diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 930cce83..8f1c9690 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -55,7 +55,7 @@ Replace the below example with any added or modified files, and briefly describe ## Additional supporting information - + Python version: 3.x WOMBAT version (`wombat.__version__`): 0.x diff --git a/.gitignore b/.gitignore index 129c47bb..add80dc5 100644 --- a/.gitignore +++ b/.gitignore @@ -156,7 +156,9 @@ ENV/ /library/code_comparison/iea26/outputs /library/code_comparison/dinwoodie/results /library/code_comparison/iea26/results +/library/code_comparison/avanessova_diss/results /library/corewind/results +/library/default/results /tests/library/results # Text editors diff --git a/CHANGELOG.md b/CHANGELOG.md index a261e52b..5fd97cec 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,90 @@ # CHANGELOG +## v0.13 - 23 December 2025 + +### Default Data Now Available + +- For complete details, please see the + [default data section of the user guide](https://wisdem.github.io/WOMBAT/examples/index.html#default-data) +- `"default"` library is now made available with a validated fixed and floating offshore wind reference + case based on the 2025 Cost of Wind Energy Review (COWER), and an experimental land-based + reference data set based on a variety of incomplete sources in onshore O&M studies. +- A new example + ([examples/default_data_demonstration.ipynb](https://github.com/WISDEM/WOMBAT/blob/develop/examples/default_data_demonstration.ipynb)) + is available to see key statistics of each scenario. +- A new example + ([examples/COWER_om_workflow.ipynb](https://github.com/WISDEM/WOMBAT/blob/develop/examples/COWER_om_workflow.ipynb)) + is available to reproduce the current year's offshore COWER results. + +### Tow-To-Port Improvements + +- Creation of the `PortManager` to manage the tugboats and other port-managed vessels. +- Tugboats are now mobilized when there are no currently available tugboats, including when another + tugboat might be busy towing or returning from site. +- Tugboats accumulate downtime costs while at port and unused. +- Ensures that a nonexistent `site_distance` passed to the `PortConfig` is still set from the + primary configuration's `port_distance`. +- Fixes a bug where the towing costs are logged twice, and double counting the values in the results. +- Fixes a bug in `Metrics.equipment_labor_cost_breakdown()` filters out some costs because a + filtering column has a NaN value. This primarily occurred in the towing operations. +- Fixes a bug in `Metrics.equipment_labor_cost_breakdown()` where the mooring and unmooring + processes were entirely removed from the cost breakdown. +- Adds the `monthly_fee` as an alternative to the `annual_fee` to the port configuration inputs. +- Adds the `daily_use_fee` for use when turbine(s) are at the port. + +### Convenience Upgrades + +- Adds the `units` input to `FixedCosts` that allows for costs to be defined on per kW + basis (default, "\$/kw/yr") or as a set cost ("\$/yr"). +- Adds distance-based coordinates in meters that can be used by providing + `layout_coords: distance` in the primary configuration file. The default is "wgs-84 + to maintain compatibility with existing workflows. +- Moves the standard weather loading routines to the `wombat/core/library.py, which includes the + following: + - Ability to read pre-processed Parquet data files for smaller file sizes and efficient I/O. + - `load_weather()` automatically reads the Parquet data or reads and transforms the CSV data. + - `read_weather_csv()` reads and converts the datetime column from standard datetime formats. + - `format_weather()` converts a resultant DataFrame from `read_weather_csv()` into a + WOMBAT-compatible weather profile. +- `Port` and `ServiceEquipment` now have a `name` attribute that allows for quicker access to the + highly used `.settings.name` attribute. +- The COREWIND Morro Bay Port has been updated to a 139 km `site_distance`, and the associated + tugboats have charter periods of 10 days. +- Tugboats can be defined in the `vessels` section of the primary configuration and be configured + as copies of each other similar to the vessels in the primary configuration, as seen below. + + ```yaml + ... + tugboats: + - - tugboat.yaml + - 2 + ... + ``` + +### Minor Improvements and Bug Fixes + +- Adds the configuration data and analysis code used in the code comparison study, + published at http://dx.doi.org/10.7488/era/5854. +- Adds an internal validation routine for the wind farm layout file to avoid uncaught layout + creation errors. +- Add more robust type handling and checking for `format_weather()` +- Allow for a `FixedCosts` `dict` to be passed directly to `Metrics` without loading additional data. +- Fixes a test collection error introduced in Pytest v9. +- Updates the bibliography style in the documentation. +- Fixes a previously rare bug where an ongoing repair at a system can cause a queued + servicing equipment dispatching to be canceled, and derail the remainder of the + simulation. Servicing equipment now check for the existence of any matching requests + instead of a failed repair attempt prior to canceling its dispatching. +- Servicing equipment now check for new requests every two hours rather than waiting + for the next shift to avoid previously rare scenarios where requests go unaddressed + during a chartering. + +## v0.12.3 - 1 December 2025 + +- Converts `PosixPath` objects to `str` before passing to pytest's `args` during custom unit and + regression test collection. +- Pins the maximum version of Jupyter-Book to be "<2" until the documentation has been migrated. + ## v0.12.3 - 1 December 2025 - Converts `PosixPath` objects to `str` before passing to pytest's `args` during custom unit and @@ -33,7 +118,7 @@ - Fixes a bug primarily impacting tow-to-port scenarios where individual maintenance and failure models are not being reset upon either replacement or following a tow-to-port repair under certain conditions. This allows for these additional processes to be perpetuated throughout the lifecycle - of the simulation while succumbing to the same inital flaw, compounding the number of erroneously + of the simulation while succumbing to the same initial flaw, compounding the number of erroneously additional events. The issue is resolved by the following: 1. Multiple subassemblies can now be passed to a `Cable` or `System` object during an interruption, allowing for simpler logic handling. diff --git a/README.md b/README.md index 638b32b8..7ac320cc 100644 --- a/README.md +++ b/README.md @@ -16,11 +16,11 @@ distributed, land-based, and offshore windfarms using a discrete event simultaio framework. WOMBAT is written around the [`SimPy`](https://gitlab.com/team-simpy/simpy) discrete -event simulation framework. Additionally, this is supported using a flexible and modular +event simulation framework. Additionally, this is supported using a flexible object-oriented code base, which enables the modeling of arbitrarily large (or small) windfarms with as many or as few failure and maintenance tasks that can be encoded. -Please note that this is still heavily under development, so you may find some functionality +Please note that this is still under development, so you may find some functionality to be incomplete at the current moment, but rest assured the functionality is expanding. With that said, it would be greatly appreciated for issues or PRs to be submitted for any improvements at all, from fixing typos (guaranteed to be a few) to features to @@ -66,12 +66,11 @@ folder, but here are a few highlights: `validation exercise `_. * Presentations: `slides `_. - ## Setup ### Requirements -* Python 3.11 through 3.13. Python 3.14 will be compatible once PyArrow 22.0 is released. +* Python 3.11+ ### Environment Setup @@ -117,10 +116,7 @@ Install it directly into an activated virtual environment: ```console git clone https://github.com/WISDEM/WOMBAT.git -cd wombat -python setup.py install - -# Alternatively: +cd WOMBAT pip install . ``` @@ -187,7 +183,7 @@ Basic pre-commit issues that users might encounter and their remedies: ```console git clone https://github.com/WISDEM/WOMBAT.git -cd wombat +cd WOMBAT pip install -e '.[docs]' ``` @@ -195,8 +191,8 @@ Build the site > **Note** > You may want to change the "execute_notebooks" parameter in the `docs/_config.yaml` -> file to "off" unless you're updating the coded examples, or they will be run every -> time you build the site. +> file to "off" unless you're updating the coded examples, or they will run every time +> there is an update to the base files. ```console jupyter-book build docs @@ -216,11 +212,11 @@ pip install -e '.[all]' Standard dependencies: -* attrs>=21 +* attrs>=24.1 * numpy>=1.21 * scipy>=1.8 * pandas>=2 -* polars>=0.17 +* polars>=1.33.1 * pyarrow>=10 * jupyterlab>=3 * simpy>=4.0.1 @@ -232,6 +228,8 @@ Standard dependencies: * types-typed-ast>=1.5 * types-PyYAML>=6 * types-python-dateutil>=2.8 +* python-dateutil +* "polars-lts-cpu; sys_platform == 'darwin'", Optional "dev" dependencies: @@ -239,13 +237,13 @@ Optional "dev" dependencies: * isort>=5.10 * pytest>=7 * pytest-cov>=4 -* mypy==0.991 +* mypy * ruff>=0.2 * pyupgrade Optional "docs" dependencies: -* jupyter-book>=0.15 +* jupyter-book>1,<2 * myst-nb>=0.16 * myst-parser>=0.17 * linkify-it-py>=2 diff --git a/docs/API/core.md b/docs/API/core.md index 2d80a44f..1ebd8daa 100644 --- a/docs/API/core.md +++ b/docs/API/core.md @@ -51,7 +51,14 @@ how each of the APIs are powered to enable the full flexibility of modeling. ```{eval-rst} .. automodule:: wombat.core.port - :exclude-members: Port + :exclude-members: Port, PortManager + +.. autoclass:: wombat.core.port.PortManager + :members: register_tugboat, update_charter_map, manage_vessels, dispatch_vessel, + return_vessel + :inherited-members: + :undoc-members: + :exclude-members: .. autoclass:: wombat.core.port.Port :members: transfer_requests_from_manager, repair_single, run_repairs, @@ -59,6 +66,6 @@ how each of the APIs are powered to enable the full flexibility of modeling. :inherited-members: :undoc-members: :exclude-members: env, windfarm, manager, settings, requests_serviced, turbine_manager, - crew_manager, tugboat_manager, active_repairs, GetQueue, PutQueue, capacity, get, - get_queue, items, put, put_queue + crew_manager, service_equipment_manager, active_repairs, GetQueue, PutQueue, + capacity, get, get_queue, items, put, put_queue ``` diff --git a/docs/API/utilities.md b/docs/API/utilities.md index 048cb228..18764ac0 100644 --- a/docs/API/utilities.md +++ b/docs/API/utilities.md @@ -1,11 +1,17 @@ # Helpers and Plotting (converting-and-structuring)= -## Importing and Converting From Old Versions +## Importing and Converting Data ```{eval-rst} .. autofunction:: wombat.core.library.load_yaml +.. autofunction:: wombat.core.library.load_weather + +.. autofunction:: wombat.core.library.read_weather_csv + +.. autofunction:: wombat.core.library.format_weather + .. autofunction:: wombat.core.library.create_library_structure .. autofunction:: wombat.core.library.convert_failure_data diff --git a/docs/_config.yml b/docs/_config.yml index 01ff7809..781cb70b 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -28,7 +28,10 @@ latex: # Add a bibtex file so that we can create citations bibtex_bibfiles: - - references.bib + - refs.bib + +bibliography: + - refs.bib # Information about where the book exists on the web repository: @@ -79,6 +82,7 @@ sphinx: - sphinxcontrib.autoyaml - sphinxcontrib.bibtex config: + bibtex_bibfiles: ["refs.bib"] html_theme: sphinx_book_theme html_theme_options: icon_links: [ @@ -93,12 +97,6 @@ sphinx: icon: "https://img.shields.io/pypi/v/WOMBAT", type: url, }, - { - name: PyPI downloads, - url: "https://pypi.org/project/WOMBAT/", - icon: "https://img.shields.io/pypi/dm/WOMBAT", - type: url, - }, { name: Launch Binder, url: "https://mybinder.org/v2/gh/WISDEM/WOMBAT/main?filepath=examples", diff --git a/docs/_toc.yml b/docs/_toc.yml index 3ee2af09..1ee421b3 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -20,6 +20,7 @@ parts: - file: examples/how_to - file: examples/strategy_demonstration - file: examples/metrics_demonstration + - file: examples/default_data_demonstration - file: examples/examples_reference - file: API/index sections: diff --git a/docs/examples/default_data_demonstration.md b/docs/examples/default_data_demonstration.md new file mode 100644 index 00000000..06e73e7f --- /dev/null +++ b/docs/examples/default_data_demonstration.md @@ -0,0 +1,198 @@ +--- +jupytext: + formats: md:myst + text_representation: + extension: .md + format_name: myst +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Default Data Demonstration + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/WISDEM/WOMBAT/main?filepath=examples) + +In this example, we'll show what each of the reference plants look like and what results they yield when simulated. + +```{important} +The land-based data are expiremental, and should only be used as a starting point for +developing a more robust simulation. Please see the +[default data section of the user guide for further details](index.md#default-data) +``` + +## Imports + +```{code-cell} ipython3 +from time import perf_counter + +import numpy as np +import pandas as pd + +from wombat import Simulation +from wombat.utilities import plot +from wombat.core.library import DEFAULT_DATA + +pd.set_option("display.max_rows", 50) +pd.set_option("display.max_columns", 20) +pd.options.display.float_format = '{:,.2f}'.format +``` + +## Initialize the simulations + +```{code-cell} ipython3 +SEED = 48 + +lbw_sim = Simulation(DEFAULT_DATA, "base_lbw.yaml", random_seed=SEED) +osw_fixed_sim = Simulation(DEFAULT_DATA, "base_osw_fixed.yaml", random_seed=SEED) +osw_floating_sim = Simulation(DEFAULT_DATA, "base_osw_floating.yaml", random_seed=SEED) +``` + +### View the farms + +Using the plotting library, we can observe the layout for each of the farms + +```{code-cell} ipython3 +print(f"N turbines: {len(lbw_sim.windfarm.turbine_id)}, N substations: {len(lbw_sim.windfarm.substation_id)}") +plot.plot_farm_layout(lbw_sim.windfarm) +``` + +```{code-cell} ipython3 +print(f"N turbines: {len(osw_fixed_sim.windfarm.turbine_id)}, N substations: {len(osw_fixed_sim.windfarm.substation_id)}") +plot.plot_farm_layout(osw_fixed_sim.windfarm) +``` + +```{code-cell} ipython3 +print(f"N turbines: {len(osw_floating_sim.windfarm.turbine_id)}, N substations: {len(osw_floating_sim.windfarm.substation_id)}") +plot.plot_farm_layout(osw_floating_sim.windfarm) +``` + +## Run the simulations + +Now we can run all three simulations, delete the simulation log files, and view the run time for +each simulation. + +```{code-cell} ipython3 +start = perf_counter() +lbw_sim.run(delete_logs=True, save_metrics_inputs=False) +run_time = perf_counter() - start +print(f"Run time: {run_time // 60} minutes {run_time % 60:.1f} seconds") + +start = perf_counter() +osw_fixed_sim.run(delete_logs=True, save_metrics_inputs=False) +run_time = perf_counter() - start +print(f"Run time: {run_time // 60} minutes {run_time % 60:.1f} seconds") + +start = perf_counter() +osw_floating_sim.run(delete_logs=True, save_metrics_inputs=False) +run_time = perf_counter() - start +print(f"Run time: {run_time // 60} minutes {run_time % 60:.1f} seconds") +``` + +### Gather the results + +First we will simplify the process of getting results, ensure that all simulations are +20 years, and gather the capacities. + +```{code-cell} ipython3 +metrics_lbw = lbw_sim.metrics +metrics_osw_fixed = osw_fixed_sim.metrics +metrics_osw_floating = osw_floating_sim.metrics + +years_check = ( + lbw_sim.env.simulation_years, + osw_fixed_sim.env.simulation_years, + osw_floating_sim.env.simulation_years +) +print(f"All are 20 years: {all(x == 20 for x in years_check)}") +years = 20 + +capacities = [ # MW + metrics_lbw.project_capacity, + metrics_osw_fixed.project_capacity, + metrics_osw_floating.project_capacity, +] + +columns = ["Metric", "Units", "Land-Based", "OSW-Fixed", "OSW-Floating"] +sort_order = [ + "Project Capacity", + "Production Based Availability", + "Total OpEx", + "Materials", + "Direct Labor", + "Port Fees", + "Fixed Costs (operations, indirect labor, etc.)", + "Service Equipment", + "Truck 1", + "Truck 2", + "Truck 3", + "Crawler Crane - 1350 tonnes", + "Crew Transfer Vessel 1", + "Crew Transfer Vessel 2", + "Crew Transfer Vessel 3", + "Cable Laying Vessel", + "Diving Support Vessel", + "Heavy Lift Vessel", + "Anchor Handling Tug", + "Tugboat 1", + "Tugboat 2", +] +``` + +Now, we can gather some of the high level availability and cost statistics for each +of the base scenarios. + +```{code-cell} ipython3 +capacity = pd.DataFrame([["Project Capacity", "MW"] + capacities], columns=columns).set_index(["Metric", "Units"]) + +availability = [ + "Production Based Availability", + "%", + metrics_lbw.production_based_availability("project", "windfarm").squeeze() * 100, + metrics_osw_fixed.production_based_availability("project", "windfarm").squeeze() * 100, + metrics_osw_floating.production_based_availability("project", "windfarm").squeeze() * 100, +] +availability = pd.DataFrame([availability], columns=columns).set_index(["Metric", "Units"]) +``` + +```{code-cell} ipython3 +opex = ( + metrics_lbw.opex("project", "windfarm").rename({0: "Land-Based"}).T + .join(metrics_osw_fixed.opex("project", "windfarm").rename({0: "OSW-Fixed"}).T) + .join(metrics_osw_floating.opex("project", "windfarm").rename({0: "OSW-Floating"}).T) + / years + / (np.array(capacities) * 1000) +) +opex.index = [ + "Fixed Costs (operations, indirect labor, etc.)", + "Port Fees", + "Service Equipment", + "Direct Labor", + "Materials", + "Total OpEx" +] +opex["Units"] = "$/kw/yr" +opex = opex.set_index("Units", append=True).iloc[::-1] +``` + +```{code-cell} ipython3 +equipment = ( + metrics_lbw.equipment_costs("project", by_equipment=True).rename({0: "Land-Based"}).T + .join(metrics_osw_fixed.equipment_costs("project", by_equipment=True).rename({0: "OSW-Fixed"}).T, how="outer") + .join(metrics_osw_floating.equipment_costs("project", by_equipment=True).rename({0: "OSW-Floating"}).T, how="outer") + .fillna(0.0) + / years + / (np.array(capacities) * 1000) +) +equipment["Units"] = "$/kw/yr" +equipment = equipment.set_index("Units", append=True) +``` + +With each of the core costs gathered, we can combine them into a single DataFrame and +view them. + +```{code-cell} ipython3 +results = pd.concat([capacity, availability, opex, equipment]).loc[sort_order] +results +``` diff --git a/docs/examples/examples_reference.md b/docs/examples/examples_reference.md index d4d3fe35..34590da4 100644 --- a/docs/examples/examples_reference.md +++ b/docs/examples/examples_reference.md @@ -9,6 +9,14 @@ This contains the old notebooks and data used to prepare the unveiling of the mo DOE. The results are displayed in [presentations section](presentations:fy20-doe) of the documentation. +## Ongoing Analysis Work & Default Data Results Demonstration + +### `COWER_om_worflow.ipynb` + +This workflow example demonstrates the new (as of v0.13) default offshore wind data sets and the +O&M portion of the NREL Cost of Wind Energy Review (COWER) analysis. The underlying data and example +are kept up to date every year for this project, so the data will be updated annually (as of 2025). + ## Explanations The following notebooks are aimed at demonstrating and explaining various functionality @@ -25,6 +33,12 @@ This is a Jupyter Notebook version of the [*Strategy Demonstration* section](./strategy_demonstration.md) of the documentation that allows users to interact with the walk through. +### `default_data_demonstration.ipynb` + +This is a Jupyter Notebook version of the +[*Default Data Demonstration* section](./default_data_demonstration.md) of the documentation +that allows users to interact with the examples. + ### `metrics_demonstration.ipynb` This is a Jupyter Notebook version of the @@ -48,6 +62,13 @@ These example are no longer recalibrated for updated releases, and are merely ke to date with WOMBAT's input changes. ``` +### `avanessova_2025.ipynb` + +This example demonstrates the WOMBAT analysis portion of the 2025 doctoral dissertation by +Nadezda Avanessova [^avanessova2025], which in part focuses on a comparison of +ORE Catapult's COMPASS, WavEC's O&M Tool, and NREL's WOMBAT for offshore wind O&M. This example +was originally run in v0.8.1, and updated to v0.12, so some results will differ from what was published. + ### `dinwoodie_validation.ipynb` This shows the latest results of the model validation using the modeling parameters from @@ -66,3 +87,4 @@ the listed times for v0.7 in the notebooks will not match those in this example. [^dinwoodie2015reference]: Iain Dinwoodie, Ole-Erik V Endrerud, Matthias Hofmann, Rebecca Martin, and Iver Bakken Sperstad. Reference cases for verification of operation and maintenance simulation models for offshore wind farms. *Wind Engineering*, 39(1):1–14, 2015. [^smart2016iea]: Gavin Smart, Aaron Smith, Ethan Warner, Iver Bakken Sperstad, Bob Prinsen, and Roberto Lacal-Arantegui. Iea wind task 26: offshore wind farm baseline documentation. Technical Report, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2016. +[^avanessova2025]: Avanessova, Nadezda. "Developing a holistic operation and maintenance simulation tool for emerging offshore wind projects." (2025). diff --git a/docs/examples/index.md b/docs/examples/index.md index 3002b311..7eb0b0dc 100644 --- a/docs/examples/index.md +++ b/docs/examples/index.md @@ -7,8 +7,8 @@ demonstrated through example notebooks. To fully follow the particulars of each it is recommended to see how each model's configuration files are composed. For thorough explanations of the design and implementation ethos of the model, please -see our NREL Technical Report: https://www.osti.gov/biblio/1894867, which was published -alongside v0.5.1, so some functionality has been updated. +see our NREL Technical Report: https://www.osti.gov/biblio/1894867 {cite}`osti_1894867`, +which was published alongside v0.5.1, so some functionality has been updated. ## Feature Overview @@ -152,20 +152,25 @@ modeling assumptions as well as the basic operations of the maintenance and fail - Operating limits can be applied for both transiting and repair logic to ensure site safety is considered in the model. - Ports + :::{note} + Major improvements as of v0.13 + ::: - Currently only used for tow-to-port repairs or tugboat based repairs, which adds the following additional capabilities: - TOW: tugboat or towing equipment - AHV: anchor handling vessel (tugboat that doesn't trigger tow-to-port) - - There are a few limitations to the tow-to-port model as follows: - - Port usage fees are only modeled by the single monthly access fee `equipment_rate`. - - Tugboats are not formally mobilized, so cost and vessel wait times are not - applied when a tow-to-port simulation is kicked off. - - Tugboats are called out on the first request from the requesting system, - regardless of the user encoding. - - All subsequent repair and maintenance requests following a "TOW" request will not - be addressed until the system is at the port. This can create a significant - backlog of repairs if there are not enough tugboats, crews, or active turbine - slots encoded at the port. + - Port access fees can be modeled in the `FixedCosts` module, or applied as a monthly + fee using either the `annual_fee` or `monthly_fee` input. + - Port usage fees are applied when any turbine is at the port for repairs, and can + set using the `daily_use_fee` parameter. + - Tugboats are mobilized, and will stay at port for the duration of their + `charter_days` parameter. + - Tugboats are called out on the first request from the requesting system, + regardless of the user encoding. + - All subsequent repair and maintenance requests following a "TOW" request will not + be addressed until the system is at the port. This can create a significant + backlog of repairs if there are not enough tugboats, crews, or active turbine + slots encoded at the port. - See the [API docs](core:port) for more details ## Examples and Validation Work @@ -176,5 +181,92 @@ Below are a few examples to get started, for users interested in the validation the [code-to-code comparison presentations](presentations:code-comparison), the notebooks generating [the most up-to-date results can be found in the main repository](https://github.com/WISDEM/WOMBAT/examples/), where there is a separate analysis for the -[Dinwoodie, et al., 2015 comparison](https://github.com/WISDEM/WOMBAT/blob/main/examples/dinwoodie_validation.ipynb), -and for the [IEA Task 26, 2016 comparison](https://github.com/WISDEM/blob/main/WOMBAT/examples/iea_26_validation.ipynb). +[{cite:t}`dinwoodie2015reference` comparison](https://github.com/WISDEM/WOMBAT/blob/main/examples/dinwoodie_validation.ipynb), +and for the [{cite:t}`smart2016iea` comparison](https://github.com/WISDEM/blob/main/WOMBAT/examples/iea_26_validation.ipynb). + + + +## Default Data + +As of version 0.13, a default reference data set has been made available for users. Simply specify +a simulation as `Simulation.from_config("DEFAULT", "osw_fixed.yaml")` or +`Simulation.from_config("DEFAULT", "osw_floating.yaml")` to use either of the fixed-bottom or floating +offshore wind data sets, respectively. See the `examples/COWER_om_workflow.ipnyb` for more details. + +The default library provides a validated, ready-to-use data set for fixed and floating offshore wind, +and an experimental land-based data set. For all three, users can use the pre-configured base models +or use them as a starting point for building custom models. Example results can be seen in +`examples/default_data_demonstration.ipynb` or [online](default_data_demonstration.md) + +### Overview + +#### Offshore + +The **default library** provides a consistent set of cost and performance inputs for offshore wind +energy systems, standardized to 2024 USD. This library supports reproducible analyses in the +*Cost of Wind Energy Review (COWER): 2025 Edition* (forthcoming), including both fixed-bottom and +floating offshore wind cases. + +Unlike other datasets in this repository, which largely reflect publicly available sources, this +dataset incorporates **internal adjustments and harmonizations** to align with research-focused +scenarios for NREL products that may require outputs to be in 2024 USD, like for example, the Annual +Technology Baseline, or the Cost of Wind Energy Review. + +#### Land-Based + +The default library provides an experimental set of land-based data that is hybridized from a series +of incomplete sources of offshore and onshore costs, and scaling factors between them. It is highly +recommended to merely use this data as a starting point for any analysis work. + +### Core Assumptions + +#### Offshore + +Material costs for repairs and replacements, as well as failure data, are sourced from +{cite:t}`corewind_d61_2020, corewind_d42_2021`. Fixed cost data is primarily derived from +{cite:t}`bvg_guide_2023, bvg_guide_2025`, while vessel day rate and mobilization assumptions are +compiled from a range of public sources. For detailed reference information, please contact Daniel +Mulas Hernando (Daniel.MulasHernando@nrel.gov) to request access to WOMBAT_cost_history.xlsx. + +- **Cost Year:** All monetary values are standardized to **2024 USD**. +- **Data Integration:** Inputs were consolidated from multiple public sources and internal records (`WOMBAT_cost_history.xlsx`), with historical exchange rates and inflation applied to transform costs to 2024 USD. +- **Technology Coverage:** Includes representative inputs for offshore (fixed-bottom and floating) technologies. + +#### Land-Based + +Failure rates, repair times, and materials costs not provided in {cite:t}`corewind_d42_2021` are +largely taken from {cite:t}`carroll2016failure` with some custom substitutions or modifications. All +failure rates are converted to a Weibull scale factor. Using {cite:t}`carroll2016failure` we adjust +offshoreWeibull scale factors for minor repairs for the generator, blades, pitch system, yaw system, +and drive train (wind-affected subassemblies) by a factor of 5.912, for non-wind-affected +subassemblies a factor of 1.217 is applied, and for all major repairs and replacements, a factor of +2.432 is applied. These scaling factors are primarily derived from {cite:t}`carroll2016failure` + +All costs are rescaled from the 12 MW baseline to a 3.5 MW baseline using the COWER 2025 turbine +CapEx figures of \$1,117/kw-yr for onshore and \$1,770/kw-yr for offshore, a 0.6311 scaling factor. + +Additional subassemblies are derived from {cite:t}`carroll2016failure` and scaled using the above +assumptions. Fixed costs are derived from {cite:t}`WISER201946`, with labor coming supplemented by +{cite:t}`osti_1995015`, and equipment costs come from LandBOSSE and internal source adjustments. + +### Intended Use + +- Serve as a **baseline input** for replicable analyses of the offshore fixed and floating *COWER-2025* results. +- Serve as a **baseline input** for the development of land-based analyses. +- Support **scenario development** and **sensitivity analyses** exploring the impact of cost evolution, operational performance, and logistics assumptions. + +### Reproducibility + +The accompanying notebook, **`examples/COWER_om_workflow.ipynb`**, demonstrates how to replicate O&M results from the *Cost of Wind Energy Review: 2025 Edition*. It runs 50 simulations per case and summarizes mean and standard deviation results to identify sources of variability within cost components. + +#### Notes + +- `WOMBAT_cost_history.xlsx` is an internal NREL document. For questions, contact **Daniel.MulasHernando@nrel.gov**. +- This dataset should be treated as a **scenario-based reference**, not as purely empirical or historical data. + +## References + +```{bibliography} +:style: unsrt +:filter: docname in docnames +``` diff --git a/docs/index.md b/docs/index.md index eb3b58fe..cd247dd0 100644 --- a/docs/index.md +++ b/docs/index.md @@ -45,31 +45,52 @@ rob.hammond@nrel.gov. ## Latest Changes? +Please see the CHANGELOG, linked below for complete information, and the appropriate +documentation pages for specific details. + +* On this site: https://wisdem.github.io/WOMBAT/changelog.html +* On GitHub: https://github.com/WISDEM/WOMBAT/blob/main/CHANGELOG.md + +Below is a shortlist of some of the most impactful changes made in recent versions. + +As of v0.13, a series of convenience features have been made, and are outlined below: +* Major tow-to-port model upgrades! + * Tow-to-port simulations now mobilize tugboats, and log downtime during their chartering + more closely replicating real life conditions. + * Ports can now use an `annual_fee` or `monthly_fee`, and have direct use fee applied + during any at-port repair operations, using the `daily_use_fee` input. +* New default data model! + * `"default"` library now standardizes all baselinecost inputs to 2024 USD. + * Contains a validated fixed and floating offshore wind base scenario, and an experimental + land-based scenario. + * The offshore data set is highlighted in the `COWER_om_workflow.ipynb` notebook, and can be used + to replicate the OpEx results presented in the Cost of Wind Energy Review: 2025 Edition. +* Fixed costs can now be provided as \$/yr instead of only \$/kw/yr. +* Layout coordinates can be provided as meters-based relative coordinates by defining + the `layout_coords` input to the primary configuration as `"distance"`. +* Weather profile loading tools are now made accessible through `wombat.core.library.load_weather()`. +* Parquet data files are enabled to reduce file sizes and simplify the simulation loading process. + Using the `load_weather()` function, users can save the output Polars DataFrame to Parquet. + As of v0.10, a series of convenience features and consistency updates have been made. -- A breaking change to failure data has been made by using lists instead of dictionaries +* A breaking change to failure data has been made by using lists instead of dictionaries in the configuration of cables, turbines, and substations. To ease adoption, a function has been provided to convert to the new format: https://wisdem.github.io/WOMBAT/API/utilities.html#importing-and-converting-from-old-versions. -- Single configuration files are now supported for all non-CSV data. This means that +* Single configuration files are now supported for all non-CSV data. This means that servicing equipment, cables, turbines, and substations can all be included in the primary configuration file. -- Simplification of duplicated servicing equipment definitions: simply provide a list +* Simplification of duplicated servicing equipment definitions: simply provide a list of the filename/id and the number of them that should be in the simulation. -- Date-based maintenance scheduling is now possible. - -Please see the CHANGELOG for complete information, and the appropriate documentation -pages. - -* On this site: https://wisdem.github.io/WOMBAT/changelog.html -* On GitHub: https://github.com/WISDEM/WOMBAT/blob/main/CHANGELOG.md +* Date-based maintenance scheduling is now possible. ## Part of the WETO Stack WOMBAT is primarily developed with the support of the U.S. Department of Energy and is part of the [WETO Software Stack](https://nrel.github.io/WETOStack). For more information and other integrated modeling software, see: -- [Portfolio Overview](https://nrel.github.io/WETOStack/portfolio_analysis/overview.html) -- [Entry Guide](https://nrel.github.io/WETOStack/_static/entry_guide/index.html) -- [Techno-Economic Modeling Workshop](https://nrel.github.io/WETOStack/workshops/user_workshops_2024.html#tea-and-cost-modeling) -- [Systems Engineering Workshop](https://nrel.github.io/WETOStack/workshops/user_workshops_2024.html#systems-engineering) +* [Portfolio Overview](https://nrel.github.io/WETOStack/portfolio_analysis/overview.html) +* [Entry Guide](https://nrel.github.io/WETOStack/_static/entry_guide/index.html) +* [Techno-Economic Modeling Workshop](https://nrel.github.io/WETOStack/workshops/user_workshops_2024.html#tea-and-cost-modeling) +* [Systems Engineering Workshop](https://nrel.github.io/WETOStack/workshops/user_workshops_2024.html#systems-engineering) ## The Model in 30 Seconds Or Less diff --git a/docs/install.md b/docs/install.md index 95bbbae1..358b0a03 100644 --- a/docs/install.md +++ b/docs/install.md @@ -44,8 +44,8 @@ using the `pip install -e '.[dev]'` prompt in the next section ```bash git clone https://github.com/WISDEM/WOMBAT.git -cd wombat -pip install wombat/ +cd WOMBAT +pip install . ``` ### For contributors @@ -57,7 +57,7 @@ functionality run and pass before submitting pull requests. ```bash git clone https://github.com/WISDEM/WOMBAT.git -cd wombat +cd WOMBAT pip install -e '.[dev]' # some users may need double quotes here, not single quotes # Required for automatic code formatting! pre-commit install @@ -85,7 +85,7 @@ packages. ```bash git clone https://github.com/WISDEM/WOMBAT.git -cd wombat +cd WOMBAT pip install -e '.[docs]' ``` diff --git a/docs/presentations.md b/docs/presentations.md index 9d2182ed..9ce7b71a 100644 --- a/docs/presentations.md +++ b/docs/presentations.md @@ -19,6 +19,13 @@ Hammond, Rob, & Cooperman, Aubryn. Windfarm Operations and Maintenance cost-Bene } ``` +## Developing a holistic operation and maintenance simulation tool for emerging offshore wind projects, 2025 + +The 2025 doctoral dissertation by Nadezda Avanessova {cite}`avanessova_2025` highlights a +comparison of ORE Catapult's COMPASS, WavEC's O&M Tool, and NREL's WOMBAT for offshore wind O&M. +
+[Edinburgh University Archive](http://dx.doi.org/10.7488/era/5854) + ## 2023 NAWEA WindTech Workshop This document is the actual presentation given in associating with the conference @@ -41,17 +48,20 @@ of the model. This document serves as a demonstration of the capabilities of this work in reference to two major works to document simulated O&M results from various industry and research software systems. Here we compare our software to the published results from -Dinwoodie, et al., 2015 [^dinwoodie2015reference] and IEA, 2016 [^smart2016iea]. +{cite:t}`dinwoodie2015reference` and {cite:t}`smart2016iea`.
{download}`presentation PDF <../presentation_material/code_comparison.pdf>` - ## IEA Task 26 Presentation -This presentation was given to the IEA Task Force responsible for [^smart2016iea] +This presentation was given to the IEA Task Force responsible for {cite:t}`smart2016iea` as a demonstration of NREL's efforts on O&M model development.
{download}`presentation PDF <../presentation_material/WOMBAT_IEA_task_26_presentation_6_May_2021.pdf>` -[^dinwoodie2015reference]: Iain Dinwoodie, Ole-Erik V Endrerud, Matthias Hofmann, Rebecca Martin, and Iver Bakken Sperstad. Reference cases for verification of operation and maintenance simulation models for offshore wind farms. *Wind Engineering*, 39(1):1–14, 2015. -[^smart2016iea]: Gavin Smart, Aaron Smith, Ethan Warner, Iver Bakken Sperstad, Bob Prinsen, and Roberto Lacal-Arantegui. Iea wind task 26: offshore wind farm baseline documentation. Technical Report, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2016. +## References + +```{bibliography} +:style: unsrt +:filter: docname in docnames +``` diff --git a/docs/refs.bib b/docs/refs.bib index 6919ef76..737921a1 100644 --- a/docs/refs.bib +++ b/docs/refs.bib @@ -1,8 +1,24 @@ +@techreport{osti_1894867, + author = {Hammond, Rob and Cooperman, Aubryn}, + title = {Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT)}, + institution = {National Renewable Energy Laboratory (NREL), Golden, CO (United States)}, + doi = {10.2172/1894867}, + url = {https://www.osti.gov/biblio/1894867}, + place = {United States}, + year = {2022}, + month = {10} +} + @techreport{smart2016iea, - title={IEA wind task 26: offshore wind farm baseline documentation}, - author={Smart, Gavin and Smith, Aaron and Warner, Ethan and Sperstad, Iver Bakken and Prinsen, Bob and Lacal-Arantegui, Roberto}, - year={2016}, - institution={National Renewable Energy Lab.(NREL), Golden, CO (United States)} + author = {Smart, Gavin and Smith, Aaron and Warner, Ethan and Sperstad, Iver Bakken and Prinsen, Bob and Lacal-Arantegui, Roberto}, + title = {IEA Wind Task 26: Offshore Wind Farm Baseline Documentation}, + institution = {National Renewable Energy Lab. (NREL), Golden, CO (United States)}, + annote = {This document has been produced to provide the definition and rationale for the Baseline Offshore Wind Farm established within IEA Wind Task 26--Cost of Wind Energy. The Baseline has been developed to provide a common starting point for country comparisons and sensitivity analysis on key offshore wind cost and value drivers. The baseline project reflects an approximate average of the characteristics of projects installed between 2012 and 2014, with the project life assumed to be 20 years. The baseline wind farm is located 40 kilometres (km) from construction and operations and maintenance (O&M) ports and from export cable landfall. The wind farm consists of 100 4-megawatt (MW) wind turbines mounted on monopile foundations in an average water depth of 25 metres (m), connected by 33-kilovolt (kV) inter-array cables. The arrays are connected to a single offshore substation (33kV/220kV) mounted on a jacket foundation, with the substation connected via a single 220kV export cable to an onshore substation, 10km from landfall. The wind farm employs a port-based O&M strategy using crew-transfer vessels.}, + doi = {10.2172/1259255}, + url = {https://www.osti.gov/biblio/1259255}, + place = {United States}, + year = {2016}, + month = {06} } @article{dinwoodie2015reference, @@ -13,5 +29,111 @@ @article{dinwoodie2015reference number={1}, pages={1--14}, year={2015}, - publisher={SAGE Publications Sage UK: London, England} + publisher={SAGE Publications Sage UK: London, England}, + doi={10.1260/0309-524X.39.1.1}, + url={https://doi.org/10.1260/0309-524X.39.1.1}, +} + +@misc{osti_1995015, + author = {National Renewable Energy Laboratory (NREL)}, + title = {Clean Energy Employment Impacts}, + url = {https://www.osti.gov/biblio/1995015}, + place = {United States}, + year = {2023}, + month = {08} +} + +@article{WISER201946, + title = {Assessing wind power operating costs in the United States: Results from a survey of wind industry experts}, + journal = {Renewable Energy Focus}, + volume = {30}, + pages = {46-57}, + year = {2019}, + issn = {1755-0084}, + doi = {https://doi.org/10.1016/j.ref.2019.05.003}, + url = {https://www.sciencedirect.com/science/article/pii/S1755008419300092}, + author = {Ryan Wiser and Mark Bolinger and Eric Lantz}, +} + +@article{carroll2016failure, + title={Failure rate, repair time and unscheduled O\&M cost analysis of offshore wind turbines}, + author={Carroll, James and McDonald, Alasdair and McMillan, David}, + journal={Wind energy}, + volume={19}, + number={6}, + pages={1107--1119}, + year={2016}, + publisher={Wiley Online Library}, + doi = {https://doi.org/10.1002/we.1887}, + url = {https://onlinelibrary.wiley.com/doi/full/10.1002/we.1887}, +} + +@misc{bvg_guide_2023, + title = {Guide to a floating offshore wind farm | An informative resource for floating offshore wind}, + copyright = {This report and its content is copyright of BVG Associates Limited - © BVG Associates 2023. All rights are reserved.}, + shorttitle = {Guide to a floating offshore wind farm}, + url = {https://guidetofloatingoffshorewind.com/}, + language = {en-GB}, + urldate = {2025-12-10}, + month = oct, + year = {2023}, +} + +@misc{bvg_guide_2025, + title = {Guide to an offshore wind farm | An informative resource for offshore wind}, + copyright = {This report and its content is copyright of BVG Associates Limited - © BVG Associates 2023. All rights are reserved.}, + shorttitle = {Guide to an offshore wind farm}, + url = {https://guidetoanoffshorewindfarm.com/}, + language = {en-GB}, + urldate = {2025-12-10}, + month = jun, + year = {2025}, +} + +@techreport{corewind_d42_2021, + title = {D4.2 Floating Wind O\&M Strategies Assessment}, + url = {https://corewind.eu/publications/}, + urldate = {2023-01-30}, + author = {Schwarzkopf, Marie-Antoinette and Borisade, Friedemann and Espelage, Jannis and Johnston, Eve and Durán Vicente, Rubén and Muñoz, Sara and Hylland, Pål and He, Wei and Urbano, Joaquín and Javier Comas, Francisco and Arribas, Andrea and Somoano Rodriguez, Miguel and Fernandez Ruano, Sergio and Meneses Aja, Lucía and Guanche García, Raul and Rodríguez Luis, Álvaro}, + institution = {COREWIND}, + month = aug, + year = {2021}, + pages = {107}, +} + +@techreport{corewind_d61_2020, + title = {D6.1 {General} frame of the analysis and description of the new FOW assessment app}, + shorttitle = {General frame of the analysis and description of the new FOW assessment app}, + url = {https://corewind.eu/publications/}, + urldate = {2023-01-30}, + author = {Verma, Jai and Ignacio Rapha, José and Luis Domínguez, José and Ferreira, Victor}, + institution = {COREWIND}, + month = nov, + year = {2020}, + pages = {52}, +} + +@conference{osti_2212471, + author = {Hammond, Rob}, + title = {WOMBAT Tutorial}, + url = {https://www.osti.gov/biblio/2212471}, + place = {United States}, + organization = {National Renewable Energy Laboratory (NREL), Golden, CO (United States)}, + year = {2023}, + month = {11} +} + +@phdthesis{avanessova_2025, + title = {Developing a holistic operation and maintenance simulation tool for emerging offshore wind projects}, + url = {https://era.ed.ac.uk/handle/1842/43313}, + doi = {10.7488/era/5854}, + language = {en}, + urldate = {2025-12-12}, + author = {Avanessova, Nadezda}, + month = mar, + year = {2025}, + day = {26}, + school = {The University of Edinburgh}, +} + } diff --git a/examples/COWER_om_workflow.ipynb b/examples/COWER_om_workflow.ipynb new file mode 100644 index 00000000..a36248bc --- /dev/null +++ b/examples/COWER_om_workflow.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7d1b251c-0721-4975-8c8a-87b6c8b8cb1d", + "metadata": {}, + "source": [ + "# COWER: 2025 Edition: Runs for Fixed-Bottom & Floating Offshore Wind\n", + "\n", + "National Renewable Energy Laboratory\\\n", + "Daniel Mulas Hernando\\\n", + "21 November 2025\\\n", + "WOMBAT version of originally published notebook: 0.13\n", + "\n", + "This notebook serves as a demonstration of the default data fixed-bottom and floating offshore wind data, and\n", + "offers a reproducible workflow for the OpEx results for the fixed-bottom and floating offshore wind farms presented in the Cost of Wind Energy Review: 2025 Edition.\n", + "\n", + "As such, these data were produced as a part of the annual Cost of Wind Energy Review (COWER) analysis project.\n", + "Similarly, this notebook, and the underlying data will represent the work and updates for the current\n", + "year's analysis work.\n", + "\n", + "This notebook allows you to specify the random seeds\u2014and thus control the number of simulations\u2014to run for this particular case.\n", + "For each simulation, it calculates and saves the availability, OpEx, and vessel cost breakdowns.\n", + "All results are stored in the `library/default/results` folder for easy access and analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "00e8ffd6-55bd-41b2-911f-59ead5673b7c", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import yaml\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from wombat.core import Simulation, Metrics\n", + "from wombat.core.library import DEFAULT_DATA\n", + "\n", + "\n", + "N_RUNS = 50\n", + "RANDOM_SEEDS = list(range(1, N_RUNS + 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2102b56b-faaa-4365-bfe9-304f96d00e82", + "metadata": {}, + "outputs": [], + "source": [ + "def run_windfarm_simulations(technology: str, random_seeds: list):\n", + " \"\"\"\n", + " Run simulations for a specified wind farm technology ('floating' or 'fixed_bottom')\n", + " and save availability, OpEx, and vessel results for all runs. Saves CSV files in\n", + " library/default/results with appropriate naming.\n", + "\n", + " Parameters\n", + " ----------\n", + " technology : str\n", + " One of \"floating\" or \"fixed_bottom\".\n", + " random_seeds : list[int]\n", + " Random seeds to use to vary Weibull sampled failures.\n", + " \"\"\"\n", + " # === CONFIGURATION ===\n", + " results_dir = DEFAULT_DATA / \"results\"\n", + " if not results_dir.is_dir():\n", + " results_dir.mkdir()\n", + "\n", + " # Select config file based on technology\n", + " if technology.lower() == \"floating\":\n", + " config_name = \"base_osw_floating.yaml\"\n", + " elif technology.lower() == \"fixed_bottom\":\n", + " config_name = \"base_osw_fixed.yaml\"\n", + " else:\n", + " raise ValueError(\"Invalid technology. Must be 'floating' or 'fixed_bottom'.\")\n", + "\n", + " # Initialize storage lists\n", + " availability_records = []\n", + " opex_records = []\n", + " vessel_records = []\n", + " repair_time_records = []\n", + "\n", + " # === RUN SIMULATIONS SEQUENTIALLY ===\n", + " N = len(random_seeds)\n", + " for i, seed in enumerate(random_seeds, start=1):\n", + " print(\n", + " f\"\ud83d\ude80 Running simulation {i}/N ({technology}) with random seed {seed}\",\n", + " end=\"\\r\",\n", + " )\n", + "\n", + " # Run simulation\n", + " sim = Simulation(DEFAULT_DATA, config_name, random_seed=seed)\n", + " sim.run(create_metrics=True, save_metrics_inputs=True)\n", + "\n", + " # Load metrics\n", + " fpath = sim.env.metrics_input_fname.parent\n", + " fname = sim.env.metrics_input_fname.name\n", + " metrics = Metrics.from_simulation_outputs(fpath, fname)\n", + "\n", + " # === 1. Availability Results ===\n", + " time_avail = metrics.time_based_availability(frequency=\"project\", by=\"windfarm\")\n", + " prod_avail = metrics.production_based_availability(\n", + " frequency=\"project\", by=\"windfarm\"\n", + " )\n", + " time_value = time_avail.iloc[0, 0]\n", + " prod_value = prod_avail.iloc[0, 0]\n", + " availability_records.append(\n", + " {\n", + " \"run\": i,\n", + " \"random_seed\": seed,\n", + " \"time_based_availability\": time_value,\n", + " \"production_based_availability\": prod_value,\n", + " }\n", + " )\n", + "\n", + " # === 2. OpEx Results ===\n", + " opex_df = metrics.opex(frequency=\"annual\", by_category=True).reset_index()\n", + " opex_df.insert(0, \"random_seed\", seed)\n", + " opex_df.insert(0, \"run\", i)\n", + " opex_records.append(opex_df)\n", + "\n", + " # === 3. Vessel Costs ===\n", + " vessel_df = metrics.equipment_costs(\n", + " frequency=\"annual\", by_equipment=True\n", + " ).reset_index()\n", + " vessel_df.insert(0, \"random_seed\", seed)\n", + " vessel_df.insert(0, \"run\", i)\n", + " vessel_records.append(vessel_df)\n", + "\n", + " # === 4. Repair Time at Port ===\n", + "\n", + " # Build full path to config file\n", + " config_path = DEFAULT_DATA / \"project\" / \"config\" / config_name\n", + "\n", + " # Load YAML\n", + " with open(config_path, \"r\") as f:\n", + " config_data = yaml.safe_load(f)\n", + "\n", + " # Extract port name\n", + " port_name = config_data.get(\"port\", None)\n", + "\n", + " if port_name is None:\n", + " raise KeyError(\n", + " f\"'port' key not found in {config_path}, can not calculate time at port\"\n", + " )\n", + "\n", + " port_name = port_name.replace(\".yaml\", \"\")\n", + "\n", + " events_df = sim.env.load_events_log_dataframe()\n", + " events_df[\"duration\"] = pd.to_numeric(events_df[\"duration\"], errors=\"coerce\")\n", + " df_port = events_df[events_df[\"agent\"] == port_name]\n", + " total_hours = df_port[\"duration\"].sum()\n", + " simulation_years = sim.env.end_year - sim.env.start_year + 1\n", + " avg_hours_per_year = total_hours / simulation_years\n", + " avg_days_per_year = avg_hours_per_year / 24\n", + " avg_months_per_year = avg_hours_per_year / (24 * 30.4375)\n", + "\n", + " repair_time_records.append(\n", + " {\n", + " \"run\": i,\n", + " \"random_seed\": seed,\n", + " \"avg_repair_time_months\": avg_months_per_year,\n", + " \"avg_repair_time_days\": avg_days_per_year,\n", + " }\n", + " )\n", + "\n", + " # Cleanup logs for this simulation\n", + " sim.env.cleanup_log_files()\n", + "\n", + " # === COMBINE AND SAVE RESULTS ===\n", + " df_availability = pd.DataFrame(availability_records)\n", + " df_opex = pd.concat(opex_records, ignore_index=True)\n", + " df_vessels = pd.concat(vessel_records, ignore_index=True)\n", + " df_repair_time = pd.DataFrame(repair_time_records)\n", + "\n", + " df_availability.to_csv(\n", + " results_dir / f\"COWER-2025-{technology}_all_availability_results.csv\",\n", + " index=False,\n", + " )\n", + " df_opex.to_csv(\n", + " results_dir / f\"COWER-2025-{technology}_all_opex_results.csv\", index=False\n", + " )\n", + " df_vessels.to_csv(\n", + " results_dir / f\"COWER-2025-{technology}_all_vessel_results.csv\", index=False\n", + " )\n", + " df_repair_time.to_csv(\n", + " results_dir / f\"COWER-2025-{technology}_repair_time_at_port_results.csv\",\n", + " index=False,\n", + " )\n", + "\n", + " print(f\"\u2705 All {technology} simulations complete. Results saved to {results_dir}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c4709f9a-e6c6-4a46-84f4-a1a60ffac5b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u2705 All fixed_bottom simulations complete. Results saved to C:\\WOMBAT_COWER_2025\\WOMBAT\\library\\default\\results\n", + "\u2705 All floating simulations complete. Results saved to C:\\WOMBAT_COWER_2025\\WOMBAT\\library\\default\\results\n" + ] + } + ], + "source": [ + "# Run fixed-bottom simulations\n", + "run_windfarm_simulations(\"fixed_bottom\", random_seeds=RANDOM_SEEDS)\n", + "\n", + "# Run floating simulations\n", + "run_windfarm_simulations(\"floating\", random_seeds=RANDOM_SEEDS)" + ] + }, + { + "cell_type": "markdown", + "id": "6cbf8832-2213-480a-8351-1fb4a5d00fb9", + "metadata": {}, + "source": [ + "## Summarize Results from Multiple Simulations in One Table" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f1745f7-6e23-4209-9e3e-9c600c71679e", + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_simulation(\n", + " library_path=DEFAULT_DATA / \"results\",\n", + " project_capacity_mw=600,\n", + "):\n", + " \"\"\"\n", + " Compute overall average and standard deviation results per technology (fixed_bottom, floating),\n", + " averaging over all years and all simulation runs, returning a formatted DataFrame\n", + " with monetary values in $/kW-yr, availability in %, and a Units column.\n", + "\n", + " Parameters\n", + " ----------\n", + " library_path : str or Path\n", + " Path to the folder containing the CSV results.\n", + " project_capacity_mw : float\n", + " Project capacity in MW to normalize costs to $/kW. Default is 600 MW.\n", + "\n", + " Returns\n", + " -------\n", + " df_summary : pd.DataFrame\n", + " Formatted, transposed DataFrame with categories as rows and columns as technologies,\n", + " including Units column, values rounded to 1 decimal, 0 replaced with NaN, NaN displayed as \"-\".\n", + " Vessel types are shown as indented subcategories of Equipment Cost.\n", + " Columns for mean and standard deviation per technology are included.\n", + " \"\"\"\n", + " summary_dict = {}\n", + " capacity_kw = project_capacity_mw * 1_000 # convert MW to kW\n", + "\n", + " # Step 1: Identify all vessel columns across both technologies\n", + " vessel_cols_all = set()\n", + " for tech in [\"fixed_bottom\", \"floating\"]:\n", + " df_vessels = pd.read_csv(\n", + " Path(library_path) / f\"COWER-2025-{tech}_all_vessel_results.csv\"\n", + " )\n", + " vessel_cols = [\n", + " c for c in df_vessels.columns if c not in [\"run\", \"random_seed\", \"year\"]\n", + " ]\n", + " vessel_cols_all.update(vessel_cols)\n", + " vessel_cols_all = sorted(vessel_cols_all)\n", + "\n", + " for tech in [\"fixed_bottom\", \"floating\"]:\n", + " # Load CSVs\n", + " df_avail = pd.read_csv(\n", + " Path(library_path) / f\"COWER-2025-{tech}_all_availability_results.csv\"\n", + " )\n", + " df_opex = pd.read_csv(\n", + " Path(library_path) / f\"COWER-2025-{tech}_all_opex_results.csv\"\n", + " )\n", + " df_vessels = pd.read_csv(\n", + " Path(library_path) / f\"COWER-2025-{tech}_all_vessel_results.csv\"\n", + " )\n", + "\n", + " # --- Average and std availability over all runs and years ---\n", + " summary_dict.setdefault(\"avg_time_based_availability\", {})[f\"{tech} Mean\"] = (\n", + " df_avail[\"time_based_availability\"].mean() * 100\n", + " )\n", + " summary_dict.setdefault(\"avg_time_based_availability\", {})[f\"{tech} Std\"] = (\n", + " df_avail[\"time_based_availability\"].std() * 100\n", + " )\n", + "\n", + " summary_dict.setdefault(\"avg_production_based_availability\", {})[\n", + " f\"{tech} Mean\"\n", + " ] = df_avail[\"production_based_availability\"].mean() * 100\n", + " summary_dict.setdefault(\"avg_production_based_availability\", {})[\n", + " f\"{tech} Std\"\n", + " ] = df_avail[\"production_based_availability\"].std() * 100\n", + "\n", + " # --- Average and std OpEx over all runs and years ($/kW-yr) ---\n", + " opex_cols = [\"operations\", \"port_fees\", \"total_labor_cost\", \"materials_cost\"]\n", + " for col in opex_cols:\n", + " summary_dict.setdefault(col, {})[f\"{tech} Mean\"] = (\n", + " df_opex[col].mean() / capacity_kw\n", + " )\n", + " summary_dict.setdefault(col, {})[f\"{tech} Std\"] = (\n", + " df_opex[col].std() / capacity_kw\n", + " )\n", + "\n", + " # --- Equipment cost as sum of vessels ($/kW-yr) ---\n", + " vessel_total = df_vessels[\n", + " [c for c in vessel_cols_all if c in df_vessels.columns]\n", + " ].sum(axis=1)\n", + " summary_dict.setdefault(\"equipment_cost\", {})[f\"{tech} Mean\"] = (\n", + " vessel_total.mean() / capacity_kw\n", + " )\n", + " summary_dict.setdefault(\"equipment_cost\", {})[f\"{tech} Std\"] = (\n", + " vessel_total.std() / capacity_kw\n", + " )\n", + "\n", + " # --- Individual vessel costs ($/kW-yr) ---\n", + " for col in vessel_cols_all:\n", + " if col in df_vessels.columns:\n", + " summary_dict.setdefault(f\" - {col}\", {})[f\"{tech} Mean\"] = (\n", + " df_vessels[col].mean() / capacity_kw\n", + " )\n", + " summary_dict.setdefault(f\" - {col}\", {})[f\"{tech} Std\"] = (\n", + " df_vessels[col].std() / capacity_kw\n", + " )\n", + " else:\n", + " summary_dict.setdefault(f\" - {col}\", {})[f\"{tech} Mean\"] = np.nan\n", + " summary_dict.setdefault(f\" - {col}\", {})[f\"{tech} Std\"] = np.nan\n", + "\n", + " # --- OpEx total ($/kW-yr) ---\n", + " op_ex_total = (\n", + " df_opex[\n", + " [\"operations\", \"port_fees\", \"total_labor_cost\", \"materials_cost\"]\n", + " ].sum(axis=1)\n", + " + vessel_total\n", + " )\n", + " summary_dict.setdefault(\"OpEx_total\", {})[f\"{tech} Mean\"] = (\n", + " op_ex_total.mean() / capacity_kw\n", + " )\n", + " summary_dict.setdefault(\"OpEx_total\", {})[f\"{tech} Std\"] = (\n", + " op_ex_total.std() / capacity_kw\n", + " )\n", + "\n", + " # Convert dict to DataFrame\n", + " df_summary = pd.DataFrame(summary_dict).T\n", + "\n", + " # Reorder rows (\ud83d\udfe9 inserted repair time row after availability)\n", + " avail_rows = [\n", + " \"avg_time_based_availability\",\n", + " \"avg_production_based_availability\",\n", + " ]\n", + " opex_rows = [\"operations\", \"port_fees\", \"total_labor_cost\", \"materials_cost\"]\n", + " vessel_rows = [\"equipment_cost\"] + [f\" - {v}\" for v in vessel_cols_all]\n", + " ordered_rows = avail_rows + opex_rows + vessel_rows + [\"OpEx_total\"]\n", + " df_summary = df_summary.loc[[r for r in ordered_rows if r in df_summary.index]]\n", + "\n", + " # Capitalize metrics and analysis names, and replace underscores\n", + " df_summary.columns = [col.replace(\"_\", \" \").title() for col in df_summary.columns]\n", + " df_summary.index = df_summary.index.str.replace(\"_\", \" \").str.title()\n", + "\n", + " # Add Units column\n", + " units = []\n", + " for idx in df_summary.index:\n", + " if \"availability\" in idx:\n", + " units.append(\"%\")\n", + " elif \"repair\" in idx:\n", + " units.append(\"months / yr\")\n", + " else:\n", + " units.append(\"$ / kW-yr\")\n", + " df_summary.insert(0, \"Units\", units)\n", + "\n", + " # Replace 0 with NaN and drop empty rows\n", + " df_summary.replace(0, np.nan, inplace=True)\n", + " df_summary.dropna(how=\"all\", inplace=True)\n", + "\n", + " df_summary.rename(index={\"Opex Total\": \"Total OpEx\"}, inplace=True)\n", + "\n", + " return df_summary" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7319e0d1-43d4-455c-a3fd-8326e6197853", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 UnitsFixed Bottom MeanFixed Bottom StdFloating MeanFloating Std
Avg Time Based Availability$ / kW-yr94.30.192.00.3
Avg Production Based Availability$ / kW-yr93.90.292.00.4
Operations$ / kW-yr47.40.150.90.1
Port Fees$ / kW-yr0.5-17.44.4
Total Labor Cost$ / kW-yr----
Materials Cost$ / kW-yr2.10.63.21.0
Equipment Cost$ / kW-yr73.024.973.017.8
- Anchor Handling Tug$ / kW-yr--4.32.4
- Cable Laying Vessel$ / kW-yr13.49.920.015.2
- Crew Transfer Vessel 1$ / kW-yr1.90.01.90.0
- Crew Transfer Vessel 2$ / kW-yr1.90.01.90.0
- Crew Transfer Vessel 3$ / kW-yr1.90.01.90.0
- Diving Support Vessel$ / kW-yr--2.51.3
- Heavy Lift Vessel$ / kW-yr53.722.4--
- Tugboat 1$ / kW-yr--20.63.8
- Tugboat 2$ / kW-yr--19.83.5
Total OpEx$ / kW-yr123.025.4144.620.4
