From 8afa31cfd9f053de2ee83e0db3e111ab09f20579 Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 10:12:41 +0200 Subject: [PATCH 01/13] from pyprob to pyro probprog engine --- .github/workflows/build.yml | 6 - kessler/cdm.py | 8 +- kessler/model.py | 845 +++++++++++++++++++++--------------- kessler/util.py | 99 ++++- setup.py | 2 +- tests/test_util.py | 32 +- 6 files changed, 615 insertions(+), 377 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index e7fd3a7..154e3e0 100644 --- a/.github/workflows/build.yml +++ b/.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 diff --git a/kessler/cdm.py b/kessler/cdm.py index c08f6a3..69879fe 100644 --- a/kessler/cdm.py +++ b/kessler/cdm.py @@ -258,10 +258,10 @@ def set_state(self, object_id, state): self._update_miss_distance() def _update_miss_distance(self): - state_object1 = self.get_state(0) + state_object1 = self.get_state(0)*1e3 # if np.isnan(state_object1.sum()): # warnings.warn('state_object1 has NaN') - state_object2 = self.get_state(1) + state_object2 = self.get_state(1)*1e3 # if np.isnan(state_object2.sum()): # warnings.warn('state_object2 has NaN') @@ -286,10 +286,10 @@ def relative_state(state_obj_1, state_obj_2): relative_state[1] = np.array([np.dot(rot_matrix[0], rel_velocity_xyz), np.dot(rot_matrix[1], rel_velocity_xyz), np.dot(rot_matrix[2], rel_velocity_xyz)]) return relative_state - state_object1 = self.get_state(0) + state_object1 = self.get_state(0)*1e3 # if np.isnan(state_object1.sum()): # warnings.warn('state_object1 has NaN') - state_object2 = self.get_state(1) + state_object2 = self.get_state(1)*1e3 # if np.isnan(state_object2.sum()): # warnings.warn('state_object2 has NaN') diff --git a/kessler/model.py b/kessler/model.py index 6fd3c14..f36e1d3 100644 --- a/kessler/model.py +++ b/kessler/model.py @@ -1,72 +1,27 @@ -import numpy as np import dsgp4 -import torch +import numpy as np import uuid -# import warnings +import torch +import pyro +import pyro.distributions as dist -from . import util, ConjunctionDataMessage -from dsgp4.tle import TLE +from . import GNSS, Radar, ConjunctionDataMessage, util +from dsgp4 import TLE -import pyprob -from pyprob import Model -from pyprob.distributions import Mixture, TruncatedNormal, Uniform, Normal, Bernoulli +from torch.distributions import Normal, constraints +from torch.distributions.distribution import Distribution +from torch.distributions.utils import broadcast_all +from pyro.poutine import trace +from pyro.distributions import MixtureSameFamily, Categorical, Uniform, Normal, Bernoulli -def default_prior(): - """ - This function returns a dictionary of TLE elements priors, corresponding to the 22nd of - May 2020. Each prior is a probability density function. - - Returns: - - p (``dict``): dictionary of ``pyprob.distributions`` - """ - p={} - p['mean_motion_prior']=Mixture( - distributions=[TruncatedNormal(0.0010028142482042313, 0.00004670943133533001, low=0.0, high=0.004), - TruncatedNormal(0.00017592836171388626, 0.00003172305878251791, low=0.0, high=0.004), - TruncatedNormal(0.0010926761478185654, 0.000027726569678634405, low=0.0, high=0.004), - TruncatedNormal(0.0003353552892804146, 0.00007733114063739777, low=0.0, high=0.004), - TruncatedNormal(0.0007777251303195953, 0.00013636205345392227, low=0.0, high=0.004), - TruncatedNormal(0.001032940074801445, 0.00002651428570970893, low=0.0, high=0.004)], - probs=[0.12375596165657043, 0.05202080309391022, 0.21220888197422028, 0.0373813770711422, 0.01674230769276619, 0.5578906536102295]) - p['mean_anomaly_prior'] = Uniform(low=0.0, high=6.2831854820251465) - p['eccentricity_prior'] = Mixture( - distributions=[TruncatedNormal(0.0028987403493374586, 0.002526970813050866, low=0.0, high=0.8999999761581421), - TruncatedNormal(0.6150050163269043, 0.07872536778450012, low=0.0, high=0.8999999761581421), - TruncatedNormal(0.05085373669862747, 0.024748045951128006, low=0.0, high=0.8999999761581421), - TruncatedNormal(0.3420163094997406, 0.18968918919563293, low=0.0, high=0.8999999761581421), - TruncatedNormal(0.7167646288871765, 0.011966796591877937, low=0.0, high=0.8999999761581421), - TruncatedNormal(0.013545362278819084, 0.0068586356937885284, low=0.0, high=0.8999999761581421)], - probs=[0.5433819890022278, 0.04530993849039078, 0.08378008753061295, 0.02705608867108822, 0.03350389748811722, 0.2669680118560791]) - p['inclination_prior'] = Mixture( - distributions=[TruncatedNormal(0.09954200685024261, 0.04205162078142166, low=0, high=3.1415), - TruncatedNormal(1.4393062591552734, 0.012214339338243008, low=0, high=3.1415), - TruncatedNormal(1.736578106880188, 0.11822951585054398, low=0, high=3.1415), - TruncatedNormal(1.0963480472564697, 0.010178830474615097, low=0, high=3.1415), - TruncatedNormal(0.48166394233703613, 0.04073172062635422, low=0, high=3.1415), - TruncatedNormal(0.9063634872436523, 0.04156989976763725, low=0, high=3.1415), - TruncatedNormal(1.275956392288208, 0.02754846028983593, low=0, high=3.1415), - TruncatedNormal(2.5208728313446045, 0.003279004478827119, low=0, high=3.1415), - TruncatedNormal(1.5189905166625977, 0.02461068518459797, low=0, high=3.1415), - TruncatedNormal(0.3474450707435608, 0.0433642603456974, low=0, high=3.1415), - TruncatedNormal(0.6648743152618408, 0.11472384631633759, low=0, high=3.1415), - TruncatedNormal(1.1465401649475098, 0.014345825649797916, low=0, high=3.1415), - TruncatedNormal(1.7207987308502197, 0.012212350033223629, low=0, high=3.1415)], - probs=[0.028989605605602264, 0.10272273421287537, 0.02265254408121109, 0.019256576895713806, 0.028676774352788925, 0.06484941393136978, 0.13786117732524872, 0.0010146398562937975, 0.047179922461509705, 0.01607278548181057, 0.020023610442876816, 0.06644929945468903, 0.4442509114742279]) - p['argument_of_perigee_prior'] = Uniform(low=0.0, high=6.2829999923706055) - p['raan_prior'] = Uniform(low=0.0, high=6.2829999923706055) - p['mean_motion_first_derivative_prior'] = Normal(4.937096738377722e-13, 5.807570136601159e-13) - p['b_star_prior'] = Mixture( - distributions=[Normal(0.0002002030232688412, 0.0011279708705842495), - Normal(0.3039868175983429, 0.06403032690286636), - Normal(0.003936616238206625, 0.012939595617353916), - Normal(-0.04726288095116615, 0.17036935687065125), - Normal(0.08823495358228683, 0.061987943947315216)], - probs=[0.9688150882720947, 0.0012630978599190712, 0.024090370163321495, 0.0009446446783840656, 0.0048867943696677685]) - return p - +import torch +from torch.distributions import Normal, Distribution +from torch.distributions import constraints -def find_conjunction(tr0, tr1, miss_dist_threshold): +def find_conjunction(tr0, + tr1, + miss_dist_threshold): """" Find the closest and first conjunction between two trajectories. Args: @@ -95,163 +50,349 @@ def find_conjunction(tr0, tr1, miss_dist_threshold): d_conj = squared_norm[i_conj].sqrt() return i_min, d_min, i_conj, d_conj -class Conjunction(Model): + +class TruncatedNormal(Distribution): """ - A class for simulating conjunctions. + Truncated Normal distribution with specified lower and upper bounds. + This class inherits from the Pyro Distribution class and implements + the log probability and sampling methods for a truncated normal distribution. Args: - time0 (``float``): The time of the first observation in MJD. - max_duration_days (``float``): The maximum duration of the observations in days. - time_resolution (``float``): The time resolution of the observations in seconds. - time_upsample_factor (``int``): The factor by which the time resolution is upsampled. - miss_dist_threshold (``float``): The miss distance threshold in meters. - prior_dict (``dict``): A dictionary of priors for the parameters of the conjunctions. If None, a default dictionary is used. - t_prob_new_obs (``float``): The probability of a new observation at the next CDM update. - c_prob_new_obs (``float``): The probability of a new observation at the next CDM update. - cdm_update_every_hours (``float``): The number of hours between consecutive CDM updates. - mc_samples (``int``): The number of samples for Monte Carlo integration. - mc_upsample_factor (``int``): The factor by which the time resolution is upsampled for Monte Carlo integration. - pc_method (``str``): The method for computing the probability of collision. Can be 'MC' or 'CDM'. - collision_threshold (``float``): The collision threshold in meters. - likelihood_t_stddev (``list``): The standard deviation of the likelihood for the true anomaly - likelihood_c_stddev (``list``): The standard deviation of the likelihood for the collision distance - likelihood_time_to_tca_stdev (``list``): The standard deviation of the likelihood for the time to TCA. - - Returns: - A model for conjunctions. + loc (``torch.Tensor``): The mean of the normal distribution. + scale (``torch.Tensor``): The standard deviation of the normal distribution. + low (``torch.Tensor``, optional): The lower bound for truncation. Default is None. + high (``torch.Tensor``, optional): The upper bound for truncation. Default is None. + validate_args (bool, optional): Whether to validate the arguments. Default is None. + + Attributes: + loc (``torch.Tensor``): The mean of the normal distribution. + scale (``torch.Tensor``): The standard deviation of the normal distribution. + low (``torch.Tensor``): The lower bound for truncation. + high (``torch.Tensor``): The upper bound for truncation. + base_dist (``Normal``): The base normal distribution. + + Methods: + log_prob(value): Computes the log probability of the given value. + sample(sample_shape): Samples from the truncated normal distribution. + """ + arg_constraints = { + 'loc': constraints.real, # loc can be any real number + 'scale': constraints.positive, # scale must be positive + 'low': constraints.real, # low can be any real number + 'high': constraints.real # high can be any real number + } + + def __init__(self, loc, scale, low=None, high=None, validate_args=None): + # Convert inputs to tensors and handle None values + self.loc, self.scale, self.low, self.high = broadcast_all( + torch.as_tensor(loc), torch.as_tensor(scale), + torch.as_tensor(low) if low is not None else None, + torch.as_tensor(high) if high is not None else None + ) + # Validate bounds (low < high) + if self.low is not None and self.high is not None: + if (self.low >= self.high).any(): + raise ValueError("Invalid bounds: low must be less than high.") + # Create a base Normal distribution + self.base_dist = Normal(loc, scale) + super().__init__(batch_shape=self.loc.shape, validate_args=validate_args) + + def log_prob(self, value): + # Calculate log probability for the normal distribution + log_prob = self.base_dist.log_prob(value) + # Adjust for truncation at the low bound (if any) + if self.low is not None: + log_prob -= torch.log(torch.clamp(self.base_dist.cdf(self.low), min=1e-10)) + # Adjust for truncation at the high bound (if any) + if self.high is not None: + log_prob -= torch.log(torch.clamp(1 - self.base_dist.cdf(self.high), min=1e-10)) + + return log_prob + + def sample(self, sample_shape=torch.Size()): + shape = self._extended_shape(sample_shape) + rand = torch.rand(shape, dtype=self.loc.dtype, device=self.loc.device) + if self.low is not None: + rand = rand * (1 - self.base_dist.cdf(self.low)) + self.base_dist.cdf(self.low) + if self.high is not None: + rand = rand * self.base_dist.cdf(self.high) + return self.base_dist.icdf(rand) + +def default_prior(): + """ + This function returns a dictionary of TLE elements priors. + Each prior is a probability density function defined using Pyro distributions. + The population of objects from which these priors were derived is the one of May 2022. + The priors are defined as follows: + - mean_motion_prior: MixtureSameFamily + - mean_anomaly_prior: Uniform + - eccentricity_prior: MixtureSameFamily + - inclination_prior: MixtureSameFamily + - argument_of_perigee_prior: Uniform + - raan_prior: Uniform + - mean_motion_first_derivative_prior: Uniform + - b_star_prior: MixtureSameFamily + The parameters of the distributions are based on the population of objects. + The priors are defined in the following ranges: + - mean_motion: [0.0, 0.004] + - mean_anomaly: [0.0, 2 * pi] + - eccentricity: [0.0, 0.9] + - inclination: [0.0, pi] + - argument_of_perigee: [0.0, 2 * pi] + - raan: [0.0, 2 * pi] + - mean_motion_first_derivative: [4.937096738377722e-13, 5.807570136601159e-13] + - b_star: (-inf, inf) + """ + p = {} + + # Mean Motion Prior + p['mean_motion_prior'] = MixtureSameFamily( + mixture_distribution=Categorical(probs=torch.tensor([ + 0.12375596165657043, 0.05202080309391022, 0.21220888197422028, + 0.0373813770711422, 0.01674230769276619, 0.5578906536102295])), + component_distribution=TruncatedNormal( + low=0.0, high=0.004, loc=torch.tensor([ + 0.0010028142482042313, 0.00017592836171388626, 0.0010926761478185654, + 0.0003353552892804146, 0.0007777251303195953, 0.001032940074801445]), + scale=torch.tensor([ + 0.00004670943133533001, 0.00003172305878251791, 0.000027726569678634405, + 0.00007733114063739777, 0.00013636205345392227, 0.00002651428570970893])) + ) + + # Mean Anomaly Prior + p['mean_anomaly_prior'] = Uniform(low=0.0, high=2 * torch.pi) + + # Eccentricity Prior + p['eccentricity_prior'] = MixtureSameFamily( + mixture_distribution=Categorical(probs=torch.tensor([ + 0.5433819890022278, 0.04530993849039078, 0.08378008753061295, + 0.02705608867108822, 0.03350389748811722, 0.2669680118560791])), + component_distribution=TruncatedNormal( + low=0.0, high=0.8999999761581421, loc=torch.tensor([ + 0.0028987403493374586, 0.6150050163269043, 0.05085373669862747, + 0.3420163094997406, 0.7167646288871765, 0.013545362278819084]), + scale=torch.tensor([ + 0.002526970813050866, 0.07872536778450012, 0.024748045951128006, + 0.18968918919563293, 0.011966796591877937, 0.0068586356937885284])) + ) + + # Inclination Prior + p['inclination_prior'] = MixtureSameFamily( + mixture_distribution=Categorical(probs=torch.tensor([ + 0.028989605605602264, 0.10272273421287537, 0.02265254408121109, 0.019256576895713806, + 0.028676774352788925, 0.06484941393136978, 0.13786117732524872, 0.0010146398562937975, + 0.047179922461509705, 0.01607278548181057, 0.020023610442876816, 0.06644929945468903, + 0.4442509114742279])), + component_distribution=TruncatedNormal( + low=0.0, high=torch.pi, loc=torch.tensor([ + 0.09954200685024261, 1.4393062591552734, 1.736578106880188, 1.0963480472564697, + 0.48166394233703613, 0.9063634872436523, 1.275956392288208, 2.5208728313446045, + 1.5189905166625977, 0.3474450707435608, 0.6648743152618408, 1.1465401649475098, + 1.7207987308502197]), + scale=torch.tensor([ + 0.04205162078142166, 0.012214339338243008, 0.11822951585054398, 0.010178830474615097, + 0.04073172062635422, 0.04156989976763725, 0.02754846028983593, 0.003279004478827119, + 0.02461068518459797, 0.0433642603456974, 0.11472384631633759, 0.014345825649797916, + 0.012212350033223629])) + ) + + # Argument of Perigee Prior + p['argument_of_perigee_prior'] = Uniform(low=0.0, high=2 * torch.pi) + + # RAAN Prior + p['raan_prior'] = Uniform(low=0.0, high=2 * torch.pi) + + # Mean Motion First Derivative Prior + p['mean_motion_first_derivative_prior'] = Uniform(4.937096738377722e-13, 5.807570136601159e-13) + + # B* Prior + p['b_star_prior'] = MixtureSameFamily( + mixture_distribution=Categorical(probs=torch.tensor([ + 0.9688150882720947, 0.0012630978599190712, 0.024090370163321495, 0.0009446446783840656, + 0.0048867943696677685])), + component_distribution=Normal( + loc=torch.tensor([0.0002002030232688412, 0.3039868175983429, 0.003936616238206625, + -0.04726288095116615, 0.08823495358228683]), + scale=torch.tensor([0.0011279708705842495, 0.06403032690286636, 0.012939595617353916, + 0.17036935687065125, 0.061987943947315216])) + ) + + return p + + +class Conjunction: + """ + This class implements the Conjunction class, which is used to generate conjunction data messages (CDM) for two objects in space. + The class uses the Pyro probabilistic programming library to define the model and perform inference. + The class has the following attributes: + - time0: The time of the first observation in MJD. + - max_duration_days: The maximum duration of the simulation in days. + - time_resolution: The time resolution of the simulation in seconds. + - time_upsample_factor: The upsample factor for the time resolution. + - miss_dist_threshold: The miss distance threshold in meters. + - prior_dict: A dictionary containing the priors for the TLE elements. + - t_prob_new_obs: The probability of a new observation for the target. + - c_prob_new_obs: The probability of a new observation for the chaser. + - cdm_update_every_hours: The interval at which to update the CDM in hours. + - mc_samples: The number of Monte Carlo samples to use for uncertainty propagation. + - mc_upsample_factor: The upsample factor for the Monte Carlo samples. + - pc_method: The method to use for calculating the probability of collision. + - collision_threshold: The threshold for considering a collision in meters. + - likelihood_t_stddev: The standard deviation of the likelihood for the target. + - likelihood_c_stddev: The standard deviation of the likelihood for the chaser. + - likelihood_time_to_tca_stddev: The standard deviation of the likelihood for time to TCA. + + Example: + >>> from kessler import Conjunction + >>> conj = Conjunction() """ def __init__(self, time0=58991.90384230018, max_duration_days=7.0, time_resolution=6e5, time_upsample_factor=100, - miss_dist_threshold=20e3, + miss_dist_threshold=5e3, prior_dict=None, - t_prob_new_obs = 0.96, - c_prob_new_obs = 0.4, - cdm_update_every_hours = 8., - mc_samples = 100, - mc_upsample_factor = 100, - pc_method = 'MC', - collision_threshold = 70, - likelihood_t_stddev = [3.71068006e+02, 9.99999999e-02, 1.72560879e-01], - likelihood_c_stddev = [3.71068006e+02, 9.99999999e-02, 1.72560879e-01], - likelihood_time_to_tca_stddev = 0.7, - t_observing_instruments = [], - c_observing_instruments = [], - t_tle = None, - c_tle = None - ): - + t_prob_new_obs=0.96, + c_prob_new_obs=0.4, + cdm_update_every_hours=8., + mc_samples=100, + mc_upsample_factor=100, + pc_method='MC', + up_method='MC', + collision_threshold=70, + likelihood_t_stddev=[3.71068006e+02, 9.99999999e-02, 1.72560879e-01], + likelihood_c_stddev=[3.71068006e+02, 9.99999999e-02, 1.72560879e-01], + likelihood_time_to_tca_stddev=0.7, + t_observing_instruments=None, + c_observing_instruments=None, + t_tle=None, + c_tle=None): self._time0 = time0 self._max_duration_days = max_duration_days self._time_resolution = time_resolution self._time_upsample_factor = time_upsample_factor self._delta_time = max_duration_days / time_resolution self._miss_dist_threshold = miss_dist_threshold # miss distance threshold in [m] - if prior_dict is None: - self._prior_dict = default_prior() - else: - self._prior_dict = prior_dict + self._prior_dict = prior_dict or default_prior() self._t_prob_new_obs = t_prob_new_obs self._c_prob_new_obs = c_prob_new_obs self._cdm_update_every_hours = cdm_update_every_hours self._mc_samples = mc_samples self._mc_upsample_factor = mc_upsample_factor + if pc_method not in ['MC', 'FOSTER-1992']: + raise ValueError( + f"Unknown method for probability of collision: {pc_method}. Currently, we only support MC and FOSTER-1992.