from pyprob to pyro probprog engine

.github/workflows/build.yml

@@ -26,12 +26,6 @@ jobs:
     - name: Install
       run: |
         python -m pip install --upgrade pip
-        pip install matplotlib
-        pip install jupyter
-        pip install numpy
-        pip install dsgp4
-        pip install pyprob
-        pip install skyfield
         pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
         pip install .[dev]
         pip install sphinx

README.md

@@ -22,26 +22,19 @@
 
 -----------------------------------------
 [![Build Status](https://github.com/kesslerlib/kessler/workflows/build/badge.svg)](https://github.com/kesslerlib/kessler/actions)
-[![Documentation Status](https://readthedocs.org/projects/kessler/badge/?version=latest)](https://kessler.readthedocs.io/en/latest/?badge=latest)
 [![codecov](https://codecov.io/gh/kesslerlib/kessler/branch/master/graph/badge.svg?token=EQ9CLXD909)](https://codecov.io/gh/kesslerlib/kessler)
 
 Kessler is a Python package for simulation-based inference and machine learning for space collision avoidance and assessment. It is named in honor of NASA scientist [Donald J. Kessler](https://en.wikipedia.org/wiki/Donald_J._Kessler) known for his studies regarding [space debris](https://en.wikipedia.org/wiki/Space_debris) and proposing the [Kessler syndrome](https://en.wikipedia.org/wiki/Kessler_syndrome).
 
-Developed by the [FDL Europe](https://fdleurope.org/) Constellations team in collaboration with [European Space Operations Centre (ESOC)](http://www.esa.int/esoc) of the [European Space Agency (ESA)](http://www.esa.int).
+Initially developed by the [FDL Europe](https://fdleurope.org/) Constellations team in collaboration with [European Space Operations Centre (ESOC)](http://www.esa.int/esoc) of the [European Space Agency (ESA)](http://www.esa.int).
 
 ## Documentation and roadmap
 
 To get started, follow the [documentation](https://kesslerlib.github.io/kessler/) examples.
 
 ## Authors
 
-* Giacomo Acciarini, University of Surrey
-* Francesco Pinto, University of Oxford
-* Francesca Letizia, European Space Agency
-* Chris Bridges, University of Surrey
-* Atılım Güneş Baydin, University of Oxford
-
-Kessler was initially developed by the Constellations team at the Frontier Development Lab (FDL) Europe 2020, a public–private partnership between the European Space Agency (ESA), Trillium Technologies, and University of Oxford.
+Kessler was initiated by the Constellations team at the Frontier Development Lab (FDL) Europe 2020, a public–private partnership between the European Space Agency (ESA), Trillium Technologies, and University of Oxford. The main developer is [Giacomo Acciarini](https://www.esa.int/gsp/ACT/team/giacomo_acciarini/).
 
 Constellations team members: Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin
 
@@ -51,7 +44,8 @@ Kessler is distributed under the GNU General Public License version 3. Get in to
 
 ## More info and how to cite
 
-If you would like to learn more about or cite the techniques Kessler uses, please see the following papers:
+If you use `kessler`, we would be grateful if you could star the repository and/or cite our work.
+If you would like to learn more about or cite the techniques `kessler` uses, please see the following papers:
 
 * Giacomo Acciarini, Nicola Baresi, Christopher Bridges, Leonard Felicetti, Stephen Hobbs, Atılım Güneş Baydin. 2023. [“Observation Strategies and Megaconstellations Impact on Current LEO Population.”](https://conference.sdo.esoc.esa.int/proceedings/neosst2/paper/88) In 2nd NEO and Debris Detection Conference.
 ```
@@ -63,7 +57,7 @@ If you would like to learn more about or cite the techniques Kessler uses, pleas
 }
 ```
 * Giacomo Acciarini, Francesco Pinto, Francesca Letizia, José A. Martinez-Heras, Klaus Merz, Christopher Bridges, and Atılım Güneş Baydin. 2021. [“Kessler: a Machine Learning Library for Spacecraft Collision Avoidance.”](https://conference.sdo.esoc.esa.int/proceedings/sdc8/paper/226) In 8th European Conference on Space Debris.
-```
+```bibtex
 @inproceedings{acciarini-2020-kessler,
   title = {Kessler: a Machine Learning Library for Spacecraft Collision Avoidance},
   author = {Acciarini, Giacomo and Pinto, Francesco and Letizia, Francesca and Martinez-Heras, José A. and Merz, Klaus and Bridges, Christopher and Baydin, Atılım Güneş},
@@ -72,7 +66,7 @@ If you would like to learn more about or cite the techniques Kessler uses, pleas
 }
 ```
 * Francesco Pinto, Giacomo Acciarini, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, and Atılım Güneş Baydin. 2020. “Towards Automated Satellite Conjunction Management with Bayesian Deep Learning.” In AI for Earth Sciences Workshop at NeurIPS 2020, Vancouver, Canada. [arXiv:2012.12450](https://arxiv.org/abs/2012.12450)
-```
+```bibtex
 @inproceedings{pinto-2020-automated,
   title = {Towards Automated Satellite Conjunction Management with Bayesian Deep Learning},
   author = {Pinto, Francesco and Acciarini, Giacomo and Metz, Sascha and Boufelja, Sarah and Kaczmarek, Sylvester and Merz, Klaus and Martinez-Heras, José A. and Letizia, Francesca and Bridges, Christopher and Baydin, Atılım Güneş},
@@ -81,7 +75,7 @@ If you would like to learn more about or cite the techniques Kessler uses, pleas
 }
 ```
 * Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, and Atılım Güneş Baydin. 2020. “Spacecraft Collision Risk Assessment with Probabilistic Programming.” In Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada. [arXiv:2012.10260](https://arxiv.org/abs/2012.10260)
-```
+```bibtex
 @inproceedings{acciarini-2020-spacecraft,
   title = {Spacecraft Collision Risk Assessment with Probabilistic Programming},
   author = {Acciarini, Giacomo and Pinto, Francesco and Metz, Sascha and Boufelja, Sarah and Kaczmarek, Sylvester and Merz, Klaus and Martinez-Heras, José A. and Letizia, Francesca and Bridges, Christopher and Baydin, Atılım Güneş},
@@ -92,7 +86,7 @@ If you would like to learn more about or cite the techniques Kessler uses, pleas
 