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_summary = summarize_simulation()\n", + "df_summary.style.format(na_rep=\"-\", precision=1)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "md:myst", + "text_representation": { + "extension": ".md", + "format_name": "myst" + } + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + }, + "source_map": [ + 11, + 29, + 49, + 97, + 115, + 181, + 186, + 189, + 195, + 201, + 207, + 213, + 217, + 224, + 228, + 249, + 255, + 277, + 285, + 304, + 310, + 314, + 331, + 335, + 358, + 364, + 368, + 391, + 396, + 412, + 416, + 435, + 442, + 446, + 481, + 487, + 491, + 511, + 557, + 596, + 602, + 606, + 624, + 627, + 631, + 639, + 645, + 649, + 670, + 675, + 678, + 696, + 701, + 704, + 715, + 718, + 738, + 744, + 750, + 754, + 777, + 781, + 799 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/dinwoodie_validation_100pct_operation_reduction.ipynb b/examples/archival/dinwoodie_validation_100pct_operation_reduction.ipynb similarity index 100% rename from examples/dinwoodie_validation_100pct_operation_reduction.ipynb rename to examples/archival/dinwoodie_validation_100pct_operation_reduction.ipynb diff --git a/examples/avanessova_2025.ipynb b/examples/avanessova_2025.ipynb new file mode 100644 index 00000000..9279bd5b --- /dev/null +++ b/examples/avanessova_2025.ipynb @@ -0,0 +1,4985 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "664d389c-0035-4e68-98bd-44635c233b77", + "metadata": {}, + "source": [ + "# Avanessova, 2025 Dissertation WOMBAT Results Replication\n", + "\n", + "National Renewable Energy Laboratory\\\n", + "Rob Hammond\\\n", + "Last Updated: 13 October 2025\n", + "\n", + "**Note**: The results in this notebook will differ from the published results because this example\n", + "was created during the v0.12 release cycle, and the published results are were using WOMBAT\n", + "v0.8.1. As such there are numerous bug fixes, improvements, and new features in the model that\n", + "will change the results.\n", + "\n", + "For complete details on this study, please see: http://dx.doi.org/10.7488/era/5854." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ce2287d5-761f-417d-837e-cee6868ebe97", + "metadata": {}, + "outputs": [], + "source": [ + "from time import perf_counter\n", + "from pathlib import Path\n", + "from itertools import product\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from wombat import Simulation\n", + "from wombat.core.post_processor import _check_frequency, Metrics\n", + "from wombat.core.library import AVANESSOVA_DISS\n", + "\n", + "pd.options.display.float_format = \"{:,.3f}\".format" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca34002c-f9b0-4a8c-9f5e-7d4d1bffca8d", + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: the original analysis ran 50 simulations, but this will\n", + "# run 5 each for demonstration purposes.\n", + "N = 5\n", + "\n", + "ANALYSIS_LIBRARY = AVANESSOVA_DISS\n", + "RESULTS_DIR = AVANESSOVA_DISS / \"results\"\n", + "\n", + "RESULTS_BASE = {\n", + " \"opex_total\": 0,\n", + " \"opex_levelized\": 0,\n", + " \"vessel_annual\": 0,\n", + " \"vessel_levelized\": 0,\n", + " \"ctv_annual\": 0,\n", + " \"rov_annual\": 0,\n", + " \"cab_annual\": 0,\n", + " \"ahv_annual\": 0,\n", + " \"jackup_annual\": 0,\n", + " \"tugboat_annual\": 0,\n", + " \"cab_mobilization\": 0,\n", + " \"ahv_mobilization\": 0,\n", + " \"jackup_mobilization\": 0,\n", + " \"activities_annual\": 0,\n", + " \"labor_annual\": 0,\n", + " \"activities_levelized\": 0,\n", + " \"labor_levelized\": 0,\n", + " \"energy\": 0,\n", + " \"opex_energy\": 0,\n", + " \"time_availability\": 0,\n", + " \"energy_availability\": 0,\n", + " \"planned_visits\": 0,\n", + " \"unplanned_visits\": 0,\n", + " \"time_to_repair_start\": 0,\n", + " \"quayside_annual\": 0,\n", + " \"gcf\": 0,\n", + " \"ncf\": 0,\n", + "}\n", + "results_columns = [*RESULTS_BASE]\n", + "\n", + "results_order = [\n", + " \"opex_total\",\n", + " \"opex_levelized\",\n", + " \"vessel_annual\",\n", + " \"vessel_levelized\",\n", + " \"quayside_annual\",\n", + " \"ctv_annual\",\n", + " \"rov_annual\",\n", + " \"cab_annual\",\n", + " \"ahv_annual\",\n", + " \"jackup_annual\",\n", + " \"tugboat_annual\",\n", + " \"cab_mobilization\",\n", + " \"ahv_mobilization\",\n", + " \"jackup_mobilization\",\n", + " \"activities_annual\",\n", + " \"labor_annual\",\n", + " \"activities_levelized\",\n", + " \"labor_levelized\",\n", + " \"energy\",\n", + " \"opex_energy\",\n", + " \"time_availability\",\n", + " \"energy_availability\",\n", + " \"planned_visits\",\n", + " \"unplanned_visits\",\n", + " \"time_to_repair_start\",\n", + " \"gcf\",\n", + " \"ncf\",\n", + "]\n", + "vessel_results_order = [\n", + " \"Weather Delay\",\n", + " \"Vessel Usage\",\n", + " \"Mobilization\",\n", + " \"Vessel Inactive\",\n", + " \"Vessel Cost\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "8ce72957-9457-43f5-be28-158d7ebfea26", + "metadata": {}, + "source": [ + "## Results Gathering Functionality" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7175cf8c-e994-4cf8-842d-af39948fd824", + "metadata": {}, + "outputs": [], + "source": [ + "def equipment_breakdowns(\n", + " metrics: Metrics, frequency: str, by_category: bool = False\n", + ") -> pd.DataFrame:\n", + " \"\"\"Calculates the producitivty cost breakdowns for the simulation at a project,\n", + " annual, or monthly level that can be broken out to include the equipment and\n", + " labor components.\n", + "\n", + " .. note:: Doesn't produce a value if there's no cost associated with a \"reason\".\n", + "\n", + " Parameters\n", + " ----------\n", + " frequency : str\n", + " One of \"project\", \"annual\", \"monthly\", or \"month-year\".\n", + " by_category : bool, optional\n", + " Indicates whether to include the equipment and labor categories (True) or\n", + " not (False), by default False.\n", + "\n", + " Returns\n", + " -------\n", + " pd.DataFrame\n", + " Returns pandas ``DataFrame`` with columns:\n", + " - year (if appropriate for frequency)\n", + " - month (if appropriate for frequency)\n", + " - reason\n", + " - time (hours)\n", + " - equipment_cost (if by_category == ``True``)\n", + "\n", + " Raises\n", + " ------\n", + " ValueError\n", + " If ``frequency`` is not one of \"project\", \"annual\", \"monthly\", or\n", + " \"month-year\".\n", + " ValueError\n", + " If ``by_category`` is not one of ``True`` or ``False``.\n", + " \"\"\"\n", + " frequency = _check_frequency(frequency, which=\"all\")\n", + " if not isinstance(by_category, bool):\n", + " raise ValueError(\"``by_equipment`` must be one of ``True`` or ``False``\")\n", + "\n", + " group_filter = [\"action\", \"reason\", \"additional\", \"agent\"]\n", + " if frequency in (\"annual\", \"month-year\"):\n", + " group_filter.insert(0, \"year\")\n", + " elif frequency == \"monthly\":\n", + " group_filter.insert(0, \"month\")\n", + " if frequency == \"month-year\":\n", + " group_filter.insert(1, \"month\")\n", + "\n", + " action_list = [\n", + " \"delay\",\n", + " \"repair\",\n", + " \"maintenance\",\n", + " \"mobilization\",\n", + " \"transferring crew\",\n", + " \"traveling\",\n", + " \"towing\",\n", + " \"mooring reconnection\",\n", + " \"unmooring\",\n", + " ]\n", + " equipment = metrics.events[\n", + " metrics.events[metrics._equipment_cost] > 0\n", + " ].agent.unique()\n", + " time_costs = (\n", + " metrics.events.loc[\n", + " metrics.events.agent.isin(equipment)\n", + " & metrics.events.action.isin(action_list)\n", + " & ~metrics.events.additional.isin([\"work is complete\"]),\n", + " group_filter + [metrics._equipment_cost, \"duration\"],\n", + " ]\n", + " .groupby(group_filter)\n", + " .sum()\n", + " .reset_index()\n", + " )\n", + " time_costs[\"display_reason\"] = [\"\"] * time_costs.shape[0]\n", + "\n", + " non_shift_hours = (\n", + " \"not in working hours\",\n", + " \"work shift has ended; waiting for next shift to start\",\n", + " \"no more return visits will be made\",\n", + " \"will return next year\",\n", + " \"waiting for next operational period\",\n", + " )\n", + " weather_hours = (\"weather delay\", \"weather unsuitable to transfer crew\")\n", + " time_costs.loc[\n", + " (time_costs.action == \"delay\") & (time_costs.additional.isin(non_shift_hours)),\n", + " \"display_reason\",\n", + " ] = \"Not in Shift\"\n", + " time_costs.loc[time_costs.action == \"repair\", \"display_reason\"] = \"Repair\"\n", + " time_costs.loc[time_costs.action == \"maintenance\", \"display_reason\"] = \"Maintenance\"\n", + " time_costs.loc[time_costs.action == \"transferring crew\", \"display_reason\"] = (\n", + " \"Crew Transfer\"\n", + " )\n", + " time_costs.loc[time_costs.action == \"traveling\", \"display_reason\"] = \"Site Travel\"\n", + " time_costs.loc[time_costs.action == \"mobilization\", \"display_reason\"] = (\n", + " \"Mobilization\"\n", + " )\n", + " time_costs.loc[time_costs.additional.isin(weather_hours), \"display_reason\"] = (\n", + " \"Weather Delay\"\n", + " )\n", + " time_costs.loc[time_costs.reason == \"no requests\", \"display_reason\"] = \"No Requests\"\n", + "\n", + " time_costs.reason = time_costs.display_reason\n", + "\n", + " drop_columns = [\"display_reason\", \"additional\", \"action\"]\n", + " group_filter.pop(group_filter.index(\"additional\"))\n", + " group_filter.pop(group_filter.index(\"action\"))\n", + " time_costs = time_costs.drop(columns=drop_columns)\n", + " time_costs = time_costs.groupby(group_filter).sum().reset_index()\n", + "\n", + " month_year = frequency == \"month-year\"\n", + " if frequency in (\"annual\", \"month-year\"):\n", + " years = time_costs.year.unique()\n", + " reasons = time_costs.reason.unique()\n", + " comparison_values = product(years, reasons)\n", + " if month_year:\n", + " months = time_costs.month.unique()\n", + " comparison_values = product(years, months, reasons)\n", + "\n", + " zeros = np.zeros(time_costs.shape[1] - 2).tolist()\n", + " for _year, *_month, _reason in comparison_values:\n", + " row_filter = time_costs.year.values == _year\n", + " row = [_year, _reason] + zeros\n", + " if month_year:\n", + " _month = _month[0]\n", + " row_filter &= time_costs.month.values == _month\n", + " row = [_year, _month, _reason] + zeros[:-1]\n", + "\n", + " row_filter &= time_costs.reason.values == _reason\n", + " if time_costs.loc[row_filter].size > 0:\n", + " continue\n", + " time_costs.loc[time_costs.shape[0]] = row\n", + " elif frequency == \"monthly\":\n", + " months = time_costs.month.unique()\n", + " reasons = time_costs.reason.unique()\n", + " comparison_values = product(months, reasons)\n", + " zeros = np.zeros(time_costs.shape[1] - 2).tolist()\n", + " for _month, _reason in comparison_values:\n", + " row_filter = time_costs.month.values == _month\n", + " row_filter &= time_costs.reason.values == _reason\n", + " row = [_month, _reason] + zeros\n", + " if time_costs.loc[row_filter].size > 0:\n", + " continue\n", + " time_costs.loc[time_costs.shape[0]] = row\n", + "\n", + " new_sort = [\n", + " \"Maintenance\",\n", + " \"Repair\",\n", + " \"Crew Transfer\",\n", + " \"Site Travel\",\n", + " \"Mobilization\",\n", + " \"Weather Delay\",\n", + " \"No Requests\",\n", + " \"Not in Shift\",\n", + " ]\n", + " time_costs.reason = pd.Categorical(time_costs.reason, new_sort)\n", + " time_costs = time_costs.set_index(group_filter)\n", + " if frequency == \"project\":\n", + " return time_costs.sort_values(by=\"reason\")\n", + " if frequency == \"annual\":\n", + " return time_costs.sort_values(by=[\"year\", \"reason\"])\n", + " if frequency == \"monthly\":\n", + " return time_costs.sort_values(by=[\"month\", \"reason\"])\n", + " return time_costs.sort_values(by=[\"year\", \"month\", \"reason\"])\n", + "\n", + "\n", + "def vessel_summary(metrics: Metrics, frequency=\"project\") -> pd.DataFrame:\n", + " keep = [\"Weather Delay\", \"Vessel Usage\", \"Mobilization\", \"Vessel Inactive\"]\n", + " order = [\n", + " \"Crew Transfer Vessel 1\",\n", + " \"Crew Transfer Vessel 2\",\n", + " \"Crew Transfer Vessel 3\",\n", + " \"ROV Support Vessel\",\n", + " \"Cable Laying Vessel\",\n", + " \"Anchor Handling Vessel\",\n", + " \"Heavy Lift Vessel\",\n", + " \"Tugboat 1\",\n", + " ]\n", + " vessel_results = metrics.equipment_labor_cost_breakdowns(\n", + " frequency, by_category=True, by_equipment=True\n", + " )\n", + " vessel_results = vessel_results.unstack(level=\"equipment_name\").fillna(0)\n", + "\n", + " if \"Towing\" not in vessel_results.index:\n", + " vessel_results.loc[\"Towing\"] = 0\n", + "\n", + " vessel_results.loc[\"Vessel Usage\"] = 0\n", + " vessel_results.loc[\"Vessel Inactive\"] = 0\n", + " vessel_results.loc[\"Vessel Usage\"] = (\n", + " vessel_results.loc[\n", + " [\"Maintenance\", \"Repair\", \"Crew Transfer\", \"Site Travel\", \"Towing\"], :\n", + " ]\n", + " .sum(axis=0)\n", + " .to_frame(name=\"Vessel Usage\")\n", + " .T.values\n", + " )\n", + " vessel_results.loc[\"Vessel Inactive\"] = (\n", + " vessel_results.loc[[\"Not in Shift\", \"No Requests\"], :]\n", + " .sum(axis=0)\n", + " .to_frame(name=\"Vessel Inactive\")\n", + " .T.values\n", + " )\n", + " vessel_results = vessel_results.loc[keep]\n", + "\n", + " vessel_results = pd.concat(\n", + " [\n", + " vessel_results.loc[:, \"total_hours\"],\n", + " vessel_results.loc[:, \"equipment_cost\"]\n", + " .sum()\n", + " .to_frame(name=\"Vessel Cost\")\n", + " .T,\n", + " ]\n", + " )\n", + " vessel_results.columns.name = None\n", + " vessel_results.index.name = \"Name\"\n", + " return vessel_results[[el for el in order if el in vessel_results.columns]]\n", + "\n", + "\n", + "def gather_results(sim: Simulation) -> dict:\n", + " \"\"\"Calculates the primary set of results without granular breakdowns.\"\"\"\n", + " project_years = 20\n", + " n_turbines = len(sim.windfarm.turbine_id)\n", + " metrics = sim.metrics\n", + " events = metrics.events\n", + " capacity_kw = sim.windfarm.capacity\n", + " results = {}\n", + "\n", + " opex = metrics.opex(\"project\").values[0][0]\n", + " opex_levelized = opex / capacity_kw / project_years\n", + "\n", + " port_annual = metrics.port_fees(\"project\").values[0][0] / project_years\n", + "\n", + " mobilization = (\n", + " events.loc[events.action == \"mobilization\", [\"agent\", \"total_cost\"]]\n", + " .groupby(\"agent\")\n", + " .sum()\n", + " )\n", + " cab_mobilization = (\n", + " mobilization.loc[\n", + " [el for el in mobilization.index if \"Cable Laying Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " )\n", + " ahv_mobilization = (\n", + " mobilization.loc[\n", + " [el for el in mobilization.index if \"Anchor Handling Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " )\n", + " jackup_mobilization = (\n", + " mobilization.loc[\n", + " [el for el in mobilization.index if \"Heavy Lift Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " )\n", + "\n", + " vessels = metrics.equipment_costs(frequency=\"project\", by_equipment=True)\n", + " vessel_annual = vessels.values.sum() / project_years\n", + " vessel_levelized = vessel_annual / capacity_kw\n", + " ctv_annual = (\n", + " vessels[\n", + " [el for el in vessels.columns if \"Crew Transfer Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " )\n", + " rov_annual = (\n", + " vessels[\n", + " [el for el in vessels.columns if \"ROV Support Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " )\n", + " cab_annual = (\n", + " vessels[\n", + " [el for el in vessels.columns if \"Cable Laying Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " - cab_mobilization\n", + " )\n", + " ahv_annual = (\n", + " vessels[\n", + " [el for el in vessels.columns if \"Anchor Handling Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " - ahv_mobilization\n", + " )\n", + " jackup_annual = (\n", + " vessels[\n", + " [el for el in vessels.columns if \"Heavy Lift Vessel\" in el]\n", + " ].values.sum()\n", + " / project_years\n", + " - jackup_mobilization\n", + " )\n", + " tugboat_annual = (\n", + " vessels[[el for el in vessels.columns if \"Tugboat\" in el]].values.sum()\n", + " / project_years\n", + " )\n", + "\n", + " materials_cost = metrics.events.materials_cost.sum()\n", + " materials_annual = materials_cost / project_years\n", + " materials_levelized = materials_annual / capacity_kw\n", + "\n", + " labor_annual = (\n", + " metrics.project_fixed_costs(\"project\", \"high\").labor.values[0]\n", + " + metrics.labor_costs(\"project\", False).total_labor_cost.values[0]\n", + " ) / project_years\n", + " labor_levelized = labor_annual / capacity_kw\n", + "\n", + " energy = metrics.power_production(\"project\", \"windfarm\", \"kwh\").values[0][0]\n", + " opex_energy = (\n", + " opex / metrics.power_production(\"project\", \"windfarm\", \"mwh\").values[0][0]\n", + " )\n", + " time_availability = metrics.time_based_availability(\n", + " frequency=\"project\", by=\"windfarm\"\n", + " ).values[0][0]\n", + " energy_availability = metrics.production_based_availability(\n", + " frequency=\"project\", by=\"windfarm\"\n", + " ).values[0][0]\n", + "\n", + " serviced_requests = pd.Series(\n", + " events.loc[\n", + " events.agent.isin(metrics.service_equipment_names)\n", + " ].request_id.unique(),\n", + " name=\"request_id\",\n", + " )\n", + " n_maintenance = (\n", + " serviced_requests.str.startswith(\"MNT\").sum() / n_turbines / project_years\n", + " )\n", + " n_repair = (\n", + " serviced_requests.str.startswith(\"RPR\").sum() / n_turbines / project_years\n", + " )\n", + "\n", + " timing = metrics.process_times()\n", + " average_time_to_start = timing.sum().T.time_to_start / timing.sum().T.N\n", + "\n", + " gcf = metrics.capacity_factor(\"gross\", \"project\", \"windfarm\").values[0][0]\n", + " ncf = metrics.capacity_factor(\"net\", \"project\", \"windfarm\").values[0][0]\n", + "\n", + " results[\"opex_total\"] = opex\n", + " results[\"opex_levelized\"] = opex_levelized\n", + " results[\"vessel_annual\"] = vessel_annual\n", + " results[\"vessel_levelized\"] = vessel_levelized\n", + " results[\"quayside_annual\"] = port_annual\n", + " results[\"ctv_annual\"] = ctv_annual\n", + " results[\"rov_annual\"] = rov_annual\n", + " results[\"cab_annual\"] = cab_annual\n", + " results[\"ahv_annual\"] = ahv_annual\n", + " results[\"jackup_annual\"] = jackup_annual\n", + " results[\"tugboat_annual\"] = tugboat_annual\n", + " results[\"cab_mobilization\"] = cab_mobilization\n", + " results[\"ahv_mobilization\"] = ahv_mobilization\n", + " results[\"jackup_mobilization\"] = jackup_mobilization\n", + " results[\"activities_annual\"] = materials_annual\n", + " results[\"labor_annual\"] = labor_annual\n", + " results[\"activities_levelized\"] = materials_levelized\n", + " results[\"labor_levelized\"] = labor_levelized\n", + " results[\"energy\"] = energy\n", + " results[\"opex_energy\"] = opex_energy\n", + " results[\"time_availability\"] = time_availability\n", + " results[\"energy_availability\"] = energy_availability\n", + " results[\"planned_visits\"] = n_maintenance\n", + " results[\"unplanned_visits\"] = n_repair\n", + " results[\"time_to_repair_start\"] = average_time_to_start\n", + " results[\"gcf\"] = gcf\n", + " results[\"ncf\"] = ncf\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "id": "78109a33-9398-44b2-8694-ea0b0ad07c7e", + "metadata": {}, + "source": [ + "## Analysis Running Functionality" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cbd8d793-36f5-4783-967c-fa79956c0677", + "metadata": {}, + "outputs": [], + "source": [ + "def load_and_run(name: str, i: int, rng) -> Simulation:\n", + " \"\"\"Run a given configuration, and output the summary runtime stats and primary results.\"\"\"\n", + " print(f\"{name.rjust(40)} | {i:>3.0f}\", end=\" | \")\n", + " start = perf_counter()\n", + " sim = Simulation(ANALYSIS_LIBRARY, f\"{name}.yaml\", random_generator=rng)\n", + " end = perf_counter()\n", + " print(f\"Load: {(end - start) / 60:.2f} m\", end=\" | \")\n", + "\n", + " start = perf_counter()\n", + " sim.run()\n", + " end = perf_counter()\n", + " print(f\"Run: {(end - start) / 60:5.2f} m\", end=\" | \")\n", + "\n", + " avail = sim.metrics.production_based_availability(\n", + " frequency=\"project\", by=\"windfarm\"\n", + " ).values[0][0]\n", + " opex = sim.metrics.opex(\"project\").values[0][0] / sim.windfarm.capacity\n", + " print(f\"Avail: {avail:6.2%} | Opex ($/kw): {opex: 8,.2f}\")\n", + " sim.env.cleanup_log_files()\n", + " return sim\n", + "\n", + "\n", + "def compile_results(sim: Simulation) -> dict:\n", + " \"\"\"Gathers the primary results, vessel usage summaries, and activities summaries.\"\"\"\n", + " results = gather_results(sim)\n", + "\n", + " # Calculate the vessel breakdown\n", + " vessel_results = vessel_summary(sim.metrics)\n", + "\n", + " # Summarize the repair activities\n", + " materials = sim.metrics.events.loc[\n", + " sim.metrics.events.materials_cost > 0, [\"part_name\", \"reason\", \"materials_cost\"]\n", + " ]\n", + " materials.loc[materials.part_name.str.startswith(\"CB\"), \"part_name\"] = \"cable\"\n", + " materials_results = (\n", + " materials.groupby([\"part_name\", \"reason\"])\n", + " .agg([\"count\", \"sum\"])\n", + " .sort_index()\n", + " .T.droplevel(0)\n", + " .T\n", + " )\n", + "\n", + " return results, vessel_results, materials_results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b430f2cf-3f0c-4fd5-b9d7-264676473292", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " layout_1_in_situ_base | 0 | Load: 0.01 m | Run: 0.81 m | Avail: 93.05% | Opex ($/kw): 1,132.75\n", + " layout_1_in_situ_base | 1 | Load: 0.01 m | Run: 0.81 m | Avail: 93.12% | Opex ($/kw): 1,181.78\n", + " layout_1_in_situ_base | 2 | Load: 0.01 m | Run: 0.84 m | Avail: 93.12% | Opex ($/kw): 1,111.74\n", + " layout_1_in_situ_base | 3 | Load: 0.01 m | Run: 0.83 m | Avail: 93.17% | Opex ($/kw): 1,134.37\n", + " layout_1_in_situ_base | 4 | Load: 0.01 m | Run: 0.85 m | Avail: 93.12% | Opex ($/kw): 1,150.53\n", + " layout_1_in_situ_base_24hr | 0 | Load: 0.01 m | Run: 0.69 m | Avail: 95.16% | Opex ($/kw): 1,087.79\n", + " layout_1_in_situ_base_24hr | 1 | Load: 0.01 m | Run: 0.72 m | Avail: 95.15% | Opex ($/kw): 1,107.02\n", + " layout_1_in_situ_base_24hr | 2 | Load: 0.01 m | Run: 0.75 m | Avail: 95.12% | Opex ($/kw): 1,096.10\n", + " layout_1_in_situ_base_24hr | 3 | Load: 0.01 m | Run: 0.78 m | Avail: 95.17% | Opex ($/kw): 1,117.96\n", + " layout_1_in_situ_base_24hr | 4 | Load: 0.01 m | Run: 0.70 m | Avail: 95.13% | Opex ($/kw): 1,082.55\n", + " layout_1_in_situ_no_major | 0 | Load: 0.01 m | Run: 0.77 m | Avail: 93.27% | Opex ($/kw): 849.08\n", + " layout_1_in_situ_no_major | 1 | Load: 0.01 m | Run: 0.78 m | Avail: 93.33% | Opex ($/kw): 831.17\n", + " layout_1_in_situ_no_major | 2 | Load: 0.01 m | Run: 0.79 m | Avail: 93.35% | Opex ($/kw): 831.69\n", + " layout_1_in_situ_no_major | 3 | Load: 0.01 m | Run: 0.80 m | Avail: 93.31% | Opex ($/kw): 839.04\n", + " layout_1_in_situ_no_major | 4 | Load: 0.01 m | Run: 0.82 m | Avail: 93.33% | Opex ($/kw): 839.43\n", + " layout_1_in_situ_increase_maintenance | 0 | Load: 0.01 m | Run: 0.88 m | Avail: 92.70% | Opex ($/kw): 1,142.81\n", + " layout_1_in_situ_increase_maintenance | 1 | Load: 0.01 m | Run: 0.72 m | Avail: 92.72% | Opex ($/kw): 1,186.83\n", + " layout_1_in_situ_increase_maintenance | 2 | Load: 0.01 m | Run: 0.89 m | Avail: 92.75% | Opex ($/kw): 1,102.30\n", + " layout_1_in_situ_increase_maintenance | 3 | Load: 0.01 m | Run: 0.90 m | Avail: 92.73% | Opex ($/kw): 1,152.98\n", + " layout_1_in_situ_increase_maintenance | 4 | Load: 0.01 m | Run: 0.91 m | Avail: 92.72% | Opex ($/kw): 1,143.34\n", + " layout_1_in_situ_increase_failure | 0 | Load: 0.01 m | Run: 1.27 m | Avail: 92.14% | Opex ($/kw): 1,833.65\n", + " layout_1_in_situ_increase_failure | 1 | Load: 0.01 m | Run: 1.46 m | Avail: 92.01% | Opex ($/kw): 1,968.36\n", + " layout_1_in_situ_increase_failure | 2 | Load: 0.01 m | Run: 1.51 m | Avail: 92.03% | Opex ($/kw): 1,988.94\n", + " layout_1_in_situ_increase_failure | 3 | Load: 0.01 m | Run: 1.28 m | Avail: 92.09% | Opex ($/kw): 1,954.56\n", + " layout_1_in_situ_increase_failure | 4 | Load: 0.01 m | Run: 1.51 m | Avail: 92.09% | Opex ($/kw): 1,931.92\n", + " layout_1_in_situ_increase_failure_24hr | 0 | Load: 0.01 m | Run: 1.05 m | Avail: 93.77% | Opex ($/kw): 1,848.62\n", + " layout_1_in_situ_increase_failure_24hr | 1 | Load: 0.01 m | Run: 0.80 m | Avail: 93.71% | Opex ($/kw): 1,879.34\n", + " layout_1_in_situ_increase_failure_24hr | 2 | Load: 0.01 m | Run: 1.10 m | Avail: 93.74% | Opex ($/kw): 1,872.91\n", + " layout_1_in_situ_increase_failure_24hr | 3 | Load: 0.01 m | Run: 1.12 m | Avail: 93.69% | Opex ($/kw): 1,876.42\n", + " layout_1_in_situ_increase_failure_24hr | 4 | Load: 0.01 m | Run: 0.81 m | Avail: 93.73% | Opex ($/kw): 1,860.72\n", + " layout_1_in_situ_increase_wave | 0 | Load: 0.01 m | Run: 1.04 m | Avail: 95.00% | Opex ($/kw): 1,141.69\n", + " layout_1_in_situ_increase_wave | 1 | Load: 0.01 m | Run: 0.69 m | Avail: 95.00% | Opex ($/kw): 1,109.38\n", + " layout_1_in_situ_increase_wave | 2 | Load: 0.01 m | Run: 0.69 m | Avail: 94.94% | Opex ($/kw): 1,087.88\n", + " layout_1_in_situ_increase_wave | 3 | Load: 0.01 m | Run: 1.07 m | Avail: 94.90% | Opex ($/kw): 1,124.42\n", + " layout_1_in_situ_increase_wave | 4 | Load: 0.01 m | Run: 0.68 m | Avail: 94.98% | Opex ($/kw): 1,044.47\n", + " layout_1_tow_base | 0 | Load: 0.01 m | Run: 0.71 m | Avail: 93.17% | Opex ($/kw): 1,351.05\n", + " layout_1_tow_base | 1 | Load: 0.01 m | Run: 1.12 m | Avail: 93.20% | Opex ($/kw): 1,338.87\n", + " layout_1_tow_base | 2 | Load: 0.01 m | Run: 0.71 m | Avail: 93.26% | Opex ($/kw): 1,340.14\n", + " layout_1_tow_base | 3 | Load: 0.01 m | Run: 0.73 m | Avail: 93.21% | Opex ($/kw): 1,350.28\n", + " layout_1_tow_base | 4 | Load: 0.01 m | Run: 1.17 m | Avail: 93.13% | Opex ($/kw): 1,359.72\n", + " layout_1_tow_base_24hr | 0 | Load: 0.01 m | Run: 0.61 m | Avail: 95.03% | Opex ($/kw): 1,357.37\n", + " layout_1_tow_base_24hr | 1 | Load: 0.01 m | Run: 0.63 m | Avail: 94.98% | Opex ($/kw): 1,365.84\n", + " layout_1_tow_base_24hr | 2 | Load: 0.01 m | Run: 1.15 m | Avail: 95.11% | Opex ($/kw): 1,348.14\n", + " layout_1_tow_base_24hr | 3 | Load: 0.01 m | Run: 0.63 m | Avail: 95.06% | Opex ($/kw): 1,362.67\n", + " layout_1_tow_base_24hr | 4 | Load: 0.01 m | Run: 0.62 m | Avail: 95.05% | Opex ($/kw): 1,336.16\n", + " layout_1_tow_increase_wave | 0 | Load: 0.01 m | Run: 0.69 m | Avail: 95.07% | Opex ($/kw): 1,367.21\n", + " layout_1_tow_increase_wave | 1 | Load: 0.01 m | Run: 1.25 m | Avail: 95.00% | Opex ($/kw): 1,316.86\n", + " layout_1_tow_increase_wave | 2 | Load: 0.01 m | Run: 0.71 m | Avail: 94.91% | Opex ($/kw): 1,304.73\n", + " layout_1_tow_increase_wave | 3 | Load: 0.01 m | Run: 0.70 m | Avail: 95.01% | Opex ($/kw): 1,310.84\n", + " layout_1_tow_increase_wave | 4 | Load: 0.01 m | Run: 0.68 m | Avail: 94.90% | Opex ($/kw): 1,376.95\n", + " layout_1_tow_increase_failure | 0 | Load: 0.01 m | Run: 1.83 m | Avail: 91.43% | Opex ($/kw): 2,006.75\n", + " layout_1_tow_increase_failure | 1 | Load: 0.01 m | Run: 1.12 m | Avail: 91.27% | Opex ($/kw): 2,011.43\n", + " layout_1_tow_increase_failure | 2 | Load: 0.01 m | Run: 1.12 m | Avail: 91.28% | Opex ($/kw): 2,041.24\n", + " layout_1_tow_increase_failure | 3 | Load: 0.01 m | Run: 1.13 m | Avail: 91.31% | Opex ($/kw): 2,034.28\n", + " layout_1_tow_increase_failure | 4 | Load: 0.01 m | Run: 1.86 m | Avail: 91.27% | Opex ($/kw): 2,001.02\n", + " layout_1_tow_increase_maintenance | 0 | Load: 0.01 m | Run: 0.73 m | Avail: 92.70% | Opex ($/kw): 1,365.57\n", + " layout_1_tow_increase_maintenance | 1 | Load: 0.01 m | Run: 0.72 m | Avail: 92.70% | Opex ($/kw): 1,364.93\n", + " layout_1_tow_increase_maintenance | 2 | Load: 0.01 m | Run: 0.71 m | Avail: 92.75% | Opex ($/kw): 1,360.25\n", + " layout_1_tow_increase_maintenance | 3 | Load: 0.01 m | Run: 1.46 m | Avail: 92.74% | Opex ($/kw): 1,373.95\n", + " layout_1_tow_increase_maintenance | 4 | Load: 0.01 m | Run: 0.73 m | Avail: 92.75% | Opex ($/kw): 1,329.85\n", + " layout_2_tow_base | 0 | Load: 0.01 m | Run: 0.71 m | Avail: 93.12% | Opex ($/kw): 1,347.40\n", + " layout_2_tow_base | 1 | Load: 0.01 m | Run: 0.71 m | Avail: 93.16% | Opex ($/kw): 1,360.99\n", + " layout_2_tow_base | 2 | Load: 0.01 m | Run: 0.72 m | Avail: 93.22% | Opex ($/kw): 1,327.05\n", + " layout_2_tow_base | 3 | Load: 0.01 m | Run: 1.66 m | Avail: 93.10% | Opex ($/kw): 1,355.57\n", + " layout_2_tow_base | 4 | Load: 0.01 m | Run: 0.73 m | Avail: 93.19% | Opex ($/kw): 1,339.61\n", + " layout_2_in_situ_base | 0 | Load: 0.01 m | Run: 0.75 m | Avail: 93.12% | Opex ($/kw): 1,162.44\n", + " layout_2_in_situ_base | 1 | Load: 0.01 m | Run: 0.72 m | Avail: 93.12% | Opex ($/kw): 1,161.21\n", + " layout_2_in_situ_base | 2 | Load: 0.01 m | Run: 0.72 m | Avail: 93.11% | Opex ($/kw): 1,179.81\n", + " layout_2_in_situ_base | 3 | Load: 0.01 m | Run: 1.84 m | Avail: 93.05% | Opex ($/kw): 1,183.89\n", + " layout_2_in_situ_base | 4 | Load: 0.01 m | Run: 0.73 m | Avail: 93.11% | Opex ($/kw): 1,137.19\n" + ] + } + ], + "source": [ + "configs = [\n", + " \"layout_1_in_situ_base\",\n", + " \"layout_1_in_situ_base_24hr\",\n", + " \"layout_1_in_situ_no_major\",\n", + " \"layout_1_in_situ_increase_maintenance\",\n", + " \"layout_1_in_situ_increase_failure\",\n", + " \"layout_1_in_situ_increase_failure_24hr\",\n", + " \"layout_1_in_situ_increase_wave\",\n", + " \"layout_1_tow_base\",\n", + " \"layout_1_tow_base_24hr\",\n", + " \"layout_1_tow_increase_wave\",\n", + " \"layout_1_tow_increase_failure\",\n", + " \"layout_1_tow_increase_maintenance\",\n", + " \"layout_2_tow_base\",\n", + " \"layout_2_in_situ_base\",\n", + "]\n", + "\n", + "results_dict = {\n", + " \"primary\": {},\n", + " \"vessel\": {},\n", + " \"activities\": {},\n", + "}\n", + "for config in configs:\n", + " results_list = []\n", + " vessel_results_list = []\n", + " activities_results_list = []\n", + "\n", + " # Reset the random generator for the scenario, and run each scenario 50 times\n", + " rng = np.random.default_rng(seed=34)\n", + " for i in range(N):\n", + " sim = load_and_run(config, i, rng)\n", + " results, vessel_results, materials_results = compile_results(sim)\n", + "\n", + " # Create the primary results data frame and append each to the list of results\n", + " results = pd.DataFrame.from_dict(results, orient=\"index\", columns=[config]).loc[\n", + " results_order\n", + " ]\n", + " results.index.name = \"metric\"\n", + " results_list.append(results)\n", + " vessel_results_list.append(vessel_results)\n", + " activities_results_list.append(materials_results)\n", + "\n", + " sim.env.cleanup_log_files()\n", + "\n", + " # Concatenate each set of results into a single dataframe\n", + " results = (\n", + " pd.concat(results_list)\n", + " .groupby([\"metric\"])\n", + " .agg([\"mean\", \"std\"])\n", + " .loc[results_order]\n", + " )\n", + " vessel_results = (\n", + " pd.concat(vessel_results_list)\n", + " .groupby([\"Name\"])\n", + " .agg([\"mean\", \"std\"])\n", + " .loc[vessel_results_order]\n", + " )\n", + " activities_results = (\n", + " pd.concat(activities_results_list)\n", + " .groupby([\"part_name\", \"reason\"])\n", + " .agg([\"mean\", \"std\"])\n", + " )\n", + "\n", + " # Set the main attributes to make the saved results legible\n", + " vessel_results.columns.names = [\"Vessel\", \"Value\"]\n", + " vessel_results = vessel_results.unstack().to_frame()\n", + " vessel_results.index = (\n", + " vessel_results.index.swaplevel(\"Vessel\", \"Name\")\n", + " .swaplevel(\"Value\", \"Vessel\")\n", + " .set_names([\"Metric\", \"Vessel\", \"Value\"])\n", + " )\n", + " vessel_results = vessel_results.unstack()\n", + " vessel_results.columns = vessel_results.columns.droplevel(0).set_names([None])\n", + "\n", + " # Save to file\n", + " results_dict[\"primary\"][config] = results\n", + " results_dict[\"vessel\"][config] = vessel_results\n", + " results_dict[\"activities\"][config] = activities_results" + ] + }, + { + "cell_type": "markdown", + "id": "fc097510-ed9b-4b35-8f20-db8adcb1f5de", + "metadata": {}, + "source": [ + "## Results Gathering\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78d5b203-5f94-4bdd-8f24-097cb6e38290", + "metadata": {}, + "outputs": [], + "source": [ + "column_order = [\n", + " \"layout_1_in_situ_base\",\n", + " \"layout_1_in_situ_no_major\",\n", + " \"layout_1_in_situ_increase_failure\",\n", + " \"layout_1_in_situ_increase_maintenance\",\n", + " \"layout_1_in_situ_increase_wave\",\n", + " \"layout_1_tow_base\",\n", + " \"layout_1_tow_increase_failure\",\n", + " \"layout_1_tow_increase_maintenance\",\n", + " \"layout_1_tow_increase_wave\",\n", + " \"layout_2_tow_base\",\n", + " \"layout_2_in_situ_base\",\n", + " \"layout_1_in_situ_base_24hr\",\n", + " \"layout_1_in_situ_increase_failure_24hr\",\n", + " \"layout_1_tow_base_24hr\",\n", + "]\n", + "\n", + "\n", + "def calculate_summary_stats(name, df):\n", + " df = df.droplevel(0, axis=1)\n", + " mean = df[[\"mean\"]].rename(columns={\"mean\": name})\n", + " std = df[[\"std\"]].rename(columns={\"std\": name})\n", + " return mean, std\n", + "\n", + "\n", + "def calculate_vessel_stats(name, df):\n", + " return df[[\"mean\"]].rename(columns={\"mean\": name}), df[[\"std\"]].rename(\n", + " columns={\"std\": name}\n", + " )\n", + "\n", + "\n", + "def calculate_activities_stats(name, df):\n", + " ix_drop = ~df.index.get_level_values(\"part_name\").isna()\n", + " count_mean = (\n", + " df[[\"count\"]].droplevel(0, axis=1)[[\"mean\"]].rename(columns={\"mean\": name})\n", + " )\n", + " count_std = (\n", + " df[[\"count\"]].droplevel(0, axis=1)[[\"std\"]].rename(columns={\"std\": name})\n", + " )\n", + " sum_mean = df[[\"sum\"]].droplevel(0, axis=1)[[\"mean\"]].rename(columns={\"mean\": name})\n", + " sum_std = df[[\"sum\"]].droplevel(0, axis=1)[[\"std\"]].rename(columns={\"std\": name})\n", + " return count_mean, count_std, sum_mean, sum_std\n", + "\n", + "\n", + "summary_list = [\n", + " el\n", + " for el in RESULTS_DIR.iterdir()\n", + " if el.name.startswith(\"layout\")\n", + " and el.name.endswith(\"summary.csv\")\n", + " and \"vessel\" not in el.name\n", + "]\n", + "vessel_list = [\n", + " el\n", + " for el in RESULTS_DIR.iterdir()\n", + " if el.name.startswith(\"layout\")\n", + " and el.name.endswith(\"summary.csv\")\n", + " and \"vessel\" in el.name\n", + "]\n", + "activities_list = [\n", + " el\n", + " for el in RESULTS_DIR.iterdir()\n", + " if el.name.startswith(\"layout\") and el.name.endswith(\"activities.csv\")\n", + "]\n", + "\n", + "# Split between mean and standard deviation data\n", + "\n", + "# Overall results\n", + "summary_data = [calculate_summary_stats(*el) for el in results_dict[\"primary\"].items()]\n", + "summary_means = pd.concat([el[0] for el in summary_data], axis=1, join=\"outer\").fillna(\n", + " 0.0\n", + ")\n", + "summary_stds = pd.concat([el[1] for el in summary_data], axis=1, join=\"outer\").fillna(\n", + " 0.0\n", + ")\n", + "\n", + "# Vessels\n", + "vessel_data = [calculate_vessel_stats(*el) for el in results_dict[\"vessel\"].items()]\n", + "vessel_means = (\n", + " pd.concat([el[0] for el in vessel_data], axis=1, join=\"outer\").fillna(0.0) / 20.0\n", + ")\n", + "vessel_stds = (\n", + " pd.concat([el[1] for el in vessel_data], axis=1, join=\"outer\").fillna(0.0) / 20.0\n", + ")\n", + "\n", + "vessel_means = vessel_means.sort_index().reset_index(drop=False)\n", + "vessel_means.Metric = pd.Categorical(\n", + " vessel_means[\"Metric\"],\n", + " ordered=True,\n", + " categories=[\n", + " \"Weather Delay\",\n", + " \"Mobilization\",\n", + " \"Vessel Usage\",\n", + " \"Vessel Inactive\",\n", + " \"Vessel Cost\",\n", + " ],\n", + ")\n", + "vessel_means.Vessel = pd.Categorical(\n", + " vessel_means[\"Vessel\"],\n", + " ordered=True,\n", + " categories=[\n", + " \"Crew Transfer Vessel - 1\",\n", + " \"Crew Transfer Vessel - 2\",\n", + " \"Crew Transfer Vessel - 3\",\n", + " \"ROV Support Vessel\",\n", + " \"Cable Laying Vessel\",\n", + " \"Anchor Handling Vessel\",\n", + " \"Heavy Lift Vessel\",\n", + " \"Tugboat 1\",\n", + " ],\n", + ")\n", + "vessel_means = vessel_means.sort_values([\"Metric\", \"Vessel\"]).set_index(\n", + " [\"Metric\", \"Vessel\"]\n", + ")\n", + "\n", + "vessel_stds = vessel_stds.sort_index().reset_index(drop=False)\n", + "vessel_stds.Metric = pd.Categorical(\n", + " vessel_stds[\"Metric\"],\n", + " ordered=True,\n", + " categories=[\n", + " \"Weather Delay\",\n", + " \"Mobilization\",\n", + " \"Vessel Usage\",\n", + " \"Vessel Inactive\",\n", + " \"Vessel Cost\",\n", + " ],\n", + ")\n", + "vessel_stds.Vessel = pd.Categorical(\n", + " vessel_stds[\"Vessel\"],\n", + " ordered=True,\n", + " categories=[\n", + " \"Crew Transfer Vessel - 1\",\n", + " \"Crew Transfer Vessel - 2\",\n", + " \"Crew Transfer Vessel - 3\",\n", + " \"ROV Support Vessel\",\n", + " \"Cable Laying Vessel\",\n", + " \"Anchor Handling Vessel\",\n", + " \"Heavy Lift Vessel\",\n", + " \"Tugboat 1\",\n", + " ],\n", + ")\n", + "vessel_stds = vessel_stds.sort_values([\"Metric\", \"Vessel\"]).set_index(\n", + " [\"Metric\", \"Vessel\"]\n", + ")\n", + "\n", + "# Activities summary\n", + "activities_data = [\n", + " calculate_activities_stats(*el) for el in results_dict[\"activities\"].items()\n", + "]\n", + "\n", + "count_means = pd.concat([el[0] for el in activities_data], axis=1, join=\"outer\").fillna(\n", + " 0.0\n", + ")[column_order]\n", + "count_stds = pd.concat([el[1] for el in activities_data], axis=1, join=\"outer\").fillna(\n", + " 0.0\n", + ")[column_order]\n", + "sum_means = pd.concat([el[2] for el in activities_data], axis=1, join=\"outer\").fillna(\n", + " 0.0\n", + ")[column_order]\n", + "sum_stds = pd.concat([el[3] for el in activities_data], axis=1, join=\"outer\").fillna(\n", + " 0.0\n", + ")[column_order]\n", + "\n", + "summary_means = summary_means[column_order]\n", + "summary_stds = summary_stds[column_order]\n", + "vessel_means = vessel_means[column_order]\n", + "vessel_stds = vessel_stds[column_order]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "acb9d61b-a173-4f43-9cc2-9d8b5a64ede2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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layout_1_in_situ_baselayout_1_in_situ_no_majorlayout_1_in_situ_increase_failurelayout_1_in_situ_increase_maintenancelayout_1_in_situ_increase_wavelayout_1_tow_baselayout_1_tow_increase_failurelayout_1_tow_increase_maintenancelayout_1_tow_increase_wavelayout_2_tow_baselayout_2_in_situ_baselayout_1_in_situ_base_24hrlayout_1_in_situ_increase_failure_24hrlayout_1_tow_base_24hr
metric
opex_total685,338,709.875502,849,645.5651,161,292,754.234687,391,585.545660,941,569.828808,807,735.1851,211,366,611.759815,345,471.900801,191,529.129807,675,492.692698,943,702.799658,969,586.7991,120,561,355.173812,422,182.539
opex_levelized57.11241.90496.77457.28355.07867.401100.94767.94566.76667.30658.24554.91493.38067.702
vessel_annual11,879,520.0214,416,686.88518,325,853.39512,195,069.50811,348,914.5486,310,451.3477,689,981.2896,617,803.9236,675,270.5136,225,043.40612,512,395.6259,966,689.37914,081,114.6915,724,130.301
vessel_levelized19.7997.36130.54320.32518.91510.51712.81711.03011.12510.37520.85416.61123.4699.540
quayside_annual0.0000.0000.0000.0000.00011,862,500.00011,862,500.00011,862,500.00011,862,500.00011,862,500.0000.0000.0000.00011,862,500.000
ctv_annual2,738,881.6912,738,881.4662,738,895.5042,738,893.9432,738,832.9212,738,881.5202,738,892.6022,738,893.6662,738,833.6212,738,880.9972,738,881.8112,738,820.3492,738,783.5832,738,822.817
rov_annual1,825,736.4991,677,805.4191,890,200.0322,173,903.5671,734,259.7121,823,791.2991,970,584.5882,185,506.3231,775,809.6881,799,714.5151,788,680.4261,358,006.0461,354,359.5591,399,965.840
cab_annual595,193.2560.000920,045.356429,981.1301,345,091.692532,675.157833,017.323577,781.7551,276,340.613482,248.133559,201.186396,454.472728,258.937432,742.329
ahv_annual45,753.9780.00022,066.31031,114.51941,760.44982,355.91379,353.20158,667.30938,047.32048,042.12833,371.43043,677.14631,144.56840,118.353
jackup_annual5,326,704.5970.00010,460,146.1955,611,926.3494,309,969.7730.0000.0000.0000.0000.0005,988,510.7734,019,481.3666,859,568.0430.000
tugboat_annual0.0000.0000.0000.0000.000717,997.4581,471,633.575669,954.870527,239.271797,407.6330.0000.0000.000728,480.962
cab_mobilization395,000.0000.000585,000.000270,000.000295,000.000340,000.000525,000.000335,000.000280,000.000310,000.000380,000.000315,000.000575,000.000345,000.000
ahv_mobilization42,250.0000.00022,750.00029,250.00042,250.00074,750.00071,500.00052,000.00039,000.00048,750.00032,500.00045,500.00032,500.00039,000.000
jackup_mobilization910,000.0000.0001,686,750.000910,000.000841,750.0000.0000.0000.0000.0000.000991,250.0001,049,750.0001,761,500.0000.000
activities_annual20,626,385.00018,964,765.00037,977,749.00020,413,475.00020,897,618.00020,506,405.00039,254,815.00020,525,935.00020,721,260.00020,535,201.00020,673,759.00021,220,781.00040,185,957.00021,273,469.000
labor_annual1,761,030.4721,761,030.3931,761,035.3161,761,034.769800,545.9441,761,030.4121,761,034.2991,761,034.672800,545.9441,761,030.2291,761,030.5141,761,008.9611,760,996.0681,761,009.826
activities_levelized34.37731.60863.29634.02234.82934.17765.42534.21034.53534.22534.45635.36866.97735.456
labor_levelized2.9352.9352.9352.9351.3342.9352.9352.9351.3342.9352.9352.9352.9352.935
energy60,961,156,323.47161,093,452,513.47960,279,143,932.72360,706,473,860.90962,172,281,596.53861,012,939,010.97559,780,756,517.53760,708,890,021.61362,180,988,715.20060,989,469,465.06760,952,698,054.34362,290,817,243.08161,362,061,618.93762,226,679,511.117
opex_energy11.2428.23119.26511.32310.63113.25620.26413.43012.88513.24311.46710.57918.26113.056
time_availability0.9340.9360.9210.9290.9530.9350.9130.9290.9530.9340.9340.9570.9420.956
energy_availability0.9310.9330.9210.9270.9500.9320.9130.9270.9500.9320.9310.9510.9370.950
planned_visits4.1894.1881.4874.0954.2714.1341.4654.0144.2524.1274.1864.4504.3334.426
unplanned_visits11.27211.17916.60111.11311.52711.17017.02411.17911.40311.14411.24511.54322.65411.562
time_to_repair_start807.809823.8146,804.278891.486294.322766.2088,967.330867.236289.423751.301814.567255.456407.884252.236
gcf0.6220.6220.6220.6220.6220.6220.6220.6220.6220.6220.6220.6220.6220.622
ncf0.5800.5810.5730.5770.5910.5800.5680.5770.5910.5800.5790.5920.5830.592
\n", + "
" + ], + "text/plain": [ + " layout_1_in_situ_base layout_1_in_situ_no_major \\\n", + "metric \n", + "opex_total 685,338,709.875 502,849,645.565 \n", + "opex_levelized 57.112 41.904 \n", + "vessel_annual 11,879,520.021 4,416,686.885 \n", + "vessel_levelized 19.799 7.361 \n", + "quayside_annual 0.000 0.000 \n", + "ctv_annual 2,738,881.691 2,738,881.466 \n", + "rov_annual 1,825,736.499 1,677,805.419 \n", + "cab_annual 595,193.256 0.000 \n", + "ahv_annual 45,753.978 0.000 \n", + "jackup_annual 5,326,704.597 0.000 \n", + "tugboat_annual 0.000 0.000 \n", + "cab_mobilization 395,000.000 0.000 \n", + "ahv_mobilization 42,250.000 0.000 \n", + "jackup_mobilization 910,000.000 0.000 \n", + "activities_annual 20,626,385.000 18,964,765.000 \n", + "labor_annual 1,761,030.472 1,761,030.393 \n", + "activities_levelized 34.377 31.608 \n", + "labor_levelized 2.935 2.935 \n", + "energy 60,961,156,323.471 61,093,452,513.479 \n", + "opex_energy 11.242 8.231 \n", + "time_availability 0.934 0.936 \n", + "energy_availability 0.931 0.933 \n", + "planned_visits 4.189 4.188 \n", + "unplanned_visits 11.272 11.179 \n", + "time_to_repair_start 807.809 823.814 \n", + "gcf 0.622 0.622 \n", + "ncf 0.580 0.581 \n", + "\n", + " layout_1_in_situ_increase_failure \\\n", + "metric \n", + "opex_total 1,161,292,754.234 \n", + "opex_levelized 96.774 \n", + "vessel_annual 18,325,853.395 \n", + "vessel_levelized 30.543 \n", + "quayside_annual 0.000 \n", + "ctv_annual 2,738,895.504 \n", + "rov_annual 1,890,200.032 \n", + "cab_annual 920,045.356 \n", + "ahv_annual 22,066.310 \n", + "jackup_annual 10,460,146.195 \n", + "tugboat_annual 0.000 \n", + "cab_mobilization 585,000.000 \n", + "ahv_mobilization 22,750.000 \n", + "jackup_mobilization 1,686,750.000 \n", + "activities_annual 37,977,749.000 \n", + "labor_annual 1,761,035.316 \n", + "activities_levelized 63.296 \n", + "labor_levelized 2.935 \n", + "energy 60,279,143,932.723 \n", + "opex_energy 19.265 \n", + "time_availability 0.921 \n", + "energy_availability 0.921 \n", + "planned_visits 1.487 \n", + "unplanned_visits 16.601 \n", + "time_to_repair_start 6,804.278 \n", + "gcf 0.622 \n", + "ncf 0.573 \n", + "\n", + " layout_1_in_situ_increase_maintenance \\\n", + "metric \n", + "opex_total 687,391,585.545 \n", + "opex_levelized 57.283 \n", + "vessel_annual 12,195,069.508 \n", + "vessel_levelized 20.325 \n", + "quayside_annual 0.000 \n", + "ctv_annual 2,738,893.943 \n", + "rov_annual 2,173,903.567 \n", + "cab_annual 429,981.130 \n", + "ahv_annual 31,114.519 \n", + "jackup_annual 5,611,926.349 \n", + "tugboat_annual 0.000 \n", + "cab_mobilization 270,000.000 \n", + "ahv_mobilization 29,250.000 \n", + "jackup_mobilization 910,000.000 \n", + "activities_annual 20,413,475.000 \n", + "labor_annual 1,761,034.769 \n", + "activities_levelized 34.022 \n", + "labor_levelized 2.935 \n", + "energy 60,706,473,860.909 \n", + "opex_energy 11.323 \n", + "time_availability 0.929 \n", + "energy_availability 0.927 \n", + "planned_visits 4.095 \n", + "unplanned_visits 11.113 \n", + "time_to_repair_start 891.486 \n", + "gcf 0.622 \n", + "ncf 0.577 \n", + "\n", + " layout_1_in_situ_increase_wave layout_1_tow_base \\\n", + "metric \n", + "opex_total 660,941,569.828 808,807,735.185 \n", + "opex_levelized 55.078 67.401 \n", + "vessel_annual 11,348,914.548 6,310,451.347 \n", + "vessel_levelized 18.915 10.517 \n", + "quayside_annual 0.000 11,862,500.000 \n", + "ctv_annual 2,738,832.921 2,738,881.520 \n", + "rov_annual 1,734,259.712 1,823,791.299 \n", + "cab_annual 1,345,091.692 532,675.157 \n", + "ahv_annual 41,760.449 82,355.913 \n", + "jackup_annual 4,309,969.773 0.000 \n", + "tugboat_annual 0.000 717,997.458 \n", + "cab_mobilization 295,000.000 340,000.000 \n", + "ahv_mobilization 42,250.000 74,750.000 \n", + "jackup_mobilization 841,750.000 0.000 \n", + "activities_annual 20,897,618.000 20,506,405.000 \n", + "labor_annual 800,545.944 1,761,030.412 \n", + "activities_levelized 34.829 34.177 \n", + "labor_levelized 1.334 2.935 \n", + "energy 62,172,281,596.538 61,012,939,010.975 \n", + "opex_energy 10.631 13.256 \n", + "time_availability 0.953 0.935 \n", + "energy_availability 0.950 0.932 \n", + "planned_visits 4.271 4.134 \n", + "unplanned_visits 11.527 11.170 \n", + "time_to_repair_start 294.322 766.208 \n", + "gcf 0.622 0.622 \n", + "ncf 0.591 0.580 \n", + "\n", + " layout_1_tow_increase_failure \\\n", + "metric \n", + "opex_total 1,211,366,611.759 \n", + "opex_levelized 100.947 \n", + "vessel_annual 7,689,981.289 \n", + "vessel_levelized 12.817 \n", + "quayside_annual 11,862,500.000 \n", + "ctv_annual 2,738,892.602 \n", + "rov_annual 1,970,584.588 \n", + "cab_annual 833,017.323 \n", + "ahv_annual 79,353.201 \n", + "jackup_annual 0.000 \n", + "tugboat_annual 1,471,633.575 \n", + "cab_mobilization 525,000.000 \n", + "ahv_mobilization 71,500.000 \n", + "jackup_mobilization 0.000 \n", + "activities_annual 39,254,815.000 \n", + "labor_annual 1,761,034.299 \n", + "activities_levelized 65.425 \n", + "labor_levelized 2.935 \n", + "energy 59,780,756,517.537 \n", + "opex_energy 20.264 \n", + "time_availability 0.913 \n", + "energy_availability 0.913 \n", + "planned_visits 1.465 \n", + "unplanned_visits 17.024 \n", + "time_to_repair_start 8,967.330 \n", + "gcf 0.622 \n", + "ncf 0.568 \n", + "\n", + " layout_1_tow_increase_maintenance \\\n", + "metric \n", + "opex_total 815,345,471.900 \n", + "opex_levelized 67.945 \n", + "vessel_annual 6,617,803.923 \n", + "vessel_levelized 11.030 \n", + "quayside_annual 11,862,500.000 \n", + "ctv_annual 2,738,893.666 \n", + "rov_annual 2,185,506.323 \n", + "cab_annual 577,781.755 \n", + "ahv_annual 58,667.309 \n", + "jackup_annual 0.000 \n", + "tugboat_annual 669,954.870 \n", + "cab_mobilization 335,000.000 \n", + "ahv_mobilization 52,000.000 \n", + "jackup_mobilization 0.000 \n", + "activities_annual 20,525,935.000 \n", + "labor_annual 1,761,034.672 \n", + "activities_levelized 34.210 \n", + "labor_levelized 2.935 \n", + "energy 60,708,890,021.613 \n", + "opex_energy 13.430 \n", + "time_availability 0.929 \n", + "energy_availability 0.927 \n", + "planned_visits 4.014 \n", + "unplanned_visits 11.179 \n", + "time_to_repair_start 867.236 \n", + "gcf 0.622 \n", + "ncf 0.577 \n", + "\n", + " layout_1_tow_increase_wave layout_2_tow_base \\\n", + "metric \n", + "opex_total 801,191,529.129 807,675,492.692 \n", + "opex_levelized 66.766 67.306 \n", + "vessel_annual 6,675,270.513 6,225,043.406 \n", + "vessel_levelized 11.125 10.375 \n", + "quayside_annual 11,862,500.000 11,862,500.000 \n", + "ctv_annual 2,738,833.621 2,738,880.997 \n", + "rov_annual 1,775,809.688 1,799,714.515 \n", + "cab_annual 1,276,340.613 482,248.133 \n", + "ahv_annual 38,047.320 48,042.128 \n", + "jackup_annual 0.000 0.000 \n", + "tugboat_annual 527,239.271 797,407.633 \n", + "cab_mobilization 280,000.000 310,000.000 \n", + "ahv_mobilization 39,000.000 48,750.000 \n", + "jackup_mobilization 0.000 0.000 \n", + "activities_annual 20,721,260.000 20,535,201.000 \n", + "labor_annual 800,545.944 1,761,030.229 \n", + "activities_levelized 34.535 34.225 \n", + "labor_levelized 1.334 2.935 \n", + "energy 62,180,988,715.200 60,989,469,465.067 \n", + "opex_energy 12.885 13.243 \n", + "time_availability 0.953 0.934 \n", + "energy_availability 0.950 0.932 \n", + "planned_visits 4.252 4.127 \n", + "unplanned_visits 11.403 11.144 \n", + "time_to_repair_start 289.423 751.301 \n", + "gcf 0.622 0.622 \n", + "ncf 0.591 0.580 \n", + "\n", + " layout_2_in_situ_base layout_1_in_situ_base_24hr \\\n", + "metric \n", + "opex_total 698,943,702.799 658,969,586.799 \n", + "opex_levelized 58.245 54.914 \n", + "vessel_annual 12,512,395.625 9,966,689.379 \n", + "vessel_levelized 20.854 16.611 \n", + "quayside_annual 0.000 0.000 \n", + "ctv_annual 2,738,881.811 2,738,820.349 \n", + "rov_annual 1,788,680.426 1,358,006.046 \n", + "cab_annual 559,201.186 396,454.472 \n", + "ahv_annual 33,371.430 43,677.146 \n", + "jackup_annual 5,988,510.773 4,019,481.366 \n", + "tugboat_annual 0.000 0.000 \n", + "cab_mobilization 380,000.000 315,000.000 \n", + "ahv_mobilization 32,500.000 45,500.000 \n", + "jackup_mobilization 991,250.000 1,049,750.000 \n", + "activities_annual 20,673,759.000 21,220,781.000 \n", + "labor_annual 1,761,030.514 1,761,008.961 \n", + "activities_levelized 34.456 35.368 \n", + "labor_levelized 2.935 2.935 \n", + "energy 60,952,698,054.343 62,290,817,243.081 \n", + "opex_energy 11.467 10.579 \n", + "time_availability 0.934 0.957 \n", + "energy_availability 0.931 0.951 \n", + "planned_visits 4.186 4.450 \n", + "unplanned_visits 11.245 11.543 \n", + "time_to_repair_start 814.567 255.456 \n", + "gcf 0.622 0.622 \n", + "ncf 0.579 0.592 \n", + "\n", + " layout_1_in_situ_increase_failure_24hr \\\n", + "metric \n", + "opex_total 1,120,561,355.173 \n", + "opex_levelized 93.380 \n", + "vessel_annual 14,081,114.691 \n", + "vessel_levelized 23.469 \n", + "quayside_annual 0.000 \n", + "ctv_annual 2,738,783.583 \n", + "rov_annual 1,354,359.559 \n", + "cab_annual 728,258.937 \n", + "ahv_annual 31,144.568 \n", + "jackup_annual 6,859,568.043 \n", + "tugboat_annual 0.000 \n", + "cab_mobilization 575,000.000 \n", + "ahv_mobilization 32,500.000 \n", + "jackup_mobilization 1,761,500.000 \n", + "activities_annual 40,185,957.000 \n", + "labor_annual 1,760,996.068 \n", + "activities_levelized 66.977 \n", + "labor_levelized 2.935 \n", + "energy 61,362,061,618.937 \n", + "opex_energy 18.261 \n", + "time_availability 0.942 \n", + "energy_availability 0.937 \n", + "planned_visits 4.333 \n", + "unplanned_visits 22.654 \n", + "time_to_repair_start 407.884 \n", + "gcf 0.622 \n", + "ncf 0.583 \n", + "\n", + " layout_1_tow_base_24hr \n", + "metric \n", + "opex_total 812,422,182.539 \n", + "opex_levelized 67.702 \n", + "vessel_annual 5,724,130.301 \n", + "vessel_levelized 9.540 \n", + "quayside_annual 11,862,500.000 \n", + "ctv_annual 2,738,822.817 \n", + "rov_annual 1,399,965.840 \n", + "cab_annual 432,742.329 \n", + "ahv_annual 40,118.353 \n", + "jackup_annual 0.000 \n", + "tugboat_annual 728,480.962 \n", + "cab_mobilization 345,000.000 \n", + "ahv_mobilization 39,000.000 \n", + "jackup_mobilization 0.000 \n", + "activities_annual 21,273,469.000 \n", + "labor_annual 1,761,009.826 \n", + "activities_levelized 35.456 \n", + "labor_levelized 2.935 \n", + "energy 62,226,679,511.117 \n", + "opex_energy 13.056 \n", + "time_availability 0.956 \n", + "energy_availability 0.950 \n", + "planned_visits 4.426 \n", + "unplanned_visits 11.562 \n", + "time_to_repair_start 252.236 \n", + "gcf 0.622 \n", + "ncf 0.592 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_means" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1da7e0ab-752e-457a-8808-cdd4e0e3144c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MetricVessel
Weather DelayROV Support Vessel45.90944.50042.84549.87119.25444.35345.39651.56318.44744.88245.52862.33481.02363.212
Cable Laying Vessel23.3770.00041.44919.5227.21325.91737.79430.7968.75421.17822.73223.89942.47123.909
Anchor Handling Vessel2.1280.0000.2521.7641.1954.1314.5102.7150.7250.6941.1731.3503.1376.889
Heavy Lift Vessel91.5140.000172.240103.45839.9360.0000.0000.0000.0000.000106.661130.381220.4160.000
Tugboat 10.0000.0000.0000.0000.000166.014334.314152.27094.207188.2620.0000.0000.000163.818
MobilizationROV Support Vessel0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Cable Laying Vessel134.4000.000196.56090.720201.600114.240176.400112.560188.160104.160127.680105.840194.880115.920
Anchor Handling Vessel21.8400.00014.70018.90054.60038.64036.96026.88040.32025.20016.80025.20016.80025.200
Heavy Lift Vessel672.0000.0001,245.600672.000870.2400.0000.0000.0000.0000.000734.400777.6001,303.2000.000
Tugboat 10.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Vessel UsageROV Support Vessel803.004804.883790.8351,145.130777.776792.777772.4511,135.418777.084791.508800.623530.635533.605528.132
Cable Laying Vessel66.6250.000100.68745.54443.38057.27289.88261.23447.14852.76061.14730.24056.16434.086
Anchor Handling Vessel11.6260.0006.8789.76113.53820.77219.98614.9569.92212.0968.4726.7204.8007.200
Heavy Lift Vessel277.5270.000554.430288.016243.1410.0000.0000.0000.0000.000309.864150.891269.2440.000
Tugboat 10.0000.0000.0000.0000.0008.46117.4697.9007.8869.2360.0000.0000.0009.015
Vessel InactiveROV Support Vessel4,504.4803,944.9584,871.4694,304.1584,261.6304,422.7774,791.9844,532.7854,303.0414,518.8474,396.7314,092.1444,389.9104,153.440
Cable Laying Vessel88.5560.000133.87763.929352.93576.614122.22981.305327.00170.73683.88164.798119.84271.828
Anchor Handling Vessel12.7060.0008.82210.96715.45522.72521.39516.25611.35614.9939.65417.18910.07414.912
Heavy Lift Vessel270.1630.000528.548281.957234.1190.0000.0000.0000.0000.000302.097201.067333.4880.000
Tugboat 10.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Vessel CostROV Support Vessel1,825,736.4991,677,805.4191,890,200.0322,173,903.5671,734,259.7121,823,791.2991,970,584.5882,185,506.3231,775,809.6881,799,714.5151,788,680.4261,358,006.0461,354,359.5591,399,965.840
Cable Laying Vessel990,193.2560.0001,505,045.356699,981.1301,640,091.692872,675.1571,358,017.323912,781.7551,556,340.613792,248.133939,201.186711,454.4721,303,258.937777,742.329
Anchor Handling Vessel88,003.9780.00056,020.38775,455.649105,013.062157,105.913150,853.201110,667.30977,047.32096,792.12865,871.43089,177.14663,644.56898,897.941
Heavy Lift Vessel6,236,704.5970.00012,146,896.1956,521,926.3495,151,719.7730.0000.0000.0000.0000.0006,979,760.7735,069,231.3668,621,068.0430.000
Tugboat 10.0000.0000.0000.0000.000404,201.305814,963.618371,059.237236,515.717457,536.3210.0000.0000.000400,396.130
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layout_1_in_situ_baselayout_1_in_situ_no_majorlayout_1_in_situ_increase_failurelayout_1_in_situ_increase_maintenancelayout_1_in_situ_increase_wavelayout_1_tow_baselayout_1_tow_increase_failurelayout_1_tow_increase_maintenancelayout_1_tow_increase_wavelayout_2_tow_baselayout_2_in_situ_baselayout_1_in_situ_base_24hrlayout_1_in_situ_increase_failure_24hrlayout_1_tow_base_24hr
part_namereason
cablecable & scour survey610.000603.800605.000573.600609.800606.800610.400565.600606.200606.200607.200683.400679.200680.400
cable replacement16.8000.00024.60011.00012.00013.80022.00014.20012.80013.00015.20012.60023.40014.200
central_hydraulics_systemmedium repair422.600430.600818.200444.000438.000410.000846.200408.600442.200421.000419.600424.200870.200439.800
minor repair418.400430.600254.200424.400442.800427.800734.000406.800432.800424.200418.400436.000828.000431.400
drive_train_systemmajor repair1.3330.0001.3331.0001.3331.0001.2001.0001.3331.4001.2001.6673.0001.000
medium repair286.200285.600572.600295.800302.800280.400580.400297.400294.800294.600301.600303.000578.400292.400
minor repair301.200295.000180.000276.400286.000280.600496.800301.600301.200283.600293.600285.000586.200309.600
electrical_systemmajor repair3.5000.0003.0003.2501.0004.0003.2002.0002.5002.6002.2001.7504.2504.250
major replacement2.6000.0003.6002.8001.6001.6003.2001.7502.0003.0002.0002.0005.8002.000
gearboxmajor repair6.6000.00013.6007.2005.4005.20010.0006.8006.6007.2005.8008.40011.6005.600
major replacement6.2000.00012.2008.4007.4006.20013.0004.2007.6007.2007.8007.40012.6007.600
generator_systemmajor repair3.8000.0004.4002.8002.7502.5004.6002.2503.6002.4003.0002.4004.6002.500
major replacement2.4000.0004.2002.0002.7501.8005.2001.6001.5001.8002.4002.8004.0002.000
medium repair425.200436.000838.800421.200424.800415.800840.000439.400421.400426.400422.400447.400852.000432.200
minor repair432.000422.000242.800394.400443.000431.800692.600427.000419.800417.800438.200440.400876.000432.400
main_bearingmajor replacement1.0000.0001.7501.3331.6671.5002.0001.2501.0001.0001.0001.5001.8001.000
mooring_linesmooring and cable inspection580.800587.200584.400543.800582.400585.000579.400553.800588.200582.400581.400610.000621.000614.800
mooring replacement2.6000.0001.7502.2503.2504.6004.4003.4002.4003.0002.0002.8002.0003.000
rotor_bladesmajor repair19.0000.00045.20019.80019.00021.60049.40019.80021.80021.80021.80027.00044.00024.000
major replacement20.6000.00043.20018.20020.60023.40048.00024.20018.60024.00023.60023.20043.00022.400
rotor_systemmedium repair674.600678.2001,330.600664.000677.200666.0001,319.000685.400704.200663.000667.400678.2001,384.600692.400
minor repair662.200664.400389.400661.200677.400667.4001,131.000672.200680.400669.000658.200695.2001,352.000682.000
substructuremedium repair71.40072.200152.20079.60071.60067.800145.40074.40071.60071.60072.20076.600160.60082.400
minor repair372.600377.200227.000363.200383.800371.600622.200372.200388.600365.600379.200384.200766.200391.200
transmission_systemmedium repair1,353.2001,335.6002,662.2001,325.4001,377.8001,359.6002,656.0001,362.4001,348.6001,334.6001,349.8001,367.6002,690.6001,388.200
minor repair1,338.4001,353.4002,643.6001,325.4001,410.4001,338.8002,615.0001,316.2001,360.8001,332.8001,348.0001,372.0002,704.2001,384.600
turbineannual service - HV components720.000719.8000.000719.400740.600719.000484.800715.800740.800719.400720.000755.000721.200754.400
annual service - lifting & safety equipment720.000719.8000.000719.400740.800719.200484.800715.800741.200719.400720.000755.200721.800754.400
annual service - turbine720.200720.2000.000719.800742.200719.200485.000717.000742.800719.400720.000755.400722.200754.600
medium repair203.600206.800384.200202.400198.800203.200386.600200.200201.400194.800198.400197.200400.200214.000
minor repair196.800196.600121.400206.000203.000197.600332.600205.000223.000195.600210.000217.000402.800205.800
yaw_systemmedium repair881.000895.6001,773.000869.400899.400873.8001,719.400861.600881.000869.400866.400906.0001,744.400902.800
minor repair892.600861.200530.200858.200907.000882.8001,491.800863.600873.200889.600863.000912.6001,767.800889.200
\n", + "
" + ], + "text/plain": [ + " layout_1_in_situ_base \\\n", + "part_name reason \n", + "cable cable & scour survey 610.000 \n", + " cable replacement 16.800 \n", + "central_hydraulics_system medium repair 422.600 \n", + " minor repair 418.400 \n", + "drive_train_system major repair 1.333 \n", + " medium repair 286.200 \n", + " minor repair 301.200 \n", + "electrical_system major repair 3.500 \n", + " major replacement 2.600 \n", + "gearbox major repair 6.600 \n", + " major replacement 6.200 \n", + "generator_system major repair 3.800 \n", + " major replacement 2.400 \n", + " medium repair 425.200 \n", + " minor repair 432.000 \n", + "main_bearing major replacement 1.000 \n", + "mooring_lines mooring and cable inspection 580.800 \n", + " mooring replacement 2.600 \n", + "rotor_blades major repair 19.000 \n", + " major replacement 20.600 \n", + "rotor_system medium repair 674.600 \n", + " minor repair 662.200 \n", + "substructure medium repair 71.400 \n", + " minor repair 372.600 \n", + "transmission_system medium repair 1,353.200 \n", + " minor repair 1,338.400 \n", + "turbine annual service - HV components 720.000 \n", + " annual service - lifting & safety equipment 720.000 \n", + " annual service - turbine 720.200 \n", + " medium repair 203.600 \n", + " minor repair 196.800 \n", + "yaw_system medium repair 881.000 \n", + " minor repair 892.600 \n", + "\n", + " layout_1_in_situ_no_major \\\n", + "part_name reason \n", + "cable cable & scour survey 603.800 \n", + " cable replacement 0.000 \n", + "central_hydraulics_system medium repair 430.600 \n", + " minor repair 430.600 \n", + "drive_train_system major repair 0.000 \n", + " medium repair 285.600 \n", + " minor repair 295.000 \n", + "electrical_system major repair 0.000 \n", + " major replacement 0.000 \n", + "gearbox major repair 0.000 \n", + " major replacement 0.000 \n", + "generator_system major repair 0.000 \n", + " major replacement 0.000 \n", + " medium repair 436.000 \n", + " minor repair 422.000 \n", + "main_bearing major replacement 0.000 \n", + "mooring_lines mooring and cable inspection 587.200 \n", + " mooring replacement 0.000 \n", + "rotor_blades major repair 0.000 \n", + " major replacement 0.000 \n", + "rotor_system medium repair 678.200 \n", + " minor repair 664.400 \n", + "substructure medium repair 72.200 \n", + " minor repair 377.200 \n", + "transmission_system medium repair 1,335.600 \n", + " minor repair 1,353.400 \n", + "turbine annual service - HV components 719.800 \n", + " annual service - lifting & safety equipment 719.800 \n", + " annual service - turbine 720.200 \n", + " medium repair 206.800 \n", + " minor repair 196.600 \n", + "yaw_system medium repair 895.600 \n", + " minor repair 861.200 \n", + "\n", + " layout_1_in_situ_increase_failure \\\n", + "part_name reason \n", + "cable cable & scour survey 605.000 \n", + " cable replacement 24.600 \n", + "central_hydraulics_system medium repair 818.200 \n", + " minor repair 254.200 \n", + "drive_train_system major repair 1.333 \n", + " medium repair 572.600 \n", + " minor repair 180.000 \n", + "electrical_system major repair 3.000 \n", + " major replacement 3.600 \n", + "gearbox major repair 13.600 \n", + " major replacement 12.200 \n", + "generator_system major repair 4.400 \n", + " major replacement 4.200 \n", + " medium repair 838.800 \n", + " minor repair 242.800 \n", + "main_bearing major replacement 1.750 \n", + "mooring_lines mooring and cable inspection 584.400 \n", + " mooring replacement 1.750 \n", + "rotor_blades major repair 45.200 \n", + " major replacement 43.200 \n", + "rotor_system medium repair 1,330.600 \n", + " minor repair 389.400 \n", + "substructure medium repair 152.200 \n", + " minor repair 227.000 \n", + "transmission_system medium repair 2,662.200 \n", + " minor repair 2,643.600 \n", + "turbine annual service - HV components 0.000 \n", + " annual service - lifting & safety equipment 0.000 \n", + " annual service - turbine 0.000 \n", + " medium repair 384.200 \n", + " minor repair 121.400 \n", + "yaw_system medium repair 1,773.000 \n", + " minor repair 530.200 \n", + "\n", + " layout_1_in_situ_increase_maintenance \\\n", + "part_name reason \n", + "cable cable & scour survey 573.600 \n", + " cable replacement 11.000 \n", + "central_hydraulics_system medium repair 444.000 \n", + " minor repair 424.400 \n", + "drive_train_system major repair 1.000 \n", + " medium repair 295.800 \n", + " minor repair 276.400 \n", + "electrical_system major repair 3.250 \n", + " major replacement 2.800 \n", + "gearbox major repair 7.200 \n", + " major replacement 8.400 \n", + "generator_system major repair 2.800 \n", + " major replacement 2.000 \n", + " medium repair 421.200 \n", + " minor repair 394.400 \n", + "main_bearing major replacement 1.333 \n", + "mooring_lines mooring and cable inspection 543.800 \n", + " mooring replacement 2.250 \n", + "rotor_blades major repair 19.800 \n", + " major replacement 18.200 \n", + "rotor_system medium repair 664.000 \n", + " minor repair 661.200 \n", + "substructure medium repair 79.600 \n", + " minor repair 363.200 \n", + "transmission_system medium repair 1,325.400 \n", + " minor repair 1,325.400 \n", + "turbine annual service - HV components 719.400 \n", + " annual service - lifting & safety equipment 719.400 \n", + " annual service - turbine 719.800 \n", + " medium repair 202.400 \n", + " minor repair 206.000 \n", + "yaw_system medium repair 869.400 \n", + " minor repair 858.200 \n", + "\n", + " layout_1_in_situ_increase_wave \\\n", + "part_name reason \n", + "cable cable & scour survey 609.800 \n", + " cable replacement 12.000 \n", + "central_hydraulics_system medium repair 438.000 \n", + " minor repair 442.800 \n", + "drive_train_system major repair 1.333 \n", + " medium repair 302.800 \n", + " minor repair 286.000 \n", + "electrical_system major repair 1.000 \n", + " major replacement 1.600 \n", + "gearbox major repair 5.400 \n", + " major replacement 7.400 \n", + "generator_system major repair 2.750 \n", + " major replacement 2.750 \n", + " medium repair 424.800 \n", + " minor repair 443.000 \n", + "main_bearing major replacement 1.667 \n", + "mooring_lines mooring and cable inspection 582.400 \n", + " mooring replacement 3.250 \n", + "rotor_blades major repair 19.000 \n", + " major replacement 20.600 \n", + "rotor_system medium repair 677.200 \n", + " minor repair 677.400 \n", + "substructure medium repair 71.600 \n", + " minor repair 383.800 \n", + "transmission_system medium repair 1,377.800 \n", + " minor repair 1,410.400 \n", + "turbine annual service - HV components 740.600 \n", + " annual service - lifting & safety equipment 740.800 \n", + " annual service - turbine 742.200 \n", + " medium repair 198.800 \n", + " minor repair 203.000 \n", + "yaw_system medium repair 899.400 \n", + " minor repair 907.000 \n", + "\n", + " layout_1_tow_base \\\n", + "part_name reason \n", + "cable cable & scour survey 606.800 \n", + " cable replacement 13.800 \n", + "central_hydraulics_system medium repair 410.000 \n", + " minor repair 427.800 \n", + "drive_train_system major repair 1.000 \n", + " medium repair 280.400 \n", + " minor repair 280.600 \n", + "electrical_system major repair 4.000 \n", + " major replacement 1.600 \n", + "gearbox major repair 5.200 \n", + " major replacement 6.200 \n", + "generator_system major repair 2.500 \n", + " major replacement 1.800 \n", + " medium repair 415.800 \n", + " minor repair 431.800 \n", + "main_bearing major replacement 1.500 \n", + "mooring_lines mooring and cable inspection 585.000 \n", + " mooring replacement 4.600 \n", + "rotor_blades major repair 21.600 \n", + " major replacement 23.400 \n", + "rotor_system medium repair 666.000 \n", + " minor repair 667.400 \n", + "substructure medium repair 67.800 \n", + " minor repair 371.600 \n", + "transmission_system medium repair 1,359.600 \n", + " minor repair 1,338.800 \n", + "turbine annual service - HV components 719.000 \n", + " annual service - lifting & safety equipment 719.200 \n", + " annual service - turbine 719.200 \n", + " medium repair 203.200 \n", + " minor repair 197.600 \n", + "yaw_system medium repair 873.800 \n", + " minor repair 882.800 \n", + "\n", + " layout_1_tow_increase_failure \\\n", + "part_name reason \n", + "cable cable & scour survey 610.400 \n", + " cable replacement 22.000 \n", + "central_hydraulics_system medium repair 846.200 \n", + " minor repair 734.000 \n", + "drive_train_system major repair 1.200 \n", + " medium repair 580.400 \n", + " minor repair 496.800 \n", + "electrical_system major repair 3.200 \n", + " major replacement 3.200 \n", + "gearbox major repair 10.000 \n", + " major replacement 13.000 \n", + "generator_system major repair 4.600 \n", + " major replacement 5.200 \n", + " medium repair 840.000 \n", + " minor repair 692.600 \n", + "main_bearing major replacement 2.000 \n", + "mooring_lines mooring and cable inspection 579.400 \n", + " mooring replacement 4.400 \n", + "rotor_blades major repair 49.400 \n", + " major replacement 48.000 \n", + "rotor_system medium repair 1,319.000 \n", + " minor repair 1,131.000 \n", + "substructure medium repair 145.400 \n", + " minor repair 622.200 \n", + "transmission_system medium repair 2,656.000 \n", + " minor repair 2,615.000 \n", + "turbine annual service - HV components 484.800 \n", + " annual service - lifting & safety equipment 484.800 \n", + " annual service - turbine 485.000 \n", + " medium repair 386.600 \n", + " minor repair 332.600 \n", + "yaw_system medium repair 1,719.400 \n", + " minor repair 1,491.800 \n", + "\n", + " layout_1_tow_increase_maintenance \\\n", + "part_name reason \n", + "cable cable & scour survey 565.600 \n", + " cable replacement 14.200 \n", + "central_hydraulics_system medium repair 408.600 \n", + " minor repair 406.800 \n", + "drive_train_system major repair 1.000 \n", + " medium repair 297.400 \n", + " minor repair 301.600 \n", + "electrical_system major repair 2.000 \n", + " major replacement 1.750 \n", + "gearbox major repair 6.800 \n", + " major replacement 4.200 \n", + "generator_system major repair 2.250 \n", + " major replacement 1.600 \n", + " medium repair 439.400 \n", + " minor repair 427.000 \n", + "main_bearing major replacement 1.250 \n", + "mooring_lines mooring and cable inspection 553.800 \n", + " mooring replacement 3.400 \n", + "rotor_blades major repair 19.800 \n", + " major replacement 24.200 \n", + "rotor_system medium repair 685.400 \n", + " minor repair 672.200 \n", + "substructure medium repair 74.400 \n", + " minor repair 372.200 \n", + "transmission_system medium repair 1,362.400 \n", + " minor repair 1,316.200 \n", + "turbine annual service - HV components 715.800 \n", + " annual service - lifting & safety equipment 715.800 \n", + " annual service - turbine 717.000 \n", + " medium repair 200.200 \n", + " minor repair 205.000 \n", + "yaw_system medium repair 861.600 \n", + " minor repair 863.600 \n", + "\n", + " layout_1_tow_increase_wave \\\n", + "part_name reason \n", + "cable cable & scour survey 606.200 \n", + " cable replacement 12.800 \n", + "central_hydraulics_system medium repair 442.200 \n", + " minor repair 432.800 \n", + "drive_train_system major repair 1.333 \n", + " medium repair 294.800 \n", + " minor repair 301.200 \n", + "electrical_system major repair 2.500 \n", + " major replacement 2.000 \n", + "gearbox major repair 6.600 \n", + " major replacement 7.600 \n", + "generator_system major repair 3.600 \n", + " major replacement 1.500 \n", + " medium repair 421.400 \n", + " minor repair 419.800 \n", + "main_bearing major replacement 1.000 \n", + "mooring_lines mooring and cable inspection 588.200 \n", + " mooring replacement 2.400 \n", + "rotor_blades major repair 21.800 \n", + " major replacement 18.600 \n", + "rotor_system medium repair 704.200 \n", + " minor repair 680.400 \n", + "substructure medium repair 71.600 \n", + " minor repair 388.600 \n", + "transmission_system medium repair 1,348.600 \n", + " minor repair 1,360.800 \n", + "turbine annual service - HV components 740.800 \n", + " annual service - lifting & safety equipment 741.200 \n", + " annual service - turbine 742.800 \n", + " medium repair 201.400 \n", + " minor repair 223.000 \n", + "yaw_system medium repair 881.000 \n", + " minor repair 873.200 \n", + "\n", + " layout_2_tow_base \\\n", + "part_name reason \n", + "cable cable & scour survey 606.200 \n", + " cable replacement 13.000 \n", + "central_hydraulics_system medium repair 421.000 \n", + " minor repair 424.200 \n", + "drive_train_system major repair 1.400 \n", + " medium repair 294.600 \n", + " minor repair 283.600 \n", + "electrical_system major repair 2.600 \n", + " major replacement 3.000 \n", + "gearbox major repair 7.200 \n", + " major replacement 7.200 \n", + "generator_system major repair 2.400 \n", + " major replacement 1.800 \n", + " medium repair 426.400 \n", + " minor repair 417.800 \n", + "main_bearing major replacement 1.000 \n", + "mooring_lines mooring and cable inspection 582.400 \n", + " mooring replacement 3.000 \n", + "rotor_blades major repair 21.800 \n", + " major replacement 24.000 \n", + "rotor_system medium repair 663.000 \n", + " minor repair 669.000 \n", + "substructure medium repair 71.600 \n", + " minor repair 365.600 \n", + "transmission_system medium repair 1,334.600 \n", + " minor repair 1,332.800 \n", + "turbine annual service - HV components 719.400 \n", + " annual service - lifting & safety equipment 719.400 \n", + " annual service - turbine 719.400 \n", + " medium repair 194.800 \n", + " minor repair 195.600 \n", + "yaw_system medium repair 869.400 \n", + " minor repair 889.600 \n", + "\n", + " layout_2_in_situ_base \\\n", + "part_name reason \n", + "cable cable & scour survey 607.200 \n", + " cable replacement 15.200 \n", + "central_hydraulics_system medium repair 419.600 \n", + " minor repair 418.400 \n", + "drive_train_system major repair 1.200 \n", + " medium repair 301.600 \n", + " minor repair 293.600 \n", + "electrical_system major repair 2.200 \n", + " major replacement 2.000 \n", + "gearbox major repair 5.800 \n", + " major replacement 7.800 \n", + "generator_system major repair 3.000 \n", + " major replacement 2.400 \n", + " medium repair 422.400 \n", + " minor repair 438.200 \n", + "main_bearing major replacement 1.000 \n", + "mooring_lines mooring and cable inspection 581.400 \n", + " mooring replacement 2.000 \n", + "rotor_blades major repair 21.800 \n", + " major replacement 23.600 \n", + "rotor_system medium repair 667.400 \n", + " minor repair 658.200 \n", + "substructure medium repair 72.200 \n", + " minor repair 379.200 \n", + "transmission_system medium repair 1,349.800 \n", + " minor repair 1,348.000 \n", + "turbine annual service - HV components 720.000 \n", + " annual service - lifting & safety equipment 720.000 \n", + " annual service - turbine 720.000 \n", + " medium repair 198.400 \n", + " minor repair 210.000 \n", + "yaw_system medium repair 866.400 \n", + " minor repair 863.000 \n", + "\n", + " layout_1_in_situ_base_24hr \\\n", + "part_name reason \n", + "cable cable & scour survey 683.400 \n", + " cable replacement 12.600 \n", + "central_hydraulics_system medium repair 424.200 \n", + " minor repair 436.000 \n", + "drive_train_system major repair 1.667 \n", + " medium repair 303.000 \n", + " minor repair 285.000 \n", + "electrical_system major repair 1.750 \n", + " major replacement 2.000 \n", + "gearbox major repair 8.400 \n", + " major replacement 7.400 \n", + "generator_system major repair 2.400 \n", + " major replacement 2.800 \n", + " medium repair 447.400 \n", + " minor repair 440.400 \n", + "main_bearing major replacement 1.500 \n", + "mooring_lines mooring and cable inspection 610.000 \n", + " mooring replacement 2.800 \n", + "rotor_blades major repair 27.000 \n", + " major replacement 23.200 \n", + "rotor_system medium repair 678.200 \n", + " minor repair 695.200 \n", + "substructure medium repair 76.600 \n", + " minor repair 384.200 \n", + "transmission_system medium repair 1,367.600 \n", + " minor repair 1,372.000 \n", + "turbine annual service - HV components 755.000 \n", + " annual service - lifting & safety equipment 755.200 \n", + " annual service - turbine 755.400 \n", + " medium repair 197.200 \n", + " minor repair 217.000 \n", + "yaw_system medium repair 906.000 \n", + " minor repair 912.600 \n", + "\n", + " layout_1_in_situ_increase_failure_24hr \\\n", + "part_name reason \n", + "cable cable & scour survey 679.200 \n", + " cable replacement 23.400 \n", + "central_hydraulics_system medium repair 870.200 \n", + " minor repair 828.000 \n", + "drive_train_system major repair 3.000 \n", + " medium repair 578.400 \n", + " minor repair 586.200 \n", + "electrical_system major repair 4.250 \n", + " major replacement 5.800 \n", + "gearbox major repair 11.600 \n", + " major replacement 12.600 \n", + "generator_system major repair 4.600 \n", + " major replacement 4.000 \n", + " medium repair 852.000 \n", + " minor repair 876.000 \n", + "main_bearing major replacement 1.800 \n", + "mooring_lines mooring and cable inspection 621.000 \n", + " mooring replacement 2.000 \n", + "rotor_blades major repair 44.000 \n", + " major replacement 43.000 \n", + "rotor_system medium repair 1,384.600 \n", + " minor repair 1,352.000 \n", + "substructure medium repair 160.600 \n", + " minor repair 766.200 \n", + "transmission_system medium repair 2,690.600 \n", + " minor repair 2,704.200 \n", + "turbine annual service - HV components 721.200 \n", + " annual service - lifting & safety equipment 721.800 \n", + " annual service - turbine 722.200 \n", + " medium repair 400.200 \n", + " minor repair 402.800 \n", + "yaw_system medium repair 1,744.400 \n", + " minor repair 1,767.800 \n", + "\n", + " layout_1_tow_base_24hr \n", + "part_name reason \n", + "cable cable & scour survey 680.400 \n", + " cable replacement 14.200 \n", + "central_hydraulics_system medium repair 439.800 \n", + " minor repair 431.400 \n", + "drive_train_system major repair 1.000 \n", + " medium repair 292.400 \n", + " minor repair 309.600 \n", + "electrical_system major repair 4.250 \n", + " major replacement 2.000 \n", + "gearbox major repair 5.600 \n", + " major replacement 7.600 \n", + "generator_system major repair 2.500 \n", + " major replacement 2.000 \n", + " medium repair 432.200 \n", + " minor repair 432.400 \n", + "main_bearing major replacement 1.000 \n", + "mooring_lines mooring and cable inspection 614.800 \n", + " mooring replacement 3.000 \n", + "rotor_blades major repair 24.000 \n", + " major replacement 22.400 \n", + "rotor_system medium repair 692.400 \n", + " minor repair 682.000 \n", + "substructure medium repair 82.400 \n", + " minor repair 391.200 \n", + "transmission_system medium repair 1,388.200 \n", + " minor repair 1,384.600 \n", + "turbine annual service - HV components 754.400 \n", + " annual service - lifting & safety equipment 754.400 \n", + " annual service - turbine 754.600 \n", + " medium repair 214.000 \n", + " minor repair 205.800 \n", + "yaw_system medium repair 902.800 \n", + " minor repair 889.200 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count_means" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "76e0aed4-a7b3-4700-944f-84075c009d43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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layout_1_in_situ_baselayout_1_in_situ_no_majorlayout_1_in_situ_increase_failurelayout_1_in_situ_increase_maintenancelayout_1_in_situ_increase_wavelayout_1_tow_baselayout_1_tow_increase_failurelayout_1_tow_increase_maintenancelayout_1_tow_increase_wavelayout_2_tow_baselayout_2_in_situ_baselayout_1_in_situ_base_24hrlayout_1_in_situ_increase_failure_24hrlayout_1_tow_base_24hr