\n" + "We are happy to receive your contributions through pull requests to extend Kessler's support to other Pc methods.") self._pc_method = pc_method + if up_method not in ['MC', 'STM']: + raise ValueError( + f"Unknown method for uncertainty propagation: {up_method}. Currently, we only support MC and STM.\n" + "We are happy to receive your contributions through pull requests to extend Kessler's support to other UP methods.") + self._up_method = up_method self._collision_threshold = collision_threshold self._likelihood_t_stddev = likelihood_t_stddev self._likelihood_c_stddev = likelihood_c_stddev self._likelihood_time_to_tca_stddev = likelihood_time_to_tca_stddev - if len(t_observing_instruments)==0 or len(c_observing_instruments)==0: + if t_observing_instruments is None: + t_instrument_characteristics={'bias_xyz': np.array([[0., 0., 0.],[0., 0., 0.]]), 'covariance_rtn': np.array([1e-9, 1.115849341564346, 0.059309835843067, 1e-9, 1e-9, 1e-9])**2} + t_observing_instruments=[GNSS(t_instrument_characteristics)] + print(f'No observing instruments for target, using default one with diagonal covariance {t_observing_instruments[0]._instrument_characteristics['covariance_rtn']}') + if c_observing_instruments is None: + c_instrument_characteristics={'bias_xyz': np.array([[0., 0., 0.],[0., 0., 0.]]), 'covariance_rtn': np.array([1.9628939405514678, 2.2307686944695706, 0.9660907831563862, 1e-9, 1e-9, 1e-9])**2} + c_observing_instruments=[Radar(c_instrument_characteristics)] + print(f'No observing instruments for chaser, using default one with diagonal covariance {c_observing_instruments[0]._instrument_characteristics['covariance_rtn']}') + if len(t_observing_instruments) == 0 or len(c_observing_instruments) == 0: raise ValueError("We need at least one observing instrument for target and chaser!") self._t_observing_instruments = t_observing_instruments self._c_observing_instruments = c_observing_instruments self._t_tle = t_tle self._c_tle = c_tle - super().__init__(name='Conjunction') def make_target(self): - """" - This function creates a target object, as a TLE (``dsgp4.tle.TLE``). - """ if self._t_tle is None: d = {} - d['mean_motion'] = pyprob.sample(self._prior_dict['mean_motion_prior'], name='t_mean_motion') - d['mean_anomaly'] = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name='t_mean_anomaly') - d['eccentricity'] = pyprob.sample(self._prior_dict['eccentricity_prior'], name='t_eccentricity') - d['inclination'] = pyprob.sample(self._prior_dict['inclination_prior'], name='t_inclination') - d['argument_of_perigee'] = pyprob.sample(self._prior_dict['argument_of_perigee_prior'], name='t_argument_of_perigee') - d['raan'] = pyprob.sample(self._prior_dict['raan_prior'], name='t_raan') - d['mean_motion_first_derivative'] = pyprob.sample(self._prior_dict['mean_motion_first_derivative_prior'], name='t_mean_motion_first_derivative') - d['mean_motion_second_derivative'] = 0.0 # pybrob.sample(Uniform(0.0,1e-17)) - pyprob.tag(d['mean_motion_second_derivative'], 't_mean_motion_second_derivative') - d['b_star'] = pyprob.sample(self._prior_dict['b_star_prior'], name='t_b_star') + d['mean_motion'] = pyro.sample('t_mean_motion', self._prior_dict['mean_motion_prior']) + d['mean_anomaly'] = pyro.sample('t_mean_anomaly', self._prior_dict['mean_anomaly_prior']) + d['eccentricity'] = pyro.sample('t_eccentricity', self._prior_dict['eccentricity_prior']) + d['inclination'] = pyro.sample('t_inclination', self._prior_dict['inclination_prior']) + d['argument_of_perigee'] = pyro.sample('t_argument_of_perigee', self._prior_dict['argument_of_perigee_prior']) + d['raan'] = pyro.sample('t_raan', self._prior_dict['raan_prior']) + d['mean_motion_first_derivative'] = pyro.sample('t_mean_motion_first_derivative', self._prior_dict['mean_motion_first_derivative_prior']) + d['mean_motion_second_derivative'] = 0.0 + pyro.deterministic('t_mean_motion_second_derivative', torch.tensor(d['mean_motion_second_derivative'])) + d['b_star'] = pyro.sample('t_b_star', self._prior_dict['b_star_prior']) d['satellite_catalog_number'] = 43437 d['classification'] = 'U' d['international_designator'] = '18100A' d['ephemeris_type'] = 0 d['element_number'] = 9996 d['revolution_number_at_epoch'] = 56353 - d['epoch_year'] = util.from_mjd_to_datetime(self._time0).year - d['epoch_days'] = util.from_mjd_to_epoch_days_after_1_jan(self._time0) + d['epoch_year'] = dsgp4.util.from_mjd_to_datetime(self._time0).year + d['epoch_days'] = dsgp4.util.from_mjd_to_epoch_days_after_1_jan(self._time0) tle = TLE(d) return tle else: - mean_anomaly = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name = 't_mean_anomaly') + mean_anomaly = pyro.sample('t_mean_anomaly', self._prior_dict['mean_anomaly_prior']) tle = self._t_tle.copy() tle.update({'mean_anomaly': mean_anomaly}) - pyprob.tag(tle.mean_motion, name='t_mean_motion') - pyprob.tag(tle.eccentricity, name='t_eccentricity') - pyprob.tag(tle.inclination, name='t_inclination') - pyprob.tag(tle.argument_of_perigee, name='t_argument_of_perigee') - pyprob.tag(tle.raan, name='t_raan') - pyprob.tag(tle.mean_motion_first_derivative, name='t_mean_motion_first_derivative') - pyprob.tag(tle.mean_motion_second_derivative, name='t_mean_motion_second_derivative') - pyprob.tag(tle.b_star, name='t_b_star') + pyro.deterministic('t_mean_motion', torch.tensor(tle.mean_motion)) + pyro.deterministic('t_eccentricity', torch.tensor(tle.eccentricity)) + pyro.deterministic('t_inclination', torch.tensor(tle.inclination)) + pyro.deterministic('t_argument_of_perigee', torch.tensor(tle.argument_of_perigee)) + pyro.deterministic('t_raan', torch.tensor(tle.raan)) + pyro.deterministic('t_mean_motion_first_derivative', torch.tensor(tle.mean_motion_first_derivative)) + pyro.deterministic('t_mean_motion_second_derivative', torch.tensor(tle.mean_motion_second_derivative)) + pyro.deterministic('t_b_star', torch.tensor(tle.b_star)) return tle - def make_chaser(self): """ This function creates a chaser object, as a TLE (``dsgp4.tle.TLE``). """ if self._c_tle is None: d = {} - d['mean_motion'] = pyprob.sample(self._prior_dict['mean_motion_prior'], name='c_mean_motion') - d['mean_anomaly'] = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name='c_mean_anomaly') - d['eccentricity'] = pyprob.sample(self._prior_dict['eccentricity_prior'], name='c_eccentricity') - d['inclination'] = pyprob.sample(self._prior_dict['inclination_prior'], name='c_inclination') - d['argument_of_perigee'] = pyprob.sample(self._prior_dict['argument_of_perigee_prior'], name='c_argument_of_perigee') - d['raan'] = pyprob.sample(self._prior_dict['raan_prior'], name='c_raan') - d['mean_motion_first_derivative'] = pyprob.sample(self._prior_dict['mean_motion_first_derivative_prior'], name='c_mean_motion_first_derivative') + d['mean_motion'] = pyro.sample('c_mean_motion',self._prior_dict['mean_motion_prior']) + d['mean_anomaly'] = pyro.sample('c_mean_anomaly',self._prior_dict['mean_anomaly_prior']) + d['eccentricity'] = pyro.sample('c_eccentricity',self._prior_dict['eccentricity_prior']) + d['inclination'] = pyro.sample('c_inclination',self._prior_dict['inclination_prior']) + d['argument_of_perigee'] = pyro.sample('c_argument_of_perigee',self._prior_dict['argument_of_perigee_prior']) + d['raan'] = pyro.sample('c_raan',self._prior_dict['raan_prior']) + d['mean_motion_first_derivative'] = pyro.sample('c_mean_motion_first_derivative',self._prior_dict['mean_motion_first_derivative_prior']) d['mean_motion_second_derivative'] = 0.0 # pybrob.sample(Uniform(0.0,1e-17)) - pyprob.tag(d['mean_motion_second_derivative'], 'c_mean_motion_second_derivative') - d['b_star'] = pyprob.sample(self._prior_dict['b_star_prior'], name='c_b_star') + pyro.deterministic('c_mean_motion_second_derivative',torch.tensor(d['mean_motion_second_derivative'])) + d['b_star'] = pyro.sample('c_b_star',self._prior_dict['b_star_prior']) d['satellite_catalog_number'] = 43437 d['classification'] = 'U' d['international_designator'] = '18100A' d['ephemeris_type'] = 0 d['element_number'] = 9996 d['revolution_number_at_epoch'] = 56353 - d['epoch_year'] = util.from_mjd_to_datetime(self._time0).year - d['epoch_days'] = util.from_mjd_to_epoch_days_after_1_jan(self._time0) + d['epoch_year'] = dsgp4.util.from_mjd_to_datetime(self._time0).year + d['epoch_days'] = dsgp4.util.from_mjd_to_epoch_days_after_1_jan(self._time0) tle = TLE(d) return tle else: - mean_anomaly = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name = 'c_mean_anomaly') + mean_anomaly = pyro.sample('c_mean_anomaly', self._prior_dict['mean_anomaly_prior']) tle = self._c_tle.copy() tle.update({'mean_anomaly': mean_anomaly}) - pyprob.tag(tle.mean_motion, name='c_mean_motion') - pyprob.tag(tle.eccentricity, name='c_eccentricity') - pyprob.tag(tle.inclination, name='c_inclination') - pyprob.tag(tle.argument_of_perigee, name='c_argument_of_perigee') - pyprob.tag(tle.raan, name='c_raan') - pyprob.tag(tle.mean_motion_first_derivative, name='c_mean_motion_first_derivative') - pyprob.tag(tle.mean_motion_second_derivative, name='c_mean_motion_second_derivative') - pyprob.tag(tle.b_star, name='c_b_star') + pyro.deterministic('c_mean_motion',tle.mean_motion) + pyro.deterministic('c_eccentricity',tle.eccentricity) + pyro.deterministic('c_inclination',tle.inclination) + pyro.deterministic('c_argument_of_perigee',tle.argument_of_perigee) + pyro.deterministic('c_raan',tle.raan) + pyro.deterministic('c_mean_motion_first_derivative',tle.mean_motion_first_derivative) + pyro.deterministic('c_mean_motion_second_derivative',tle.mean_motion_second_derivative) + pyro.deterministic('c_b_star',tle.b_star) return tle - def generate_cdm(self, t_state_new_obs, c_state_new_obs, time_obs_mjd, time_conj_mjd, t_tle, c_tle, previous_cdm): + def generate_cdm(self, + t_state_new_obs, + c_state_new_obs, + time_obs_mjd, + time_conj_mjd, + t_tle, + c_tle, + previous_cdm): """ This function generates a conjunction data message (``kessler.cdm.ConjunctionDataMessage``) from the current state of the chaser and target. @@ -262,15 +403,12 @@ def generate_cdm(self, t_state_new_obs, c_state_new_obs, time_obs_mjd, time_conj time_conj_mjd (``float``): The time of the conjunction in MJD. t_tle (``dsgp4.tle.TLE``): The TLE of the target. c_tle (``dsgp4.tle.TLE``): The TLE of the chaser. - previous_cdm (``kessler.cdm.ConjunctionDataMessage``): The previous conjunction data message. + previous_cdm (``kessler.cdm.ConjunctionDataMessage``): The previous conjunction data message (it can be None in case there's no history of CDMs). Returns: cdm (``kessler.cdm.ConjunctionDataMessage``): The conjunction data message. None, if no CDM has to be generated. """ - # time_conj_mjd = time_conj_mjd + pyprob.sample(Normal(0, 0.00001)) if c_state_new_obs is not None or t_state_new_obs is not None: - # print('\n\n') - # print('new cdm') if previous_cdm: cdm = previous_cdm.copy() else: @@ -325,83 +463,65 @@ def generate_cdm(self, t_state_new_obs, c_state_new_obs, time_obs_mjd, time_conj cdm.set_relative_metadata('TCA', util.from_jd_to_cdm_datetime_str(tca_jd)) cdm.set_header('CREATION_DATE', util.from_jd_to_cdm_datetime_str(obs_jd)) if t_state_new_obs is not None: -# print("target") -# print(t_tle,time_obs_mjd) + obs_tle_t,_=dsgp4.newton_method(t_tle,time_obs_mjd,verbose=True) # we return the state in XYZ, the state in RTN and the cov matrix in RTN - if self._pc_method == 'MC': - obs_tle_t,_=dsgp4.newton_method(t_tle,time_obs_mjd) + if self._up_method == 'MC': t_mean_state_tca_xyz_TEME, t_cov_state_tca_rtn = self.propagate_uncertainty_monte_carlo(state_xyz = t_state_new_obs, cov_matrix_diagonal_rtn = self._t_cov_matrix_diagonal_obs_noise, - time_obs_mjd = time_obs_mjd, time_ca_mjd = time_conj_mjd, obs_tle = obs_tle_t) - t_mean_state_tca_xyz_TEME = t_mean_state_tca_xyz_TEME / 1e3 - t_mean_state_tca_xyz_ITRF = util.from_TEME_to_ITRF(t_mean_state_tca_xyz_TEME, tca_jd) + t_mean_state_tca_xyz_ITRF = util.from_TEME_to_ITRF(t_mean_state_tca_xyz_TEME, tca_jd)/ 1e3 cdm.set_state(0, t_mean_state_tca_xyz_ITRF) cdm.set_covariance(0, t_cov_state_tca_rtn) - + #elif self._up_method == 'STM': if c_state_new_obs is not None: -# print("chaser") -# print(c_tle,time_obs_mjd) - obs_tle_c,_=dsgp4.newton_method(c_tle,time_obs_mjd) - if self._pc_method == 'MC': + obs_tle_c,_=dsgp4.newton_method(c_tle,time_obs_mjd,verbose=True) + if self._up_method == 'MC': c_mean_state_tca_xyz_TEME, c_cov_state_tca_rtn = self.propagate_uncertainty_monte_carlo(state_xyz = c_state_new_obs, cov_matrix_diagonal_rtn = self._c_cov_matrix_diagonal_obs_noise, - time_obs_mjd = time_obs_mjd, time_ca_mjd = time_conj_mjd, obs_tle = obs_tle_c) - c_mean_state_tca_xyz_TEME = c_mean_state_tca_xyz_TEME / 1e3 - c_mean_state_tca_xyz_ITRF = util.from_TEME_to_ITRF(c_mean_state_tca_xyz_TEME, tca_jd) + c_mean_state_tca_xyz_ITRF = util.from_TEME_to_ITRF(c_mean_state_tca_xyz_TEME, tca_jd)/ 1e3 cdm.set_state(1, c_mean_state_tca_xyz_ITRF) cdm.set_covariance(1, c_cov_state_tca_rtn) - #I now resample at TCA to find Pc: + #elif self._up_method == 'STM': + #in this case we propagate the covariances using the state transition matrix (first-order approximation) + #Recommended standards to compute Pc: + #FOSTER-1992, CHAN-1997,PATERA-2001, and, ALFANO-2005 if self._pc_method == 'MC': - #WARNING WARNING WARNING: - #the following is a quick hack waiting for Pyprob to be able to sample w/o side effects - rng_state = torch.random.get_rng_state() + #We extract the state and transform it to RTN t_state_tca_rtn, _ = util.from_cartesian_to_rtn(cdm.get_state(0)*1e3) c_state_tca_rtn, _ = util.from_cartesian_to_rtn(cdm.get_state(1)*1e3) - + #and we extract the covariance matrix in the RTN frame: t_cov_pos_tca=cdm.get_covariance(0)[:3,:3] c_cov_pos_tca=cdm.get_covariance(1)[:3,:3] - + #we now sample the state of the target and chaser at the time of closest approach t_samples_tca=torch.distributions.MultivariateNormal(torch.tensor(t_state_tca_rtn[0]),torch.tensor(t_cov_pos_tca)).sample((int(self._mc_samples*self._mc_upsample_factor),)) c_samples_tca=torch.distributions.MultivariateNormal(torch.tensor(c_state_tca_rtn[0]),torch.tensor(c_cov_pos_tca)).sample((int(self._mc_samples*self._mc_upsample_factor),)) + #we compute all vs all miss distances: miss_distances=torch.cdist(t_samples_tca,c_samples_tca) - #miss_distances=torch.norm(t_samples_tca-c_samples_tca,dim=-1) + #we compute the probability of collision as the number of samples that are below the threshold probability_of_collision=(miss_distances TLE Covariance ###### state_=torch.tensor(state_xyz) #we extract the partials of TLE elements w.r.t. cartesian coordinates - dtle_dx=util.keplerian_cartesian_partials(state_.requires_grad_(),self._mu_earth) + dtle_dx=util.keplerian_cartesian_partials(state_.requires_grad_(), self._mu_earth) #we construct the covariance matrix of the TLE elements via similarity transformation, using the partials of TLE elements w.r.t. cartesian coordinates Cov_tle=np.matmul(np.matmul(dtle_dx,C_xyz),dtle_dx.T) + if ~torch.all(torch.from_numpy(np.real(np.linalg.eig(Cov_tle)[0])) > 1e-11): + #Covariance matrix is not sdp, forcing symmetry and adding a small value to the diagonal + Cov_tle = 0.5 * (Cov_tle + Cov_tle.T) + Cov_tle += 1e-10 * np.eye(Cov_tle.shape[0]) #we extract the mean TLE elements from the TLE object, that will be used for the sampling - mean_tle_els=torch.tensor([obs_tle._no_kozai,obs_tle._ecco,obs_tle._inclo,obs_tle._nodeo,obs_tle._argpo,obs_tle._mo]) - #the below is another option, however, it breaks for circular and zero inclination orbits, - #due to singularity on the elements -# dx_dtle,y0=util.transformation_jacobian(obs_tle,0.) -# Cov_tle=np.matmul(np.matmul(np.linalg.pinv(dx_tle),C_xyz),np.linalg.pinv(dx_tle.T)) + mean_tle_els=torch.tensor([obs_tle.semi_major_axis,obs_tle._ecco,obs_tle._inclo,obs_tle._nodeo,obs_tle._argpo,obs_tle._mo]) ###### TLEs construction and propagation from the given samples ###### tle_data={} @@ -440,16 +559,12 @@ def propagate_uncertainty_monte_carlo(self, state_xyz, cov_matrix_diagonal_rtn, tle_data['ephemeris_type'] = obs_tle.ephemeris_type tle_data['element_number'] = obs_tle.element_number tle_data['revolution_number_at_epoch'] = obs_tle.revolution_number_at_epoch - xpdotp = 1440.0 / (2.0 *np.pi) - no_kozai_conversion_factor=xpdotp/43200.0* np.pi - ###### Covariance matrix sampling (directly in TLE elements) ###### - #WARNING WARNING WARNING: - #the following is a quick hack waiting for Pyprob to be able to sample w/o side effects - rng_state = torch.random.get_rng_state() try: dist=torch.distributions.MultivariateNormal(loc=mean_tle_els,covariance_matrix=torch.tensor(Cov_tle)) samples=dist.sample((self._mc_samples,)) + #we convert all the sampled semi-major axis into mean motion: + samples[:,0]= (self._mu_earth/samples[:,0]**3)**(0.5) except Exception as e: if "Expected parameter covariance_matrix" in str(e): if mean_tle_els[1]<0.: @@ -459,11 +574,7 @@ def propagate_uncertainty_monte_carlo(self, state_xyz, cov_matrix_diagonal_rtn, tle_data['argument_of_perigee']=mean_tle_els[4] tle_data['inclination']=mean_tle_els[2] tle_data['mean_anomaly']=mean_tle_els[5] - val=mean_tle_els[0]*no_kozai_conversion_factor - #if 2*np.pi / val >= 225.0: - # tle_data['mean_motion']=(2*np.pi/(225.0*0.99)) - #else: - tle_data['mean_motion']=val + tle_data['mean_motion']=(self._mu_earth/mean_tle_els[0]**3)**(1./2.) tle_data['raan']=mean_tle_els[3] # Propagate object at tca tle_object = TLE(tle_data) @@ -475,6 +586,7 @@ def propagate_uncertainty_monte_carlo(self, state_xyz, cov_matrix_diagonal_rtn, raise e mc_states_tca_xyz=[] + tle_objects=[] for sample in samples: if sample[1]<0.: tle_data['eccentricity']=torch.tensor(0.) @@ -483,23 +595,20 @@ def propagate_uncertainty_monte_carlo(self, state_xyz, cov_matrix_diagonal_rtn, tle_data['argument_of_perigee']=sample[4] tle_data['inclination']=sample[2] tle_data['mean_anomaly']=sample[5] - val=sample[0]*no_kozai_conversion_factor -# if 2*np.pi / val >= 225.0: -# tle_data['mean_motion']=(2*np.pi/(225.0*0.99)) -# else: - tle_data['mean_motion']=val + tle_data['mean_motion']=sample[0] tle_data['raan']=sample[3] # Propagate object at tca - tle_object = TLE(tle_data) - dsgp4.initialize_tle(tle_object) - tsinces=(torch.tensor(time_ca_mjd)-dsgp4.util.from_datetime_to_mjd(tle_object._epoch))*1440. - mc_states_tca_xyz.append(dsgp4.propagate(tle_object,tsinces).numpy()*1e3) - #WARNING WARNING WARNING: - #the following is a quick hack waiting for Pyprob to be able to sample w/o side effects - torch.random.set_rng_state(rng_state) - mc_states_tca_xyz = np.stack(mc_states_tca_xyz) + tle_objects.append(TLE(tle_data).copy()) + try: + _,tle_batch=dsgp4.initialize_tle(tle_objects) + except Exception as e: + pyro.deterministic('conj',torch.tensor(False)) + return + tsinces=torch.stack([(torch.tensor(time_ca_mjd)-dsgp4.util.from_datetime_to_mjd(tle_objects[0]._epoch))*1440.]