 ## Installation
 
-To install kessler, do the following:
+To install `kessler` locally, you can do the following:
 
 ```
 git clone https://github.com/kesslerlib/kessler.git

docs/notebooks/basics.ipynb

@@ -8,15 +8,6 @@
     "\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import kessler"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -45,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,92 +53,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loading CDMS (with extension .cdm.kvn.txt) from directory: /Users/giacomoacciarini/cdm_data/cdms_kvn/\n",
-      "Loaded 39 CDMs grouped into 4 events\n"
+      "Loading CDMS (with extension .kvn) from directory: synthetic_cdms/\n",
+      "Loaded 14 CDMs grouped into 2 events\n"
      ]
     }
    ],
    "source": [
-    "path_to_cdms_folder='cdm_data/cdms_kvn/'\n",
+    "path_to_cdms_folder='synthetic_cdms/'\n",
     "\n",
-    "events=EventDataset(path_to_cdms_folder)\n",
+    "events=EventDataset(path_to_cdms_folder,cdm_extension='.kvn')\n",
     "#A message appears confirming that the loading has happened, with the number of CDMs and events."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading CDMs from pandas ``DataFrame`` object\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "In this tutorial, we show how to load CDMs from pandas ``DataFrame`` object.\n",
-    "\n",
-    "First we perform the relevant imports:\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import kessler\n",
-    "import pandas as pd\n",
-    "from kessler import EventDataset\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, we create the ``EventDataset`` object, after having uploaded the pandas dataframe and created the ``DataFrame`` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataframe with 2 rows and 231 columns\n",
-      "Dropping columns with NaNs\n",
-      "Dataframe with 2 rows and 104 columns\n",
-      "Grouping by event_id\n",
-      "Grouped into 1 event(s)\n",
-      "Converting DataFrame to EventDataset\n",
-      "Time spent  | Time remain.| Progress             | Events | Events/sec\n",
-      "0d:00:00:00 | 0d:00:00:00 | #################### | 1/1 | 404.06         \n",
-      "\n",
-      "EventDataset(Events:1, number of CDMs per event: 2 (min), 2 (max), 2.00 (mean))\n"
-     ]
-    }
-   ],
-   "source": [
-    "file_name='cdm_data/cdms_csv/sample.csv'\n",
-    "df=pd.read_csv(file_name)\n",
-    "events=EventDataset.from_pandas(df)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -173,27 +97,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cannot import dbm.gnu: No module named '_gdbm'\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/giacomoacciarini/miniconda3/envs/fdl/lib/python3.7/site-packages/pyprob/util.py:327: UserWarning: Empirical distributions on disk may perform slow because GNU DBM is not available. Please install and configure gdbm library for Python for better speed.\n",
-      "  warnings.warn('Empirical distributions on disk may perform slow because GNU DBM is not available. Please install and configure gdbm library for Python for better speed.')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import kessler\n",
     "from kessler.data import kelvins_to_event_dataset"
    ]
   },
@@ -230,7 +137,7 @@
     }
    ],
    "source": [
-    "file_name='cdm_data/kelvins_data/test_data.csv'\n",
+    "file_name='kelvins_data/test_data.csv'\n",
     "events=kelvins_to_event_dataset(file_name, drop_features=['c_rcs_estimate', 't_rcs_estimate'], num_events=1000)\n",
     "#The output will show the number of CDMs and events loaded, as they progress.\n"
    ]

docs/notebooks/kelvins_dataset.ipynb

@@ -7,7 +7,6 @@
    "outputs": [],
    "source": [
     "import kessler\n",
-    "from kessler import EventDataset\n",
     "from kessler.nn import LSTMPredictor\n",
     "from kessler.data import kelvins_to_event_dataset\n",
     "import pandas as pd\n",
@@ -28,13 +27,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "322e9b06",
    "metadata": {},
    "outputs": [],
    "source": [
     "#As an example, we first show the case in which the data comes from the Kelvins competition.\n",
     "#For this, we built a specific converter that takes care of the conversion from Kelvins format\n",
     "#to standard CDM format (the data can be downloaded at https://kelvins.esa.int/collision-avoidance-challenge/data/):\n",
-    "file_name = '/home/gunes/data/kelvins/train_data/train_data.csv'\n",
+    "file_name='kelvins_data/train_data.csv'\n",
     "events = kelvins_to_event_dataset(file_name, drop_features=['c_rcs_estimate', 't_rcs_estimate'], num_events=1000) #we use only 200 events"
    ]
   },