part_namereason
cablecable & scour survey6,100,000.0006,038,000.0006,050,000.0005,736,000.0006,098,000.0006,068,000.0006,104,000.0005,656,000.0006,062,000.0006,062,000.0006,072,000.0006,834,000.0006,792,000.0006,804,000.000
cable replacement3,360,000.0000.0004,920,000.0002,200,000.0002,400,000.0002,760,000.0004,400,000.0002,840,000.0002,560,000.0002,600,000.0003,040,000.0002,520,000.0004,680,000.0002,840,000.000
central_hydraulics_systemmedium repair32,836,020.00033,457,620.00063,574,140.00034,498,800.00034,032,600.00031,857,000.00065,749,740.00031,748,220.00034,358,940.00032,711,700.00032,602,920.00032,960,340.00067,614,540.00034,172,460.000
minor repair1,757,280.0001,808,520.0001,067,640.0001,782,480.0001,859,760.0001,796,760.0003,082,800.0001,708,560.0001,817,760.0001,781,640.0001,757,280.0001,831,200.0003,477,600.0001,811,880.000
drive_train_systemmajor repair80,000.0000.00080,000.00060,000.00080,000.00060,000.00072,000.00060,000.00080,000.00084,000.00072,000.000100,000.000180,000.00060,000.000
medium repair22,237,740.00022,191,120.00044,491,020.00022,983,660.00023,527,560.00021,787,080.00045,097,080.00023,107,980.00022,905,960.00022,890,420.00023,434,320.00023,543,100.00044,941,680.00022,719,480.000
minor repair1,265,040.0001,239,000.000756,000.0001,160,880.0001,201,200.0001,178,520.0002,086,560.0001,266,720.0001,265,040.0001,191,120.0001,233,120.0001,197,000.0002,462,040.0001,300,320.000
electrical_systemmajor repair350,000.0000.000300,000.000325,000.000100,000.000400,000.000320,000.000200,000.000250,000.000260,000.000220,000.000175,000.000425,000.000425,000.000
major replacement1,300,000.0000.0001,800,000.0001,400,000.000800,000.000800,000.0001,600,000.000875,000.0001,000,000.0001,500,000.0001,000,000.0001,000,000.0002,900,000.0001,000,000.000
gearboxmajor repair1,386,000.0000.0002,856,000.0001,512,000.0001,134,000.0001,092,000.0002,100,000.0001,428,000.0001,386,000.0001,512,000.0001,218,000.0001,764,000.0002,436,000.0001,176,000.000
major replacement6,510,000.0000.00012,810,000.0008,820,000.0007,770,000.0006,510,000.00013,650,000.0004,410,000.0007,980,000.0007,560,000.0008,190,000.0007,770,000.00013,230,000.0007,980,000.000
generator_systemmajor repair1,140,000.0000.0001,320,000.000840,000.000825,000.000750,000.0001,380,000.000675,000.0001,080,000.000720,000.000900,000.000720,000.0001,380,000.000750,000.000
major replacement3,600,000.0000.0006,300,000.0003,000,000.0004,125,000.0002,700,000.0007,800,000.0002,400,000.0002,250,000.0002,700,000.0003,600,000.0004,200,000.0006,000,000.0003,000,000.000
medium repair33,038,040.00033,877,200.00065,174,760.00032,727,240.00033,006,960.00032,307,660.00065,268,000.00034,141,380.00032,742,780.00033,131,280.00032,820,480.00034,762,980.00066,200,400.00033,581,940.000
minor repair1,814,400.0001,772,400.0001,019,760.0001,656,480.0001,860,600.0001,813,560.0002,908,920.0001,793,400.0001,763,160.0001,754,760.0001,840,440.0001,849,680.0003,679,200.0001,816,080.000
main_bearingmajor replacement300,000.0000.000525,000.000400,000.000500,000.000450,000.000600,000.000375,000.000300,000.000300,000.000300,000.000450,000.000540,000.000300,000.000
mooring_linesmooring and cable inspection5,808,000.0005,872,000.0005,844,000.0005,438,000.0005,824,000.0005,850,000.0005,794,000.0005,538,000.0005,882,000.0005,824,000.0005,814,000.0006,100,000.0006,210,000.0006,148,000.000
mooring replacement1,482,000.0000.000997,500.0001,282,500.0001,852,500.0002,622,000.0002,508,000.0001,938,000.0001,368,000.0001,710,000.0001,140,000.0001,596,000.0001,140,000.0001,710,000.000
rotor_bladesmajor repair2,470,000.0000.0005,876,000.0002,574,000.0002,470,000.0002,808,000.0006,422,000.0002,574,000.0002,834,000.0002,834,000.0002,834,000.0003,510,000.0005,720,000.0003,120,000.000
major replacement13,390,000.0000.00028,080,000.00011,830,000.00013,390,000.00015,210,000.00031,200,000.00015,730,000.00012,090,000.00015,600,000.00015,340,000.00015,080,000.00027,950,000.00014,560,000.000
rotor_systemmedium repair52,416,420.00052,696,140.000103,387,620.00051,592,800.00052,618,440.00051,748,200.000102,486,300.00053,255,580.00054,716,340.00051,515,100.00051,856,980.00052,696,140.000107,583,420.00053,799,480.000
minor repair2,781,240.0002,790,480.0001,635,480.0002,777,040.0002,845,080.0002,803,080.0004,750,200.0002,823,240.0002,857,680.0002,809,800.0002,764,440.0002,919,840.0005,678,400.0002,864,400.000
substructuremedium repair5,547,780.0005,609,940.00011,825,940.0006,184,920.0005,563,320.0005,268,060.00011,297,580.0005,780,880.0005,563,320.0005,563,320.0005,609,940.0005,951,820.00012,478,620.0006,402,480.000
minor repair1,564,920.0001,584,240.000953,400.0001,525,440.0001,611,960.0001,560,720.0002,613,240.0001,563,240.0001,632,120.0001,535,520.0001,592,640.0001,613,640.0003,218,040.0001,643,040.000
transmission_systemmedium repair105,143,640.000103,776,120.000206,852,940.000102,983,580.000107,055,060.000105,640,920.000206,371,200.000105,858,480.000104,786,220.000103,698,420.000104,879,460.000106,262,520.000209,059,620.000107,863,140.000
minor repair5,621,280.0005,684,280.00011,103,120.0005,566,680.0005,923,680.0005,622,960.00010,983,000.0005,528,040.0005,715,360.0005,597,760.0005,661,600.0005,762,400.00011,357,640.0005,815,320.000
turbineannual service - HV components3,600,000.0003,599,000.0000.0003,597,000.0003,703,000.0003,595,000.0002,424,000.0003,579,000.0003,704,000.0003,597,000.0003,600,000.0003,775,000.0003,606,000.0003,772,000.000
annual service - lifting & safety equipment3,600,000.0003,599,000.0000.0003,597,000.0003,704,000.0003,596,000.0002,424,000.0003,579,000.0003,706,000.0003,597,000.0003,600,000.0003,776,000.0003,609,000.0003,772,000.000
annual service - turbine3,601,000.0003,601,000.0000.0003,599,000.0003,711,000.0003,596,000.0002,425,000.0003,585,000.0003,714,000.0003,597,000.0003,600,000.0003,777,000.0003,611,000.0003,773,000.000
medium repair15,819,720.00016,068,360.00029,852,340.00015,726,480.00015,446,760.00015,788,640.00030,038,820.00015,555,540.00015,648,780.00015,135,960.00015,415,680.00015,322,440.00031,095,540.00016,627,800.000
minor repair826,560.000825,720.000509,880.000865,200.000852,600.000829,920.0001,396,920.000861,000.000936,600.000821,520.000882,000.000911,400.0001,691,760.000864,360.000
yaw_systemmedium repair68,453,700.00069,588,120.000137,762,100.00067,552,380.00069,883,380.00067,894,260.000133,597,380.00066,946,320.00068,453,700.00067,552,380.00067,319,280.00070,396,200.000135,539,880.00070,147,560.000
minor repair3,748,920.0003,617,040.0002,226,840.0003,604,440.0003,809,400.0003,707,760.0006,265,560.0003,627,120.0003,667,440.0003,736,320.0003,624,600.0003,832,920.0007,424,760.0003,734,640.000
\n", + "
" + ], + "text/plain": [ + " layout_1_in_situ_base \\\n", + "part_name reason \n", + "cable cable & scour survey 6,100,000.000 \n", + " cable replacement 3,360,000.000 \n", + "central_hydraulics_system medium repair 32,836,020.000 \n", + " minor repair 1,757,280.000 \n", + "drive_train_system major repair 80,000.000 \n", + " medium repair 22,237,740.000 \n", + " minor repair 1,265,040.000 \n", + "electrical_system major repair 350,000.000 \n", + " major replacement 1,300,000.000 \n", + "gearbox major repair 1,386,000.000 \n", + " major replacement 6,510,000.000 \n", + "generator_system major repair 1,140,000.000 \n", + " major replacement 3,600,000.000 \n", + " medium repair 33,038,040.000 \n", + " minor repair 1,814,400.000 \n", + "main_bearing major replacement 300,000.000 \n", + "mooring_lines mooring and cable inspection 5,808,000.000 \n", + " mooring replacement 1,482,000.000 \n", + "rotor_blades major repair 2,470,000.000 \n", + " major replacement 13,390,000.000 \n", + "rotor_system medium repair 52,416,420.000 \n", + " minor repair 2,781,240.000 \n", + "substructure medium repair 5,547,780.000 \n", + " minor repair 1,564,920.000 \n", + "transmission_system medium repair 105,143,640.000 \n", + " minor repair 5,621,280.000 \n", + "turbine annual service - HV components 3,600,000.000 \n", + " annual service - lifting & safety equipment 3,600,000.000 \n", + " annual service - turbine 3,601,000.000 \n", + " medium repair 15,819,720.000 \n", + " minor repair 826,560.000 \n", + "yaw_system medium repair 68,453,700.000 \n", + " minor repair 3,748,920.000 \n", + "\n", + " layout_1_in_situ_no_major \\\n", + "part_name reason \n", + "cable cable & scour survey 6,038,000.000 \n", + " cable replacement 0.000 \n", + "central_hydraulics_system medium repair 33,457,620.000 \n", + " minor repair 1,808,520.000 \n", + "drive_train_system major repair 0.000 \n", + " medium repair 22,191,120.000 \n", + " minor repair 1,239,000.000 \n", + "electrical_system major repair 0.000 \n", + " major replacement 0.000 \n", + "gearbox major repair 0.000 \n", + " major replacement 0.000 \n", + "generator_system major repair 0.000 \n", + " major replacement 0.000 \n", + " medium repair 33,877,200.000 \n", + " minor repair 1,772,400.000 \n", + "main_bearing major replacement 0.000 \n", + "mooring_lines mooring and cable inspection 5,872,000.000 \n", + " mooring replacement 0.000 \n", + "rotor_blades major repair 0.000 \n", + " major replacement 0.000 \n", + "rotor_system medium repair 52,696,140.000 \n", + " minor repair 2,790,480.000 \n", + "substructure medium repair 5,609,940.000 \n", + " minor repair 1,584,240.000 \n", + "transmission_system medium repair 103,776,120.000 \n", + " minor repair 5,684,280.000 \n", + "turbine annual service - HV components 3,599,000.000 \n", + " annual service - lifting & safety equipment 3,599,000.000 \n", + " annual service - turbine 3,601,000.000 \n", + " medium repair 16,068,360.000 \n", + " minor repair 825,720.000 \n", + "yaw_system medium repair 69,588,120.000 \n", + " minor repair 3,617,040.000 \n", + "\n", + " layout_1_in_situ_increase_failure \\\n", + "part_name reason \n", + "cable cable & scour survey 6,050,000.000 \n", + " cable replacement 4,920,000.000 \n", + "central_hydraulics_system medium repair 63,574,140.000 \n", + " minor repair 1,067,640.000 \n", + "drive_train_system major repair 80,000.000 \n", + " medium repair 44,491,020.000 \n", + " minor repair 756,000.000 \n", + "electrical_system major repair 300,000.000 \n", + " major replacement 1,800,000.000 \n", + "gearbox major repair 2,856,000.000 \n", + " major replacement 12,810,000.000 \n", + "generator_system major repair 1,320,000.000 \n", + " major replacement 6,300,000.000 \n", + " medium repair 65,174,760.000 \n", + " minor repair 1,019,760.000 \n", + "main_bearing major replacement 525,000.000 \n", + "mooring_lines mooring and cable inspection 5,844,000.000 \n", + " mooring replacement 997,500.000 \n", + "rotor_blades major repair 5,876,000.000 \n", + " major replacement 28,080,000.000 \n", + "rotor_system medium repair 103,387,620.000 \n", + " minor repair 1,635,480.000 \n", + "substructure medium repair 11,825,940.000 \n", + " minor repair 953,400.000 \n", + "transmission_system medium repair 206,852,940.000 \n", + " minor repair 11,103,120.000 \n", + "turbine annual service - HV components 0.000 \n", + " annual service - lifting & safety equipment 0.000 \n", + " annual service - turbine 0.000 \n", + " medium repair 29,852,340.000 \n", + " minor repair 509,880.000 \n", + "yaw_system medium repair 137,762,100.000 \n", + " minor repair 2,226,840.000 \n", + "\n", + " layout_1_in_situ_increase_maintenance \\\n", + "part_name reason \n", + "cable cable & scour survey 5,736,000.000 \n", + " cable replacement 2,200,000.000 \n", + "central_hydraulics_system medium repair 34,498,800.000 \n", + " minor repair 1,782,480.000 \n", + "drive_train_system major repair 60,000.000 \n", + " medium repair 22,983,660.000 \n", + " minor repair 1,160,880.000 \n", + "electrical_system major repair 325,000.000 \n", + " major replacement 1,400,000.000 \n", + "gearbox major repair 1,512,000.000 \n", + " major replacement 8,820,000.000 \n", + "generator_system major repair 840,000.000 \n", + " major replacement 3,000,000.000 \n", + " medium repair 32,727,240.000 \n", + " minor repair 1,656,480.000 \n", + "main_bearing major replacement 400,000.000 \n", + "mooring_lines mooring and cable inspection 5,438,000.000 \n", + " mooring replacement 1,282,500.000 \n", + "rotor_blades major repair 2,574,000.000 \n", + " major replacement 11,830,000.000 \n", + "rotor_system medium repair 51,592,800.000 \n", + " minor repair 2,777,040.000 \n", + "substructure medium repair 6,184,920.000 \n", + " minor repair 1,525,440.000 \n", + "transmission_system medium repair 102,983,580.000 \n", + " minor repair 5,566,680.000 \n", + "turbine annual service - HV components 3,597,000.000 \n", + " annual service - lifting & safety equipment 3,597,000.000 \n", + " annual service - turbine 3,599,000.000 \n", + " medium repair 15,726,480.000 \n", + " minor repair 865,200.000 \n", + "yaw_system medium repair 67,552,380.000 \n", + " minor repair 3,604,440.000 \n", + "\n", + " layout_1_in_situ_increase_wave \\\n", + "part_name reason \n", + "cable cable & scour survey 6,098,000.000 \n", + " cable replacement 2,400,000.000 \n", + "central_hydraulics_system medium repair 34,032,600.000 \n", + " minor repair 1,859,760.000 \n", + "drive_train_system major repair 80,000.000 \n", + " medium repair 23,527,560.000 \n", + " minor repair 1,201,200.000 \n", + "electrical_system major repair 100,000.000 \n", + " major replacement 800,000.000 \n", + "gearbox major repair 1,134,000.000 \n", + " major replacement 7,770,000.000 \n", + "generator_system major repair 825,000.000 \n", + " major replacement 4,125,000.000 \n", + " medium repair 33,006,960.000 \n", + " minor repair 1,860,600.000 \n", + "main_bearing major replacement 500,000.000 \n", + "mooring_lines mooring and cable inspection 5,824,000.000 \n", + " mooring replacement 1,852,500.000 \n", + "rotor_blades major repair 2,470,000.000 \n", + " major replacement 13,390,000.000 \n", + "rotor_system medium repair 52,618,440.000 \n", + " minor repair 2,845,080.000 \n", + "substructure medium repair 5,563,320.000 \n", + " minor repair 1,611,960.000 \n", + "transmission_system medium repair 107,055,060.000 \n", + " minor repair 5,923,680.000 \n", + "turbine annual service - HV components 3,703,000.000 \n", + " annual service - lifting & safety equipment 3,704,000.000 \n", + " annual service - turbine 3,711,000.000 \n", + " medium repair 15,446,760.000 \n", + " minor repair 852,600.000 \n", + "yaw_system medium repair 69,883,380.000 \n", + " minor repair 3,809,400.000 \n", + "\n", + " layout_1_tow_base \\\n", + "part_name reason \n", + "cable cable & scour survey 6,068,000.000 \n", + " cable replacement 2,760,000.000 \n", + "central_hydraulics_system medium repair 31,857,000.000 \n", + " minor repair 1,796,760.000 \n", + "drive_train_system major repair 60,000.000 \n", + " medium repair 21,787,080.000 \n", + " minor repair 1,178,520.000 \n", + "electrical_system major repair 400,000.000 \n", + " major replacement 800,000.000 \n", + "gearbox major repair 1,092,000.000 \n", + " major replacement 6,510,000.000 \n", + "generator_system major repair 750,000.000 \n", + " major replacement 2,700,000.000 \n", + " medium repair 32,307,660.000 \n", + " minor repair 1,813,560.000 \n", + "main_bearing major replacement 450,000.000 \n", + "mooring_lines mooring and cable inspection 5,850,000.000 \n", + " mooring replacement 2,622,000.000 \n", + "rotor_blades major repair 2,808,000.000 \n", + " major replacement 15,210,000.000 \n", + "rotor_system medium repair 51,748,200.000 \n", + " minor repair 2,803,080.000 \n", + "substructure medium repair 5,268,060.000 \n", + " minor repair 1,560,720.000 \n", + "transmission_system medium repair 105,640,920.000 \n", + " minor repair 5,622,960.000 \n", + "turbine annual service - HV components 3,595,000.000 \n", + " annual service - lifting & safety equipment 3,596,000.000 \n", + " annual service - turbine 3,596,000.000 \n", + " medium repair 15,788,640.000 \n", + " minor repair 829,920.000 \n", + "yaw_system medium repair 67,894,260.000 \n", + " minor repair 3,707,760.000 \n", + "\n", + " layout_1_tow_increase_failure \\\n", + "part_name reason \n", + "cable cable & scour survey 6,104,000.000 \n", + " cable replacement 4,400,000.000 \n", + "central_hydraulics_system medium repair 65,749,740.000 \n", + " minor repair 3,082,800.000 \n", + "drive_train_system major repair 72,000.000 \n", + " medium repair 45,097,080.000 \n", + " minor repair 2,086,560.000 \n", + "electrical_system major repair 320,000.000 \n", + " major replacement 1,600,000.000 \n", + "gearbox major repair 2,100,000.000 \n", + " major replacement 13,650,000.000 \n", + "generator_system major repair 1,380,000.000 \n", + " major replacement 7,800,000.000 \n", + " medium repair 65,268,000.000 \n", + " minor repair 2,908,920.000 \n", + "main_bearing major replacement 600,000.000 \n", + "mooring_lines mooring and cable inspection 5,794,000.000 \n", + " mooring replacement 2,508,000.000 \n", + "rotor_blades major repair 6,422,000.000 \n", + " major replacement 31,200,000.000 \n", + "rotor_system medium repair 102,486,300.000 \n", + " minor repair 4,750,200.000 \n", + "substructure medium repair 11,297,580.000 \n", + " minor repair 2,613,240.000 \n", + "transmission_system medium repair 206,371,200.000 \n", + " minor repair 10,983,000.000 \n", + "turbine annual service - HV components 2,424,000.000 \n", + " annual service - lifting & safety equipment 2,424,000.000 \n", + " annual service - turbine 2,425,000.000 \n", + " medium repair 30,038,820.000 \n", + " minor repair 1,396,920.000 \n", + "yaw_system medium repair 133,597,380.000 \n", + " minor repair 6,265,560.000 \n", + "\n", + " layout_1_tow_increase_maintenance \\\n", + "part_name reason \n", + "cable cable & scour survey 5,656,000.000 \n", + " cable replacement 2,840,000.000 \n", + "central_hydraulics_system medium repair 31,748,220.000 \n", + " minor repair 1,708,560.000 \n", + "drive_train_system major repair 60,000.000 \n", + " medium repair 23,107,980.000 \n", + " minor repair 1,266,720.000 \n", + "electrical_system major repair 200,000.000 \n", + " major replacement 875,000.000 \n", + "gearbox major repair 1,428,000.000 \n", + " major replacement 4,410,000.000 \n", + "generator_system major repair 675,000.000 \n", + " major replacement 2,400,000.000 \n", + " medium repair 34,141,380.000 \n", + " minor repair 1,793,400.000 \n", + "main_bearing major replacement 375,000.000 \n", + "mooring_lines mooring and cable inspection 5,538,000.000 \n", + " mooring replacement 1,938,000.000 \n", + "rotor_blades major repair 2,574,000.000 \n", + " major replacement 15,730,000.000 \n", + "rotor_system medium repair 53,255,580.000 \n", + " minor repair 2,823,240.000 \n", + "substructure medium repair 5,780,880.000 \n", + " minor repair 1,563,240.000 \n", + "transmission_system medium repair 105,858,480.000 \n", + " minor repair 5,528,040.000 \n", + "turbine annual service - HV components 3,579,000.000 \n", + " annual service - lifting & safety equipment 3,579,000.000 \n", + " annual service - turbine 3,585,000.000 \n", + " medium repair 15,555,540.000 \n", + " minor repair 861,000.000 \n", + "yaw_system medium repair 66,946,320.000 \n", + " minor repair 3,627,120.000 \n", + "\n", + " layout_1_tow_increase_wave \\\n", + "part_name reason \n", + "cable cable & scour survey 6,062,000.000 \n", + " cable replacement 2,560,000.000 \n", + "central_hydraulics_system medium repair 34,358,940.000 \n", + " minor repair 1,817,760.000 \n", + "drive_train_system major repair 80,000.000 \n", + " medium repair 22,905,960.000 \n", + " minor repair 1,265,040.000 \n", + "electrical_system major repair 250,000.000 \n", + " major replacement 1,000,000.000 \n", + "gearbox major repair 1,386,000.000 \n", + " major replacement 7,980,000.000 \n", + "generator_system major repair 1,080,000.000 \n", + " major replacement 2,250,000.000 \n", + " medium repair 32,742,780.000 \n", + " minor repair 1,763,160.000 \n", + "main_bearing major replacement 300,000.000 \n", + "mooring_lines mooring and cable inspection 5,882,000.000 \n", + " mooring replacement 1,368,000.000 \n", + "rotor_blades major repair 2,834,000.000 \n", + " major replacement 12,090,000.000 \n", + "rotor_system medium repair 54,716,340.000 \n", + " minor repair 2,857,680.000 \n", + "substructure medium repair 5,563,320.000 \n", + " minor repair 1,632,120.000 \n", + "transmission_system medium repair 104,786,220.000 \n", + " minor repair 5,715,360.000 \n", + "turbine annual service - HV components 3,704,000.000 \n", + " annual service - lifting & safety equipment 3,706,000.000 \n", + " annual service - turbine 3,714,000.000 \n", + " medium repair 15,648,780.000 \n", + " minor repair 936,600.000 \n", + "yaw_system medium repair 68,453,700.000 \n", + " minor repair 3,667,440.000 \n", + "\n", + " layout_2_tow_base \\\n", + "part_name reason \n", + "cable cable & scour survey 6,062,000.000 \n", + " cable replacement 2,600,000.000 \n", + "central_hydraulics_system medium repair 32,711,700.000 \n", + " minor repair 1,781,640.000 \n", + "drive_train_system major repair 84,000.000 \n", + " medium repair 22,890,420.000 \n", + " minor repair 1,191,120.000 \n", + "electrical_system major repair 260,000.000 \n", + " major replacement 1,500,000.000 \n", + "gearbox major repair 1,512,000.000 \n", + " major replacement 7,560,000.000 \n", + "generator_system major repair 720,000.000 \n", + " major replacement 2,700,000.000 \n", + " medium repair 33,131,280.000 \n", + " minor repair 1,754,760.000 \n", + "main_bearing major replacement 300,000.000 \n", + "mooring_lines mooring and cable inspection 5,824,000.000 \n", + " mooring replacement 1,710,000.000 \n", + "rotor_blades major repair 2,834,000.000 \n", + " major replacement 15,600,000.000 \n", + "rotor_system medium repair 51,515,100.000 \n", + " minor repair 2,809,800.000 \n", + "substructure medium repair 5,563,320.000 \n", + " minor repair 1,535,520.000 \n", + "transmission_system medium repair 103,698,420.000 \n", + " minor repair 5,597,760.000 \n", + "turbine annual service - HV components 3,597,000.000 \n", + " annual service - lifting & safety equipment 3,597,000.000 \n", + " annual service - turbine 3,597,000.000 \n", + " medium repair 15,135,960.000 \n", + " minor repair 821,520.000 \n", + "yaw_system medium repair 67,552,380.000 \n", + " minor repair 3,736,320.000 \n", + "\n", + " layout_2_in_situ_base \\\n", + "part_name reason \n", + "cable cable & scour survey 6,072,000.000 \n", + " cable replacement 3,040,000.000 \n", + "central_hydraulics_system medium repair 32,602,920.000 \n", + " minor repair 1,757,280.000 \n", + "drive_train_system major repair 72,000.000 \n", + " medium repair 23,434,320.000 \n", + " minor repair 1,233,120.000 \n", + "electrical_system major repair 220,000.000 \n", + " major replacement 1,000,000.000 \n", + "gearbox major repair 1,218,000.000 \n", + " major replacement 8,190,000.000 \n", + "generator_system major repair 900,000.000 \n", + " major replacement 3,600,000.000 \n", + " medium repair 32,820,480.000 \n", + " minor repair 1,840,440.000 \n", + "main_bearing major replacement 300,000.000 \n", + "mooring_lines mooring and cable inspection 5,814,000.000 \n", + " mooring replacement 1,140,000.000 \n", + "rotor_blades major repair 2,834,000.000 \n", + " major replacement 15,340,000.000 \n", + "rotor_system medium repair 51,856,980.000 \n", + " minor repair 2,764,440.000 \n", + "substructure medium repair 5,609,940.000 \n", + " minor repair 1,592,640.000 \n", + "transmission_system medium repair 104,879,460.000 \n", + " minor repair 5,661,600.000 \n", + "turbine annual service - HV components 3,600,000.000 \n", + " annual service - lifting & safety equipment 3,600,000.000 \n", + " annual service - turbine 3,600,000.000 \n", + " medium repair 15,415,680.000 \n", + " minor repair 882,000.000 \n", + "yaw_system medium repair 67,319,280.000 \n", + " minor repair 3,624,600.000 \n", + "\n", + " layout_1_in_situ_base_24hr \\\n", + "part_name reason \n", + "cable cable & scour survey 6,834,000.000 \n", + " cable replacement 2,520,000.000 \n", + "central_hydraulics_system medium repair 32,960,340.000 \n", + " minor repair 1,831,200.000 \n", + "drive_train_system major repair 100,000.000 \n", + " medium repair 23,543,100.000 \n", + " minor repair 1,197,000.000 \n", + "electrical_system major repair 175,000.000 \n", + " major replacement 1,000,000.000 \n", + "gearbox major repair 1,764,000.000 \n", + " major replacement 7,770,000.000 \n", + "generator_system major repair 720,000.000 \n", + " major replacement 4,200,000.000 \n", + " medium repair 34,762,980.000 \n", + " minor repair 1,849,680.000 \n", + "main_bearing major replacement 450,000.000 \n", + "mooring_lines mooring and cable inspection 6,100,000.000 \n", + " mooring replacement 1,596,000.000 \n", + "rotor_blades major repair 3,510,000.000 \n", + " major replacement 15,080,000.000 \n", + "rotor_system medium repair 52,696,140.000 \n", + " minor repair 2,919,840.000 \n", + "substructure medium repair 5,951,820.000 \n", + " minor repair 1,613,640.000 \n", + "transmission_system medium repair 106,262,520.000 \n", + " minor repair 5,762,400.000 \n", + "turbine annual service - HV components 3,775,000.000 \n", + " annual service - lifting & safety equipment 3,776,000.000 \n", + " annual service - turbine 3,777,000.000 \n", + " medium repair 15,322,440.000 \n", + " minor repair 911,400.000 \n", + "yaw_system medium repair 70,396,200.000 \n", + " minor repair 3,832,920.000 \n", + "\n", + " layout_1_in_situ_increase_failure_24hr \\\n", + "part_name reason \n", + "cable cable & scour survey 6,792,000.000 \n", + " cable replacement 4,680,000.000 \n", + "central_hydraulics_system medium repair 67,614,540.000 \n", + " minor repair 3,477,600.000 \n", + "drive_train_system major repair 180,000.000 \n", + " medium repair 44,941,680.000 \n", + " minor repair 2,462,040.000 \n", + "electrical_system major repair 425,000.000 \n", + " major replacement 2,900,000.000 \n", + "gearbox major repair 2,436,000.000 \n", + " major replacement 13,230,000.000 \n", + "generator_system major repair 1,380,000.000 \n", + " major replacement 6,000,000.000 \n", + " medium repair 66,200,400.000 \n", + " minor repair 3,679,200.000 \n", + "main_bearing major replacement 540,000.000 \n", + "mooring_lines mooring and cable inspection 6,210,000.000 \n", + " mooring replacement 1,140,000.000 \n", + "rotor_blades major repair 5,720,000.000 \n", + " major replacement 27,950,000.000 \n", + "rotor_system medium repair 107,583,420.000 \n", + " minor repair 5,678,400.000 \n", + "substructure medium repair 12,478,620.000 \n", + " minor repair 3,218,040.000 \n", + "transmission_system medium repair 209,059,620.000 \n", + " minor repair 11,357,640.000 \n", + "turbine annual service - HV components 3,606,000.000 \n", + " annual service - lifting & safety equipment 3,609,000.000 \n", + " annual service - turbine 3,611,000.000 \n", + " medium repair 31,095,540.000 \n", + " minor repair 1,691,760.000 \n", + "yaw_system medium repair 135,539,880.000 \n", + " minor repair 7,424,760.000 \n", + "\n", + " layout_1_tow_base_24hr \n", + "part_name reason \n", + "cable cable & scour survey 6,804,000.000 \n", + " cable replacement 2,840,000.000 \n", + "central_hydraulics_system medium repair 34,172,460.000 \n", + " minor repair 1,811,880.000 \n", + "drive_train_system major repair 60,000.000 \n", + " medium repair 22,719,480.000 \n", + " minor repair 1,300,320.000 \n", + "electrical_system major repair 425,000.000 \n", + " major replacement 1,000,000.000 \n", + "gearbox major repair 1,176,000.000 \n", + " major replacement 7,980,000.000 \n", + "generator_system major repair 750,000.000 \n", + " major replacement 3,000,000.000 \n", + " medium repair 33,581,940.000 \n", + " minor repair 1,816,080.000 \n", + "main_bearing major replacement 300,000.000 \n", + "mooring_lines mooring and cable inspection 6,148,000.000 \n", + " mooring replacement 1,710,000.000 \n", + "rotor_blades major repair 3,120,000.000 \n", + " major replacement 14,560,000.000 \n", + "rotor_system medium repair 53,799,480.000 \n", + " minor repair 2,864,400.000 \n", + "substructure medium repair 6,402,480.000 \n", + " minor repair 1,643,040.000 \n", + "transmission_system medium repair 107,863,140.000 \n", + " minor repair 5,815,320.000 \n", + "turbine annual service - HV components 3,772,000.000 \n", + " annual service - lifting & safety equipment 3,772,000.000 \n", + " annual service - turbine 3,773,000.000 \n", + " medium repair 16,627,800.000 \n", + " minor repair 864,360.000 \n", + "yaw_system medium repair 70,147,560.000 \n", + " minor repair 3,734,640.000 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_means" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/default_data_demonstration.ipynb b/examples/default_data_demonstration.ipynb new file mode 100644 index 00000000..3ebb3269 --- /dev/null +++ b/examples/default_data_demonstration.ipynb @@ -0,0 +1,709 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6ff58593", + "metadata": {}, + "source": [ + "# Default Data Demonstration\n", + "\n", + "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/WISDEM/WOMBAT/main?filepath=examples)\n", + "\n", + "In this example, we'll show what each of the reference plants look like and what results they yield when simulated.\n", + "\n", + "```{important}\n", + "The land-based data are expiremental, and should only be used as a starting point for\n", + "developing a more robust simulation. Please see the\n", + "[default data section of the user guide for further details](index.md#default-data)\n", + "```\n", + "\n", + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b6ba2950", + "metadata": {}, + "outputs": [], + "source": [ + "from time import perf_counter\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from wombat import Simulation\n", + "from wombat.utilities import plot\n", + "from wombat.core.library import DEFAULT_DATA\n", + "\n", + "pd.set_option(\"display.max_rows\", 50)\n", + "pd.set_option(\"display.max_columns\", 20)\n", + "pd.options.display.float_format = \"{:,.2f}\".format" + ] + }, + { + "cell_type": "markdown", + "id": "72a3c8ab", + "metadata": {}, + "source": [ + "## Initialize the simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dcb462d5", + "metadata": {}, + "outputs": [], + "source": [ + "SEED = 48\n", + "\n", + "lbw_sim = Simulation(DEFAULT_DATA, \"base_lbw.yaml\", random_seed=SEED)\n", + "osw_fixed_sim = Simulation(DEFAULT_DATA, \"base_osw_fixed.yaml\", random_seed=SEED)\n", + "osw_floating_sim = Simulation(DEFAULT_DATA, \"base_osw_floating.yaml\", random_seed=SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "c75fd445", + "metadata": {}, + "source": [ + "### View the farms\n", + "\n", + "Using the plotting library, we can observe the layout for each of the farms" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2c01d1af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N turbines: 57, N substations: 1\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\n", + " f\"N turbines: {len(lbw_sim.windfarm.turbine_id)}, N substations: {len(lbw_sim.windfarm.substation_id)}\"\n", + ")\n", + "plot.plot_farm_layout(lbw_sim.windfarm)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a817f841", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N turbines: 50, N substations: 1\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\n", + " f\"N turbines: {len(osw_fixed_sim.windfarm.turbine_id)}, N substations: {len(osw_fixed_sim.windfarm.substation_id)}\"\n", + ")\n", + "plot.plot_farm_layout(osw_fixed_sim.windfarm)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "203705e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N turbines: 50, N substations: 1\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\n", + " f\"N turbines: {len(osw_floating_sim.windfarm.turbine_id)}, N substations: {len(osw_floating_sim.windfarm.substation_id)}\"\n", + ")\n", + "plot.plot_farm_layout(osw_floating_sim.windfarm)" + ] + }, + { + "cell_type": "markdown", + "id": "36de6c60", + "metadata": {}, + "source": [ + "## Run the simulations\n", + "\n", + "Now we can run all three simulations, delete the simulation log files, and view the run time for\n", + "each simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1751ef16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run time: 0.0 minutes 35.9 seconds\n", + "Run time: 0.0 minutes 35.5 seconds\n", + "Run time: 0.0 minutes 39.8 seconds\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run time: 0.0 minutes 35.5 seconds\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run time: 0.0 minutes 39.8 seconds\n" + ] + } + ], + "source": [ + "start = perf_counter()\n", + "lbw_sim.run(delete_logs=True, save_metrics_inputs=False)\n", + "run_time = perf_counter() - start\n", + "print(f\"Run time: {run_time // 60} minutes {run_time % 60:.1f} seconds\")\n", + "\n", + "start = perf_counter()\n", + "osw_fixed_sim.run(delete_logs=True, save_metrics_inputs=False)\n", + "run_time = perf_counter() - start\n", + "print(f\"Run time: {run_time // 60} minutes {run_time % 60:.1f} seconds\")\n", + "\n", + "start = perf_counter()\n", + "osw_floating_sim.run(delete_logs=True, save_metrics_inputs=False)\n", + "run_time = perf_counter() - start\n", + "print(f\"Run time: {run_time // 60} minutes {run_time % 60:.1f} seconds\")" + ] + }, + { + "cell_type": "markdown", + "id": "ccb05270", + "metadata": {}, + "source": [ + "### Gather the results\n", + "\n", + "First we will simplify the process of getting results, ensure that all simulations are\n", + "20 years, and gather the capacities." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ded1d043", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All are 20 years: True\n" + ] + } + ], + "source": [ + "metrics_lbw = lbw_sim.metrics\n", + "metrics_osw_fixed = osw_fixed_sim.metrics\n", + "metrics_osw_floating = osw_floating_sim.metrics\n", + "\n", + "years_check = (\n", + " lbw_sim.env.simulation_years,\n", + " osw_fixed_sim.env.simulation_years,\n", + " osw_floating_sim.env.simulation_years,\n", + ")\n", + "print(f\"All are 20 years: {all(x == 20 for x in years_check)}\")\n", + "years = 20\n", + "\n", + "capacities = [ # MW\n", + " metrics_lbw.project_capacity,\n", + " metrics_osw_fixed.project_capacity,\n", + " metrics_osw_floating.project_capacity,\n", + "]\n", + "\n", + "columns = [\"Metric\", \"Units\", \"Land-Based\", \"OSW-Fixed\", \"OSW-Floating\"]\n", + "sort_order = [\n", + " \"Project Capacity\",\n", + " \"Production Based Availability\",\n", + " \"Total OpEx\",\n", + " \"Materials\",\n", + " \"Direct Labor\",\n", + " \"Port Fees\",\n", + " \"Fixed Costs (operations, indirect labor, etc.)\",\n", + " \"Service Equipment\",\n", + " \"Truck 1\",\n", + " \"Truck 2\",\n", + " \"Truck 3\",\n", + " \"Crawler Crane - 1350 tonnes\",\n", + " \"Crew Transfer Vessel 1\",\n", + " \"Crew Transfer Vessel 2\",\n", + " \"Crew Transfer Vessel 3\",\n", + " \"Cable Laying Vessel\",\n", + " \"Diving Support Vessel\",\n", + " \"Heavy Lift Vessel\",\n", + " \"Anchor Handling Tug\",\n", + " \"Tugboat 1\",\n", + " \"Tugboat 2\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "e657a83c", + "metadata": {}, + "source": [ + "Now, we can gather some of the high level availability and cost statistics for each\n", + "of the base scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0fd1c476", + "metadata": {}, + "outputs": [], + "source": [ + "capacity = pd.DataFrame(\n", + " [[\"Project Capacity\", \"MW\"] + capacities], columns=columns\n", + ").set_index([\"Metric\", \"Units\"])\n", + "\n", + "availability = [\n", + " \"Production Based Availability\",\n", + " \"%\",\n", + " metrics_lbw.production_based_availability(\"project\", \"windfarm\").squeeze() * 100,\n", + " metrics_osw_fixed.production_based_availability(\"project\", \"windfarm\").squeeze()\n", + " * 100,\n", + " metrics_osw_floating.production_based_availability(\"project\", \"windfarm\").squeeze()\n", + " * 100,\n", + "]\n", + "availability = pd.DataFrame([availability], columns=columns).set_index(\n", + " [\"Metric\", \"Units\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb9a38fb", + "metadata": {}, + "outputs": [], + "source": [ + "opex = (\n", + " metrics_lbw.opex(\"project\", \"windfarm\")\n", + " .rename({0: \"Land-Based\"})\n", + " .T.join(metrics_osw_fixed.opex(\"project\", \"windfarm\").rename({0: \"OSW-Fixed\"}).T)\n", + " .join(\n", + " metrics_osw_floating.opex(\"project\", \"windfarm\").rename({0: \"OSW-Floating\"}).T\n", + " )\n", + " / years\n", + " / (np.array(capacities) * 1000)\n", + ")\n", + "opex.index = [\n", + " \"Fixed Costs (operations, indirect labor, etc.)\",\n", + " \"Port Fees\",\n", + " \"Service Equipment\",\n", + " \"Direct Labor\",\n", + " \"Materials\",\n", + " \"Total OpEx\",\n", + "]\n", + "opex[\"Units\"] = \"$/kw/yr\"\n", + "opex = opex.set_index(\"Units\", append=True).iloc[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c2202cb8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/q5/tfpytqxn0r396dfg7rk5sj8rwq9tvv/T/ipykernel_43581/2109753505.py:5: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " .fillna(0.0)\n" + ] + } + ], + "source": [ + "equipment = (\n", + " metrics_lbw.equipment_costs(\"project\", by_equipment=True)\n", + " .rename({0: \"Land-Based\"})\n", + " .T.join(\n", + " metrics_osw_fixed.equipment_costs(\"project\", by_equipment=True)\n", + " .rename({0: \"OSW-Fixed\"})\n", + " .T,\n", + " how=\"outer\",\n", + " )\n", + " .join(\n", + " metrics_osw_floating.equipment_costs(\"project\", by_equipment=True)\n", + " .rename({0: \"OSW-Floating\"})\n", + " .T,\n", + " how=\"outer\",\n", + " )\n", + " .fillna(0.0)\n", + " / years\n", + " / (np.array(capacities) * 1000)\n", + ")\n", + "equipment[\"Units\"] = \"$/kw/yr\"\n", + "equipment = equipment.set_index(\"Units\", append=True)" + ] + }, + { + "cell_type": "markdown", + "id": "780465b6", + "metadata": {}, + "source": [ + "With each of the core costs gathered, we can combine them into a single DataFrame and\n", + "view them." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a893fd35", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Land-BasedOSW-FixedOSW-Floating
Units
Project CapacityMW199.50600.00600.00
Production Based Availability%97.4493.7492.52
Total OpEx$/kw/yr35.73123.42145.91
Materials$/kw/yr1.662.133.39
Direct Labor$/kw/yr0.000.000.00
Port Fees$/kw/yr0.000.5016.74
Fixed Costs (operations, indirect labor, etc.)$/kw/yr18.8147.3850.94
Service Equipment$/kw/yr15.2573.4174.84
Truck 1$/kw/yr0.040.000.00
Truck 2$/kw/yr0.040.000.00
Truck 3$/kw/yr0.040.000.00
Crawler Crane - 1350 tonnes$/kw/yr15.130.000.00
Crew Transfer Vessel 1$/kw/yr0.001.951.95
Crew Transfer Vessel 2$/kw/yr0.001.951.95
Crew Transfer Vessel 3$/kw/yr0.001.951.95
Cable Laying Vessel$/kw/yr0.0012.1422.87
Diving Support Vessel$/kw/yr0.000.942.49
Heavy Lift Vessel$/kw/yr0.0054.480.00
Anchor Handling Tug$/kw/yr0.000.004.52
Tugboat 1$/kw/yr0.000.0019.91
Tugboat 2$/kw/yr0.000.0019.21
\n", + "
" + ], + "text/plain": [ + " Land-Based OSW-Fixed \\\n", + " Units \n", + "Project Capacity MW 199.50 600.00 \n", + "Production Based Availability % 97.44 93.74 \n", + "Total OpEx $/kw/yr 35.73 123.42 \n", + "Materials $/kw/yr 1.66 2.13 \n", + "Direct Labor $/kw/yr 0.00 0.00 \n", + "Port Fees $/kw/yr 0.00 0.50 \n", + "Fixed Costs (operations, indirect labor, etc.) $/kw/yr 18.81 47.38 \n", + "Service Equipment $/kw/yr 15.25 73.41 \n", + "Truck 1 $/kw/yr 0.04 0.00 \n", + "Truck 2 $/kw/yr 0.04 0.00 \n", + "Truck 3 $/kw/yr 0.04 0.00 \n", + "Crawler Crane - 1350 tonnes $/kw/yr 15.13 0.00 \n", + "Crew Transfer Vessel 1 $/kw/yr 0.00 1.95 \n", + "Crew Transfer Vessel 2 $/kw/yr 0.00 1.95 \n", + "Crew Transfer Vessel 3 $/kw/yr 0.00 1.95 \n", + "Cable Laying Vessel $/kw/yr 0.00 12.14 \n", + "Diving Support Vessel $/kw/yr 0.00 0.94 \n", + "Heavy Lift Vessel $/kw/yr 0.00 54.48 \n", + "Anchor Handling Tug $/kw/yr 0.00 0.00 \n", + "Tugboat 1 $/kw/yr 0.00 0.00 \n", + "Tugboat 2 $/kw/yr 0.00 0.00 \n", + "\n", + " OSW-Floating \n", + " Units \n", + "Project Capacity MW 600.00 \n", + "Production Based Availability % 92.52 \n", + "Total OpEx $/kw/yr 145.91 \n", + "Materials $/kw/yr 3.39 \n", + "Direct Labor $/kw/yr 0.00 \n", + "Port Fees $/kw/yr 16.74 \n", + "Fixed Costs (operations, indirect labor, etc.) $/kw/yr 50.94 \n", + "Service Equipment $/kw/yr 74.84 \n", + "Truck 1 $/kw/yr 0.00 \n", + "Truck 2 $/kw/yr 0.00 \n", + "Truck 3 $/kw/yr 0.00 \n", + "Crawler Crane - 1350 tonnes $/kw/yr 0.00 \n", + "Crew Transfer Vessel 1 $/kw/yr 1.95 \n", + "Crew Transfer Vessel 2 $/kw/yr 1.95 \n", + "Crew Transfer Vessel 3 $/kw/yr 1.95 \n", + "Cable Laying Vessel $/kw/yr 22.87 \n", + "Diving Support Vessel $/kw/yr 2.49 \n", + "Heavy Lift Vessel $/kw/yr 0.00 \n", + "Anchor Handling Tug $/kw/yr 4.52 \n", + "Tugboat 1 $/kw/yr 19.91 \n", + "Tugboat 2 $/kw/yr 19.21 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = pd.concat([capacity, availability, opex, equipment]).loc[sort_order]\n", + "results" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "md:myst", + "text_representation": { + "extension": ".md", + "format_name": "myst" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + }, + "source_map": [ + 11, + 27, + 40, + 44, + 50, + 56, + 61, + 66, + 69, + 76, + 91, + 98, + 141, + 146, + 159, + 179, + 190, + 195 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/how_to.ipynb b/examples/how_to.ipynb index 50ea39da..f071cd22 100644 --- a/examples/how_to.ipynb +++ b/examples/how_to.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "80f717db", + "id": "b62d241c", "metadata": {}, "source": [ "# How To Use WOMBAT\n", @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "d573b567", + "id": "02964931", "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "markdown", - "id": "1f6dd61d", + "id": "432854c7", "metadata": {}, "source": [ "## Defining the Simulation\n", @@ -112,7 +112,7 @@ "```{note}\n", "Electrolyzers can also be provided in the same manner as turbines, substations, cables,\n", "and vessels.\n", - "``` \n", + "```\n", "\n", "```yaml\n", "# WOMBAT/library/corewind/project/config/morro_bay_in_situ_consolidated.yaml\n", @@ -653,7 +653,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "829a6d58", + "id": "aa57a388", "metadata": {}, "outputs": [], "source": [ @@ -662,7 +662,7 @@ }, { "cell_type": "markdown", - "id": "fe46b4d0", + "id": "3647d3f2", "metadata": {}, "source": [ "### The configuration file\n", @@ -687,7 +687,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "afc9e9c2", + "id": "83b6a40e", "metadata": {}, "outputs": [], "source": [ @@ -696,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "e1da0d2a", + "id": "f0dd2971", "metadata": {}, "source": [ "```{code-block} yaml\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "5e0a1fec", + "id": "33d8ea57", "metadata": {}, "outputs": [], "source": [ @@ -797,7 +797,7 @@ }, { "cell_type": "markdown", - "id": "d66ef045", + "id": "023b7810", "metadata": {}, "source": [ "```{important}\n", @@ -824,7 +824,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "771f462b", + "id": "aaa4812f", "metadata": {}, "outputs": [], "source": [ @@ -837,7 +837,7 @@ }, { "cell_type": "markdown", - "id": "51f77896", + "id": "9567fed4", "metadata": {}, "source": [ "### Seeding the simulation random variable\n", @@ -850,7 +850,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "3c2270bc", + "id": "019df99b", "metadata": {}, "outputs": [], "source": [ @@ -865,7 +865,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "de15de67", + "id": "fc21d990", "metadata": {}, "outputs": [], "source": [ @@ -879,7 +879,7 @@ }, { "cell_type": "markdown", - "id": "9a6abd38", + "id": "72b905cd", "metadata": {}, "source": [ "## Run the analysis\n", @@ -894,14 +894,14 @@ { "cell_type": "code", "execution_count": 8, - "id": "0c00db45", + "id": "bc29c31a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Run time: 0.33 minutes\n" + "Run time: 0.37 minutes\n" ] } ], @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "9c8abe9e", + "id": "62e81a17", "metadata": {}, "source": [ "### Optional: Delete the logging files\n", @@ -933,7 +933,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "83c76b14", + "id": "df17c66e", "metadata": {}, "outputs": [], "source": [ @@ -942,7 +942,7 @@ }, { "cell_type": "markdown", - "id": "179665cc", + "id": "519547d6", "metadata": {}, "source": [ "For additional convenience, in `Simulation.run()`, `save_metrics_inputs=True` and\n", @@ -959,14 +959,14 @@ { "cell_type": "code", "execution_count": 10, - "id": "91583d92", + "id": "8b7b0cbc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Net Capacity Factor: 46.3%\n", + " Net Capacity Factor: 46.2%\n", "Gross Capacity Factor: 47.7%\n" ] } @@ -985,7 +985,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "040bf9e6", + "id": "f7db2962", "metadata": {}, "outputs": [ { @@ -993,16 +993,16 @@ "output_type": "stream", "text": [ " Project time-based availability: 97.0%\n", - "Project energy-based availability: 97.1%\n", - " Project equipment costs: $446,586.89/MW\n" + "Project energy-based availability: 97.0%\n", + " Project equipment costs: $453,518.85/MW\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Project energy-based availability: 97.1%\n", - " Project equipment costs: $446,586.89/MW\n" + "Project energy-based availability: 97.0%\n", + " Project equipment costs: $453,518.85/MW\n" ] } ], @@ -1043,7 +1043,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.13.11" }, "source_map": [ 10, diff --git a/examples/metrics_demonstration.ipynb b/examples/metrics_demonstration.ipynb index 0377d825..11e9bddc 100644 --- a/examples/metrics_demonstration.ipynb +++ b/examples/metrics_demonstration.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5909b86b", + "id": "ee30d560", "metadata": {}, "source": [ "(metrics-demo)=\n", @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "8cac9b98", + "id": "2050343d", "metadata": { "tags": [ "output_scroll" @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "de72f970", + "id": "73e21eb8", "metadata": {}, "source": [ "(metrics-demo:toc)=\n", @@ -108,7 +108,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "05d7ab47", + "id": "899d686f", "metadata": { "tags": [ "output_scroll" @@ -136,7 +136,7 @@ }, { "cell_type": "markdown", - "id": "09b7a4dd", + "id": "6a1e6c00", "metadata": {}, "source": [ "(metrics-demo:common-parameters)=\n", @@ -207,7 +207,7 @@ { "cell_type": "code", "execution_count": 3, - 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 windfarmwindfarm
time_availability0.91time_availability0.91
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -250,7 +250,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "2afff2ee", + "id": "4eede24a", "metadata": { "tags": [ "output_scroll" @@ -262,23 +262,23 @@ "text/html": [ "\n", - "\n", + "
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energy_availability0.91energy_availability0.91
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year
20020.9520020.94
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20070.9020070.90
20080.9120080.91
20090.9220090.92
20100.8620100.90
20110.8920110.90
20120.9120120.91
20130.9120130.91
20140.9320140.92
20150.8920150.92
20160.9120160.90
20170.9120170.91
20180.9220180.90
20190.9020190.91
20200.9220200.92
20210.9120210.86
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month
10.8910.87
20.8520.87
30.8830.88
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50.8850.89
60.9160.91
70.9370.93
80.9580.95
90.9690.95
100.94100.94
110.93110.93
120.92120.91
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" \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
  windfarmwindfarm
year
200210.93200210.92
20.9520.95
30.9730.90
40.9940.96
50.9750.96
60.9460.96
70.9770.96
80.9780.96
90.9890.97
100.98100.97
110.82110.92
120.93120.91
200310.72200310.57
20.8520.79
30.9030.90
40.9040.90
50.9150.90
60.9160.90
70.9270.91
80.9880.97
90.9790.98
100.94100.94
110.95110.96
120.92120.91
200410.85200410.66
20.7420.90
30.9030.89
40.9040.89
50.9050.90
60.9060.89
70.9370.93
80.9880.98
90.9690.96
100.97100.96
110.96110.95
120.93120.71
200510.83200510.84
20.8520.77
30.9030.90
40.9040.90
50.9050.90
60.9160.91
70.9670.96
80.9880.98
90.9890.97
100.93100.92
110.97110.95
120.95120.94
200610.81200610.89
20.5520.82
30.9030.89
40.9040.89
50.9050.90
60.8960.90
70.9070.90
80.9280.93
90.9690.96
100.97100.97
110.93110.96
120.92120.92
200710.92200710.92
20.6520.59
30.8830.90
40.8940.91
50.9050.90
60.9060.90
70.9070.90
80.9280.91
90.9790.97
100.94100.95
110.94110.94
120.92120.94
200810.91200810.92
20.7620.86
30.8930.80
40.9440.93
50.8950.90
60.8960.89
70.9070.89
80.9080.89
90.9590.96
100.95100.94
110.96110.95
120.95120.93
200910.93200910.93
20.8820.91
30.9030.87
40.9140.93
50.9250.93
60.9260.93
70.9170.90
80.9580.94
90.9490.93
100.94100.95
110.92110.93
120.92120.92
201010.92201010.92
20.7520.90
30.5730.90
40.6640.73
50.9250.93
60.9160.91
70.9170.90
80.9480.94
90.9590.94
100.91100.91
110.90110.90
120.94120.94
201110.91201110.91
20.9220.91
30.7530.87
40.6540.79
50.9350.93
60.9360.97
70.9270.93
80.9280.90
90.9290.93
100.92100.91
110.92110.91
120.93120.89
201210.92201210.93
20.9220.91
30.9030.90
40.7540.78
50.9450.94
60.9460.92
70.9470.92
80.9280.93
90.9390.90
100.92100.93
110.92110.91
120.90120.89
201310.89201310.90
20.8920.87
30.8930.79
40.6640.89
50.9350.95
60.9360.93
70.9770.96
80.9680.96
90.9490.94
100.93100.92
110.92110.90
120.95120.95
201410.94201410.96
20.9420.94
30.9130.87
40.9340.84
50.9150.97
60.9160.90
70.9870.96
80.9780.97
90.9590.96
100.93100.92
110.91110.89
120.90120.89
201510.89201510.91
20.9220.94
30.9530.87
40.7340.74
50.5750.93
60.9560.96
70.9770.97
80.9880.98
90.9490.96
100.93100.95
110.92110.90
120.91120.90
201610.89201610.90
20.8920.90
30.8930.90
40.9040.75
50.8650.90
60.9060.90
70.9470.92
80.9680.96
90.9590.96
100.92100.94
110.89110.90
120.89120.89
201710.90201710.89
20.9020.90
30.9030.90
40.8940.76
50.8550.89
60.9060.90
70.9070.91
80.9680.96
90.9690.97
100.94100.92
110.95110.97
120.93120.94
201810.92201810.92
20.9120.91
30.9130.90
40.9140.62
50.9050.89
60.9160.91
70.9470.95
80.9680.96
90.9790.95
100.94100.95
110.93110.96
120.89120.91
201910.90201910.89
20.9020.90
30.9030.81
40.9040.85
50.7150.88
60.8460.90
70.9470.94
80.9580.96
90.9690.96
100.95100.97
110.95110.95
120.94120.92
202010.90202010.90
20.9020.90
30.9030.91
40.9140.90
50.8250.77
60.9160.92
70.9370.93
80.9680.96
90.9790.97
100.97100.97
110.96110.96
120.92120.93
202110.90202110.75
20.9120.91
30.9030.90
40.9040.89
50.9050.51
60.8460.73
70.9070.90
80.8980.96
90.9790.95
100.95100.96
110.95110.95
120.95120.96
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -1746,7 +1746,7 @@ }, { "cell_type": "markdown", - "id": "34bb90ab", + "id": "613ea8e4", "metadata": {}, "source": [ "### Plotting Availability\n", @@ -1758,7 +1758,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "a284cd7f", + "id": "01718e40", "metadata": { "tags": [ "output_scroll" @@ -1767,7 +1767,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1783,7 +1783,7 @@ }, { "cell_type": "markdown", - "id": "b38f77df", + "id": "c4f20cf6", "metadata": {}, "source": [ "(metrics-demo:cf)=\n", @@ -1809,7 +1809,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "aa990d56", + "id": "63c94c56", "metadata": { "tags": [ "output_scroll" @@ -1820,7 +1820,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Net capacity factor: 50.35%\n", + " Net capacity factor: 50.34%\n", "Gross capacity factor: 55.59%\n" ] } @@ -1838,7 +1838,7 @@ }, { "cell_type": "markdown", - "id": "8af66066", + "id": "8dc029f1", "metadata": {}, "source": [ "(metrics-demo:task-completion)=\n", @@ -1865,7 +1865,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "b1525395", + "id": "2e71f81b", "metadata": { "tags": [ "output_scroll" @@ -1876,9 +1876,9 @@ "name": "stdout", "output_type": "stream", "text": [ - " Scheduled Task Completion Rate: 96.22%\n", - "Unscheduled Task Completion Rate: 96.86%\n", - " Overall Task Completion Rate: 96.62%\n" + " Scheduled Task Completion Rate: 95.86%\n", + "Unscheduled Task Completion Rate: 95.25%\n", + " Overall Task Completion Rate: 95.48%\n" ] } ], @@ -1897,7 +1897,7 @@ }, { "cell_type": "markdown", - "id": "408274f3", + "id": "c4df85ec", "metadata": {}, "source": [ "(metrics-demo:equipment-costs)=\n", @@ -1921,7 +1921,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "effd2e94", + "id": "8ef1eb56", "metadata": { "tags": [ "output_scroll" @@ -1933,23 +1933,23 @@ "text/html": [ "\n", - "\n", + "
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 equipment_costequipment_cost
02,268,860,530.2002,292,120,278.72
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -1965,7 +1965,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "2d1a2b88", + "id": "ffed3e95", "metadata": { "tags": [ "output_scroll" @@ -1977,43 +1977,43 @@ "text/html": [ "\n", - "\n", + "
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 Anchor Handling TugCable Laying VesselCrew Transfer Vessel 1Crew Transfer Vessel 2Crew Transfer Vessel 3Crew Transfer Vessel 4Crew Transfer Vessel 5Crew Transfer Vessel 6Crew Transfer Vessel 7Diving Support VesselHeavy Lift VesselAnchor Handling TugCable Laying VesselCrew Transfer Vessel 1Crew Transfer Vessel 2Crew Transfer Vessel 3Crew Transfer Vessel 4Crew Transfer Vessel 5Crew Transfer Vessel 6Crew Transfer Vessel 7Diving Support VesselHeavy Lift Vessel
00.890.921.001.001.001.001.001.001.000.950.9900.910.951.001.001.001.001.001.001.000.971.00
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 Total Crew Hours at SeaTotal Crew Hours at Sea
0676,243.620669,289.94