*len(tle_objects)) + mc_states_tca_xyz=dsgp4.propagate_batch(tle_batch,tsinces).numpy()*1e3 + #torch.random.set_rng_state(rng_state) + #mc_states_tca_xyz = np.stack(mc_states_tca_xyz) mean_state_tca_xyz_TEME = mc_states_tca_xyz.mean(axis=0) - rotation_matrix_tca = util.rotation_matrix(mean_state_tca_xyz_TEME) mc_states_tca_rtn = np.zeros_like(mc_states_tca_xyz) @@ -507,60 +616,64 @@ def propagate_uncertainty_monte_carlo(self, state_xyz, cov_matrix_diagonal_rtn, mc_states_tca_rtn[i], _ = util.from_cartesian_to_rtn(mc_state_tca_xyz, rotation_matrix_tca) cov_state_tca_rtn = np.cov(mc_states_tca_rtn.reshape(-1, 6).transpose()) return mean_state_tca_xyz_TEME, cov_state_tca_rtn + def forward(self): # Create the target & chaser: t_tle = self.make_target() - self._t_tle_name=t_tle.name c_tle = self.make_chaser() #we immediately exclude cases where target and chaser are the same object: - if t_tle.name==c_tle.name: - pyprob.tag(False, 'conj') - return - pyprob.tag(t_tle, 't_tle0') - pyprob.tag(c_tle, 'c_tle0') + # if t_tle.international_designator==c_tle.international_designator: + # pyro.deterministic('conj',torch.tensor(False)) + # return + # pyro.deterministic('t_tle0_line1',t_tle.line1) + # pyro.deterministic('t_tle0_line2',t_tle.line2) + # pyro.deterministic('c_tle0_line1',c_tle.line1) + # pyro.deterministic('c_tle0_line2',c_tle.line2) #we use a perigee/apogee filter to immediately exclude cases if (((t_tle.apogee_alt()+self._miss_dist_threshold+1000)>> trace,it = model.get_conjunction() + >>> cdms = trace.nodes['cdms']['infer']['cdms'] + >>> for cdm in cdms: + print(cdm) + """ found = False - iter=0 + iteration = 0 while not found: - iter+=1 - trace = self.get_trace() - if trace['conj']: + iteration += 1 + traced_model = pyro.poutine.trace(self.forward).get_trace() + if traced_model.nodes['conj']['value']: found = True - print(f"After {iter} iterations, generated event with {len(trace['cdms'])} CDMs") - return trace,iter - -from pyprob.distributions import Categorical + print(f"After {iteration} iterations, generated event with {len(traced_model.nodes['cdms']['infer']['cdms'])} CDMs") + return traced_model, iteration + class ConjunctionSimplified(Conjunction): + """ + + This class is a simplified version of the Conjunction class. To generate the conjunction, it shuffles + two TLEs from a given TLE population file, randomizing the mean anomaly, and checking for conjunctions. + + Example: + >>> from kessler.model import ConjunctionSimplified + >>> from dsgp4 import tle + >>> tles = dsgp4.tle.load('tles_sample_population.txt') + >>> model = ConjunctionSimplified(tles=tles) + >>> tr = model.get_conjunction() + """ + def __init__(self, tles, exclude_object_name=None, *args, **kwargs): self._tles = tles self._exclude_object_name=exclude_object_name @@ -713,59 +848,59 @@ def __init__(self, tles, exclude_object_name=None, *args, **kwargs): def make_target(self): if self._t_tle is None: - mean_anomaly = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name = 't_mean_anomaly') - tle_index = pyprob.sample( Categorical(range(len(self._tles))) , name='t_sampled_index') + mean_anomaly = pyro.sample('t_mean_anomaly',self._prior_dict['mean_anomaly_prior']) + tle_index = pyro.sample('t_sampled_index', Categorical(torch.tensor(range(len(self._tles))))) tle = self._tles[tle_index].copy() tle.update({'mean_anomaly': mean_anomaly}) - pyprob.tag(tle.mean_motion, name='t_mean_motion') - pyprob.tag(tle.eccentricity, name='t_eccentricity') - pyprob.tag(tle.inclination, name='t_inclination') - pyprob.tag(tle.argument_of_perigee, name='t_argument_of_perigee') - pyprob.tag(tle.raan, name='t_raan') - pyprob.tag(tle.mean_motion_first_derivative, name='t_mean_motion_first_derivative') - pyprob.tag(tle.mean_motion_second_derivative, name='t_mean_motion_second_derivative') - pyprob.tag(tle.b_star, name='t_b_star') + pyro.deterministic('t_mean_motion', torch.tensor(tle.mean_motion)) + pyro.deterministic('t_eccentricity', torch.tensor(tle.eccentricity)) + pyro.deterministic('t_inclination',torch.tensor(tle.inclination)) + pyro.deterministic('t_argument_of_perigee', torch.tensor(tle.argument_of_perigee)) + pyro.deterministic('t_raan',torch.tensor(tle.raan)) + pyro.deterministic('t_mean_motion_first_derivative',torch.tensor(tle.mean_motion_first_derivative)) + pyro.deterministic('t_mean_motion_second_derivative', torch.tensor(tle.mean_motion_second_derivative)) + pyro.deterministic('t_b_star',torch.tensor(tle.b_star)) else: - mean_anomaly = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name = 't_mean_anomaly') + mean_anomaly = pyro.sample('t_mean_anomaly', self._prior_dict['mean_anomaly_prior']) tle = self._t_tle.copy() tle.update({'mean_anomaly': mean_anomaly}) - pyprob.tag(tle.mean_motion, name='t_mean_motion') - pyprob.tag(tle.eccentricity, name='t_eccentricity') - pyprob.tag(tle.inclination, name='t_inclination') - pyprob.tag(tle.argument_of_perigee, name='t_argument_of_perigee') - pyprob.tag(tle.raan, name='t_raan') - pyprob.tag(tle.mean_motion_first_derivative, name='t_mean_motion_first_derivative') - pyprob.tag(tle.mean_motion_second_derivative, name='t_mean_motion_second_derivative') - pyprob.tag(tle.b_star, name='t_b_star') + pyro.deterministic('t_mean_motion', torch.tensor(tle.mean_motion)) + pyro.deterministic('t_eccentricity', torch.tensor(tle.eccentricity)) + pyro.deterministic('t_inclination',torch.tensor(tle.inclination)) + pyro.deterministic('t_argument_of_perigee', torch.tensor(tle.argument_of_perigee)) + pyro.deterministic('t_raan',torch.tensor(tle.raan)) + pyro.deterministic('t_mean_motion_first_derivative',torch.tensor(tle.mean_motion_first_derivative)) + pyro.deterministic('t_mean_motion_second_derivative', torch.tensor(tle.mean_motion_second_derivative)) + pyro.deterministic('t_b_star',torch.tensor(tle.b_star)) return tle def make_chaser(self): if self._c_tle is None: - mean_anomaly = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name = 'c_mean_anomaly') + mean_anomaly = pyro.sample('c_mean_anomaly',self._prior_dict['mean_anomaly_prior']) if self._exclude_object_name is not None and self._t_tle_name.startswith(self._exclude_object_name): - tle_index = pyprob.sample( Categorical(self._include_idxs) , name='c_sampled_index') + tle_index = pyro.sample('c_sampled_index',Categorical(torch.tensor(self._include_idxs))) else: - tle_index = pyprob.sample( Categorical(range(len(self._tles))) , name='c_sampled_index') + tle_index = pyro.sample('c_sampled_index', Categorical(torch.tensor(range(len(self._tles))))) tle = self._tles[tle_index].copy() tle.update({'mean_anomaly': mean_anomaly}) - pyprob.tag(tle.mean_motion, name='c_mean_motion') - pyprob.tag(tle.eccentricity, name='c_eccentricity') - pyprob.tag(tle.inclination, name='c_inclination') - pyprob.tag(tle.argument_of_perigee, name='c_argument_of_perigee') - pyprob.tag(tle.raan, name='c_raan') - pyprob.tag(tle.mean_motion_first_derivative, name='c_mean_motion_first_derivative') - pyprob.tag(tle.mean_motion_second_derivative, name='c_mean_motion_second_derivative') - pyprob.tag(tle.b_star, name='c_b_star') + pyro.deterministic('c_mean_motion', torch.tensor(tle.mean_motion)) + pyro.deterministic('c_eccentricity', torch.tensor(tle.eccentricity)) + pyro.deterministic('c_inclination',torch.tensor(tle.inclination)) + pyro.deterministic('c_argument_of_perigee', torch.tensor(tle.argument_of_perigee)) + pyro.deterministic('c_raan',torch.tensor(tle.raan)) + pyro.deterministic('c_mean_motion_first_derivative',torch.tensor(tle.mean_motion_first_derivative)) + pyro.deterministic('c_mean_motion_second_derivative', torch.tensor(tle.mean_motion_second_derivative)) + pyro.deterministic('c_b_star',torch.tensor(tle.b_star)) else: - mean_anomaly = pyprob.sample(self._prior_dict['mean_anomaly_prior'], name = 'c_mean_anomaly') + mean_anomaly = pyro.sample('c_mean_anomaly',self._prior_dict['mean_anomaly_prior']) tle = self._c_tle.copy() tle.update({'mean_anomaly': mean_anomaly}) - pyprob.tag(tle.mean_motion, name='c_mean_motion') - pyprob.tag(tle.eccentricity, name='c_eccentricity') - pyprob.tag(tle.inclination, name='c_inclination') - pyprob.tag(tle.argument_of_perigee, name='c_argument_of_perigee') - pyprob.tag(tle.raan, name='c_raan') - pyprob.tag(tle.mean_motion_first_derivative, name='c_mean_motion_first_derivative') - pyprob.tag(tle.mean_motion_second_derivative, name='c_mean_motion_second_derivative') - pyprob.tag(tle.b_star, name='c_b_star') - return tle + pyro.deterministic('c_mean_motion', torch.tensor(tle.mean_motion)) + pyro.deterministic('c_eccentricity', torch.tensor(tle.eccentricity)) + pyro.deterministic('c_inclination',torch.tensor(tle.inclination)) + pyro.deterministic('c_argument_of_perigee', torch.tensor(tle.argument_of_perigee)) + pyro.deterministic('c_raan',torch.tensor(tle.raan)) + pyro.deterministic('c_mean_motion_first_derivative',torch.tensor(tle.mean_motion_first_derivative)) + pyro.deterministic('c_mean_motion_second_derivative', torch.tensor(tle.mean_motion_second_derivative)) + pyro.deterministic('c_b_star',torch.tensor(tle.b_star)) + return tle \ No newline at end of file diff --git a/kessler/util.py b/kessler/util.py index 4ba28d3..28eea84 100644 --- a/kessler/util.py +++ b/kessler/util.py @@ -8,7 +8,6 @@ # # GNU General Public License version 3. See LICENSE in root of repository. - import numpy as np import torch import math @@ -61,39 +60,119 @@ def is_number(s): except ValueError: return False + +def from_cartesian_to_keplerian(r_vec, v_vec, mu): + """ + This function converts the provided state from Cartesian to Keplerian elements. + It mirrors the function from_cartesian_to_keplerian in (`dsgp4`)[https://github.com/esa/dSGP4], but uses torch tensors instead of numpy arrays. + + Args: + r_vec (``torch.Tensor``): position vector in Cartesian coordinates + v_vec (``torch.Tensor``): velocity vector in Cartesian coordinates + mu (``float``): gravitational parameter of the central body + + Returns: + ``torch.Tensor``: tensor of Keplerian elements: (a, e, i, Omega, omega, M) + (i.e., semi-major axis, eccentricity, inclination, + right ascension of ascending node, argument of perigee, + mean anomaly). All the angles are in radians, eccentricity is unitless + and semi-major axis is in SI. + """ + + r = torch.norm(r_vec) + v = torch.norm(v_vec) + + h_vec = torch.cross(r_vec, v_vec) + h = torch.norm(h_vec) + + # Inclination + i = torch.where(h != 0, torch.acos(h_vec[2] / h), torch.tensor(0.0, device=r_vec.device)) + + # Node vector + K = torch.tensor([0.0, 0.0, 1.0], device=r_vec.device) + n_vec = torch.cross(K, h_vec) + n = torch.norm(n_vec) + + # Eccentricity vector and magnitude + e_vec = (1 / mu) * ((v ** 2 - mu / r) * r_vec - torch.dot(r_vec, v_vec) * v_vec) + e = torch.norm(e_vec) + + # Specific orbital energy + energy = v ** 2 / 2 - mu / r + + # Semi-major axis + a = torch.where(torch.abs(e - 1.0) > 1e-8, -mu / (2 * energy), torch.tensor(float('inf'), device=r_vec.device)) + + # Right Ascension of Ascending Node (RAAN) + cos_Omega = n_vec[0] / n + cos_Omega = torch.clamp(cos_Omega, -1.0, 1.0) + raw_Omega = torch.acos(cos_Omega) + Omega = torch.where(n_vec[1] >= 0, raw_Omega, 2 * torch.pi - raw_Omega) + Omega = torch.where(n != 0, Omega, torch.tensor(0.0, device=r_vec.device)) + + # Argument of perigee + cos_omega = torch.dot(n_vec, e_vec) / (n * e + 1e-12) + cos_omega = torch.clamp(cos_omega, -1.0, 1.0) + raw_omega = torch.acos(cos_omega) + omega = torch.where(e_vec[2] >= 0, raw_omega, 2 * torch.pi - raw_omega) + omega = torch.where((n != 0) & (e != 0), omega, torch.tensor(0.0, device=r_vec.device)) + + # True anomaly + cos_nu = torch.dot(e_vec, r_vec) / (e * r + 1e-12) + cos_nu = torch.clamp(cos_nu, -1.0, 1.0) + raw_nu = torch.acos(cos_nu) + nu = torch.where(torch.dot(r_vec, v_vec) >= 0, raw_nu, 2 * torch.pi - raw_nu) + nu = torch.where(e != 0, nu, torch.tensor(0.0, device=r_vec.device)) + + # Eccentric anomaly (E) and Mean anomaly (M) + elliptic = e < 1.0 + hyperbolic = e > 1.0 + #parabolic = ~elliptic & ~hyperbolic + if elliptic: + E = 2 * torch.atan(torch.tan(nu / 2) * torch.sqrt((1 - e) / (1 + e))) + M = E - e * torch.sin(E) + elif hyperbolic: + F = 2 * torch.atanh(torch.tan(nu / 2) * torch.sqrt((e - 1) / (e + 1))) + M = e * torch.sinh(F) - F + # Normalize angles to [0, 2π) + Omega=Omega - (2 * torch.pi) * torch.floor(Omega / (2 * torch.pi)) + omega=omega - (2 * torch.pi) * torch.floor(omega / (2 * torch.pi)) + M=M - (2 * torch.pi) * torch.floor(M / (2 * torch.pi)) + return a, e, i, Omega, omega, M + def keplerian_cartesian_partials(state,mu): """ Computes the partial derivatives of the cartesian state with respect to the keplerian elements. Args: - state (`numpy.array`): numpy array of 2 rows and 3 columns, where + state (``numpy.array``): numpy array of 2 rows and 3 columns, where the first row represents position, and the second velocity. - mu (`float`): gravitational parameter of the central body + mu (``float``): gravitational parameter of the central body Returns: - `numpy.array`: numpy array of the partial derivatives of the cartesian state with respect to the keplerian elements. + ``numpy.array``: numpy array of the partial derivatives of the cartesian state with respect to the keplerian elements. """ state_1=dsgp4.util.clone_w_grad(state) state_2=dsgp4.util.clone_w_grad(state) state_3=dsgp4.util.clone_w_grad(state) state_4=dsgp4.util.clone_w_grad(state) state_5=dsgp4.util.clone_w_grad(state) - a=dsgp4.util.from_cartesian_to_keplerian_torch(state,mu=mu)[0] + a=from_cartesian_to_keplerian(state[0], state[1], mu=mu)[0] a.backward() gradient_a=state.grad.flatten() - e=dsgp4.util.from_cartesian_to_keplerian_torch(state_1,mu=mu)[1] + e=from_cartesian_to_keplerian(state_1[0], state_1[1],mu=mu)[1] e.backward() gradient_e=state_1.grad.flatten() - i=dsgp4.util.from_cartesian_to_keplerian_torch(state_2,mu=mu)[2] + i=from_cartesian_to_keplerian(state_2[0], state_2[1], mu=mu)[2] i.backward() gradient_i=state_2.grad.flatten() - Omega=dsgp4.util.from_cartesian_to_keplerian_torch(state_3,mu=mu)[3] + Omega=from_cartesian_to_keplerian(state_3[0], state_3[1], mu=mu)[3] Omega.backward() gradient_Omega=state_3.grad.flatten() - omega=dsgp4.util.from_cartesian_to_keplerian_torch(state_4,mu=mu)[4] + omega=from_cartesian_to_keplerian(state_4[0], state_4[1], mu=mu)[4] omega.backward() gradient_omega=state_4.grad.flatten() - mean_anomaly=dsgp4.util.from_cartesian_to_keplerian_torch(state_5,mu=mu)[5] + mean_anomaly=from_cartesian_to_keplerian(state_5[0], state_5[1], mu=mu)[5] mean_anomaly.backward() gradient_mean_anomaly=state_5.grad.flatten() DF=np.stack((gradient_a, gradient_e, gradient_i, gradient_Omega, gradient_omega, gradient_mean_anomaly)) diff --git a/setup.py b/setup.py index 319373c..5e7dd18 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ def read_package_variable(key): author='ESA FDL Europe Constellations Team', # author_email='', packages=find_packages(), - install_requires=['pyprob', 'numpy', 'matplotlib', 'torch>=1.5.1', 'dsgp4', 'skyfield>=1.26', 'pandas'], + install_requires=['pyro', 'numpy', 'matplotlib', 'torch>=1.5.1', 'dsgp4', 'skyfield>=1.26', 'pandas'], extras_require={'dev': ['pytest', 'coverage', 'pytest-xdist']}, # url='https://github.com/kessler/kessler', classifiers=['License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3'], diff --git a/tests/test_util.py b/tests/test_util.py index 31eb35d..b36d405 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -12,7 +12,8 @@ import unittest import numpy as np import datetime - +import torch +import dsgp4 import kessler import kessler.util @@ -101,4 +102,33 @@ def test_doy_2_date(self): self.assertEqual(kessler.util.doy_2_date(example3, doy_3, year_3, 5), test_case3_correct) self.assertEqual(kessler.util.doy_2_date(example4, doy_4, year_4, 5), test_case4_correct) self.assertEqual(kessler.util.doy_2_date(example5, doy_5, year_5, 5), test_case5_correct) + + def test_from_cartesian_to_keplerian_torch(self): + lines=[] + lines.append("0 COSMOS 2251 DEB") + lines.append("1 34427U 93036RU 22068.94647328 .00008100 00000-0 11455-2 0 9999") + lines.append("2 34427 74.0145 306.8269 0033346 13.0723 347.1308 14.76870515693886") + tle=dsgp4.TLE(lines) + dsgp4.initialize_tle(tle) + # Extract the state vector from the dSGP4 output + st=dsgp4.sgp4(tle,torch.tensor(0.0))*1e3 # Convert to meters and meters/second + #now let's retrieve the poliastro gravitational parameter of the Earth: + mu = 398600441800000.0000000000000000#Earth.k.to(u.m**3 / u.s**2).value + # Let's then convert Cartesian -> Keplerian using our function + a,e,i,Omega,omega,M=kessler.util.from_cartesian_to_keplerian(r_vec=st[0], v_vec=st[1], mu=mu) + + a_poliastro=7023679.5817881366237998 + e_poliastro=0.0041649630912143 + i_poliastro=1.2919744331609118 + Omega_poliastro=5.3551396410293757 + omega_poliastro=0.4409272281996022 + M_poliastro=5.8458093777349722 + #let's now test they are close: + self.assertAlmostEqual(a.item(), a_poliastro, places=5) + self.assertAlmostEqual(e.item(), e_poliastro, places=5) + self.assertAlmostEqual(i.item(), i_poliastro, places=5) + self.assertAlmostEqual(Omega.item(), Omega_poliastro, places=5) + self.assertAlmostEqual(omega.item(), omega_poliastro, places=5) + self.assertAlmostEqual(M.item(), M_poliastro, places=5) + From 9c2787708f0f3dcc6daf05943e971050ffb0d5ac Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 10:18:34 +0200 Subject: [PATCH 02/13] fix imports [skip ci] --- kessler/model.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/kessler/model.py b/kessler/model.py index f36e1d3..0755115 100644 --- a/kessler/model.py +++ b/kessler/model.py @@ -8,17 +8,12 @@ from . import GNSS, Radar, ConjunctionDataMessage, util from dsgp4 import TLE -from torch.distributions import Normal, constraints +from torch.distributions import constraints from torch.distributions.distribution import Distribution from torch.distributions.utils import broadcast_all -from pyro.poutine import trace from pyro.distributions import MixtureSameFamily, Categorical, Uniform, Normal, Bernoulli -import torch -from torch.distributions import Normal, Distribution -from torch.distributions import constraints - def find_conjunction(tr0, tr1, miss_dist_threshold): From 4a5b5f29a2f3793d447a9a90789cdf2ef64181df Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 10:33:49 +0200 Subject: [PATCH 03/13] update doc probprog [skip ci] --- .../probabilistic_programming_module.ipynb | 741 ++++-------------- kessler/model.py | 4 +- 2 files changed, 156 insertions(+), 589 deletions(-) diff --git a/docs/notebooks/probabilistic_programming_module.ipynb b/docs/notebooks/probabilistic_programming_module.ipynb index e6fa668..c499537 100644 --- a/docs/notebooks/probabilistic_programming_module.ipynb +++ b/docs/notebooks/probabilistic_programming_module.ipynb @@ -14,26 +14,26 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "d6f58e4e", "metadata": {}, "outputs": [], "source": [ "import kessler\n", - "import pyprob\n", + "import pyro\n", "import numpy as np\n", "import dsgp4" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "c0f44a09", "metadata": {}, "outputs": [], "source": [ "#we seed everything for reproducibility\n", - "pyprob.seed(1)\n", + "pyro.set_rng_seed(10)\n", "\n", "#we define the observing instruments\n", "#GNSS first:\n", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "245991f4", "metadata": {}, "outputs": [ @@ -117,25 +117,37 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "id": "20c49ee3", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ga00693/Develop/kessler/kessler/model.py:765: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " pyro.deterministic('conj',torch.tensor(conj))\n", + "/Users/ga00693/Develop/kessler/kessler/util.py:85: UserWarning: Using torch.cross without specifying the dim arg is deprecated.\n", + "Please either pass the dim explicitly or simply use torch.linalg.cross.\n", + "The default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at /Users/runner/miniforge3/conda-bld/libtorch_1738206012956/work/aten/src/ATen/native/Cross.cpp:66.)\n", + " h_vec = torch.cross(r_vec, v_vec)\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "After 835 iterations, generated event with 7 CDMs\n" + "After 1170 iterations, generated event with 2 CDMs\n" ] } ], "source": [ - "trace=conjunction_model.get_conjunction()" + "trace,iterations=conjunction_model.get_conjunction()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "id": "d1fcae4b", "metadata": {}, "outputs": [ @@ -145,461 +157,16 @@ "[CCSDS_CDM_VERS = 1.0\n", " CREATION_DATE = 2025-02-21T03:20:09.135184\n", " ORIGINATOR = KESSLER_SOFTWARE\n", - " MESSAGE_ID = KESSLER_SOFTWARE_65189812-1387-11f0-a1a9-f2a9f71f7e19\n", - " TCA = 2025-02-23T13:01:31.347952\n", - " MISS_DISTANCE = 620.5391897367289\n", - " RELATIVE_SPEED = 8.500887282727685\n", - " RELATIVE_POSITION_R = -30.90865879375995\n", - " RELATIVE_POSITION_T = -580.2512313942899\n", - 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" CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18\n", - " OBJECT = OBJECT2\n", - " OBJECT_DESIGNATOR = 65082PT\n", - " CATALOG_NAME = 3462\n", - " OBJECT_NAME = TITAN 3C TRANSTAGE DEB\n", - " INTERNATIONAL_DESIGNATOR = 65082PT\n", - " EPHEMERIS_NAME = NONE\n", - " COVARIANCE_METHOD = CALCULATED\n", - " MANEUVERABLE = N/A\n", - " ORBIT_CENTER = EARTH\n", - " REF_FRAME = ITRF\n", - " X = -2873.0254251831598\n", - " Y = 5114.827628051727\n", - " Z = -3608.1214947949693\n", - " X_DOT = -6.040577212554574\n", - " Y_DOT = -3.8350893476292636\n", - " Z_DOT = -0.6043614138577811\n", - " CR_R = 46.757440226303586\n", - " CT_R = -8957.86420553608\n", - " CT_T = 1782174.497494517\n", - " CN_R = 1.1154153322347222\n", - " CN_T = -221.62762382182441\n", - " CN_N = 0.02756389817328491\n", - 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" CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18\n", - " OBJECT = OBJECT2\n", - " OBJECT_DESIGNATOR = 65082PT\n", - " CATALOG_NAME = 3462\n", - " OBJECT_NAME = TITAN 3C TRANSTAGE DEB\n", - " INTERNATIONAL_DESIGNATOR = 65082PT\n", - " EPHEMERIS_NAME = NONE\n", - " COVARIANCE_METHOD = CALCULATED\n", - " MANEUVERABLE = N/A\n", - " ORBIT_CENTER = EARTH\n", - " REF_FRAME = ITRF\n", - " X = -2874.503418692707\n", - " Y = 5113.900250637651\n", - " Z = -3608.261909514749\n", - " X_DOT = -6.039832942280944\n", - " Y_DOT = -3.836416281232265\n", - " Z_DOT = -0.6033522297755305\n", - " CR_R = 3.852952621853669\n", - " CT_R = 0.0\n", - " CT_T = 4.976328968225473\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.9333314012997196\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18,\n", - " CCSDS_CDM_VERS = 1.0\n", - " CREATION_DATE = 2025-02-22T11:19:33.102361\n", - " ORIGINATOR = KESSLER_SOFTWARE\n", - " MESSAGE_ID = KESSLER_SOFTWARE_682ac1e2-1387-11f0-a1a9-f2a9f71f7e19\n", - " TCA = 2025-02-23T13:01:31.347952\n", - " MISS_DISTANCE = 327.1071065092106\n", - " RELATIVE_SPEED = 8.49477258058075\n", - " RELATIVE_POSITION_R = -8.851838708103571\n", - " RELATIVE_POSITION_T = -228.85298210225113\n", - " RELATIVE_POSITION_N = -233.5530275188496\n", - " RELATIVE_VELOCITY_R = -0.24019476540712978\n", - " RELATIVE_VELOCITY_T = -5.088429141322612\n", - " RELATIVE_VELOCITY_N = -6.797893537280234\n", - " COLLISION_PROBABILITY = 0.0\n", - " COLLISION_PROBABILITY_METHOD = MC\n", - " OBJECT = OBJECT1\n", - " OBJECT_DESIGNATOR = 76126AP\n", - " CATALOG_NAME = 9827\n", - " OBJECT_NAME = COSMOS 886 DEB\n", - " INTERNATIONAL_DESIGNATOR = 76126AP\n", - " EPHEMERIS_NAME = NONE\n", - " COVARIANCE_METHOD = CALCULATED\n", - " MANEUVERABLE = N/A\n", - " ORBIT_CENTER = EARTH\n", - " REF_FRAME = ITRF\n", - " X = -2847.1668519000614\n", - " Y = 5306.04976534258\n", - " Z = -3344.9553671663302\n", - " X_DOT = -4.323999308182716\n", - " Y_DOT = 1.4473467271883949\n", - " Z_DOT = 5.8230717063439394\n", - " CR_R = 1.000E-18\n", - " CT_R = 0.0\n", - " CT_T = 1.2451197530695846\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.0035176566277315556\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18\n", - " OBJECT = OBJECT2\n", - " OBJECT_DESIGNATOR = 65082PT\n", - " CATALOG_NAME = 3462\n", - " OBJECT_NAME = TITAN 3C TRANSTAGE DEB\n", - " INTERNATIONAL_DESIGNATOR = 65082PT\n", - " EPHEMERIS_NAME = NONE\n", - " COVARIANCE_METHOD = CALCULATED\n", - " MANEUVERABLE = N/A\n", - " ORBIT_CENTER = EARTH\n", - " REF_FRAME = ITRF\n", - " X = -2874.503418692707\n", - " Y = 5113.900250637651\n", - " Z = -3608.261909514749\n", - " X_DOT = -6.039832942280944\n", - " Y_DOT = -3.836416281232265\n", - " Z_DOT = -0.6033522297755305\n", - " CR_R = 3.852952621853669\n", - " CT_R = 0.0\n", - " CT_T = 4.976328968225473\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.9333314012997196\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18,\n", - " CCSDS_CDM_VERS = 1.0\n", - " CREATION_DATE = 2025-02-22T19:18:57.382150\n", - " ORIGINATOR = KESSLER_SOFTWARE\n", - " MESSAGE_ID = KESSLER_SOFTWARE_686fd494-1387-11f0-a1a9-f2a9f71f7e19\n", - " TCA = 2025-02-23T13:01:31.347952\n", - " MISS_DISTANCE = 304.00199947770375\n", - " RELATIVE_SPEED = 8.494039443021444\n", - " RELATIVE_POSITION_R = -7.734808394529005\n", - " RELATIVE_POSITION_T = -193.60549377766796\n", - " RELATIVE_POSITION_N = -234.25264396511537\n", - " RELATIVE_VELOCITY_R = -0.25275836956828907\n", - " RELATIVE_VELOCITY_T = -5.087505222676052\n", - " RELATIVE_VELOCITY_N = -6.797213390460911\n", + " MESSAGE_ID = KESSLER_SOFTWARE_ac66059e-20e6-11f0-b2ee-f2a9f71f7e1a\n", + " TCA = 2025-02-22T01:17:28.408663\n", + " MISS_DISTANCE = 141514.9960370336\n", + " RELATIVE_SPEED = 8783.071096663718\n", + " RELATIVE_POSITION_R = -3769.8761935982398\n", + " RELATIVE_POSITION_T = -110791.09624826182\n", + " RELATIVE_POSITION_N = -87963.71484285413\n", + " RELATIVE_VELOCITY_R = -144.7709891581174\n", + " RELATIVE_VELOCITY_T = -5424.100602146948\n", + " RELATIVE_VELOCITY_N = -6906.555719570857\n", " COLLISION_PROBABILITY = 0.0\n", " COLLISION_PROBABILITY_METHOD = MC\n", " OBJECT = OBJECT1\n", @@ -612,33 +179,33 @@ " MANEUVERABLE = N/A\n", " ORBIT_CENTER = EARTH\n", " REF_FRAME = ITRF\n", - " X = -2826.2062399489596\n", - " Y = 5299.708069047362\n", - " Z = -3372.550109153248\n", - " X_DOT = -4.340982987372497\n", - " Y_DOT = 1.4752253221709153\n", - " Z_DOT = 5.803603238097498\n", - " CR_R = 1.000E-18\n", - " CT_R = 0.0\n", - " CT_T = 1.2451197530695846\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.0035176566277315556\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18\n", + " X = 2980.1734541932515\n", + " Y = -5181.782698754123\n", + " Z = -3422.116689930527\n", + " X_DOT = 4.425575967702924\n", + " Y_DOT = -1.3655090728792418\n", + " Z_DOT = 5.7673902574303915\n", + " CR_R = 434.5847746412686\n", + " CT_R = 3804.230266317108\n", + " CT_T = 41269.012800060416\n", + " CN_R = -224.26822649455545\n", + " CN_T = -3739.194423008724\n", + " CN_N = 4893.388541408624\n", + " CRDOT_R = -2.690240978331646\n", + " CRDOT_T = -32.45608595843143\n", + " CRDOT_N = 3.3748910113664388\n", + " CRDOT_RDOT = 0.026616660879604667\n", + " CTDOT_R = -0.45587521823581134\n", + " CTDOT_T = -3.891974449268055\n", + " CTDOT_N = 0.21602245879137436\n", + " CTDOT_RDOT = 0.002711690719702106\n", + " CTDOT_TDOT = 0.000479432666082355\n", + " CNDOT_R = 0.27513941996415164\n", + " CNDOT_T = 0.4561951334680935\n", + " CNDOT_N = -0.44165624627125066\n", + " CNDOT_RDOT = 0.0004400365334853134\n", + " CNDOT_TDOT = -0.0003127338855863759\n", + " CNDOT_NDOT = 0.004822710541832547\n", " OBJECT = OBJECT2\n", " OBJECT_DESIGNATOR = 65082PT\n", " CATALOG_NAME = 3462\n", @@ -649,46 +216,46 @@ " MANEUVERABLE = N/A\n", " ORBIT_CENTER = EARTH\n", " REF_FRAME = ITRF\n", - " X = -2874.503418692707\n", - " Y = 5113.900250637651\n", - " Z = -3608.261909514749\n", - " X_DOT = -6.039832942280944\n", - " Y_DOT = -3.836416281232265\n", - " Z_DOT = -0.6033522297755305\n", - " CR_R = 3.852952621853669\n", - " CT_R = 0.0\n", - " CT_T = 4.976328968225473\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.9333314012997196\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18,\n", + " X = 2972.4172682119224\n", + " Y = -5103.530721008279\n", + " Z = -3539.772814553296\n", + " X_DOT = 5.826757151768794\n", + " Y_DOT = 4.085711393314795\n", + " Z_DOT = -0.9752519996834083\n", + " CR_R = 15.608895194536064\n", + " CT_R = -1831.7454009406745\n", + " CT_T = 387066.15903594147\n", + " CN_R = 71.5596813096827\n", + " CN_T = -6122.2782569260735\n", + " CN_N = 5424.209709946298\n", + " CRDOT_R = 2.1418685416237584\n", + " CRDOT_T = -416.38358827942716\n", + " CRDOT_N = 8.17067859275866\n", + " CRDOT_RDOT = 0.45361166277724996\n", + " CTDOT_R = -0.005166751853750279\n", + " CTDOT_T = -0.1424221310036492\n", + " CTDOT_N = -0.019218390871709318\n", + " CTDOT_RDOT = 1.609E-05\n", + " CTDOT_TDOT = 5.202E-06\n", + " CNDOT_R = 0.06170571196677281\n", + " CNDOT_T = -8.629383890813989\n", + " CNDOT_N = 1.1567101080406283\n", + " CNDOT_RDOT = 0.010208457878684038\n", + " CNDOT_TDOT = -1.756E-05\n", + " CNDOT_NDOT = 0.0021928221709182245,\n", " CCSDS_CDM_VERS = 1.0\n", - " CREATION_DATE = 2025-02-23T03:19:44.317937\n", + " CREATION_DATE = 2025-02-21T11:20:08.694966\n", " ORIGINATOR = KESSLER_SOFTWARE\n", - " MESSAGE_ID = KESSLER_SOFTWARE_68be23ec-1387-11f0-a1a9-f2a9f71f7e19\n", - " TCA = 2025-02-23T13:01:31.347952\n", - " MISS_DISTANCE = 294.6326615148539\n", - " RELATIVE_SPEED = 8.493194057687942\n", - " RELATIVE_POSITION_R = -7.419299740360892\n", - " RELATIVE_POSITION_T = -174.57021712348944\n", - " RELATIVE_POSITION_N = -237.23110781713865\n", - " RELATIVE_VELOCITY_R = -0.26204858929400876\n", - " RELATIVE_VELOCITY_T = -5.086450727376211\n", - " RELATIVE_VELOCITY_N = -6.796594355732054\n", + " MESSAGE_ID = KESSLER_SOFTWARE_adc65ed4-20e6-11f0-b2ee-f2a9f71f7e1a\n", + " TCA = 2025-02-22T01:17:28.408663\n", + " MISS_DISTANCE = 119296.4071367849\n", + " RELATIVE_SPEED = 8781.77597072965\n", + " RELATIVE_POSITION_R = -3392.400548990203\n", + " RELATIVE_POSITION_T = -79317.283331336\n", + " RELATIVE_POSITION_N = -89044.33131422414\n", + " RELATIVE_VELOCITY_R = -154.91187193411943\n", + " RELATIVE_VELOCITY_T = -5422.481073457308\n", + " RELATIVE_VELOCITY_N = -6905.960506693898\n", " COLLISION_PROBABILITY = 0.0\n", " COLLISION_PROBABILITY_METHOD = MC\n", " OBJECT = OBJECT1\n", @@ -701,33 +268,33 @@ " MANEUVERABLE = N/A\n", " ORBIT_CENTER = EARTH\n", " REF_FRAME = ITRF\n", - " X = -2815.953763263901\n", - " Y = 5296.190984284624\n", - " Z = -3386.8113002022865\n", - " X_DOT = -4.349799473304206\n", - " Y_DOT = 1.4891053026520324\n", - " Z_DOT = 5.793480676499484\n", - " CR_R = 1.000E-18\n", - " CT_R = 0.0\n", - " CT_T = 1.2451197530695846\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.0035176566277315556\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18\n", + " X = 2961.416138954747\n", + " Y = -5176.42090647565\n", + " Z = -3446.5166602514805\n", + " X_DOT = 4.441367932633197\n", + " Y_DOT = -1.3893739558637068\n", + " Z_DOT = 5.749634804852701\n", + " CR_R = 418.4789881653135\n", + " CT_R = 2833.654900508839\n", + " CT_T = 26275.07871526111\n", + " CN_R = 235.96450630189815\n", + " CN_T = 1379.1837684683846\n", + " CN_N = 3824.354403059597\n", + " CRDOT_R = -1.629021196870671\n", + " CRDOT_T = -18.917640141448356\n", + " CRDOT_N = -0.6906003255748716\n", + " CRDOT_RDOT = 0.015134294118617029\n", + " CTDOT_R = -0.44696031062349284\n", + " CTDOT_T = -2.9384291761156875\n", + " CTDOT_N = -0.2522263711844156\n", + " CTDOT_RDOT = 0.001641663225350465\n", + " CTDOT_TDOT = 0.0004784788899478311\n", + " CNDOT_R = 0.15032615582479048\n", + " CNDOT_T = 0.0059359771572660164\n", + " CNDOT_N = -0.3262798823098171\n", + " CNDOT_RDOT = 0.0004967254843707441\n", + " CNDOT_TDOT = -0.00017283616485068076\n", + " CNDOT_NDOT = 0.005746080835028397\n", " OBJECT = OBJECT2\n", " OBJECT_DESIGNATOR = 65082PT\n", " CATALOG_NAME = 3462\n", @@ -738,42 +305,42 @@ " MANEUVERABLE = N/A\n", " ORBIT_CENTER = EARTH\n", " REF_FRAME = ITRF\n", - " X = -2877.333943848403\n", - " Y = 5112.12102362322\n", - " Z = -3608.5298791370465\n", - " X_DOT = -6.038407838525875\n", - " Y_DOT = -3.838957489663574\n", - " Z_DOT = -0.6014152547562583\n", - " CR_R = 3.852952621853669\n", - " CT_R = 0.0\n", - " CT_T = 4.976328968225473\n", - " CN_R = 0.0\n", - " CN_T = 0.0\n", - " CN_N = 0.9333314012997196\n", - " CRDOT_R = 0.0\n", - " CRDOT_T = 0.0\n", - " CRDOT_N = 0.0\n", - " CRDOT_RDOT = 1.000E-18\n", - " CTDOT_R = 0.0\n", - " CTDOT_T = 0.0\n", - " CTDOT_N = 0.0\n", - " CTDOT_RDOT = 0.0\n", - " CTDOT_TDOT = 1.000E-18\n", - " CNDOT_R = 0.0\n", - " CNDOT_T = 0.0\n", - " CNDOT_N = 0.0\n", - " CNDOT_RDOT = 0.0\n", - " CNDOT_TDOT = 0.0\n", - " CNDOT_NDOT = 1.000E-18]" + " X = 2973.071864839906\n", + " Y = -5103.077176433223\n", + " Z = -3539.878719318852\n", + " X_DOT = 5.826399482002183\n", + " Y_DOT = 4.0863283565475435\n", + " Z_DOT = -0.9747953563722136\n", + " CR_R = 19.416112754758714\n", + " CT_R = -1063.96729664468\n", + " CT_T = 122541.70103023999\n", + " CN_R = -35.02940219476802\n", + " CN_T = 1550.9822755102928\n", + " CN_N = 5255.002744568752\n", + " CRDOT_R = 1.2925239374152526\n", + " CRDOT_T = -126.57004812480902\n", + " CRDOT_N = -2.22107658156981\n", + " CRDOT_RDOT = 0.1356785119000294\n", + " CTDOT_R = -0.010480352857442607\n", + " CTDOT_T = 0.30203170115238487\n", + " CTDOT_N = 0.03313019924386015\n", + " CTDOT_RDOT = -0.00046144760169145884\n", + " CTDOT_TDOT = 7.133E-06\n", + " CNDOT_R = -0.01550631317382298\n", + " CNDOT_T = -0.7484418552935522\n", + " CNDOT_N = 1.171583503661754\n", + " CNDOT_RDOT = 0.0008530656635604496\n", + " CNDOT_TDOT = 1.209E-05\n", + " CNDOT_NDOT = 0.0018826631138264438]" ] }, - "execution_count": 25, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trace[0]['cdms']" + "trace.nodes['cdms']['infer']['cdms']" ] }, { @@ -784,8 +351,8 @@ "outputs": [], "source": [ "#let's save all cdms to kvn files:\n", - "#for i in range(len(trace[0]['cdms'])): \n", - "# trace[0]['cdms'][i].save(f'event2_{i}.kvn')" + "#for i in range(len(trace.nodes['cdms']['infer']['cdms'])): \n", + "# trace.nodes['cdms']['infer']['cdms'][i].save(f'event2_{i}.kvn')" ] } ], diff --git a/kessler/model.py b/kessler/model.py index 0755115..e818225 100644 --- a/kessler/model.py +++ b/kessler/model.py @@ -458,7 +458,7 @@ def generate_cdm(self, cdm.set_relative_metadata('TCA', util.from_jd_to_cdm_datetime_str(tca_jd)) cdm.set_header('CREATION_DATE', util.from_jd_to_cdm_datetime_str(obs_jd)) if t_state_new_obs is not None: - obs_tle_t,_=dsgp4.newton_method(t_tle,time_obs_mjd,verbose=True) + obs_tle_t,_=dsgp4.newton_method(t_tle,time_obs_mjd,verbose=False) # we return the state in XYZ, the state in RTN and the cov matrix in RTN if self._up_method == 'MC': t_mean_state_tca_xyz_TEME, t_cov_state_tca_rtn = self.propagate_uncertainty_monte_carlo(state_xyz = t_state_new_obs, @@ -470,7 +470,7 @@ def generate_cdm(self, cdm.set_covariance(0, t_cov_state_tca_rtn) #elif self._up_method == 'STM': if c_state_new_obs is not None: - obs_tle_c,_=dsgp4.newton_method(c_tle,time_obs_mjd,verbose=True) + obs_tle_c,_=dsgp4.newton_method(c_tle,time_obs_mjd,verbose=False) if self._up_method == 'MC': c_mean_state_tca_xyz_TEME, c_cov_state_tca_rtn = self.propagate_uncertainty_monte_carlo(state_xyz = c_state_new_obs, cov_matrix_diagonal_rtn = self._c_cov_matrix_diagonal_obs_noise, From b80359304f8b06fd7a8251a6af5d2256b16bf5ce Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 16:26:52 +0200 Subject: [PATCH 04/13] some old updates --- README.md | 22 +++----- docs/notebooks/trace.pickle | Bin 215078 -> 147423 bytes kessler/__init__.py | 5 +- kessler/cdm.py | 5 +- kessler/data.py | 4 +- kessler/event.py | 5 +- kessler/model.py | 88 +++++--------------------------- kessler/nn.py | 4 +- kessler/observation_model.py | 10 ++++ setup.py | 11 ++-- tests/test_cdm.py | 4 +- tests/test_event.py | 4 +- tests/test_model.py | 10 ++++ tests/test_observation_model.py | 10 ++++ 14 files changed, 71 insertions(+), 111 deletions(-) diff --git a/README.md b/README.md index 33be64e..b11133e 100644 --- a/README.md +++ b/README.md @@ -22,12 +22,11 @@ ----------------------------------------- [![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 @@ -35,13 +34,7 @@ To get started, follow the [documentation](https://kesslerlib.github.io/kessler/ ## 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 diff --git a/docs/notebooks/trace.pickle b/docs/notebooks/trace.pickle index 4ff2f168c756cf9dcfd1f9704252eefa1d3a03c7..c06d620e4cc8fec392fe6df6033a508b90e06e29 100644 GIT binary patch literal 147423 zcmeIb349a9`#;_rC}+7rz@pqj$>t7sX$zE=6x(trM@-YubZHx!ra(~?JUD}h1jGXY 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zguX?Jh)@nrG!dcRND&1w3@IW)l}Hgsmu~evK3{phu$lBF55-6fu!!B1H`24M-8g_#9Hi2<}CS