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -2185,7 +2185,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "8ed61307", + "id": "5d2a16cd", "metadata": { "tags": [ "output_scroll" @@ -2200,10 +2200,10 @@ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Annual project totals, broken out by vessel\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m style(\u001b[43mmetrics\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvessel_crew_hours_at_sea\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mannual\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby_equipment\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/WOMBAT/wombat/core/post_processor.py:1030\u001b[39m, in \u001b[36mMetrics.vessel_crew_hours_at_sea\u001b[39m\u001b[34m(self, frequency, by_equipment, vessel_crew_assumption)\u001b[39m\n\u001b[32m 1024\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pd.DataFrame(at_sea.sum()[[\u001b[33m\"\u001b[39m\u001b[33mduration\u001b[39m\u001b[33m\"\u001b[39m]]).T.rename(\n\u001b[32m 1025\u001b[39m columns={\u001b[33m\"\u001b[39m\u001b[33mduration\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mTotal Crew Hours at Sea\u001b[39m\u001b[33m\"\u001b[39m}\n\u001b[32m 1026\u001b[39m )\n\u001b[32m 1027\u001b[39m additional_cols = frequency.group_cols\n\u001b[32m 1028\u001b[39m total_hours = (\n\u001b[32m 1029\u001b[39m \u001b[43mtotal_hours\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop_cols\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m-> \u001b[39m\u001b[32m1030\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_cols\u001b[49m\u001b[43m)\u001b[49m[[\u001b[33m\"\u001b[39m\u001b[33mN\u001b[39m\u001b[33m\"\u001b[39m]]\n\u001b[32m 1031\u001b[39m .sum()\n\u001b[32m 1032\u001b[39m )\n\u001b[32m 1034\u001b[39m columns = additional_cols + columns\n\u001b[32m 1035\u001b[39m group_cols.extend(additional_cols)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/wombat/lib/python3.12/site-packages/pandas/core/frame.py:9183\u001b[39m, in \u001b[36mDataFrame.groupby\u001b[39m\u001b[34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[39m\n\u001b[32m 9180\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m level \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m by \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 9181\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mYou have to supply one of \u001b[39m\u001b[33m'\u001b[39m\u001b[33mby\u001b[39m\u001b[33m'\u001b[39m\u001b[33m and \u001b[39m\u001b[33m'\u001b[39m\u001b[33mlevel\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m9183\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 9184\u001b[39m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 9185\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9186\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9187\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9188\u001b[39m \u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9189\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9190\u001b[39m \u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9191\u001b[39m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m=\u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9192\u001b[39m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9193\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/wombat/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1329\u001b[39m, in \u001b[36mGroupBy.__init__\u001b[39m\u001b[34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[39m\n\u001b[32m 1326\u001b[39m \u001b[38;5;28mself\u001b[39m.dropna = dropna\n\u001b[32m 1328\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1329\u001b[39m grouper, exclusions, obj = \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1330\u001b[39m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1331\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1332\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1333\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1334\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1335\u001b[39m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mno_default\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1336\u001b[39m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1337\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1339\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m observed \u001b[38;5;129;01mis\u001b[39;00m lib.no_default:\n\u001b[32m 1340\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(ping._passed_categorical \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m grouper.groupings):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/wombat/lib/python3.12/site-packages/pandas/core/groupby/grouper.py:1043\u001b[39m, in \u001b[36mget_grouper\u001b[39m\u001b[34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[39m\n\u001b[32m 1041\u001b[39m in_axis, level, gpr = \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1042\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1043\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[32m 1044\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr.key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1045\u001b[39m \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[32m 1046\u001b[39m exclusions.add(gpr.key)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/NREL/WOMBAT/wombat/core/post_processor.py:1031\u001b[39m, in \u001b[36mMetrics.vessel_crew_hours_at_sea\u001b[39m\u001b[34m(self, frequency, by_equipment, vessel_crew_assumption)\u001b[39m\n\u001b[32m 1025\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pd.DataFrame(at_sea.sum()[[\u001b[33m\"\u001b[39m\u001b[33mduration\u001b[39m\u001b[33m\"\u001b[39m]]).T.rename(\n\u001b[32m 1026\u001b[39m columns={\u001b[33m\"\u001b[39m\u001b[33mduration\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mTotal Crew Hours at Sea\u001b[39m\u001b[33m\"\u001b[39m}\n\u001b[32m 1027\u001b[39m )\n\u001b[32m 1028\u001b[39m additional_cols = frequency.group_cols\n\u001b[32m 1029\u001b[39m total_hours = (\n\u001b[32m 1030\u001b[39m \u001b[43mtotal_hours\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop_cols\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m-> \u001b[39m\u001b[32m1031\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_cols\u001b[49m\u001b[43m)\u001b[49m[[\u001b[33m\"\u001b[39m\u001b[33mN\u001b[39m\u001b[33m\"\u001b[39m]]\n\u001b[32m 1032\u001b[39m .sum()\n\u001b[32m 1033\u001b[39m )\n\u001b[32m 1035\u001b[39m columns = additional_cols + columns\n\u001b[32m 1036\u001b[39m group_cols.extend(additional_cols)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/wombat-nrel/lib/python3.13/site-packages/pandas/core/frame.py:9210\u001b[39m, in \u001b[36mDataFrame.groupby\u001b[39m\u001b[34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[39m\n\u001b[32m 9207\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m level \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m by \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 9208\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mYou have to supply one of \u001b[39m\u001b[33m'\u001b[39m\u001b[33mby\u001b[39m\u001b[33m'\u001b[39m\u001b[33m and \u001b[39m\u001b[33m'\u001b[39m\u001b[33mlevel\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m9210\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 9211\u001b[39m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 9212\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9213\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9214\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9215\u001b[39m \u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9216\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9217\u001b[39m \u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9218\u001b[39m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m=\u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9219\u001b[39m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9220\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/wombat-nrel/lib/python3.13/site-packages/pandas/core/groupby/groupby.py:1331\u001b[39m, in \u001b[36mGroupBy.__init__\u001b[39m\u001b[34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[39m\n\u001b[32m 1328\u001b[39m \u001b[38;5;28mself\u001b[39m.dropna = dropna\n\u001b[32m 1330\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1331\u001b[39m grouper, exclusions, obj = \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1332\u001b[39m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1333\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1334\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1335\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1336\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1337\u001b[39m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mno_default\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1338\u001b[39m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1339\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1341\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m observed \u001b[38;5;129;01mis\u001b[39;00m lib.no_default:\n\u001b[32m 1342\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(ping._passed_categorical \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m grouper.groupings):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/wombat-nrel/lib/python3.13/site-packages/pandas/core/groupby/grouper.py:1043\u001b[39m, in \u001b[36mget_grouper\u001b[39m\u001b[34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[39m\n\u001b[32m 1041\u001b[39m in_axis, level, gpr = \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1042\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1043\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[32m 1044\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr.key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1045\u001b[39m \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[32m 1046\u001b[39m exclusions.add(gpr.key)\n", "\u001b[31mKeyError\u001b[39m: 'agent'" ] } @@ -2215,7 +2215,7 @@ }, { "cell_type": "markdown", - "id": "5c42c0ce", + "id": "c603ff96", "metadata": {}, "source": [ "(metrics-demo:n-tows)=\n", @@ -2243,7 +2243,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "d62945c8", + "id": "ab568b68", "metadata": { "tags": [ "output_scroll" @@ -2255,23 +2255,23 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 total_towstotal_tows
0000
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -2287,7 +2287,7 @@ }, { "cell_type": "markdown", - "id": "d0555087", + "id": "5cb8fe13", "metadata": {}, "source": [ "(metrics-demo:dispatch-summary)=\n", @@ -2308,7 +2308,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "b96a6763", + "id": "d591f1f5", "metadata": { "tags": [ "output_scroll" @@ -2320,12 +2320,12 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2335,65 +2335,65 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 N MobilizationsAverage Charter DaysN MobilizationsAverage Charter Days
agent
Anchor Handling Tug4430.22Anchor Handling Tug5125.39
Cable Laying Vessel5339.16Cable Laying Vessel3745.46
Crew Transfer Vessel 117,303.67Crew Transfer Vessel 117,303.00
Crew Transfer Vessel 217,303.00Crew Transfer Vessel 217,303.00
Crew Transfer Vessel 317,303.00Crew Transfer Vessel 317,303.00
Crew Transfer Vessel 417,303.00Crew Transfer Vessel 417,303.00
Crew Transfer Vessel 517,303.67Crew Transfer Vessel 517,303.00
Crew Transfer Vessel 617,303.00Crew Transfer Vessel 617,303.00
Crew Transfer Vessel 717,303.67Crew Transfer Vessel 717,303.67
Diving Support Vessel1557.21Diving Support Vessel1547.04
Heavy Lift Vessel14938.00Heavy Lift Vessel13941.91
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -2408,7 +2408,7 @@ }, { "cell_type": "markdown", - "id": "3808f1e8", + "id": "dddc900a", "metadata": {}, "source": [ "(metrics-demo:labor-costs)=\n", @@ -2432,7 +2432,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "1338e944", + "id": "c0d914f8", "metadata": { "tags": [ "output_scroll" @@ -2443,7 +2443,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Project total: $41,463.52/MW\n" + "Project total: $40,812.81/MW\n" ] } ], @@ -2456,7 +2456,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "ea8e8d73", + "id": "5119454c", "metadata": { "tags": [ "output_scroll" @@ -2468,27 +2468,27 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 hourly_labor_costsalary_labor_costtotal_labor_costhourly_labor_costsalary_labor_costtotal_labor_cost
00.0049,756,225.0949,756,225.0900.0048,975,369.1048,975,369.10
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -2503,7 +2503,7 @@ }, { "cell_type": "markdown", - "id": "ef3fbce5", + "id": "fe571000", "metadata": {}, "source": [ "(metrics-demo:equipment-labor-costs)=\n", @@ -2543,7 +2543,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "c2f39c7f", + "id": "0c39d189", "metadata": { "tags": [ "output_scroll" @@ -2555,12 +2555,12 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2570,50 +2570,50 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 total_costtotal_hourstotal_costtotal_hours
reason
Maintenance24,040,075.8773,484.70Maintenance23,778,851.3273,358.06
Repair573,378,455.37163,269.40Repair595,677,483.18162,777.30
Crew Transfer9,389,341.3411,482.50Crew Transfer8,535,967.2211,355.75
Site Travel95,272,147.55125,427.20Site Travel94,036,128.41125,127.94
Mobilization119,095,000.00131,088.00Mobilization116,260,000.00126,792.00
Weather Delay1,377,310,792.68608,123.97Weather Delay1,406,583,437.36606,268.66
No Requests91,735,224.97380,130.88No Requests67,551,908.19378,246.13
Not in Shift28,395,717.51110,027.92Not in Shift28,671,872.14108,757.42
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -2629,7 +2629,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "e921da0c", + "id": "458731f1", "metadata": { "tags": [ "output_scroll" @@ -2641,16 +2641,16 @@ "text/html": [ "\n", - "\n", + "
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 hourly_labor_costsalary_labor_costtotal_labor_costequipment_costtotal_costtotal_hourshourly_labor_costsalary_labor_costtotal_labor_costequipment_costtotal_costtotal_hours
reason
Maintenance02,250,896.182,250,896.1821,789,179.6924,040,075.8773,484.70
Repair07,611,032.917,611,032.91565,767,422.47573,378,455.37163,269.40
Crew Transfer0360,320.50360,320.509,029,020.839,389,341.3411,482.50
Site Travel03,953,327.643,953,327.6491,318,819.9195,272,147.55125,427.20
Mobilization00.000.00119,095,000.00119,095,000.00131,088.00
Weather Delay021,846,648.0421,846,648.041,355,464,144.641,377,310,792.68608,123.97
No Requests010,566,759.4310,566,759.4381,168,465.5491,735,224.97380,130.88
Not in Shift03,167,240.393,167,240.3925,228,477.1228,395,717.51110,027.92Maintenance02,242,399.942,242,399.9421,536,451.3923,778,851.3273,358.06
Repair07,461,788.667,461,788.66588,215,694.52595,677,483.18162,777.30
Crew Transfer0354,628.67354,628.678,181,338.548,535,967.2211,355.75
Site Travel03,923,784.583,923,784.5890,112,343.8394,036,128.41125,127.94
Mobilization00.000.00116,260,000.00116,260,000.00126,792.00
Weather Delay021,467,372.8121,467,372.811,385,116,064.551,406,583,437.36606,268.66
No Requests010,387,521.6910,387,521.6957,164,386.5167,551,908.19378,246.13
Not in Shift03,137,872.763,137,872.7625,533,999.3828,671,872.14108,757.42
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -2754,7 +2754,7 @@ }, { "cell_type": "markdown", - "id": "5dfa25f6", + "id": "bf0f908e", "metadata": {}, "source": [ "(metrics-demo:emissions)=\n", @@ -2779,7 +2779,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "0c2dbd83", + "id": "df846ac6", "metadata": {}, "outputs": [ { @@ -2787,14 +2787,14 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2806,310 +2806,310 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  durationdistance_kmemissionsdurationdistance_kmemissions
agent
Anchor Handling Tugidle at port3,955.200.00988.80Anchor Handling Tugidle at port4,647.630.001,161.91
idle at site30,005.080.0030,005.08idle at site30,051.300.0030,051.30
maneuvering1,603.460.004,810.38maneuvering1,884.170.005,652.52
transit19,775.99inf79,103.95transit23,238.13inf92,952.53
Cable Laying Vesselidle at port3,460.270.00692.05Cable Laying Vesselidle at port2,419.220.00483.84
idle at site42,726.790.008,545.36idle at site35,691.570.007,138.31
maneuvering1,402.810.009,819.68maneuvering980.770.006,865.36
transit17,301.34inf69,205.36transit12,096.11inf48,384.43
Crew Transfer Vessel 1idle at port3,091.840.00772.96Crew Transfer Vessel 1idle at port3,108.790.00777.20
idle at site32,111.080.0016,055.54idle at site32,088.900.0016,044.45
maneuvering1,253.450.003,760.35maneuvering1,260.320.003,780.97
transit15,459.21inf61,836.82transit15,543.97inf62,175.88
Crew Transfer Vessel 2idle at port3,055.130.00763.78Crew Transfer Vessel 2idle at port3,080.230.00770.06
idle at site32,067.850.0016,033.92idle at site32,076.100.0016,038.05
maneuvering1,238.560.003,715.69maneuvering1,248.740.003,746.22
transit15,275.63inf61,102.51transit15,401.14inf61,604.57
Crew Transfer Vessel 3idle at port3,145.130.00786.28Crew Transfer Vessel 3idle at port3,131.850.00782.96
idle at site32,775.940.0016,387.97idle at site32,853.210.0016,426.60
maneuvering1,275.050.003,825.16maneuvering1,269.670.003,809.01
transit15,725.66inf62,902.65transit15,659.27inf62,637.10
Crew Transfer Vessel 4idle at port3,098.300.00774.58Crew Transfer Vessel 4idle at port3,115.550.00778.89
idle at site32,469.580.0016,234.79idle at site32,560.860.0016,280.43
maneuvering1,256.070.003,768.21maneuvering1,263.060.003,789.18
transit15,491.52inf61,966.10transit15,577.75inf62,311.01
Crew Transfer Vessel 5idle at port3,126.480.00781.62Crew Transfer Vessel 5idle at port3,054.810.00763.70
idle at site32,935.270.0016,467.64idle at site31,462.080.0015,731.04
maneuvering1,267.490.003,802.47maneuvering1,238.440.003,715.31
transit15,632.40inf62,529.58transit15,274.05inf61,096.20
Crew Transfer Vessel 6idle at port3,127.520.00781.88Crew Transfer Vessel 6idle at port3,147.440.00786.86
idle at site32,631.030.0016,315.51idle at site32,830.540.0016,415.27
maneuvering1,267.910.003,803.74maneuvering1,275.990.003,827.97
transit15,637.62inf62,550.46transit15,737.22inf62,948.89
Crew Transfer Vessel 7idle at port3,101.320.00775.33Crew Transfer Vessel 7idle at port3,110.510.00777.63
idle at site32,935.840.0016,467.92idle at site32,400.070.0016,200.03
maneuvering1,257.290.003,771.87maneuvering1,261.020.003,783.05
transit15,506.59inf62,026.35transit15,552.55inf62,210.18
Diving Support Vesselidle at port11,437.990.002,287.60Diving Support Vesselidle at port11,375.060.002,275.01
idle at site5,556.420.001,111.28idle at site5,498.830.001,099.77
maneuvering4,637.020.0032,459.17maneuvering4,611.510.0032,280.57
transit57,189.96126,063.06228,759.84transit56,875.29126,466.16227,501.16
Field Support Vesselidle at port0.000.000.00Field Support Vesselidle at port0.000.000.00
idle at site0.000.000.00idle at site0.000.000.00
maneuvering0.000.000.00maneuvering0.000.000.00
transit0.000.000.00transit0.000.000.00
Heavy Lift Vesselidle at port7,478.250.003,739.12Heavy Lift Vesselidle at port7,030.830.003,515.42
idle at site109,940.760.00109,940.76idle at site116,312.800.00116,312.80
maneuvering3,031.720.0021,222.05maneuvering2,850.340.0019,952.36
transit37,391.23inf448,694.81transit35,154.16inf421,849.97
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -3175,7 +3175,7 @@ }, { "cell_type": "markdown", - "id": "ea527e9f", + "id": "724d5f58", "metadata": {}, "source": [ "(metrics-demo:component-costs)=\n", @@ -3219,7 +3219,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "56e14794", + "id": "58eefa63", "metadata": { "tags": [ "output_scroll" @@ -3231,11 +3231,11 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3244,66 +3244,66 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 total_costtotal_cost
subassembly
anchor53,506,741.10anchor41,360,149.10
array cable161,710,290.39array cable130,486,048.09
ballast pump187,977.28ballast pump187,880.34
drive_train144,100,858.41drive_train90,568,651.56
electrical system48,340,735.59electrical system30,195,017.04
export cable6,412,325.79export cable9,018,545.04
generator231,912,478.98generator353,201,983.07
hydraulic pitch system33,986,885.80hydraulic pitch system36,092,593.82
mooring lines62,110,421.59mooring lines69,805,258.40
power converter1,111,706,405.46power converter1,117,814,098.16
rotor_blades98,854,324.30rotor_blades126,968,866.91
supporting_structure81,597,783.98supporting_structure81,800,486.10
transformer1,140,854.70transformer1,149,213.96
yaw_system18,417,915.73yaw_system16,318,484.82
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -3319,7 +3319,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "007688ec", + "id": "9fcbb0b8", "metadata": { "tags": [ "output_scroll" @@ -3331,15 +3331,15 @@ "text/html": [ "\n", - "\n", + "
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  materials_costtotal_labor_costequipment_costtotal_costmaterials_costtotal_labor_costequipment_costtotal_cost
subassembly
anchordelay0483,343.3614,554,549.7015,037,893.06anchordelay0388,250.1211,691,079.3712,079,329.49
repair9,829,000920,533.2427,719,314.7938,468,848.04repair7,119,000712,318.4321,449,501.1829,280,819.62
array cabledelay02,634,439.9090,146,451.4992,780,891.39array cabledelay02,167,507.9874,168,764.8176,336,272.79
repair6,790,0001,764,399.0060,375,000.0068,929,399.00repair5,520,0001,380,803.9447,248,971.3554,149,775.29
ballast pumpdelay024,824.37132,136.90156,961.27ballast pumpdelay022,167.69117,995.72140,163.40
repair13,0002,849.3415,166.6731,016.01repair20,0004,383.6023,333.3347,716.93
drive_traindelay0831,895.00103,714,897.49104,546,792.49drive_traindelay0631,499.3260,243,064.9360,874,564.24
repair3,590,000227,399.2535,736,666.6739,554,065.92repair2,894,000194,503.9926,605,583.3329,694,087.32
electrical systemdelay0825,535.7238,081,419.2438,906,954.97electrical systemdelay0858,201.1322,184,813.2823,043,014.42
repair838,000172,238.958,423,541.679,433,780.62repair814,000178,716.396,159,286.237,152,002.62
export cabledelay0155,513.495,321,430.565,476,944.05export cabledelay0229,761.377,862,077.978,091,839.34
maintenance9,00019,972.55683,429.69712,402.24maintenance9,00019,726.20675,000.00703,726.20
repair30,0005,479.50187,500.00222,979.50repair30,0005,479.50187,500.00222,979.50
generatordelay04,325,881.87158,778,398.42163,104,280.29generatordelay04,718,061.38243,854,065.33248,572,126.71
maintenance2,145,000940,282.205,005,000.008,090,282.20maintenance2,142,000938,967.124,998,000.008,078,967.12
repair3,831,040487,100.3356,399,776.1660,717,916.49repair6,182,700613,496.4189,754,692.8296,550,889.24
hydraulic pitch systemdelay03,619,135.1722,484,741.9226,103,877.09hydraulic pitch systemdelay03,728,495.6223,848,311.7027,576,807.31
repair1,175,900920,323.815,786,784.907,883,008.70repair1,148,200884,336.516,483,250.008,515,786.51
mooring linesdelay01,780,155.9624,723,544.4726,503,700.44mooring linesdelay01,467,186.7821,189,074.8922,656,261.67
repair7,684,0001,107,920.6026,814,800.5635,606,721.16repair8,892,5001,453,163.4036,803,333.3347,148,996.73
power converterdelay04,193,893.76786,810,250.49791,004,144.25power converterdelay04,256,697.54790,463,384.77794,720,082.30
repair8,285,0001,464,109.59310,953,151.62320,702,261.21repair8,538,0001,489,081.26313,066,934.60323,094,015.86
rotor_bladesdelay01,686,604.3763,580,632.1265,267,236.49rotor_bladesdelay01,517,059.2881,426,330.0382,943,389.31
repair5,317,320448,715.0427,821,052.7833,587,087.82repair5,957,856459,604.9337,608,016.6744,025,477.60
supporting_structuredelay03,972,108.9359,237,370.1463,209,479.07supporting_structuredelay04,104,856.6359,558,057.9963,662,914.62
maintenance1,154,9001,265,654.9115,967,750.0018,388,304.91maintenance1,148,4001,258,720.1015,730,451.3918,137,571.48
transformerdelay0148,571.07790,824.49939,395.56transformerdelay0146,667.73780,693.25927,360.97
maintenance19,00024,986.52133,000.00176,986.52maintenance19,00024,986.52133,000.00176,986.52
repair12,0001,972.6210,500.0024,472.62repair22,0003,616.4719,250.0044,866.47
yaw_systemdelay0328,711.2512,318,546.1812,647,257.43yaw_systemdelay0357,953.8512,923,705.4713,281,659.32
repair158,50087,991.645,524,166.675,770,658.30repair148,50082,283.832,806,041.673,036,825.49
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 25, @@ -3601,7 +3601,7 @@ }, { "cell_type": "markdown", - "id": "5529ff74", + "id": "65004ec9", "metadata": {}, "source": [ "(metrics-demo:fixed-costs)=\n", @@ -3624,7 +3624,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "02bb6df7", + "id": "3485caa4", "metadata": { "tags": [ "output_scroll" @@ -3659,7 +3659,7 @@ }, { "cell_type": "markdown", - "id": "e71627f6", + "id": "42c0015d", "metadata": {}, "source": [ "**Example Usage**:" @@ -3668,7 +3668,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "62edffe7", + "id": "ac292a0e", "metadata": { "tags": [ "output_scroll" @@ -3680,23 +3680,23 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 operationsoperations
00.0000.00
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -3714,7 +3714,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "90f658c8", + "id": "164a2de6", "metadata": { "tags": [ "output_scroll" @@ -3726,35 +3726,35 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 operations_management_administrationinsuranceannual_leases_feesoperating_facilitiesenvironmental_health_safety_monitoringonshore_electrical_maintenancelaboroperations_management_administrationinsuranceannual_leases_feesoperating_facilitiesenvironmental_health_safety_monitoringonshore_electrical_maintenancelabor
00.000.000.000.000.000.000.0000.000.000.000.000.000.000.00
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\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 project_management_administrationmarine_managementweather_forecastingcondition_monitoringbrokers_feeoperations_all_riskbusiness_interruptionthird_party_liabilitystorm_coveragesubmerge_land_lease_coststransmission_charges_rightsoperating_facilitiesenvironmental_health_safety_monitoringonshore_electrical_maintenancelaborproject_management_administrationmarine_managementweather_forecastingcondition_monitoringbrokers_feeoperations_all_riskbusiness_interruptionthird_party_liabilitystorm_coveragesubmerge_land_lease_coststransmission_charges_rightsoperating_facilitiesenvironmental_health_safety_monitoringonshore_electrical_maintenancelabor
00.000.000.000.000.000.000.000.000.000.000.000.000.000.000.0000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 29, @@ -3841,7 +3841,7 @@ }, { "cell_type": "markdown", - "id": "13d7cd49", + "id": "93cc62a7", "metadata": {}, "source": [ "(metrics-demo:opex)=\n", @@ -3867,7 +3867,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "246fb863", + "id": "86f43925", "metadata": { "tags": [ "output_scroll" @@ -3879,11 +3879,11 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3892,90 +3892,90 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 OpExOpEx
year
200286,855,544.812002109,960,887.59
2003101,135,289.092003105,497,310.32
2004112,950,842.072004120,621,518.21
2005107,034,105.522005112,320,335.88
2006129,067,303.682006138,066,385.78
2007120,796,065.412007106,223,730.38
2008137,976,178.542008133,945,414.35
2009113,217,639.702009109,262,966.14
2010131,517,102.162010113,657,102.52
2011118,687,738.022011124,949,595.74
2012120,303,401.332012141,738,946.66
2013129,663,415.652013124,495,778.92
2014113,043,271.902014112,414,262.51
2015120,048,532.392015122,915,424.43
2016127,186,189.392016113,339,913.69
2017127,120,102.072017118,287,747.95
2018112,474,848.942018122,648,182.71
2019130,928,179.712019123,486,698.92
2020106,947,654.342020116,173,524.81
2021122,545,010.552021121,695,076.30
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 operationsport_feesequipment_costtotal_labor_costmaterials_costOpExoperationsport_feesequipment_costtotal_labor_costmaterials_costOpEx
year
20020.000.0083,446,206.522,100,938.291,308,40086,855,544.81
20030.000.0096,648,869.342,361,647.742,124,772101,135,289.09
20040.000.00108,879,456.532,443,033.541,628,352112,950,842.07
20050.000.00102,456,444.652,241,120.862,336,540107,034,105.52
20060.000.00123,696,875.562,615,656.122,754,772129,067,303.68
20070.000.00113,967,876.032,487,449.394,340,740120,796,065.41
20080.000.00131,868,092.692,790,485.853,317,600137,976,178.54
20090.000.00108,544,953.562,409,006.142,263,680113,217,639.70
20100.000.00125,723,969.432,786,792.733,006,340131,517,102.16
20110.000.00113,743,455.992,420,382.032,523,900118,687,738.02
20120.000.00115,681,746.082,464,275.252,157,380120,303,401.33
20130.000.00124,514,389.132,522,386.522,626,640129,663,415.65
20140.000.00109,088,997.262,350,794.641,603,480113,043,271.90
20150.000.00113,254,789.162,520,099.234,273,644120,048,532.39
20160.000.00122,323,207.862,728,477.532,134,504127,186,189.39
20170.000.00121,620,848.002,711,074.062,788,180127,120,102.07
20180.000.00108,078,791.952,350,432.992,045,624112,474,848.94
20190.000.00125,634,291.212,632,088.502,661,800130,928,179.71
20200.000.00102,595,609.162,197,805.182,154,240106,947,654.34
20210.000.00117,091,660.082,622,278.472,831,072122,545,010.5520020.000.00105,768,399.652,281,587.931,910,900109,960,887.59
20030.000.00101,360,248.712,106,381.622,030,680105,497,310.32
20040.000.00115,054,503.762,443,310.453,123,704120,621,518.21
20050.000.00106,867,107.602,352,396.283,100,832112,320,335.88
20060.000.00132,263,716.092,898,929.692,903,740138,066,385.78
20070.000.00102,102,285.942,240,832.451,880,612106,223,730.38
20080.000.00128,532,885.662,758,388.692,654,140133,945,414.35
20090.000.00104,642,932.192,377,341.952,242,692109,262,966.14
20100.000.00109,591,867.932,281,454.591,783,780113,657,102.52
20110.000.00120,000,557.222,447,938.532,501,100124,949,595.74
20120.000.00135,294,912.532,886,034.133,558,000141,738,946.66
20130.000.00118,946,944.172,428,534.753,120,300124,495,778.92
20140.000.00108,387,079.482,240,539.021,786,644112,414,262.51
20150.000.00116,403,373.742,624,050.693,888,000122,915,424.43
20160.000.00109,054,606.782,356,766.911,928,540113,339,913.69
20170.000.00113,689,371.562,383,504.392,214,872118,287,747.95
20180.000.00116,602,587.152,519,215.563,526,380122,648,182.71
20190.000.00119,610,100.942,405,897.981,470,700123,486,698.92
20200.000.00111,275,788.192,359,636.622,538,100116,173,524.81
20210.000.00116,671,009.432,582,626.872,441,440121,695,076.30
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  time_to_completionprocess_timedowntimetime_to_startNtime_to_completionprocess_timedowntimetime_to_startN
subassembly
anchoranchor replacement16,940.048,989.108,985.807,985.3019
major anchor repair14,625.166,541.746,540.618,355.9217
array cablearray cable major repair44,985.5229,725.130.0017,221.0946
array cable replacement30,111.0719,407.390.0011,232.5527
ballast pumpminor ballast pump repair1,892.651,187.151,170.241,070.8513
drive_trainmain shaft major repair498,830.134,337.764,324.67494,851.6543
main shaft minor repair58,542.7923,162.9522,918.3641,285.30344
main shaft replacement24,983.487,399.527,395.5518,109.2212
electrical systempower electrical system major repair192,434.501,529.261,523.14191,133.9418
power electrical system major replacement8,173.462,137.142,135.896,778.215
power electrical system minor repair77,049.4935,800.1935,250.5451,456.75524
export cableexport cable major repair396.5160.500.00336.262
export cable subsea inspection9,916.512,412.740.008,342.5920
generatorannual turbine inspection1,440,221.55158,046.33156,570.281,294,696.751,430
direct drive generator major repair391,197.845,935.595,924.17385,872.2945
direct drive generator major replacement22,511.6210,534.6510,529.7711,984.6011
direct drive generator minor repair127,733.4457,020.2456,411.9782,747.32785
hydraulic pitch systemmajor pitch system repair74,170.6756,754.4656,489.7822,802.37277
major pitch system replacement4,217.53348.50348.503,869.281
minor pitch system repair241,622.97136,392.94135,292.91128,828.451,276
mooring linesbuoyancy module replacement16,192.9313,746.0413,679.193,557.5655
marine growth removal63,684.0346,982.0146,814.9921,240.82186
mooring line major repair18,494.778,580.958,579.3010,189.6724
mooring line replacement12,754.417,541.927,540.995,369.9811
power converterpower converter major repair4,007,565.2332,964.1732,822.643,980,927.06425
power converter minor repair129,530.0465,736.9565,040.0577,457.75769
power converter replacement202,019.9460,847.4860,814.53144,183.58102
rotor_bladesblades major repair130,856.732,106.462,101.37128,757.8613
blades major replacement4,958.774,812.954,812.31146.711
blades minor repair139,058.4378,064.1077,419.9473,649.59711anchoranchor replacement13,464.467,823.577,821.105,759.4013
major anchor repair11,678.254,299.834,298.717,487.0017
array cablearray cable major repair31,087.3520,289.680.0011,133.5435
array cable replacement29,585.5419,244.980.0011,012.3522
ballast pumpminor ballast pump repair2,278.861,134.451,114.301,451.1321
drive_trainmain shaft major repair378,139.321,842.721,838.05376,923.3235
main shaft minor repair55,153.4321,714.2021,431.3838,932.25350
main shaft replacement34,707.285,337.265,333.7929,582.3410
electrical systempower electrical system major repair177,277.521,404.131,402.68176,413.2217
power electrical system major replacement6,058.53765.38765.235,752.034
power electrical system minor repair90,894.3640,480.3739,916.3860,084.21579
export cableexport cable major repair1,125.17789.160.00336.261
export cable subsea inspection10,149.452,588.510.008,384.0720
generatorannual turbine inspection1,335,623.02154,781.01153,221.031,194,511.991,428
direct drive generator major repair753,291.426,623.526,614.40747,369.5247
direct drive generator major replacement69,642.4519,809.8319,772.0750,273.8522
direct drive generator minor repair136,266.5263,552.8362,784.8786,947.93786
hydraulic pitch systemmajor pitch system repair68,128.2054,902.0354,651.3718,083.60273
major pitch system replacement1,838.23491.53490.001,348.732
minor pitch system repair237,647.80140,973.67139,828.90122,318.991,203
mooring linesbuoyancy module replacement14,228.8711,777.3411,718.893,902.4251
marine growth removal55,411.8839,755.6539,557.8919,039.09205
mooring line major repair16,639.727,521.777,516.718,829.3920
mooring line replacement22,446.8911,364.1911,360.8611,310.0324
power converterpower converter major repair5,462,751.2224,300.1924,241.725,442,626.80410
power converter minor repair134,299.4568,571.9367,875.8381,096.45815
power converter replacement287,512.7069,601.2269,563.62221,079.46116
rotor_bladesblades major repair180,153.401,751.171,749.41178,724.1014
blades major replacement13,133.487,703.837,702.555,818.733
blades minor repair130,295.8166,621.4466,026.6472,831.52657
supporting_structurestructural annual inspection1,646,987.58154,775.47153,616.681,497,725.511,439supporting_structurestructural annual inspection1,570,418.92160,853.93159,578.531,415,251.081,439
structural subsea inspection1,425,228.1523,572.8023,021.571,408,144.48719
structural subsea inspection1,519,291.6723,294.7122,779.301,504,198.02719
transformeross annual inspection12,979.447,139.977,084.666,913.7938
transformeross annual inspection14,199.066,338.956,304.218,087.9238
oss minor repair655.02316.13312.87396.036
oss minor repair2,236.411,108.131,102.231,367.1311
yaw_systemyaw system major repair110,975.82730.19727.42110,255.9011
yaw system major replacement2,772.02599.35599.202,173.071
yaw system minor repair43,978.8815,245.9815,067.8333,557.76250
yaw_systemyaw system major repair138,166.081,252.941,251.83137,648.0111
yaw system minor repair44,068.5516,620.2316,412.2131,406.51267
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  time_to_completionprocess_timedowntimetime_to_startNtime_to_completionprocess_timedowntimetime_to_startN
subassembly
anchoranchor replacement16,940.048,989.108,985.807,985.3017
major anchor repair14,625.166,541.746,540.618,355.9215
array cablearray cable major repair44,985.5229,725.130.0017,221.0943
array cable replacement30,111.0719,407.390.0011,232.5525
ballast pumpminor ballast pump repair1,892.651,187.151,170.241,070.8513
drive_trainmain shaft major repair498,830.134,337.764,324.67494,851.6533
main shaft minor repair58,542.7923,162.9522,918.3641,285.30344
main shaft replacement24,983.487,399.527,395.5518,109.2212
electrical systempower electrical system major repair192,434.501,529.261,523.14191,133.9413
power electrical system major replacement8,173.462,137.142,135.896,778.215
power electrical system minor repair77,017.2535,776.1935,235.3551,456.75523
export cableexport cable major repair396.5160.500.00336.261
export cable subsea inspection9,798.512,371.710.008,257.2918
generatorannual turbine inspection1,440,221.55158,046.33156,570.281,294,696.751,430
direct drive generator major repair391,197.845,935.595,924.17385,872.2931
direct drive generator major replacement22,511.6210,534.6510,529.7711,984.6011
direct drive generator minor repair127,733.4457,020.2456,411.9782,747.32785
hydraulic pitch systemmajor pitch system repair73,992.8956,586.4656,330.5922,686.53276
major pitch system replacement4,217.53348.50348.503,869.281
minor pitch system repair241,483.72136,347.56135,255.77128,726.091,275
mooring linesbuoyancy module replacement16,192.9313,746.0413,679.193,557.5655
marine growth removal63,684.0346,982.0146,814.9921,240.82186
mooring line major repair18,494.778,580.958,579.3010,189.6721
mooring line replacement12,754.417,541.927,540.995,369.9811
power converterpower converter major repair3,993,181.1132,923.9432,815.123,966,550.21288
power converter minor repair129,530.0465,736.9565,040.0577,457.75769
power converter replacement202,019.9460,847.4860,814.53144,183.58100
rotor_bladesblades major repair130,856.732,106.462,101.37128,757.8610
blades major replacement4,958.774,812.954,812.31146.711
blades minor repair139,058.4378,064.1077,419.9473,649.59711anchoranchor replacement13,464.467,823.577,821.105,759.4012
major anchor repair11,678.254,299.834,298.717,487.0013
array cablearray cable major repair31,087.3520,289.680.0011,133.5430
array cable replacement29,585.5419,244.980.0011,012.3521
ballast pumpminor ballast pump repair2,278.861,134.451,114.301,451.1320
drive_trainmain shaft major repair378,139.321,842.721,838.05376,923.3216
main shaft minor repair55,153.4321,714.2021,431.3838,932.25350
main shaft replacement34,707.285,337.265,333.7929,582.3410
electrical systempower electrical system major repair177,277.521,404.131,402.68176,413.227
power electrical system major replacement6,058.53765.38765.235,752.034
power electrical system minor repair90,894.3640,480.3739,916.3860,084.21579
export cableexport cable major repair1,125.17789.160.00336.261
export cable subsea inspection10,149.452,588.510.008,384.0718
generatorannual turbine inspection1,335,623.02154,781.01153,221.031,194,511.991,428
direct drive generator major repair753,291.426,623.526,614.40747,369.5230
direct drive generator major replacement68,489.3418,833.4718,827.9550,064.6021
direct drive generator minor repair136,266.5263,552.8362,784.8786,947.93786
hydraulic pitch systemmajor pitch system repair68,128.2054,902.0354,651.3718,083.60273
major pitch system replacement1,838.23491.53490.001,348.732
minor pitch system repair237,647.80140,973.67139,828.90122,318.991,203
mooring linesbuoyancy module replacement14,228.8711,777.3411,718.893,902.4251
marine growth removal55,411.8839,755.6539,557.8919,039.09205
mooring line major repair16,156.977,516.027,513.978,829.3919
mooring line replacement22,446.8911,364.1911,360.8611,310.0323
power converterpower converter major repair5,462,751.2224,300.1924,241.725,442,626.80192
power converter minor repair134,263.2968,530.5567,842.9581,092.92814
power converter replacement287,512.7069,601.2269,563.62221,079.46116
rotor_bladesblades major repair180,153.401,751.171,749.41178,724.108
blades major replacement13,133.487,703.837,702.555,818.733
blades minor repair130,295.8166,621.4466,026.6472,831.52657
supporting_structurestructural annual inspection1,646,987.58154,775.47153,616.681,497,725.511,439supporting_structurestructural annual inspection1,570,418.92160,853.93159,578.531,415,251.081,439
structural subsea inspection1,425,228.1523,572.8023,021.571,408,144.48583
structural subsea inspection1,515,174.2223,254.2522,746.991,500,112.63570
transformeross annual inspection12,979.447,139.977,084.666,913.7938
transformeross annual inspection14,199.066,338.956,304.218,087.9238
oss minor repair655.02316.13312.87396.036
oss minor repair2,236.411,108.131,102.231,367.1311
yaw_systemyaw system major repair110,975.82730.19727.42110,255.907
yaw system major replacement2,772.02599.35599.202,173.071
yaw system minor repair43,978.8815,245.9815,067.8333,557.76250
yaw_systemyaw system major repair138,166.081,252.941,251.83137,648.015
yaw system minor repair44,068.5516,620.2316,412.2131,406.51267
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 33, @@ -4970,7 +4954,7 @@ }, { "cell_type": "markdown", - "id": "653ce2d6", + "id": "e13f29fa", "metadata": {}, "source": [ "(metrics-demo:request-sumamry)=\n", @@ -4986,7 +4970,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "b965359c", + "id": "afc5826c", "metadata": { "tags": [ "output_scroll" @@ -4998,15 +4982,15 @@ "text/html": [ "\n", - "\n", + "
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  total_requestscanceled_requestsincomplete_requestscompleted_requeststotal_requestscanceled_requestsincomplete_requestscompleted_requests
subassembly
anchoranchor replacement190217anchoranchor replacement130112
major anchor repair170215major anchor repair170413
array cablearray cable major repair460343array cablearray cable major repair350530
array cable replacement270225array cable replacement220121
ballast pumpminor ballast pump repair130013ballast pumpminor ballast pump repair210120
drive_trainmain shaft major repair4301033drive_trainmain shaft major repair3501916
main shaft minor repair34400344main shaft minor repair35000350
main shaft replacement120012main shaft replacement100010
electrical systempower electrical system major repair180513electrical systempower electrical system major repair170107
power electrical system major replacement5005power electrical system major replacement4004
power electrical system minor repair52401523power electrical system minor repair57900579
export cableexport cable major repair2011export cableexport cable major repair1001
export cable subsea inspection200218export cable subsea inspection200218
generatorannual turbine inspection1,430001,430generatorannual turbine inspection1,430201,428
direct drive generator major repair4501431direct drive generator major repair4921730
direct drive generator major replacement110011direct drive generator major replacement220121
direct drive generator minor repair78500785direct drive generator minor repair78600786
hydraulic pitch systemmajor pitch system repair27701276hydraulic pitch systemmajor pitch system repair27300273
major pitch system replacement1001major pitch system replacement2002
minor pitch system repair1,276011,275minor pitch system repair1,203001,203
mooring linesbuoyancy module replacement550055mooring linesbuoyancy module replacement510051
marine growth removal18600186marine growth removal20500205
mooring line major repair240321mooring line major repair200119
mooring line replacement110011mooring line replacement240123
power converterpower converter major repair47954137288power converterpower converter major repair516106218192
power converter minor repair76900769power converter minor repair81721814
power converter replacement10202100power converter replacement11710116
rotor_bladesblades major repair130310rotor_bladesblades major repair14068
blades major replacement1001blades major replacement3003
blades minor repair71100711blades minor repair65700657
supporting_structurestructural annual inspection1,439001,439supporting_structurestructural annual inspection1,439001,439
structural subsea inspection7190136583structural subsea inspection7190149570
transformeross annual inspection380038transformeross annual inspection380038
oss minor repair6006oss minor repair110011
yaw_systemyaw system major repair11047yaw_systemyaw system major repair11065
yaw system major replacement1001
yaw system minor repair25000250yaw system minor repair26700267
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 34, @@ -5309,7 +5286,7 @@ }, { "cell_type": "markdown", - "id": "46b2b3f6", + "id": "d69214f9", "metadata": {}, "source": [ "(metrics-demo:power-production)=\n", @@ -5334,7 +5311,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "6ca66ae7", + "id": "d517f843", "metadata": { "tags": [ "output_scroll" @@ -5346,23 +5323,23 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 windfarmwindfarm
Project Energy Production (kWh)105,935,766,142.00Project Energy Production (kWh)105,917,518,696.50
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 35, @@ -5378,7 +5355,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "9c0cb861", + "id": "8796ee4f", "metadata": { "tags": [ "output_scroll" @@ -5390,23 +5367,23 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 windfarmwindfarm
Project Energy Production (MWh)105,935,766.14Project Energy Production (MWh)105,917,518.70
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 36, @@ -5422,7 +5399,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "9ca4ddc0", + "id": "6dd363e2", "metadata": { "tags": [ "output_scroll" @@ -5434,23 +5411,23 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 windfarmwindfarm
Project Energy Production (GWh)105,935.77Project Energy Production (GWh)105,917.52
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 37, @@ -5465,7 +5442,7 @@ }, { "cell_type": "markdown", - "id": "9b86e0a2", + "id": "84a2dfd2", "metadata": {}, "source": [ "(metrics-demo:h2-production)=\n", @@ -5493,7 +5470,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "1706f39b", + "id": "203977d9", "metadata": { "tags": [ "output_scroll" @@ -5508,7 +5485,7 @@ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[38]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Project totals, in kWh, at the wind farm level\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m style(\u001b[43mmetrics\u001b[49m\u001b[43m.\u001b[49m\u001b[43mh2_production\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfrequency\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mproject\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtotal\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mkgph\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/WOMBAT/wombat/core/post_processor.py:2322\u001b[39m, in \u001b[36mMetrics.h2_production\u001b[39m\u001b[34m(self, frequency, by, units)\u001b[39m\n\u001b[32m 2286\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calculates the hydrogen production for the simulation at a project, annual,\u001b[39;00m\n\u001b[32m 2287\u001b[39m \u001b[33;03mor monthly level that can be broken out by electrolyzer.\u001b[39;00m\n\u001b[32m 2288\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 2319\u001b[39m \u001b[33;03m If :py:attr:`units` is not one of \"kg\" or \"tn\".\u001b[39;00m\n\u001b[32m 2320\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 2321\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.electrolyzer_rated_production.size == \u001b[32m0\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m2322\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo electrolyzers available to analyze.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2323\u001b[39m frequency = _check_frequency(frequency, which=\u001b[33m\"\u001b[39m\u001b[33mall\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2325\u001b[39m by = by.lower().strip()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/GitHub_Public/NREL/WOMBAT/wombat/core/post_processor.py:2332\u001b[39m, in \u001b[36mMetrics.h2_production\u001b[39m\u001b[34m(self, frequency, by, units)\u001b[39m\n\u001b[32m 2296\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Calculates the hydrogen production for the simulation at a project, annual,\u001b[39;00m\n\u001b[32m 2297\u001b[39m \u001b[33;03mor monthly level that can be broken out by electrolyzer.\u001b[39;00m\n\u001b[32m 2298\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 2329\u001b[39m \u001b[33;03m If :py:attr:`units` is not one of \"kg\" or \"tn\".\u001b[39;00m\n\u001b[32m 2330\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 2331\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.electrolyzer_rated_production.size == \u001b[32m0\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m2332\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo electrolyzers available to analyze.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2333\u001b[39m frequency = _check_frequency(frequency, which=\u001b[33m\"\u001b[39m\u001b[33mall\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2335\u001b[39m by = by.lower().strip()\n", "\u001b[31mValueError\u001b[39m: No electrolyzers available to analyze." ] } @@ -5520,7 +5497,7 @@ }, { "cell_type": "markdown", - "id": "80f975d6", + "id": "45dc49f8", "metadata": {}, "source": [ "(metrics-demo:npv)=\n", @@ -5543,7 +5520,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "c39620e6", + "id": "7f8e35c0", "metadata": { "tags": [ "output_scroll" @@ -5555,11 +5532,11 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5568,90 +5545,90 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 OpExOpEx
year
200286,855,544.812002109,960,887.59
2003101,135,289.092003105,497,310.32
2004112,950,842.072004120,621,518.21
2005107,034,105.522005112,320,335.88
2006129,067,303.682006138,066,385.78
2007120,796,065.412007106,223,730.38
2008137,976,178.542008133,945,414.35
2009113,217,639.702009109,262,966.14
2010131,517,102.162010113,657,102.52
2011118,687,738.022011124,949,595.74
2012120,303,401.332012141,738,946.66
2013129,663,415.652013124,495,778.92
2014113,043,271.902014112,414,262.51
2015120,048,532.392015122,915,424.43
2016127,186,189.392016113,339,913.69
2017127,120,102.072017118,287,747.95
2018112,474,848.942018122,648,182.71
2019130,928,179.712019123,486,698.92
2020106,947,654.342020116,173,524.81
2021122,545,010.552021121,695,076.30
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 39, @@ -5687,7 +5664,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.13.11" }, "source_map": [ 11, diff --git a/examples/strategy_demonstration.ipynb b/examples/strategy_demonstration.ipynb index 090f1877..68e462c0 100644 --- a/examples/strategy_demonstration.ipynb +++ b/examples/strategy_demonstration.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d99337a2", + "id": "f4c866d0", "metadata": {}, "source": [ "# Servicing Strategies\n", @@ -47,7 +47,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "93e56ff0", + "id": "1ae6d8cd", "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "markdown", - "id": "4627e389", + "id": "11e4345c", "metadata": {}, "source": [ "## Simulation and results setup\n", @@ -84,7 +84,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "25a42ea6", + "id": "eb110d66", "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "markdown", - "id": "bd77887b", + "id": "8a6773c8", "metadata": {}, "source": [ "## Run the simulations and display the results" @@ -131,7 +131,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "34dc0632", + "id": "f1b46ec9", "metadata": {}, "outputs": [ { @@ -167,23 +167,23 @@ " \n", " \n", " base_scheduled\n", - " 0.01\n", - " 0.31\n", + " 0.02\n", + " 0.35\n", " \n", " \n", " base_requests\n", - " 0.01\n", - " 0.32\n", + " 0.02\n", + " 0.37\n", " \n", " \n", " base_downtime\n", - " 0.01\n", - " 0.32\n", + " 0.02\n", + " 0.37\n", " \n", " \n", " base_tow_to_port\n", " 0.02\n", - " 0.34\n", + " 0.36\n", " \n", " \n", "\n", @@ -192,10 +192,10 @@ "text/plain": [ " Load Time (min) Run Time (min)\n", "Scenario \n", - "base_scheduled 0.01 0.31\n", - "base_requests 0.01 0.32\n", - "base_downtime 0.01 0.32\n", - "base_tow_to_port 0.02 0.34" + "base_scheduled 0.02 0.35\n", + "base_requests 0.02 0.37\n", + "base_downtime 0.02 0.37\n", + "base_tow_to_port 0.02 0.36" ] }, "execution_count": 3, @@ -321,7 +321,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7ae84e6e", + "id": "32006d25", "metadata": {}, "outputs": [ { @@ -357,14 +357,14 @@ " 0.97\n", " 0.97\n", " 0.97\n", - " 0.96\n", + " 0.97\n", " \n", " \n", " availability - production based\n", " 0.97\n", " 0.97\n", " 0.97\n", - " 0.96\n", + " 0.97\n", " \n", " \n", " capacity factor - net\n", @@ -382,31 +382,31 @@ " \n", " \n", " power production\n", - " 9,724.71\n", - " 9,725.97\n", - " 9,744.79\n", - " 9,669.09\n", + " 9,724.34\n", + " 9,725.49\n", + " 9,742.92\n", + " 9,727.74\n", " \n", " \n", " task completion rate\n", " 1.00\n", " 1.00\n", " 0.99\n", - " 0.99\n", + " 1.00\n", " \n", " \n", " annual direct O&M cost\n", - " 18.57\n", - " 13.68\n", - " 5.43\n", - " 10.16\n", + " 18.38\n", + " 16.14\n", + " 5.47\n", + " 9.58\n", " \n", " \n", " annual vessel cost\n", - " 12.80\n", - " 8.41\n", + " 12.75\n", + " 10.25\n", " 1.92\n", - " 2.03\n", + " 2.02\n", " \n", " \n", " ctv cost\n", @@ -418,14 +418,14 @@ " \n", " fsv cost\n", " 0.27\n", - " 0.24\n", + " 0.21\n", " 0.00\n", " 0.00\n", " \n", " \n", " hlv cost\n", - " 10.62\n", - " 6.25\n", + " 10.56\n", + " 8.12\n", " 0.00\n", " 0.00\n", " \n", @@ -434,14 +434,14 @@ " 0.00\n", " 0.00\n", " 0.00\n", - " 0.11\n", + " 0.10\n", " \n", " \n", " annual repair cost\n", - " 4.17\n", - " 3.67\n", - " 1.91\n", - " 4.53\n", + " 4.04\n", + " 4.29\n", + " 1.95\n", + " 3.97\n", " \n", " \n", " annual technician cost\n", @@ -460,14 +460,14 @@ " \n", " fsv utilization\n", " 1.00\n", - " 0.93\n", + " 0.95\n", " 0.00\n", " 0.00\n", " \n", " \n", " hlv utilization\n", + " 0.97\n", " 0.96\n", - " 0.93\n", " 0.00\n", " 0.00\n", " \n", @@ -476,7 +476,7 @@ " 0.00\n", " 0.00\n", " 0.00\n", - " 0.82\n", + " 0.83\n", " \n", " \n", "\n", @@ -488,40 +488,40 @@ "availability - production based 0.97 0.97 0.97 \n", "capacity factor - net 0.46 0.46 0.46 \n", "capacity factor - gross 0.48 0.48 0.48 \n", - "power production 9,724.71 9,725.97 9,744.79 \n", + "power production 9,724.34 9,725.49 9,742.92 \n", "task completion rate 1.00 1.00 0.99 \n", - "annual direct O&M cost 18.57 13.68 5.43 \n", - "annual vessel cost 12.80 8.41 1.92 \n", + "annual direct O&M cost 18.38 16.14 5.47 \n", + "annual vessel cost 12.75 10.25 1.92 \n", "ctv cost 1.92 1.92 1.92 \n", - "fsv cost 0.27 0.24 0.00 \n", - "hlv cost 10.62 6.25 0.00 \n", + "fsv cost 0.27 0.21 0.00 \n", + "hlv cost 10.56 8.12 0.00 \n", "tow cost 0.00 0.00 0.00 \n", - "annual repair cost 4.17 3.67 1.91 \n", + "annual repair cost 4.04 4.29 1.95 \n", "annual technician cost 1.60 1.60 1.60 \n", "ctv utilization 1.00 1.00 1.00 \n", - "fsv utilization 1.00 0.93 0.00 \n", - "hlv utilization 0.96 0.93 0.00 \n", + "fsv utilization 1.00 0.95 0.00 \n", + "hlv utilization 0.97 0.96 0.00 \n", "tow utilization 0.00 0.00 0.00 \n", "\n", " base_tow_to_port \n", - "availability - time based 0.96 \n", - "availability - production based 0.96 \n", + "availability - time based 0.97 \n", + "availability - production based 0.97 \n", "capacity factor - net 0.46 \n", "capacity factor - gross 0.48 \n", - "power production 9,669.09 \n", - "task completion rate 0.99 \n", - "annual direct O&M cost 10.16 \n", - "annual vessel cost 2.03 \n", + "power production 9,727.74 \n", + "task completion rate 1.00 \n", + "annual direct O&M cost 9.58 \n", + "annual vessel cost 2.02 \n", "ctv cost 1.92 \n", "fsv cost 0.00 \n", "hlv cost 0.00 \n", - "tow cost 0.11 \n", - "annual repair cost 4.53 \n", + "tow cost 0.10 \n", + "annual repair cost 3.97 \n", "annual technician cost 1.60 \n", "ctv utilization 1.00 \n", "fsv utilization 0.00 \n", "hlv utilization 0.00 \n", - "tow utilization 0.82 " + "tow utilization 0.83 " ] }, "execution_count": 4, @@ -560,7 +560,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.13.11" }, "source_map": [ 11, diff --git a/library/code_comparison/avanessova_diss/cables/__init__.py b/library/code_comparison/avanessova_diss/cables/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/cables/array_cable.yaml b/library/code_comparison/avanessova_diss/cables/array_cable.yaml new file mode 100644 index 00000000..75c4261e --- /dev/null +++ b/library/code_comparison/avanessova_diss/cables/array_cable.yaml @@ -0,0 +1,17 @@ +name: array_cable +maintenance: +- description: cable & scour survey + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 +failures: +- scale: 69.4444 + shape: 1 + time: 48 + materials: 200000 + description: cable replacement + service_equipment: CAB + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/cables/array_cable_increase_failure.yaml b/library/code_comparison/avanessova_diss/cables/array_cable_increase_failure.yaml new file mode 100644 index 00000000..8305d850 --- /dev/null +++ b/library/code_comparison/avanessova_diss/cables/array_cable_increase_failure.yaml @@ -0,0 +1,17 @@ +name: array_cable +maintenance: +- description: cable & scour survey + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 +failures: +- scale: 34.7222 + shape: 1 + time: 48 + materials: 200000 + description: cable replacement + service_equipment: CAB + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/cables/array_cable_increase_maintenance.yaml b/library/code_comparison/avanessova_diss/cables/array_cable_increase_maintenance.yaml new file mode 100644 index 00000000..9f882281 --- /dev/null +++ b/library/code_comparison/avanessova_diss/cables/array_cable_increase_maintenance.yaml @@ -0,0 +1,17 @@ +name: array_cable +maintenance: +- description: cable & scour survey + time: 12 + materials: 10000 + service_equipment: RMT + frequency: 365 +failures: +- scale: 69.4444 + shape: 1 + time: 48 + materials: 200000 + description: cable replacement + service_equipment: CAB + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/cables/array_cable_no_major.yaml b/library/code_comparison/avanessova_diss/cables/array_cable_no_major.yaml new file mode 100644 index 00000000..cab7fade --- /dev/null +++ b/library/code_comparison/avanessova_diss/cables/array_cable_no_major.yaml @@ -0,0 +1,17 @@ +name: array_cable +maintenance: +- description: cable & scour survey + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 +failures: +- scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 4 diff --git a/library/code_comparison/avanessova_diss/cables/export_cable.yaml b/library/code_comparison/avanessova_diss/cables/export_cable.yaml new file mode 100644 index 00000000..fad3e8d8 --- /dev/null +++ b/library/code_comparison/avanessova_diss/cables/export_cable.yaml @@ -0,0 +1,16 @@ +name: export_cable +maintenance: +- description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 +failures: +- scale: 0 + shape: 0 + time: 0 + materials: 0 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: na diff --git a/library/code_comparison/avanessova_diss/project/config/__init__.py b/library/code_comparison/avanessova_diss/project/config/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/project/config/fixed_costs.yaml b/library/code_comparison/avanessova_diss/project/config/fixed_costs.yaml new file mode 100644 index 00000000..5b84d81d --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/fixed_costs.yaml @@ -0,0 +1 @@ +labor: 1.33333 # £80K/yr x 100 technicians / 600K kW diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base.yaml new file mode 100644 index 00000000..9ee59e0b --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Base +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base_24hr.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base_24hr.yaml new file mode 100644 index 00000000..01e59218 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base_24hr.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Base 24 Hour Shift +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_base.csv +workday_start: 0 +workday_end: 24 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base_copy.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base_copy.yaml new file mode 100644 index 00000000..9ee59e0b --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_base_copy.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Base +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_failure.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_failure.yaml new file mode 100644 index 00000000..7820cc5e --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_failure.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Increase Failure Rates +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_increase_failure.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_failure_24hr.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_failure_24hr.yaml new file mode 100644 index 00000000..e6ee770d --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_failure_24hr.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Increase Failure Rates +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_increase_failure.csv +workday_start: 0 +workday_end: 24 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_maintenance.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_maintenance.yaml new file mode 100644 index 00000000..2847c780 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_maintenance.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Increase Maintenance Duration +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_increase_maintenance.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_wave.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_wave.yaml new file mode 100644 index 00000000..13fe09df --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_increase_wave.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - Increase Wave Limit +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv_increase_wave.yaml, 3] + - cab_increase_wave.yaml + - hlv_increase_wave.yaml + - ahv_increase_wave.yaml + - rov_increase_wave.yaml +layout: broadshore_layout_1_in_situ_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_no_major.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_no_major.yaml new file mode 100644 index 00000000..40c53e38 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_in_situ_no_major.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - In Situ - No Major Repairs or Replacements +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_in_situ_no_major.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_tow_base.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_base.yaml new file mode 100644 index 00000000..f19349ce --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_base.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - Tow-to-Port - Base +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_tow_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port: port_base.yaml +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_tow_base_24hr.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_base_24hr.yaml new file mode 100644 index 00000000..27e08cdd --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_base_24hr.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - Tow-to-Port - 24 Hour Shift +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_tow_base.csv +workday_start: 0 +workday_end: 24 +project_capacity: 600 # 15MW x 40 turbines +port: port_base.yaml +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_failure.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_failure.yaml new file mode 100644 index 00000000..88036d38 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_failure.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - Tow-to-Port - Increase Failure Rates +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_tow_increase_failure.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port: port_base.yaml +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_maintenance.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_maintenance.yaml new file mode 100644 index 00000000..a6f61434 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_maintenance.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - Tow-to-Port - Increase Maintenance Duration +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_1_tow_increase_maintenance.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port: port_base.yaml +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_wave.yaml b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_wave.yaml new file mode 100644 index 00000000..083f7220 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_1_tow_increase_wave.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 1 - Tow-to-Port - Increase Wave Limit +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv_increase_wave.yaml, 3] + - cab_increase_wave.yaml + - ahv_increase_wave.yaml + - rov_increase_wave.yaml +layout: broadshore_layout_1_tow_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port: port_increase_wave.yaml +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_2_in_situ_base.yaml b/library/code_comparison/avanessova_diss/project/config/layout_2_in_situ_base.yaml new file mode 100644 index 00000000..8b2ba314 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_2_in_situ_base.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 2 - In Situ - Base +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_2_in_situ_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/config/layout_2_tow_base.yaml b/library/code_comparison/avanessova_diss/project/config/layout_2_tow_base.yaml new file mode 100644 index 00000000..3c197bd9 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/config/layout_2_tow_base.yaml @@ -0,0 +1,17 @@ +name: Broadshore Layout 2 - Tow-to-Port - Base +start_year: 2000 +end_year: 2019 +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - ahv.yaml + - rov.yaml +layout: broadshore_layout_2_tow_base.csv +workday_start: 7 +workday_end: 19 +project_capacity: 600 # 15MW x 40 turbines +port: port_base.yaml +port_distance: 45.79 +weather: ore_validation_58.0_-1.5.pqt +inflation_rate: 0 +fixed_costs: fixed_costs.yaml diff --git a/library/code_comparison/avanessova_diss/project/plant/Cable_Layout_1.png b/library/code_comparison/avanessova_diss/project/plant/Cable_Layout_1.png new file mode 100644 index 00000000..27305b07 Binary files /dev/null and b/library/code_comparison/avanessova_diss/project/plant/Cable_Layout_1.png differ diff --git a/library/code_comparison/avanessova_diss/project/plant/Cable_Layout_2.png b/library/code_comparison/avanessova_diss/project/plant/Cable_Layout_2.png new file mode 100644 index 00000000..e14ef197 Binary files /dev/null and b/library/code_comparison/avanessova_diss/project/plant/Cable_Layout_2.png differ diff --git a/library/code_comparison/avanessova_diss/project/plant/__init__.py b/library/code_comparison/avanessova_diss/project/plant/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_base.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_base.csv new file mode 100644 index 00000000..2de25047 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_base.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB28 +WTG_36,SS01,WTG_36,turbine,-1.6344,58,1,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB36 +WTG_19,SS01,WTG_19,turbine,-1.668,58.0362,2,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB19 +WTG_27,SS01,WTG_27,turbine,-1.668,58.0181,2,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB27 +WTG_35,SS01,WTG_35,turbine,-1.668,58,2,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB35 +WTG_18,SS01,WTG_18,turbine,-1.7016,58.0362,3,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB18 +WTG_26,SS01,WTG_26,turbine,-1.7016,58.0181,3,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB26 +WTG_34,SS01,WTG_34,turbine,-1.7016,58,3,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB34 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,4,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB17 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,4,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB25 +WTG_33,SS01,WTG_33,turbine,-1.7352,58,4,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB33 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,5,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB10 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,5,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB02 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,5,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB01 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,5,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB09 +WTG_15,SS02,WTG_15,turbine,-1.5336,58.0543,6,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB15 +WTG_7,SS02,WTG_7,turbine,-1.5336,58.0724,6,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB07 +WTG_8,SS02,WTG_8,turbine,-1.5,58.0724,6,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB08 +WTG_16,SS02,WTG_16,turbine,-1.5,58.0543,6,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB16 +WTG_24,SS02,WTG_24,turbine,-1.5,58.0362,7,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB24 +WTG_32,SS02,WTG_32,turbine,-1.5,58.0181,7,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB32 +WTG_40,SS02,WTG_40,turbine,-1.5,58,7,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB40 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,8,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB23 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,8,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB31 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,8,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB39 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,9,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB22 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,9,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB30 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,9,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB38 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,10,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB21 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,10,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB29 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,10,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB37 +WTG_14,SS02,WTG_14,turbine,-1.5672,58.0543,11,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB14 +WTG_6,SS02,WTG_6,turbine,-1.5672,58.0724,11,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB06 +WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_increase_failure.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_increase_failure.csv new file mode 100644 index 00000000..314b4b8e --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_increase_failure.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB28 +WTG_36,SS01,WTG_36,turbine,-1.6344,58,1,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB36 +WTG_19,SS01,WTG_19,turbine,-1.668,58.0362,2,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB19 +WTG_27,SS01,WTG_27,turbine,-1.668,58.0181,2,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB27 +WTG_35,SS01,WTG_35,turbine,-1.668,58,2,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB35 +WTG_18,SS01,WTG_18,turbine,-1.7016,58.0362,3,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB18 +WTG_26,SS01,WTG_26,turbine,-1.7016,58.0181,3,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB26 +WTG_34,SS01,WTG_34,turbine,-1.7016,58,3,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB34 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,4,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB17 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,4,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB25 +WTG_33,SS01,WTG_33,turbine,-1.7352,58,4,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB33 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,5,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB10 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,5,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB02 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,5,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB01 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,5,3,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB09 +WTG_15,SS02,WTG_15,turbine,-1.5336,58.0543,6,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB15 +WTG_7,SS02,WTG_7,turbine,-1.5336,58.0724,6,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB07 +WTG_8,SS02,WTG_8,turbine,-1.5,58.0724,6,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB08 +WTG_16,SS02,WTG_16,turbine,-1.5,58.0543,6,3,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB16 +WTG_24,SS02,WTG_24,turbine,-1.5,58.0362,7,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB24 +WTG_32,SS02,WTG_32,turbine,-1.5,58.0181,7,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB32 +WTG_40,SS02,WTG_40,turbine,-1.5,58,7,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB40 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,8,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB23 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,8,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB31 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,8,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB39 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,9,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB22 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,9,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB30 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,9,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB38 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,10,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB21 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,10,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB29 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,10,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB37 +WTG_14,SS02,WTG_14,turbine,-1.5672,58.0543,11,0,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB14 +WTG_6,SS02,WTG_6,turbine,-1.5672,58.0724,11,1,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB06 +WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_in_situ_increase_failure.yaml,array_cable_increase_failure.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_increase_maintenance.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_increase_maintenance.csv new file mode 100644 index 00000000..8304a1ed --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_increase_maintenance.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB28 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+WTG_14,SS02,WTG_14,turbine,-1.5672,58.0543,11,0,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB14 +WTG_6,SS02,WTG_6,turbine,-1.5672,58.0724,11,1,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB06 +WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_no_major.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_no_major.csv new file mode 100644 index 00000000..76e06a26 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_in_situ_no_major.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB28 +WTG_36,SS01,WTG_36,turbine,-1.6344,58,1,2,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB36 +WTG_19,SS01,WTG_19,turbine,-1.668,58.0362,2,0,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB19 +WTG_27,SS01,WTG_27,turbine,-1.668,58.0181,2,1,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB27 +WTG_35,SS01,WTG_35,turbine,-1.668,58,2,2,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB35 +WTG_18,SS01,WTG_18,turbine,-1.7016,58.0362,3,0,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB18 +WTG_26,SS01,WTG_26,turbine,-1.7016,58.0181,3,1,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB26 +WTG_34,SS01,WTG_34,turbine,-1.7016,58,3,2,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB34 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,4,0,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB17 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+WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_in_situ_no_major.yaml,array_cable_no_major.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_base.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_base.csv new file mode 100644 index 00000000..bb0a8f76 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_base.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB28 +WTG_36,SS01,WTG_36,turbine,-1.6344,58,1,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB36 +WTG_19,SS01,WTG_19,turbine,-1.668,58.0362,2,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB19 +WTG_27,SS01,WTG_27,turbine,-1.668,58.0181,2,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB27 +WTG_35,SS01,WTG_35,turbine,-1.668,58,2,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB35 +WTG_18,SS01,WTG_18,turbine,-1.7016,58.0362,3,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB18 +WTG_26,SS01,WTG_26,turbine,-1.7016,58.0181,3,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB26 +WTG_34,SS01,WTG_34,turbine,-1.7016,58,3,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB34 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,4,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB17 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,4,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB25 +WTG_33,SS01,WTG_33,turbine,-1.7352,58,4,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB33 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,5,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB10 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,5,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB02 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,5,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB01 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,5,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB09 +WTG_15,SS02,WTG_15,turbine,-1.5336,58.0543,6,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB15 +WTG_7,SS02,WTG_7,turbine,-1.5336,58.0724,6,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB07 +WTG_8,SS02,WTG_8,turbine,-1.5,58.0724,6,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB08 +WTG_16,SS02,WTG_16,turbine,-1.5,58.0543,6,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB16 +WTG_24,SS02,WTG_24,turbine,-1.5,58.0362,7,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB24 +WTG_32,SS02,WTG_32,turbine,-1.5,58.0181,7,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB32 +WTG_40,SS02,WTG_40,turbine,-1.5,58,7,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB40 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,8,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB23 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,8,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB31 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,8,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB39 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,9,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB22 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,9,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB30 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,9,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB38 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,10,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB21 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,10,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB29 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,10,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB37 +WTG_14,SS02,WTG_14,turbine,-1.5672,58.0543,11,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB14 +WTG_6,SS02,WTG_6,turbine,-1.5672,58.0724,11,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB06 +WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_increase_failure.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_increase_failure.csv new file mode 100644 index 00000000..0db238db --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_increase_failure.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB28 +WTG_36,SS01,WTG_36,turbine,-1.6344,58,1,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB36 +WTG_19,SS01,WTG_19,turbine,-1.668,58.0362,2,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB19 +WTG_27,SS01,WTG_27,turbine,-1.668,58.0181,2,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB27 +WTG_35,SS01,WTG_35,turbine,-1.668,58,2,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB35 +WTG_18,SS01,WTG_18,turbine,-1.7016,58.0362,3,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB18 +WTG_26,SS01,WTG_26,turbine,-1.7016,58.0181,3,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB26 +WTG_34,SS01,WTG_34,turbine,-1.7016,58,3,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB34 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,4,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB17 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,4,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB25 +WTG_33,SS01,WTG_33,turbine,-1.7352,58,4,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB33 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,5,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB10 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,5,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB02 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,5,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB01 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,5,3,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB09 +WTG_15,SS02,WTG_15,turbine,-1.5336,58.0543,6,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB15 +WTG_7,SS02,WTG_7,turbine,-1.5336,58.0724,6,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB07 +WTG_8,SS02,WTG_8,turbine,-1.5,58.0724,6,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB08 +WTG_16,SS02,WTG_16,turbine,-1.5,58.0543,6,3,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB16 +WTG_24,SS02,WTG_24,turbine,-1.5,58.0362,7,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB24 +WTG_32,SS02,WTG_32,turbine,-1.5,58.0181,7,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB32 +WTG_40,SS02,WTG_40,turbine,-1.5,58,7,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB40 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,8,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB23 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,8,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB31 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,8,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB39 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,9,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB22 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,9,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB30 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,9,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB38 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,10,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB21 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,10,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB29 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,10,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB37 +WTG_14,SS02,WTG_14,turbine,-1.5672,58.0543,11,0,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB14 +WTG_6,SS02,WTG_6,turbine,-1.5672,58.0724,11,1,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB06 +WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_tow_increase_failure.yaml,array_cable_increase_failure.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_increase_maintenance.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_increase_maintenance.csv new file mode 100644 index 00000000..1e103df5 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_1_tow_increase_maintenance.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,0,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB11 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,0,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB12 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,0,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB03 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,0,3,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB04 +WTG_20,SS01,WTG_20,turbine,-1.6344,58.0362,1,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB20 +WTG_28,SS01,WTG_28,turbine,-1.6344,58.0181,1,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB28 +WTG_36,SS01,WTG_36,turbine,-1.6344,58,1,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB36 +WTG_19,SS01,WTG_19,turbine,-1.668,58.0362,2,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB19 +WTG_27,SS01,WTG_27,turbine,-1.668,58.0181,2,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB27 +WTG_35,SS01,WTG_35,turbine,-1.668,58,2,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB35 +WTG_18,SS01,WTG_18,turbine,-1.7016,58.0362,3,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB18 +WTG_26,SS01,WTG_26,turbine,-1.7016,58.0181,3,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB26 +WTG_34,SS01,WTG_34,turbine,-1.7016,58,3,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB34 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,4,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB17 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,4,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB25 +WTG_33,SS01,WTG_33,turbine,-1.7352,58,4,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB33 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,5,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB10 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,5,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB02 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,5,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB01 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,5,3,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB09 +WTG_15,SS02,WTG_15,turbine,-1.5336,58.0543,6,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB15 +WTG_7,SS02,WTG_7,turbine,-1.5336,58.0724,6,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB07 +WTG_8,SS02,WTG_8,turbine,-1.5,58.0724,6,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB08 +WTG_16,SS02,WTG_16,turbine,-1.5,58.0543,6,3,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB16 +WTG_24,SS02,WTG_24,turbine,-1.5,58.0362,7,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB24 +WTG_32,SS02,WTG_32,turbine,-1.5,58.0181,7,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB32 +WTG_40,SS02,WTG_40,turbine,-1.5,58,7,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB40 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,8,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB23 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,8,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB31 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,8,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB39 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,9,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB22 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,9,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB30 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,9,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB38 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,10,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB21 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,10,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB29 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,10,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB37 +WTG_14,SS02,WTG_14,turbine,-1.5672,58.0543,11,0,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB14 +WTG_6,SS02,WTG_6,turbine,-1.5672,58.0724,11,1,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB06 +WTG_5,SS02,WTG_5,turbine,-1.6008,58.0724,11,2,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB05 +WTG_13,SS02,WTG_13,turbine,-1.6008,58.0543,11,3,2.4,IEA_15MW_240_RWT_tow_increase_maintenance.yaml,array_cable_increase_maintenance.yaml,CB13 +SS02,SS01,SS02,substation,-1.5504,58.04525,12,0,,substation.yaml,export_cable.yaml,CB41 +SS01,SS01,SS01,substation,-1.6848,58.04525,12,1,,substation.yaml,export_cable.yaml,CB42 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_2_in_situ_base.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_2_in_situ_base.csv new file mode 100644 index 00000000..23fef5dc --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_2_in_situ_base.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +SS01,SS02,SS01,substation,-1.6176,58.06335,8,0,,substation.yaml,export_cable.yaml,CB41 +SS02,SS02,SS02,substation,-1.6176,58.02715,8,1,,substation.yaml,export_cable.yaml,CB42 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,3,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB01 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,3,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB02 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,3,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB_0 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,3,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB04 +WTG_5,SS01,WTG_5,turbine,-1.6008,58.0724,0,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB05 +WTG_6,SS01,WTG_6,turbine,-1.5672,58.0724,0,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB06 +WTG_7,SS01,WTG_7,turbine,-1.5336,58.0724,0,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB07 +WTG_8,SS01,WTG_8,turbine,-1.5,58.0724,0,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB08 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,2,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB09 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,2,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB10 +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,2,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB11 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,2,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB12 +WTG_13,SS01,WTG_13,turbine,-1.6008,58.0543,1,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB13 +WTG_14,SS01,WTG_14,turbine,-1.5672,58.0543,1,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB14 +WTG_15,SS01,WTG_15,turbine,-1.5336,58.0543,1,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB15 +WTG_16,SS01,WTG_16,turbine,-1.5,58.0543,1,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB16 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,2,4,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB17 +WTG_18,SS02,WTG_18,turbine,-1.7016,58.0362,7,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB18 +WTG_19,SS02,WTG_19,turbine,-1.668,58.0362,7,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB19 +WTG_20,SS02,WTG_20,turbine,-1.6344,58.0362,7,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB20 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,4,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB21 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,4,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB22 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,4,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB23 +WTG_24,SS01,WTG_24,turbine,-1.5,58.0362,1,4,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB24 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,2,5,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB25 +WTG_26,SS02,WTG_26,turbine,-1.7016,58.0181,7,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB26 +WTG_27,SS02,WTG_27,turbine,-1.668,58.0181,7,4,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB27 +WTG_28,SS02,WTG_28,turbine,-1.6344,58.0181,6,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB28 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,5,0,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB29 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,4,4,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB30 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,4,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB31 +WTG_32,SS01,WTG_32,turbine,-1.5,58.0181,1,5,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB32 +WTG_33,SS02,WTG_33,turbine,-1.7352,58,6,4,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB33 +WTG_34,SS02,WTG_34,turbine,-1.7016,58,6,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB34 +WTG_35,SS02,WTG_35,turbine,-1.668,58,6,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB35 +WTG_36,SS02,WTG_36,turbine,-1.6344,58,6,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB36 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,5,1,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB37 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,5,2,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB38 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,5,3,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB39 +WTG_40,SS02,WTG_40,turbine,-1.5,58,5,4,2.4,IEA_15MW_240_RWT_in_situ_base.yaml,array_cable.yaml,CB40 diff --git a/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_2_tow_base.csv b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_2_tow_base.csv new file mode 100644 index 00000000..4834cfd4 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/plant/broadshore_layout_2_tow_base.csv @@ -0,0 +1,43 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable,upstream_cable_name +SS01,SS02,SS01,substation,-1.6176,58.06335,8,0,,substation.yaml,export_cable.yaml,CB41 +SS02,SS02,SS02,substation,-1.6176,58.02715,8,1,,substation.yaml,export_cable.yaml,CB42 +WTG_1,SS01,WTG_1,turbine,-1.7352,58.0724,3,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB01 +WTG_2,SS01,WTG_2,turbine,-1.7016,58.0724,3,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB02 +WTG_3,SS01,WTG_3,turbine,-1.668,58.0724,3,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB_0 +WTG_4,SS01,WTG_4,turbine,-1.6344,58.0724,3,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB04 +WTG_5,SS01,WTG_5,turbine,-1.6008,58.0724,0,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB05 +WTG_6,SS01,WTG_6,turbine,-1.5672,58.0724,0,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB06 +WTG_7,SS01,WTG_7,turbine,-1.5336,58.0724,0,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB07 +WTG_8,SS01,WTG_8,turbine,-1.5,58.0724,0,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB08 +WTG_9,SS01,WTG_9,turbine,-1.7352,58.0543,2,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB09 +WTG_10,SS01,WTG_10,turbine,-1.7016,58.0543,2,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB10 +WTG_11,SS01,WTG_11,turbine,-1.668,58.0543,2,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB11 +WTG_12,SS01,WTG_12,turbine,-1.6344,58.0543,2,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB12 +WTG_13,SS01,WTG_13,turbine,-1.6008,58.0543,1,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB13 +WTG_14,SS01,WTG_14,turbine,-1.5672,58.0543,1,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB14 +WTG_15,SS01,WTG_15,turbine,-1.5336,58.0543,1,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB15 +WTG_16,SS01,WTG_16,turbine,-1.5,58.0543,1,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB16 +WTG_17,SS01,WTG_17,turbine,-1.7352,58.0362,2,4,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB17 +WTG_18,SS02,WTG_18,turbine,-1.7016,58.0362,7,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB18 +WTG_19,SS02,WTG_19,turbine,-1.668,58.0362,7,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB19 +WTG_20,SS02,WTG_20,turbine,-1.6344,58.0362,7,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB20 +WTG_21,SS02,WTG_21,turbine,-1.6008,58.0362,4,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB21 +WTG_22,SS02,WTG_22,turbine,-1.5672,58.0362,4,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB22 +WTG_23,SS02,WTG_23,turbine,-1.5336,58.0362,4,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB23 +WTG_24,SS01,WTG_24,turbine,-1.5,58.0362,1,4,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB24 +WTG_25,SS01,WTG_25,turbine,-1.7352,58.0181,2,5,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB25 +WTG_26,SS02,WTG_26,turbine,-1.7016,58.0181,7,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB26 +WTG_27,SS02,WTG_27,turbine,-1.668,58.0181,7,4,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB27 +WTG_28,SS02,WTG_28,turbine,-1.6344,58.0181,6,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB28 +WTG_29,SS02,WTG_29,turbine,-1.6008,58.0181,5,0,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB29 +WTG_30,SS02,WTG_30,turbine,-1.5672,58.0181,4,4,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB30 +WTG_31,SS02,WTG_31,turbine,-1.5336,58.0181,4,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB31 +WTG_32,SS01,WTG_32,turbine,-1.5,58.0181,1,5,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB32 +WTG_33,SS02,WTG_33,turbine,-1.7352,58,6,4,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB33 +WTG_34,SS02,WTG_34,turbine,-1.7016,58,6,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB34 +WTG_35,SS02,WTG_35,turbine,-1.668,58,6,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB35 +WTG_36,SS02,WTG_36,turbine,-1.6344,58,6,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB36 +WTG_37,SS02,WTG_37,turbine,-1.6008,58,5,1,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB37 +WTG_38,SS02,WTG_38,turbine,-1.5672,58,5,2,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB38 +WTG_39,SS02,WTG_39,turbine,-1.5336,58,5,3,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB39 +WTG_40,SS02,WTG_40,turbine,-1.5,58,5,4,2.4,IEA_15MW_240_RWT_tow_base.yaml,array_cable.yaml,CB40 diff --git a/library/code_comparison/avanessova_diss/project/port/__init__.py b/library/code_comparison/avanessova_diss/project/port/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/project/port/port_base.yaml b/library/code_comparison/avanessova_diss/project/port/port_base.yaml new file mode 100644 index 00000000..e845c755 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/port/port_base.yaml @@ -0,0 +1,16 @@ +name: Base Port +tugboats: + - tug1.yaml +max_operations: 1 +n_crew: 1 +crew: + n_day_rate: 0 + day_rate: 0 + n_hourly_rate: 0 + hourly_rate: 0 +workday_start: 7 +workday_end: 19 +site_distance: 20 +port_distance: 57.71 +annual_fee: 11862500 # 130,000 EUR day rate x 365 x 0.25 (usage assumption) +# annual_fee: 47450000 # 130,000 EUR day rate x 365 diff --git a/library/code_comparison/avanessova_diss/project/port/port_base_24hr.yaml b/library/code_comparison/avanessova_diss/project/port/port_base_24hr.yaml new file mode 100644 index 00000000..38b5af60 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/port/port_base_24hr.yaml @@ -0,0 +1,16 @@ +name: Base Port +tugboats: + - tug1.yaml +max_operations: 1 +n_crew: 1 +crew: + n_day_rate: 0 + day_rate: 0 + n_hourly_rate: 0 + hourly_rate: 0 +workday_start: 0 +workday_end: 24 +site_distance: 20 +port_distance: 57.71 +annual_fee: 11862500 # 130,000 EUR day rate x 365 x 0.25 (usage assumption) +# annual_fee: 47450000 # 130,000 EUR day rate x 365 diff --git a/library/code_comparison/avanessova_diss/project/port/port_increase_wave.yaml b/library/code_comparison/avanessova_diss/project/port/port_increase_wave.yaml new file mode 100644 index 00000000..aaa4a049 --- /dev/null +++ b/library/code_comparison/avanessova_diss/project/port/port_increase_wave.yaml @@ -0,0 +1,16 @@ +name: Port - Increase Wave Limit +tugboats: + - tug1_increase_wave.yaml +max_operations: 1 +n_crew: 1 +crew: + n_day_rate: 0 + day_rate: 0 + n_hourly_rate: 0 + hourly_rate: 0 +workday_start: 7 +workday_end: 19 +site_distance: 20 +port_distance: 57.71 +annual_fee: 11862500 # 130,000 EUR day rate x 365 x 0.25 (usage assumption) +# annual_fee: 47450000 # 130,000 EUR day rate x 365 diff --git a/library/code_comparison/avanessova_diss/results/__init__.py b/library/code_comparison/avanessova_diss/results/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/substations/__init__.py b/library/code_comparison/avanessova_diss/substations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/substations/substation.yaml b/library/code_comparison/avanessova_diss/substations/substation.yaml new file mode 100644 index 00000000..e262f35b --- /dev/null +++ b/library/code_comparison/avanessova_diss/substations/substation.yaml @@ -0,0 +1,19 @@ +capacity_kw: 300000.0 +capex_kw: 1000 +substation: + name: substation + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0 + shape: 0 + time: 0 + materials: 0 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: na diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT.csv b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT.csv new file mode 100644 index 00000000..d9f500cd --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT.csv @@ -0,0 +1,60 @@ +windspeed_ms,power_kw,Cp,thrust_kN,Ct,,,,, +2.999999831,70.021377,0.100335552,593.964861,0.819748943,,,,, +3.499999916,301.9937,0.272197208,659.768436,0.801112031,,,,, +4,595.088475,0.359305118,744.019844,0.808268424,,,,, +4.500000084,964.887394,0.409156685,844.283886,0.821910918,,,,, +4.750000126,1185.081978,0.42729002,896.218567,0.822876237,,,,, +5.000000169,1429.216889,0.441819614,950.66647,0.823265981,,,,, +5.249999874,1695.245223,0.452708049,1013.407273,0.830989358,,,,, +5.999999663,2656.263808,0.475270298,1206.649681,0.834932456,,,,, +6.199999966,2957.216831,0.479478644,1260.317718,0.833618598,,,,, +6.40000027,3275.743373,0.482871263,1315.060648,0.83180478,,,,, +6.499999747,3442.669566,0.484447627,1340.858086,0.829011103,,,,, +6.55000016,3528.654678,0.485186337,1353.03462,0.826909201,,,,, +6.599999899,3614.989064,0.485905846,1365.170435,0.824740997,,,,, +6.700000051,3791.164267,0.487114154,1389.573781,0.820429675,,,,, +6.800000202,3971.967845,0.488094793,1414.180748,0.816176257,,,,, +6.900000354,4155.58904,0.488866488,1437.989395,0.811200233,,,,, +6.919999845,4192.387051,0.488988058,1442.160463,0.809740903,,,,, +6.929999928,4210.773549,0.489050152,1443.944251,0.808780765,,,,, +6.94000001,4228.841377,0.489088858,1446.095476,0.808102306,,,,, +6.950000093,4247.186435,0.489114531,1448.433817,0.807566626,,,,, +6.960000175,4265.458105,0.489127764,1451.063716,0.807251977,,,,, +6.969999584,4283.898392,0.48915576,1453.280931,0.80662442,,,,, +6.980000341,4301.948494,0.489165047,1456.152592,0.806495512,,,,, +6.989999749,4320.289911,0.489154466,1459.581324,0.806806173,,,,, +6.999999831,4339.296326,0.489163867,1462.408574,0.806651158,,,,, +7.499999916,5338.82324,0.489224161,1618.598616,0.805469658,,,,, +8,6481.116995,0.489263048,1785.688325,0.804571567,,,,, +8.500000084,7774.570984,0.48928802,1963.797766,0.803949121,,,,, +9.000000169,9229.227024,0.489293116,2153.76621,0.803904895,,,,, +9.500000253,10855.04374,0.489304304,2354.153957,0.803708734,,,,, +10.00000034,12661.25448,0.489319143,2565.156695,0.80345211,,,,, +10.2499997,13638.148,0.48938292,2670.036159,0.801706154,,,,, +10.49999975,14660.65727,0.489378059,2782.638159,0.801777393,,,,, +10.60000057,14994.84635,0.486507177,2727.830431,0.768657554,,,,, +10.70000005,14994.64979,0.472991558,2582.325993,0.70731525,,,,, +10.72000022,14994.61195,0.470349316,2563.16707,0.698507743,,,,, +10.73999971,14994.55374,0.467724941,2545.414142,0.690211963,,,,, +10.76000055,14994.52082,0.465119143,2528.795404,0.682335591,,,,, +10.78000004,14994.55424,0.462537044,2513.190807,0.674835939,,,,, +10.78400034,14994.52965,0.462021882,2510.161326,0.673371183,,,,, +10.78600049,14994.53211,0.461765284,2508.666998,0.672646111,,,,, +10.78699989,14994.53141,0.461636557,2507.917813,0.672283185,,,,, +10.78799997,14994.51543,0.46150846,2507.17275,0.671921569,,,,, +10.78899937,14994.52466,0.461380941,2506.43983,0.671564033,,,,, +10.78950042,14994.59142,0.461317766,2506.078932,0.671386994,,,,, +10.8000002,14994.53984,0.459972382,2498.382011,0.667639697,,,,, +10.89999968,14994.4265,0.447424184,2433.69366,0.635292304,,,,, +10.99999983,14994.26629,0.435324299,2379.709865,0.607277698,,,,, +11.24999987,14994.02192,0.406934831,2271.612586,0.548965866,,,,, +11.50000059,14994.10762,0.380965216,2185.962064,0.501379105,,,,, +11.75000063,14994.17514,0.357157168,2114.041428,0.460982977,,,,, +11.99999933,14994.17331,0.335295525,2051.783979,0.425965654,,,,, +12.99999949,14994.76256,0.263718683,1861.059797,0.32116631,,,,, +13.99999966,14994.76121,0.211136579,1724.382126,0.2511023,,,,, +14.99999983,14994.75771,0.171651956,1619.130709,0.201415182,,,,, +17.50000025,14994.82665,0.108080744,1434.534952,0.125653944,,,,, +20.00000067,14994.82754,0.072394937,1314.275979,0.08506697,,,,, +22.49999975,14996.27008,0.050842443,1229.73494,0.061026446,,,,, +24.99999882,14997.62687,0.037062292,1168.739896,0.045814967,,,,, diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_base.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_base.yaml new file mode 100644 index 00000000..0d581182 --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_base.yaml @@ -0,0 +1,378 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.1111 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.1111 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 33.3333 + shape: 1 + time: 24 + materials: 130000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 33.3333 + shape: 1 + time: 48 + materials: 650000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 333.3333 + shape: 1 + time: 24 + materials: 300000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 96 + materials: 1500000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 333.3333 + shape: 1 + time: 24 + materials: 100000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 48 + materials: 500000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 111.1111 + shape: 1 + time: 24 + materials: 210000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 111.1111 + shape: 1 + time: 72 + materials: 1050000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.5641 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 2.5641 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 1000 + shape: 1 + time: 24 + materials: 60000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +main_bearing: + name: main_bearing + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1000 + shape: 1 + time: 96 + materials: 300000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.551 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.551 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8547 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8547 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 16 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 4 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 8 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 3.7037 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 3.7037 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 2 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 10 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 277.7778 + shape: 1 + time: 48 + materials: 570000 + description: mooring replacement + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_increase_failure.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_increase_failure.yaml new file mode 100644 index 00000000..9dae9f98 --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_increase_failure.yaml @@ -0,0 +1,378 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.5556 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.5556 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 16.6667 + shape: 1 + time: 24 + materials: 130000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 16.6667 + shape: 1 + time: 48 + materials: 650000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8772 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8772 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 166.6667 + shape: 1 + time: 24 + materials: 300000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 166.6667 + shape: 1 + time: 96 + materials: 1500000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 166.6667 + shape: 1 + time: 24 + materials: 100000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 166.6667 + shape: 1 + time: 48 + materials: 500000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 55.5556 + shape: 1 + time: 24 + materials: 210000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 55.5556 + shape: 1 + time: 72 + materials: 1050000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.2821 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.2821 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 500.0 + shape: 1 + time: 24 + materials: 60000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +main_bearing: + name: main_bearing + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 500.0 + shape: 1 + time: 96 + materials: 300000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.2755 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.2755 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.4274 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.4274 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 0.8772 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8772 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 16 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 4 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 8 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 1.8519 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.8519 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 1.0 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 5.0 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 277.7778 + shape: 1 + time: 48 + materials: 570000 + description: mooring replacement + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml new file mode 100644 index 00000000..4ba9f2dc --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_increase_maintenance.yaml @@ -0,0 +1,378 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.1111 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.1111 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 33.3333 + shape: 1 + time: 24 + materials: 130000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 33.3333 + shape: 1 + time: 48 + materials: 650000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 333.3333 + shape: 1 + time: 24 + materials: 300000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 96 + materials: 1500000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 333.3333 + shape: 1 + time: 24 + materials: 100000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 48 + materials: 500000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 111.1111 + shape: 1 + time: 24 + materials: 210000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 + - scale: 111.1111 + shape: 1 + time: 72 + materials: 1050000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.5641 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 2.5641 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 1000 + shape: 1 + time: 24 + materials: 60000 + description: major repair + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +main_bearing: + name: main_bearing + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1000 + shape: 1 + time: 96 + materials: 300000 + description: major replacement + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 4 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.551 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.551 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8547 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8547 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 24 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 6 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 12 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 3.7037 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 3.7037 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 2 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 10 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 12 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 277.7778 + shape: 1 + time: 48 + materials: 570000 + description: mooring replacement + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_no_major.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_no_major.yaml new file mode 100644 index 00000000..ed47d10a --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_in_situ_no_major.yaml @@ -0,0 +1,324 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.1111 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.1111 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.5641 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 2.5641 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 3 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.551 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.551 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8547 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8547 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 16 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 4 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 8 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 3.7037 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 3.7037 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 2 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 10 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 0 + shape: 0 + time: 0 + materials: 0 + description: na + service_equipment: LCN + operation_reduction: 0 + replacement: false + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_base.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_base.yaml new file mode 100644 index 00000000..8d7689a5 --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_base.yaml @@ -0,0 +1,378 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.1111 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.1111 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 33.3333 + shape: 1 + time: 24 + materials: 130000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 33.3333 + shape: 1 + time: 48 + materials: 650000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 333.3333 + shape: 1 + time: 24 + materials: 300000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 96 + materials: 1500000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 333.3333 + shape: 1 + time: 24 + materials: 100000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 48 + materials: 500000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 111.1111 + shape: 1 + time: 24 + materials: 210000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 111.1111 + shape: 1 + time: 72 + materials: 1050000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.5641 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 2.5641 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 1000 + shape: 1 + time: 24 + materials: 60000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 +main_bearing: + name: main_bearing + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1000 + shape: 1 + time: 96 + materials: 300000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.551 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.551 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8547 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8547 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 16 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 4 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 8 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 3.7037 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 3.7037 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 2 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 10 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 277.7778 + shape: 1 + time: 48 + materials: 570000 + description: mooring replacement + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_increase_failure.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_increase_failure.yaml new file mode 100644 index 00000000..12973f3c --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_increase_failure.yaml @@ -0,0 +1,378 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.5556 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.5556 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 16.6667 + shape: 1 + time: 24 + materials: 130000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 16.6667 + shape: 1 + time: 48 + materials: 650000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8772 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8772 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 166.6667 + shape: 1 + time: 24 + materials: 300000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 166.6667 + shape: 1 + time: 96 + materials: 1500000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 166.6667 + shape: 1 + time: 24 + materials: 100000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 166.6667 + shape: 1 + time: 48 + materials: 500000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 55.5556 + shape: 1 + time: 24 + materials: 210000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 55.5556 + shape: 1 + time: 72 + materials: 1050000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.2821 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.2821 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 500.0 + shape: 1 + time: 24 + materials: 60000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 +main_bearing: + name: main_bearing + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 500.0 + shape: 1 + time: 96 + materials: 300000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.2755 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.2755 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.4274 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.4274 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 0.8772 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8772 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 16 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 4 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 8 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 1.8519 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.8519 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 1.0 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 5.0 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 8 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 138.8889 + shape: 1 + time: 48 + materials: 570000 + description: mooring replacement + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_increase_maintenance.yaml b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_increase_maintenance.yaml new file mode 100644 index 00000000..167a026a --- /dev/null +++ b/library/code_comparison/avanessova_diss/turbines/IEA_15MW_240_RWT_tow_increase_maintenance.yaml @@ -0,0 +1,378 @@ +capacity_kw: 15000 +capex_kw: 1300 +power_curve: + file: IEA_15MW_240_RWT.csv + bin_width: 0.25 +rotor_system: + name: rotor_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.1111 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.1111 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 33.3333 + shape: 1 + time: 24 + materials: 130000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 33.3333 + shape: 1 + time: 48 + materials: 650000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +generator_system: + name: generator_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 11 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 333.3333 + shape: 1 + time: 24 + materials: 300000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 96 + materials: 1500000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +electrical_system: + name: electrical_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 333.3333 + shape: 1 + time: 24 + materials: 100000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 333.3333 + shape: 1 + time: 48 + materials: 500000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +gearbox: + name: gearbox + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 111.1111 + shape: 1 + time: 24 + materials: 210000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 + - scale: 111.1111 + shape: 1 + time: 72 + materials: 1050000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +drive_train_system: + name: drive_train_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.5641 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 2.5641 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 1000 + shape: 1 + time: 24 + materials: 60000 + description: major repair + service_equipment: TOW + operation_reduction: 0 + replacement: false + level: 3 +main_bearing: + name: main_bearing + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1000 + shape: 1 + time: 96 + materials: 300000 + description: major replacement + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 4 +transmission_system: + name: transmission_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.551 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 + - scale: 0.551 + shape: 1 + time: 16 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 0.8547 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 0.8547 + shape: 1 + time: 5 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +central_hydraulics_system: + name: central_hydraulics_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.7544 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 1.7544 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +turbine: + name: turbine + maintenance: + - description: annual service - turbine + time: 24 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - lifting & safety equipment + time: 6 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + - description: annual service - HV components + time: 12 + materials: 5000 + service_equipment: CTV + frequency: 365 + operation_reduction: 0 + failures: + - scale: 3.7037 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 3.7037 + shape: 1 + time: 6 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +substructure: + name: substructure + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: RMT + frequency: 0 + failures: + - scale: 2 + shape: 1 + time: 4 + materials: 4200 + description: minor repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 1 + - scale: 10 + shape: 1 + time: 8 + materials: 77700 + description: medium repair + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 2 +mooring_lines: + name: mooring_lines + maintenance: + - description: mooring and cable inspection + time: 12 + materials: 10000 + service_equipment: RMT + frequency: 365 + failures: + - scale: 277.7778 + shape: 1 + time: 48 + materials: 570000 + description: mooring replacement + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 4 diff --git a/library/code_comparison/avanessova_diss/turbines/__init__.py b/library/code_comparison/avanessova_diss/turbines/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/vessels/__init__.py b/library/code_comparison/avanessova_diss/vessels/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/vessels/ahv.yaml b/library/code_comparison/avanessova_diss/vessels/ahv.yaml new file mode 100644 index 00000000..df6f705f --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/ahv.yaml @@ -0,0 +1,20 @@ +name: Anchor Handling Vessel +equipment_rate: 41500 # £/day +charter_days: 7 +capability: AHV +mobilization_cost: 325000 +mobilization_days: 7 +speed: 22.224 # 12 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.5 +max_waveheight_repair: 2.5 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/ahv_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/ahv_increase_wave.yaml new file mode 100644 index 00000000..884320c9 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/ahv_increase_wave.yaml @@ -0,0 +1,20 @@ +name: Anchor Handling Vessel +equipment_rate: 41500 # £/day +charter_days: 7 +capability: AHV +mobilization_cost: 325000 +mobilization_days: 14 +speed: 22.224 # 12 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 3.0 +max_waveheight_repair: 3.0 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/cab.yaml b/library/code_comparison/avanessova_diss/vessels/cab.yaml new file mode 100644 index 00000000..b9f48e2c --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/cab.yaml @@ -0,0 +1,20 @@ +name: Cable Laying Vessel +equipment_rate: 80000 # £/day +charter_days: 7 +capability: CAB +mobilization_cost: 500000 +mobilization_days: 7 +speed: 37.04 # 12 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.0 +max_waveheight_repair: 2.0 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/cab_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/cab_increase_wave.yaml new file mode 100644 index 00000000..7b0240e3 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/cab_increase_wave.yaml @@ -0,0 +1,20 @@ +name: Cable Laying Vessel +equipment_rate: 80000 # £/day +charter_days: 28 +capability: CAB +mobilization_cost: 500000 +mobilization_days: 14 +speed: 37.04 # 12 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.5 +max_waveheight_repair: 2.5 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/ctv.yaml b/library/code_comparison/avanessova_diss/vessels/ctv.yaml new file mode 100644 index 00000000..0d320c49 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/ctv.yaml @@ -0,0 +1,26 @@ +name: Crew Transfer Vessel 1 +equipment_rate: 2500 # £/day +start_month: 1 +start_day: 1 +end_month: 12 +end_day: 31 +start_year: 2000 +end_year: 2019 +onsite: True +capability: CTV +mobilization_cost: 0 +mobilization_days: 0 +speed: 37.04 # 20 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 1.5 +max_waveheight_repair: 1.5 +speed_reduction_factor: 0 +strategy: scheduled +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 219.17808 # $80k/year + n_day_rate: 4 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/ctv_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/ctv_increase_wave.yaml new file mode 100644 index 00000000..23348167 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/ctv_increase_wave.yaml @@ -0,0 +1,25 @@ +name: Crew Transfer Vessel 1 +equipment_rate: 2500 # £/day +start_month: 1 +start_day: 1 +end_month: 12 +end_day: 31 +start_year: 2000 +end_year: 2019 +onsite: True +capability: CTV +mobilization_cost: 0 +mobilization_days: 0 +speed: 37.04 # 20 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.0 +max_waveheight_repair: 2.0 +strategy: scheduled +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 219.17808 # $80k/year + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/hlv.yaml b/library/code_comparison/avanessova_diss/vessels/hlv.yaml new file mode 100644 index 00000000..8f82e4f2 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/hlv.yaml @@ -0,0 +1,20 @@ +name: Heavy Lift Vessel +equipment_rate: 200000 # £/day +capability: LCN +mobilization_cost: 325000 +mobilization_days: 10 +charter_days: 5 +speed: 20.372 # 11 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2 +max_waveheight_repair: 2 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/hlv_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/hlv_increase_wave.yaml new file mode 100644 index 00000000..793446bc --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/hlv_increase_wave.yaml @@ -0,0 +1,20 @@ +name: Heavy Lift Vessel +equipment_rate: 200000 # £/day +capability: LCN +mobilization_cost: 325000 +mobilization_days: 14 +charter_days: 5 +speed: 20.372 # 11 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.5 +max_waveheight_repair: 2.5 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/rov.yaml b/library/code_comparison/avanessova_diss/vessels/rov.yaml new file mode 100644 index 00000000..aaf93fb9 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/rov.yaml @@ -0,0 +1,22 @@ +name: ROV Support Vessel +equipment_rate: 24000 # £/day +capability: RMT +mobilization_cost: 0 +mobilization_days: 0 +speed: 22.224 # 12 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.5 +max_waveheight_repair: 2.5 +strategy: requests +strategy_threshold: 1 +charter_days: 3 +non_operational_start: "10/1" +non_operational_end: "3/31" +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/rov_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/rov_increase_wave.yaml new file mode 100644 index 00000000..9ea0893c --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/rov_increase_wave.yaml @@ -0,0 +1,22 @@ +name: ROV Support Vessel +equipment_rate: 24000 # £/day +capability: RMT +mobilization_cost: 0 +mobilization_days: 0 +speed: 22.224 # 12 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 3.0 +max_waveheight_repair: 3.0 +strategy: requests +strategy_threshold: 1 +charter_days: 3 +non_operational_start: "10/1" +non_operational_end: "3/31" +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/tug1.yaml b/library/code_comparison/avanessova_diss/vessels/tug1.yaml new file mode 100644 index 00000000..9f6c9e0e --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/tug1.yaml @@ -0,0 +1,23 @@ +name: Tugboat 1 +equipment_rate: 55600 # £/day +charter_days: 28 +capability: TOW +mobilization_cost: 200000 +mobilization_days: 14 +speed: 37.04 # 20 knots +tow_speed: 5.556 # 3 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 3 +max_waveheight_repair: 1.5 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +unmoor_hours: 12 +reconnection_hours: 12 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/tug1_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/tug1_increase_wave.yaml new file mode 100644 index 00000000..9d043da6 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/tug1_increase_wave.yaml @@ -0,0 +1,23 @@ +name: Tugboat 1 +equipment_rate: 55600 # £/day +charter_days: 28 +capability: TOW +mobilization_cost: 200000 +mobilization_days: 14 +speed: 37.04 # 20 knots +tow_speed: 5.556 # 3 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 3.5 +max_waveheight_repair: 2.0 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +unmoor_hours: 12 +reconnection_hours: 12 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/tug2.yaml b/library/code_comparison/avanessova_diss/vessels/tug2.yaml new file mode 100644 index 00000000..91a984fc --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/tug2.yaml @@ -0,0 +1,23 @@ +name: Tugboat 2 +equipment_rate: 55600 # £/day +charter_days: 28 +capability: TOW +mobilization_cost: 200000 +mobilization_days: 14 +speed: 37.04 # 20 knots +tow_speed: 5.556 # 3 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 3 +max_waveheight_repair: 1.5 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +unmoor_hours: 12 +reconnection_hours: 12 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/vessels/tug2_increase_wave.yaml b/library/code_comparison/avanessova_diss/vessels/tug2_increase_wave.yaml new file mode 100644 index 00000000..b6985433 --- /dev/null +++ b/library/code_comparison/avanessova_diss/vessels/tug2_increase_wave.yaml @@ -0,0 +1,23 @@ +name: Tugboat 2 +equipment_rate: 55600 # £/day +charter_days: 28 +capability: TOW +mobilization_cost: 200000 +mobilization_days: 14 +speed: 37.04 # 20 knots +tow_speed: 5.556 # 3 knots +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 3.5 +max_waveheight_repair: 2.0 +strategy: requests +strategy_threshold: 1 +crew_transfer_time: 0 +unmoor_hours: 12 +reconnection_hours: 12 +n_crews: 1 +crew: + day_rate: 0 # crew comes with vessel + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/code_comparison/avanessova_diss/weather/__init__.py b/library/code_comparison/avanessova_diss/weather/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/code_comparison/avanessova_diss/weather/ore_validation_58.0_-1.5.pqt b/library/code_comparison/avanessova_diss/weather/ore_validation_58.0_-1.5.pqt new file mode 100644 index 00000000..8505ad45 Binary files /dev/null and b/library/code_comparison/avanessova_diss/weather/ore_validation_58.0_-1.5.pqt differ diff --git a/library/code_comparison/dinwoodie/project/config/SAM_Singleowner_defaults.yaml b/library/code_comparison/dinwoodie/project/config/SAM_Singleowner_defaults.yaml deleted file mode 100644 index b9933a47..00000000 --- a/library/code_comparison/dinwoodie/project/config/SAM_Singleowner_defaults.yaml +++ /dev/null @@ -1,18811 +0,0 @@ -adjust:constant: 0 -analysis_period: 25 -avail_bop_loss: 0.5 -avail_grid_loss: 1.5 -avail_turb_loss: 3.58 -cbi_fed_amount: 0 -cbi_fed_deprbas_fed: 0 -cbi_fed_deprbas_sta: 0 -cbi_fed_maxvalue: 1.0e+38 -cbi_fed_tax_fed: 1 -cbi_fed_tax_sta: 1 -cbi_oth_amount: 0 -cbi_oth_deprbas_fed: 0 -cbi_oth_deprbas_sta: 0 -cbi_oth_maxvalue: 1.0e+38 -cbi_oth_tax_fed: 1 -cbi_oth_tax_sta: 1 -cbi_sta_amount: 0 -cbi_sta_deprbas_fed: 0 -cbi_sta_deprbas_sta: 0 -cbi_sta_maxvalue: 1.0e+38 -cbi_sta_tax_fed: 1 -cbi_sta_tax_sta: 1 -cbi_uti_amount: 0 -cbi_uti_deprbas_fed: 0 -cbi_uti_deprbas_sta: 0 -cbi_uti_maxvalue: 1.0e+38 -cbi_uti_tax_fed: 1 -cbi_uti_tax_sta: 1 -compute_module_0: windpower -compute_module_1: grid -compute_module_2: singleowner -construction_financing_cost: 1296000 -cost_debt_closing: 450000 -cost_debt_fee: 2.75 -cost_other_financing: 0 -cp_battery_nameplate: 0 -cp_capacity_credit_percent: -- 0 -cp_capacity_payment_amount: -- 0 -cp_capacity_payment_esc: 0 -cp_capacity_payment_type: 0 -cp_system_nameplate: 48 -debt_option: 1 -debt_percent: 60 -degradation: -- 0 -depr_alloc_custom_percent: 0 -depr_alloc_macrs_15_percent: 1.5 -depr_alloc_macrs_5_percent: 90 -depr_alloc_sl_15_percent: 2.5 -depr_alloc_sl_20_percent: 3 -depr_alloc_sl_39_percent: 0 -depr_alloc_sl_5_percent: 0 -depr_bonus_fed: 0 -depr_bonus_fed_custom: 0 -depr_bonus_fed_macrs_15: 1 -depr_bonus_fed_macrs_5: 1 -depr_bonus_fed_sl_15: 0 -depr_bonus_fed_sl_20: 0 -depr_bonus_fed_sl_39: 0 -depr_bonus_fed_sl_5: 0 -depr_bonus_sta: 0 -depr_bonus_sta_custom: 0 -depr_bonus_sta_macrs_15: 1 -depr_bonus_sta_macrs_5: 1 -depr_bonus_sta_sl_15: 0 -depr_bonus_sta_sl_20: 0 -depr_bonus_sta_sl_39: 0 -depr_bonus_sta_sl_5: 0 -depr_custom_schedule: -- 0 -depr_fedbas_method: 1 -depr_itc_fed_custom: 0 -depr_itc_fed_macrs_15: 0 -depr_itc_fed_macrs_5: 1 -depr_itc_fed_sl_15: 0 -depr_itc_fed_sl_20: 0 -depr_itc_fed_sl_39: 0 -depr_itc_fed_sl_5: 0 -depr_itc_sta_custom: 0 -depr_itc_sta_macrs_15: 0 -depr_itc_sta_macrs_5: 1 -depr_itc_sta_sl_15: 0 -depr_itc_sta_sl_20: 0 -depr_itc_sta_sl_39: 0 -depr_itc_sta_sl_5: 0 -depr_stabas_method: 1 -dispatch_factor1: 1 -dispatch_factor2: 1 -dispatch_factor3: 1 -dispatch_factor4: 1 -dispatch_factor5: 1 -dispatch_factor6: 1 -dispatch_factor7: 1 -dispatch_factor8: 1 -dispatch_factor9: 1 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1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 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0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 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1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 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-- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 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1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 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1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 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- - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 -- - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 - - 1 -dscr: 1.3 -dscr_reserve_months: 6 -elec_eff_loss: 1.91 -elec_parasitic_loss: 0.1 -enable_interconnection_limit: 0 -env_degrad_loss: 1.8 -env_env_loss: 0.4 -env_exposure_loss: 0 -env_icing_loss: 0.21 -equip1_reserve_cost: 0.25 -equip1_reserve_freq: 12 -equip2_reserve_cost: 0 -equip2_reserve_freq: 15 -equip3_reserve_cost: 0 -equip3_reserve_freq: 3 -equip_reserve_depr_fed: 0 -equip_reserve_depr_sta: 0 -federal_tax_rate: -- 21 -flip_target_percent: 11 -flip_target_year: 20 -grid_curtailment: -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -grid_curtailment_price: -- 0 -grid_curtailment_price_esc: 0 -grid_interconnection_limit_kwac: 100000 -ibi_fed_amount: 0 -ibi_fed_amount_deprbas_fed: 0 -ibi_fed_amount_deprbas_sta: 0 -ibi_fed_amount_tax_fed: 1 -ibi_fed_amount_tax_sta: 1 -ibi_fed_percent: 0 -ibi_fed_percent_deprbas_fed: 0 -ibi_fed_percent_deprbas_sta: 0 -ibi_fed_percent_maxvalue: 1.0e+38 -ibi_fed_percent_tax_fed: 1 -ibi_fed_percent_tax_sta: 1 -ibi_oth_amount: 0 -ibi_oth_amount_deprbas_fed: 0 -ibi_oth_amount_deprbas_sta: 0 -ibi_oth_amount_tax_fed: 1 -ibi_oth_amount_tax_sta: 1 -ibi_oth_percent: 0 -ibi_oth_percent_deprbas_fed: 0 -ibi_oth_percent_deprbas_sta: 0 -ibi_oth_percent_maxvalue: 1.0e+38 -ibi_oth_percent_tax_fed: 1 -ibi_oth_percent_tax_sta: 1 -ibi_sta_amount: 0 -ibi_sta_amount_deprbas_fed: 0 -ibi_sta_amount_deprbas_sta: 0 -ibi_sta_amount_tax_fed: 1 -ibi_sta_amount_tax_sta: 1 -ibi_sta_percent: 0 -ibi_sta_percent_deprbas_fed: 0 -ibi_sta_percent_deprbas_sta: 0 -ibi_sta_percent_maxvalue: 1.0e+38 -ibi_sta_percent_tax_fed: 1 -ibi_sta_percent_tax_sta: 1 -ibi_uti_amount: 0 -ibi_uti_amount_deprbas_fed: 0 -ibi_uti_amount_deprbas_sta: 0 -ibi_uti_amount_tax_fed: 1 -ibi_uti_amount_tax_sta: 1 -ibi_uti_percent: 0 -ibi_uti_percent_deprbas_fed: 0 -ibi_uti_percent_deprbas_sta: 0 -ibi_uti_percent_maxvalue: 1.0e+38 -ibi_uti_percent_tax_fed: 1 -ibi_uti_percent_tax_sta: 1 -inflation_rate: 2.5 -insurance_rate: 0.5 -itc_fed_amount: 0 -itc_fed_amount_deprbas_fed: 1 -itc_fed_amount_deprbas_sta: 1 -itc_fed_percent: 0 -itc_fed_percent_deprbas_fed: 1 -itc_fed_percent_deprbas_sta: 1 -itc_fed_percent_maxvalue: 1.0e+38 -itc_sta_amount: 0 -itc_sta_amount_deprbas_fed: 0 -itc_sta_amount_deprbas_sta: 0 -itc_sta_percent: 0 -itc_sta_percent_deprbas_fed: 0 -itc_sta_percent_deprbas_sta: 0 -itc_sta_percent_maxvalue: 1.0e+38 -loan_moratorium: 0 -metric_0: annual_energy -metric_0_label: Annual energy (year 1) -metric_1: capacity_factor -metric_10: flip_actual_irr -metric_10_label: Internal rate of return (IRR) -metric_11: flip_actual_year -metric_11_label: Year IRR is achieved -metric_12: project_return_aftertax_irr -metric_12_label: IRR at end of project -metric_13: cost_installed -metric_13_label: Net capital cost -metric_14: size_of_equity -metric_14_label: Equity -metric_15: size_of_debt -metric_15_label: Size of debt -metric_1_label: Capacity factor (year 1) -metric_2: annual_energy_p90 -metric_2_label: p90 Energy (year 1) -metric_3: ppa -metric_3_label: PPA price (year 1) -metric_4: ppa_escalation -metric_4_label: PPA price escalation -metric_5: lppa_nom -metric_5_label: Levelized PPA price (nominal) -metric_6: lppa_real -metric_6_label: Levelized PPA price (real) -metric_7: lcoe_nom -metric_7_label: Levelized COE (nominal) -metric_8: lcoe_real -metric_8_label: Levelized COE (real) -metric_9: project_return_aftertax_npv -metric_9_label: Net present value -months_receivables_reserve: 0 -months_working_reserve: 6 -number_compute_modules: 3 -number_metrics: 16 -om_capacity: -- 42 -om_capacity_escal: 0 -om_fixed: -- 0 -om_fixed_escal: 0 -om_fuel_cost: -- 0 -om_fuel_cost_escal: 0 -om_production: -- 0 -om_production_escal: 0 -om_replacement_cost1: -- 0 -om_replacement_cost_escal: 0 -ops_env_loss: 1 -ops_grid_loss: 0.84 -ops_load_loss: 0.99 -ops_strategies_loss: 0 -payment_option: 0 -pbi_fed_amount: -- 0 -pbi_fed_escal: 0 -pbi_fed_for_ds: 0 -pbi_fed_tax_fed: 1 -pbi_fed_tax_sta: 1 -pbi_fed_term: 0 -pbi_oth_amount: -- 0 -pbi_oth_escal: 0 -pbi_oth_for_ds: 0 -pbi_oth_tax_fed: 1 -pbi_oth_tax_sta: 1 -pbi_oth_term: 0 -pbi_sta_amount: -- 0 -pbi_sta_escal: 0 -pbi_sta_for_ds: 0 -pbi_sta_tax_fed: 1 -pbi_sta_tax_sta: 1 -pbi_sta_term: 0 -pbi_uti_amount: -- 0 -pbi_uti_escal: 0 -pbi_uti_for_ds: 0 -pbi_uti_tax_fed: 1 -pbi_uti_tax_sta: 1 -pbi_uti_term: 0 -ppa_escalation: 1 -ppa_multiplier_model: 0 -ppa_price_input: -- 0.04 -ppa_soln_mode: 0 -prop_tax_assessed_decline: 0 -prop_tax_cost_assessed_percent: 100 -property_tax_rate: 0 -ptc_fed_amount: -- 0.022 -ptc_fed_escal: 2.5 -ptc_fed_term: 10 -ptc_sta_amount: -- 0 -ptc_sta_escal: 0 -ptc_sta_term: 10 -real_discount_rate: 6.4 -reserves_interest: 1.75 -roe_input: -- 0 -salvage_percentage: 0 -state_tax_rate: -- 7 -system_capacity: 48000 -system_use_lifetime_output: 0 -system_use_recapitalization: 0 -term_int_rate: 7 -term_tenor: 18 -total_installed_cost: 64800000 -total_uncert: 12.085201263427734 -turb_generic_loss: 1.7 -turb_hysteresis_loss: 0.4 -turb_perf_loss: 1.1 -turb_specific_loss: 0.81 -wake_ext_loss: 1.1 -wake_future_loss: 0 -wake_int_loss: 0 -weibull_k_factor: 2 -weibull_reference_height: 50 -weibull_wind_speed: 7.25 -wind_farm_wake_model: 0 -wind_farm_xCoordinates: -- 0 -- 616 -- 1232 -- 1848 -- 2464 -- 3080 -- 3696 -- 4312 -- 308 -- 924 -- 1540 -- 2156 -- 2772 -- 3388 -- 4004 -- 4620 -- 0 -- 616 -- 1232 -- 1848 -- 2464 -- 3080 -- 3696 -- 4312 -- 308 -- 924 -- 1540 -- 2156 -- 2772 -- 3388 -- 4004 -- 4620 -wind_farm_yCoordinates: -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 616 -- 616 -- 616 -- 616 -- 616 -- 616 -- 616 -- 616 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -wind_resource_distribution: -- - 0 - - 0 - - 1 -wind_resource_filename: /Applications/SAM.app/Contents/wind_resource/WY Southern-Flat - Lands.srw -wind_resource_model_choice: 0 -wind_resource_shear: 0.14 -wind_resource_turbulence_coeff: 0.1 -wind_turbine_hub_ht: 80 -wind_turbine_max_cp: 0.45 -wind_turbine_powercurve_powerout: -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 21.32 -- 33.51 -- 45.69 -- 65.21 -- 79.83 -- 104.25 -- 128.66 -- 157.97 -- 187.27 -- 216.58 -- 250.78 -- 292.32 -- 333.85 -- 375.4 -- 426.72 -- 475.6 -- 534.27 -- 597.81 -- 656.49 -- 724.94 -- 798.29 -- 871.63 -- 940.08 -- 1010 -- 1060 -- 1130 -- 1190 -- 1240 -- 1290 -- 1330 -- 1370 -- 1390 -- 1410 -- 1430 -- 1440 -- 1460 -- 1470 -- 1475 -- 1480 -- 1485 -- 1490 -- 1495 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -wind_turbine_powercurve_windspeeds: -- 0 -- 0.25 -- 0.5 -- 0.75 -- 1 -- 1.25 -- 1.5 -- 1.75 -- 2 -- 2.25 -- 2.5 -- 2.75 -- 3 -- 3.25 -- 3.5 -- 3.75 -- 4 -- 4.25 -- 4.5 -- 4.75 -- 5 -- 5.25 -- 5.5 -- 5.75 -- 6 -- 6.25 -- 6.5 -- 6.75 -- 7 -- 7.25 -- 7.5 -- 7.75 -- 8 -- 8.25 -- 8.5 -- 8.75 -- 9 -- 9.25 -- 9.5 -- 9.75 -- 10 -- 10.25 -- 10.5 -- 10.75 -- 11 -- 11.25 -- 11.5 -- 11.75 -- 12 -- 12.25 -- 12.5 -- 12.75 -- 13 -- 13.25 -- 13.5 -- 13.75 -- 14 -- 14.25 -- 14.5 -- 14.75 -- 15 -- 15.25 -- 15.5 -- 15.75 -- 16 -- 16.25 -- 16.5 -- 16.75 -- 17 -- 17.25 -- 17.5 -- 17.75 -- 18 -- 18.25 -- 18.5 -- 18.75 -- 19 -- 19.25 -- 19.5 -- 19.75 -- 20 -- 20.25 -- 20.5 -- 20.75 -- 21 -- 21.25 -- 21.5 -- 21.75 -- 22 -- 22.25 -- 22.5 -- 22.75 -- 23 -- 23.25 -- 23.5 -- 23.75 -- 24 -- 24.25 -- 24.5 -- 24.75 -- 25 -- 25.25 -- 25.5 -- 25.75 -- 26 -- 26.25 -- 26.5 -- 26.75 -- 27 -- 27.25 -- 27.5 -- 27.75 -- 28 -- 28.25 -- 28.5 -- 28.75 -- 29 -- 29.25 -- 29.5 -- 29.75 -- 30 -- 30.25 -- 30.5 -- 30.75 -- 31 -- 31.25 -- 31.5 -- 31.75 -- 32 -- 32.25 -- 32.5 -- 32.75 -- 33 -- 33.25 -- 33.5 -- 33.75 -- 34 -- 34.25 -- 34.5 -- 34.75 -- 35 -- 35.25 -- 35.5 -- 35.75 -- 36 -- 36.25 -- 36.5 -- 36.75 -- 37 -- 37.25 -- 37.5 -- 37.75 -- 38 -- 38.25 -- 38.5 -- 38.75 -- 39 -- 39.25 -- 39.5 -- 39.75 -- 40 -wind_turbine_rotor_diameter: 77 diff --git a/library/code_comparison/dinwoodie/project/config/annual_service_only.yaml b/library/code_comparison/dinwoodie/project/config/annual_service_only.yaml index 81dac6b6..fb929b2e 100644 --- a/library/code_comparison/dinwoodie/project/config/annual_service_only.yaml +++ b/library/code_comparison/dinwoodie/project/config/annual_service_only.yaml @@ -1,6 +1,6 @@ name: dinwoodie_annual_service_only library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_annual_service_only.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/annual_service_only_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/annual_service_only_100pct_reduction.yaml index 06636706..5b5cab34 100644 --- a/library/code_comparison/dinwoodie/project/config/annual_service_only_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/annual_service_only_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_annual_service_only_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_annual_service_only_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/base.yaml b/library/code_comparison/dinwoodie/project/config/base.yaml index 381cb925..51e7b7ea 100644 --- a/library/code_comparison/dinwoodie/project/config/base.yaml +++ b/library/code_comparison/dinwoodie/project/config/base.yaml @@ -1,6 +1,6 @@ name: dinwoodie_base library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv # located in: dinwoodie / weather +weather: alpha_ventus_weather_2002_2014.pqt # located in: dinwoodie / weather service_equipment: # YAML-encoded list, but could also be created in standard Python list notation with # square brackets: [ctv1.yaml, ctv2.yaml, ..., hlv_requests.yaml] diff --git a/library/code_comparison/dinwoodie/project/config/base_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/base_100pct_reduction.yaml index b26dcb15..0e9a0ce0 100644 --- a/library/code_comparison/dinwoodie/project/config/base_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/base_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_base_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/base_downtime.yaml b/library/code_comparison/dinwoodie/project/config/base_downtime.yaml index 5114d81d..413b447e 100644 --- a/library/code_comparison/dinwoodie/project/config/base_downtime.yaml +++ b/library/code_comparison/dinwoodie/project/config/base_downtime.yaml @@ -1,6 +1,6 @@ name: downtime_base_downtime library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_downtime.yaml, hlv_downtime.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/base_requests.yaml b/library/code_comparison/dinwoodie/project/config/base_requests.yaml index c4b8703f..552c607f 100644 --- a/library/code_comparison/dinwoodie/project/config/base_requests.yaml +++ b/library/code_comparison/dinwoodie/project/config/base_requests.yaml @@ -1,6 +1,6 @@ name: dinwoodie_base_requests library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/base_scheduled.yaml b/library/code_comparison/dinwoodie/project/config/base_scheduled.yaml index 63a686db..e1dae4a5 100644 --- a/library/code_comparison/dinwoodie/project/config/base_scheduled.yaml +++ b/library/code_comparison/dinwoodie/project/config/base_scheduled.yaml @@ -1,6 +1,6 @@ name: dinwoodie_base library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_scheduled.yaml, hlv_1_scheduled.yaml, hlv_2_scheduled.yaml, hlv_3_scheduled.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/base_tow_to_port.yaml b/library/code_comparison/dinwoodie/project/config/base_tow_to_port.yaml index 01a7e592..ee754a74 100644 --- a/library/code_comparison/dinwoodie/project/config/base_tow_to_port.yaml +++ b/library/code_comparison/dinwoodie/project/config/base_tow_to_port.yaml @@ -1,6 +1,6 @@ name: dinwoodie_base_tow_to_port library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_tow_to_port.csv port: base_port.yaml diff --git a/library/code_comparison/dinwoodie/project/config/failure_200.yaml b/library/code_comparison/dinwoodie/project/config/failure_200.yaml index 9dc6a6c0..a6d369f9 100644 --- a/library/code_comparison/dinwoodie/project/config/failure_200.yaml +++ b/library/code_comparison/dinwoodie/project/config/failure_200.yaml @@ -1,6 +1,6 @@ name: dinwoodie_failure_200 library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_failure_200.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/failure_200_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/failure_200_100pct_reduction.yaml index 00d0c852..b99742fa 100644 --- a/library/code_comparison/dinwoodie/project/config/failure_200_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/failure_200_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_failure_200_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_failure_200_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/failure_50.yaml b/library/code_comparison/dinwoodie/project/config/failure_50.yaml index fc9722b7..2170bf55 100644 --- a/library/code_comparison/dinwoodie/project/config/failure_50.yaml +++ b/library/code_comparison/dinwoodie/project/config/failure_50.yaml @@ -1,6 +1,6 @@ name: dinwoodie_failure_50 library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_failure_50.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/failure_50_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/failure_50_100pct_reduction.yaml index bdd3b7a7..15a049d0 100644 --- a/library/code_comparison/dinwoodie/project/config/failure_50_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/failure_50_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_failure_50_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_failure_50_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/fewer_ctvs.yaml b/library/code_comparison/dinwoodie/project/config/fewer_ctvs.yaml index 49d67eea..98ecd7f5 100644 --- a/library/code_comparison/dinwoodie/project/config/fewer_ctvs.yaml +++ b/library/code_comparison/dinwoodie/project/config/fewer_ctvs.yaml @@ -1,6 +1,6 @@ name: dinwoodie_fewer_ctvs library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/fewer_ctvs_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/fewer_ctvs_100pct_reduction.yaml index d47cabfa..8a1a6074 100644 --- a/library/code_comparison/dinwoodie/project/config/fewer_ctvs_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/fewer_ctvs_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_fewer_ctvs_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/fewer_techs.yaml b/library/code_comparison/dinwoodie/project/config/fewer_techs.yaml index 1b3ad688..c1bae04c 100644 --- a/library/code_comparison/dinwoodie/project/config/fewer_techs.yaml +++ b/library/code_comparison/dinwoodie/project/config/fewer_techs.yaml @@ -1,6 +1,6 @@ name: dinwoodie_fewer_techs library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/fewer_techs_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/fewer_techs_100pct_reduction.yaml index b8b1e459..7b4dbf4a 100644 --- a/library/code_comparison/dinwoodie/project/config/fewer_techs_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/fewer_techs_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_fewer_techs_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/historic_weather.yaml b/library/code_comparison/dinwoodie/project/config/historic_weather.yaml index 58eb5eb3..6da1add5 100644 --- a/library/code_comparison/dinwoodie/project/config/historic_weather.yaml +++ b/library/code_comparison/dinwoodie/project/config/historic_weather.yaml @@ -1,6 +1,6 @@ name: dinwoodie_historic_weather library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/historic_weather_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/historic_weather_100pct_reduction.yaml index 817871bc..c6e35ed7 100644 --- a/library/code_comparison/dinwoodie/project/config/historic_weather_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/historic_weather_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_historic_weather_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/major_repairs_only.yaml b/library/code_comparison/dinwoodie/project/config/major_repairs_only.yaml index f7c5325d..6658d241 100644 --- a/library/code_comparison/dinwoodie/project/config/major_repairs_only.yaml +++ b/library/code_comparison/dinwoodie/project/config/major_repairs_only.yaml @@ -1,6 +1,6 @@ name: dinwoodie_major_repairs_only library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml] layout: layout_major_repairs_only.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/major_repairs_only_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/major_repairs_only_100pct_reduction.yaml index cc41e80f..a30e647d 100644 --- a/library/code_comparison/dinwoodie/project/config/major_repairs_only_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/major_repairs_only_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_major_repairs_only_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml] layout: layout_major_repairs_only_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/major_replacements_only.yaml b/library/code_comparison/dinwoodie/project/config/major_replacements_only.yaml index 3deafad3..6d365ae7 100644 --- a/library/code_comparison/dinwoodie/project/config/major_replacements_only.yaml +++ b/library/code_comparison/dinwoodie/project/config/major_replacements_only.yaml @@ -1,6 +1,6 @@ name: dinwoodie_major_replacements_only library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, hlv_requests.yaml] layout: layout_major_replacements_only.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/major_replacements_only_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/major_replacements_only_100pct_reduction.yaml index dcb64fef..1cdd9253 100644 --- a/library/code_comparison/dinwoodie/project/config/major_replacements_only_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/major_replacements_only_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_major_replacements_only_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, hlv_requests.yaml] layout: layout_major_replacements_only_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/manual_resets_only.yaml b/library/code_comparison/dinwoodie/project/config/manual_resets_only.yaml index deae1229..7f31a17b 100644 --- a/library/code_comparison/dinwoodie/project/config/manual_resets_only.yaml +++ b/library/code_comparison/dinwoodie/project/config/manual_resets_only.yaml @@ -1,6 +1,6 @@ name: dinwoodie_manual_resets_only library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_manual_resets_only.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/manual_resets_only_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/manual_resets_only_100pct_reduction.yaml index d86afa98..84004c0e 100644 --- a/library/code_comparison/dinwoodie/project/config/manual_resets_only_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/manual_resets_only_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_manual_resets_only_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_manual_resets_only_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/medium_repairs_only.yaml b/library/code_comparison/dinwoodie/project/config/medium_repairs_only.yaml index a6c725f2..39a3d121 100644 --- a/library/code_comparison/dinwoodie/project/config/medium_repairs_only.yaml +++ b/library/code_comparison/dinwoodie/project/config/medium_repairs_only.yaml @@ -1,6 +1,6 @@ name: dinwoodie_medium_repairs_only library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_medium_repairs_only.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/medium_repairs_only_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/medium_repairs_only_100pct_reduction.yaml index cd72b8ce..291bcb25 100644 --- a/library/code_comparison/dinwoodie/project/config/medium_repairs_only_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/medium_repairs_only_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_medium_repairs_only_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_medium_repairs_only_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/minor_repairs_only.yaml b/library/code_comparison/dinwoodie/project/config/minor_repairs_only.yaml index 93943a9f..6e2df227 100644 --- a/library/code_comparison/dinwoodie/project/config/minor_repairs_only.yaml +++ b/library/code_comparison/dinwoodie/project/config/minor_repairs_only.yaml @@ -1,6 +1,6 @@ name: dinwoodie_minor_repairs_only library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_minor_repairs_only.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/minor_repairs_only_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/minor_repairs_only_100pct_reduction.yaml index 924678ea..d92eef1b 100644 --- a/library/code_comparison/dinwoodie/project/config/minor_repairs_only_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/minor_repairs_only_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_minor_repairs_only_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml] layout: layout_minor_repairs_only_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/more_ctvs.yaml b/library/code_comparison/dinwoodie/project/config/more_ctvs.yaml index 989d86d1..2ba78e8b 100644 --- a/library/code_comparison/dinwoodie/project/config/more_ctvs.yaml +++ b/library/code_comparison/dinwoodie/project/config/more_ctvs.yaml @@ -1,6 +1,6 @@ name: dinwoodie_more_ctvs library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, ctv4.yaml, ctv5.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/more_ctvs_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/more_ctvs_100pct_reduction.yaml index 7a8711df..cf52ac70 100644 --- a/library/code_comparison/dinwoodie/project/config/more_ctvs_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/more_ctvs_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_more_ctvs_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, ctv4.yaml, ctv5.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/more_techs.yaml b/library/code_comparison/dinwoodie/project/config/more_techs.yaml index d99e0785..19a777ca 100644 --- a/library/code_comparison/dinwoodie/project/config/more_techs.yaml +++ b/library/code_comparison/dinwoodie/project/config/more_techs.yaml @@ -1,6 +1,6 @@ name: dinwoodie_more_techs library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/more_techs_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/more_techs_100pct_reduction.yaml index 6b7fa281..e0fa2cba 100644 --- a/library/code_comparison/dinwoodie/project/config/more_techs_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/more_techs_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_more_techs_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/no_hlvs.yaml b/library/code_comparison/dinwoodie/project/config/no_hlvs.yaml index 41822822..63dc31f6 100644 --- a/library/code_comparison/dinwoodie/project/config/no_hlvs.yaml +++ b/library/code_comparison/dinwoodie/project/config/no_hlvs.yaml @@ -1,6 +1,6 @@ name: dinwoodie_no_hlvs library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml] layout: layout_no_hlvs.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/no_hlvs_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/no_hlvs_100pct_reduction.yaml index ab3b86d9..5dbbdb5e 100644 --- a/library/code_comparison/dinwoodie/project/config/no_hlvs_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/no_hlvs_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_no_hlvs_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014.csv +weather: alpha_ventus_weather_2002_2014.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml] layout: layout_no_hlvs_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/no_weather.yaml b/library/code_comparison/dinwoodie/project/config/no_weather.yaml index 4d3e7994..c906357d 100644 --- a/library/code_comparison/dinwoodie/project/config/no_weather.yaml +++ b/library/code_comparison/dinwoodie/project/config/no_weather.yaml @@ -1,6 +1,6 @@ name: dinwoodie_no_weather library: DINWOODIE -weather: alpha_ventus_weather_2002_2014_zeros.csv +weather: alpha_ventus_weather_2002_2014_zeros.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/project/config/no_weather_100pct_reduction.yaml b/library/code_comparison/dinwoodie/project/config/no_weather_100pct_reduction.yaml index 2cb797eb..88258b78 100644 --- a/library/code_comparison/dinwoodie/project/config/no_weather_100pct_reduction.yaml +++ b/library/code_comparison/dinwoodie/project/config/no_weather_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: dinwoodie_no_weather_100pct_reduction library: DINWOODIE -weather: alpha_ventus_weather_2002_2014_zeros.csv +weather: alpha_ventus_weather_2002_2014_zeros.pqt service_equipment: [ctv1.yaml, ctv2.yaml, ctv3.yaml, fsv_requests.yaml, hlv_requests.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/dinwoodie/weather/alpha_ventus_weather_2002_2014.csv b/library/code_comparison/dinwoodie/weather/alpha_ventus_weather_2002_2014.csv deleted file mode 100644 index b7071914..00000000 --- a/library/code_comparison/dinwoodie/weather/alpha_ventus_weather_2002_2014.csv +++ /dev/null @@ -1,113953 +0,0 @@ -datetime,windspeed,waveheight -1/1/02 0:00,11.75561096,1.281772405 -1/1/02 1:00,10.41321252,1.586584315 -1/1/02 2:00,8.959270788,1.725690828 -1/1/02 3:00,9.10014808,1.680982063 -1/1/02 4:00,9.945059601,1.552232138 -1/1/02 5:00,11.50807971,1.335083363 -1/1/02 6:00,12.44757098,1.294392727 -1/1/02 7:00,13.33747206,1.187261471 -1/1/02 8:00,13.73767254,1.161398299 -1/1/02 9:00,13.90839292,1.116859314 -1/1/02 10:00,15.20178021,0.956417099 -1/1/02 11:00,15.16679404,1.022461566 -1/1/02 12:00,16.03606673,1.057606051 -1/1/02 13:00,16.01126969,1.040118699 -1/1/02 14:00,16.34378873,1.046740124 -1/1/02 15:00,16.8960269,1.169038404 -1/1/02 16:00,17.63065384,1.197844431 -1/1/02 17:00,17.5821912,1.239270781 -1/1/02 18:00,17.8293809,1.253305938 -1/1/02 19:00,17.31883271,1.437064622 -1/1/02 20:00,17.04679936,1.486753604 -1/1/02 21:00,17.1166448,1.494620083 -1/1/02 22:00,17.9860875,1.143288419 -1/1/02 23:00,17.35486012,1.310295465 -1/2/02 0:00,17.07238888,1.418332215 -1/2/02 1:00,17.1705808,1.411484417 -1/2/02 2:00,16.15249532,1.689810629 -1/2/02 3:00,15.46708778,1.95772366 -1/2/02 4:00,14.75968704,1.967174754 -1/2/02 5:00,13.8651614,2.065194477 -1/2/02 6:00,12.97814328,2.222524056 -1/2/02 7:00,12.26468349,2.276061387 -1/2/02 8:00,11.56965779,2.286983908 -1/2/02 9:00,11.31251841,2.22410867 -1/2/02 10:00,9.910373921,2.232201523 -1/2/02 11:00,6.673472487,2.152970799 -1/2/02 12:00,4.3579899,2.039048338 -1/2/02 13:00,4.551171686,1.924220383 -1/2/02 14:00,5.501910463,1.81663638 -1/2/02 15:00,6.835946836,1.717994129 -1/2/02 16:00,7.844314926,1.627444731 -1/2/02 17:00,8.583774848,1.542498077 -1/2/02 18:00,9.279926155,1.458626698 -1/2/02 19:00,9.294270826,1.37815092 -1/2/02 20:00,9.63547847,1.318105351 -1/2/02 21:00,10.20529673,1.245835612 -1/2/02 22:00,9.633606012,1.195184543 -1/2/02 23:00,9.790521088,1.139666443 -1/3/02 0:00,9.879944867,1.076055491 -1/3/02 1:00,9.761126596,1.016009922 -1/3/02 2:00,10.06073886,0.957039626 -1/3/02 3:00,10.56309636,0.890259445 -1/3/02 4:00,11.24356245,0.835080906 -1/3/02 5:00,11.56122423,0.784939177 -1/3/02 6:00,11.53710839,0.738306237 -1/3/02 7:00,11.69115898,0.71034911 -1/3/02 8:00,11.70318645,0.677581547 -1/3/02 9:00,11.64519712,0.661056282 -1/3/02 10:00,11.63255899,0.684712312 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20:00,0,0 -12/30/14 21:00,0,0 -12/30/14 22:00,0,0 -12/30/14 23:00,0,0 -12/31/14 0:00,0,0 -12/31/14 1:00,0,0 -12/31/14 2:00,0,0 -12/31/14 3:00,0,0 -12/31/14 4:00,0,0 -12/31/14 5:00,0,0 -12/31/14 6:00,0,0 -12/31/14 7:00,0,0 -12/31/14 8:00,0,0 -12/31/14 9:00,0,0 -12/31/14 10:00,0,0 -12/31/14 11:00,0,0 -12/31/14 12:00,0,0 -12/31/14 13:00,0,0 -12/31/14 14:00,0,0 -12/31/14 15:00,0,0 -12/31/14 16:00,0,0 -12/31/14 17:00,0,0 -12/31/14 18:00,0,0 -12/31/14 19:00,0,0 -12/31/14 20:00,0,0 -12/31/14 21:00,0,0 -12/31/14 22:00,0,0 -12/31/14 23:00,0,0 diff --git a/library/code_comparison/dinwoodie/weather/alpha_ventus_weather_2002_2014_zeros.pqt b/library/code_comparison/dinwoodie/weather/alpha_ventus_weather_2002_2014_zeros.pqt new file mode 100644 index 00000000..4e0c4ce5 Binary files /dev/null and b/library/code_comparison/dinwoodie/weather/alpha_ventus_weather_2002_2014_zeros.pqt differ diff --git a/library/code_comparison/iea26/project/config/SAM_Singleowner_defaults.yaml b/library/code_comparison/iea26/project/config/SAM_Singleowner_defaults.yaml deleted file mode 100644 index b9933a47..00000000 --- a/library/code_comparison/iea26/project/config/SAM_Singleowner_defaults.yaml +++ /dev/null @@ -1,18811 +0,0 @@ -adjust:constant: 0 -analysis_period: 25 -avail_bop_loss: 0.5 -avail_grid_loss: 1.5 -avail_turb_loss: 3.58 -cbi_fed_amount: 0 -cbi_fed_deprbas_fed: 0 -cbi_fed_deprbas_sta: 0 -cbi_fed_maxvalue: 1.0e+38 -cbi_fed_tax_fed: 1 -cbi_fed_tax_sta: 1 -cbi_oth_amount: 0 -cbi_oth_deprbas_fed: 0 -cbi_oth_deprbas_sta: 0 -cbi_oth_maxvalue: 1.0e+38 -cbi_oth_tax_fed: 1 -cbi_oth_tax_sta: 1 -cbi_sta_amount: 0 -cbi_sta_deprbas_fed: 0 -cbi_sta_deprbas_sta: 0 -cbi_sta_maxvalue: 1.0e+38 -cbi_sta_tax_fed: 1 -cbi_sta_tax_sta: 1 -cbi_uti_amount: 0 -cbi_uti_deprbas_fed: 0 -cbi_uti_deprbas_sta: 0 -cbi_uti_maxvalue: 1.0e+38 -cbi_uti_tax_fed: 1 -cbi_uti_tax_sta: 1 -compute_module_0: windpower -compute_module_1: grid -compute_module_2: singleowner -construction_financing_cost: 1296000 -cost_debt_closing: 450000 -cost_debt_fee: 2.75 -cost_other_financing: 0 -cp_battery_nameplate: 0 -cp_capacity_credit_percent: -- 0 -cp_capacity_payment_amount: -- 0 -cp_capacity_payment_esc: 0 -cp_capacity_payment_type: 0 -cp_system_nameplate: 48 -debt_option: 1 -debt_percent: 60 -degradation: -- 0 -depr_alloc_custom_percent: 0 -depr_alloc_macrs_15_percent: 1.5 -depr_alloc_macrs_5_percent: 90 -depr_alloc_sl_15_percent: 2.5 -depr_alloc_sl_20_percent: 3 -depr_alloc_sl_39_percent: 0 -depr_alloc_sl_5_percent: 0 -depr_bonus_fed: 0 -depr_bonus_fed_custom: 0 -depr_bonus_fed_macrs_15: 1 -depr_bonus_fed_macrs_5: 1 -depr_bonus_fed_sl_15: 0 -depr_bonus_fed_sl_20: 0 -depr_bonus_fed_sl_39: 0 -depr_bonus_fed_sl_5: 0 -depr_bonus_sta: 0 -depr_bonus_sta_custom: 0 -depr_bonus_sta_macrs_15: 1 -depr_bonus_sta_macrs_5: 1 -depr_bonus_sta_sl_15: 0 -depr_bonus_sta_sl_20: 0 -depr_bonus_sta_sl_39: 0 -depr_bonus_sta_sl_5: 0 -depr_custom_schedule: -- 0 -depr_fedbas_method: 1 -depr_itc_fed_custom: 0 -depr_itc_fed_macrs_15: 0 -depr_itc_fed_macrs_5: 1 -depr_itc_fed_sl_15: 0 -depr_itc_fed_sl_20: 0 -depr_itc_fed_sl_39: 0 -depr_itc_fed_sl_5: 0 -depr_itc_sta_custom: 0 -depr_itc_sta_macrs_15: 0 -depr_itc_sta_macrs_5: 1 -depr_itc_sta_sl_15: 0 -depr_itc_sta_sl_20: 0 -depr_itc_sta_sl_39: 0 -depr_itc_sta_sl_5: 0 -depr_stabas_method: 1 -dispatch_factor1: 1 -dispatch_factor2: 1 -dispatch_factor3: 1 -dispatch_factor4: 1 -dispatch_factor5: 1 -dispatch_factor6: 1 -dispatch_factor7: 1 -dispatch_factor8: 1 -dispatch_factor9: 1 -dispatch_factors_ts: -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 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1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 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1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 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-- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1.2 -- 1.2 -- 1.2 -- 1.2 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 2.064 -- 1.2 -- 1.2 -- 1.2 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 1 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 1.1 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.7 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 -- 0.8 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1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -- 1.0e+38 -grid_curtailment_price: -- 0 -grid_curtailment_price_esc: 0 -grid_interconnection_limit_kwac: 100000 -ibi_fed_amount: 0 -ibi_fed_amount_deprbas_fed: 0 -ibi_fed_amount_deprbas_sta: 0 -ibi_fed_amount_tax_fed: 1 -ibi_fed_amount_tax_sta: 1 -ibi_fed_percent: 0 -ibi_fed_percent_deprbas_fed: 0 -ibi_fed_percent_deprbas_sta: 0 -ibi_fed_percent_maxvalue: 1.0e+38 -ibi_fed_percent_tax_fed: 1 -ibi_fed_percent_tax_sta: 1 -ibi_oth_amount: 0 -ibi_oth_amount_deprbas_fed: 0 -ibi_oth_amount_deprbas_sta: 0 -ibi_oth_amount_tax_fed: 1 -ibi_oth_amount_tax_sta: 1 -ibi_oth_percent: 0 -ibi_oth_percent_deprbas_fed: 0 -ibi_oth_percent_deprbas_sta: 0 -ibi_oth_percent_maxvalue: 1.0e+38 -ibi_oth_percent_tax_fed: 1 -ibi_oth_percent_tax_sta: 1 -ibi_sta_amount: 0 -ibi_sta_amount_deprbas_fed: 0 -ibi_sta_amount_deprbas_sta: 0 -ibi_sta_amount_tax_fed: 1 -ibi_sta_amount_tax_sta: 1 -ibi_sta_percent: 0 -ibi_sta_percent_deprbas_fed: 0 -ibi_sta_percent_deprbas_sta: 0 -ibi_sta_percent_maxvalue: 1.0e+38 -ibi_sta_percent_tax_fed: 1 -ibi_sta_percent_tax_sta: 1 -ibi_uti_amount: 0 -ibi_uti_amount_deprbas_fed: 0 -ibi_uti_amount_deprbas_sta: 0 -ibi_uti_amount_tax_fed: 1 -ibi_uti_amount_tax_sta: 1 -ibi_uti_percent: 0 -ibi_uti_percent_deprbas_fed: 0 -ibi_uti_percent_deprbas_sta: 0 -ibi_uti_percent_maxvalue: 1.0e+38 -ibi_uti_percent_tax_fed: 1 -ibi_uti_percent_tax_sta: 1 -inflation_rate: 2.5 -insurance_rate: 0.5 -itc_fed_amount: 0 -itc_fed_amount_deprbas_fed: 1 -itc_fed_amount_deprbas_sta: 1 -itc_fed_percent: 0 -itc_fed_percent_deprbas_fed: 1 -itc_fed_percent_deprbas_sta: 1 -itc_fed_percent_maxvalue: 1.0e+38 -itc_sta_amount: 0 -itc_sta_amount_deprbas_fed: 0 -itc_sta_amount_deprbas_sta: 0 -itc_sta_percent: 0 -itc_sta_percent_deprbas_fed: 0 -itc_sta_percent_deprbas_sta: 0 -itc_sta_percent_maxvalue: 1.0e+38 -loan_moratorium: 0 -metric_0: annual_energy -metric_0_label: Annual energy (year 1) -metric_1: capacity_factor -metric_10: flip_actual_irr -metric_10_label: Internal rate of return (IRR) -metric_11: flip_actual_year -metric_11_label: Year IRR is achieved -metric_12: project_return_aftertax_irr -metric_12_label: IRR at end of project -metric_13: cost_installed -metric_13_label: Net capital cost -metric_14: size_of_equity -metric_14_label: Equity -metric_15: size_of_debt -metric_15_label: Size of debt -metric_1_label: Capacity factor (year 1) -metric_2: annual_energy_p90 -metric_2_label: p90 Energy (year 1) -metric_3: ppa -metric_3_label: PPA price (year 1) -metric_4: ppa_escalation -metric_4_label: PPA price escalation -metric_5: lppa_nom -metric_5_label: Levelized PPA price (nominal) -metric_6: lppa_real -metric_6_label: Levelized PPA price (real) -metric_7: lcoe_nom -metric_7_label: Levelized COE (nominal) -metric_8: lcoe_real -metric_8_label: Levelized COE (real) -metric_9: project_return_aftertax_npv -metric_9_label: Net present value -months_receivables_reserve: 0 -months_working_reserve: 6 -number_compute_modules: 3 -number_metrics: 16 -om_capacity: -- 42 -om_capacity_escal: 0 -om_fixed: -- 0 -om_fixed_escal: 0 -om_fuel_cost: -- 0 -om_fuel_cost_escal: 0 -om_production: -- 0 -om_production_escal: 0 -om_replacement_cost1: -- 0 -om_replacement_cost_escal: 0 -ops_env_loss: 1 -ops_grid_loss: 0.84 -ops_load_loss: 0.99 -ops_strategies_loss: 0 -payment_option: 0 -pbi_fed_amount: -- 0 -pbi_fed_escal: 0 -pbi_fed_for_ds: 0 -pbi_fed_tax_fed: 1 -pbi_fed_tax_sta: 1 -pbi_fed_term: 0 -pbi_oth_amount: -- 0 -pbi_oth_escal: 0 -pbi_oth_for_ds: 0 -pbi_oth_tax_fed: 1 -pbi_oth_tax_sta: 1 -pbi_oth_term: 0 -pbi_sta_amount: -- 0 -pbi_sta_escal: 0 -pbi_sta_for_ds: 0 -pbi_sta_tax_fed: 1 -pbi_sta_tax_sta: 1 -pbi_sta_term: 0 -pbi_uti_amount: -- 0 -pbi_uti_escal: 0 -pbi_uti_for_ds: 0 -pbi_uti_tax_fed: 1 -pbi_uti_tax_sta: 1 -pbi_uti_term: 0 -ppa_escalation: 1 -ppa_multiplier_model: 0 -ppa_price_input: -- 0.04 -ppa_soln_mode: 0 -prop_tax_assessed_decline: 0 -prop_tax_cost_assessed_percent: 100 -property_tax_rate: 0 -ptc_fed_amount: -- 0.022 -ptc_fed_escal: 2.5 -ptc_fed_term: 10 -ptc_sta_amount: -- 0 -ptc_sta_escal: 0 -ptc_sta_term: 10 -real_discount_rate: 6.4 -reserves_interest: 1.75 -roe_input: -- 0 -salvage_percentage: 0 -state_tax_rate: -- 7 -system_capacity: 48000 -system_use_lifetime_output: 0 -system_use_recapitalization: 0 -term_int_rate: 7 -term_tenor: 18 -total_installed_cost: 64800000 -total_uncert: 12.085201263427734 -turb_generic_loss: 1.7 -turb_hysteresis_loss: 0.4 -turb_perf_loss: 1.1 -turb_specific_loss: 0.81 -wake_ext_loss: 1.1 -wake_future_loss: 0 -wake_int_loss: 0 -weibull_k_factor: 2 -weibull_reference_height: 50 -weibull_wind_speed: 7.25 -wind_farm_wake_model: 0 -wind_farm_xCoordinates: -- 0 -- 616 -- 1232 -- 1848 -- 2464 -- 3080 -- 3696 -- 4312 -- 308 -- 924 -- 1540 -- 2156 -- 2772 -- 3388 -- 4004 -- 4620 -- 0 -- 616 -- 1232 -- 1848 -- 2464 -- 3080 -- 3696 -- 4312 -- 308 -- 924 -- 1540 -- 2156 -- 2772 -- 3388 -- 4004 -- 4620 -wind_farm_yCoordinates: -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 616 -- 616 -- 616 -- 616 -- 616 -- 616 -- 616 -- 616 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1232 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -- 1848 -wind_resource_distribution: -- - 0 - - 0 - - 1 -wind_resource_filename: /Applications/SAM.app/Contents/wind_resource/WY Southern-Flat - Lands.srw -wind_resource_model_choice: 0 -wind_resource_shear: 0.14 -wind_resource_turbulence_coeff: 0.1 -wind_turbine_hub_ht: 80 -wind_turbine_max_cp: 0.45 -wind_turbine_powercurve_powerout: -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 21.32 -- 33.51 -- 45.69 -- 65.21 -- 79.83 -- 104.25 -- 128.66 -- 157.97 -- 187.27 -- 216.58 -- 250.78 -- 292.32 -- 333.85 -- 375.4 -- 426.72 -- 475.6 -- 534.27 -- 597.81 -- 656.49 -- 724.94 -- 798.29 -- 871.63 -- 940.08 -- 1010 -- 1060 -- 1130 -- 1190 -- 1240 -- 1290 -- 1330 -- 1370 -- 1390 -- 1410 -- 1430 -- 1440 -- 1460 -- 1470 -- 1475 -- 1480 -- 1485 -- 1490 -- 1495 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 1500 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -- 0 -wind_turbine_powercurve_windspeeds: -- 0 -- 0.25 -- 0.5 -- 0.75 -- 1 -- 1.25 -- 1.5 -- 1.75 -- 2 -- 2.25 -- 2.5 -- 2.75 -- 3 -- 3.25 -- 3.5 -- 3.75 -- 4 -- 4.25 -- 4.5 -- 4.75 -- 5 -- 5.25 -- 5.5 -- 5.75 -- 6 -- 6.25 -- 6.5 -- 6.75 -- 7 -- 7.25 -- 7.5 -- 7.75 -- 8 -- 8.25 -- 8.5 -- 8.75 -- 9 -- 9.25 -- 9.5 -- 9.75 -- 10 -- 10.25 -- 10.5 -- 10.75 -- 11 -- 11.25 -- 11.5 -- 11.75 -- 12 -- 12.25 -- 12.5 -- 12.75 -- 13 -- 13.25 -- 13.5 -- 13.75 -- 14 -- 14.25 -- 14.5 -- 14.75 -- 15 -- 15.25 -- 15.5 -- 15.75 -- 16 -- 16.25 -- 16.5 -- 16.75 -- 17 -- 17.25 -- 17.5 -- 17.75 -- 18 -- 18.25 -- 18.5 -- 18.75 -- 19 -- 19.25 -- 19.5 -- 19.75 -- 20 -- 20.25 -- 20.5 -- 20.75 -- 21 -- 21.25 -- 21.5 -- 21.75 -- 22 -- 22.25 -- 22.5 -- 22.75 -- 23 -- 23.25 -- 23.5 -- 23.75 -- 24 -- 24.25 -- 24.5 -- 24.75 -- 25 -- 25.25 -- 25.5 -- 25.75 -- 26 -- 26.25 -- 26.5 -- 26.75 -- 27 -- 27.25 -- 27.5 -- 27.75 -- 28 -- 28.25 -- 28.5 -- 28.75 -- 29 -- 29.25 -- 29.5 -- 29.75 -- 30 -- 30.25 -- 30.5 -- 30.75 -- 31 -- 31.25 -- 31.5 -- 31.75 -- 32 -- 32.25 -- 32.5 -- 32.75 -- 33 -- 33.25 -- 33.5 -- 33.75 -- 34 -- 34.25 -- 34.5 -- 34.75 -- 35 -- 35.25 -- 35.5 -- 35.75 -- 36 -- 36.25 -- 36.5 -- 36.75 -- 37 -- 37.25 -- 37.5 -- 37.75 -- 38 -- 38.25 -- 38.5 -- 38.75 -- 39 -- 39.25 -- 39.5 -- 39.75 -- 40 -wind_turbine_rotor_diameter: 77 diff --git a/library/code_comparison/iea26/project/config/one_mobilization.yaml b/library/code_comparison/iea26/project/config/one_mobilization.yaml index d97a9222..29d717cc 100644 --- a/library/code_comparison/iea26/project/config/one_mobilization.yaml +++ b/library/code_comparison/iea26/project/config/one_mobilization.yaml @@ -1,6 +1,6 @@ name: iea_26_one_mobilization library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_scheduled.yaml, hlv_1.yaml, rmt.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/one_mobilization_100pct_reduction.yaml b/library/code_comparison/iea26/project/config/one_mobilization_100pct_reduction.yaml index 9b772a67..f089f24a 100644 --- a/library/code_comparison/iea26/project/config/one_mobilization_100pct_reduction.yaml +++ b/library/code_comparison/iea26/project/config/one_mobilization_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: iea_26_one_mobilization_100pct_reduction library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_scheduled.yaml, hlv_2.yaml, rmt.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/requests.yaml b/library/code_comparison/iea26/project/config/requests.yaml index 40e26546..fefeec12 100644 --- a/library/code_comparison/iea26/project/config/requests.yaml +++ b/library/code_comparison/iea26/project/config/requests.yaml @@ -1,6 +1,6 @@ name: iea_26_requests library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_requests.yaml, hlv_requests.yaml, rmt.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/requests_100pct_reduction.yaml b/library/code_comparison/iea26/project/config/requests_100pct_reduction.yaml index 8a2076d0..4da7fbfe 100644 --- a/library/code_comparison/iea26/project/config/requests_100pct_reduction.yaml +++ b/library/code_comparison/iea26/project/config/requests_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: iea_26_requests_100pct_reduction library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_requests.yaml, hlv_requests.yaml, rmt.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/three_mobilizations.yaml b/library/code_comparison/iea26/project/config/three_mobilizations.yaml index 41052418..c8e6d634 100644 --- a/library/code_comparison/iea26/project/config/three_mobilizations.yaml +++ b/library/code_comparison/iea26/project/config/three_mobilizations.yaml @@ -1,6 +1,6 @@ name: iea_26_three_mobilizations library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_scheduled.yaml, hlv_1.yaml, hlv_2.yaml, hlv_3.yaml, rmt.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/three_mobilizations_100pct_reduction.yaml b/library/code_comparison/iea26/project/config/three_mobilizations_100pct_reduction.yaml index c01c92d7..e7a98dfd 100644 --- a/library/code_comparison/iea26/project/config/three_mobilizations_100pct_reduction.yaml +++ b/library/code_comparison/iea26/project/config/three_mobilizations_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: iea_26_three_mobilizations_100pct_reduction library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_scheduled.yaml, hlv_1.yaml, hlv_2.yaml, hlv_3.yaml, rmt.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/two_mobilizations.yaml b/library/code_comparison/iea26/project/config/two_mobilizations.yaml index 1124799b..1348d95d 100644 --- a/library/code_comparison/iea26/project/config/two_mobilizations.yaml +++ b/library/code_comparison/iea26/project/config/two_mobilizations.yaml @@ -1,6 +1,6 @@ name: iea_26_two_mobilizations library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_scheduled.yaml, hlv_1.yaml, hlv_2.yaml, rmt.yaml] layout: layout.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/project/config/two_mobilizations_100pct_reduction.yaml b/library/code_comparison/iea26/project/config/two_mobilizations_100pct_reduction.yaml index f5ffec4c..a730f8cc 100644 --- a/library/code_comparison/iea26/project/config/two_mobilizations_100pct_reduction.yaml +++ b/library/code_comparison/iea26/project/config/two_mobilizations_100pct_reduction.yaml @@ -1,6 +1,6 @@ name: iea_26_two_mobilizations_100pct_reduction library: IEA_26 -weather: hornsrev3_weather_1996_2015.csv +weather: hornsrev3_weather_1996_2015.pqt service_equipment: [ctv1.yaml, ctv2.yaml, dsv.yaml, cabling_scheduled.yaml, hlv_1.yaml, hlv_3.yaml, rmt.yaml] layout: layout_100pct_reduction.csv inflation_rate: 0 diff --git a/library/code_comparison/iea26/weather/hornsrev3_weather_1996_2015.csv b/library/code_comparison/iea26/weather/hornsrev3_weather_1996_2015.csv deleted file mode 100644 index 944949f4..00000000 --- a/library/code_comparison/iea26/weather/hornsrev3_weather_1996_2015.csv +++ /dev/null @@ -1,175321 +0,0 @@ -datetime,windspeed,waveheight -1/1/96 0:00,14.65873587,0.446721201 -1/1/96 1:00,14.56073715,0.440976098 -1/1/96 2:00,14.66247288,0.434036467 -1/1/96 3:00,14.71935541,0.389952752 -1/1/96 4:00,14.72166756,0.388473815 -1/1/96 5:00,14.70261208,0.369532038 -1/1/96 6:00,14.87994341,0.347518621 -1/1/96 7:00,15.17653735,0.347177328 -1/1/96 8:00,15.20248254,0.350419614 -1/1/96 9:00,15.28668349,0.356392246 -1/1/96 10:00,15.82246089,0.389782106 -1/1/96 11:00,15.81056889,0.395356562 -1/1/96 12:00,15.79534048,0.402580604 -1/1/96 13:00,15.36996806,0.412591873 -1/1/96 14:00,15.41836401,0.420782912 -1/1/96 15:00,15.42771805,0.416914921 -1/1/96 16:00,15.36237817,0.424138962 -1/1/96 17:00,15.52442514,0.413729517 -1/1/96 18:00,15.52847121,0.400191551 -1/1/96 19:00,15.41482606,0.400817255 -1/1/96 20:00,15.08147621,0.416004806 -1/1/96 21:00,14.60773569,0.426698662 -1/1/96 22:00,14.34444424,0.437563165 -1/1/96 23:00,14.03886648,0.436709932 -1/2/96 0:00,14.0259903,0.431306121 -1/2/96 1:00,14.05480414,0.433126352 -1/2/96 2:00,13.85153365,0.429542773 -1/2/96 3:00,13.57695317,0.337564234 -1/2/96 4:00,13.24576428,0.222207105 -1/2/96 5:00,12.8456758,0.062823143 -1/2/96 6:00,12.6090152,0.060149679 -1/2/96 7:00,12.26829951,0.059751504 -1/2/96 8:00,11.63253241,0.062709379 -1/2/96 9:00,11.29524185,0.062595614 -1/2/96 10:00,10.75251477,0.055144044 -1/2/96 11:00,10.2673999,0.072379355 -1/2/96 12:00,9.736269747,0.148203345 -1/2/96 13:00,9.183866448,0.155199858 -1/2/96 14:00,8.697369584,0.268793638 -1/2/96 15:00,8.739743796,0.166974476 -1/2/96 16:00,8.786780806,0.153038334 -1/2/96 17:00,8.5074233,0.273514861 -1/2/96 18:00,8.007220857,0.263674239 -1/2/96 19:00,7.585871944,0.38733617 -1/2/96 20:00,6.979848069,0.224823687 -1/2/96 21:00,6.078301678,0.354628898 -1/2/96 22:00,5.23859921,0.544160433 -1/2/96 23:00,5.072205219,0.494843554 -1/3/96 0:00,5.491474714,0.441829331 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16:00,12.83619279,0.598286739 -12/28/15 17:00,13.27269611,0.477636841 -12/28/15 18:00,13.96536476,0.383029205 -12/28/15 19:00,14.69516868,0.331888242 -12/28/15 20:00,15.45615974,0.296284571 -12/28/15 21:00,15.9284228,0.262819636 -12/28/15 22:00,16.51048011,0.19689623 -12/28/15 23:00,17.51620906,0.220736851 -12/29/15 0:00,16.95221968,0.246716208 -12/29/15 1:00,17.09213827,0.251056585 -12/29/15 2:00,17.03043064,0.362648305 -12/29/15 3:00,17.12643639,0.376801708 -12/29/15 4:00,17.38364657,0.341323844 -12/29/15 5:00,17.29912445,0.446247739 -12/29/15 6:00,17.39161553,0.46021243 -12/29/15 7:00,17.31866069,0.484367571 -12/29/15 8:00,17.63152409,0.46977384 -12/29/15 9:00,17.27620694,0.466188311 -12/29/15 10:00,16.59666276,0.489966028 -12/29/15 11:00,16.49097316,0.484430475 -12/29/15 12:00,16.31801538,0.494998349 -12/29/15 13:00,15.09849429,0.546516736 -12/29/15 14:00,14.36593917,0.58482528 -12/29/15 15:00,14.002906,0.59344313 -12/29/15 16:00,13.91738673,0.604514236 -12/29/15 17:00,14.15344139,0.577088086 -12/29/15 18:00,14.19266083,0.533810125 -12/29/15 19:00,14.09265207,0.513932457 -12/29/15 20:00,13.71906404,0.536892422 -12/29/15 21:00,13.90263487,0.553499081 -12/29/15 22:00,12.87417945,0.615459534 -12/29/15 23:00,12.92602708,0.573628366 -12/30/15 0:00,13.33142968,0.505629127 -12/30/15 1:00,13.44106387,0.477825553 -12/30/15 2:00,13.72499468,0.493991885 -12/30/15 3:00,14.57532438,0.509969504 -12/30/15 4:00,15.03483917,0.51978253 -12/30/15 5:00,15.24961848,0.532677853 -12/30/15 6:00,15.95294636,0.549347416 -12/30/15 7:00,16.39373474,0.582246215 -12/30/15 8:00,16.54408973,0.609798173 -12/30/15 9:00,17.06637946,0.717867268 -12/30/15 10:00,17.70275021,0.671255394 -12/30/15 11:00,17.83792006,0.828075099 -12/30/15 12:00,17.47657622,0.906201884 -12/30/15 13:00,17.79757886,0.991185206 -12/30/15 14:00,18.11181031,1.042326169 -12/30/15 15:00,18.03652829,1.046603642 -12/30/15 16:00,18.45951993,1.108375383 -12/30/15 17:00,18.42035805,1.151338824 -12/30/15 18:00,18.00340592,1.221413895 -12/30/15 19:00,17.91542592,1.29280995 -12/30/15 20:00,17.40163351,1.231415633 -12/30/15 21:00,17.34849571,1.188515096 -12/30/15 22:00,16.8561194,1.146180695 -12/30/15 23:00,16.1418621,1.154232408 -12/31/15 0:00,16.33182421,1.1149174 -12/31/15 1:00,16.2855562,1.04956013 -12/31/15 2:00,16.1914285,1.028613094 -12/31/15 3:00,16.73354093,0.989612606 -12/31/15 4:00,17.00630929,0.901798603 -12/31/15 5:00,16.91418989,0.865502987 -12/31/15 6:00,17.27059382,0.859527106 -12/31/15 7:00,17.64889214,0.797252133 -12/31/15 8:00,17.2249258,0.828263811 -12/31/15 9:00,16.15934282,0.887456488 -12/31/15 10:00,16.21366189,0.840907518 -12/31/15 11:00,15.52198052,0.961117087 -12/31/15 12:00,13.92166287,1.258086934 -12/31/15 13:00,14.28066628,1.269598369 -12/31/15 14:00,14.34798435,1.047735914 -12/31/15 15:00,13.411763,1.47416222 -12/31/15 16:00,12.65289834,1.582986163 -12/31/15 17:00,10.79829451,1.784279006 -12/31/15 18:00,11.01128646,1.710807118 -12/31/15 19:00,11.24020923,1.545243755 -12/31/15 20:00,10.10431678,1.736471955 -12/31/15 21:00,8.193357407,1.819631061 -12/31/15 22:00,9.040457554,1.724142769 -12/31/15 23:00,11.90564331,1.538638834 diff --git a/library/code_comparison/iea26/weather/hornsrev3_weather_1996_2015.pqt b/library/code_comparison/iea26/weather/hornsrev3_weather_1996_2015.pqt new file mode 100644 index 00000000..62c93e41 Binary files /dev/null and b/library/code_comparison/iea26/weather/hornsrev3_weather_1996_2015.pqt differ diff --git a/library/corewind/project/config/morro_bay_in_situ.yaml b/library/corewind/project/config/morro_bay_in_situ.yaml index 0508479b..559af0ef 100644 --- a/library/corewind/project/config/morro_bay_in_situ.yaml +++ b/library/corewind/project/config/morro_bay_in_situ.yaml @@ -1,17 +1,11 @@ name: COREWIND Morro Bay In Situ -weather: central_ca.csv +weather: central_ca.pqt service_equipment: - - ctv1.yaml - - ctv2.yaml - - ctv3.yaml - - ctv4.yaml - - ctv5.yaml - - ctv6.yaml - - ctv7.yaml + - [ctv.yaml, 7] - cab.yaml - dsv.yaml - ahv.yaml - - hlv1.yaml + - hlv.yaml layout: morro_bay_9D_layout.csv port_distance: 60 # 20/100 for West of Barra Island; 10 for Gran Canaria Island inflation_rate: 0 diff --git a/library/corewind/project/config/morro_bay_in_situ_consolidated.yaml b/library/corewind/project/config/morro_bay_in_situ_consolidated.yaml index 6c4fa0eb..f1a88d8e 100644 --- a/library/corewind/project/config/morro_bay_in_situ_consolidated.yaml +++ b/library/corewind/project/config/morro_bay_in_situ_consolidated.yaml @@ -7,7 +7,7 @@ service_equipment: - dsv - ahv - hlv -layout: morro_bay_9D_layout.csv +layout: morro_bay_9D_layout.pqt port_distance: 60 inflation_rate: 0 workday_start: 6 diff --git a/library/corewind/project/config/morro_bay_tow_to_port.yaml b/library/corewind/project/config/morro_bay_tow_to_port.yaml index 14a78078..8462454c 100644 --- a/library/corewind/project/config/morro_bay_tow_to_port.yaml +++ b/library/corewind/project/config/morro_bay_tow_to_port.yaml @@ -1,14 +1,8 @@ name: COREWIND Morro Bay Tow To Port library: library -weather: central_ca.csv +weather: central_ca.pqt service_equipment: - - ctv1.yaml - - ctv2.yaml - - ctv3.yaml - - ctv4.yaml - - ctv5.yaml - - ctv6.yaml - - ctv7.yaml + - [ctv.yaml, 7] - cab.yaml # Alternative to SOV w/ROV - dsv.yaml # Alternative to SOV w/ROV - ahv.yaml @@ -21,4 +15,3 @@ start_year: 2002 end_year: 2021 project_capacity: 1200 port: morro_bay_port.yaml -# NOTE: No labor cost data is available in the assumptions diff --git a/library/corewind/project/port/morro_bay_port.yaml b/library/corewind/project/port/morro_bay_port.yaml index c71504e3..e05a0566 100644 --- a/library/corewind/project/port/morro_bay_port.yaml +++ b/library/corewind/project/port/morro_bay_port.yaml @@ -1,5 +1,5 @@ name: Morro Bay Port -tugboats: [tugboat1.yaml, tugboat2.yaml] +tugboats: [[tugboat.yaml, 2]] n_crews: 3 crew: day_rate: 219.18 # 80K/yr / 365 days @@ -7,4 +7,5 @@ crew: hourly_rate: 0 n_hourly_rate: 0 max_operations: 2 +site_distance: 139 annual_fee: 24e6 # From ORBIT diff --git a/library/corewind/vessels/ctv1.yaml b/library/corewind/vessels/ctv.yaml similarity index 91% rename from library/corewind/vessels/ctv1.yaml rename to library/corewind/vessels/ctv.yaml index 6a0b02e0..f23b20ac 100644 --- a/library/corewind/vessels/ctv1.yaml +++ b/library/corewind/vessels/ctv.yaml @@ -1,4 +1,4 @@ -name: Crew Transfer Vessel 1 +name: Crew Transfer Vessel equipment_rate: 3500 capability: CTV speed: 37.04 # 20 knots diff --git a/library/corewind/vessels/ctv10.yaml b/library/corewind/vessels/ctv10.yaml deleted file mode 100644 index 8e94dd7d..00000000 --- a/library/corewind/vessels/ctv10.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 10 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv11.yaml b/library/corewind/vessels/ctv11.yaml deleted file mode 100644 index 8d047aa0..00000000 --- a/library/corewind/vessels/ctv11.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 11 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv12.yaml b/library/corewind/vessels/ctv12.yaml deleted file mode 100644 index f5f85122..00000000 --- a/library/corewind/vessels/ctv12.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 12 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv13.yaml b/library/corewind/vessels/ctv13.yaml deleted file mode 100644 index baeb0a86..00000000 --- a/library/corewind/vessels/ctv13.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 13 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv14.yaml b/library/corewind/vessels/ctv14.yaml deleted file mode 100644 index 87928bf3..00000000 --- a/library/corewind/vessels/ctv14.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 14 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv3.yaml b/library/corewind/vessels/ctv3.yaml deleted file mode 100644 index 8d459090..00000000 --- a/library/corewind/vessels/ctv3.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 3 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 219.18 # 80K/yr / 365 days - n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv4.yaml b/library/corewind/vessels/ctv4.yaml deleted file mode 100644 index 968beff6..00000000 --- a/library/corewind/vessels/ctv4.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 4 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 219.18 # 80K/yr / 365 days - n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv5.yaml b/library/corewind/vessels/ctv5.yaml deleted file mode 100644 index 0833a74b..00000000 --- a/library/corewind/vessels/ctv5.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 5 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 219.18 # 80K/yr / 365 days - n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv6.yaml b/library/corewind/vessels/ctv6.yaml deleted file mode 100644 index 2b964c08..00000000 --- a/library/corewind/vessels/ctv6.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 6 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 219.18 # 80K/yr / 365 days - n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv7.yaml b/library/corewind/vessels/ctv7.yaml deleted file mode 100644 index 3c4bfd03..00000000 --- a/library/corewind/vessels/ctv7.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 7 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 219.18 # 80K/yr / 365 days - n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv8.yaml b/library/corewind/vessels/ctv8.yaml deleted file mode 100644 index 0559c2ea..00000000 --- a/library/corewind/vessels/ctv8.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 8 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/ctv9.yaml b/library/corewind/vessels/ctv9.yaml deleted file mode 100644 index 7f2af0c7..00000000 --- a/library/corewind/vessels/ctv9.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: Crew Transfer Vessel 9 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 # crew comes with vessel - n_day_rate: 0 - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 diff --git a/library/corewind/vessels/hlv1.yaml b/library/corewind/vessels/hlv.yaml similarity index 100% rename from library/corewind/vessels/hlv1.yaml rename to library/corewind/vessels/hlv.yaml diff --git a/library/corewind/vessels/hlv2.yaml b/library/corewind/vessels/hlv2.yaml deleted file mode 100644 index 8a482d61..00000000 --- a/library/corewind/vessels/hlv2.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: Heavy Lift Vessel 2 -equipment_rate: 290000 -capability: LCN -speed: 20.37 # 11 knots -strategy: requests -strategy_threshold: 1 -max_windspeed_transport: 10 -max_windspeed_repair: 10 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: false -mobilization_cost: 325000 -mobilization_days: 10 -workday_start: 0 -workday_end: 24 -charter_days: 20 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 - n_day_rate: 0 # crew comes with the vessel - hourly_rate: 0 - n_hourly_rate: 0 diff --git a/library/corewind/vessels/hlv3.yaml b/library/corewind/vessels/hlv3.yaml deleted file mode 100644 index 70a2aeb4..00000000 --- a/library/corewind/vessels/hlv3.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: Heavy Lift Vessel 3 -equipment_rate: 290000 -capability: LCN -speed: 20.37 # 11 knots -strategy: requests -strategy_threshold: 1 -max_windspeed_transport: 10 -max_windspeed_repair: 10 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: false -mobilization_cost: 325000 -mobilization_days: 10 -workday_start: 0 -workday_end: 24 -charter_days: 20 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 - n_day_rate: 0 # crew comes with the vessel - hourly_rate: 0 - n_hourly_rate: 0 diff --git a/library/corewind/vessels/hlv4.yaml b/library/corewind/vessels/hlv4.yaml deleted file mode 100644 index b0319f25..00000000 --- a/library/corewind/vessels/hlv4.