zn7(7t7!iXv9Vue)!blP0b`w&>q`iz3F=XE$MGVmPM3t;Z ziYS_Uks=1_Riub9x)PT@Q6M)XMHI+AND&3{7*a%myoeN0AUlvE3gk1ShywWzDWX8S zqY@wrWF%5Vft-jGQ6Lpa5e3qK6j30{kRl4?N~A;uk^@mrTVputHUH5M&ciTn-O@DH z>?B4i{Z+LpI9Vkv80p;@tmomdOAQ}Oos^~p%BMnIiToGIY4kVOhXc{lTY;C5bcU*Q oOr($hbboDI7|zJ>Va#x~`fH<8Cowm(v)~QdYRa7H%cBMV2bWdM-~a#s diff --git a/kessler/__init__.py b/kessler/__init__.py index fd2dcaa..70f08b4 100644 --- a/kessler/__init__.py +++ b/kessler/__init__.py @@ -1,14 +1,13 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. - __version__ = '1.0.0' from .util import seed diff --git a/kessler/cdm.py b/kessler/cdm.py index 69879fe..3d6db85 100644 --- a/kessler/cdm.py +++ b/kessler/cdm.py @@ -1,14 +1,13 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. - import numpy as np import warnings import datetime diff --git a/kessler/data.py b/kessler/data.py index 58005e2..2cf7e17 100644 --- a/kessler/data.py +++ b/kessler/data.py @@ -1,9 +1,9 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. diff --git a/kessler/event.py b/kessler/event.py index 8a6d8d1..3c22fdc 100644 --- a/kessler/event.py +++ b/kessler/event.py @@ -1,14 +1,13 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. - import pandas as pd import numpy as np import matplotlib as mpl diff --git a/kessler/model.py b/kessler/model.py index e818225..2afc160 100644 --- a/kessler/model.py +++ b/kessler/model.py @@ -1,3 +1,13 @@ +# This code is part of Kessler, a machine learning library for spacecraft collision avoidance. +# +# Copyright (c) 2020- +# Trillium Technologies +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) +# and other contributors, see README in root of repository. +# +# GNU General Public License version 3. See LICENSE in root of repository. + import dsgp4 import numpy as np import uuid @@ -8,10 +18,6 @@ from . import GNSS, Radar, ConjunctionDataMessage, util from dsgp4 import TLE -from torch.distributions import constraints -from torch.distributions.distribution import Distribution -from torch.distributions.utils import broadcast_all - from pyro.distributions import MixtureSameFamily, Categorical, Uniform, Normal, Bernoulli def find_conjunction(tr0, @@ -45,74 +51,6 @@ def find_conjunction(tr0, d_conj = squared_norm[i_conj].sqrt() return i_min, d_min, i_conj, d_conj - -class TruncatedNormal(Distribution): - """ - Truncated Normal distribution with specified lower and upper bounds. - This class inherits from the Pyro Distribution class and implements - the log probability and sampling methods for a truncated normal distribution. - - Args: - loc (``torch.Tensor``): The mean of the normal distribution. - scale (``torch.Tensor``): The standard deviation of the normal distribution. - low (``torch.Tensor``, optional): The lower bound for truncation. Default is None. - high (``torch.Tensor``, optional): The upper bound for truncation. Default is None. - validate_args (bool, optional): Whether to validate the arguments. Default is None. - - Attributes: - loc (``torch.Tensor``): The mean of the normal distribution. - scale (``torch.Tensor``): The standard deviation of the normal distribution. - low (``torch.Tensor``): The lower bound for truncation. - high (``torch.Tensor``): The upper bound for truncation. - base_dist (``Normal``): The base normal distribution. - - Methods: - log_prob(value): Computes the log probability of the given value. - sample(sample_shape): Samples from the truncated normal distribution. - """ - arg_constraints = { - 'loc': constraints.real, # loc can be any real number - 'scale': constraints.positive, # scale must be positive - 'low': constraints.real, # low can be any real number - 'high': constraints.real # high can be any real number - } - - def __init__(self, loc, scale, low=None, high=None, validate_args=None): - # Convert inputs to tensors and handle None values - self.loc, self.scale, self.low, self.high = broadcast_all( - torch.as_tensor(loc), torch.as_tensor(scale), - torch.as_tensor(low) if low is not None else None, - torch.as_tensor(high) if high is not None else None - ) - # Validate bounds (low < high) - if self.low is not None and self.high is not None: - if (self.low >= self.high).any(): - raise ValueError("Invalid bounds: low must be less than high.") - # Create a base Normal distribution - self.base_dist = Normal(loc, scale) - super().__init__(batch_shape=self.loc.shape, validate_args=validate_args) - - def log_prob(self, value): - # Calculate log probability for the normal distribution - log_prob = self.base_dist.log_prob(value) - # Adjust for truncation at the low bound (if any) - if self.low is not None: - log_prob -= torch.log(torch.clamp(self.base_dist.cdf(self.low), min=1e-10)) - # Adjust for truncation at the high bound (if any) - if self.high is not None: - log_prob -= torch.log(torch.clamp(1 - self.base_dist.cdf(self.high), min=1e-10)) - - return log_prob - - def sample(self, sample_shape=torch.Size()): - shape = self._extended_shape(sample_shape) - rand = torch.rand(shape, dtype=self.loc.dtype, device=self.loc.device) - if self.low is not None: - rand = rand * (1 - self.base_dist.cdf(self.low)) + self.base_dist.cdf(self.low) - if self.high is not None: - rand = rand * self.base_dist.cdf(self.high) - return self.base_dist.icdf(rand) - def default_prior(): """ This function returns a dictionary of TLE elements priors. @@ -145,7 +83,7 @@ def default_prior(): mixture_distribution=Categorical(probs=torch.tensor([ 0.12375596165657043, 0.05202080309391022, 0.21220888197422028, 0.0373813770711422, 0.01674230769276619, 0.5578906536102295])), - component_distribution=TruncatedNormal( + component_distribution=util.TruncatedNormal( low=0.0, high=0.004, loc=torch.tensor([ 0.0010028142482042313, 0.00017592836171388626, 0.0010926761478185654, 0.0003353552892804146, 0.0007777251303195953, 0.001032940074801445]), @@ -162,7 +100,7 @@ def default_prior(): mixture_distribution=Categorical(probs=torch.tensor([ 0.5433819890022278, 0.04530993849039078, 0.08378008753061295, 0.02705608867108822, 0.03350389748811722, 0.2669680118560791])), - component_distribution=TruncatedNormal( + component_distribution=util.TruncatedNormal( low=0.0, high=0.8999999761581421, loc=torch.tensor([ 0.0028987403493374586, 0.6150050163269043, 0.05085373669862747, 0.3420163094997406, 0.7167646288871765, 0.013545362278819084]), @@ -178,7 +116,7 @@ def default_prior(): 0.028676774352788925, 0.06484941393136978, 0.13786117732524872, 0.0010146398562937975, 0.047179922461509705, 0.01607278548181057, 0.020023610442876816, 0.06644929945468903, 0.4442509114742279])), - component_distribution=TruncatedNormal( + component_distribution=util.TruncatedNormal( low=0.0, high=torch.pi, loc=torch.tensor([ 0.09954200685024261, 1.4393062591552734, 1.736578106880188, 1.0963480472564697, 0.48166394233703613, 0.9063634872436523, 1.275956392288208, 2.5208728313446045, diff --git a/kessler/nn.py b/kessler/nn.py index b516d78..effa1c5 100644 --- a/kessler/nn.py +++ b/kessler/nn.py @@ -1,9 +1,9 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. diff --git a/kessler/observation_model.py b/kessler/observation_model.py index 57feba2..a401b98 100644 --- a/kessler/observation_model.py +++ b/kessler/observation_model.py @@ -1,3 +1,13 @@ +# This code is part of Kessler, a machine learning library for spacecraft collision avoidance. +# +# Copyright (c) 2020- +# Trillium Technologies +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) +# and other contributors, see README in root of repository. +# +# GNU General Public License version 3. See LICENSE in root of repository. + import numpy as np diff --git a/setup.py b/setup.py index 5e7dd18..dde561d 100644 --- a/setup.py +++ b/setup.py @@ -26,14 +26,15 @@ def read_package_variable(key): setup( name='kessler', version=read_package_variable('__version__'), - description='Simulation-based inference and machine learning for space collision assessment and avoidance.', - author='ESA FDL Europe Constellations Team', - # author_email='', + description='Machine learning and simulation-based inference and machine learning for space collision assessment and avoidance.', + long_description=open('README.md').read(), + author='Giacomo Acciarini', + author_email='giacomo.acciarini@gmail.com', packages=find_packages(), install_requires=['pyro', 'numpy', 'matplotlib', 'torch>=1.5.1', 'dsgp4', 'skyfield>=1.26', 'pandas'], extras_require={'dev': ['pytest', 'coverage', 'pytest-xdist']}, - # url='https://github.com/kessler/kessler', + url='https://kesslerlib.github.io/kessler/', classifiers=['License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3'], license='BSD' - # keywords='', + keywords='Spacecraft Collision Avoidance Kessler Machine Learning Artificial Intelligence Probabilistic Programming', ) diff --git a/tests/test_cdm.py b/tests/test_cdm.py index 3b46b3b..4a4881d 100644 --- a/tests/test_cdm.py +++ b/tests/test_cdm.py @@ -1,9 +1,9 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. diff --git a/tests/test_event.py b/tests/test_event.py index 6ad5b57..3171ecd 100644 --- a/tests/test_event.py +++ b/tests/test_event.py @@ -1,9 +1,9 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. diff --git a/tests/test_model.py b/tests/test_model.py index 3ce560a..b981ee6 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -1,3 +1,13 @@ +# This code is part of Kessler, a machine learning library for spacecraft collision avoidance. +# +# Copyright (c) 2020- +# Trillium Technologies +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) +# and other contributors, see README in root of repository. +# +# GNU General Public License version 3. See LICENSE in root of repository. + import numpy as np import unittest import dsgp4 diff --git a/tests/test_observation_model.py b/tests/test_observation_model.py index 13bb086..253d19c 100644 --- a/tests/test_observation_model.py +++ b/tests/test_observation_model.py @@ -1,3 +1,13 @@ +# This code is part of Kessler, a machine learning library for spacecraft collision avoidance. +# +# Copyright (c) 2020- +# Trillium Technologies +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) +# and other contributors, see README in root of repository. +# +# GNU General Public License version 3. See LICENSE in root of repository. + import numpy as np import unittest From ddb166a1862e595156d7ca00d8c1984ba040a917 Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 16:28:07 +0200 Subject: [PATCH 05/13] initial updates to plotting module to suppor pyro, docs update, test --- docs/notebooks/plotting.ipynb | 80 ++++++----- kessler/plot.py | 78 ++++++----- kessler/util.py | 243 +++++++++++++++++++++++++++------- tests/test_util.py | 25 +++- 4 files changed, 303 insertions(+), 123 deletions(-) diff --git a/docs/notebooks/plotting.ipynb b/docs/notebooks/plotting.ipynb index 55c11a2..7b211d3 100644 --- a/docs/notebooks/plotting.ipynb +++ b/docs/notebooks/plotting.ipynb @@ -28,13 +28,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import kessler\n", "import dsgp4\n", - "import pickle" + "import pickle\n", + "import numpy as np" ] }, { @@ -48,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -64,12 +65,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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q3RtvvKG3335bkuTn56fOnTvr0UcfVWBgoK5cuaLNmzfru+++05UrV/Tkk09q8+bNeuyxx+xur1evXtq2bZuqV6+up556SpUqVdK1a9fMswxnVkREhJo0aWKebTokJET9+vVTlSpVFBsbq/Xr1+u7775Tp06dVLNmzQz7mzFjhgYOHCjDMOTt7a0nn3xSTZo0UVBQkK5du6adO3dqwYIFunHjhvr27at8+fLpqaeeSrevPn36aP78+ZKkgIAAdevWTQ8//LCKFCmiixcvavXq1Vq9erXOnj2rxo0b65dfflHlypWz9Pzvxn/+8x/NnDlTkuTl5aWePXuqcePG8vX11f79+zVr1ix9+umnOnv2bIZ9nTlzRo888ogZhxo1aqhz586677775OXlpePHj2vevHk6deqUlixZomvXrmn16tXp/grdFV5TAQAAgLwiISFBx44dM++XL1/ebvuc+P5FkhYuXGhepad3795mrtCnTx9NmDBBhmFo1qxZevHFF7P0/Hx9fTVu3Dg9++yzSkxM1PDhw7V69eos9ZHTPvroo1RX12nfvr3atGkjf39/HTlyRLNnz9Z3331nXkbYnujoaDVs2FC///67pKSrmHXv3l3Vq1eXr6+vwsPDtXDhQu3fv1/btm1Ts2bNtGvXLvn5+aXp66233tL7778vKekqUZ06dVKrVq1UqFAhHT9+XLNnz9aCBQsyvMoYAAAAALib6dOnm8vNmzdPt03Xrl3Nq0FVrVpVXbt2Vfny5RUQEKDo6GgdO3ZM27Zt088//5zqcU2aNNHSpUu1efNmTZkyRZL00ksvqUmTJnbHdOvWLSUkJCgkJERNmzbVAw88oFKlSilfvnyKiIjQrl279O233+rGjRsaPXq0ihcvnqkc3FHHfK1k3bp1unHjhqSkHN2efv36adWqVQoNDdVTTz2lkJAQXbx4UV999ZV++eUXRUREqHPnzjpw4IDatGmjX375RY0bN1aHDh0UHBysM2fO6Msvv9SxY8d05swZPfvss2a9AAAghzm7ihsAHO21114zf2k3Y8aMVOvefPNNc91HH31kt587f7lZuXJl488//7TZPuWZlgoXLmzs2rUrTZuIiAijZs2aqfrN7TNR6/+fHejWrVtp2j3zzDNmmzp16phna0o+61Oy+Ph4o3Hjxun+4jWl4cOHm22eeuopIzY2Nt12U6dOTTW29KQcv7e3t7F48eI0bRISElKdYWnSpEnp9uWoX66mlJiYaDz44IOp4pbePv7+++8NHx8fQ5Lh5eVl86zDmT1TcmZl5UzU3bt3N9v269fP/PuaNWvMs049+OCDxh9//JHu4//3v/8ZhQsXNv9PxsfHp2mT8le5koxBgwYZCQkJdseV3NbWmah79+5ttmnSpEm6/99Wrlxp5MuXL9W209u/Bw4cMHx9fc1f+u7fvz/dbR49etQoW7asIcnw9/c3Ll++nKbN9OnTU43r4sWL6fa1bNky8/9Gw4YNbe+IDGT1LMs7duww41qgQAHjxx9/TNPmn3/+MapWrZrha1diYqJ5BmoPDw9j8uTJaV4/DMMw4uLiUp1h+s7XasNw7GvqnTgTNQAAANxJys/L9nz00UepPoNHR0fbbe+o71/uVLduXfOxx48fT7XuscceM9ft27fPbj93nonaMJKuwpPyylbpfSfgrDNRnzp1yvDz8zO/L0jvTOBXr15NtQ/s9d2jRw+zzSuvvJLudxSJiYnG66+/nmY/pXTs2DEzj/bx8TGWL1+eps21a9eM5s2bZ2pcKXG2LAAAAACuyF6enZiYaFy5csXYunWr0blzZ7NdtWrV0r066549e8w23bp1M27fvm1zu3/++We6xzXvvMJ2Ri5fvmxs377dbpvTp0+bZ8cOCAgwYmJi0m2XnWO+mWGVM1EnPz44ODjdY553Pv/hw4enW1vRpEmTNHUY06dPT9NfdHS0Ua1aNbPtnj17MhwjuTUA3D1PAUAekpCQoHnz5klKOktu165dU63v06ePuTxr1qxM9+vh4aFvvvnG7hmAP/zwQ3P5gw8+0COPPJKmTWBgoL755ht5ezvvQgBVqlTRrFmz5OPjk2bd22+/bZ7h6ZdfflGTJk301ltvpTlDrLe3t8aPH2/eX7t2bZq+Ll68qI8//liSVKdOHX399dcqWLBgumMaNGiQevXqJUn65ptv9Pfff9t9DiNGjEgTWynpDLqTJk0y769Zs8ZuP460evVqHTx4UFLSmXdnzpyZ7j7u2LGj3njjDUnS7du3zTM5WcX8+fO1ePFi8363bt3M5VGjRskwDPn7+2v16tWqWLFiun3Ur19fkydPliT9+eefWrJkid1thoaG6pNPPpGXl1e2x33hwgUtXLhQUtKZnhcuXJju/7c2bdpo2LBhGfY3duxYxcXFycvLS8uXL7d55uoqVapozpw5kqSYmBjNmDEj1fq4uDiNGzdOklSuXDktX77c5pnW27dvb45t586d2r17d4bjdITJkyfLMAxJ0rvvvqvHH388TZvg4GAtWrQowxitWLFCP/30kyTp3//+t1555ZV0zzCdL18+zZ492/w/lPz/JSVXeU0FAAAAXNn169e1d+9eDR48WK+++qr595deekn+/v42H5dT378cOnRIe/bskZSUW953332p1vft2zdb/Sbz9PTUu+++a95//fXXs9xHTpk6dapu3rwpSRoyZIi6d++epk3hwoW1aNEiu7GRpIMHD+qbb76RlPQ9xOTJk9P9jsLDw0MTJ07Uo48+ao4hLi4uVZspU6bo1q1bkqShQ4eqXbt2afopUKCA/vvf/6po0aKZeKYAAAAAkHd4eHikunl6eqpo0aJq1KiRlixZotKlS2vIkCH66aef0r0a8smTJ83lPn36yNPTdhlXSEiIKlSocNdjLlasmJkH2lKhQgVNmzZNknT16lUtX748w34dcczXSm7fvq2VK1dKktq1a5fuMc+UGjVqpHfffTfd2ork48VSUh1Gv379NHDgwDR9+Pv7a8SIEeb99OowAACORxE1gDxlxYoVunDhgiSpQ4cOCggISLW+UqVKZkJw+PDhNJe8seXRRx9V7dq1ba6/efOm1q1bJ0kqWrRoqoN6d6pataqeeOKJTG03J/zrX/+Sr69vuuvKlSuX6nK9L7/8ss1+HnnkEfMA3G+//ZZm/aJFi8yDf0OHDs0wWerdu7ekpGRk06ZNNtt5enraHVflypVVrlw5m+PKKSkLhYcOHWq3qPPf//63mSSvWLFC8fHxOT6+lI4ePaply5aZtyVLlmjKlClq1aqVnnnmGbOgtm3btmrZsqWkpAPZe/fulZR0KaYyZcrY3cZTTz1l7oPkuWHLoEGD7jqZXrVqlbkfe/XqpZIlS9ps+9JLL9mNT1RUlPlFQPPmze3OfUlq1qyZSpcuLSntc12/fr3OnTsnKel5FipUyG5fyfMgvb5yQlxcnFatWiUp6WD8888/b7Ptgw8+qBYtWtjt76uvvpKU9IXRa6+9Zrdtvnz51KNHD0lJ/yf/+usvc50rvaYCAAAAruTOg7sFCxbUQw89pM8++0yJiYmSpJ49e2rs2LF2+8mp719mzpxpLqcsxE7WtWtXM59esGBBmoLfzGjTpo3CwsIkST///LO+/fbbLPeRE77//ntJSd97vPLKKzbbBQcH6+mnn7bbV3JuJmWuUPyZZ56RlHRg/M4f9Cbnx15eXhoyZIjNPgIDAzMcFwAAAAC4Gx8fHxUsWFC3b99Od33Kk0L9+uuvuTWsTGnYsKG5vGvXrgzbO+KYr5Xs2LFDly9flpT03UdG/v3vf9tcl7K2QrJfh/HYY4+Zy7lZ7wAA7ozT9gHIU1KehSi9g21S0lmLduzYIUmaPXu2Hn744Qz7TflBNT0HDhwwCzgbNGigfPny2W3fuHFjrVixIsPt5oT69evbXR8UFKTw8HBJUr169Wy28/HxUfHixXX+/HlduXIlzfpt27aZy1euXNGyZcvsbjfl2ad///13m+2qVKmi4sWL2+2rTJkyOnPmTLrjyikpE8fkwmNbChcurAYNGmjjxo26ceOGDhw4oDp16uT0EE2LFi3SokWL7Lbp1q2beYZlKXU8vby8MoynJBUqVEhRUVF24yllPL8yI/lMZVLS/LKnZMmSqlatmnnm8Dvt3LnTLB7w9/fP9HOV0v7fTbnf4uLiMuwrZUF9RvvNEQ4cOGCeUaxhw4by8/Oz275p06Z2z/Ce/HyLFSuWqTNpp5yjv//+u3m2f1d6TQUAAADyiqCgIM2bN0/NmzfPsG1OfP8SFxenBQsWSJJ8fX3TPROzv7+/OnXqpPnz5+vKlStaunSp+ePMrHj//ffNq92MGjVKHTt2dOoVbi5evKg///xTUtL3HmXLlrXbvmnTpuYZwdKTnJt5eHjozJkz5o97bbnzO5nkKxRduHBBZ86ckZT0A9agoCC7/TRu3FhTpkyx2wYAAAAA8pKlS5em+dv169cVHh6u5cuX6+eff9a7776rBQsWaOPGjWmuuNSwYUMVKFBA169f1/jx43X58mU988wzCg0NzfDMx3fr5MmTmjdvnrZt26Zjx47p6tWrunHjRrptz549m2F/jjjmayXJx3X9/f3VpEmTDNvbq8NIWVtRoEABPfDAAzbbpsy9c7PeAQDcGUXUAPKMf/75x7ycSXBwsM2Dft26ddOQIUN0/fp1LVy4UB9++GG6l85JKaODV//884+5XKlSpQzHmpk2OSUwMNDu+pRnqc5s2+QzTqeUXIgtJZ39OisiIyNtrstoTCnHlZ0zUmVX8gFJf3//DA8qSkkHRTdu3Cgp9f8fZ/Dy8lLhwoVVvnx51atXT88884waNGiQqk3KeH722Wf67LPPMt2/vXhKGc+vzMjOHLRVRJ3yuX777bdZOivZnc81ZV9jxozJdD/p9ZUTUu63O7+0Sc+9995rc921a9d06dIlSdLly5fVsWPHLI0l5fN1pddUAAAAwJWkPLgbFxenv/76S0uWLNHu3bt1/vx5vfXWW3r44YfTnFk6pZz6/mXZsmXmGZ7at2+vIkWKpNuuT58+mj9/vqSkYu7sFFE//PDD6tKli7777judOHFCX375pV588cUs9+MojszNpP/LRQ3DUNeuXbM0FnIzAAAAAMg8e2coHjlypD766CO98sor+uuvv9SxY0ft27cv1RmJixUrpk8++UQDBw5UQkKCPvnkE33yyScqUqSIGjRooMcff1wtWrTI8Mq5WTV27Fi9/fbbSkhIyFT76OjoDNs44pivlSRfmemJJ57I8IRPUuZrK4oXL263QD5lvUZ6dRgAAMfzdPYAAMBR5s6da14Gp1evXjYvFePv728W90VHR+u7777LsO/8+fPbXX/t2jVzOaOC7My2ySmenpl/6c9K2ztFRUVl+7HJZ8ZNz92MKSfFxMRISn3JJXuSz1yc8rG5ZcyYMTIMw7wlJCQoMjJS+/bt07Rp09IUUEs5F08p4/mVGY6cg3fzXFOeSfpu+8povzlCbGysuZyZ/Wbv//fdPFcp9fN1pddUAAAAwJV06NDBvHXv3l2vvfaadu3apY8++khS0hmMO3fubF6dJz059f1LyrNb9+7d22a7Jk2amFex2bRpU6ofr2bFO++8Y559evz48anyo9zmyNxMclwuSm4GAAAAAHfnP//5j3m1n99++y3d3Pi5557Ttm3b1KpVKzPHjoqK0urVqzV8+HCFhobqwQcftHu12Kz44IMPNG7cOCUkJMjT01NNmzbV6NGjNXPmTC1atEhLly41b8mSvwewxxHHfK3i4MGDOn36tKSkH3pnRmbrGKxa7wAA7oxXZgB5gmEYmj17tnl/0qRJ8vDwsHlLvjyslPogXXalPHh1/fr1DNunPAjlCJlJWnJbyiLhK1eupCrazeg2d+5c5w08m/z9/SVlPrYpD5AmP9bKUsZz2bJlWYpndg9oZ4Uj52DK5/rxxx9n6bkahmGzr/3792epn61bt2ZhD2RPyvE5cr/VqlUry/utb9++5uOd/ZoKAAAAuJt///vf6tmzp6SkwuRPPvkk3XY59f3Ln3/+qU2bNpn3n3zySZt9enl56a+//jLHM2fOnGw95/vuu0/PPfecJOnChQuaPHlytvpxBEfmZin7K1KkSJZzs7Fjx5r9kJsBAAAAwN174oknzOUNGzak26Zhw4Zas2aNLl26pB9++EEjRozQo48+ahZVHzp0SK1bt77r4+g3b97U+PHjJSXljj/99JM2btyocePGqX///urWrZv542tbV55yB8lnofbx8VHr1q2dPBoAQE6jiBpAnvDjjz/q1KlT2Xrstm3bdOLEibvafunSpc3lzIzjjz/+sLs+5SVaMjobrWEYqS61ahUpL9fz22+/OXEkuSM4OFhS0lmlz58/n2H748ePm8sp//9YVcp4Hj582IkjSZ8j56Ajn6vV91uZMmXM5ZMnT2bY3l6bgIAA8wcBJ06cuKszaTv6NRUAAABAxiZNmmSeNWr8+PG6fPlymjY59f3LnDlz7J792p67eeyYMWPMQuHJkyfr4sWL2ernbqXMge42N5P+LxeNiorS33//7ZBxkZsBAAAAQPYUL17cXM4oRytSpIjatm2rd955R9u3b9e5c+c0aNAgc/2rr76a5sq4WfHTTz+ZJ/saOHCgHn74YZttk8/E7I6WLVsmSQoLC1ORIkWcOhYAQM7zdvYAAMARUp7NqGPHjnrwwQczfMzPP/9sXvJm9uzZevfdd7O9/Zo1a8rHx0fx8fHauXOnbt26pXz58tlsv2XLFrv9pfwgnlEitX///kydDSi3NWrUSCtXrpQkff/992rYsKGTR/R/Ul4i584zB2dXvXr1dOTIEUnSunXr1KdPH5ttY2Ji9L///U9S0mWNatas6ZAx5KRGjRqZy99//71GjRrlvMGko27dupo+fbqkpPnVpUsXm20vXrxot7A/LCxMHh4eMgxDK1euzHA+29OoUSNNnTpVUtJ+69WrV7b6ySkPPvigfH19FRcXpx07dujmzZvy8/Oz2T7lmeHSExYWppUrV+ratWtat26d2rZtm61xOfo1FQAAAEDGgoOD9a9//UsffvihoqKiNHHiRH3wwQep2uTE9y+JiYmpzib9n//8R4ULF86w38WLF+vIkSM6c+aMNmzYoJYtW2b4mDsFBQXplVde0YQJExQTE6MJEyY4JUcvWbKkKlSooPDwcB09elR///13qh+93imj3KxRo0bmD3m///57vfTSS9kaV6lSpVSuXDmdOXNGR44c0fnz5xUUFGSzPbkZAAAAAKR16dIlcznlFX8yo0SJEpo6dap27NihAwcOKDIyUr/99ptq1apltsnKse+UJwO799577bZdvXp1lsaaV5w9e1Z79+6VJHXo0MG5gwEA5AqKqAG4vKtXr2rJkiWSJC8vL33++ed2D+gkO378uHkQ76uvvtJbb71lXg4nq/z8/NSyZUutXLlSUVFRmjt3rp5//vl02x49etTcri358+fXPffcoz/++EM///yzoqOjbR5A/PDDD7M15pzWo0cPjRo1SnFxcZo+fbr+9a9/ZZiI5ZaUl8l11KVmu3TpYh70nTx5snr16iVv7/TfZj/55BNzu+3atZOPj49DxpCTQkND9cADD+jQoUPau3evFi5cqKeeesrZwzK1adPGLLpdsGCBxo4dqxIlSqTbdsqUKbp9+7bNvgIDA9WmTRutXLlS58+f1+TJkzVixIhsjeuJJ55QyZIldfHiRS1dulQ7d+601A8KfH191bp1ay1dulTR0dGaOXOmBg8enG7bw4cPa/369Xb769u3r/njiTfffFPNmze3W5Rti6NfUwEAAABkztChQ/XZZ58pLi5On3/+uYYOHapSpUpJyrnvXzZu3Ki//vpLklStWrVMf89RunRpDRw4UFJScXd2iqgl6bXXXtP06dMVERGhL774Qq+++mq2+rlbHTt21EcffaTExER99NFHmjRpUrrtLly4oAULFtjtq0+fPuYPeidOnKinnnpKgYGB2RpX+/btNXXqVCUmJurTTz/VO++8k267S5cu6euvv87WNgAAAAAgL0t5HKtatWrZ6qNixYo6cOCAJCkhISHVuqwc+05ZxG3vKkdXrlzRxx9/nI2Rur7ks1BLScfyAQB5n2fGTQDA2v773//qxo0bkqQWLVpk6gCeJFWuXFn16tWTJJ07d+6uf0n5yiuvmMuvvfaadu/enabNpUuX1KNHjzSJTXqeeOIJSdLNmzdtFnB+/PHHmj9/fjZHnLPKlCmj//znP5Kk69evq2XLltq3b5/dxxw+fFgvvPBCjo+tYsWK5nLyr0jv1hNPPGGererQoUN6/vnn072U0g8//KAJEyZISjroPGzYMIdsP6d5eHjo/fffl4eHhyTpueee0zfffGP3MRcuXND48eN18ODBHB9fqVKlzKLuq1evqkePHul+SbBq1Sq9//77Gfb31ltvydfXV5L0xhtv6JNPPrH7y+2rV6/q448/1saNG1P9vUCBAho/frykpF9+d+jQIcMzhoWHh+vVV1/NtctIDx061Izr8OHDtWPHjjRtLly4oO7du9stPpekTp06qX79+pKkAwcOqH379oqIiLDZPjExURs2bNBbb72VZp2jX1MBAAAAZCw4OFj9+vWTlJTLT5w40VyXU9+/pDy7tb2rOt2pe/fu5o82ly9fnurMXlnh7++vN998U5IUHx+vKVOmZKufuzV48GDz+Xz88cf67rvv0rSJiYlR9+7dFR0dbbevOnXqqEePHpKkf/75Ry1btszwMsy7du3Sa6+9lu64kn/8PWnSJP3www9p2ly/fl09e/ZUVFSU3W0AAAAAgLv56KOPtH37dklJZ4xOztWSLViwQLNnz7Zb/Hzs2DHz+KKfn5+qVKmSan1Wjn3XqVPHPC44c+ZMnTp1Kk2byMhIdejQQefOnbPbV161fPlySdJDDz2kcuXKOXk0AIDcwJmoAbi8lAfbevfunaXH9u7dW7t27TL7adu2bbbH0bhxY/Xv31+zZs1SdHS0HnvsMT399NN6/PHH5evrq4MHD2rWrFmKiIhQt27dtHjxYkmpL6+T0ssvv6xZs2bp5s2b+vzzz3X8+HF17dpVRYsW1ZkzZ/Tdd9/pp59+UlhYmE6ePKm///4722PPKW+99ZYOHDigNWvW6I8//lCdOnXUqlUrNWnSRGXKlJGHh4cuX76sw4cPa+vWrTpy5Ii8vLw0ffr0HB3X448/rnz58unWrVvmpYlr1qxpFs3mz59fYWFhWerTw8NDCxYsUL169RQbG6s5c+bop59+Uu/evXXPPfcoOjpaa9as0dKlS83HjBs3TqGhoY57YjmsVatWeuuttzRq1Chdv35dTz31lN5//321a9dO9957r3x9fXX16lUdP35cu3bt0s6dO5WYmKgmTZrkyvgmTZqkDRs26Ny5c9q8ebOqVaumfv36qWrVqoqNjdX69ev17bffqlixYqpVq5b5ZUN6c7BmzZqaOXOm+vTpo8TERP373//W559/ro4dO+r+++9XwYIFFRMTo1OnTunnn3/Wjz/+qFu3bqV71q2BAwdq7969+vLLL3Xp0iU1a9ZMjz/+uFq1aqXy5cvLx8dHkZGROnLkiHbs2KFff/1VkswfIeS0Bg0a6KWXXtKnn36qa9euqVGjRurVq5caN24sX19f7d+/XzNnzlRkZKQ6deqk77//3mZfHh4eWrJkierXr68///xT69evV8WKFdWlSxc98sgjKlGihOLi4nT+/Hnt379fGzZs0IULF9S0aVO98cYbqfpy9GsqAAAAgMx5/fXXNXPmTMXHx2v69Ol67bXXVLp06Rz5/uXy5cvmwUlPT089/fTTme4zICBA7dq10+LFi3Xr1i3Nnz9f//73v7M0rmQvvPCCPvnkE506dcphV6zKqnvuuUfvvPOOXnnlFd2+fVtdu3ZVx44d1bp1a/n7++vIkSOaPXu2zpw5k2FuJkkzZszQ8ePHtXfvXu3du1dVqlRR+/bt9dh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", 