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: Heavy Lift Vessel 4 -equipment_rate: 290000 -capability: LCN -speed: 20.37 # 11 knots -strategy: requests -strategy_threshold: 1 -max_windspeed_transport: 10 -max_windspeed_repair: 10 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: false -mobilization_cost: 325000 -mobilization_days: 10 -workday_start: 0 -workday_end: 24 -charter_days: 20 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 0 - n_day_rate: 0 # crew comes with the vessel - hourly_rate: 0 - n_hourly_rate: 0 diff --git a/library/corewind/vessels/tugboat1.yaml b/library/corewind/vessels/tugboat.yaml similarity index 85% rename from library/corewind/vessels/tugboat1.yaml rename to library/corewind/vessels/tugboat.yaml index d5bd8639..01dd3ae7 100644 --- a/library/corewind/vessels/tugboat1.yaml +++ b/library/corewind/vessels/tugboat.yaml @@ -1,6 +1,6 @@ -name: Tugboat 1 +name: Tugboat equipment_rate: 30000 -# mobilization_cost: 200000 +mobilization_cost: 200000 capability: [TOW] speed: 37.04 # 20 knots strategy: requests @@ -13,7 +13,7 @@ port_distance: 0 # automatically set from the port configuration # workday_start: 6 # workday_end: 18 strategy_threshold: 1 # needs to be here, but doesn't do anything -charter_days: 3 # needs to be here, but doesn't do anything +charter_days: 10 tow_speed: 9.26 # 5 knots unmoor_hours: 2 reconnection_hours: 4 diff --git a/library/corewind/weather/central_ca.csv b/library/corewind/weather/central_ca.csv deleted file mode 100644 index 25ce3f1f..00000000 --- a/library/corewind/weather/central_ca.csv +++ /dev/null @@ -1,184081 +0,0 @@ -datetime,windspeed,waveheight -1/1/2002 0:00,1.775592869,2.864133 -1/1/2002 1:00,1.469833571,2.8786383 -1/1/2002 2:00,1.050208457,2.884513 -1/1/2002 3:00,2.6066314,2.8836555 -1/1/2002 4:00,5.137701431,2.8775454 -1/1/2002 5:00,4.474026684,2.8627014 -1/1/2002 6:00,1.743806731,2.8385687 -1/1/2002 7:00,1.214418735,2.805872 -1/1/2002 8:00,2.431012183,2.7705402 -1/1/2002 9:00,3.107835182,2.7316217 -1/1/2002 10:00,1.12916764,2.6884336 -1/1/2002 11:00,2.199400997,2.6432571 -1/1/2002 12:00,3.669561873,2.5975037 -1/1/2002 13:00,5.153392629,2.5526419 -1/1/2002 14:00,5.265992855,2.5078459 -1/1/2002 15:00,5.242363949,2.4645452 -1/1/2002 16:00,6.02950749,2.4258642 -1/1/2002 17:00,6.8766671,2.394062 -1/1/2002 18:00,7.779646747,2.3726206 -1/1/2002 19:00,8.342269232,2.4342632 -1/1/2002 20:00,8.684076099,2.429412 -1/1/2002 21:00,8.764601829,2.4339828 -1/1/2002 22:00,7.300412849,2.426649 -1/1/2002 23:00,8.000883083,2.4307175 -1/2/2002 0:00,8.647278946,2.4462776 -1/2/2002 1:00,9.081767568,2.471614 -1/2/2002 2:00,9.817494026,2.5073605 -1/2/2002 3:00,10.59141725,2.5523767 -1/2/2002 4:00,11.13597242,2.609477 -1/2/2002 5:00,10.90711777,2.6784315 -1/2/2002 6:00,10.76848149,2.7427006 -1/2/2002 7:00,10.49163433,2.6304836 -1/2/2002 8:00,10.61913878,2.7069492 -1/2/2002 9:00,11.35482254,2.7827835 -1/2/2002 10:00,9.267665307,2.842184 -1/2/2002 11:00,9.516549859,2.9025126 -1/2/2002 12:00,9.987393049,2.9618864 -1/2/2002 13:00,10.43880201,3.0197115 -1/2/2002 14:00,11.32122971,3.0752707 -1/2/2002 15:00,12.53342594,3.1332026 -1/2/2002 16:00,13.76928699,3.1964417 -1/2/2002 17:00,14.23488007,3.258316 -1/2/2002 18:00,13.37287382,3.2975693 -1/2/2002 19:00,11.81653804,3.2943506 -1/2/2002 20:00,10.13029847,3.2660723 -1/2/2002 21:00,8.035977893,3.2330027 -1/2/2002 22:00,7.128813149,3.2078876 -1/2/2002 23:00,5.369022801,3.1816149 -1/3/2002 0:00,4.465996605,3.154356 -1/3/2002 1:00,4.262355316,3.1293688 -1/3/2002 2:00,4.607294653,3.107131 -1/3/2002 3:00,5.140410164,3.087925 -1/3/2002 4:00,5.684925347,3.0718222 -1/3/2002 5:00,5.88133998,3.0586367 -1/3/2002 6:00,6.274076564,3.048483 -1/3/2002 7:00,6.74714664,3.0406961 -1/3/2002 8:00,7.134026713,3.034895 -1/3/2002 9:00,7.466164785,3.0310612 -1/3/2002 10:00,8.945931036,3.0308704 -1/3/2002 11:00,9.092001207,3.0319195 -1/3/2002 12:00,8.839817564,3.032113 -1/3/2002 13:00,8.014258981,3.028143 -1/3/2002 14:00,6.933792715,3.0180416 -1/3/2002 15:00,6.363704995,3.0051622 -1/3/2002 16:00,6.07494393,2.9918175 -1/3/2002 17:00,5.826758144,2.9793472 -1/3/2002 18:00,5.557607717,2.9682007 -1/3/2002 19:00,5.96212241,2.959547 -1/3/2002 20:00,7.38392234,2.9465284 -1/3/2002 21:00,9.397779458,2.9551125 -1/3/2002 22:00,11.03314763,2.9773035 -1/3/2002 23:00,11.37446609,3.006134 -1/4/2002 0:00,11.40284369,3.0233612 -1/4/2002 1:00,11.62033689,3.0243149 -1/4/2002 2:00,11.87094701,3.0218163 -1/4/2002 3:00,11.40917007,3.009222 -1/4/2002 4:00,10.20613903,2.9799967 -1/4/2002 5:00,8.975292737,2.9345274 -1/4/2002 6:00,7.899536007,2.8857832 -1/4/2002 7:00,6.788903052,2.8275347 -1/4/2002 8:00,6.235341403,2.7778606 -1/4/2002 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8:00,12.27486593,1.7523727 -10/22/2011 9:00,12.12053136,1.7253046 -10/22/2011 10:00,11.54343828,1.7337008 -10/22/2011 11:00,10.65623793,1.7190704 -10/22/2011 12:00,10.1970332,1.6914902 -10/22/2011 13:00,9.883631012,1.6629477 -10/22/2011 14:00,9.514887817,1.6338758 -10/22/2011 15:00,9.184358052,1.6058292 -10/22/2011 16:00,8.801406272,1.5786285 -10/22/2011 17:00,8.553554769,1.5499086 -10/22/2011 18:00,8.467595119,1.5241356 -10/22/2011 19:00,8.383861939,1.5023994 -10/22/2011 20:00,8.015740761,1.4817915 -10/22/2011 21:00,7.52629301,1.458188 -10/22/2011 22:00,6.990557596,1.4347315 -10/22/2011 23:00,6.762030664,1.4447632 -10/23/2011 0:00,6.438881157,1.4158077 -10/23/2011 1:00,6.108485522,1.3875008 -10/23/2011 2:00,5.793012441,1.3613625 -10/23/2011 3:00,5.522244127,1.3381786 -10/23/2011 4:00,5.26463073,1.3191576 -10/23/2011 5:00,4.921497458,1.3037443 -10/23/2011 6:00,4.467783196,1.2917986 -10/23/2011 7:00,4.079267532,1.2835741 -10/23/2011 8:00,3.764168098,1.278636 -10/23/2011 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-12/28/2022 18:00,6.083089602,3.7182121 -12/28/2022 19:00,5.668532676,3.648778 -12/28/2022 20:00,5.472882207,3.5852861 -12/28/2022 21:00,7.499534445,3.5314522 -12/28/2022 22:00,7.115235432,3.4816866 -12/28/2022 23:00,7.541949113,3.4313164 -12/29/2022 0:00,8.326425369,3.3887768 -12/29/2022 1:00,7.717047328,3.3491497 -12/29/2022 2:00,7.171434531,3.3046532 -12/29/2022 3:00,6.221129069,3.2553082 -12/29/2022 4:00,4.729459431,3.2053366 -12/29/2022 5:00,3.446177795,3.156207 -12/29/2022 6:00,2.14466816,3.108714 -12/29/2022 7:00,1.650706132,3.0644112 -12/29/2022 8:00,3.601947169,3.0214405 -12/29/2022 9:00,4.447399124,2.9790668 -12/29/2022 10:00,4.52404936,2.9366941 -12/29/2022 11:00,4.265553914,2.892497 -12/29/2022 12:00,3.549113288,2.847209 -12/29/2022 13:00,5.552787066,2.8050413 -12/29/2022 14:00,7.389961093,2.7731018 -12/29/2022 15:00,9.629373982,2.7577057 -12/29/2022 16:00,10.83604673,2.7725563 -12/29/2022 17:00,11.25001913,2.8103848 -12/29/2022 18:00,10.60548942,2.814641 -12/29/2022 19:00,9.388924606,2.774866 -12/29/2022 20:00,6.263136245,2.7082806 -12/29/2022 21:00,4.950568095,2.6377058 -12/29/2022 22:00,4.298062041,2.5733652 -12/29/2022 23:00,3.884851723,2.5139236 -12/30/2022 0:00,2.894049189,2.458191 -12/30/2022 1:00,1.57271637,2.4057512 -12/30/2022 2:00,1.026154043,2.3552065 -12/30/2022 3:00,1.013969484,2.295434 -12/30/2022 4:00,1.221478977,2.2476645 -12/30/2022 5:00,1.953853546,2.2029305 -12/30/2022 6:00,3.136933832,2.1611319 -12/30/2022 7:00,4.383219099,2.1216593 -12/30/2022 8:00,5.743704256,2.0855436 -12/30/2022 9:00,7.052002854,2.0528908 -12/30/2022 10:00,7.937782448,2.0227804 -12/30/2022 11:00,8.174078593,1.994978 -12/30/2022 12:00,8.130837382,1.9675999 -12/30/2022 13:00,7.174918988,1.9400167 -12/30/2022 14:00,6.048098722,1.9115238 -12/30/2022 15:00,5.650876707,1.878128 -12/30/2022 16:00,6.602252395,1.8513079 -12/30/2022 17:00,7.533735022,1.8309488 -12/30/2022 18:00,8.176554995,1.8156424 -12/30/2022 19:00,8.690842985,1.8043661 -12/30/2022 20:00,8.810520427,1.7970724 -12/30/2022 21:00,8.920953542,1.7943993 -12/30/2022 22:00,10.36052566,1.806694 -12/30/2022 23:00,10.18145104,1.8200302 -12/31/2022 0:00,9.657770325,1.8300056 -12/31/2022 1:00,9.239039861,1.8355913 -12/31/2022 2:00,9.012156539,1.8418579 -12/31/2022 3:00,8.611220131,1.9230423 -12/31/2022 4:00,8.755778218,1.9277325 -12/31/2022 5:00,9.329053602,1.9361649 -12/31/2022 6:00,9.621068458,1.9491224 -12/31/2022 7:00,9.527059464,1.9654655 -12/31/2022 8:00,9.367966839,1.9845762 -12/31/2022 9:00,9.185328618,2.005766 -12/31/2022 10:00,8.11427934,2.025361 -12/31/2022 11:00,7.876952333,2.051258 -12/31/2022 12:00,8.990932178,2.0905485 -12/31/2022 13:00,10.63169678,2.1458597 -12/31/2022 14:00,11.91299063,2.204692 -12/31/2022 15:00,13.01751443,2.314334 -12/31/2022 16:00,13.49717394,2.4296026 -12/31/2022 17:00,12.63653399,2.516118 -12/31/2022 18:00,12.99225681,2.5897055 -12/31/2022 19:00,14.35617685,2.6806498 -12/31/2022 20:00,15.27174855,2.819765 -12/31/2022 21:00,16.27679872,3.0179663 -12/31/2022 22:00,17.34215415,3.1488962 -12/31/2022 23:00,17.51866906,3.3263721 diff --git a/library/corewind/weather/central_ca.pqt b/library/corewind/weather/central_ca.pqt new file mode 100644 index 00000000..bc78910b Binary files /dev/null and b/library/corewind/weather/central_ca.pqt differ diff --git a/library/default/README.md b/library/default/README.md new file mode 100644 index 00000000..ed4b584b --- /dev/null +++ b/library/default/README.md @@ -0,0 +1,83 @@ +# Default Library + +The default library provides a validated, ready-to-use data set for fixed and floating offshore wind, +and an experimental land-based data set. For all three, users can use the pre-configured base models +or use them as a starting point for building custom models. + +## Overview + +### Offshore + +The **default library** provides a consistent set of cost and performance inputs for offshore wind +energy systems, standardized to 2024 USD. This library supports reproducible analyses in the +*Cost of Wind Energy Review (COWER): 2025 Edition*, including both fixed-bottom and floating +offshore wind cases. + +Unlike other datasets in this repository, which largely reflect publicly available sources, this +dataset incorporates **internal adjustments and harmonizations** to align with research-focused +scenarios for NREL products that may require outputs to be in 2024 USD, like for example, the Annual +Technology Baseline, or the Cost of Wind Energy Review. + +### Land-Based + +The default library provides an experimental set of land-based data that is hybridized from a series +of incomplete sources of offshore and onshore costs, and scaling factors between them. It is highly +recommended to merely use this data as a starting point for any analysis work. + +## Core Assumptions + +### Offshore + +Material costs for repairs and replacements, as well as failure data, are sourced from COREWIND (2020, 2021). Fixed cost data is primarily derived from BVG Associates (2025a, 2025b), while vessel day rate and mobilization assumptions are compiled from a range of public sources. For detailed reference information, please contact Daniel Mulas Hernando (Daniel.MulasHernando@nrel.gov) to request access to WOMBAT_cost_history.xlsx. + +- **Cost Year:** All monetary values are standardized to **2024 USD**. +- **Data Integration:** Inputs were consolidated from multiple public sources and internal records (`WOMBAT_cost_history.xlsx`), with historical exchange rates and inflation applied to transform costs to 2024 USD. +- **Technology Coverage:** Includes representative inputs for offshore (fixed-bottom and floating) technologies. + +### Land-Based + +Failure rates, repair times, and materials costs not provided in COREWIND (2020) are are largely +taken from Carroll, et al. (2016) with some custom substitutions or modifications. All failure rates +are converted to a Weibull scale factor. Using Carroll, et al. (2016) we adjust offshore Weibull +scale factors for minor repairs for the generator, blades, pitch system, yaw system, and drive train +(wind-affected subassemblies) by a factor of 5.912, for non-wind-affected subassemblies a factor of +1.217 is applied, and for all major repairs and replacements, a factor of 2.432 is applied. These +scaling factors are primarily derived from Carroll et, al. (2016). + +All costs are rescaled from the 12 MW baseline to a 3.5 MW baseline using the COWER 2025 turbine +CapEx figures of \$1,117/kw-yr for onshore and \$1,770/kw-yr for offshore, a 0.6311 scaling factor. + +Additional subassemblies are derived from Carroll, et al. (2016) and scaled using the above +assumptions. Fixed costs are derived from Wiser, et al. (2019), and equipment costs come from +LandBOSSE and internal source adjustments. + +## Intended Use + +- Serve as a **baseline input** for replicable analyses of the offshore fixed and floating *COWER-2025* results. +- Serve as a **baseline input** for the development of land-based analyses. +- Support **scenario development** and **sensitivity analyses** exploring the impact of cost evolution, operational performance, and logistics assumptions. + +## Reproducibility + +The accompanying notebook, **`examples/COWER_om_workflow.ipynb`**, demonstrates how to replicate O&M results from the *Cost of Wind Energy Review: 2025 Edition*. It runs 50 simulations per case and summarizes mean and standard deviation results to identify sources of variability within cost components. + +## Notes + +- `WOMBAT_cost_history.xlsx` is an internal NREL document. For questions, contact **Daniel.MulasHernando@nrel.gov**. +- This dataset should be treated as a **scenario-based reference**, not as purely empirical or historical data. + +## Data Sources + +[1] COREWIND, 2020: https://corewind.eu/wp-content/uploads/files/publications/COREWIND-D6.1-General-frame-of-the-analysis-and-description-of-the-new-FOW-assessment-app.pdf + +[2] COREWIND, 2021: https://corewind.eu/wp-content/uploads/files/publications/COREWIND-D4.2-Floating-Wind-O-and-M-Strategies-Assessment.pdf + +[3] BVG Associates, 2025a: https://guidetoanoffshorewindfarm.com/wind-farm-costs/ + +[4] BVG Associates, 2025b: https://guidetofloatingoffshorewind.com/wind-farm-costs/ + +[5] Carroll, James, Alasdair McDonald, and David McMillan. "Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines." Wind energy 19, no. 6 (2016): 1107-1119. + +[6] Wiser, Ryan, Mark Bolinger, and Eric Lantz. "Assessing wind power operating costs in the United States: Results from a survey of wind industry experts." Renewable Energy Focus 30 (2019): 46-57. + +[7] National Renewable Energy Laboratory (NREL), "Clean Energy Employment Impacts," (2023). https://www.osti.gov/biblio/1995015. diff --git a/library/default/cables/__init__.py b/library/default/cables/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/cables/lbw_array.yaml b/library/default/cables/lbw_array.yaml new file mode 100644 index 00000000..0b038517 --- /dev/null +++ b/library/default/cables/lbw_array.yaml @@ -0,0 +1,19 @@ +name: array cable +maintenance: +- description: na + time: 0 + materials: 0 + service_equipment: DSV + frequency: 0 + level: 1 +failures: +- scale: 0 + shape: 0 + time: 0 + materials: 0 + operation_reduction: 0 + service_equipment: + - CAB + replacement: false + level: 1 + description: na diff --git a/library/default/cables/lbw_export.yaml b/library/default/cables/lbw_export.yaml new file mode 100644 index 00000000..7f9cd94b --- /dev/null +++ b/library/default/cables/lbw_export.yaml @@ -0,0 +1,19 @@ +name: export cable +maintenance: +- description: na + time: 0 + materials: 0 + service_equipment: DSV + frequency: 0 + level: 1 +failures: +- scale: 0 + shape: 0 + time: 0 + materials: 0 + operation_reduction: 0 + service_equipment: + - CAB + replacement: false + level: 1 + description: na diff --git a/library/default/cables/osw_array.yaml b/library/default/cables/osw_array.yaml new file mode 100644 index 00000000..a957d85d --- /dev/null +++ b/library/default/cables/osw_array.yaml @@ -0,0 +1,28 @@ +name: array cable +maintenance: +- description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 +failures: +- scale: 40 + shape: 1 + time: 240 + materials: 41086.4 + operation_reduction: 0 + service_equipment: + - CAB + replacement: false + level: 2 + description: array cable major repair +- scale: 62.5 + shape: 1 + time: 360 + materials: 301300.3 + operation_reduction: 0 + service_equipment: + - CAB + replacement: true + level: 3 + description: array cable replacement diff --git a/library/default/cables/osw_export.yaml b/library/default/cables/osw_export.yaml new file mode 100644 index 00000000..cd3a2944 --- /dev/null +++ b/library/default/cables/osw_export.yaml @@ -0,0 +1,19 @@ +name: export cable +maintenance: +- description: export cable subsea inspection + time: 12 + materials: 684.8 + service_equipment: DSV + frequency: 730 + level: 1 +failures: +- scale: 50 + shape: 1 + time: 60 + materials: 41086.4 + operation_reduction: 0 + service_equipment: + - CAB + replacement: false + level: 2 + description: export cable major repair diff --git a/library/default/project/__init__.py b/library/default/project/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/project/config/__init__.py b/library/default/project/config/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/project/config/base_lbw.yaml b/library/default/project/config/base_lbw.yaml new file mode 100644 index 00000000..70ff2f96 --- /dev/null +++ b/library/default/project/config/base_lbw.yaml @@ -0,0 +1,13 @@ +name: lbw_test +weather: merra2_32.5N_-100.625W_1980_2024.pqt +service_equipment: + - [truck.yaml, 3] + - crawler_large.yaml +layout: base_lbw.csv +fixed_costs: fixed_costs_lbw.yaml +inflation_rate: 0 +workday_start: 7 +workday_end: 19 +start_year: 2000 +end_year: 2019 +project_capacity: 199.5 diff --git a/library/default/project/config/base_osw_fixed.yaml b/library/default/project/config/base_osw_fixed.yaml new file mode 100644 index 00000000..a1219f81 --- /dev/null +++ b/library/default/project/config/base_osw_fixed.yaml @@ -0,0 +1,17 @@ +name: base_fixed_bottom_2024 +weather: era5_40.0N_72.5W_1990_2020.pqt +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - hlv.yaml + - dsv.yaml +layout: base_osw_fixed.csv +inflation_rate: 0 +fixed_costs: fixed_costs_osw_fixed.yaml +workday_start: 7 +workday_end: 19 +start_year: 2000 +end_year: 2019 +project_capacity: 600 +site_distance: 116 +port: port_116km.yaml diff --git a/library/default/project/config/base_osw_floating.yaml b/library/default/project/config/base_osw_floating.yaml new file mode 100644 index 00000000..d60d16c2 --- /dev/null +++ b/library/default/project/config/base_osw_floating.yaml @@ -0,0 +1,17 @@ +name: base_floating_2024 +weather: era5_41.0N_125.0W_1989_2019.pqt +service_equipment: + - [ctv.yaml, 3] + - cab.yaml + - dsv.yaml + - ahv.yaml +layout: base_osw_floating.csv +inflation_rate: 0 +fixed_costs: fixed_costs_osw_floating.yaml +workday_start: 7 +workday_end: 19 +start_year: 1999 +end_year: 2018 +project_capacity: 600 +site_distance: 139 +port: port_139km.yaml diff --git a/library/default/project/config/fixed_costs_lbw.yaml b/library/default/project/config/fixed_costs_lbw.yaml new file mode 100644 index 00000000..afc7621c --- /dev/null +++ b/library/default/project/config/fixed_costs_lbw.yaml @@ -0,0 +1,2 @@ +operations_management_administration: 12.4 # Wiser, et al., 2019 in 2024 USD +labor: 6.4 # $80K per technician; 1 tech per 12.5 MW; $/kw-yr (excludes equipment-based labor) diff --git a/library/default/project/config/fixed_costs_osw_fixed.yaml b/library/default/project/config/fixed_costs_osw_fixed.yaml new file mode 100644 index 00000000..bbe736e0 --- /dev/null +++ b/library/default/project/config/fixed_costs_osw_fixed.yaml @@ -0,0 +1,6 @@ +# All USD figures coming from the WOMBAT_cost_history.xlsx database +labor: 5.125 # $102.5K/year * 30 workers / Capacity (kW) +operations_management_administration: 23.5 # includes Training (fixed), Onshore logistics (fixed), Technical resource (onshore and off) (fixed) and Admin and support staff (onshore) (fixed) +operating_facilities: 1.7 #Operations control centre (fixed) +insurance: 14.3 # insurance (fixed) +annual_leases_fees: 2.72 # Calculation on the WOMBAT_cost_history.xlsx database; assumed net CF = 49%, ISO-NE NREL Cambium projected wholesale electricity prices diff --git a/library/default/project/config/fixed_costs_osw_floating.yaml b/library/default/project/config/fixed_costs_osw_floating.yaml new file mode 100644 index 00000000..c0f8a981 --- /dev/null +++ b/library/default/project/config/fixed_costs_osw_floating.yaml @@ -0,0 +1,6 @@ +# All USD figures coming from the WOMBAT_cost_history.xlsx database +labor: 5.125 # $102.5K/year * 30 workers / Capacity (kW) +operations_management_administration: 23.5 # includes Training (floating), Onshore logistics (floating), Technical resource (onshore and off) (floating) and Admin and support staff (onshore) (floating) +operating_facilities: 1.7 #Operations control centre (floating) +insurance: 17.9 # insurance (floating) +annual_leases_fees: 2.68 # Calculation on the WOMBAT_cost_history.xlsx database; assumed net CF = 45%, CAISO NREL Cambium projected wholesale electricity prices diff --git a/library/default/project/plant/__init__.py b/library/default/project/plant/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/project/plant/base_lbw.csv b/library/default/project/plant/base_lbw.csv new file mode 100644 index 00000000..046e8070 --- /dev/null +++ b/library/default/project/plant/base_lbw.csv @@ -0,0 +1,59 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable +OSS1,OSS1,OSS1,substation,-100.39,32.33556,,,,lbw_substation.yaml,lbw_export.yaml +S00T00,OSS1,S00T00,turbine,-100.407089,32.345234,0,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S00T01,OSS1,S00T01,turbine,-100.404289,32.344509,0,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S00T02,OSS1,S00T02,turbine,-100.401581,32.344788,0,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S00T03,OSS1,S00T03,turbine,-100.398628,32.355999,0,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S00T04,OSS1,S00T04,turbine,-100.396378,32.355755,0,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S00T05,OSS1,S00T05,turbine,-100.393661,32.355812,0,5,0,3.5MW_lbw.yaml,lbw_array.yaml +S00T06,OSS1,S00T06,turbine,-100.391174,32.356098,0,6,0,3.5MW_lbw.yaml,lbw_array.yaml +S01T00,OSS1,S01T00,turbine,-100.395782,32.342808,1,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S01T01,OSS1,S01T01,turbine,-100.392639,32.343369,1,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S01T02,OSS1,S01T02,turbine,-100.389656,32.343941,1,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S01T03,OSS1,S01T03,turbine,-100.385796,32.347607,1,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S01T04,OSS1,S01T04,turbine,-100.382828,32.347691,1,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T00,OSS1,S02T00,turbine,-100.374413,32.352451,2,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T01,OSS1,S02T01,turbine,-100.377167,32.352554,2,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T02,OSS1,S02T02,turbine,-100.380089,32.352699,2,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T03,OSS1,S02T03,turbine,-100.38266,32.352764,2,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T04,OSS1,S02T04,turbine,-100.386856,32.361858,2,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T05,OSS1,S02T05,turbine,-100.384193,32.364651,2,5,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T06,OSS1,S02T06,turbine,-100.381866,32.364479,2,6,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T07,OSS1,S02T07,turbine,-100.379509,32.364315,2,7,0,3.5MW_lbw.yaml,lbw_array.yaml +S02T08,OSS1,S02T08,turbine,-100.375916,32.364136,2,8,0,3.5MW_lbw.yaml,lbw_array.yaml +S03T00,OSS1,S03T00,turbine,-100.364281,32.339458,3,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S03T01,OSS1,S03T01,turbine,-100.361618,32.33934,3,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S03T02,OSS1,S03T02,turbine,-100.358978,32.339222,3,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S03T03,OSS1,S03T03,turbine,-100.356323,32.339108,3,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S03T04,OSS1,S03T04,turbine,-100.353867,32.338997,3,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T00,OSS1,S04T00,turbine,-100.380531,32.329418,4,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T01,OSS1,S04T01,turbine,-100.377937,32.32922,4,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T02,OSS1,S04T02,turbine,-100.375351,32.329018,4,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T03,OSS1,S04T03,turbine,-100.372765,32.328819,4,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T04,OSS1,S04T04,turbine,-100.368851,32.328514,4,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T05,OSS1,S04T05,turbine,-100.366066,32.328423,4,5,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T06,OSS1,S04T06,turbine,-100.361893,32.327972,4,6,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T07,OSS1,S04T07,turbine,-100.359406,32.327785,4,7,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T08,OSS1,S04T08,turbine,-100.356812,32.327587,4,8,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T09,OSS1,S04T09,turbine,-100.354225,32.327385,4,9,0,3.5MW_lbw.yaml,lbw_array.yaml +S04T10,OSS1,S04T10,turbine,-100.350998,32.327129,4,10,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T00,OSS1,S05T00,turbine,-100.378525,32.319187,5,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T01,OSS1,S05T01,turbine,-100.37571,32.318211,5,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T02,OSS1,S05T02,turbine,-100.372902,32.318054,5,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T03,OSS1,S05T03,turbine,-100.370102,32.317898,5,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T04,OSS1,S05T04,turbine,-100.367317,32.317577,5,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T05,OSS1,S05T05,turbine,-100.364494,32.317589,5,5,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T06,OSS1,S05T06,turbine,-100.361725,32.317608,5,6,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T07,OSS1,S05T07,turbine,-100.358627,32.317268,5,7,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T08,OSS1,S05T08,turbine,-100.356087,32.31712,5,8,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T09,OSS1,S05T09,turbine,-100.353554,32.316978,5,9,0,3.5MW_lbw.yaml,lbw_array.yaml +S05T10,OSS1,S05T10,turbine,-100.350479,32.316811,5,10,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T00,OSS1,S06T00,turbine,-100.374229,32.308487,6,0,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T01,OSS1,S06T01,turbine,-100.371788,32.308304,6,1,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T02,OSS1,S06T02,turbine,-100.369331,32.308113,6,2,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T03,OSS1,S06T03,turbine,-100.366882,32.30793,6,3,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T04,OSS1,S06T04,turbine,-100.364433,32.307743,6,4,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T05,OSS1,S06T05,turbine,-100.361984,32.30756,6,5,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T06,OSS1,S06T06,turbine,-100.359207,32.308258,6,6,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T07,OSS1,S06T07,turbine,-100.356659,32.308064,6,7,0,3.5MW_lbw.yaml,lbw_array.yaml +S06T08,OSS1,S06T08,turbine,-100.354027,32.307865,6,8,0,3.5MW_lbw.yaml,lbw_array.yaml diff --git a/library/default/project/plant/base_osw_fixed.csv b/library/default/project/plant/base_osw_fixed.csv new file mode 100644 index 00000000..8f2f7e7d --- /dev/null +++ b/library/default/project/plant/base_osw_fixed.csv @@ -0,0 +1,52 @@ 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+E1,OSS,E1,turbine,-41.36986256,-74.71659038,9.0,4.0,9.0,4.0,0.0,12MW_osw_floating.yaml,osw_array.yaml,7575.75,0.0,7575.75,0.0,4.994242133,0.0 +OSS,OSS,OSS,substation,-41.66684471,-74.71874487,,,,,,osw_substation.yaml,osw_export.yaml,0.0,4545.45,0.0,4545.45,, diff --git a/library/default/project/port/__init__.py b/library/default/project/port/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/project/port/port_116km.yaml b/library/default/project/port/port_116km.yaml new file mode 100644 index 00000000..b33819bf --- /dev/null +++ b/library/default/project/port/port_116km.yaml @@ -0,0 +1,13 @@ +name: port_116km +tugboats: [[tugboat.yaml, 2]] +max_operations: 2 # max number of turbines at the port at one time +workday_start: 6 +workday_end: 18 +site_distance: 116 # will set the tugboats to this distance if not individually defined +crew: + day_rate: 0 + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 +n_crews: 2 # maximum number of simultaneously operating crews to repair individual failures on a turbine +annual_fee: 300000 # Operations, maintenance and service port (fixed) from the WOMBAT_cost_history.xlsx database 0.5 $/kW-yr x 600,000 kW = $300,000 per year diff --git a/library/default/project/port/port_139km.yaml b/library/default/project/port/port_139km.yaml new file mode 100644 index 00000000..674e26f8 --- /dev/null +++ b/library/default/project/port/port_139km.yaml @@ -0,0 +1,13 @@ +name: port_139km +tugboats: [[tugboat.yaml, 2]] +max_operations: 2 # max number of turbines at the port at one time +workday_start: 6 +workday_end: 18 +site_distance: 139 # will set the tugboats to this distance if not individually defined +crew: + day_rate: 0 + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 +n_crews: 2 # maximum number of simultaneously operating crews to repair individual failures on a turbine +daily_use_fee: 83266 # Calculated based on monthly port fee in 2024 USD from ORBIT [2.498e6](more appropiate for TTP operations given that additional berth space and heavy-lift capacity would be required compared to a fixed-bottom port with just some space for CTVs) divided by 30 days in a month diff --git a/library/default/results/.gitkeep b/library/default/results/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/library/default/substations/__init__.py b/library/default/substations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/substations/lbw_substation.yaml b/library/default/substations/lbw_substation.yaml new file mode 100644 index 00000000..c8c6565b --- /dev/null +++ b/library/default/substations/lbw_substation.yaml @@ -0,0 +1,28 @@ +capacity_kw: 600 +capex_kw: 120 +transformer: + name: transformer + maintenance: + - description: oss annual inspection + time: 24 + materials: 228.27 + service_equipment: CTV + frequency: 365 + level: 1 + failures: + - scale: 5 + shape: 1 + time: 12 + materials: 913.03 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: oss minor repair + - scale: 100 + shape: 1 + time: 60 + materials: 45651.57 + service_equipment: CTV + operation_reduction: 0 + level: 2 + description: oss major repair diff --git a/library/default/substations/osw_substation.yaml b/library/default/substations/osw_substation.yaml new file mode 100644 index 00000000..3aef5c67 --- /dev/null +++ b/library/default/substations/osw_substation.yaml @@ -0,0 +1,28 @@ +capacity_kw: 600 +capex_kw: 120 +transformer: + name: transformer + maintenance: + - description: oss annual inspection + time: 24 + materials: 684.8 + service_equipment: CTV + frequency: 365 + level: 1 + failures: + - scale: 5 + shape: 1 + time: 12 + materials: 2739.1 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: oss minor repair + - scale: 100 + shape: 1 + time: 60 + materials: 136954.7 + service_equipment: CTV + operation_reduction: 0 + level: 2 + description: oss major repair diff --git a/library/default/turbines/12MW_216_RWT.csv b/library/default/turbines/12MW_216_RWT.csv new file mode 100644 index 00000000..3a66f996 --- /dev/null +++ b/library/default/turbines/12MW_216_RWT.csv @@ -0,0 +1,24 @@ +windspeed_ms,power_kw +3,56 +4,474 +5,1139 +6,2116 +7,3459 +8,5164 +9,7353 +10,10088 +11,12000 +12,12000 +13,12000 +14,12000 +15,12000 +16,12000 +17,12000 +18,12000 +19,12000 +20,12000 +21,12000 +22,12000 +23,12000 +24,12000 +25,12000 diff --git a/library/default/turbines/12MW_osw_fixed.yaml b/library/default/turbines/12MW_osw_fixed.yaml new file mode 100644 index 00000000..70775ed7 --- /dev/null +++ b/library/default/turbines/12MW_osw_fixed.yaml @@ -0,0 +1,282 @@ +capacity_kw: 12000 +capex_kw: 1770 +power_curve: + file: 12MW_216_RWT.csv + bin_width: 0.5 +power_converter: + name: power converter + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.859 + shape: 1 + time: 14 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: power converter minor repair + - scale: 2.959 + shape: 1 + time: 28 + materials: 7669.44 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: power converter major repair + - scale: 12.99 + shape: 1 + time: 170 + materials: 60260.08 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: power converter replacement +electrical_system: + name: electrical system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.793 + shape: 1 + time: 10 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: power electrical system minor repair + - scale: 62.5 + shape: 1 + time: 28 + materials: 5478.16 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: power electrical system major repair + - scale: 500 + shape: 1 + time: 54 + materials: 54781.84 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: power electrical system major replacement +hydraulic_pitch_system: + name: hydraulic pitch system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.214 + shape: 1 + time: 18 + materials: 547.84 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: minor pitch system repair + - scale: 5.587 + shape: 1 + time: 38 + materials: 2081.68 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: major pitch system repair + - scale: 1000 + shape: 1 + time: 75 + materials: 15338.96 + service_equipment: CTV + operation_reduction: 0 + replacement: true + level: 3 + description: major pitch system replacement +ballast_pump: + name: ballast pump + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 100 + shape: 1 + time: 8 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: minor ballast pump repair +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 6.173 + shape: 1 + time: 10 + materials: 547.84 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: yaw system minor repair + - scale: 166.7 + shape: 1 + time: 40 + materials: 3286.88 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: yaw system major repair + - scale: 1000 + shape: 1 + time: 147 + materials: 13695.44 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: yaw system major replacement +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.193 + shape: 1 + time: 18 + materials: 5478.16 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: blades minor repair + - scale: 100 + shape: 1 + time: 42 + materials: 47232.96 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: blades major repair + - scale: 1000 + shape: 1 + time: 864 + materials: 487558.64 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: blades major replacement +generator: + name: generator + maintenance: + - description: annual turbine inspection + time: 24 + materials: 1643.44 + service_equipment: CTV + frequency: 365 + failures: + - scale: 1.832 + shape: 1 + time: 13 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: direct drive generator minor repair + - scale: 33.333 + shape: 1 + time: 49 + materials: 15711.44 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: direct drive generator major repair + - scale: 111.1111 + shape: 1 + time: 244 + materials: 259118.24 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: direct drive generator major replacement +supporting_structure: + name: supporting_structure + maintenance: + - description: structural annual inspection + time: 24 + materials: 657.36 + service_equipment: CTV + frequency: 365 + - description: structural subsea inspection + time: 6 + materials: 547.84 + service_equipment: DSV + frequency: 730 + failures: + - description: na + scale: 0 + shape: 0 + time: 0 + materials: 0 + service_equipment: CTV + level: 0 + operation_reduction: 0 +drive_train: + name: drive_train + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 4.329 + shape: 1 + time: 10 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: main shaft minor repair + - scale: 38.462 + shape: 1 + time: 36 + materials: 15338.96 + service_equipment: CTV + operation_reduction: 0 + level: 2 + description: main shaft major repair + - scale: 111.111 + shape: 1 + time: 144 + materials: 254187.84 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: main shaft replacement diff --git a/library/default/turbines/12MW_osw_floating.yaml b/library/default/turbines/12MW_osw_floating.yaml new file mode 100644 index 00000000..b5ef0e68 --- /dev/null +++ b/library/default/turbines/12MW_osw_floating.yaml @@ -0,0 +1,351 @@ +capacity_kw: 12000 +capex_kw: 1770 +power_curve: + file: 12MW_216_RWT.csv + bin_width: 0.5 +power_converter: + name: power converter + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.859 + shape: 1 + time: 14 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: power converter minor repair + - scale: 2.959 + shape: 1 + time: 28 + materials: 7669.44 + service_equipment: TOW + operation_reduction: 0.0 + level: 2 + description: power converter major repair + - scale: 12.99 + shape: 1 + time: 170 + materials: 60260.08 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: power converter replacement +electrical_system: + name: electrical system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.793 + shape: 1 + time: 10 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: power electrical system minor repair + - scale: 62.5 + shape: 1 + time: 28 + materials: 5478.16 + service_equipment: TOW + operation_reduction: 0.0 + level: 2 + description: power electrical system major repair + - scale: 500 + shape: 1 + time: 54 + materials: 54781.84 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: power electrical system major replacement +hydraulic_pitch_system: + name: hydraulic pitch system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 1.214 + shape: 1 + time: 18 + materials: 547.84 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: minor pitch system repair + - scale: 5.587 + shape: 1 + time: 38 + materials: 2081.68 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: major pitch system repair + - scale: 1000 + shape: 1 + time: 75 + materials: 15338.96 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: major pitch system replacement +ballast_pump: + name: ballast pump + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 100 + shape: 1 + time: 8 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: minor ballast pump repair +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 6.173 + shape: 1 + time: 10 + materials: 547.84 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: yaw system minor repair + - scale: 166.7 + shape: 1 + time: 40 + materials: 3286.88 + service_equipment: TOW + operation_reduction: 0.0 + level: 2 + description: yaw system major repair + - scale: 1000 + shape: 1 + time: 147 + materials: 13695.44 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: yaw system major replacement +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.193 + shape: 1 + time: 18 + materials: 5478.16 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: blades minor repair + - scale: 100 + shape: 1 + time: 42 + materials: 47232.96 + service_equipment: TOW + operation_reduction: 0.0 + level: 2 + description: blades major repair + - scale: 1000 + shape: 1 + time: 864 + materials: 487558.64 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: blades major replacement +generator: + name: generator + maintenance: + - description: annual turbine inspection + time: 24 + materials: 1643.44 + service_equipment: CTV + frequency: 365 + failures: + - scale: 1.832 + shape: 1 + time: 13 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: direct drive generator minor repair + - scale: 33.333 + shape: 1 + time: 49 + materials: 15711.44 + service_equipment: TOW + operation_reduction: 0.0 + level: 2 + description: direct drive generator major repair + - scale: 111.1111 + shape: 1 + time: 244 + materials: 259118.24 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: direct drive generator major replacement +supporting_structure: + name: supporting_structure + maintenance: + - description: structural annual inspection + time: 24 + materials: 657.36 + service_equipment: CTV + frequency: 365 + - description: structural subsea inspection + time: 6 + materials: 547.84 + service_equipment: DSV + frequency: 730 + failures: + - description: na + scale: 0 + shape: 0 + time: 0 + materials: 0 + service_equipment: CTV + level: 0 + operation_reduction: 0 +anchor: + name: anchor + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: DSV + frequency: 0 + failures: + - scale: 66.67 + shape: 1 + time: 240 + materials: 82172.8 + service_equipment: AHV + operation_reduction: 0 + level: 1 + description: major anchor repair + - scale: 80 + shape: 1 + time: 360 + materials: 560966.32 + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 2 + description: anchor replacement +mooring_lines: + name: mooring lines + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: DSV + frequency: 0 + failures: + - scale: 8.33 + shape: 1 + time: 40 + materials: 1643.44 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: marine growth removal + - scale: 66.67 + shape: 1 + time: 240 + materials: 21912.72 + service_equipment: AHV + operation_reduction: 0 + level: 2 + description: mooring line major repair + - scale: 80 + shape: 1 + time: 360 + materials: 147911.04 + service_equipment: AHV + operation_reduction: 0 + replacement: true + level: 3 + description: mooring line replacement + - scale: 30.3 + shape: 1 + time: 40 + materials: 109563.76 + service_equipment: CTV + operation_reduction: 0 + replacement: false + level: 3 + description: buoyancy module replacement +drive_train: + name: drive_train + maintenance: + - description: n/a + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 4.329 + shape: 1 + time: 10 + materials: 1095.6 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: main shaft minor repair + - scale: 38.462 + shape: 1 + time: 36 + materials: 15338.96 + service_equipment: TOW + operation_reduction: 0 + level: 2 + description: main shaft major repair + - scale: 111.111 + shape: 1 + time: 144 + materials: 254187.84 + service_equipment: TOW + operation_reduction: 0 + replacement: true + level: 3 + description: main shaft replacement diff --git a/library/default/turbines/3.5MW_lbw.yaml b/library/default/turbines/3.5MW_lbw.yaml new file mode 100644 index 00000000..a6a538b3 --- /dev/null +++ b/library/default/turbines/3.5MW_lbw.yaml @@ -0,0 +1,467 @@ +capacity_kw: 3500 +capex_kw: 1117 +power_curve: + file: IEA_3.4MW_130_RWT.csv + bin_width: 0.25 +power_converter: + name: power converter + maintenance: + - description: n/a + time: 0 + materials: 0.0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 2.262 + shape: 1 + time: 14 + materials: 691.4 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: power converter minor repair + - scale: 7.196 + shape: 1 + time: 28 + materials: 4839.98 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: power converter major repair + - scale: 31.592 + shape: 1 + time: 170 + materials: 38028.54 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: power converter replacement +electrical_system: + name: electrical system + maintenance: + - description: n/a + time: 0 + materials: 0.0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 3.399 + shape: 1 + time: 10 + materials: 691.4 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: power electrical system minor repair + - scale: 152.0 + shape: 1 + time: 28 + materials: 3457.12 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: power electrical system major repair + - scale: 1216.0 + shape: 1 + time: 54 + materials: 34571.36 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: power electrical system major replacement +hydraulic_pitch_system: + name: hydraulic pitch system + maintenance: + - description: n/a + time: 0 + materials: 0.0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 7.177 + shape: 1 + time: 18 + materials: 345.73 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: minor pitch system repair + - scale: 13.588 + shape: 1 + time: 38 + materials: 1313.69 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: major pitch system repair + - scale: 2432.0 + shape: 1 + time: 75 + materials: 9680.01 + service_equipment: CTV + operation_reduction: 0 + replacement: true + level: 3 + description: major pitch system replacement +yaw_system: + name: yaw_system + maintenance: + - description: n/a + time: 0 + materials: 0.0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 36.495 + shape: 1 + time: 10 + materials: 345.73 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: yaw system minor repair + - scale: 405.414 + shape: 1 + time: 40 + materials: 2074.26 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: yaw system major repair + - scale: 2432.0 + shape: 1 + time: 147 + materials: 8642.83 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: yaw system major replacement +rotor_blades: + name: rotor_blades + maintenance: + - description: n/a + time: 0 + materials: 0.0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 12.965 + shape: 1 + time: 18 + materials: 3457.12 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: blades minor repair + - scale: 243.2 + shape: 1 + time: 42 + materials: 29807.47 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: blades major repair + - scale: 2432.0 + shape: 1 + time: 864 + materials: 307685.31 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: blades major replacement +generator: + name: generator + maintenance: + - description: annual turbine inspection + time: 24 + materials: 1037.13 + service_equipment: CTV + frequency: 365 + failures: + - scale: 10.831 + shape: 1 + time: 13 + materials: 691.4 + service_equipment: CTV + operation_reduction: 0.0 + level: 1 + description: direct drive generator minor repair + - scale: 81.066 + shape: 1 + time: 49 + materials: 9915.07 + service_equipment: CTV + operation_reduction: 0.0 + level: 2 + description: direct drive generator major repair + - scale: 270.222 + shape: 1 + time: 244 + materials: 163522.64 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: direct drive generator major replacement +supporting_structure: + name: supporting_structure + maintenance: + - description: structural annual inspection + time: 24 + materials: 414.84 + service_equipment: CTV + frequency: 365 + failures: + - description: minor repair + scale: 13.228 + shape: 1 + time: 5 + materials: 117.51 + service_equipment: CTV + level: 1 + operation_reduction: 0 + - description: major repair + scale: 27.326 + shape: 1 + time: 2 + materials: 923.26 + service_equipment: CTV + level: 2 + operation_reduction: 0 +drive_train: + name: drive_train + maintenance: + - description: n/a + time: 0 + materials: 0.0 + service_equipment: CTV + frequency: 0 + failures: + - scale: 25.593 + shape: 1 + time: 10 + materials: 691.4 + service_equipment: CTV + operation_reduction: 0 + level: 1 + description: main shaft minor repair + - scale: 93.54 + shape: 1 + time: 36 + materials: 9680.01 + service_equipment: CTV + operation_reduction: 0 + level: 2 + description: main shaft major repair + - scale: 270.222 + shape: 1 + time: 144 + materials: 160411.2 + service_equipment: LCN + operation_reduction: 0 + replacement: true + level: 3 + description: main shaft replacement +safety: + name: safety + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 3.263 + shape: 1 + time: 1 + materials: 109.11 + service_equipment: CTV + level: 0 + operation_reduction: 0 + - description: major repair + scale: 608 + shape: 1 + time: 7 + materials: 2014.39 + service_equipment: CTV + level: 2 + operation_reduction: 0 +sensors: + name: sensors + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 4.927 + shape: 1 + time: 8 + materials: 125.90 + service_equipment: CTV + level: 0 + operation_reduction: 0 + - description: major repair + scale: 34.743 + shape: 1 + time: 6 + materials: 2098.32 + service_equipment: CTV + level: 2 + operation_reduction: 0 +hvac: + name: hvac + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 6.405 + shape: 1 + time: 5 + materials: 390.29 + service_equipment: CTV + level: 1 + operation_reduction: 0 + - description: major repair + scale: 347.429 + shape: 1 + time: 14 + materials: 1091.13 + service_equipment: CTV + level: 2 + operation_reduction: 0 +oil: + name: oil + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 2.99 + shape: 1 + time: 4 + materials: 134.29 + service_equipment: CTV + level: 1 + operation_reduction: 0 + - description: major repair + scale: 405.33 + shape: 1 + time: 18 + materials: 1678.66 + service_equipment: CTV + level: 2 + operation_reduction: 0 +controls: + name: controls + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 3.428 + shape: 1 + time: 8 + materials: 167.87 + service_equipment: CTV + level: 1 + operation_reduction: 0 + - description: major repair + scale: 45.037 + shape: 1 + time: 14 + materials: 1678.66 + service_equipment: CTV + level: 2 + operation_reduction: 0 + - description: major replacement + scale: 2432 + shape: 1 + time: 12 + materials: 10911.26 + service_equipment: CTV + level: 2 + operation_reduction: 0 +hub: + name: hub + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 6.687 + shape: 1 + time: 10 + materials: 134.29 + service_equipment: CTV + level: 1 + operation_reduction: 0 + - description: major repair + scale: 64 + shape: 1 + time: 40 + materials: 1258.99 + service_equipment: CTV + level: 2 + operation_reduction: 0 + - description: major replacement + scale: 2432 + shape: 1 + time: 298 + materials: 79736.13 + service_equipment: CTV + level: 2 + operation_reduction: 0 +other: + name: other + maintenance: + - description: na + time: 0 + materials: 0 + service_equipment: CTV + frequency: 0 + failures: + - description: minor repair + scale: 1.499 + shape: 1 + time: 5 + materials: 92.33 + service_equipment: CTV + level: 1 + operation_reduction: 0 + - description: major repair + scale: 57.905 + shape: 1 + time: 21 + materials: 2014.39 + service_equipment: CTV + level: 2 + operation_reduction: 0 + - description: major replacement + scale: 2432 + shape: 1 + time: 36 + materials: 8393.28 + service_equipment: CTV + level: 2 + operation_reduction: 0 diff --git a/library/default/turbines/IEA_3.4MW_130_RWT.csv b/library/default/turbines/IEA_3.4MW_130_RWT.csv new file mode 100644 index 00000000..e0380d0c --- /dev/null +++ b/library/default/turbines/IEA_3.4MW_130_RWT.csv @@ -0,0 +1,51 @@ +windspeed_ms,power_kw,,Cp_[-],thrust_kN,Ct_[-] +3,51.6203274,0.2368,59.15902157,0.814 +3.5392,123.3681588,0.3446,81.33753438,0.8042 +4.0485,213.241509,0.398,105.6415977,0.7982 +4.5267,318.837998,0.4257,131.4666955,0.7945 +4.973,435.9564867,0.439,158.0225341,0.7913 +5.3862,560.8983052,0.4445,184.1549163,0.7861 +5.7654,688.906312,0.4452,208.2935131,0.776 +6.1098,819.1257759,0.4448,231.0204376,0.7664 +6.4186,949.7236726,0.4448,254.9655571,0.7664 +6.6912,1075.927005,0.4448,277.0800216,0.7664 +6.9269,1193.682699,0.4448,296.9449476,0.7664 +7.1252,1299.172102,0.4448,314.191524,0.7664 +7.2857,1388.952513,0.4448,328.5047139,0.7664 +7.408,1460.081742,0.4448,339.6264098,0.7664 +7.4919,1510.222643,0.4448,347.3580161,0.7664 +7.5371,1537.725022,0.4448,351.5624331,0.7664 +7.5435,1541.682933,0.4448,352.1654264,0.7664 +7.5758,1561.569465,0.4448,355.1873959,0.7664 +7.6468,1605.876078,0.4448,361.8745379,0.7664 +7.7563,1675.862931,0.4448,372.3136886,0.7664 +7.9041,1773.500601,0.4448,386.6377314,0.7664 +8.0899,1901.498529,0.4448,405.0239174,0.7664 +8.3132,2063.341406,0.4448,427.6916155,0.7664 +8.5735,2263.331677,0.4448,454.8995164,0.7664 +8.8703,2506.635948,0.4448,486.9423153,0.7664 +9.203,2799.332771,0.4448,524.1469056,0.7664 +9.5707,3148.458978,0.4448,566.86812,0.7664 +9.8127,3370,0.4417,627.2778066,0.8068 +10.408,3370.000035,0.3702,464.1349468,0.5306 +10.8757,3370.000717,0.3244,426.6328697,0.4467 +11.3748,3370.000086,0.2836,397.1305987,0.3801 +11.9041,3370.000018,0.2474,372.3838258,0.3254 +12.4625,3370.011453,0.2156,350.9650611,0.2798 +13.0487,3370.00003,0.1878,332.0121364,0.2415 +13.6615,3370.001728,0.1637,315.0514158,0.209 +14.2995,3370.008641,0.1427,299.8321163,0.1816 +14.9612,3370.033669,0.1246,286.0848232,0.1583 +15.6452,3370.000061,0.109,273.6208499,0.1384 +16.35,3370.000238,0.0955,262.0838634,0.1214 +17.074,3370.001502,0.0838,251.6840928,0.1069 +17.8155,3370.002288,0.0738,242.3162553,0.0945 +18.5731,3370.002284,0.0651,233.7869761,0.0839 +19.3448,3370.018233,0.0576,226.0189728,0.0748 +20.1292,3370.034011,0.0512,218.9436879,0.0669 +20.9244,3370.004589,0.0456,212.4962122,0.0601 +21.7286,3369.999956,0.0407,206.5729122,0.0542 +22.5401,3370.000667,0.0364,201.2740021,0.0491 +23.357,3370.003948,0.0328,196.4747518,0.0446 +24.1776,3370.00009,0.0295,191.788032,0.0406 +25,3370.104925,0.0267,187.8573918,0.0372 diff --git a/library/default/turbines/__init__.py b/library/default/turbines/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/vessels/__init__.py b/library/default/vessels/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/vessels/ahv.yaml b/library/default/vessels/ahv.yaml new file mode 100644 index 00000000..9ef8621e --- /dev/null +++ b/library/default/vessels/ahv.yaml @@ -0,0 +1,25 @@ +name: Anchor Handling Tug +charter_days: 3 +equipment_rate: 37870.0 # Reference: WOMBAT_Cost_History.xslx +strategy: requests +strategy_threshold: 1 +onsite: false +capability: AHV +speed: 24.08 # 13 knots +tow_speed: 9.26 +mobilization_cost: 416223.5 # Reference: WOMBAT_Cost_History.xslx +mobilization_days: 20 +max_windspeed_transport: 20 +max_windspeed_repair: 20 +max_waveheight_transport: 2.5 +max_waveheight_repair: 2.5 +workday_start: 0 +workday_end: 24 +crew_transfer_time: 0.25 +port_distance: 0 # will get automatically set from the port configuration if not provided +n_crews: 1 +crew: + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx + n_day_rate: 10 # N technicians for all AHV jobs + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/default/vessels/cab.yaml b/library/default/vessels/cab.yaml new file mode 100644 index 00000000..17f23387 --- /dev/null +++ b/library/default/vessels/cab.yaml @@ -0,0 +1,23 @@ +name: Cable Laying Vessel +equipment_rate: 241171.4 # Reference: WOMBAT_Cost_History.xslx +capability: CAB +speed: 25.93 # 14 knots +strategy: requests +strategy_threshold: 1 +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2 +max_waveheight_repair: 2 +onsite: false +mobilization_cost: 0.0 # Reference: WOMBAT_Cost_History.xslx; assumes domestic CLV (no mobilization costs) +mobilization_days: 14 +workday_start: 0 +workday_end: 24 +charter_days: 10 +crew_transfer_time: 0.25 +n_crews: 1 +crew: + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx + n_day_rate: 10 # N technicians for all array cable tasks + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/default/vessels/crawler_large.yaml b/library/default/vessels/crawler_large.yaml new file mode 100644 index 00000000..5eb1d78c --- /dev/null +++ b/library/default/vessels/crawler_large.yaml @@ -0,0 +1,22 @@ +name: Crawler Crane - 1350 tonnes +# IEA 3.6 MW - 120m RD +equipment_rate: 34328.7 +capability: LCN +speed: 1.61 # 1 mph +strategy: requests +strategy_threshold: 1 +max_windspeed_transport: 9 +max_windspeed_repair: 9 +max_waveheight_transport: 99 +max_waveheight_repair: 99 +onsite: false +mobilization_cost: 253366.1 +mobilization_days: 10 +charter_days: 20 +crew_transfer_time: 0.25 +n_crews: 1 +crew: + day_rate: 0 + n_day_rate: 0 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/corewind/vessels/ctv2.yaml b/library/default/vessels/ctv.yaml similarity index 60% rename from library/corewind/vessels/ctv2.yaml rename to library/default/vessels/ctv.yaml index fbbe35f2..217adca1 100644 --- a/library/corewind/vessels/ctv2.yaml +++ b/library/default/vessels/ctv.yaml @@ -1,29 +1,27 @@ -name: Crew Transfer Vessel 2 -equipment_rate: 3500 -capability: CTV -speed: 37.04 # 20 knots -strategy: scheduled -max_windspeed_transport: 16 -max_windspeed_repair: 16 -max_waveheight_transport: 2 -max_waveheight_repair: 2 -onsite: True -mobilization_cost: 0 -mobilization_days: 0 -port_distance: 30 -# workday_start: 0 -# workday_end: 24 -start_month: 1 -start_day: 1 -end_month: 12 -end_day: 31 -start_year: 2002 -end_year: 2021 -crew_transfer_time: 0.25 -n_crews: 1 -crew: - day_rate: 219.18 # 80K/yr / 365 days - n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging - hourly_rate: 0 - n_hourly_rate: 0 -# capex: 3000000 +name: Crew Transfer Vessel +equipment_rate: 3201.7 # Reference: WOMBAT_Cost_History.xslx +capability: CTV +speed: 37.04 # 20 knots +strategy: scheduled +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2 +max_waveheight_repair: 2 +onsite: True +mobilization_cost: 0 +mobilization_days: 0 +port_distance: 30 +start_month: 1 +start_day: 1 +end_month: 12 +end_day: 31 +start_year: 1998 +end_year: 2025 +crew_transfer_time: 0.25 +n_crews: 1 +crew: + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx + n_day_rate: 3 # Most tasks are 2, but some larger ones are 3, 4, or 5, so averaging + hourly_rate: 0 + n_hourly_rate: 0 +# capex: 3000000 diff --git a/library/default/vessels/dsv.yaml b/library/default/vessels/dsv.yaml new file mode 100644 index 00000000..f1162390 --- /dev/null +++ b/library/default/vessels/dsv.yaml @@ -0,0 +1,21 @@ +name: Diving Support Vessel +equipment_rate: 30736.5 # Reference: WOMBAT_Cost_History.xslx +capability: DSV +speed: 29.63 # 16 knots +strategy: requests +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2 +max_waveheight_repair: 2 +onsite: false +mobilization_cost: 0 # Reference: WOMBAT_Cost_History.xslx +mobilization_days: 15 +strategy_threshold: 1 +charter_days: 4 +crew_transfer_time: 0.25 +n_crews: 1 +crew: + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx + n_day_rate: 10 # N technicians for all DSV tasks + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/default/vessels/hlv.yaml b/library/default/vessels/hlv.yaml new file mode 100644 index 00000000..5b6c9909 --- /dev/null +++ b/library/default/vessels/hlv.yaml @@ -0,0 +1,23 @@ +name: Heavy Lift Vessel +equipment_rate: 400000.0 # Reference: WOMBAT_Cost_History.xslx +capability: LCN +speed: 20.37 # 11 knots +strategy: requests +strategy_threshold: 1 +max_windspeed_transport: 10 +max_windspeed_repair: 10 +max_waveheight_transport: 2 +max_waveheight_repair: 2 +onsite: false +mobilization_cost: 0 # Reference: WOMBAT_Cost_History.xsl, assumed domestic WTIV (no mobilization costs) +mobilization_days: 10 +workday_start: 0 +workday_end: 24 +charter_days: 20 +crew_transfer_time: 0.25 +n_crews: 1 +crew: + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx + n_day_rate: 5 # Most tasks are 3 or 4, but some larger ones are 8, 10, and 21 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/default/vessels/truck.yaml b/library/default/vessels/truck.yaml new file mode 100644 index 00000000..a8ff28ed --- /dev/null +++ b/library/default/vessels/truck.yaml @@ -0,0 +1,29 @@ +name: Truck +equipment_rate: 25 +capability: CTV +speed: 50 +strategy: scheduled +start_month: 1 +start_day: 1 +start_year: 1998 +end_month: 12 +end_day: 31 +end_year: 2017 +max_windspeed_transport: 16 +max_windspeed_repair: 16 +max_waveheight_transport: 99 +max_waveheight_repair: 99 +onsite: true +crew_transfer_time: 0 +n_crews: 1 +crew: + day_rate: 0 + n_day_rate: 0 + hourly_rate: 0 # 118.75 + n_hourly_rate: 0 # 3 + +# rate: 20 +# QC/QA tech: 2 x 90 +# Electrician: 1 x 90 +# Per Diem (144/day) added to hourly rate as 6/hr +# cpi of 1.25 for 2019 -> 2024 diff --git a/library/default/vessels/tugboat.yaml b/library/default/vessels/tugboat.yaml new file mode 100644 index 00000000..57458a9b --- /dev/null +++ b/library/default/vessels/tugboat.yaml @@ -0,0 +1,24 @@ +name: Tugboat +equipment_rate: 75031 # tugboat day rate x2 assuming 1 tugs and 1 support tug, day rate WOMBAT_Cost_History.xslx +mobilization_cost: 273909.3 # tugboat group mobilization rate from WOMBAT_Cost_History.xslx +capability: [TOW] +speed: 37.04 # 20 knots +strategy: requests +max_windspeed_transport: 99 +max_windspeed_repair: 99 +max_waveheight_transport: 2.5 +max_waveheight_repair: 2.5 +onsite: false +port_distance: 0 # automatically set from the port configuration +strategy_threshold: 1 # needs to be here, but doesn't do anything +charter_days: 10 +tow_speed: 9.26 # 5 knots +unmoor_hours: 2 +reconnection_hours: 4 +crew_transfer_time: 0.25 +n_crews: 1 +crew: + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx + n_day_rate: 5 # Most tasks are 3 or 4, but some larger ones are 8, 10, and 21 + hourly_rate: 0 + n_hourly_rate: 0 diff --git a/library/default/weather/__init__.py b/library/default/weather/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/library/default/weather/era5_40.0N_72.5W_1990_2020.pqt b/library/default/weather/era5_40.0N_72.5W_1990_2020.pqt new file mode 100644 index 00000000..bfdcc29b Binary files /dev/null and b/library/default/weather/era5_40.0N_72.5W_1990_2020.pqt differ diff --git a/library/default/weather/era5_41.0N_125.0W_1989_2019.pqt b/library/default/weather/era5_41.0N_125.0W_1989_2019.pqt new file mode 100644 index 00000000..1b503a20 Binary files /dev/null and b/library/default/weather/era5_41.0N_125.0W_1989_2019.pqt differ diff --git a/library/default/weather/merra2_32.5N_-100.625W_1980_2024.pqt b/library/default/weather/merra2_32.5N_-100.625W_1980_2024.pqt new file mode 100644 index 00000000..1df919a0 Binary files /dev/null and b/library/default/weather/merra2_32.5N_-100.625W_1980_2024.pqt differ diff --git a/tests/library/project/config/ttp.yml b/tests/library/project/config/ttp.yml new file mode 100644 index 00000000..a31b2e16 --- /dev/null +++ b/tests/library/project/config/ttp.yml @@ -0,0 +1,17 @@ +name: base +weather: test_weather.csv +service_equipment: + - - ctv.yaml + - 2 + - fsv_requests.yaml + - [2, hlv_requests.yaml] +layout: layout_simulation.csv +port_distance: 20 +inflation_rate: 0 +workday_start: 6 +workday_end: 22 +start_year: 2002 +end_year: 2003 +maintenance_start: "6/1/2002" +project_capacity: 18 +port: base.yml diff --git a/tests/library/project/plant/layout_distance.csv b/tests/library/project/plant/layout_distance.csv new file mode 100644 index 00000000..528612fd --- /dev/null +++ b/tests/library/project/plant/layout_distance.csv @@ -0,0 +1,8 @@ +id,substation_id,name,type,longitude,latitude,string,order,distance,subassembly,upstream_cable +OSS1,OSS1,OSS1,substation,0,0,,,,offshore_substation.yaml,export.yaml +S00T1,OSS1,WTG001,turbine,-200,200,0,0,0,vestas_v90.yaml,array_33kv_630mm.yaml +S00T2,OSS1,WTG002,turbine,400,400,0,1,0,vestas_v90.yaml,array_33kv_240mm.yaml +S00T3,OSS1,WTG003,turbine,600,600,0,2,0,vestas_v90.yaml,array_33kv_240mm.yaml +S01T4,OSS1,WTG004,turbine,200,-200,1,0,0,vestas_v90.yaml,array_33kv_630mm.yaml +S01T5,OSS1,WTG005,turbine,-400,400,1,1,0,vestas_v90.yaml,array_33kv_240mm.yaml +S01T6,OSS1,WTG006,turbine,-600,600,1,2,0,vestas_v90.yaml,array_33kv_240mm.yaml diff --git a/tests/library/project/plant/layout_missing_columns.csv b/tests/library/project/plant/layout_missing_columns.csv new file mode 100644 index 00000000..9e5a1865 --- /dev/null +++ b/tests/library/project/plant/layout_missing_columns.csv @@ -0,0 +1,9 @@ +id,string,order,subassembly +OSS1,,,offshore_substation.yaml +S00T1,0,0,vestas_v90.yaml +S00T1,0,0,vestas_v90.yaml +S00T2,0,1,vestas_v90.yaml +S00T3,0,2,vestas_v90.yaml +S01T4,1,0,vestas_v90.yaml +S01T5,1,1,vestas_v90.yaml +S01T6,1,2,vestas_v90.yaml diff --git a/tests/library/project/plant/layout_required_only.csv b/tests/library/project/plant/layout_required_only.csv new file mode 100644 index 00000000..b1fabb2d --- /dev/null +++ b/tests/library/project/plant/layout_required_only.csv @@ -0,0 +1,8 @@ +id,substation_id,name,type,string,order,subassembly,upstream_cable +OSS1,OSS1,OSS1,substation,,,offshore_substation.yaml,export.yaml +S00T1,OSS1,WTG001,turbine,0,0,vestas_v90.yaml,array_33kv_630mm.yaml +S00T2,OSS1,WTG002,turbine,0,1,vestas_v90.yaml,array_33kv_240mm.yaml +S00T3,OSS1,WTG003,turbine,0,2,vestas_v90.yaml,array_33kv_240mm.yaml +S01T4,OSS1,WTG004,turbine,1,0,vestas_v90.yaml,array_33kv_630mm.yaml +S01T5,OSS1,WTG005,turbine,1,1,vestas_v90.yaml,array_33kv_240mm.yaml +S01T6,OSS1,WTG006,turbine,1,2,vestas_v90.yaml,array_33kv_240mm.yaml diff --git a/tests/library/project/port/__init__.py b/tests/library/project/port/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/library/project/port/base.yml b/tests/library/project/port/base.yml new file mode 100644 index 00000000..41bd9b1f --- /dev/null +++ b/tests/library/project/port/base.yml @@ -0,0 +1,11 @@ +name: Test Port +tugboats: [[tugboat.yaml, 2]] +n_crews: 3 +crew: + day_rate: 219.18 # 80K/yr / 365 days + n_day_rate: 5 # Most tasks are 3 or 4, but some larger ones are 8, 10, and 21 + hourly_rate: 0 + n_hourly_rate: 0 +max_operations: 2 +site_distance: 139 +annual_fee: 24e6 # From ORBIT diff --git a/library/corewind/vessels/tugboat2.yaml b/tests/library/vessels/tugboat.yaml similarity index 75% rename from library/corewind/vessels/tugboat2.yaml rename to tests/library/vessels/tugboat.yaml index e625367c..1d5da92f 100644 --- a/library/corewind/vessels/tugboat2.yaml +++ b/tests/library/vessels/tugboat.yaml @@ -1,6 +1,6 @@ -name: Tugboat 2 +name: Tugboat equipment_rate: 30000 -# mobilization_cost: 200000 +mobilization_cost: 200000 capability: [TOW] speed: 37.04 # 20 knots strategy: requests @@ -10,17 +10,15 @@ max_waveheight_transport: 2.5 max_waveheight_repair: 2.5 onsite: false port_distance: 0 # automatically set from the port configuration -# workday_start: 6 -# workday_end: 18 strategy_threshold: 1 # needs to be here, but doesn't do anything -charter_days: 3 # needs to be here, but doesn't do anything +charter_days: 10 tow_speed: 9.26 # 5 knots unmoor_hours: 2 reconnection_hours: 4 crew_transfer_time: 0.25 n_crews: 1 crew: - day_rate: 219.18 # 80K/yr / 365 days + day_rate: 0 # Labor cost assumed separately as a fixed cost based on WOMBAT_Cost_History.xslx n_day_rate: 5 # Most tasks are 3 or 4, but some larger ones are 8, 10, and 21 hourly_rate: 0 n_hourly_rate: 0 diff --git a/tests/regression/test_equipment_timing.py b/tests/regression/test_equipment_timing.py index ae0f2c67..b36c516c 100644 --- a/tests/regression/test_equipment_timing.py +++ b/tests/regression/test_equipment_timing.py @@ -1765,8 +1765,9 @@ def test_unscheduled_service_equipment_call(env_setup_full_profile): # catastrophic failure putting the windfarm at 83.3% operations, which is less than # the 90% threshold. However, because the timing will be delayed during repairs, # realized timeout will be at 14185.753253 - timeout = 14185.753253 - env.run(timeout + 1) + request_time = 14084.24 + timeout = request_time + 1 / 60 + env.run(timeout) # For checking in on the actual timing when it eventually changes # df = env.load_events_log_dataframe() # print( @@ -1791,19 +1792,24 @@ def test_unscheduled_service_equipment_call(env_setup_full_profile): # Test that the HLV was successfully mobilized timeout += 60 * 24 - env.run(timeout + 1 / 60) + env.run(timeout) + assert hlv.at_port + assert not hlv.enroute + + timeout += 52.25 + env.run(timeout) assert hlv.transferring_crew is hlv.at_site is hlv.at_system is hlv.onsite is True assert hlv.enroute is hlv.at_port is False - assert hlv.current_system == "S00T2" + assert hlv.current_system == "S00T1" # Ensure it's still here at the end, accounting for completing its last request - timeout += 30 * 24 + 10 + 15 / 60 + timeout = request_time + 60 * 24 + 30 * 24 + 1.25 env.run(timeout) assert hlv.onsite is hlv.at_site is hlv.at_system is True assert hlv.transferring_crew is hlv.at_port is hlv.enroute is False - assert hlv.current_system == "S00T2" + assert hlv.current_system == "S00T1" # Check that it's gone - timeout += 24 + 15 / 60 - env.run(timeout + 24) + timeout += 0.02 + env.run(timeout) assert hlv.onsite is False diff --git a/tests/regression/test_results.py b/tests/regression/test_results.py index 52e59a93..b6e89329 100644 --- a/tests/regression/test_results.py +++ b/tests/regression/test_results.py @@ -1,8 +1,13 @@ """Test the post-processing functionality.""" +from copy import deepcopy + +import pytest import pandas.testing as pdt from pytest_check import check +from wombat.core import FixedCosts + def test_results_consistency(setup_ttp): """Multi-facted check to ensure consistency across metrics.""" @@ -108,3 +113,21 @@ def test_event_summary_consistency(setup_ttp): pdt.assert_series_equal( combined.N_completed, combined.completed_requests, check_names=False ) + + +def test_fixed_cost_units(setup_ttp): + """Tests that the $/kw/yr and $/yr produce the same result.""" + sim = setup_ttp + metrics = sim.metrics + + fc_kw = FixedCosts(labor=(80000 / 1200.0 / 1000)) + fc_whole = FixedCosts(labor=80000, units="$/yr") + + metrics_kw = deepcopy(metrics) + metrics_whole = deepcopy(metrics) + metrics_kw.fixed_costs = fc_kw + metrics_whole.fixed_costs = fc_whole + + cost_kw = metrics_kw.project_fixed_costs("project", "high").labor.squeeze() + cost_whole = metrics_whole.project_fixed_costs("project", "high").labor.squeeze() + assert cost_whole == pytest.approx(cost_kw) diff --git a/tests/unit/test_data_classes.py b/tests/unit/test_data_classes.py index 5042a8ec..2db62eae 100644 --- a/tests/unit/test_data_classes.py +++ b/tests/unit/test_data_classes.py @@ -1072,6 +1072,7 @@ def test_FixedCosts_operations_provided(): transmission_charges_rights=90, onshore_electrical_maintenance=60, labor=100, + units="$/yr", ) assert high_res.operations == 100 @@ -1093,6 +1094,7 @@ def test_FixedCosts_operations_provided(): assert high_res.transmission_charges_rights == 0 assert high_res.onshore_electrical_maintenance == 0 assert high_res.labor == 0 + assert high_res.units == "$/yr" def test_FixedCosts_category_values_provided(): @@ -1139,6 +1141,7 @@ def test_FixedCosts_category_values_provided(): assert high_res.transmission_charges_rights == 0 assert high_res.onshore_electrical_maintenance == 60 assert high_res.labor == 100 + assert high_res.units == "$/kw/yr" def test_equipment_map(env_setup): diff --git a/tests/unit/test_library.py b/tests/unit/test_library.py index 742ff0e4..aacea769 100644 --- a/tests/unit/test_library.py +++ b/tests/unit/test_library.py @@ -1,10 +1,17 @@ """Tests for wombat/core/library.py.""" from copy import deepcopy +from pathlib import Path import pytest +from polars.testing import assert_frame_equal -from wombat.core.library import load_yaml, library_map, convert_failure_data +from wombat.core.library import ( + load_yaml, + library_map, + load_weather, + convert_failure_data, +) def test_load_yaml(): @@ -298,3 +305,18 @@ def test_convert_failure_dict(): with pytest.raises(ValueError): convert_failure_data(base_cable, "cables", return_dict=True) + + +def test_csv_pqt_conversion(): + """Tests that a Parquet data file saved from a converted CSV yields the same result + as the original CSV load and conversion. + """ + weather_csv = Path(__file__).parents[1] / "library" / "weather" / "test_weather.csv" + weather_pqt = weather_csv.with_suffix(".pqt") + + df_csv = load_weather(weather_csv) + df_csv.write_parquet(weather_pqt) + df_pqt = load_weather(weather_pqt) + weather_pqt.unlink() + + assert_frame_equal(df_csv, df_pqt) diff --git a/tests/unit/test_simulation_api.py b/tests/unit/test_simulation_api.py index 7a31497e..0a436d7e 100644 --- a/tests/unit/test_simulation_api.py +++ b/tests/unit/test_simulation_api.py @@ -71,6 +71,28 @@ def test_simulation_df_layout(): sim.run(delete_logs=True, save_metrics_inputs=False) +def test_tug_loading(): + """Test that tugboats load correctly whether consolidated or not.""" + library_path = TEST_DATA + config = load_yaml(library_path / "project/config", "ttp.yml") + + # Check for 2 tugboats being loaded via standard list-based reference to a file + sim = Simulation.from_config(library_path, config) + assert len(sim.port.service_equipment_manager.reserve_vessels.items) == 2 + sim.env.cleanup_log_files() + + # Check for 2 tugboats being loaded via list-based reference to a config-embedded + # dictionary + tug = load_yaml(library_path / "vessels", "tugboat.yaml") + port = load_yaml(library_path / "project/port", config["port"]) + port["tugboats"] = [2, "tugboat"] + config["vessels"] = {"tugboat": tug} + + sim = Simulation.from_config(library_path, config) + assert len(sim.port.service_equipment_manager.reserve_vessels.items) == 2 + sim.env.cleanup_log_files() + + @pytest.mark.skipif( sys.platform.startswith("win"), reason="Why does Windows load it differently?" ) diff --git a/tests/unit/test_windfarm.py b/tests/unit/test_windfarm.py index 64dc69e8..c95a57ee 100644 --- a/tests/unit/test_windfarm.py +++ b/tests/unit/test_windfarm.py @@ -3,6 +3,7 @@ from pathlib import Path import numpy as np +import pandas as pd import pytest import numpy.testing as npt from networkx.classes.digraph import DiGraph @@ -68,12 +69,13 @@ def test_windfarm_init(env_setup): } # Basic init items + assert windfarm.env == env assert windfarm.repair_manager == manager assert manager.windfarm == windfarm assert Path(windfarm.env.operations_log_fname).is_file() # Windfarm layout checking: 6 turbines, 1 substation, and 6 cables - assert windfarm.env == env + assert windfarm.layout_coords == "wgs-84" assert isinstance(windfarm.graph, DiGraph) assert len(windfarm.graph.nodes) == correct_N_nodes assert len(windfarm.graph.edges) == correct_N_edges @@ -221,3 +223,101 @@ def test_windfarm_failed_init(env_setup): # Test for bad substation file with pytest.raises(ValueError): Windfarm(env, "layout_array_invalid.csv", manager) + + +def test_layout_validation(env_setup): + """Tests the layout column validation.""" + env = env_setup + manager = RepairManager(env) + + # Check required only columns to ensure optional numeric columns have an initial 0 + windfarm = Windfarm(env, "layout_required_only.csv", manager) + assert (windfarm.layout_df.latitude == 0).all() + assert (windfarm.layout_df.longitude == 0).all() + assert (windfarm.layout_df.distance == 0).all() + + # Check missing columns fail + df = pd.read_csv(env.data_dir / "project/plant/layout_required_only.csv") + for col in df.columns: + _df = df.drop(columns=col) + with pytest.raises(ValueError): + Windfarm(env, _df, manager) + + +def test_windfarm_distance_coords(env_setup): + """Tests km based distances between distance-based coordinates.""" + env = env_setup + manager = RepairManager(env) + windfarm = Windfarm(env, "layout_distance.csv", manager, layout_coords="distance") + + correct_distances = np.array( + [ + [ + np.inf, + 0.282842712474619, + 0.565685424949238, + 0.848528137423857, + 0.282842712474619, + 0.565685424949238, + 0.848528137423857, + ], + [ + 0.282842712474619, + np.inf, + 0.6324555320336759, + 0.8944271909999159, + 0.565685424949238, + 0.282842712474619, + 0.565685424949238, + ], + [ + 0.565685424949238, + 0.6324555320336759, + np.inf, + 0.282842712474619, + 0.6324555320336759, + 0.8, + 1.019803902718557, + ], + [ + 0.848528137423857, + 0.8944271909999159, + 0.282842712474619, + np.inf, + 0.8944271909999159, + 1.019803902718557, + 1.2, + ], + [ + 0.282842712474619, + 0.565685424949238, + 0.6324555320336759, + 0.8944271909999159, + np.inf, + 0.848528137423857, + 1.131370849898476, + ], + [ + 0.565685424949238, + 0.282842712474619, + 0.8, + 1.019803902718557, + 0.848528137423857, + np.inf, + 0.282842712474619, + ], + [ + 0.848528137423857, + 0.565685424949238, + 1.019803902718557, + 1.2, + 1.131370849898476, + 0.282842712474619, + np.inf, + ], + ] + ) + + test_ids = windfarm.substation_id.tolist() + windfarm.turbine_id.tolist() + asset_distances = windfarm.distance_matrix.loc[test_ids, test_ids] + npt.assert_equal(asset_distances, correct_distances) diff --git a/wombat/__init__.py b/wombat/__init__.py index e6f0d073..780ae6df 100644 --- a/wombat/__init__.py +++ b/wombat/__init__.py @@ -4,4 +4,4 @@ from wombat.core.library import create_library_structure, load_yaml -__version__ = "0.12.3" +__version__ = "0.13" diff --git a/wombat/core/data_classes.py b/wombat/core/data_classes.py index 763182f1..a24800f4 100644 --- a/wombat/core/data_classes.py +++ b/wombat/core/data_classes.py @@ -1160,20 +1160,29 @@ def _set_environment_shift(self, start: int, end: int) -> None: if self.workday_start == 0 and self.workday_end == 24: # type: ignore object.__setattr__(self, "non_stop_shift", True) - def _set_port_distance(self, distance: int | float | None) -> None: + def _set_port_distance( + self, distance: int | float | None, *, port: bool = False + ) -> None: """Set ``port_distance`` from the environment's or port's variables. Parameters ---------- distance : int | float The distance to port that must be traveled for servicing equipment. + port : bool, optional + Used to flag if the port is being set, which will override the + :py:class:`PortConfig` ``site_distance`` attribute. """ if distance is None: return if distance <= 0: return - if self.port_distance <= 0: - object.__setattr__(self, "port_distance", float(distance)) + if port: + if self.site_distance <= 0: # type: ignore + object.__setattr__(self, "site_distance", float(distance)) + else: + if self.port_distance <= 0: + object.__setattr__(self, "port_distance", float(distance)) def _compare_dates( self, @@ -2087,7 +2096,8 @@ class PortConfig(FromDictMixin, DateLimitsMixin): workday_end : int The ending hour of a workshift, in 24 hour time. site_distance : int | float - Distance, in km, a tugboat has to travel to get between site and port. + Distance, in km, a tugboat has to travel to get between site and port. If not + passed, the primary configuration's ``port_distance`` will be used. annual_fee : int | float The annualized fee for access to the repair port that will be distributed monthly in the simulation and accounted for on the first of the month from the @@ -2096,7 +2106,18 @@ class PortConfig(FromDictMixin, DateLimitsMixin): .. note:: Don't include this cost in both this category and either the ``FixedCosts.operations_management_administration`` bucket or ``FixedCosts.marine_management`` category. + monthly_fee : int | float + The monthly fee for access to the repair port that will be accounted for on the + first of the month from the start of the simulation to the end of the + simulation. Do not use in conjunction with :py:attr:`annual_fee` + .. note:: Don't include this cost in both this category and either the + ``FixedCosts.operations_management_administration`` bucket or + ``FixedCosts.marine_management`` category. + + daily_use_fee : int | float + The daily cost of using the port for any repair related activities or while a + tugboat is currently dispatched. non_operational_start : str | datetime.datetime | None The starting month and day, e.g., MM/DD, M/D, MM-DD, etc. for an annualized period of prohibited operations. When defined at the port level, an undefined or @@ -2133,7 +2154,13 @@ class PortConfig(FromDictMixin, DateLimitsMixin): workday_end: int = field(default=-1, converter=int, validator=valid_hour) site_distance: float = field(default=0.0, converter=float) annual_fee: float = field( - default=0, converter=float, validator=attrs.validators.gt(0) + default=0, converter=float, validator=attrs.validators.ge(0) + ) + monthly_fee: float = field( + default=0, converter=float, validator=attrs.validators.ge(0) + ) + daily_use_fee: float = field( + default=0, converter=float, validator=attrs.validators.ge(0) ) non_operational_start: datetime.datetime = field(default=None, converter=parse_date) non_operational_end: datetime.datetime = field(default=None, converter=parse_date) @@ -2151,10 +2178,25 @@ def __attrs_post_init__(self) -> None: if self.workday_start == 0 and self.workday_end == 24: object.__setattr__(self, "non_stop_shift", True) + if self.annual_fee > 0 and self.monthly_fee > 0: + msg = ( + f"Only set one of the port `annual_fee` ({self.annual_fee}) and the" + f" `monthly_fee` ({self.monthly_fee})." + ) + raise ValueError(msg) + + if self.annual_fee > 0: + object.__setattr__(self, "monthly_fee", self.annual_fee / 12.0) + object.__setattr__(self, "annual_fee", 0) + @define(frozen=True, auto_attribs=True) class FixedCosts(FromDictMixin): - """Fixed costs for operating a windfarm. All values are assumed to be in $/kW/yr. + """Fixed costs for operating a windfarm. All values are assumed to be in $/kW/yr + unless :py:attr:`units` is set to "$/yr". In either case, all costs must use the + same units. The cost per kw is helpful if you plan to scale fixed costs with + project sizes, otherwise using $/yr allows for a fixed set of cost assumptions + across analyses. Parameters ---------- @@ -2240,6 +2282,10 @@ class FixedCosts(FromDictMixin): labor : float The costs associated with labor, if not being modeled through the simulated processes. + + units : str + The units that all costs are provided in; must be one of "$/kw/yr" or "$/yr", by + default "$/kw/yr". """ # Total Cost @@ -2272,6 +2318,12 @@ class FixedCosts(FromDictMixin): labor: float = field(default=0) + units: str = field( + default="$/kw/yr", + converter=(str, str.lower), + validator=validators.in_(["$/kw/yr", "$/yr"]), + ) + resolution: dict = field(init=False) hierarchy: dict = field(init=False) diff --git a/wombat/core/environment.py b/wombat/core/environment.py index cc446639..f989dcde 100644 --- a/wombat/core/environment.py +++ b/wombat/core/environment.py @@ -7,7 +7,6 @@ import math import logging import datetime as dt -import warnings from typing import TYPE_CHECKING from pathlib import Path from datetime import datetime, timedelta @@ -16,16 +15,15 @@ import simpy import pandas as pd import polars as pl -import pyarrow as pa -import pyarrow.csv # pylint: disable=W0611 from simpy.events import Event import wombat # pylint: disable=W0611 +from wombat.core import library from wombat.utilities import ( hours_until_future_hour, calculate_windfarm_operational_level, ) -from wombat.core.data_classes import parse_date +from wombat.utilities.time import parse_date if TYPE_CHECKING: @@ -448,7 +446,7 @@ def date_ix(self, date: dt.datetime | dt.date) -> int: def _weather_setup( self, - weather_file: str, + weather: str | Path | pd.DataFrame, start_year: int | None = None, end_year: int | None = None, ) -> pl.DataFrame: @@ -460,9 +458,10 @@ def _weather_setup( Parameters ---------- - weather_file : str - Name of the weather file to be used by the environment. Should be contained - within ``data_dir/weather``. + weather : str | Path | pd.DataFrame + Name of the weather file to be used by the environment, or a Pandas + DataFrame. Files should be contained within ``data_dir/weather``. + DataFrames should not have the "datetime" column set as the index. start_year : Optional[int], optional Custom starting year for the weather profile, by default None. If ``None`` or less than the first year of the weather profile, this will be ignored. @@ -472,75 +471,13 @@ def _weather_setup( Returns ------- - pd.DataFrame + pl.DataFrame The wind (and wave) timeseries. """ - REQUIRED = ["windspeed", "waveheight"] - - # PyArrow datetime conversion setup - convert_options = pa.csv.ConvertOptions( - timestamp_parsers=[ - "%m/%d/%y %H:%M", - "%m/%d/%y %I:%M", - "%m/%d/%y %H:%M:%S", - "%m/%d/%y %I:%M:%S", - "%m/%d/%Y %H:%M", - "%m/%d/%Y %I:%M", - "%m/%d/%Y %H:%M:%S", - "%m/%d/%Y %I:%M:%S", - "%m-%d-%y %H:%M", - "%m-%d-%y %I:%M", - "%m-%d-%y %H:%M:%S", - "%m-%d-%y %I:%M:%S", - "%m-%d-%Y %H:%M", - "%m-%d-%Y %I:%M", - "%m-%d-%Y %H:%M:%S", - "%m-%d-%Y %I:%M:%S", - "%Y-%m-%d %H:%M", - "%Y-%m-%d %I:%M", - "%Y-%m-%d %H:%M:%S", - "%Y-%m-%d %I:%M:%S", - ] - ) - weather = ( - pl.from_pandas( - pa.csv.read_csv( - self.data_dir / "weather" / weather_file, - convert_options=convert_options, - ) - .to_pandas() - .fillna(0.0) - .set_index("datetime") - .sort_index() - .resample("h") - .interpolate(limit_direction="both") - .reset_index(drop=False) - ) - .with_row_index() - .with_columns( - [ - pl.col("datetime").cast(pl.Datetime).dt.cast_time_unit("ns"), - (pl.col("datetime").dt.hour()).alias("hour"), - ] - ) - ) - - missing = set(REQUIRED).difference(weather.columns) - if missing: - msg = ( - f"The following column(s) are missing and will be set to 0:" - f" {', '.join(missing)}. Do NOT run the simulation if this is an error." - ) - warnings.warn(msg) - - weather = weather.with_columns( - pl.Series(name=col, values=np.zeros(weather.height)) - for col in REQUIRED - if col in missing - ) - column_order = ["index", "datetime", "hour", "windspeed", "waveheight"] - column_order += [col for col in weather.columns if col not in column_order] - weather = weather.select(column_order) + if isinstance(weather, str | Path): + weather = library.load_weather(self.data_dir / "weather" / weather) + else: + weather = library.format_weather(weather) # Create the start and end points self.start_datetime = weather.get_column("datetime").dt.min() diff --git a/wombat/core/library.py b/wombat/core/library.py index 1e202fde..46bbeef9 100644 --- a/wombat/core/library.py +++ b/wombat/core/library.py @@ -21,33 +21,36 @@ from __future__ import annotations import re +import warnings from copy import deepcopy from typing import Any from pathlib import Path import yaml +import numpy as np +import pandas as pd +import polars as pl +import pyarrow as pa +import pyarrow.csv # pylint: disable=W0611 ROOT = Path(__file__).parents[2].resolve() DEFAULT_LIBRARY = ROOT / "library" CODE_COMPARISON = DEFAULT_LIBRARY / "code_comparison" -BASE_CASES = DEFAULT_LIBRARY / "baseline" +DEFAULT_DATA = DEFAULT_LIBRARY / "default" DINWOODIE = CODE_COMPARISON / "dinwoodie" IEA_26 = CODE_COMPARISON / "iea26" +AVANESSOVA_DISS = CODE_COMPARISON / "avanessova_diss" COREWIND = DEFAULT_LIBRARY / "corewind" -OSW_FIXED = BASE_CASES / "offshore_fixed" -LBW = BASE_CASES / "land_based" - library_map = { + "DEFAULT": DEFAULT_DATA, + "COREWIND": COREWIND, "DINWOODIE": DINWOODIE, "IEA_26": IEA_26, "IEA26": IEA_26, - "COREWIND": COREWIND, - "OSW_FIXED": OSW_FIXED, - "LBW": LBW, - "LAND_BASED": LBW, + "AVANESSOVA_DISS": AVANESSOVA_DISS, } # YAML loader that is able to read scientific notation @@ -249,3 +252,132 @@ def convert_failure_data( if return_dict: return configuration return None + + +def read_weather_csv(filename: str | Path) -> pd.DataFrame: + """Reads the weather profile from a CSV file and converts to a Pandas ``DataFrame`` + with a converted "datetime" column. + + Parameters + ---------- + filename : str | Path + Full filename and path for the weather profile. + + Returns + ------- + pd.DataFrame + Pandas dataframe with a converted datetime column. + """ + convert_options = pa.csv.ConvertOptions( + timestamp_parsers=[ + "%m/%d/%y %H:%M", + "%m/%d/%y %I:%M", + "%m/%d/%y %H:%M:%S", + "%m/%d/%y %I:%M:%S", + "%m/%d/%Y %H:%M", + "%m/%d/%Y %I:%M", + "%m/%d/%Y %H:%M:%S", + "%m/%d/%Y %I:%M:%S", + "%m-%d-%y %H:%M", + "%m-%d-%y %I:%M", + "%m-%d-%y %H:%M:%S", + "%m-%d-%y %I:%M:%S", + "%m-%d-%Y %H:%M", + "%m-%d-%Y %I:%M", + "%m-%d-%Y %H:%M:%S", + "%m-%d-%Y %I:%M:%S", + "%Y-%m-%d %H:%M", + "%Y-%m-%d %I:%M", + "%Y-%m-%d %H:%M:%S", + "%Y-%m-%d %I:%M:%S", + ] + ) + return pa.csv.read_csv(filename, convert_options=convert_options).to_pandas() + + +def format_weather(weather: pd.DataFrame) -> pl.DataFrame: + """Format a weather profile to be compliant with WOMBAT's internal expectations. + + Parameters + ---------- + weather : pd.DataFrame + A dataframe with at least a valid DateTime column with hourly resolution. All + used columns are "datetime", "windspeed", and "waveheight". + + Returns + ------- + pl.DataFrame + A WOMBAT-compatible weather Polars DataFrame. + """ + if isinstance(weather, pl.DataFrame | pa.Table): + weather = weather.to_pandas() + + if not isinstance(weather, pd.DataFrame): + raise TypeError(f"`weather` must be a Pandas DataFrame, not: {type(weather)}") + + required = ["windspeed", "waveheight"] + column_order = ["index", "datetime", "hour", "windspeed", "waveheight"] + + weather = ( + pl.from_pandas( + weather.fillna(0.0) + .set_index("datetime") + .sort_index() + .resample("h") + .interpolate(limit_direction="both") + .reset_index(drop=False) + ) + .with_row_index() + .with_columns( + [ + pl.col("datetime").cast(pl.Datetime).dt.cast_time_unit("ns"), + (pl.col("datetime").dt.hour()).alias("hour"), + ] + ) + ) + + missing = set(required).difference(weather.columns) + if missing: + msg = ( + f"The following column(s) are missing and will be set to 0:" + f" {', '.join(missing)}. Do NOT run the simulation if this is an error." + ) + warnings.warn(msg) + + weather = weather.with_columns( + pl.Series(name=col, values=np.zeros(weather.height)) + for col in required + if col in missing + ) + column_order += [col for col in weather.columns if col not in column_order] + return weather.select(column_order) + + +def load_weather(filename: str | Path) -> pd.DataFrame: + """Load the weather profile from either a CSV or Parquet file. If using Parquet + data, then all formatting is expected to have been previously completed. + + Parameters + ---------- + filename : str | Path + The full file path and name of the file ending in ".csv" or ".pqt".capitalize + + Returns + ------- + pd.DataFrame + A WOMBAT-friendly weather profile. + + Raises + ------ + ValueError + Raised if an invalid file format is provided. + """ + filename = Path(filename).resolve() + match filename.suffix: + case ".csv": + weather = read_weather_csv(filename) + return format_weather(weather) + case ".pqt": + return pl.read_parquet(filename) + case _: + raise ValueError("Only .csv and .pqt files are accepted.") diff --git a/wombat/core/port.py b/wombat/core/port.py index adf9f7d8..56e411c2 100644 --- a/wombat/core/port.py +++ b/wombat/core/port.py @@ -9,6 +9,7 @@ from __future__ import annotations +from copy import deepcopy from typing import TYPE_CHECKING from pathlib import Path from collections.abc import Generator @@ -18,12 +19,12 @@ import pandas as pd import polars as pl from simpy.events import Process, Timeout -from simpy.resources.store import FilterStore, FilterStoreGet +from simpy.resources.store import StorePut, FilterStore, FilterStoreGet from wombat.windfarm import Windfarm from wombat.core.mixins import RepairsMixin from wombat.core.library import load_yaml -from wombat.utilities.time import hours_until_future_hour +from wombat.utilities.time import HOURS_IN_DAY, hours_until_future_hour from wombat.core.environment import WombatEnvironment from wombat.core.data_classes import ( PortConfig, @@ -35,6 +36,181 @@ from wombat.core.service_equipment import ServiceEquipment +if TYPE_CHECKING: + from wombat.core.data_classes import UnscheduledServiceEquipmentData + + +class PortManager: + """Manages the port-based vessel dispatching, demobilization, and downtime cost + accruals. + """ + + def __init__(self, env: WombatEnvironment) -> None: + """Initialize the PortManager. + + Parameters + ---------- + env : WombatEnvironment + Simulation environment. + """ + """ + TODO: include dispatch strategy for port inputs to determine a proper vessel + dispatching choice between prioritize waiting for available tug vs dispatching + more tugboats as they're needed. Similarly for extending the chartering period + for tugboats when at port. + """ + + self.dispatch_priority: bool = True + self.env = env + self.available_vessels: FilterStore = FilterStore(env) + self.reserve_vessels: FilterStore = FilterStore(env) + self.charter_map: dict[str, float] = {} + + def manage_vessels(self) -> simpy.events.Timeout: + """Runs a daily check to see if any of the :py:attr:`available_vessels` are past + their chartering period, and if so, demobilizes them. + + Yields + ------ + simpy.events.Timeout + Daily timeout between vessel chartering check-ins. + """ + while True: + yield self.env.timeout(HOURS_IN_DAY) + if not self.available_vessels.items: + continue + now = self.env.now + for name, charter_end in self.charter_map.items(): + # Avoid scenarios where just mobilized vessels that aren't yet available + # with an updated charter end time are being immediately demobilized + if name not in (el.name for el in self.available_vessels.items): + continue + if now >= charter_end: + vessel = yield self.available_vessels.get(lambda x: x.name == name) + vessel.downtime_accrual.interrupt() + vessel.demobilize() + _ = yield self.reserve_vessels.put(vessel) + + def register_tugboat(self, tugbat: ServiceEquipment) -> None: + """Add a tugboat to the collection of tugboats to be used during the simulation. + + Parameters + ---------- + tugbat : ServiceEquipment + Tugboat that is immediately available for use + """ + self.reserve_vessels.put(tugbat) + + def update_charter_map(self, vessel: ServiceEquipment) -> None: + """Updates the charter mapping for :py:attr:`vessel` to include the end of the + charter period for the :py:attr:`vessel`. + + Parameters + ---------- + vessel : ServiceEquipment + The vessel to add to the charter period mapping. + """ + if TYPE_CHECKING: + assert isinstance(vessel.settings, UnscheduledServiceEquipmentData) + now = self.env.now + charter_hours = vessel.settings.charter_days * HOURS_IN_DAY + self.charter_map[vessel.name] = now + charter_hours + + def dispatch_vessel(self, *, tugboat: bool) -> tuple[FilterStoreGet, bool]: + """Retrieves an available tugboat. + + If a tugboat is already at port, and available, then it will be utilized. If + there aren't any available tugboats, and ``dispatch_priority`` is True, then + another tugboat will be mobilized if there are more in reserve, otherwise, the + next available tugboat will be made available. + + Returns + ------- + FilterStoreGet | StoreGet + Returns ``.get()`` from either the :py:attr:`available_vessels` or + :py:attr:`reserve_vessels`. + bool + If True, then the returned tugboat getter needs to be mobilized, otherwise + False. + """ + if tugboat: + if self.available_vessels.items: + for vessel in self.available_vessels.items: + if ( + vessel.at_port + and not vessel.dispatched + and EquipmentClass.TOW in vessel.settings.capability + ): + vessel.downtime_accrual.interrupt() + return self.available_vessels.get(lambda x: x is vessel), False + + if self.dispatch_priority & self.reserve_vessels.items: + return self.reserve_vessels.get( + lambda x: EquipmentClass.TOW in x.settings.capability + ), True + + vessel.downtime_accrual.interrupt() + return self.available_vessels.get( + lambda x: x.at_port + and not x.dispatched + and EquipmentClass.TOW in x.settings.capability + ), False + + return self.reserve_vessels.get( + lambda x: EquipmentClass.TOW in x.settings.capability + ), True + + if self.available_vessels.items: + for vessel in self.available_vessels.items: + if ( + vessel.at_port + and not vessel.dispatched + and "TOW" not in vessel.settings.capability + ): + vessel.downtime_accrual.interrupt() + return self.available_vessels.get(lambda x: x is vessel), False + + if self.dispatch_priority & self.reserve_vessels.items: + return self.reserve_vessels.get( + lambda x: EquipmentClass.TOW in x.settings.capability + ), True + + vessel.downtime_accrual.interrupt() + return self.available_vessels.get( + lambda x: x.at_port + and not x.dispatched + and "TOW" not in x.settings.capability + ), False + + return self.reserve_vessels.get( + lambda x: EquipmentClass.TOW in x.settings.capability + ), True + + def return_vessel(self, vessel: ServiceEquipment) -> StorePut: + """Return the :py:attr:`vessel` to the either the :py:attr:`available_vessels` + or :py:attr:`reserve_vessels` store, depending on if there is time left in its + charter period. + + Parameters + ---------- + vessel : ServiceEquipment + The vessel to be returned to the active or reserve vessels store. + + Yields + ------ + StorePut + Request to put the vessel back in either the active or reserve store. + """ + if self.env.now < self.charter_map[vessel.name]: + vessel.downtime_accrual = self.env.process( + vessel.run_downtime_accumulation() + ) + yield self.available_vessels.put(vessel) + else: + vessel.demobilize() + yield self.reserve_vessels.put(vessel) + + class Port(RepairsMixin, FilterStore): """The offshore wind base port that operates tugboats and performs tow-to-port repairs. @@ -56,6 +232,9 @@ class Port(RepairsMixin, FilterStore): A path to a YAML object or dictionary encoding the port's configuration settings. This will be loaded into a ``PortConfig`` object during initialization. + vessel_configs : dict | None + The ``vessels`` configuration data from the primary configuration, if one was + provided, otherwise None, by default None. Attributes ---------- @@ -95,6 +274,7 @@ def __init__( windfarm: Windfarm, repair_manager: RepairManager, config: dict | str | Path, + vessel_configs: dict | None = None, ) -> None: super().__init__(env, np.inf) @@ -111,9 +291,11 @@ def __init__( config = load_yaml(env.data_dir / "project/port", config) if TYPE_CHECKING: assert isinstance(config, dict) - self.settings = PortConfig.from_dict(config) + self.settings: PortConfig = PortConfig.from_dict(config) + self.name = self.settings.name self._check_working_hours(which="env") + self.settings._set_port_distance(self.env.port_distance, port=True) self.settings.set_non_operational_dates( self.env.non_operational_start, self.env.start_year, @@ -134,29 +316,97 @@ def __init__( self.turbine_manager = simpy.Resource(env, self.settings.max_operations) self.crew_manager = simpy.Resource(env, self.settings.n_crews) - service_equipment = [] - for t in self.settings.tugboats: - tugboat = ServiceEquipment(self.env, self.windfarm, repair_manager, t) - tugboat._register_port(self) - service_equipment.append(tugboat) + self.service_equipment_manager = PortManager(self.env) + self._setup_tugboats(repair_manager, vessel_configs) - self.service_equipment_manager = simpy.FilterStore(env, len(service_equipment)) - self.service_equipment_manager.items = service_equipment self.active_repairs: dict[str, dict[str, simpy.events.Event]] = {} + self.turbines_at_port: dict[str, bool] = {} self.subassembly_resets: dict[str, list[str]] = {} # Create partial functions for the labor and equipment costs for clarity self.initialize_cost_calculators(which="port") # Run the annualized fee logger - if self.settings.annual_fee > 0: - self.env.process(self._log_annual_fee()) + if self.settings.monthly_fee > 0: + self.env.process(self._log_monthly_fee()) + + self.env.process(self.service_equipment_manager.manage_vessels()) + + if (usage_fee := self.settings.daily_use_fee) > 0: + self.env.process(self._log_usage_fee(usage_fee)) + + def _initialize_servicing_equipment( + self, repair_manager: RepairManager, configuration: str | dict + ) -> None: + """ + Initializes a single tugboat. + + Parameters + ---------- + repair_manager : RepairManager + The simulation's repair management object. + configuration : str | dict + The servicing equipment configuration dictionary or YAML file. + """ + # YAML files must be loaded for repeats so that the naming can be unique + if isinstance(configuration, str): + if configuration.endswith((".yaml", ".yml")): + configuration = load_yaml(self.env.data_dir / "vessels", configuration) - def _log_annual_fee(self): - """Logs the annual port lease fee on a monthly-basis.""" + tugboat = ServiceEquipment( + self.env, self.windfarm, repair_manager, configuration + ) + tugboat._register_port(self) + self.service_equipment_manager.register_tugboat(tugboat) + + def _setup_tugboats( + self, repair_manager: RepairManager, vessel_configs: dict | None + ) -> None: + total = 0 + for tugboat in self.settings.tugboats: + n = 1 + if isinstance(tugboat, list): + n, tugboat = tugboat + if isinstance(n, str) and isinstance(tugboat, int): + n, tugboat = tugboat, n + + if TYPE_CHECKING: + assert isinstance(tugboat, str) + if not tugboat.endswith((".yml", ".yaml")): + if vessel_configs is None: + msg = ( + "The input to `vessel_configs` must be defined if file names" + f" not provided to `tugboats`. '{tugboat}' is not a" + " YAML filename." + ) + raise ValueError(msg) + tugboat = vessel_configs[tugboat] + + if n == 1: + self._initialize_servicing_equipment(repair_manager, tugboat) # type: ignore + total += 1 + continue + + if isinstance(tugboat, str): + if tugboat.endswith((".yaml", ".yml")): + tugboat = load_yaml(self.env.data_dir / "vessels", tugboat) + + if TYPE_CHECKING: + assert isinstance(tugboat, dict) + name = tugboat["name"] + for i in range(n): + config = deepcopy(tugboat) + config["name"] = f"{name} {i + 1}" + self._initialize_servicing_equipment(repair_manager, config) + total += 1 + + self.service_equipment_manager.reserve_vessels._capacity = total + + def _log_monthly_fee(self): + """Logs the monthly port lease fee.""" if TYPE_CHECKING: assert isinstance(self.settings, PortConfig) - monthly_fee = self.settings.annual_fee / 12.0 + monthly_fee = self.settings.monthly_fee ix_month_starts = self.env.weather.filter( (pl.col("datetime").dt.day() == 1) & (pl.col("datetime").dt.hour() == 0) @@ -165,7 +415,7 @@ def _log_annual_fee(self): # At time 0 log the first monthly fee self.env.log_action( - agent=self.settings.name, + agent=self.name, action="monthly lease fee", reason="port lease", equipment_cost=monthly_fee, @@ -181,12 +431,32 @@ def _log_annual_fee(self): # Log the fee at the start of each month at midnight yield self.env.timeout(time_to_next) self.env.log_action( - agent=self.settings.name, + agent=self.name, action="monthly lease fee", reason="port lease", equipment_cost=monthly_fee, ) + def _log_usage_fee(self, usage_fee): + """Logs the port usage at the end of each day by checking if there was either + any vessel activity or turbine activity during each hour of the day. + """ + while True: + hours_remaining = deepcopy(HOURS_IN_DAY) + while hours_remaining: + yield self.env.timeout(1) + hours_remaining -= 1 + ongoing_repairs = any(self.turbines_at_port.values()) + if ongoing_repairs: + self.env.log_action( + agent=self.name, + action="daily use fee", + reason="port usage", + equipment_cost=usage_fee, + ) + yield self.env.timeout(hours_remaining) + hours_remaining = 0 + def repair_single(self, request: RepairRequest) -> Generator[Timeout | Process]: """Simulation logic to process a single repair request. @@ -209,7 +479,7 @@ def repair_single(self, request: RepairRequest) -> Generator[Timeout | Process]: system = self.windfarm.system(request.system_id) subassembly = getattr(system, request.subassembly_id) shared_logging = { - "agent": self.settings.name, + "agent": self.name, "reason": request.details.description, "system_id": system.id, "system_name": system.name, @@ -253,7 +523,7 @@ def repair_single(self, request: RepairRequest) -> Generator[Timeout | Process]: system_id=system.id, part_id=subassembly.id, part_name=subassembly.name, - agent=self.settings.name, + agent=self.name, action=f"{action} complete", reason=request.details.description, materials_cost=request.details.materials, @@ -282,9 +552,7 @@ def transfer_requests_from_manager( None | list[RepairRequest] The list of repair requests that need to be completed at port. """ - requests = self.manager.get_all_requests_for_system( - self.settings.name, system_id - ) + requests = self.manager.get_all_requests_for_system(self.name, system_id) if requests is None: return requests requests = [r.value for r in requests] # type: ignore @@ -298,7 +566,7 @@ def transfer_requests_from_manager( part_name=request.subassembly_name, system_ol=float("nan"), part_ol=float("nan"), - agent=self.settings.name, + agent=self.name, action="requests moved to port", reason="at-port repair can now proceed", request_id=request.request_id, @@ -374,13 +642,16 @@ def run_tow_to_port(self, request: RepairRequest) -> Generator[Process]: The process follows the following following routine: - 1. Request a tugboat from the tugboat resource manager and wait - 2. Runs ``ServiceEquipment.tow_to_port``, which encapsulates the traveling to - site, unmooring, and return tow with a turbine + 1. Request a tugboat from the tugboat resource manager and wait for one to be + available. + 2. Mobilize the tugboat if it's not readily available at port + 3. Runs ``ServiceEquipment.tow_to_port``, which encapsulates the traveling to + site, unmooring, and return tow with a turbine. 3. Transfers the the turbine's repair log to the port, and gets all available crews to work on repairs immediately - 4. Requests a tugboat to return the turbine to site - 5. Runs ``ServiceEquipment.tow_to_site()``, which encapsulates the tow back to + 4. Requests a tugboat to return the turbine to site. + 5. If the tugboat was not already mobilized, mobilize it. + 6. Runs ``ServiceEquipment.tow_to_site()``, which encapsulates the tow back to site, reconnection, resetting the operating status, and returning back to port @@ -420,11 +691,11 @@ def run_tow_to_port(self, request: RepairRequest) -> Generator[Process]: yield system.servicing # Request a tugboat to retrieve the turbine - tugboat = yield self.service_equipment_manager.get( - lambda x: x.at_port - and (not x.dispatched) - and EquipmentClass.TOW in x.settings.capability - ) + tugboat, mobilize = self.service_equipment_manager.dispatch_vessel(tugboat=True) + tugboat = yield tugboat + if mobilize: + yield self.env.process(tugboat.mobilize()) + self.service_equipment_manager.update_charter_map(tugboat) # Check that there is enough time to complete towing, connection, and repairs # before starting the process, otherwise, wait until the next operational period @@ -439,7 +710,7 @@ def run_tow_to_port(self, request: RepairRequest) -> Generator[Process]: start_search_date=max(intersection) ) self.env.log_action( - agent=self.settings.name, + agent=self.name, action="delay", reason="non-operational period", additional="waiting for next operational period", @@ -452,9 +723,9 @@ def run_tow_to_port(self, request: RepairRequest) -> Generator[Process]: if TYPE_CHECKING: assert isinstance(tugboat, ServiceEquipment) yield self.env.process(tugboat.run_tow_to_port(request)) + self.turbines_at_port[request.system_id] = True - # Make the tugboat available again - yield self.service_equipment_manager.put(tugboat) + yield self.env.process(self.service_equipment_manager.return_vessel(tugboat)) # Transfer the repairs to the port queue, which will initiate the repair process yield self.env.process(self.run_repairs(system_id)) @@ -463,22 +734,23 @@ def run_tow_to_port(self, request: RepairRequest) -> Generator[Process]: yield simpy.AllOf(self.env, self.active_repairs[system_id].values()) # Request a tugboat to tow the turbine back to site, and open the turbine queue - tugboat = yield self.service_equipment_manager.get( - lambda x: x.at_port - and (not x.dispatched) - and EquipmentClass.TOW in x.settings.capability - ) + tugboat, mobilize = self.service_equipment_manager.dispatch_vessel(tugboat=True) + tugboat = yield tugboat + if mobilize: + yield self.env.process(tugboat.mobilize()) + self.service_equipment_manager.update_charter_map(tugboat) + self.turbine_manager.release(turbine_request) self.subassembly_resets[system_id] = list( set(self.subassembly_resets[system_id]) ) + self.turbines_at_port[request.system_id] = False yield self.env.process( tugboat.run_tow_to_site(request, self.subassembly_resets[system_id]) ) self.invalid_systems.pop(self.invalid_systems.index(system_id)) - # Make the tugboat available again - yield self.service_equipment_manager.put(tugboat) + yield self.env.process(self.service_equipment_manager.return_vessel(tugboat)) def run_unscheduled_in_situ( self, request: RepairRequest, *, initial: bool = False @@ -526,14 +798,15 @@ def run_unscheduled_in_situ( self.requests_serviced.update([request.request_id]) # Request a vessel that isn't solely a towing vessel - vessel = yield self.service_equipment_manager.get( - lambda x: x.at_port - and (not x.dispatched) - and x.settings.capability != [EquipmentClass.TOW] - ) + vessel, mobilize = self.service_equipment_manager.dispatch_vessel(tugboat=False) + vessel = yield vessel + if mobilize: + yield self.env.process(vessel.mobilize()) + self.service_equipment_manager.update_charter_map(vessel) + if TYPE_CHECKING: assert isinstance(vessel, ServiceEquipment) - request = yield self.manager.get(lambda x: x is request) + request = yield self.manager.get(lambda x: x is request) # type: ignore yield self.env.process(vessel.in_situ_repair(request, initial=True)) # If the tugboat finished mid-shift, the in-situ repair logic will keep it @@ -543,7 +816,7 @@ def run_unscheduled_in_situ( vessel.travel( start="site", end="port", - agent=vessel.settings.name, + agent=vessel.name, reason=f"{request.details.description} complete", system_id=request.system_id, system_name=request.system_name, @@ -553,5 +826,4 @@ def run_unscheduled_in_situ( ) ) - # Make the tugboat available again - yield self.service_equipment_manager.put(vessel) + yield self.service_equipment_manager.return_vessel(vessel) diff --git a/wombat/core/post_processor.py b/wombat/core/post_processor.py index 63878267..1a5cb1da 100644 --- a/wombat/core/post_processor.py +++ b/wombat/core/post_processor.py @@ -79,7 +79,7 @@ def __init__( electrolyzer_id: str | list[str], substation_turbine_map: dict[str, dict[str, list[str]]], service_equipment_names: str | list[str], - fixed_costs: str | None = None, + fixed_costs: str | dict | None = None, ) -> None: """Initializes the Metrics class. @@ -123,9 +123,9 @@ def __init__( capacity). service_equipment_names : str | list[str] The names of the servicing equipment, corresponding to - ``ServiceEquipment.settings.name`` for each ``ServiceEquipment`` in the + ``ServiceEquipment.name`` for each ``ServiceEquipment`` in the simulation. - fixed_costs : str | None + fixed_costs : str | dict | None The filename of the project's fixed costs. """ self.data_dir = Path(data_dir) @@ -139,9 +139,8 @@ def __init__( # Create a zero-cost FixedCosts object self.fixed_costs = FixedCosts.from_dict({"operations": 0}) else: - if TYPE_CHECKING: - assert isinstance(fixed_costs, str) - fixed_costs = load_yaml(self.data_dir / "project/config", fixed_costs) + if isinstance(fixed_costs, str): + fixed_costs = load_yaml(self.data_dir / "project/config", fixed_costs) if TYPE_CHECKING: assert isinstance(fixed_costs, dict) self.fixed_costs = FixedCosts.from_dict(fixed_costs) @@ -788,7 +787,9 @@ def equipment_costs( col_filter = frequency.group_cols cost_col = [self._equipment_cost] - events = self.events.loc[self.events.action != "monthly lease fee"] + events = self.events.loc[ + ~self.events.action.isin(("monthly lease fee", "daily use fee")) + ] if by_equipment: if frequency is Frequency.PROJECT: costs = ( @@ -941,7 +942,7 @@ def vessel_crew_hours_at_sea( Indicates whether the values are with resepect to each tugboat (True) or not (False), by default False. vessel_crew_assumption : dict[str, float], optional - Dictionary of vessel names (``ServiceEquipment.settings.name``) and number + Dictionary of vessel names (``ServiceEquipment.name``) and number of crew members aboard to trannsform the results from vessel hours at sea to crew hours at sea. @@ -953,7 +954,7 @@ def vessel_crew_hours_at_sea( - year (if appropriate for frequency) - month (if appropriate for frequency) - Total Crew Hours at Sea - - {ServiceEquipment.settings.name} (if broken out) + - {ServiceEquipment.name} (if broken out) Raises ------ @@ -1079,9 +1080,9 @@ def number_of_tows( - total_tows - total_tows_to_port (if broken out) - total_tows_to_site (if broken out) - - {ServiceEquipment.settings.name}_total_tows (if broken out) - - {ServiceEquipment.settings.name}_to_port (if broken out) - - {ServiceEquipment.settings.name}_to_site (if broken out) + - {ServiceEquipment.name}_total_tows (if broken out) + - {ServiceEquipment.name}_to_port (if broken out) + - {ServiceEquipment.name}_to_site (if broken out) Raises ------ @@ -1431,6 +1432,7 @@ def equipment_labor_cost_breakdowns( & ~self.events.additional.isin(["work is complete"]), group_filter + self._cost_columns + ["duration"], ] + .fillna(0) .groupby(group_filter) .sum() .reset_index() @@ -1464,7 +1466,10 @@ def equipment_labor_cost_breakdowns( ) costs.loc[costs.action == "traveling", "display_reason"] = "Site Travel" costs.loc[costs.action == "towing", "display_reason"] = "Towing" - costs.loc[costs.action.str.contains("mooring"), "display_reason"] = "Connection" + costs.loc[costs.action.eq("mooring reconnection"), "display_reason"] = ( + "Reconnection" + ) + costs.loc[costs.action.eq("unmooring"), "display_reason"] = "Disconnection" costs.loc[costs.action == "mobilization", "display_reason"] = "Mobilization" costs.loc[costs.additional.isin(weather_hours), "display_reason"] = ( "Weather Delay" @@ -1530,6 +1535,8 @@ def equipment_labor_cost_breakdowns( "Crew Transfer", "Site Travel", "Towing", + "Reconnection", + "Disconnection", "Mobilization", "Weather Delay", "No Requests", @@ -1854,7 +1861,7 @@ def port_fees(self, frequency: str) -> pd.DataFrame: column = "port_fees" port_fee = self.events.loc[ - self.events.action == "monthly lease fee", + self.events.action.isin(("monthly lease fee", "daily use fee")), ["year", "month", "equipment_cost"], ].rename(columns={"equipment_cost": column}) @@ -1908,11 +1915,9 @@ def project_fixed_costs(self, frequency: str, resolution: str) -> pd.DataFrame: # Get the appropriate values and convert to the currency base keys = self.fixed_costs.resolution[resolution] - vals = ( - np.array([[getattr(self.fixed_costs, key) for key in keys]]) - * self.project_capacity - * 1000 - ) + vals = np.array([[getattr(self.fixed_costs, key) for key in keys]]) + if self.fixed_costs.units == "$/kw/yr": + vals = vals * self.project_capacity * 1000 total = ( self.operations[["year", "month", "env_time"]] diff --git a/wombat/core/service_equipment.py b/wombat/core/service_equipment.py index b08c0b8e..80498f49 100644 --- a/wombat/core/service_equipment.py +++ b/wombat/core/service_equipment.py @@ -17,6 +17,7 @@ from collections.abc import Generator import numpy as np +import simpy import pandas as pd import polars as pl from simpy.events import Event, Process, Timeout @@ -251,6 +252,7 @@ def __init__( except Exception as e: msg = f"Could not create {data['name']}" raise ValueError(msg) from e + self.name = self.settings.name # Register servicing equipment with the repair manager if it is using an # unscheduled maintenance scenario, so it can be dispatched as needed @@ -265,6 +267,8 @@ def __init__( # Create partial functions for the labor and equipment costs for clarity self.initialize_cost_calculators(which="equipment") + self.downtime_accrual = None + def finish_setup_with_environment_variables(self) -> None: """A post-initialization step that will override unset parameters with those from the the environemt that may have already been set. @@ -419,6 +423,41 @@ def get_next_request(self): self.manager.invalidate_system(request.system_id) yield request + def check_for_matching_requests(self) -> bool: + """Checks for any capability matches with requests in the repair manager's + queue. This is intended to be used prior to undispatching a requested + servicing equipment when the initial attempt to find a request fails. + + Returns + ------- + bool + True if there any matching requests, otherwise False. + """ + if not self.manager.items: + return False + capability = set(self.settings.capability) + status = any( + capability.intersection(el.details.service_equipment) + for el in self.manager.items + ) + return status + + def demobilize(self) -> None: + """Demobilizes the servicing equipment by resetting the location booleans and + logging the site exit. + """ + self.onsite = False + self.at_port = False + self.at_site = False + self.at_system = False + self.dispatched = False + self.current_system = None + self.env.log_action( + agent=self.name, + action="leaving site", + reason="charter period has ended", + ) + def enable_string_operations(self, cable: Cable) -> None: """Traverses the upstream cable and turbine connections and resets the ``System.cable_failure`` and ``Cable.downstream_failure`` until it hits @@ -570,7 +609,7 @@ def wait_until_next_operational_period( self.onsite = False self.env.log_action( - agent=self.settings.name, + agent=self.name, action="delay", reason="work is complete", additional=additional, @@ -600,9 +639,9 @@ def mobilize_scheduled(self) -> Generator[Timeout]: ) self.enroute = True self.env.log_action( - agent=self.settings.name, + agent=self.name, action="mobilization", - reason=f"{self.settings.name} is being mobilized", + reason=f"{self.name} is being mobilized", additional="mobilization", location="enroute", duration=mobilization_hours, @@ -613,9 +652,9 @@ def mobilize_scheduled(self) -> Generator[Timeout]: self.enroute = False self.env.log_action( - agent=self.settings.name, + agent=self.name, action="mobilization", - reason=f"{self.settings.name} has arrived on site", + reason=f"{self.name} has arrived on site", additional="mobilization", equipment_cost=self.settings.mobilization_cost, location="site", @@ -635,9 +674,9 @@ def mobilize(self) -> Generator[Timeout]: self.enroute = True mobilization_hours = self.settings.mobilization_days * HOURS_IN_DAY self.env.log_action( - agent=self.settings.name, + agent=self.name, action="mobilization", - reason=f"{self.settings.name} is being mobilized", + reason=f"{self.name} is being mobilized", additional="mobilization", duration=mobilization_hours, location="enroute", @@ -648,9 +687,9 @@ def mobilize(self) -> Generator[Timeout]: self.enroute = False self.env.log_action( - agent=self.settings.name, + agent=self.name, action="mobilization", - reason=f"{self.settings.name} has arrived on site", + reason=f"{self.name} has arrived on site", additional="mobilization", equipment_cost=self.settings.mobilization_cost, location="site", @@ -785,7 +824,7 @@ def weather_delay(self, hours: int | float, **kwargs) -> Generator[Event, Any, A """ if hours < 0: raise ValueError( - f"`hours` must be greater than 0 for {self.settings.name} to process" + f"`hours` must be greater than 0 for {self.name} to process" " a weather delay" ) if hours == 0: @@ -1141,9 +1180,6 @@ def tow( self._set_location(end, set_current) self.env.log_action( action="complete towing", - salary_labor_cost=salary_labor_cost, - hourly_labor_cost=hourly_labor_cost, - equipment_cost=equipment_cost, additional="complete", location=end, **kwargs, @@ -1195,7 +1231,7 @@ def crew_transfer( "part_name": subassembly.name, "system_ol": system.operating_level, "part_ol": subassembly.operating_level, - "agent": self.settings.name, + "agent": self.name, "reason": request.details.description, "request_id": request.request_id, } @@ -1237,9 +1273,9 @@ def crew_transfer( ) if to_system: - additional = f"transferring crew from {self.settings.name} to {system.id}" + additional = f"transferring crew from {self.name} to {system.id}" else: - additional = f"transferring crew from {system.id} to {self.settings.name}" + additional = f"transferring crew from {system.id} to {self.name}" self.env.log_action( action="transferring crew", additional=additional, @@ -1310,7 +1346,7 @@ def mooring_connection( "system_id": system.id, "system_name": system.name, "system_ol": system.operating_level, - "agent": self.settings.name, + "agent": self.name, "reason": request.details.description, "request_id": request.request_id, } @@ -1429,7 +1465,7 @@ def in_situ_repair( "system_name": system.name, "part_id": subassembly.id, "part_name": subassembly.name, - "agent": self.settings.name, + "agent": self.name, "reason": request.details.description, "request_id": request.request_id, } @@ -1491,7 +1527,7 @@ def in_situ_repair( ) ) else: - raise RuntimeError(f"{self.settings.name} is lost!") + raise RuntimeError(f"{self.name} is lost!") if initial: replacement = ( @@ -1630,7 +1666,7 @@ def in_situ_repair( part_name=subassembly.name, system_ol=system.operating_level, part_ol=subassembly.operating_level, - agent=self.settings.name, + agent=self.name, action=f"{action} complete", reason=request.details.description, materials_cost=request.details.materials, @@ -1678,7 +1714,7 @@ def run_scheduled_in_situ(self) -> Generator[Process]: yield self.env.process( self.wait_until_next_shift( **{ - "agent": self.settings.name, + "agent": self.name, "reason": "not in working hours", "additional": "not in working hours", } @@ -1694,13 +1730,13 @@ def run_scheduled_in_situ(self) -> Generator[Process]: start="site", end="port", reason="no requests", - agent=self.settings.name, + agent=self.name, ) ) yield self.env.process( self.wait_until_next_shift( **{ - "agent": self.settings.name, + "agent": self.name, "reason": "no requests", "additional": "no work requests, waiting until the next shift", # noqa: E501 } @@ -1765,7 +1801,7 @@ def run_unscheduled_in_situ(self) -> Generator[Process]: else: hours_to_next = self.env.max_run_time - self.env.now self.env.log_action( - agent=self.settings.name, + agent=self.name, action="delay", reason="non-operational period", additional="waiting for next operational period", @@ -1779,13 +1815,15 @@ def run_unscheduled_in_situ(self) -> Generator[Process]: _, request = self.get_next_request() if request is None: if not self.onsite: - self.dispatched = False - return + queued_requests = self.check_for_matching_requests() + if not queued_requests: + self.dispatched = False + return yield self.env.process( self.wait_until_next_shift( **{ - "agent": self.settings.name, + "agent": self.name, "reason": "no requests", "additional": "no work requests submitted by start of shift", # noqa: E501 } @@ -1818,17 +1856,7 @@ def run_unscheduled_in_situ(self) -> Generator[Process]: while True: if self.env.now >= charter_end_env_time: - self.onsite = False - self.at_port = False - self.at_site = False - self.at_system = False - self.dispatched = False - self.current_system = None - self.env.log_action( - agent=self.settings.name, - action="leaving site", - reason="charter period has ended", - ) + self.demobilize() break if not self.onsite: @@ -1852,7 +1880,7 @@ def run_unscheduled_in_situ(self) -> Generator[Process]: yield self.env.process( self.wait_until_next_shift( **{ - "agent": self.settings.name, + "agent": self.name, "reason": "not in working hours", "additional": "not in working hours", } @@ -1861,15 +1889,21 @@ def run_unscheduled_in_situ(self) -> Generator[Process]: _, request = self.get_next_request() if request is None: - yield self.env.process( - self.wait_until_next_shift( - **{ - "agent": self.settings.name, - "reason": "no requests", - "additional": "no work requests submitted by start of shift", # noqa: E501 - } - ) + wait = 2 + salary_cost = self.calculate_salary_cost(wait) + hourly_cost = self.calculate_hourly_cost(0) + equpipment_cost = self.calculate_equipment_cost(wait) + self.env.log_action( + duration=wait, + salary_labor_cost=salary_cost, + hourly_labor_cost=hourly_cost, + equipment_cost=equpipment_cost, + agent=self.name, + action="delay", + reason="no requests", + additional="no work requests submitted", ) + yield self.env.timeout(wait) else: if request.cable: system = self.windfarm.cable(request.system_id) @@ -1904,7 +1938,7 @@ def run_tow_to_port(self, request: RepairRequest) -> Generator[Process]: "system_id": request.system_id, "system_name": request.system_name, "request_id": request.request_id, - "agent": self.settings.name, + "agent": self.name, } # Travel to the turbine @@ -1954,7 +1988,7 @@ def run_tow_to_site( self.dispatched = True system = self.windfarm.system(request.system_id) shared_logging = { - "agent": self.settings.name, + "agent": self.name, "request_id": request.request_id, "system_id": request.system_id, "system_name": request.system_name, @@ -1985,3 +2019,56 @@ def run_tow_to_site( ) ) self.dispatched = False + + def run_downtime_accumulation(self) -> Timeout: + """Creates daily logs for downtime cost accruals. + + Yields + ------ + simpy.events.Timeout + 24 hours or time between last downtime start and an interruption caused by + either a request or demobilization. + """ + # Don't run if it hasn't been registered to be in temporary storage + if not self.at_port: + return + + while True: + start = self.env.now + try: + yield self.env.timeout(HOURS_IN_DAY) + except simpy.Interrupt: + end = self.env.now + diff = end - start + salary_cost = self.calculate_salary_cost(diff) + hourly_cost = self.calculate_hourly_cost(0) + equipment_cost = self.calculate_equipment_cost(diff) + self.env.log_action( + agent=self.name, + action="delay", + reason="no requests", + location="port", + additional="no work requests, waiting for the next request", + duration=diff, + salary_labor_cost=salary_cost, + hourly_labor_cost=hourly_cost, + equipment_cost=equipment_cost, + ) + return + + end = self.env.now + diff = end - start + salary_cost = self.calculate_salary_cost(diff) + hourly_cost = self.calculate_hourly_cost(0) + equipment_cost = self.calculate_equipment_cost(diff) + self.env.log_action( + agent=self.name, + action="delay", + reason="no requests", + location="port", + additional="no work requests, waiting for the next request", + duration=diff, + salary_labor_cost=salary_cost, + hourly_labor_cost=hourly_cost, + equipment_cost=equipment_cost, + ) diff --git a/wombat/core/simulation_api.py b/wombat/core/simulation_api.py index 2da83b40..555c9e07 100644 --- a/wombat/core/simulation_api.py +++ b/wombat/core/simulation_api.py @@ -56,6 +56,10 @@ class Configuration(FromDictMixin): layout : str | pd.DataFrame The windfarm layout file or Pandas DataFrame. See ``wombat.Windfarm`` for more details. + layout_coords : str + One of "distance" or "wgs-84" to represent either a meters-based distance from + a central location or a standard WGS-84 latitude and longitude, by default + "wgs-84". service_equipment : str | list[str | list[str, int]] The equpiment that will be used in the simulation. For multiple instances of a single vessel use a list of the file name/id and the number of vessels. See @@ -155,6 +159,11 @@ class Configuration(FromDictMixin): workday_end: int = field(converter=int) inflation_rate: float = field(converter=float) project_capacity: int | float = field(converter=float) + layout_coords: str = field( + default="wgs-84", + converter=(str, str.lower), + validator=validators.in_(["wgs-84", "distance"]), + ) fixed_costs: dict | str | Path = field(default=None) port: dict | str | Path = field(default=None) start_year: int = field(default=None) @@ -329,7 +338,7 @@ def _initialize_servicing_equipment(self, configuration: str | dict) -> None: self.env, self.windfarm, self.repair_manager, configuration ) equipment.finish_setup_with_environment_variables() - name = equipment.settings.name + name = equipment.name if name in self.service_equipment: msg = ( f"Servicing equipment `{name}` already exists, please use" @@ -399,6 +408,7 @@ def _setup_simulation(self): self.windfarm = Windfarm( env=self.env, windfarm_layout=self.config.layout, + layout_coords=self.config.layout_coords, repair_manager=self.repair_manager, substations=self.config.substations, turbines=self.config.turbines, @@ -411,10 +421,15 @@ def _setup_simulation(self): # Create the port and add any tugboats to the available servicing equipment list if self.config.port is not None: self.port = Port( - self.env, self.windfarm, self.repair_manager, self.config.port + env=self.env, + windfarm=self.windfarm, + repair_manager=self.repair_manager, + config=self.config.port, + vessel_configs=self.config.vessels, ) - for service_equipment in self.port.service_equipment_manager.items: - name = service_equipment.settings.name # type: ignore + tugboats = self.port.service_equipment_manager.reserve_vessels.items + for service_equipment in tugboats: + name = service_equipment.name # type: ignore if name in self.service_equipment: raise ValueError( f"Servicing equipment `{name}` already exists, please use" diff --git a/wombat/windfarm/system/subassembly.py b/wombat/windfarm/system/subassembly.py index 1adb2db7..38c54e37 100644 --- a/wombat/windfarm/system/subassembly.py +++ b/wombat/windfarm/system/subassembly.py @@ -204,7 +204,7 @@ def run_single_maintenance(self, maintenance: Maintenance) -> Generator: Yields ------ - simpy.events. HOURS_IN_DAY + simpy.events.Timeout Time between maintenance requests. """ while True: diff --git a/wombat/windfarm/windfarm.py b/wombat/windfarm/windfarm.py index 6f57265a..b73c93b2 100644 --- a/wombat/windfarm/windfarm.py +++ b/wombat/windfarm/windfarm.py @@ -36,6 +36,7 @@ def __init__( env: WombatEnvironment, windfarm_layout: str | pd.DataFrame, repair_manager: RepairManager, + layout_coords: str = "wgs-84", substations: dict[str, dict] | None = None, turbines: dict[str, dict] | None = None, cables: dict[str, dict] | None = None, @@ -44,6 +45,11 @@ def __init__( self.env = env self.repair_manager = repair_manager + if layout_coords not in ("wgs-84", "distance"): + msg = "Windfarm `layout_coords` must be one of 'wgs-84' or 'distance'." + raise ValueError(msg) + self.layout_coords = layout_coords + # Set up the layout and instantiate all windfarm objects self._inputs: dict[str, dict | None] = { "turbine": turbines, @@ -73,6 +79,50 @@ def __init__( self.env._register_windfarm(self) self.env.process(self._log_operations()) + def _validate_layout(self, layout: pd.DataFrame) -> pd.DataFrame: + """Validate the wind farm layout data, and create any missing columns used + during the inital graph layout setup steps that the user may not have provided. + + Parameters + ---------- + layout : pd.DataFrame + The wind farm layout. + + Returns + ------- + pd.DataFrame + The input :py:attr:`layout` after checking for required columns with any + missing optional columns added as 0-valued arrays. + + Raises + ------ + ValueError + Raised if any of "id", "substation_id", "name", "type", "string", "order", + "subassembly", or "upstream_cable" are missing. + """ + required_cols = [ + "id", + "substation_id", + "name", + "type", + "string", + "order", + "subassembly", + "upstream_cable", + ] + optional_number_cols = ["longitude", "latitude", "distance"] + + missing = set(required_cols).difference(layout.columns) + if missing: + msg = f"Farm layout is missing the required columns: {', '.join(missing)}" + raise ValueError(msg) + + missing = set(optional_number_cols).difference(layout.columns) + if missing: + layout = layout.assign(**dict.fromkeys(missing, 0.0)) + + return layout.sort_values(by=["string", "order"]) + def _create_graph_layout(self, windfarm_layout: str | pd.DataFrame) -> None: """Creates a network layout of the windfarm start from the substation(s) to be able to capture downstream turbines that can be cut off in the event of a @@ -87,13 +137,12 @@ def _create_graph_layout(self, windfarm_layout: str | pd.DataFrame) -> None: # it can be traversed in sequential order later if isinstance(windfarm_layout, str): layout_path = str(self.env.data_dir / "project/plant" / windfarm_layout) - layout = ( - pd.read_csv(layout_path) - .sort_values(by=["string", "order"]) - .reset_index(drop=True) - ) + layout = pd.read_csv(layout_path).reset_index(drop=True) else: layout = windfarm_layout.copy() + + layout = self._validate_layout(layout) + layout.subassembly = layout.subassembly.fillna("") layout.upstream_cable = layout.upstream_cable.fillna("") @@ -104,9 +153,6 @@ def _create_graph_layout(self, windfarm_layout: str | pd.DataFrame) -> None: for col in ("name", "latitude", "longitude", "subassembly"): nx.set_node_attributes(windfarm, dict(layout[["id", col]].values), name=col) - if "type" not in layout.columns: - raise KeyError("Missing columng 'type' to specify system types") - if (valid := layout["type"].isin(SystemType.types())).sum() != layout.shape[0]: invalid_names = layout.loc[~valid, "name"].values.tolist() msg = f"Inputs for {invalid_names} must be one of: {SystemType.types()}." @@ -281,10 +327,25 @@ def _create_cables(self) -> None: # If the real distance/cable length is not input, then the geodesic distance # is calculated if data["length"] == 0: - data["length"] = distance.geodesic( - start_coordinates, end_coordinates, ellipsoid="WGS-84" - ).km - + match self.layout_coords: + case "wgs-84": + data["length"] = distance.geodesic( + start_coordinates, end_coordinates, ellipsoid="WGS-84" + ).km + case "distance": + data["length"] = ( + np.linalg.norm( + np.array(start_coordinates) - np.array(end_coordinates) + ) + / 1000 + ) + case _: + msg = ( + "Did you reset `Windfarm.layout_coords` manually or" + " implement a new coordinate style without updating the" + " distance calculation?" + ) + raise ValueError(msg) # Encode whether it is an array cable or an export cable if self.graph.nodes[end_node]["type"] == SystemType.TURBINE: data["type"] = "array" @@ -312,9 +373,21 @@ def calculate_distance_matrix(self) -> None: for *_, data in (*self.graph.nodes(data=True), *self.graph.edges(data=True)) ] - dist = [ - distance.geodesic(c1, c2).km for c1, c2 in itertools.combinations(coords, 2) - ] + match self.layout_coords: + case "wgs-84": + dist = [ + distance.geodesic(c1, c2).km + for c1, c2 in itertools.combinations(coords, 2) + ] + case "distance": + c1, c2 = zip(*itertools.combinations(coords, 2)) + dist = np.linalg.norm(np.array(c1) - np.array(c2), axis=1) / 1000 + case _: + msg = ( + "Did you reset `Windfarm.layout_coords` manually or implement a" + " new coordinate style without updating the distance calculation?" + ) + raise ValueError(msg) dist_arr = np.ones((len(ids), len(ids))) triangle_ix = np.triu_indices_from(dist_arr, 1) dist_arr[triangle_ix] = dist_arr.T[triangle_ix] = dist