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" ] @@ -95,23 +96,39 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "priors=kessler.util.create_priors_from_tles(tles, mixture_components = {'mean_motion': 5, 'eccentricity': 5, 'inclination': 13, 'b_star': 4})" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "priors=kessler.util.create_priors_from_tles(tles, mixture_components = {'mean_motion': 5, 'eccentricity': 5, 'inclination': 13, 'b_star': 4})\n", "#we also extract the mean motion alues from the TLEs, to then have the minimum and maximum values for the mean motion priors (else the priors will be wide and you cannot see anything)\n", "mean_motions=[el.mean_motion for el in tles]" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ga00693/miniconda3/envs/kessler/lib/python3.12/site-packages/torch/distributions/distribution.py:307: UserWarning: does not define `support` to enable sample validation. Please initialize the distribution with `validate_args=False` to turn off validation.\n", + " warnings.warn(\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -125,15 +142,16 @@ "" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "kessler.plot.plot_mix(mix=priors['mean_motion_prior'], \n", - " min_val=min(mean_motions),\n", - " max_val=max(mean_motions),\n", + "kessler.plot.plot_mix(mix = priors['mean_motion_prior'], \n", + " min_val=np.min(mean_motions), \n", + " max_val=np.max(mean_motions), \n", + " log_yscale=True,\n", " xlabel='Mean Motion',\n", " figsize=(10,8),\n", " linewidth=4.,\n", @@ -141,21 +159,14 @@ " resolution=1000)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or in log-scale" - ] - }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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pooc82G+vsNuOISIiIiKBo0RbQsYWm1ZEnZvW7DpKtCVcrc0z36NnGZaFn86uYOA67dMWERERcZ0SbQkZmwzLxgHOqGGi3aUpxBjyFSXaEurW2STalRVCO6lxIrQ11DJYr0KAIiIiIq5Toi0h4/SKySfVdOl4Yhx0bOw/vuVwTSMScdfGg/6JdnUKoZ3Uw5CQf70fvGV1i0tEREREakaJdpTIzs4mIyODzMzMYIdiyzSjXT8OWtWg4vhJnQzJ+dbDHlUel5C2yZBod2ladSG0k0zLxwtL7T/EEhEREZHAUKIdJcKhGJopGehsswy8KqZE+0iRh6Nl9Wp+MREXlPo8bDviP96lBlsn7AqibbTZliEiIiIigaFEW0JCaRlsPew/3rmGy8ZPsltqm1uSXLsLigRYXkkyXp95Rru6zrQ5167+gYiIiIgEhhJtCQm78qHY0OK7poXQTrJL0Pcp0ZYQtbfEvEeiJol224aQEOs/rkRbRERExF1KtCUk2Lb2qu2Mts3zNKMtoco20W5W/WvExpgLAWqPtoiIiIi7lGhLSNhu2JsK5qShOprWh0YJ/uNKtCVU7Sn2T7QT46BNDYsBmvrObz4EZSoEKCIiIuIaJdpRItSrju+wSbTbNard9Twe86x2rs2soUiw7TPcm52bVL/i+EmmdniFpfBdQS0DExEREZEaU6IdJUK96rhpRju1PiTXoUi4adn5/tIGlBj2gosEU4nXvNrizBosGz/JNKMNsFn7tEVERERco0RbQoIp0U6v5Wz2SaYZ7TJi2Jlft+uKOO27AvAafhybZqerYldAUAXRRERERNyjRFuCrswHOw2JdvsAJNoAO/Jr0ZhbJIC2HzHfk3b3cGU6NQbT1VQQTURERMQ9SrQl6HKPQZFhOXddE2275yvRllBjl2h3aFzza9WPhzYN/cc1oy0iIiLiHiXaEnR2FcfbN67bde0KqWnpuIQa238DtfywyVSfYJvNa4iIiIiI85RoS9BtP2web2eYlauJ5HrQrL7/+E7NaEuIMc1oN28ASbUsBmj6kCn3GJwoqd31RERERKRmlGhL0NnN5rVrXPdrmxKOHTbLdEWCxbSdoS5bJ+yeq9UcIiIiIu5Qoh0lQrmPtqmHdv04aNGg7tdON8yK78wHn6/u1xZxgrfMnADXZn/2SXaJtt2HWiIiIiLiLCXaUSKU+2ibEu12jcDjwMSzaUb7RKmH/cfrfm0RJ+w+CiVlhhntxrW/pm0hQCXaIiIiIq5Qoi1BZ5plsytkVlN219mhJbQSIuxqFHSow78Bu/teM9oiIiIi7lCiLUGVXwSHCv3HA55oK+GQELHtsHm8LkvH68dbxdROZ5fUi4iIiIiz4oIdgES3erHw6rVWArD9iLVXdfthOLOpM9e3q1yuRFtChV3bLSf6yOedtkVC972IiIiIO5RoS1AlxsGg9kD7wFy/ZbKVzBd7K47vVMIhIcKU/DZKgEaJdbtu+8bw+Z6KY7sKrOJrsVrLJCIiIhJQerslES02Btqm+I9rj7aEil2Ge9GJrROmGfFiL+w9Wvdri4iIiEjllGhLxEs3JBwqCiWh4rsC/7E2hg+Hasp034PufRERERE3KNGWiGeaHdx7FApL3Y9F5FTHiuGwoRhgG5vaAjVht8fb1LNbRERERJylRDtKZGdnk5GRQWZmZrBDcZ3dMlzTkl0RN5lmswHSHUi029pcY7fNa4qIiIiIc5RoR4msrCzWr19PTk5OsENxnSqPS6iy+7DHiaXjzRtYhQBP950+YBIREREJOCXaEvHsltCqIJoEm92MthNLx2M80Cq5+q8pIiIiIs5Roi0Rz64olGa0JdjsZpdNlfJrwzQzrkRbREREJPCUaEvES64HTev7j2uPtgTbLkPSm1wPGiY4c31Tor3nKJT5nLm+iIiIiJjFBTsAETe0TYGDJyqORXL15TIf5B2DI0VwtNj6OiEOGsRby4kbxAc7QgHzhz1tUsDjceb6piXoxV7IOw5pSc68hoiIiIj4U6ItUaFtQ1iTW3EsUma0y3ywLhc+3wOf74YNB6xl8UVe++c0SYTOTaBnGvRqCRe0M8/6S2AFqod2VdfaXaBEW0RERCSQlGhLVDDt084vsmZ8Gzm0TNdNPh+s2A3vbID5WyD3WM2ef6jw+8R8j/V1jMdKuIefAdd1g9QGzscsFRWVmv/enCiEdlJrm0T7u3w4t6VzryMiIiIiFSnRlqhgV1xqVz40au5uLHVxrBj+tx7+8xVsOujcdct8sHKP9edPS2FIJxjXG/q0cu41pKI9R83jdv2va8PuWqa94SIiIiLiHCXaEhVsE4586BEGifbRYnjlS3hppTUbHUilZTB3k/XnvDbw8/NgQHpgXzMa7bZr7eXg0vGWhvZelb22iIiIiDhDibZEBbsWX6G+T7vMB2+uh6eWwv4TVZ/vtM++g5tnwKUdYcIFcEZT92OIVHttZrRNva9rKzEOmjewip+dyq6tmIiIiIg4Q4m2RIXKlo6Hqg374VcfwJf7qv+cGA90awZdU6FDI2iRBEn1INZjFUc7XGgV4NpyCL7aV/3k/cOtsHg73NMX7s20KphL3biRaIO1muP0RFsz2iIiIiKBpbfLEhXqx0Oz+j4OnKjYNykUW3x5y+ClVfCXT61WTFVpkgiXd4ahnaBfG0ipZnE3nw+2HYaPtsG8zbD8u8rPLy2DSStgzkZ45nLoqWJadWLao+3BR4skh3p7fa91CqzaW3HMVO1cRERERJyjRDtKZGdnk52djddbjcwtQrVt6J9oh9qM9nf5cP88yNld9bl9W8EdPWFY59rNMHs80LGJ9eeuc62WYG+sg/+ssaqx29l8CK5/01pKfmcv53o+RxvTjHZqA4iPdfZ1TPu0jxRBYam1tFxEREREnBcT7ADEHVlZWaxfv56cnJxghxI0puXju/Ktmd1QsGwnXPVG1Un2+W1h2vUwfTSM6OrcMu52jeDXA2DZXVYS3TjR/tySMnjkY/jJbGs5utScKdFOS3L+ZrRbim63dF1ERERE6k6JtkSNNin+SUxBceWzt27w+eDfq+C2t+FgJXum2zeCf10Nb4y0ku1ASa4H4/vAx2Pgx+da+7vtzN8CI96ArYcCF0+kMi0dt6sSXhd217RrLyYiIiIidadEW6JGuk2Lr2Du0y4tg4c+tGaHvTaTmR6sxHf+bVZ/a7eWajdKhN9fBO/eDGe1sD9v2xG47n/VW+4ulhIv7D/uP94yADPadom2ZrRFREREAkeJtkQN04w2BG+fdmEp/HSOtS/aTrtG8OYoayl3sPbT9mgOs26Ee/tZSb/JoUK4dQbM/tbV0MJW7jEw3Y1pSc6/lhJtEREREfcp0Zao0dYm0Q7GjHZBEYyZZVX7tjOsM8y9BTLbuBeXnbgY+GV/ePVaaFbffE6RF3421+r7LZWzW7bdMtn5GW275F2JtoiIiEjgKNGWqNEmRHpp5xfBLTNg+S7zcQ9WUvvPK6390qHkovZW8t8zzXzcB/xqAUxd62pYYWffMfN4ywDMaNeLhVTDhyNKtEVEREQCR4m2RI2EOGgY618i281E+2ixNZO9Jtd8vH4cTL76+2XaIdo2Ky3Zqno+tLP5uA9r3/lra1wNK6zsseljHYgZbYCWhg+ZlGiLiIiIBI4S7TDj8/m45JJL8Hg8eDwevvnmm2CHFFaaxvlXoHJr6fiJEvjRO7Byj/l4owT470i4tJM78dRF/Xh4fjj86Fz7cyZ+BNO/di+mcLLXZkY7EHu0wTxTbheDiIiIiNRdkMorSW29+OKLfPTRR3g8Hnyh0gA6jDSLO8a2oqYVxk720g7kDHKx1+o5vfw78/GWyfDatXBms8DF4LTYGHj4IusDgr8uN5/zqwXQJBEu6ehubKHONJtcP6aYBvGBuQlNBdFyj1lV7+P0cauIiEhI8/kg77i19exIoVWE9mgxlPmsYwAN6kHDepCSYH1w3yrZWs0pwaP//WFk165d/PrXv2bYsGF8/fXXbN++PdghhZ1mcf6Nqo+XWD+wmtoU+aornw8mfAgf7zAfb5UM/xtlVRgPR/efZyVrTy3zP+b1wT1z4PWR0KeV+7GFKlOi3Ti2EAjMTWhKtMt8VouxQPTuFhERkdo5Ughf5cKafbA2DzYfhB351vvVmvBgbffr2BgymltdZM5uAV2ahu72xEijRDuMjB8/Hq/Xy/PPP8/gwYODHU5YMi0dB9h5JHCJ9rMr4E2bJdTNG8DUkeGbZJ+UlWnNcD+xxP9YYSncOQvevhE6N3E/tlCUZ1i23cjlRBushF+JtoiISPAUe+GzXbBkp/VnXa65BWhN+bB+z+89Cp+eUoC3WX04rw0MSIfLOkIrm2LBUndKtMPEa6+9xpw5c3jmmWfo0KFDsMMJW81sEu1dBdCzpfOvN+Nr+2XVzepbM70dIyT5vLuPtYxp0gr/Y0eK4MfvwMwboVGi+7GFmlzDbWgq1OeUVjbJ9J6j0CtgryoiIiImxV74ZDvM2QTzt1gdadxy4IT1unM2WfV0eqVZBW5HdIU2Dd2LIxpod973vF4va9euZcqUKdx7773079+fBg0alBcdGzt2bK2uO2vWLEaNGkX79u1JTEykRYsW9O/fn6eeeoojR45U6xr79u3j5z//OX379uXee++tVRxisZ3RDkBBtM93w68/MB9Lird6UofTnuzqePB8uPks87Eth60+26VlroYUco4Vm5d/pcQG7rdsWiUz2iIiIuKOLYfgj0vg/Mlw17vw1tfuJtkmq/fBk8tg4Mtw29vwzgZrNaLUnWa0vzd69GhmzJjh2PUKCgq45ZZbmD17doXxvLw88vLyWL58OZMmTWLatGkMGDCg0mtlZWWRn5/Pv/71L2JjYx2LMRo1jjuBBx8+Km5O2Vm9zzyqLfeYtTe5xJBUxnogezic1cLZ1wwFHg/84WI4eALmbfY//vEO+OMn8PAg92MLFXnmz3oCOqNt15/btIRdREREnOPzwcfb4YUvrKXhocoHfLLD+pNaH+7oCbefE7itldFAifb3vF5vha+bNm1Ks2bN2LhxY42vVVpayvXXX8+CBQsASEtLY9y4cWRkZHDw4EGmTp3K0qVL2bVrF1deeSVLliyhR48exmvNmDGD6dOn89BDD9GzZ8+af2NSQbynjLQk/9ZGTvbSLvHCT+dYybbJHy6Gizs493qhJi4Gnh0GN75lfUp6usmroUcLuL6766GFBLvktmEAZ7ST60FinP8n1Hb3qIiIiNRNmQ++ONqG59+M4+v9tbtGfIy1+rFTE6ueT3pDSG0AjROgYYJVHyfGY73W0WIoKLIK/O4usLZFbjsM6/Os5eI1sf+EtfUxOwdG94CfZaqmS20o0f5ev3796N69O3369KFPnz507NiRKVOmcOedd9b4Wi+99FJ5kp2RkcHChQtJS0srP56VlcUvf/lL/vKXv3D48GHGjx/PkiX+VaQOHjzIT3/6Uzp37szDDz9c+29OKmjb0MfeYxVntHcVOHf9x5dAzm7zsbv7wC1nO/daoSoxDl68Cq5+w2pFcbrfLbQqX0ba0vnqMO3PhsDOaHs80CIJdpy2csMuFhEREakdnw8+2gZPLo3jmwN9a/TchvWgf7pVqKx3S+jarO4tunw+6/f96r2wfJdVGK26iX+RF15bA/9bB7edDff0heY2q+TEnxLt702YMMGR63i9Xh599NHyr1977bUKSfZJTz75JB9++CGrV69m6dKlzJs3j6FDh1Y45xe/+AX79u3j9ddfp359rdtwSpsU+HxPxTGnemnP/hZeXm0+Nqg9/LryXQIRJS0ZXroKbnjL+kF9qhOl1qz/OzdBg/jgxBcsdrPIgZzRBmjRwJBoa0ZbRETEMSv3wJ+WwmffAVTvTWXLZLjiDBh+BvRuZa0MdJLHY/XVHtrZ+gOw76hVhO39TVbi7a2izHmR11qROHWdNbv9o3OtSRWpnP4XOWzx4sXs3bsXgEGDBtG7d2/jebGxsdx3333cddddAEydOtUv0Z41axaJiYk8+uijFZJ3oPw17rjjDho0aMDPfvYzRo0a5fS3E5HSG/r/NCkstXoK1+VTul358NsP7V7TWk4dG2XlB3u2hKcug/vn+R/beBAeXgRPD3E9rKCy26OdEsAZbbBmtE+nRLv6CkthT4E1K3Cs2PqwCCAh1vqwKC3Z2gufVC+4cYqIiPsOnrBanP5vffXOrxdrJdc3n2W12opxua91WrK1//r2c6z3AjO+sWatNx+q/HnHS+CpZTB1LUy80Erc1ZPbnhJth73//vvlj4cPH17puacenzt3rvGcwsJCFi9ebHuNnJwcAK699toaRBnd2qSYP7bbVVD7RLu0DO5/H/KL/Y+dXEbdOErbWl3bDb7YA6+u8T/25nro3za69mub9mjXi/VRPyawJT5NifaBE1ZNgXjVWKyg2Gvds8t3wdpcWJdntUKrjhZJ0D0VejS33jxltlbyLSISqXw+673MH5dYe6Or0iIJ7uoFN/WAJiGyWLVFkrW1cXxvWLYLXlppLX2vzM58GP8eDOkEj19s390k2inRdthXX31V/jgzM7PSc9PS0khPT2fnzp3k5uaSl5dH8+bNy48fPnzY9rkdOnRg+/btfP3113Tr1q3OcUeTtinm8Z1H4Nxa9tKetMJ/OfpJT1wCGc3Nx6LF7y6ElXutpOV0Dy+yEpK2UdK70TSj3aJB4D8Rbt7APL7/OLSy+TcRTY4Vw9xNVrX8pTvhmKEFW3XkHrP+LN4Oz31uLQEc0BauOtP65D9aP3ATEYk0O47ArxbA8u+qPrd9I8jKhGu71n3PdaB4PDAw3fqz8aBVCG3mN1Y1cjsLtsBnu2DChdaHB5rdrijKFrIG3oYNG8ofd+zYscrzTz3n1OdK4LQ1LB0H2FbLFl85u+HZFeZjI7vByCiarbWTGAfPXQEphpm9o8Xw4AKrYmY0MC3XTm0Q+G/eNKMN9kvZo8UXe+CXC6Dvv6z7cP6W2ifZJqVlVlu7X38AfV6C8bNhxXfWLIiIiIQfn89aZj3sv1Un2Q1jC3nkIi8f3g439gjdJPt0XZrC34bC/Nvgyi6Vn5tfDA99CLe+bW2xkh+EyV93+Dh1Fjo1NbXK85s1+6HscmUz2LVVVFREUdEPRZby860+ViUlJZSUOPhuMgyc/H6bJ5QQHxNHSVnFj9027i+jpMRreqqtEyXw4Pw4ynz+H+G1a+jj4QtKibL/zbZaJ8EfL/Zw7zz/HzvLd8GLn3v5US9D4/EIk3csjtMLpDRLtL7vQP6bbJrowfQjf3d+Kd2bRlfW5/PBJzs9PP9FDDl73Pu8ubQM3t9s/clI9fGjXl6uOsMX8rUbTt6X0fY7Q0Kf7k1x24ETMHFRLAu2Vv6Du0Gcj3G9Smib+wFXdb0EyuIpCcO3OB0bwt+HwNizPfxhSQxf5tp/30t3wrD/+vjTJV4u6xjZ7yuq+zNHibbDjh79YSNfYmLVawRPrSZeUOD8x0BPPPEEjzzyiN/4/PnzadDAZi1phFv44QJSYy9mT1nFtcqrtuczZ479fniT6QfOYntBZ7/xGMq4IekTPv7gcF1CjUjnJffis6Pt/cb/vAzY9gmt6kXux6FlPth//GpOT7QLD+6EZpS3BQyE74obAhf7jS/8bC1F67cH7HVDzbcnUpl1qAc7ixsHNY71+z08+EEcTy/O5+om6+lRf1/IL7kL5P0pUhe6N8UNmwub8nJeJvneytulnNNgDyObfkXT/ScgJnLuzzvrw+ep6cw82IOjZQnGcw4Xebh7bhwXpWxhRJN1xMeE4acL1XD8ePWWAyrRDlPbtm2r1nm//e1veeCBB8q/zs/PJz09ncsvv5yGDaNkU+z3SkpKWLBgAUOGDGHOwkT2bK54/EBZI664Yni13+zm7Pbw8UxzFakHzvdxd+8o6uVVAxcWw1XTfHxXUPF/dCmxvFsymLeu8Tre2iJUHDgOZVP8v7lzu7SBg2sYMmQI8fGB6Xd24Dg8OcV/PK3T2QzP7BGQ1wwlO/PhT8timbevZjdX/Tgf3VN9dGvmo11DaJXio2E9q9K4DygqhcNFkHvMw4582HDAw/r9Ho4WV+8HyZ6ShryYez6Zrcr4v4u8dA3B3vKn/uwM1P0pUhu6N8UNPh+8vCaGfyyLwWtYwXhSmxQfD1/o5dIOqcDFEXl/XgXcdwIeX1rGrG/tf59+XNCJvISOPDeslPQITDdOrhCuihJthyUnJ3PokFUbv7CwkOTkysvwnThxovxxSorzFYkSEhJISPD/1Ck+Pj5i/tHXVHx8PGc0jYHTEu3jpR4OFMVXqzDUiRL47UfmAhG9W8FPM2OJjVEpZ5Om8fDXy+Gm6f7//9bmxfCfdTGMM3fFC3uHDFXpAVqmxMDBwP67bNEQYj3+vTIPnIglPoLLjpeWwQtfwN8/8+/nbqdbM7i0IwzqAL1beoiPrdlUs7fMqlT+8XZ4bxOsz6v6OTl7YhjxZgzj+8B9/UKzP2k0/96Q0KZ7UwLlWLFVY2P2xsrPu6kH/P4iD8n1/H94R9r9mRYPz14BI7rB7xbad+X4er+HkW/F89xwGJDuboyBVt2/zwidNwqexo0blz/ev39/lecfOHDA+FynZWdnk5GRUWUl9GjRual5vKr+gSc9/am5eFpCrNUXOtT3XAbb+W3hxzbJ9F8+tSp5RqJcm5VGqS7s4ojxmF/HLqZIsOkgXP8/q+dnVUl2owQY2xPeuxnm3Qa/HmhVw6/NZxCxMXBOGvysH8y9BT66A36WCU2raOVSWmZVeR32X6tIm4iIBM/OfLj2f5Un2c3qw7+uhicvg+Qoa+V4aUfr9+VVlRRLO1QIt70NU76MziKgSgcc1rVr1/LHW7durfL8U8859blOy8rKYv369eV9t6PdGU3M49VJtNflwcurzcce7A+dba4tFf3S5v/ViVLrE9JI/IFs6qEN9q23nGaqPG6qgh7ufD545UsY/jqs3lf5uc0bWO3nPr0LHhkMZ7VwPp5OTeBXA6zXePJS6Ni48vO3HoYb3oTncqKnGr+ISChZvReumwbfHrA/Z1B7mHer1Us6WjVKgH9cAX+61H4lltcH/2+RVZm8hjWHw54SbYedffbZ5Y+rSmr37dvHzp07AWjRokWFHtoSWJ1qmWiX+awk8PTlt2AtGf/xuXWPLVokxsETl5qPfbwD3o7Abnd2rbSau9DeC8yJtl3yH66OFcN971v92SubxW5YD35/ISy5E37SG5JcmIlIjIObzoIFt8EfL7FvuQbWz5gnl8Htb0fmhyEiIqFqzka4cbr972wP8MD5MGUENK/k53i08Hjg5rPg3ZuotM7IG+vgR+9av6ejhRJthw0bNqz88dy5cys9d86cOeWPhw8fHrCYxF9yPUgz/HDcfLDy501dC6v2+o/Hx8BTl2nJeE2d1wZuOct87LGP4Uihu/EEml1S2yyIM9p5xyNn9cCmgzBiGrzzrf05MR647WxYNMbavhCMvdDxsXDr2bB4DGT1pdLif0t2wjVvwFe57sUnIhKtXloJP50DhaXm440T4ZVr4f7zrN8n8oMzm8Hbo+GKM+zPWbz9+w8xouQDZKUFDhs0aBAtW7YEYNGiRaxcudJ4ntfr5dlnny3/+qabbgpoXNqj7c+0bHlTJTPa+4/Dn5aaj93dF7rY7PuWyj10gTkBPHgC/rLc/XgCybQfunGitbffDS0MCX1JmbWHKtwt3Wkt89tYyYdlnRrDWzfA45e49+FGZRrEW3vBZ98EPdPsz9tzFEa9ac2yiIiI83w+q57HHz4xF7oFyEiF2TdbS8bFLKkePD/c2kpp56tcGPkmbDvsWlhBo0TbYbGxsTz88MPlX99xxx3k5vpPRTz00EOsXr0agIEDBzJ06NCAxqU92v5MBdH2HrUSPJPHP4H8Iv/xdo2sQkdSO40S4NHB5mOvralexeZwYfoE16392WC/xC3clybP+AbGzIT8Spaj/agXzLkF+rRyK6rq697cmgV44Hz7GZLCUrhnDvxjReSsQBARCQVlPmu7UXYlb5Ev7gBv3kBEtqpymsdjdc946SpIsinOveMIjPwffB1B7/FMQrCBSHBs3bqVyZMnVxhbs2ZN+eNVq1YxceLECsd79+7NyJEj/a41btw43n77bRYsWMC6devo2bMn48aNIyMjg4MHDzJ16lSWLFkCQKNGjXjhhRcC8B1JVXrYbIn/Ktf/08plO6038yaPDQ7NVjzh5Ioz4JIOsHBbxfGTv/zeHEW1+5uHMtN+LzcTbbs9wbnHoFuqe3E4xeez3hj9+VP7cxolwN+GwiUd3YurNmJjrKWI/dtae8zt2qX8+VM4WAgTL9SyRRGRuiotg18tsH+PB3DHOfD/BlW+zUf8Xd4Zpo2CO2eZ3/8cOAE3zYD/XAdnB6AQaShQevC97du38/jjj9seX7NmTYXEG2DMmDHGRDsuLo7p06dzyy23MHv2bPbu3ctjjz3md17btm2ZNm0aPXr0qPs3IDVmV134q30VE+2iUpj4kfncq7rA4A6OhxaV/t8gaz9q8WkFrHJ2w8wNcF234MTlJOOMtouFVCpLtMONzwdPLoXnv7A/56wW1hK2do3ci6uu+rWxWoJlzbWWw5tMXgX5hfCny/TGT0Sktkq8cO/7MHeT/TkPXwR39YqMD/uD4ewWMGM03DHT6qhxusOFcMsMeO1a6NXS5eBcoF/RAZKSksK7777LzJkzGTlyJOnp6SQkJJCamsp5553Hk08+ydq1axkwYECwQ41aXZtBPcPe2DWnrfT/5xfmauTJ9awfwOKMDo2t6s8mj38S/lUqC0vNS5srqzztNNMebQi/RNvng0c/rjzJvuZMmH5DeCXZJzWpD6+MsIq22Xnza8iaE32tUkREnFBaBj+fZ59kx3rgmaHwo3OVZNdVu0bW7+NeNrVI8ovg1rfh893uxuUGzWh/b/DgwfgCsPFtxIgRjBgxwvHr1lR2djbZ2dl4vXpXdlK9WOieCl+e1md3+S5ryXKMx0qw/2GzZ+eX/SEtOfBxRpOsTJjxNew+bdls3nF4cSX84vzgxOWE/TZtQlJdXDpu91p2LUxCUZkPfv8R/Ocr+3N+2tfqWx3OS6vjY62ibV2awSOLzf20399sLTOfdIVmtkVEqstbBg/Mh9k2BSbrxUL2FdbSZ3FGswYw9XoY9661evF0R4utWe/XR0bWzLZ+NUcJFUMz69fGf+xIkVWAy+eDCQv9lzKDtST19nMCH1+0aRAPE21WCbzwBeyz2bcaDuyK7DWr714MCXFWlfPThcuMts8Hf/jYPsmO8cDjF8NvBoZ3kn2qsT3hH1dYLQRN5myy3jB6y9yNS0QkHJX54NcfwKwN5uMN4uHla5RkB0KDeJh8DQy2qdp+rMRKtr/Z72pYAaVEW6LagLbm8UXb4I111uz26WI88KdLNIMUKMPPgPMNfy8nSuHpSopehTq7RLupi4k2mJeqh0ui/ewKmLzafCzWA88Og9si8AOwK7vAv6+B+jZr0GZtgN98aJ71FhERi88HExfCW1+bjyfFW3uFL2jnalhRJTEOXrwKhnQyHz9SBLe9DVsrabcbTpQqSFTr18acMP/5U/h/i8zPubMnnF1Jz1upG48HJl5gPvbm+vBtBREyibZh+Xg4LB1/eTX81aavenwMZA+Hq890NSRXXdTeqsyaXM98/M318NRSd2MSEQknzyyH/641H6sfZ9XG6Nva3ZiiUUIcPDfcmlgxyTtuFUj7Lt/duAJBibZEteR6MDDdfKzIsGS8dTI82D+wMYn1QYapyrgP+OMS18NxxIEQWDoO5irnoZ5ov/st/N9i87H4GPjnlVaLuEjXtzVMGWEtvzN5/gt45Ut3YxIRCQevfgl/X2E+lhgHL4+ATMN2QgmMerHWKjS7me3dR60CaXaTFOFCiXaUyM7OJiMjg8zMzGCHEnJG1qBt1B8uhiSbGSVx1q/6Q4KhKvzHO2DJDvfjqatDITKjberbfbQYjpe4G0d1fb4bHpxvPhbz/XLxy2x+UUeizNYw+Wrzvw2wVuK8X0mrGhGRaPPeRnh4kflYQqz1M7W/zVZCCZz4WKsGid2E19bDcNc7cCJE359UhxLtKKFiaPaGdobUaiQ7P+oFl0bRG/pga9MQfnyu+djTn1p7rcKJaUa7Xqy1J8xNdu3ETD2+g23HERg327y6BOBPl8LwLu7GFAoGpMO/rjYXSPNhVSJftdf1sEREQs6ynVYbL9NbhlgPPD9ce7KDKTEOXroKercyH1+1F34212rHFo6UaEvUqx8Pvx5Y+TnntoSHbPYNS+Dc09c847tqLyzc5no4dXKo0H+saX33+3OaZrQBckNs+fiRIhg7y37Z2MQL4cYe7sYUSi5qD38eYj5W5IWfzA7vKv0iInW16SCMf8/cPQbgycs0gRIKkupZ26IympuPf7AVJn4UfhMsoERbBIDRGXB9d/Oxc1pYFX/r2SzVlMBJSbB6Ips8/Wl4VVk+YEhkmxpabQWaaY82hNaMtrcM7p1r9bE3+dG5MK63uzGFouu6wUM2HxLmHrPeYBaWuhuTiEgoOHTCWnacX2Q+/tuBcEOGuzGJvUYJ8OoISG9oPj51LfwjDBflKtEWwZpVfHoI/N8gaN/I2rOTkQq/7A9v3uD+Plr5we3nQJohOVyfB3PDaC+qaWY2GPeV3Yx2KBVE+9tnsHi7+diQTvA7rS4pd3cf69+Iyaq98LuF4TkLICJSW8Ve64PG7UfMx390Lozv425MUrXmSVZ7NdN7o4YJcF4YFqtToi3yvRgP3NkLPh4L3/4M5t4K9/az9o9I8CTGWX8PJn9dbs1+hoODNkvH3Wa7RztEEu0FW6x+2SY9mlvFz2L1m6ucxwOPDIKLbPYYvvU1vLLG3ZhERILF54MJC+Gz78zHrz7T2nrk9rYtqZ6OTaxVpKe+926VDNNvsFryhhu9XYkSqjou4ezGHtDWsJxo00GYtcH9eGqqtAwOh0ii3SjBvA0iFJaObzkEv5hnPtYiyfrla9faKprFxliVWzs2Nh//w8fwpYqjiUgUeHElvLnefOzcltbqxRgl2SHt3Jbw3BXW31PXZvD2aDizWbCjqh0l2lFCVcclnNWLhV+cZz72zGdQYlPoJFSYkmwITqLt8ZiXj+cGOdEuLIWfzoGCYv9jJ3tlt0x2P65w0SgRXroakg3tB0vK4KdzrQJzIiKRaulO+NNS87E2KVZ1a61SDA+XdrL+vt68AVqlBDua2lOiLSJh4dpu0LmJ//iOI/DOt+7HUxN2lbODtffflGgHe+n445/A1/vNxx6+CPrYtP6QH3RpCs8ONR/blQ+/WqD92iISmXYXWG2gTEVSk+tZK6LsioFKaLqsk7UKL5wp0RaRsBAXAw+cbz6WnRPaFchNPbQhiIm24c1GMBPteZvhVZt9xKO62xf7En+XdoIsmx1C8zbDlC/djUdEJNCKSuGe98wfasd4YNIw6JbqflwiSrRFJGwM72Lep7P5UGhXILeb0W4WQjPa+48H58OK3QXWTKtJ91R4/BIVrampB863r876xBLYYLNyQEQkHP3fYli9z3zsV/3hko7uxiNykhJtEQkbMR77vtr/WBG6y2LDYem4XcG2QCrzWcXPTHuH68fBpCu0n6424mKsGRzTBzlFXrh/njUDJCIS7qatg9fXmo8N7Qz32LxnEHGDEm0RCStXnwntGvmPr98PC7e5Hk61hFyibdfiy+WCaFNWw3KbFiz/N8jacyy1k5YMfx8KpsUAX++Hvyx3PSQREUetzYXff2Q+1rkJ/GWIVkRJcCnRjhJq7yWRIi6mklntnNCc1bZLtBsnuhvHSXa9tN2sPL75kH112KvPtFq6Sd1c2B5+3Nt87MUvYNlOd+MREXHK0WKr+FmRoetIUjy8cBWkhHkhLQl/SrSjhNp7SSS5vju0MrR6WrkHPt3lfjxVMSXajROtDw2CwbR0HNwriOYtgwfnm98gtU2BP2pftmN+1R+6Geoa+LD2xh8ztFMTEQl1Dy+CrYfNx/48RCuiJDQo0RaRsFMvFsb3MR+btMLdWKrDlGgHqxAa2C8dz3Up0X5xJazaaz725yHQULMQjkmIg78Ps/7NnG5XAfz5U/djEhGpi+lfW39MftIbruzibjwidpRoi0hYuqmHOVldtsvatxVKTIl2kyAtG4dKZrRdWDq+YT/81WZ/8NieMCA98DFEm26p8JsB5mNTVkPOblfDERGptS2HYKLNvuxzW8KvbX7WiQSDEm0RCUv142Gczf7Tl1a6G0tVDhqqeTezSXbdkBgHDev5jwd66bi3DH79ARQblox3aAS/GRjY149md50L/Vr7j/uA33wAhapCLiIhrqjU2pd9vMT/WMN6VqeKeMPqHZFgUaItImHrtrMh2ZAwvvut1Z85FPh8oTejDebl44Ge0f7PV+Zepx7g6cuhQXxgXz+axXjgycsgwfAmdPMheDYEt1yIiJzqmeWwLs987E+XQXpDd+MRqYoSbREJWykJ1hLy03l98PJq18MxOlZinsEN5h5tMCfagdyjve8oPLXMfGxcb8g0zLaKszo1gQfONx/75+f2b2BFRIIt5zv45xfmY7edrX3ZEpqUaItIWLurF8QaKlRPXQsFRa6H4yfUemif1MKwdD2QM9r/t9hqx3K6Do3gwf6Be12p6Me94ewW/uNeHzz0gbW8X0QklBwthgcWWFtdTte1Gfz+ItdDEqkWJdoiEtbaNDR/kl1QDNPWuR/P6Q6EaKJtKoh2pMjaA+e0D7fCnE3mY49fYu0ZF3fExcBTl5lby63JDY1/MyIip3r8E9hxxH+8Xiz8fah+h0joUqIdJbKzs8nIyCAzMzPYoYg4zq4o2suroTTIM3ShOqNt1+Jrv8PLx4+XwMM2FWKv6wYXtHP29aRqGc3hnr7mY08us79nRUTctnArvL7WfOzB86F7c3fjEakJJdpRIisri/Xr15OTkxPsUEQcd04anNfGf3xXAcy1mUl1S8gm2nYtvhxOtJ/9zPp7OF2jBJh4obOvJdX3s0xo38h//HCh/V56ERE3HTxhdaowyWxt/yG7SKhQoi0iEcHuF+6/gtzqK2QTbZsZbScT7a2H4F+rzMcmXACpQWxxFu0S4+DRweZjb6yFVXtdDUdExM/vPzL/TmoQD3+9HGKVxUiI0y0qIhHh0o7QqbH/+Op9sDqISYNdoh30quN2M9oOFkR7fAmUGJbu92sNow3V4sVdgzvA0M7+4z5g4kcqjCYiwTNvM8zeaD728EXQzrAiRyTUKNEWkYgQ44EfnWs+9uoad2M5lakYWmIc1A9yz+gWNjPauQ4l2h9vhwVb/MdjPVYBtBhDpXhx38MXmQsJrc21KveLiLjtSJH1YZ/JJR3MbT1FQpESbRGJGNd1g4b1/Mff/RYOBLBHdGUOGRLtYM9mAzRJNLdFc2LpeIkXHvnYfOz2c+DMZnV/DXFG24Zwr02NzL8sh/wQaJEnItHliSXmD30bJ8KTl4FHH9RKmFCiLSIRI6ke3JDhP17shTeC1LbINKPdJAQS7dgYaGbqpe1Aov2fr2DTQf/xJonwi/Prfn1x1rje5m0XB0/Ac6qfKSIuWrbTfjXNwxfZr8YSCUVKtEUkotzR0zz+nzXBafUVqjPaYN6nXdel4wdPwF+Xm4892N+akZDQkhAH/2+Q+djk1bAr39VwRCRKnSiBhz40HxvUHkZ2czcekbpSoi0iEaVDYxjc3n9891H4wLBnONBMxdCCXXH8pEAk2n/7zLzcuFszuPmsul1bAmdwB+uN7OmKvfD08ljX4xGR6PPMZ7D9iP94g3j44yVaMi7hR4m2iEScMTaz2lO+dDeOYi/kF/uPNw2RWd2Wyf5j+47Vvtr0tsPw36/Mxx4eBHH6jRPSfneBuUjd7E0xbC1s4n5AIhI1vtoHL9m04/z1AKuehEi40dseEYk4gzuYW398ugu+PeBeHIcKzeOhMqPdOsV/rLQM9tdyn/bTn5qX5w/tDAPTa3dNcU/XVPtqvm8fOgufz914RCQ6eMtgwkIoM/yMObcl3HGO+zGJOEGJtohEnBiP/S/mV1yc1T5ok7CGSqLdyjCjDdYy+5r6cq9V3f10cTEw4YKaX0+C44HzIcnQem5bUVPmbta6TRFx3utrYU2u/3i9WHjqMqt4p0g40q0rIhFpdIa5P/DMDXC8xJ0YDobhjDbA7oKaXcfngyeWmo/dcpa1b17CQ/Mk+Glf87G/rYgNSkFBEYlc+4/DU8vMx7L6qh2khDcl2iISkRolWn21T3e02DzzGgimQmgQOlXHW9kk2ntqmGgv3m4tyz9dUjzc16/mcUlw/bg3tDasdthy2MP0r92PR0Qi1xNLzAU0OzaGe2w+9BMJF0q0o0R2djYZGRlkZmYGOxQR19xqU+Xarken00w9tCE0+miDOZmCmi0dL/PBn2xms3/S25ohlfCSGGe1YjP523IoKnU3HhGJTCu+g7dsPrx7bLDVelAknCnRjhJZWVmsX7+enJycYIci4pqz0+CsFv7jq/bC13mBf31TD20InRnt+vHmvtY1WTo+awN8vd9/PLW+NTMq4em6bnBGU//x3Uet/ZQiInVR4oWJH5mPXdUFLjS0GxQJN0q0RSSi3WI3q70u8K9tmtGO8VjL2kOFaVa7ukvHS8vg75+Zj913HiTXq31cElyxMfBLm1ntf6yAY4a2dSIi1fXyl7DB0AUkKR5+f5H78YgEghJtEYlo15wJ9Q3Lz97+Gk4EuCiaaUa7SaK5V3GwmAqiVXfp+MxvYOth//EOjew/4JDwMawznG1YEbL/BLy82vVwRCRC7D0Kzyw3H/vF+dDSZluTSLhRoi0iES0lAa7p6j+eXwzvbQzsa5tmtEOl4vhJpoJoeceg2Fv580q88OwK87Gfnw/xsXWPTYLL44FfDzAfe+ELOGxTVV9EpDJPLjV3/+jWDMb2dD8ekUBRoi0iEc9udjXQe01NVcdDLdE2LR33AfuOVf68Gd/A9iP+452bWKsIJDJc2A76tfbv6ZVfDJNXBSEgEQlrq/Zavz9M/nCxPqSVyKJEW0QiXs80yEj1H/9iD2wwFPJySjgk2rVp8VXihUl2s9nnWft7JTJ4PPDgeebm2S+vhiOGtjwiIiY+Hzyy2HxsZDfIbONuPCKBprdDIe7FF1/kxhtvpHv37jRt2pT4+HjS0tK46qqrmD17drDDEwkLHg/cbDOr/UaAiqL5fHDIsLS2aQgVQgPzHm2ovPL4W1/Dznz/8TObwZVdnIlLQkefVj4y6u/1Gy8ohimr3Y9HRMLTrA3WjPbpGsTDQwPdj0ck0JRoh7i//vWvzJgxg/r16zNw4EBGjhxJu3bteO+997j66qv55S9/GewQRcLCtd2s/sCnm7mh6v3ItZFfbFXlPl3TBs6/Vl3YJdqmRBqs/1eazY4+VzTeYBz/1yoo0Ky2iFTheAn8aan52E/7QpoKoEkE0luiEPfyyy9z6NAhVq5cybvvvsu0adPIycnhk08+ISkpib/85S989plNfx0RKdcwwerNebqDJ2DhVudf7+Bx83io9NA+qVUyxBt+E5iqiQO8uR6+M8x2d2sGV5zhaGgSQtonHOaidoa92kXwypdBCEhEwsoLX8AeQ0eLtikwrrf78Yi4QYl2iOvfvz/Jyf4f811wwQXceOONAHz44YduhyUSlm7IMI+/9bXzr2WqOA5We69QEhcD7Rr5j5sS7aJSq4eyyc/PD622ZeK8n/U179V+aRUcVV9tEbGxuwD++YX52G8vMK82E4kESrTDWHx8PAAJCQlBjkQkPPRrA+kN/ccXbrVaWjnJtD8bQm9GG6BjY/+xrYf8x95cb+6xnZEKQzs7HpaEmN4tfVyQ7j9+uBD+s8b9eEQkPPxpKRSW+o/3a626HhLZlGh/z+v1snbtWqZMmcK9995L//79adCgAR6PB4/Hw9ixY2t13VmzZjFq1Cjat29PYmIiLVq0oH///jz11FMcOWLojVNNq1evZtq0acTGxjJs2LBaX0ckmsR4YFR3/3GvzyrS4qQDNkvHm4Riot3Ef+xQYcU+yUWlkJ1jfv4vNJsdNe4/zzz+4kpzX1wRiW5r9pl/v3qAhwdZxUpFIpUWa3xv9OjRzJgxw7HrFRQUcMstt/hVBs/LyyMvL4/ly5czadIkpk2bxoABA6q83ssvv8zixYspLi5m+/btfPrpp8THx/P888/To0cPx+IWiXQju8MzhrIGb66HH53r3C/9cJ/RBth44Id2K3az2T2aw5BOAQtNQky/NnB+W1i+q+L4gRMwbR3c2SsoYYlICPL54Ikl5mM3ZMDZLdyNR8RtmtH+ntdbsexw06ZN6dKldutZSktLuf7668uT7LS0NCZOnMjrr7/OP/7xDwYOtHoY7Nq1iyuvvJJ166ruL7R06VJeeeUVpk6dyrJly6hfvz7PPfccP/rRj2oVo0i0atcI+rf1H//mAKzNc+51bPdoh2CifWZT8/jJ/x9VzWZrRiK63N/PPP7SSqvHuogIwOLtsGyX/3iDePhV1XNMImFPifb3+vXrx0MPPcSbb77Jli1bOHDgABMmTKjVtV566SUWLFgAQEZGBl9++SWPPfYYN998M1lZWSxZsoQHH3wQgMOHDzN+/Pgqr/mvf/0Ln8/H0aNHWbVqFTfddBM//vGPueaaaygstJk6ExEj26Jo6517jYOGRDspPjSLvnRvbi3jO9267xPtymazL+sY0NAkBPVvC31a+Y9/VwDvbnQ/HhEJPd4y+3ZeP+kNLZLcjUckGJRof2/ChAk88cQTjBo1io4da//O0ev18uijj5Z//dprr5GWluZ33pNPPkmvXr0Aa7Z63rx51bp+UlISvXr1YvLkyfz4xz/mvffe4y9/+Uut4xWJRlecYSW9p5u5wZq9dYIp0W4agrPZAMn1oENj//F1eZrNFn8eD9zT13zshc+t5aIiEt1mboCv9/uPp9ZXOy+JHkq0HbZ48WL27t0LwKBBg+jd2/zTJDY2lvvuu6/866lTp9b4tW6//XbAKrgmItXXIB6GG3aGHC6EDx3qqW1KtENxf/ZJPZr7j23YD3/7TLPZ4u/SjnCGYcvBNwdg0Xb34xGR0FFYCn/51Hzs/vOsD3dFooESbYe9//775Y+HDx9e6bmnHp87d26NX6t5c+udcV6egxtLRaLEaJvl4zMdqj5uSrRDcX/2See29B/z+uC5z83nazY7usV4YLzNrNQ/be4ZEYkOr3xpbSU5XcfGcPNZrocjEjRKtB321VdflT/OzMys9Ny0tDTS062mpLm5uTVOmD/66CMAzjjjjBpGKSKZra3CaKdbuLViW6vaCrcZ7YvaV//cnmmazRa4thu0TPYfX/4drNrrfjwiEnxHCu23G/1qAMTHuhuPSDAp0XbYhg0/TIdVZ6/3qeec+lyAzz//nFdeeYWioiK/582ePZvf/e53APz4xz+ubbgiUcvjgeu6+o+XlMGcOhZ0KiyFY4aewqE8o92lKbQyJE0mD1+k2WyBerFWSzyT5zWrLRKVnv8cjvi/beXcljBc80ISZUKw/m14O3z4cPnj1NTUKs9v1qyZ8blgtf8aO3Ys999/P3379iUtLY3Dhw+zYcMGNm/eDMCDDz7IjTfeaHv9oqKiCol6fn4+ACUlJZSUGDKBCHby+42271vsXXUG/H2Ff1W0Gd+UcUO32vcpyj0K4H/dxvW8lJSU+Y2Hyr05tFMMU9ZUPt1wTZcyejb3on9G0aOy+3NUV3j2szgKiit+8jJ/s49v80pte7SLOCFUfnaKJe84vPxlHKY+Fr86v5TS0uiqlKj7M3JV9+9UibbDjh79oWpQYmJilefXr//DFFdBQcUNLZmZmTz88MN8/PHHbNiwgSVLlhATE0Pr1q257bbbGD9+PBdccEGl13/iiSd45JFH/Mbnz59PgwYNqowvEp1svSYC0K7eRewoblJhLGd3DP955wOaxtk0w67CzqJGwGC/8V0b1zBn7w7b5wX73uxQWp8YLqPMZrFTg5hi+p74iDlz1FIwGtndn+fX786C4jMrjPnw8Ni7OxnV7Cvjc0ScFOyfnWKZfuAsCks7+433qL+X/as/Y85q92MKBbo/I8/x48erdZ4S7RDWpk0bY5JcE7/97W954IEHyr/Oz88nPT2dyy+/nIYNG9Y1xLBSUlLCggULGDJkCPHxht5OEpVyv4zhD4Zen0fbXsptvf1nn6tjyU4PvOs/Pqjf2VzW0b8STCjdm3mf+cj+wn881uNj0vAYBrW7xP2gJKiquj/7HYfFr/ko9lacxfr8REf+dmk6DRPcilSiTSj97Ix2uwvgwf+a04onr25Gt9TKCwRHIt2fkevkCuGqKNF2WHJyMocOHQKgsLCQ5OTKNz2eOPHDjFlKSorj8SQkJJCQ4P8uJz4+Pmr/0Ufz9y7+RnSHPy6DstNWtL27MZZ7z6td1ZYjxebxFilxVHbrhcK9+eAAKPHBv1b98P+kXSN4/GIPF7XXr4xoZnd/tmoEI7rCm+srjh8v9fDWhnjG93EpQIlaofCzM9r9c5VV4+R0V58JZ7eK7r8b3Z+Rp7p/nyqG5rDGjRuXP96/f3+V5x84cMD4XBFxR4skuLCd//iGA/B1LTvnHbJZWd206t0kQRcbA7+7ED68HZ4bDq+PhI/uqFlVcok+d/Uyj7/yJZTWbmGIiISJ7Yfhf+v9x2M88PPzXA9HJGQo0XZY164/lDHeunVrleefes6pz3VadnY2GRkZVbYcE4lG19r803u7lj21D9hs7W4aRmUROjWBK7vAwHSI028KqUJGcxjQ1n/8uwKYt9n9eETEPX9fYf5AbWQ3OKOp+/GIhAq9fXLY2WefXf44J8emkeD39u3bx86dOwFo0aIFzZs3D1hcWVlZrF+/vsqYRKLR0M5Q37Aq+p0N/kvKq+OAoUZGfAw0rFfza4mEC7tWX5NXuRuHiLhn40F4+xv/8bgYuF+z2RLllGg7bNiwYeWP586dW+m5c+bMKX88fHj0FYkQCRVJ9WBIJ//xPUfhs+9qfr39hkS7WQP1npbIdklH6NDIf/yLPbB6r/vxiEjg/W25+QPpGzOs+h4i0UyJtsMGDRpEy5YtAVi0aBErV640nuf1enn22WfLv77ppptciU9EzK7rZh5/txbLx/cblo43q+8/JhJJYjxwp82s9r9XuxqKiLjg6zyYvdF/PCEW7u3nfjwioUaJtsNiY2N5+OGHy7++4447yM3N9TvvoYceYvXq1QAMHDiQoUOHBjQu7dEWqdyF7aCpIRmeu7nmxZxMS8dTw2h/tkht3dDdvEXivY2w76j78YhI4Eyy2Y1469nQyvlGOiJhR71avrd161YmT55cYWzNmjXlj1etWsXEiRMrHO/duzcjR470u9a4ceN4++23WbBgAevWraNnz56MGzeOjIwMDh48yNSpU1myZAkAjRo14oUXXgjAd1RRVlYWWVlZ5Ofn06iR1vKInC4+FoZ1htfXVhw/eAKW7axZ1W3T0nEl2hINkurBjWfBS6ct5iotg6lr4efnBycuEXHWtwdgjmE2OzEO7unrfjwioUiJ9ve2b9/O448/bnt8zZo1FRJvgDFjxhgT7bi4OKZPn84tt9zC7Nmz2bt3L4899pjfeW3btmXatGn06NGj7t+AiNTZVWf6J9oA735b/UT7RAkcK/Ef19JxiRZje1oF0E7ft/n6WsjKtD7UEpHw9o8cMNUKvf0cq22miGjpeMCkpKTw7rvvMnPmTEaOHEl6ejoJCQmkpqZy3nnn8eSTT7J27VoGDBgQ7FBF5Hvnt4HmhpnneZuh2Fu9a9i19tKMtkSLtg3h0o7+4/uOwfwt7scjIs7afMj6APp0CbHwk97uxyMSqjSj/b3Bgwfj89Wij08VRowYwYgRIxy/bk1lZ2eTnZ2N11vNbEEkCsXGwBVnwKsVF69wpAiW7LCqKlfFtGwclGhLdLn9HFhgSKpfW2P1ZxeR8JWdY640fsvZms0WOZVmtKOE+miLVM/VZ5rHTZVVTUyF0EBLxyW6XNjO3Orr013W3k4RCU/bD8NMQ9/serFwdx/XwxEJaUq0RURO0bc1pBk+kZ+/GQpLq36+qbUXWH20RaJFjAduO8d87D9fuRuLiDgn+3Pwmvpm94CWye7HIxLKlGiLiJwixmNe2lpQDB9vr/r5dkvHTXu/RSLZ6AyrAvHppn8NR4vdj0dE6mZnvvXv93TxMXCPZrNF/CjRFhE5zVU2y8dNxV9OZ7d03NSjWySSNUqEawz/lo4Wm5eeikhoez7HatV3ulEZ0Kah+/GIhDol2lEiOzubjIwMMjMzgx2KSMjr3RLapPiPf7DVat9VGdPS8YYJ1v41kWhzR0/z+GtrIAD1R0UkQPYdhTcNs9mxHvip+maLGCnRjhIqhiZSfR6b5ePHS+CjbZU/17R0XMvGJVqd3QLObek//s0BWLXX/XhEpHYmrza3uRzZHdoZCh+KiBJtERGj2lYfNy0dV8VxiWZ32BRFm7rW3ThEpHaOFMF/DUUMPUCWZrNFbCnRFhExOLuF+VP6D7fCsUoKOR0wLB1XxXGJZsO7WNsnTvfut1BQ5H48IlIz/1ljLmA4vAt0bOJ+PCLhQom2iIiBxwNXGZaPF5baLx/3lpkT7VQl2hLFEuPgum7+4ydK4Z1qFBgUkeApLIV/rzIfU6Vxkcop0RYRsWFXfdxu+fjBE1BmKPCUqqXjEuVu7mEef0PLx0VC2pvrzUU+L2wHZ6e5H49IOFGiHSVUdVyk5jJSoWNj//GFNsvH9x4zXyct2dGwRMJO9+bQy/CmfE0urMtzPx4RqVppGbzwhfnYPdqbLVIlJdpRQlXHRWrO47H2oJ2uyAsLt/mP7z1qvk5akqNhiYSlm84yj6somkhoem8j7Mz3H++VBgPauh+PSLhRoi0iUgnTPm2A2Ya9pXaJdivNaItwzZmQFO8/PuubqvvTi4i7fD54/nPzsXv6Wh9Ei0jllGiLiFSieyp0auw//tE2/+Xjdol2SyXaIiTVg2u6+o/nF1szZyISOhZth6/3+493bgKXd3Y/HpFwpERbRKQSlS0f/3BrxbF9hkQ7IRYaJwYmNpFwc5NdUbR17sYhIpV70WZv9t19IEaz2SLVokRbRKQKtsvHT5uF22NItFsma4mdyEk906xVIqfL2Q0bD7ofj4j4W5cHy3b5j7dMhmsNrfpExEyJtohIFbqlWsvlTrdoGxw9Zfm4XaItIhaPx74o2jTNaouEhH+tNI/f1QvqxboaikhYU6IdJdTeS6T2qrN8vMwHuwzVWdukBDY2kXBzXVdrS8XpZn5jtRMSkeDZexTeMRT7TIqHm20+JBMRMyXaUULtvUTqxm75+MkiTnuPWon36do3ClxMIuGoUaL5g6u847B4u/vxiMgPXvnS/IHXTT2gYYL78YiEMyXaIiLV0LVZ5cvHtx8xP69d40BGJRKeRnU3j0//2t04ROQHx4rhP1/5j8d44M5z3Y9HJNwp0RYRqQaPxzyrXeSFD7bA9sPm57VrGNCwRMJS/7bQ2lC/YMEWOFLofjwiAm9+DflF/uNXnAHp+l0mUmNKtEVEqunKSpaP77CZ0dbScRF/sTFwnWFWu9hr3h8qIoHlLYN/rzIf+7Fms0VqRYm2iEg1ndkMzmjqP754O3yx13+8QTykNgh8XCLh6HotHxcJGQu2mLdA9WkFvVu5H49IJFCiLSJSTZUtH19u6DnatZl6aIvY6dwEzm3pP75qL2w+5H48ItHsX5rNFnGcEm0RkRqwWz5uclaLwMUhEgluyDCPT1/vbhwi0Wz1XsjZ7T+e3hCGdnY/HpFIoURbRKQGzmwG3ZpV79yzmgc2FpFwd9WZ5p7a07+x9oyKSODZzWb/6FyrnoKI1I7++YiI1NCNPap3Xg/NaItUqlECDOnkP773KCzb6X48ItFmdwHM2eg/3rCe/YoTEakeJdpRIjs7m4yMDDIzM4MdikjYG9kdEuMqP6dpfeie6k48IuHMrijaWyqKJhJw//kKvD7/8VvOhuR67scjEkmUaEeJrKws1q9fT05OTrBDEQl7jRPh5rMqP+eKzhCnn7AiVbqoPTQ3VOd/fzMUGHr6iogzCkth6lr/8VgPjOnpfjwikUZvA0VEauHuPpAUb398pM0snYhUFBcD13XzHy8shXmb3Y9HJFq8txEOnvAfv7wztE5xPx6RSKNEW0SkFlomwx8uNh+7oTv0be1uPCLhbJTNB1MzN7gbh0i08PlgymrzsbGazRZxhBJtEZFaGtkd/nU1pCX9MHZjD/jjpcGLSSQcdU2FDENNg6U7IfeY+/GIRLpVe2FNrv94t2ZwXhv34xGJRFWU8xERkcoM6QSXdIBtRyClHrRIqvIpImIwoius319xrMxnLW+9s1dQQhKJWFO+NI+P6Qkej7uxiEQqzWiLiNRRbAx0bqIkW6QurulqHp/5jbtxiES63GM2Lb0S4FpDvQQRqR0l2iIiIhJ0rVPMS1ZX74Nth10PRyRiTV0LJWX+4zf2gAaVFPkUkZpRoi0iIiIhYYTNrPYsFUUTcUSx1+qdfToPcMc5rocjEtGUaIuIiEhIGH6Guf/8rA1WlWQRqZv3N5kLDF7aEdo1cj8ekUimRFtERERCQpP6MLi9//jmQ7A2z/14RCJNZUXQRMRZSrRFREQkZGj5uEhgfJULX+zxH+/cBC5o5348IpFOibaIiIiEjMs6mQsyvbMBvIYCTiJSPf817M0GuKMnxKill4jjlGhHiezsbDIyMsjMzAx2KCIiIrYaxMPQzv7j+47BZ9+5H49IJMgvMq8KSa4Ho7q7H49INFCiHSWysrJYv349OTk5wQ5FRESkUlo+LuKsmd/A8RL/8eu6Wcm2iDhPibaIiIiElAvSoWl9//E5m6z2RCJSfT6f/bLxW89yNxaRaKJEW0REREJKfCxc2cV/PL8IluxwPx6RcPbFHvjmgP9471bQvbn78YhECyXaIiIiEnKuOdM8Pmeju3GIhLvX15rHbzvb3ThEoo0SbREREQk5fVtDiyT/8XmbtXxcpLoOF8Lsb/3HGyWYV42IiHOUaIuIiEjIifHYLB8vhk+0fFykWt5aD0WGD6ZGZUBinPvxiEQTJdoiIiISkuxm3N7T8nGRKlVaBE3LxkUCTol2iCspKWH+/Pncd9999O3bl9TUVBITEznjjDPIyspi+/btwQ5RREQkIPq0gpbJ/uMLNkNRqfvxiISTT3fBlsP+4/3bQucmrocjEnWUaIe4xYsXM3ToUCZNmsT+/fu58MILueKKKzhx4gTPPfcc55xzDp9++mmwwxQREXFcjAeGn+E/nl+s6uMiVbErgqbZbBF3KNEOcTExMdx4442sWLGCbdu28fbbb/P222+zefNmxo4dS35+PjfffDMlJSXBDlVERMRxdsvHZ2v5uIitvGPw/ib/8Wb1YWhn9+MRiUZKtEPcJZdcwhtvvEFmZmaF8cTERJ5//nkaNWrE9u3bWbZsWZAiFBERCZzeraCVafn4Fi0fF7Hz5nooKfMfv7EH1It1Px6RaKREO4wlJiZy5plWo9Hdu3cHORoRERHnxXhguGFWu0DVx0WMynwwdZ3/uAe4+SzXwxGJWkq0T+H1elm7di1Tpkzh3nvvpX///jRo0ACPx4PH42Hs2LG1uu6sWbMYNWoU7du3JzExkRYtWtC/f3+eeuopjhw5Uqd4t23bBkDLli1rfR0REZFQdpWWj4tU26e7YIfh7eVF7aFdI/fjEYlW6qB3itGjRzNjxgzHrldQUMAtt9zC7NmzK4zn5eWRl5fH8uXLmTRpEtOmTWPAgAE1vv5rr71GXl4eLVq0qNXzRUREwsG5LaF1Muw+WnF8wRYoLFU/YJFTTTPMZoNms0XcphntU3i93gpfN23alC5dbD5Gr0JpaSnXX399eZKdlpbGxIkTef311/nHP/7BwIEDAdi1axdXXnkl69bZ/FS0sW3bNh588EEA/vjHP5KQkFCrOEVEREKdx2b5+NFi+FhdLkXKHSk0F0FLrQ+XdXQ/HpFopkT7FP369eOhhx7izTffZMuWLRw4cIAJEybU6lovvfQSCxYsACAjI4Mvv/ySxx57jJtvvpmsrCyWLFlSnigfPnyY8ePHV/vaR44cYcSIERw8eJDRo0fzox/9qFYxioiIhIurzjSPzzEkFSLR6u0NUOT1Hx/ZHeJVBE3EVVpsdYraJtWn83q9PProo+Vfv/baa6Slpfmd9+STT/Lhhx+yevVqli5dyrx58xg6dGil1z5x4gTXXHMNa9as4ZJLLuHVV191JGYREZFQ1isN2qbAroKK4x9uhWKvKimLgP2y8Rt7uBuHiDg4o/3cc89RUFBQ9YlRYPHixezduxeAQYMG0bt3b+N5sbGx3HfffeVfT506tdLrFhcXc/311/Pxxx/Tv39/Zs2apSXjIiISFTweGHqG/3h+ESzf5X48IqHmq1xYn+c/3rcVnNHU/XhEop1jifbPfvYzWrduzbhx48jJyXHqsmHp/fffL388fPjwSs899fjcuXNtz/N6vdx6663MnTuXXr16MWfOHJKTDY1FRUREItQVnc3jpj2pItHmjbXmcc1miwSHo3u0jx8/zr///W/OP/98evfuzYsvvsjRo0erfmKE+eqrr8ofZ2ZmVnpuWloa6enpAOTm5pKX5/9RpM/n40c/+hFvvfUW3bt3Z/78+TRu3NjRmEVEREJd71ZWUafTzd8C3jL34xEJFYWl8M4G//HkenBl7er6ikgdOZZoP/roo7Rv3x6fz4fP5+PLL7/knnvuoXXr1tx9992sXLnSqZcKeRs2/PCTrmPHqks8nnrOqc896Re/+AWvvPIKnTp14oMPPqB58+bOBCoiIhJGYmPgcsOsdt5xWLnX/XhEQsWcTZBf7D9+9ZmQVM/9eETEwWJoEydO5He/+x3z5s3jxRdfZPbs2ZSWlnL06FFeeuklXnrpJXr37s3dd9/NzTffTIMGDZx66ZBz+PDh8sepqalVnt+sWTPjcwFmzZrF3//+dwDat29vW7Dt2muv5dprr/UbLyoqoqioqPzr/Px8AEpKSigpKakytkhy8vuNtu9bQp/uTQlloXZ/XtbBw+tr/d++zPnWS6/mmtaOJqF2bwbT1K9iMc2fXd+1lJISn/sBie7PCFbdv1NHq457PB6GDRvGsGHD2Lt3L//617+YPHky27dbTS5XrlzJT37yEx544AFuu+02fvKTn9CzZ08nQwgJpy6XT0xMrPL8+vV/WAd3ekG5Q4cOlT/+6KOPbK/RoUMHY6L9xBNP8Mgjj/iNz58/P6I/7KjMybZrIqFG96aEslC5P0t9Hup7ruCEL77C+Mx1hfQ8/AEeT5ACk6AJlXszWPJKklix+zK/8Vbx+Xz3+Ufs1r+JoIr2+zMSHT9+vFrneXw+X0A/5vL5fMybN48XXniB9957j9LSUuuFv/9NmJmZyd13382NN95YIeEMFVOmTOHOO+8EYMyYMUyZMqXK59SrV6/Cp1hxcZV/nnHrrbfy+uuvA/D6669z88031y3oU5hmtNPT09m/fz8NGzZ07HXCQUlJCQsWLGDIkCHEx8dX/QQRl+jelFAWivfnAwtieWej/+zdrBtK6KHdVVEjFO/NYHh6eQz/XOnf3+53A73c2VOrPIJF92fkys/PJzU1lSNHjlSaTwW8j/aps9x79uzhX//6F//+97/LZ7lXrFhBTk4Ov/jFL7j99tsZP348PXqEd3nE5OTk8pnowsLCKquDnzhxovxxSkqKo7EkJCQYW4DFx8dH7T/6aP7eJbTp3pRQFkr35/Az4Z2N/uMfbo+nV2v345HgCqV7022lZTDDUAQtPgZG9YglPl4N5oMtmu/PSFXdv09Hq45XpVWrVvz+979n06ZN/OxnPwN+mNk+cuQI2dnZnHPOOQwdOpQVK1a4GZqjTq0Ivn///irPP3DggPG5TsrOziYjI6PKKugiIiKhblB7SDRMFby/2f1YRIJp8TbIPeY/fnlnaBp6C0VFooqrifa+ffv44x//SJcuXcjOzsbj8ZRXKa9fv3754w8++IABAwbwm9/8xs3wHNO1a9fyx1u3bq3y/FPPOfW5TsrKymL9+vVR3+NcRETCX4N4K9k+3bcHYMsh/3GRSPXW1+bxm8J7cahIRHAl0Z4/fz7XX3897dq14/e//z3btm3D5/MRFxfH6NGj+eijj8jPz2fWrFkMGzYMn89HWVkZTz/9NP/973/dCNFRZ599dvnjqhLbffv2sXPnTgBatGih1l0iIiLVMMzQ5gtgnma1JUocLoQPDPM5rZJhYLr78YhIRQFLtPft28cTTzxBp06duOKKK5g5cyYlJSX4fD7S09N57LHH2LlzJ2+88QaDBg0iNjaWq6++mjlz5vDJJ5+QmpqKz+dj0qRJgQoxYIYNG1b+eO7cuZWeO2fOnPLHw4cPD1hMIiIikeTSjhBneBfz/ib3YxEJhne/hWKv//jI7lbPeREJLsf/Gc6fP59Ro0bRrl07Jk6cyPbt2zlZ2Hzo0KHMmjWLrVu38rvf/Y4WLVoYrzFw4EB+9atfAbBhg6HCQ4gbNGgQLVu2BGDRokWsXLnSeJ7X6+XZZ58t//qmm24KWEzaoy0iIpGkUSIMaOs/vnof7C7wHxeJNNNtlo1f393dOETEzLFE+09/+hOdO3fmiiuu4O233y6fvW7WrBm/+tWv2LRpE3PnzuXqq68mJqbql83IyACs8unhJjY2locffrj86zvuuIPc3Fy/8x566CFWr14NWB8uDB06NGAxaY+2iIhEmqE2y8c/rLo8ikhY23wIVu31Hz+3JXRu4n48IuLPsfZeEyZMKC9uBtC/f3/uueceRo8eTb169WoeWBW9pwNh69atTJ48ucLYmjVryh+vWrWKiRMnVjjeu3dvRo4c6XetcePG8fbbb7NgwQLWrVtHz549GTduHBkZGRw8eJCpU6eyZMkSABo1asQLL7wQgO9IREQkcg3pDL/7yH98wRa4/Rz34xFxy/T15vFRms0WCRmOZrNJSUnccsst3HPPPfTs2bNO1xo0aFC1KnY7afv27Tz++OO2x9esWVMh8QYYM2aMMdGOi4tj+vTp3HLLLcyePZu9e/fy2GOP+Z3Xtm1bpk2bFva9w0VERNyWlmTN4J0+s7dsJxQUQUpCcOISCSRvGcz4xn88IRauPtP9eETEzLFEe9KkSdxxxx2kpKQ4cr3ExETatzf07ggjKSkpvPvuu8yaNYtXX32VnJwccnNzSUlJoXPnzowcOZLx48fTqFGjgMeSnZ1NdnY2Xq+haoaIiEiYuqyjf6JdUgYf74AruwQnJpFA+nQX7DnqP35ZJ6t2gYiEBscS7aysLKcuFTSDBw8uX/rupBEjRjBixAjHr1sTWVlZZGVlkZ+f70piLyIi4oYhneDPn/qPf7BFibZEJrve2SqCJhJaHCuGdskll3DJJZewbNmyGj0vJyeHSy65hEsvvdSpUERERCRKnNkM0hv6jy/cBqVlrocjElBHi80t7Jo3gEHhvRBUJOI4NqO9aNEiPB4P+/fvr9HzDh48WP5cERERkZrweKxZ7X+vrjh+uBC+2APntQlKWCIBMWcjnCj1Hx/R1dxXXkSCR/8kRUREJKxd1sk8vmCLu3GIBJpd72xVGxcJPUFPtEtKSgCIj48PciSRLTs7m4yMDDIzM4MdioiIiKP6tYaGhk6iCzZDAEqviATFjiOw/Dv/8Yzm0L25+/GISOWCnmhv2LABgCZNmgQ5ksiWlZXF+vXrycnJCXYoIiIijoqPhcEd/Me3HYHNh1wPRyQg3ja09ALNZouEqlrt0c7Pz+fw4cPGY7m5uezYsaPS5/t8Po4dO8bKlSt5+umn8Xg8nHXWWbUJRURERIQhneCdb/3HP9gCZzR1Px4RJ/l85kQ7Lsbany0ioadWifYzzzzDo48+6jfu8/kYP358ja7l8/nweDzcfPPNtQlFREREhEEdrKTj9ErjC7bA3X2DEpKIY77cB1sP+48Pbg+pDVwPR0SqodZLx30+X4U/duNV/QG47bbbuOuuu+r+3YiIiEhUapRgrjD+xR44cNz9eEScNHODeXyklo2LhKxazWj36tWLMWPGVBh75ZVX8Hg8DB48mHbt2lX6/JiYGJKTk+nYsSOXXXaZlo2LiIhInQ3pBEt3VhzzYfXUviEjGBGJ1F1pGbxrSLST68GlHd2PR0Sqp1aJ9ogRIxgxYkSFsVdeeQWA+++/n2uuuabukYmjsrOzyc7Oxuv1BjsUERGRgLi0I/zfYv/xBVuUaEv4WroT9p/wH7+iMyTW6p28iLjBsX+ed9xxBx6Pp8rZbAmOrKwssrKyyM/Pp1GjRsEOR0RExHHtGkG3ZvDNgYrjn+yAolJIUFIiYciu2vi13dyNQ0RqxrFfOVOmTHHqUiIiIiK1cmkn/0T7eAl89h1c1D44MYnU1vESmLfZf7xFEvRv6348IlJ9Qe+jLSIiIuKUSzqYxz/a5mYUIs5YsMVKtk93zZkQq3fxIiFN/0RFREQkYpzbEpok+o8v3Op+LCJ1NVPLxkXCVo2Xjp9sw+XxeJg8ebLfeG2dfj0RERGRmoqNgUHt/dshbTsCWw9BxybBiUukpg4ch8Xb/cc7N4Gzmrsfj4jUTI0T7SlTpuDxeAAqJManjteWEm0RERGpq4s7mPsOL9wGP1KiLWFi9kbw+vzHr+0GdXzLLSIuqNXScZ/P8K/++/Ha/hERERFxwuAOEGNIRLR8XMKJ6cMigGu7uhuHiNROjWe0t241/5ayG5fQoD7aIiISLRonQu+W8PmeiuOffQfHiiGpXnDiEqmuHUdg5R7/8T6trDZ2IhL6apxot29v7o1hNy6hQX20RUQkmlzcwT/RLimDJTthaOeghCRSbba9szWbLRI2VHVcREREIs4lHc3jWj4uoc7ng1mGZeNxMXDVme7HIyK1o0RbREREIk73VGiZ7D/+0TYrkREJVWvzYPMh//GL2kHT+u7HIyK1o0RbREREIo7HYy0fP92+Y7B+v+vhiFTb7G/N4+qdLRJearxHe8eOHYGIA4B27doF7NoiIiISXS7pAFPX+o9/tBV6qA+xhCCfD941JNr142BIJ/fjEZHaq3Gi3aFDhzr3yzbxeDyUlpY6fl0RERGJTgPToV4sFJ/WcGPhNvhZv6CEJFKplXvhuwL/8SGdoEG8+/GISO3Vuo92IP6IiIiIOCWpHpzXxn981V44dML9eESqYrdsXEXQRMJPjWe0x4wZE4g4RERERBx3SQf45LRdb2U+WLQdrtOeVwkh3jKYvdF/PKUeDFIXXZGwU+NE++WXXw5EHBJg2dnZZGdn4/V6qz5ZREQkQlzSER752H98sRJtCTErdkPuMf/xoZ0hscbv2EUk2FR1PEpkZWWxfv16cnJygh2KiIiIazo0ho6N/cc/2W7NbIuECi0bF4ksSrRFREQkol1kWHa7/wSsy3M/FhGTEi/M2eQ/3jgRLkh3Px4RqTsl2iIiIhLR7Pa3frzd3ThE7CzbBQcNBfqGnwHxse7HIyJ1p0RbREREIlr/tlabr9Mp0ZZQYeqdDVo2LhLOalxa4a677gKsvteTJ0/2G6+t068nIiIi4oQG8dC3lTVreKrP90BBEaQkBCcuEYCiUphnWDbevAGcb2hPJyLhocaJ9pQpU/B4PAAVEuNTx2tLibaIiIgEwqAO/ol2aRl8ugsu7xyUkEQA+HgH5Bf7j1/ZBWK19lQkbNXqn6/PZy7T6fP5av1HREREJFAGtTOPL9bycQkyLRsXiUw1ntHeunVrjcZFREREgq1bKrRI8u9TvHg7+HxQx0V5IrVyogQWbPEfb50MfVq5H4+IOKfGiXb79ubSnXbjIiIiIsHm8cBF7eCtryuO78yHbYehY5OghCVRbuE2OF7iP37lmRCjD39Ewpp2foiIiEhUsGvztXiHu3GInPTeRvP4NVo2LhL2lGiLiIhIVLiwHZgmCRdvczsSEWvZ+ELDzst2jeDsFu7HIyLOqvHS8ZooLS1lzZo17N69m4KCAlJSUmjdujXnnHMOcXEBfWkRERGRCprUh3PS4Mt9Fcc/3WW1WErQWxNx0Ufb4ESp//hVXVQzQCQSBORXyueff87TTz/NO++8Q1FRkd/xxMRERowYwQMPPEDfvn0DEYKcJjs7m+zsbLxeb7BDERERCZpB7f0T7ROlVk/tgenBiUmi01xD72yA4We4G4eIBIbjS8d/+9vf0r9/f958800KCwuNrbxOnDjBtGnT6N+/PxMmTHA6BDHIyspi/fr15OTkBDsUERGRoLnIbp+22nyJiwpL4UPDsvG2DeEsLRsXiQiOzmj//Oc/Z9KkSeV9sVNSUhgwYABdunQhKSmJY8eOsWnTJpYtW0Z+fj5er5cnn3yS48eP87e//c3JUERERET8nNsSGtaD/OKK4x9vhwkXBCcmiT4fb4djhmrjw8/QsnGRSOFYor1o0SKeffZZPB4P9evX5w9/+AN333039evX9zu3sLCQF154gd/97nccP36cSZMmMXLkSC666CKnwhERERHxExcDA9v5L9v9ej/sOwppycGJS6LLHJtl41d2cTcOEQkcx5aOP//889YFY2KYPXs2v/jFL4xJNlh7tO+//35mz55NTIwVwj/+8Q+nQhERERGxZdfm62O1+RIXFJXCB1v8x1snQ8809+MRkcBwLNFeunQpHo+H0aNHc/HFF1frOYMHD+amm27C5/OxbNkyp0IRERERsXVRO/P4x9qnLS5YshMKiv3Hr1C1cZGI4liivX//fgAuu+yyGj3v0ksvBeDAgQNOhSIiIiJiq01DOKOp//gnO6DM5348El3mbDSPq9q4SGRxLNFu3rw5AElJSTV6XoMGDQBITU11KhQRERGRSg0yzGofKoR1ee7HItGj2AvzDcvG05Kgdyv34xGRwHEs0e7ZsycA69atq9Hz1q9fD0CvXr2cCiXirFy5kqeeeoobbriBDh064PF48Hg8bNu2LdihiYiIhKULbJaPL9U+bQmgZTshv8h//IozIEbLxkUiimOJ9l133YXP52Py5MkUFBRU6zkFBQVMnjwZj8fDXXfd5VQoEefRRx/lN7/5DW+99Rbbt2sDmYiISF2d1wbiDe+Clux0PxaJHnbVxoer2rhIxHEs0R45ciSjR49m9+7dXHnllezbt6/S8/ft28dVV13F7t27ufnmm7nuuuucCiXi9O/fn4kTJzJz5kx27dpFWppKUoqIiNRFUj3zUt0V30FhqfvxSOQr8cK8zf7jzRtAXy0bF4k4Ne6jvWOH/Zqqxx9/nNLSUmbMmMGZZ57JbbfdxmWXXUaXLl1ISkri2LFjbNq0iQULFvDf//6XgoICRo0axaOPPsqOHTto185mHVeU+81vfhPsEERERCLOwHT47LuKY0Ve+GKPdUzESZ99B4cL/ceHnQGxjk19iUioqHGifXKPcGU8Hg8FBQX885//5J///KfxHJ/Ph8fjYfr06UyfPh2Px0NpqT5CFhEREXdc0A7+utx/fOkOJdrivPdUbVwkqtTq8zOfz1fln6rOMx0PNq/Xy9q1a5kyZQr33nsv/fv3p0GDBuXFx8aOHVur686aNYtRo0bRvn17EhMTadGiBf379+epp57iyJEjzn4TIiIiUi090yC5nv+49mmL00rLzMvGm9WHfm3cj0dEAq/GM9pjxowJRBwhYfTo0cyYMcOx6xUUFHDLLbcwe/bsCuN5eXnk5eWxfPlyJk2axLRp0xgwYIBjrysiIiJVi4uB/m1hwWntltbsgyOF0CgxOHFJ5FnxHRw44T8+tLN1H4pI5Klxov3yyy8HIo6Q4PV6K3zdtGlTmjVrxsaNNmt9KlFaWsr111/PggULAEhLS2PcuHFkZGRw8OBBpk6dytKlS9m1axdXXnklS5YsoUePHo58HyIiIlI9A9P9E20fsGyX1XJJxAm21cZ1j4lErBon2pGsX79+dO/enT59+tCnTx86duzIlClTuPPOO2t8rZdeeqk8yc7IyGDhwoUVqoVnZWXxy1/+kr/85S8cPnyY8ePHs2TJEse+FxEREamaXT/tJTuUaIszynzmZeNNEuH8tu7HIyLuUKJ9igkTJjhyHa/Xy6OPPlr+9WuvvWZsyfXkk0/y4Ycfsnr1apYuXcq8efMYOnSoIzGIiIhI1c5oAmlJsO9YxfGl2qctDlm9F3KP+Y8P6QTxse7HIyLu0K6QAFi8eDF79+4FYNCgQfTu3dt4XmxsLPfdd1/511OnTnUlPhEREbF4POZZ7a2HYVe+6+FIBDLNZgMM6+xuHCLiLiXaAfD++++XPx4+fHil5556fO7cuQGLSURERMwusGnlpVltqSufzbLxpHgYaLNtQUQiQ0CWju/cuZP//Oc/LF++nF27dpGfn+9XaOx0Ho+HzZttPvILM1999VX548zMzErPTUtLIz09nZ07d5Kbm0teXh7NmzcPdIgiIiLyPbue2Ut2wI2qUyp1sPGgtTridIM7QKI2cIpENEf/iZeVlTFhwgT++te/lifWp/fH9ng8lY5Hgg0bNpQ/7tixY5Xnd+zYkZ07d5Y/V4m2iIiIe9KSoUtTKyk61dKdViGrmMh5iyIus1s2PlTLxkUinqOJdlZWFi+++GJ5Et2yZUv27t2Lx+MhNTUVn8/HwYMHKSsrA6zkuk2bNsTGRlYliMOHD5c/Tk1NrfL8Zs2aGZ970nvvvcdjjz1W/vXBg9Y7geuuu46EhAQArrzySn7/+9/7PbeoqIiioqLyr/PzrQ1nJSUllJSUVBlbJDn5/Ubb9y2hT/emhLJouT8HtIlh48GK70cOnIB1e0voVvWvcgmCcLg3522K5fSdmvExPi5sU0oIhy0OCIf7U2qnun+njiXaOTk5vPDCC3g8HgYMGMCrr75Kp06diImxfri89NJLXHPNNRQUFDB37lz++Mc/smbNGrp168a0adNo0qSJU6EE3dGjR8sfJyYmVnl+/fr1yx8XFBT4Hc/Ly+Ozzz7zG1+9enX5427duhmv/cQTT/DII4/4jc+fP58GDRpUGVskOtl2TSTU6N6UUBbp92e942nA+X7j/1qwgUsaRcbWtkgVqvfmodJEvsrz7ybTuV4un3y4PAgRSTCE6v0ptXf8+PFqnedYov3SSy8B0KRJE959913bxDklJYXRo0dz3XXXceuttzJ9+nRGjRrFhx9+6FQoEWfs2LGMHTu2Vs/97W9/ywMPPFD+dX5+Punp6Vx++eU0bNjQoQjDQ0lJCQsWLGDIkCHEx8cHOxyRcro3JZRFy/15YTH8e7IPr6/iOvFDKRkMH941SFFJZUL93nx1TQzs8h+/9bxUhveovFiuhL9Qvz+l9k6uEK6KY4n20qVL8Xg8jB49ulqz0/Hx8bz66qssX76cRYsW8d///pdbb73VqXCCKjk5mUOHDgFQWFhIcnJypeefOHGi/HFKSoqjsSQkJJQvLz9VfHx81P6jj+bvXUKb7k0JZZF+fzaNh14t4Ys9FcdX7I7BFxNDvcja5RZRQvXe/GCb/5gHGNollng10I4aoXp/Su1V9+/TsfZeu3fvBqBv377G46fuEz4pMTGRsWPH4vP5+O9//+tUKEHXuHHj8sf79++v8vwDBw4YnysiIiLuMbX5OlEKK/f4j4tU5tAJ+Ow7//FzW0FakvvxiIj7HEu0jx07BuA3m31yH/CRI0eMz8vIyABg7dq1ToUSdF27/rDEbOvWrVWef+o5pz7XSdnZ2WRkZFTZbkxERCRaXWDT13iZ+mlLDX24Fbw+/3FVGxeJHo4l2ieXPJ+6DBp+SLztEs6TCXpeXp5ToQTd2WefXf44Jyen0nP37dtX3tqrRYsWAWvtlZWVxfr166uMR0REJFr1agn1DZvqPjXssxWpjG1br07uxiEiweNYot25s/UR3ckl5CdlZGTg8/lYvHix8XkrVqwAKlbeDnfDhg0rfzx37txKz50zZ0754+HDVRhDREQkWOrFQmZr//FVe+GEOvRINZ0ogY93+I+f2Qw6Rk6THRGpgmOJdu/evfH5fHz55ZcVxi+99FIAPv300wpJJcDy5cuZMmUKHo+Hnj17OhVK0A0aNIiWLVsCsGjRIlauXGk8z+v18uyzz5Z/fdNNNwUsJi0dFxERqVr/tv5jJWX+RdJE7CzeDoWl/uNaNi4SXRxLtC+++GIAFi5cWGH89ttvL9+nfe211zJ69GgmTJjA6NGjGTRoUHnD7zFjxjgVStDFxsby8MMPl399xx13kJub63feQw89VN4Le+DAgQwd6t9r0SlaOi4iIlK1/oaCaADLtHxcqsl22bgSbZGo4lh7ryuvvJKEhAT27NnDvHnzypPGVq1a8Ze//IV77rmH0tJSpk+fXv4cn8+qEjFs2LBa94l20tatW5k8eXKFsTVr1pQ/XrVqFRMnTqxwvHfv3owcOdLvWuPGjePtt99mwYIFrFu3jp49ezJu3DgyMjI4ePAgU6dOZcmSJQA0atSIF154IQDfkYiIiNTE2S0guR4cLa44rn3aUh0lXqsQ2unapMBZgSnDIyIhyrFEOzk5mfz8fMrKyvx6i40fP56mTZvyu9/9jk2bNlV4zk9/+lMee+wxp8Kok+3bt/P444/bHl+zZk2FxBusmXhToh0XF8f06dO55ZZbmD17Nnv37jV+n23btmXatGn06NGj7t+AiIiI1ElcjLVP+6NtFcfX7INjxZBULyhhSZj47Ds44t/Rlss7g8fjfjwiEjyOJdpQefPuG264gRtuuIFt27axd+9ekpKS6NatW0Q3cE9JSeHdd99l1qxZvPrqq+Tk5JCbm0tKSgqdO3dm5MiRjB8/nkaNGgU7VBEREfle/7b+iXZpGeTshsEdghGRhAstGxeRkxxNtKujQ4cOdOjQwe2XrZbBgweXL2d30ogRIxgxYoTj162J7OxssrOz8Xq9QY1DREQk1A0wFEQDa/m4Em2x4/PBgi3+400SzdXsRSSyOVYMTUKbiqGJiIhUT0ZzaJjgP6592lKZtXmw56j/+GUdrS0JIhJdAj6jffDgQXbv3k1BQQEpKSm0bt2apk2bBvplRURERGolNgbOa+M/O/lVLuQXmZNwkQ8Ms9kAQ7RsXCQqBeTztV27dvHQQw9xxhln0Lx5c3r27MkFF1xAz549ad68OV26dGHChAns2qWPhkVERCT0mPppl/kg5zv3Y5Hw8IGh2nhCLFzYzv1YRCT4HE+0X3jhBbp3786f//xntm7dis/n8/uzZcsWnnzySbp3786LL77odAgiIiIidWJKtEH9tMVsTwGszfUfH5gODSK37q+IVMLRpeN//vOfeeihhwCrR7bH46Fr16506dKFpKQkjh07xqZNm9iwYQM+n49jx45xzz33kJ+fzy9/+UsnQ5HTqBiaiIhI9XVLtYpYHSqsOK592mJi6p0NMKSTu3GISOhwbEZ71apVTJgwoTzBvvfee9m2bRtff/0177zzDlOnTuWdd95h/fr1bN++nfvvv5/Y2Fh8Ph8TJkxg9erVToUiBiqGJiIiUn0xHmuf9unW58HhQv9xiW6mauMAl3Z0Nw4RCR2OJdqTJk3C6/Xi8XiYMmUKf//730lPTzee27ZtW5555hleffVVALxeL88++6xToYiIiIjUWX/D2xgf8Jn2acspjhWbVzqc0wLSkt2PR0RCg2OJ9sKFC/F4PAwfPpzbbrutWs+5+eabueqqq/D5fCxcuNCpUERERETqrLJ+2iInfbIDigw78y7TsnGRqOZYor1v3z4Arr766ho976qrrqrwfBEREZFQ0KUppNb3H/90p/uxSOgyVRsHJdoi0c6xRLtx48YANGnSpEbPO3n+yeeLiIiIhAKPB843zGp/cwAOHHc/Hgk93jJYaEi0WydDRqr78YhI6HAs0c7IyABg06ZNNXre5s2bKzxfAiM7O5uMjAwyMzODHYqIiEjYsGvztVz7tAVYtRcOnPAfv7ST9UGNiEQvxxLt2267DZ/PxyuvvEJxcXG1nlNcXMzLL7+Mx+Ph9ttvdyoUMVDVcRERkZozFUQD7dMWi9p6iYgdxxLtsWPHMnjwYL799ltuu+02TpwwfLx3isLCQm6//XY2btzIxRdfzNixY50KRURERMQRnRpDiyT/cSXaAua2XknxcL6hNZyIRBfHEm2Px8OsWbMYOXIkb731Ft27d+fpp59m1apVHD16FJ/Px9GjR1m9ejV//vOf6d69O2+99RajRo1i5syZToUhIiIi4hiPx7x8fNNB7dOOdtsPw8aD/uMXtYeEONfDEZEQU+MfA7GxsdU6b8eOHfzmN7+xPe7z+QCYPn0606dPx+PxUFpaWtNwRERERALq/DYwa4P/+IrdcMUZ7scjocG22nhHd+MQkdBU4xltn89X5Z/qnGs6R0RERCTUnGdTEG2FCqJFtQ8My8ZjPHBxB9dDEZEQVOMZ7YsuugiPyiiKiIhIlOjU2Oqnvf+08jOqPB69jhRZKxpO16cVNGvgfjwiEnpqnGgvWrQoAGFIoGVnZ5OdnY3X6w12KCIiImHF44F+bWDOaR1Mv86zEq5GCcGJS4Jn8TYoLfMf17JxETnJsWJoEtrU3ktERKT2+hmqSPuAzw2zmhL5TNXGAS5TWy8R+Z4SbREREZEq2LVr+kzLx6NOiRcWbfMf79AIOjdxPRwRCVFKtEVERESq0DUVGhqWiKsgWvT5fA/kF/uPX9bJ2mYgIgK12KNdXd988w0zZsxg+fLl7N69m4KCAlJSUmjdujXnn38+119/PV27dg3Uy4uIiIg4JsYD/Vr7t3T6KheOFUNSveDEJe77aJt5XMvGReRUjifa+/fvZ/z48cycOdN4fNWqVbz33ns8/PDDXHfddTz//POkpqY6HYaIiIiIo85r659ol5bBqr1wQbvgxCTuW2jon92wHvRt5X4sIhK6HF06vnPnTnr37s3MmTOr7LVdVlbGjBkz6NOnD7t27XIyDBERERHHndfaPK42X9FjZz5sPOg/fmF7iI91Px4RCV2OJdo+n48RI0awa9cufD4fqampTJw4kSVLlpCXl8eJEyfIy8tj6dKlTJw4kebNm+Pz+di5cycjRoxwKgwRERGRgOjRApLi/ce1Tzt6fGSYzQa4pIOrYYhIGHAs0Z46dSqrV6/G4/FwySWX8M033/Doo48yYMAAmjVrRkJCAs2aNaN///48+uijbNiwgUsvvRSA1atX88YbbzgVioiIiIjj4mKgr2FWe/VeKCx1Px5x38Jt5vFB7V0NQ0TCgGOJ9vTp0wFo06YNs2bNomnTppWe37hxY2bOnEnbtm0B+N///udUKCIiIiIB0c+QaBd54ct97sci7jpRAst2+o/3TIPmSe7HIyKhzbFE+/PPP8fj8XDXXXeRlFS9nzZJSUncdddd+Hw+vvjiC6dCEYPs7GwyMjLIzMwMdigiIiJh6zz1045an+6yPlQ53cUdXA9FRMKAY4l2bm4uABkZGTV63snz8/LynApFDLKysli/fj05OTnBDkVERCRsnZMGCYaiV9qnHfnslo1rf7aImDiWaCcmJgJw/PjxGj3v5Pn16qkBpYiIiIS2hDg419DG6Ys9UGKY7ZTI4POZ+2en1oez01wPR0TCgGOJdps21lqqRYsW1eh5J88/uVdbREREJJSZ2nwdL4G1WpwXsTYehF35/uODO0CMx/VwRCQMOJZoX3zxxfh8PqZOnVrt/daff/45r7/+Oh6Ph8GDBzsVioiIiEjA9NM+7ahju2y8o6thiEgYcSzR/vGPf4zH46G0tJTLL7+cN998s9Lz33zzTYYNG0ZpaSkej4ef/OQnToUiIiIiEjB9Wlmtvk6nfdqRy7RsPNYDF7RzPRQRCRNxTl2oZ8+e/OxnP2PSpEkcPnyYm266id/+9rdcfvnlnHnmmSQlJXHs2DG+/fZb5s+fz9atW/H5fHg8Hn72s59xzjnnOBWKiIiISMDUj7eKoq3cU3E85zvwlkGsY9MYEgryi+Dz3f7jma2hUYL78YhIeHAs0QZ45plnyM/P55VXXgFg69atvPDCC8ZzfT4fAGPGjOGZZ55xMgwRERGRgDqvtX+inV8M3xyAHs2DE5MExic7oLTMf1xtvUSkMo5+5hoTE8PLL7/MW2+9Rd++ffH5fLZ/MjMzmTFjBv/+97/xeFRFQkRERMLHeTY1XHMMM58S3j7aah7X/mwRqYyjM9onjRw5kpEjR7Jz506WL1/Onj17KCgoICUlhVatWnH++eeTnp4eiJcWERERCbg+rcAD+E4b/3w3jO0ZjIgkEMps2nq1SYEuTV0PR0TCiGOJ9quvvgpAy5YtufzyywFIT09XQi0iIiIRp2ECdEuFr/dXHM/ZbfVc1mK9yLA2F/af8B+/uIP+jkWkco4tHR87dix33nkny5Ytc+qSIiIiIiGrr6Gf9t6j8F2B+7FIYCzUsnERqSXHEu3k5GQAunfv7tQlRUREREJWpiHRBu3TjiSm/tkJsTDAZo++iMhJjiXarVq1AqC4uNipS4qDsrOzycjIIDMzM9ihiIiIRAS7RNvUCkrCT94x+HKf/3j/tlaLNxGRyjiWaF988cUA5OTkOHVJcVBWVhbr16/X34+IiIhDWqdA62T/cc1oR4aPd5jHL9aycRGpBscS7fHjxxMTE8Mrr7zCd99959RlRUREREKWaZ/2twfgSKH7sYizFm0zj1/Swc0oRCRcOZZon3vuuTz++OMUFBQwZMgQ1qxZ49SlRUREREKSafm4D/hij+uhiIO8ZfCJYUa7cxNo18j9eEQk/Dja3qtly5ZcccUVzJ07l969e3PBBRdw4YUX0rZtW+rXr1/lNe644w6nwhEREREJuMr2aasydfj6KhcOGVYlDGrvfiwiEp4cS7THjh2L5/uGgh6Ph7KyMj755BM++eSTaj3f4/Eo0RYREZGwcmYzaFgP8k+rBat92uFt8XbzuBJtEakux5aOA/h8vvI/p39dnT8iIiIi4SQ2Bs5t5T/+5T4oKnU/HnHGIkOinRAL56utl4hUk2Mz2i+//LJTl5LTlJSU8Je//IXXXnuNLVu2kJyczIUXXsjvf/97zj333GCHJyIiEtUyW/vPgBZ5YW0e9DEk4RLajhTC6r3+4+e3hUTH3jmLSKRz7MfFmDFjnLqUnKKkpIRhw4axcOFCWrRowdVXX82ePXt4++23ee+995g9ezZDhgwJdpgiIiJRy26fds5uJdrhaMlOKDMstNSycRGpCUeXjovznnrqKRYuXEhmZiYbN27kf//7H5988gmvv/46xcXF3HrrrRw9ejTYYYqIiEStnmkQb3hH9bn2aYclu7ZeFynRFpEacCTR3rVrF3PnzuWNN97g/fffVx9th5SWlvLMM88A8Nxzz9GwYcPyYzfffDPDhw8nLy+Pf//738EKUUREJOrVj4ezWviPf77bPDMqocvnMxdCa5MCZzRxPx4RCV91SrRzcnIYOHAg7du356qrruLWW2/lyiuvpF27dlxwwQWsWLHCqTij0tKlSzlw4AAdO3akb9++fsdvvPFGAGbNmuV2aCIiInKKvobl44cKYfMh92OR2ttwAPYd8x8f1B6+b64jIlIttU6058+fz6BBg1i+fLmxgviyZcsYNGgQ8+bNczLegPF6vaxdu5YpU6Zw77330r9/fxo0aIDH48Hj8TB27NhaXXfWrFmMGjWK9u3bk5iYSIsWLejfvz9PPfUUR44cqfS5X375JQB9+vQxHu/du3eF80RERCQ4KuunLeHDVG0ctD9bRGquVsXQCgoKGDNmDIWFheVjXbp0oXnz5uTm5rJp0yYAioqKGDNmDN9++22FZc+haPTo0cyYMcOx6xUUFHDLLbcwe/bsCuN5eXnk5eWxfPlyJk2axLRp0xgwYIDxGtu3Wz/t27Y195I4OX7gwAGOHj1KcnKyY/GLiIhI9fW1KXr2+W64+Sx3Y5HaW7zNfywuBgakux6KiIS5Ws1ov/baa+zbtw+Px0NmZibr169nw4YNLFmyhG+//ZZ169bRr18/wEosX3vtNUeDDgSv11vh66ZNm9KlS5daXau0tJTrr7++PMlOS0tj4sSJvP766/zjH/9g4MCBgLW3/corr2TdunXG6xQUFACQlJRkPH5qYn3yXBEREXFfswbQ2bCHN0cz2mHjWDF8vsd/vE8raJjgfjwiEt5qlWjPnTsXgNTUVN5//326detW4Xj37t2ZO3cuLVq0qHB+KOvXrx8PPfQQb775Jlu2bOHAgQNMmDChVtd66aWXWLBgAQAZGRl8+eWXPPbYY9x8881kZWWxZMkSHnzwQQAOHz7M+PHjjdfx+awKKh5tChIREQl5plZe24+Y9/xK6Fm+C4q9/uNaNi4itVGrRHvNmjV4PB7uuOMOmjQxl2Bs0qQJd9xxBz6fj6+++qpOQbphwoQJPPHEE4waNYqOHTvW+jper5dHH320/OvXXnuNtLQ0v/OefPJJevXqBVhFz0x72VNSUgBs23cdO3bM71wREREJDu3TDm/any0iTqpVon3w4EGA8kTRTs+ePQFrD3G0WLx4MXv37gVg0KBB5QXLThcbG8t9991X/vXUqVP9zmnf3vrJvmvXLuM1To43bdpU+7NFRESCTIl2eDO19WreADKaux+LiIS/WiXaJ2dSq5pFPZn8nThxojYvE5bef//98sfDhw+v9NxTj5uW15/8IGPlypXG558cP/mBhoiIiARPh8aQWt9//AvDvl8JLdsOW8v8T3dhO4jRDj4RqYU69dEWf6cuk8/MzKz03LS0NNLTrTKWubm55OXlVTg+cOBAmjZtypYtW/jiiy/8nv+///0PgBEjRtQ1bBEREakjjwf6GGa11+VBYan78Uj1adm4iDhNibbDNmzYUP64Onu9Tz3n1OcCxMXF8fOf/xyArKysCpXFp02bxuzZs0lNTeWuu+6qY9QiIiLihN6GgmilZbBmn/uxSPWZ2np5sGa0RURqo1Z9tE9SNWx/hw8fLn+cmppa5fnNmjUzPvek3/zmNyxcuJBFixZxxhlnMGjQIPbu3csnn3xCfHw8r732mgqhiYiIhIg+Lc3jK/dAvzbuxiLVU1QKnxrK4ZyTZrVtExGpjTol2tdee221zvP5fMTGxlZ6jsfjobQ0/NdVnVohPDExscrz69f/YTOXqRd2vXr1mDdvHk8//TT/+c9/eOedd0hOTmbEiBE8/PDDtsXWTioqKqKoqKj86/z8fABKSkooKSmpMr5IcvL7jbbvW0Kf7k0JZbo/a6ZbU4iPiaOkrOJkxOe7yygpMfSOklpz6t5cvsvDiVL/t8QXpnspKSmr07UleulnZ+Sq7t9pnRJt+KHXs4nH4ymf9a7sPKlcvXr1mDBhQq36ej/xxBM88sgjfuPz58+nQYPo/Jj2ZI9zkVCje1NCme7P6msddxHbiyu2P12+vZj33puHFgM6r6735syDGUAXv/G4XUuZs/9Qna4top+dkef48ePVOq/WiXZ1EudoTK6Tk5M5dMj6oVxYWFhl261TK7IHYgn4b3/7Wx544IHyr/Pz80lPT+fyyy+nYcOGjr9eKCspKWHBggUMGTKE+Pj4YIcjUk73poQy3Z81t3JJDFPWVBwrKEvk7AuG065RcGKKRE7dm89N83873DDBx93X9idO1YyklvSzM3KdXCFclVol2mVlWkZjp3HjxuWJ9v79+6tMtE/tMd64cWPH40lISCAhIcFvPD4+Pmr/0Ufz9y6hTfemhDLdn9WX2Qa/RBvgy/3xdK66fIvUUF3uzbxj8M0B//GB6R7qJ+h+l7rTz87IU92/T31O57CuXbuWP966dWuV5596zqnPdVp2djYZGRlVthwTERGRujFVHgerIJqEliU7zeOqNi4idaVE22Fnn312+eOcnJxKz923bx87d1o/4Vu0aEHz5s0DFldWVhbr16+vMiYRERGpm9Yp0MqwoO0LJdohZ8kO87gSbRGpKyXaDhs2bFj547lz51Z67pw5c8ofDx8+PGAxiYiIiLtMs9rf7Idjxe7HImY+H3xiSLTbNUJ76UWkzpRoO2zQoEG0bGk10Vy0aBErV640nuf1enn22WfLv77ppptciU9EREQCr48h0S7zwep97sciZhsPwr5j/uMXaTZbRBygRNthsbGxPPzww+Vf33HHHeTm5vqd99BDD7F69WoABg4cyNChQwMal/Zoi4iIuEf7tEOfaTYb4AIl2iLigDr30Y4UW7duZfLkyRXG1qz5oWToqlWrmDhxYoXjvXv3ZuTIkX7XGjduHG+//TYLFixg3bp19OzZk3HjxpGRkcHBgweZOnUqS5YsAaBRo0a88MILAfiOKsr6/+3dd3RVVdrH8d9ND6QZQhJKwCiKJFIEQqQo6KggqAxYQEYBC+Ny4jAj4th4raOIvsw7irEMOOCgIqM0RUAYBBEUpBqKICpVSGgJCRBCynn/YHJMvOemcfv9ftbKWufuu88+zz3ZhDzZ5WRlKSsrS4WFhYqNZT4UAACulN5UCg+WSsqrl7NO23tYJdpBNqlHivtjAeB/SLT/a8+ePXr++ecdvp+Tk1Mt8ZakESNGWCbaISEhmjVrloYNG6b58+crNzdXzz33nF29li1baubMmUpPTz/3DwAAALxGWLDUPlFa96vEemPu2SnkQTbPxIWzzpRLa362L++YJMXaPxUVAOqNqeMuEh0drU8++URz587V4MGDlZKSovDwcCUkJCgzM1MTJkzQli1b1KNHD0+HCgAAXKBLc/uygtPST/nujwXVbTgonSq1L2faOABnYUT7v/r06SPDMJze7sCBAzVw4ECnt1tf2dnZys7OVnl5ee2VAQDAOeucbF2+4aDUJt69saC6FQ7WZ7MRGgBnYUQ7QPAcbQAA3MvRhmis0/Y8q+dnNw6VLnPwxxEAqC8SbQAAABdIbCylxNiXb8h1fyz4RcFpKcfiMWuXt5RCg90fDwD/RKINAADgIlaj2t8flY6XuD8WnLVqn2S1WPAKpo0DcCISbQAAABfp4mD6+CZGtT2G52cDcAcS7QCRnZ2ttLQ0ZWRkeDoUAAAChqN12htYp+0RhmGdaDeLktqc5/54APgvEu0AwWZoAAC4X7sEKdLiGS9siOYZe45L+wvty3u1kmw82xyAE5FoAwAAuEhIkNTRYifrTblSeYX74wl0jqaNsz4bgLORaAMAALiQ1TrtojPSD8fcH0ugc5Ro90xxbxwA/B+JNgAAgAt1dvBs5o1siOZWZRXSV/vsy9OaSgmN3B8PAP9Gog0AAOBCnRwk2pssnuUM1/k27+xMgl+7kmnjAFyARDtAsOs4AACekdBISomxL9/IhmhutZL12QDciEQ7QLDrOAAAnnOZxaj298ekkxYjrHANq/XZ4cFS1+bujwWA/yPRBgAAcDGrRLvCkHIOuT+WQFRUYv3s8m4tpAiLx68BwLki0QYAAHAxR+u0mT7uHqt/lsoN+/JeTBsH4CIk2gAAAC6W1lQKtfitiw3R3MPR+mw2QgPgKiTaAAAALhYRIqU3tS/fcFAyLEZa4VyrLB7rlRApXZLg/lgABAYSbQAAADewmj5++JR0oMj9sQSSvJPSzmP25T1SpCCb++MBEBhItAEAANyA52l7xtcWo9nS2UQbAFyFRDtA8BxtAAA8y2rncUnamOveOAKN1bRxSepJog3AhUi0AwTP0QYAwLNax0rnRdiXbyLRdhnDkL6ySLRbxkitYt0fD4DAQaINAADgBjab9fTxnDyptNz98QSCfYXSfos18IxmA3A1Em0AAAA3sZo+XlIubT/q/lgCgaNp4z1aujcOAIGHRBsAAMBNHK3TZvq4azhMtBnRBuBiJNoAAABu0pEN0dymwsH67IubSImN3R8PgMBCog0AAOAmseHShefZlzOi7XzfH5WOFtuXsz4bgDuQaAMAALiR1fTxH/Ol46fdH4s/Y302AE8i0QYAAHAjq53HJWlTnnvj8HdWiXaQTcok0QbgBiTaASI7O1tpaWnKyMjwdCgAAAQ0h4k208edpqxCWvOzfXmHxLPT9wHA1Ui0A0RWVpa2bdumtWvXejoUAAAC2iVNpIgQ+3I2RHOenDzpxBn7cnYbB+AuJNoAAABuFBostU+0L9+UKxmG++PxR47WZ7MRGgB3IdEGAABwM6vp4/mnpT3H3R+LP7J6rFdYsNS1uftjARCYSLQBAADczGrncYnp485wukxaf9C+vHMz6yn7AOAKJNoAAABu5ijRZkO0c7fugFRSbl/OtHEA7kSiDQAA4GbNoqTExvbl3/KIr3NmNW1cItEG4F4k2gAAAG5ms0kdk+zLtx2WSi1GY1F3X+23L4sKs77fAOAqJNoAAAAe0MEi8Sspl3YcdX8s/qKwxHpWQLfmUgi/9QJwI37kAAAAeICjEdYcpo832JqfpQqLR6QxbRyAu5FoAwAAeEAHi2dpS6zTPheszwbgLUi0AQAAPOC8SKlVrH05iXbDWSXa8ZFS2wT3xwIgsJFoAwAAeIjV9PHvj0rFpe6PxdcdK5a2W6xv795SCrK5Px4AgY1EO0BkZ2crLS1NGRkZng4FAAD8l1WiXW5IWw67PxZft9pit3HpbKINAO5Goh0gsrKytG3bNq1du9bToQAAgP9iQzTnWf2zdTmJNgBPINEGAADwkEsTrac1s067/r62GNFu2ki68Dz3xwIAJNoAAAAe0ihUuijevvzbXPfH4suOnDq7tv3XureUbKzPBuABJNoAAAAe1MFi+vju49Lx0+6PxVetcTBt/HKmjQPwEBJtAAAAD2Kd9rmzmjYusT4bgOeQaAMAAHhQJweJNuu0685qx/HExlJqnNtDAQBJJNoAAAAe1TZBCgu2L8855P5YfNGRU9LOY/bll7dgfTYAzyHR9gEbNmzQSy+9pFtvvVXnn3++bDabbDabdu/e7enQAADAOQoLltIS7MsZ0a6bNQess+keKW4OBACqCPF0AKjds88+q3nz5nk6DAAA4CIdkqRNv0qsc09IeSekpCjPxOQr1vxsnWhf3sLNgQBAFSTaPqB79+5q3769unbtqq5du6pLly7Ky+PP3AAA+AtHG6J9myddR6JdozU/20/QTI6Szo9zfywAUIlE2wc88sgjng4BAAC4kNUjvqT/JtoXujcWX1JYFq4fC+xHtFmfDcDTWKMNAADgYReeJzUOtS/nEV8123naYnG7eKwXAM/zi0S7vLxcW7Zs0bRp0/THP/5R3bt3V6NGjcxNw0aOHNmgdufNm6dbbrlFrVu3VkREhBITE9W9e3e99NJLOn78uHM/BAAACFjBQVJ7i1Htb/Mkw3B/PL7ih9NNLMtJtAF4ml9MHb/ttts0e/Zsp7VXVFSkYcOGaf78+dXKDx8+rMOHD2v16tWaNGmSZs6cqR49ejjtugAAIHB1TLJ/HvTxEmnPcdYbO7LzdFO7smZRUqtYDwQDAFX4zYh2VfHx8brooosa1FZZWZluvvlmM8lOSkrSuHHj9P777+u1115Tz549JUn79+/XgAEDtHXr1nMLHgAAQDVviAZ7eSelQ2X2O8V1b8n6bACe5xcj2t26dVO7du3UpUsXdenSRampqZo2bZruuuuuerc1efJkLVmyRJKUlpamzz//XElJv/zPl5WVpbFjx2rixIkqKCjQfffdp5UrV1q2NXz4cH3zzTf1uv6gQYM0fvz4escNAAB8W00bog1s695YfIHDx3oxbRyAF/CLRPvxxx93Sjvl5eV69tlnzdfTp0+vlmRXmjBhgpYuXapNmzZp1apV+uyzz9S3b1+7env37tWOHTvqFcPBgwfrHzgAAPB5LaOl+EjpWHH1cjZEs7bmgPXETNZnA/AGfjF13Fm++OIL5ebmSpJ69+6tzp07W9YLDg7W6NGjzdczZsywrLd8+XIZhlGvr2nTpjn9cwEAAO9ns0kdEu3LtxySyircH4+3sxrRbhEtpcR4IBgA+BUS7SoWLVpkHvfv37/GulXfX7hwoctiAgAAgaNTsn1ZcZn0wzH3x+LNck9Iu4/bJ9qszwbgLUi0q9i8ebN5nJGRUWPdpKQkpaSkSJIOHTqkw4cPuzQ2AADg/xyt096U6944vN3X+63LWZ8NwFuQaFdRdT11ampqrfWr1qnvWmwAAIBfc7TzOOu0qyPRBuDt/GIzNGcpKCgwjxMSEmqt36RJE8tzne3TTz/Vc889Z74+duzs/LFBgwYpPDxckjRgwAD9z//8j925JSUlKikpMV8XFhZKkkpLS1VaWuqymL1R5ecNtM8N70ffhDejf7pXbKjUPCpEB05Un//8bV6FSkvLHZwVeL7eFyKp+j1qGW0oObJMdFV4A352+q+6fk9JtKs4ceKEeRwREVFr/cjISPO4qKjIJTFJ0uHDh7VmzRq78k2bNpnHl1xyieW548eP1zPPPGNXvnjxYjVq1MhpMfqSyse3Ad6GvglvRv90n6YVGTqg5tXKvjts6ONPFyrEZngoKu+RXxahvYX2T3tpYezTggUbPRAR4Bg/O/3PqVOn6lSPRNsHjBw5UiNHjmzQuY899pjGjBljvi4sLFRKSoquu+46xcQE1racpaWlWrJkia699lqFhoZ6OhzARN+EN6N/ut+e9UH69ld/Xy9XsC7odr0ubeqZmLzJnB02yWLq+C3dmqv/Jc3cHxBggZ+d/qtyhnBtSLSriIqKUn5+viTp9OnTioqKqrF+cfEvD7qMjo52aWwNFR4ebk4vryo0NDRg/9EH8meHd6NvwpvRP92nk4Nc8bujobqsufV7gWTtQevynq1DRBeFt+Fnp/+p6/eTzdCqiIuLM4+PHDlSa/2jR49anuuNsrOzlZaWVutu6gAAwLPaWzxLW5I2H3JvHN5qzc/2Za1ipRaBNVEPgJcj0a6ibdu25vGuXbtqrV+1TtVzvVFWVpa2bdumtWvXejoUAABQg/MipZYWE+VItM8+P3vPcfvyTEb6AXgZEu0q2rdvbx7XlpDm5eVp3759kqTExEQ1bcqiKQAA4BztLR7zteOodCbANx7/xmI0W5IyeawXAC9Dol1Fv379zOOFCxfWWHfBggXmcf/+/V0WEwAACDxW08fPlJ9NtgOZ1bRxScps4d44AKA2JNpV9O7dW8nJyZKk5cuXa8OGDZb1ysvL9eqrr5qvhw4d6pb4zgVrtAEA8B0O12nnuTcOb/PNAfuy5MaGUlifDcDLkGhXERwcrCeffNJ8PXz4cB06ZL8g6tFHHzWfYd2zZ0/17Wv/LEdvwxptAAB8Bxui2TtWLH1vMaLfrbkhm8398QBATfzi8V67du3S22+/Xa0sJyfHPN64caPGjRtX7f3OnTtr8ODBdm2NGjVKc+bM0ZIlS7R161Z17NhRo0aNUlpamo4dO6YZM2Zo5cqVkqTY2Fi99dZbLvhEAAAgkJ0XKbWMkfb/6nGtgZxoO1qfndHccG8gAFAHfpFo79mzR88//7zD93Nycqol3pI0YsQIy0Q7JCREs2bN0rBhwzR//nzl5ubqueees6vXsmVLzZw5U+np6ef+AQAAAH6lfaJ9ol25IVpYsGdi8iSHiXazCkkBeEMAeDWmjluIjo7WJ598orlz52rw4MFKSUlReHi4EhISlJmZqQkTJmjLli3q0aOHp0MFAAB+ig3RqltjsT67cVCJLjzP/bEAQG38YkS7T58+MgznTxsaOHCgBg4c6PR2PSE7O1vZ2dkqLw/w54IAAOAjatoQzdF7/qqwRNp22L68TcRR2Ww8YhWA92FEO0CwGRoAAL6FDdF+sf6gVGExpnJhRIAO7wPweiTaAAAAXqhyQ7RfC8RE29H67AvDSbQBeCcSbQAAAC9lNapduSFaIFljkWhHhRlqEXbc/cEAQB2QaAMAAHgpNkSTikulnDz78q7JhoJ4fjYAL0WiHSCys7OVlpamjIwMT4cCAADqqKYN0QLFhlyptMK+vFsLnp8NwHuRaAcINkMDAMD3sCFaTc/PJtEG4L1ItAEAALwUG6JZr8+ODJHSm5JoA/BeJNoAAABeLJA3RDtTLm04aF/euZkUFuz+eACgrki0AQAAvFggb4iWkyeVWPxBIbOF+2MBgPog0QYAAPBiHQJ4QzRH67O7kWgD8HIk2gGCXccBAPBNlwbwhmhW67NDg6TLkt0fCwDUB4l2gGDXcQAAfFOgbohWXiGts1if3TFJighxfzwAUB8k2gAAAF4uEDdE23ZEOnHGvpz12QB8AYk2AACAl7Nap+3vG6Kt2W9dzvpsAL6ARBsAAMDLOVqnvdWPp49/c8C+LMgmdW3u/lgAoL5ItAEAALxcelPr8q2H3RuHu1QY1juOX9pUigpzfzwAUF8k2gAAAF6uSSOpWZR9+RY/TbR/OCbln7YvZ9o4AF9Boh0geLwXAAC+zWr6+HeHz+7O7W+sHuslsREaAN9Boh0geLwXAAC+zWr6eHGZ9FOB20NxOatp45KUwfpsAD6CRBsAAMAHOFyn7WcbohmG9UZoFzc5+0xxAPAFJNoAAAA+wNHO4/62TvvnIin3hH15N0azAfgQEm0AAAAf0CxKOi/CvtzfRrTXWoxmS0wbB+BbSLQBAAB8gM1mPaq99fDZ6db+Yh2JNgA/QKINAADgI6zWaR8vkfYXuT8WV7Ea0W4eJbWIcX8sANBQJNoAAAA+wt83RDt+Wtpx1L68K6PZAHwMiTYAAICP8PcN0dYdtC5n2jgAX0OiHSCys7OVlpamjIwMT4cCAAAa6Pw4qXGofbm/jGizPhuAvyDRDhBZWVnatm2b1q5d6+lQAABAAwXZpHYJ9uX+MqJttT47JuzsM7QBwJeQaAMAAPgQq+njh05Kh0+6PxZnOl0mfZtnX965mRTMb6wAfAw/tgAAAHyIww3RfHxUe/Mh6Uy5fTnTxgH4IhJtAAAAH+JwQzQfX6ftcH12C/fGAQDOQKINAADgQy6Kl8KC7ct9fUTban12aJDUMcn9sQDAuSLRBgAA8CGhwdabg/nyhmgVhvWIdvskKSLE/fEAwLki0QYAAPAxl1qs0957XDpe4v5YnOGHY9axsz4bgK8i0QYAAPAxjjZE2+ajo9pW08YlEm0AvotEGwAAwMc42hBtq49uiOYo0e7SzL1xAICzkGgDAAD4mHYJUpDNvtxX12lbJdpt4qX4SPfHAgDOQKINAADgYyJDpQvOsy/3xZ3HDxZJ+wvty5k2DsCXkWgDAAD4IKsN0X44JhWXuj+Wc7HuoHU5iTYAX0aiDQAA4IPSLdZpVxjS9qPuj+VcsBEaAH9Eoh0gsrOzlZaWpoyMDE+HAgAAnMBqRFvyvQ3RrBLtxMZSSoz7YwEAZyHRDhBZWVnatm2b1q5d6+lQAACAEzh6xJcvbYhWWCJtP2JfntFcslls9gYAvoJEGwAAwAfFRkgtLUZ9fWlEe2Pu2enuv8a0cQC+jkQbAADAR1lNH99+VCotd38sDeFofXZXEm0APo5EGwAAwEdZbYh2pvzs7uO+wCrRbhx69jnhAODLSLQBAAB8lKMN0XxhnfaZcmlTrn1552ZSCL+hAvBx/BgDAADwUVYj2pL0nQ8k2lsPS6fL7MtZnw3AH5Boe7nS0lItXrxYo0ePVteuXZWQkKCIiAi1adNGWVlZ2rNnj6dDBAAAHpLYSGoSaV++zWInb2/D+mwA/oxE28t98cUX6tu3ryZNmqQjR47oiiuu0PXXX6/i4mK9/vrr6tChg77++mtPhwkAADzAZpPSLKaPbzssGRa7eXuTdRaJdrBNuizZ/bEAgLORaHu5oKAgDRkyRN988412796tOXPmaM6cOfrxxx81cuRIFRYW6vbbb1dpaamnQwUAAB5glWgfL5EOFLk/lroyDGnDQfvy9KZSo1D3xwMAzkai7eWuvvpqffDBB8rIyKhWHhERoTfeeEOxsbHas2ePvvrqKw9FCAAAPCnNwQ7d33nx9PF9hdLhU/blTBsH4C9ItH1YRESELr74YknSgQMOFjoBAAC/ZjWiLZ3dbMxbrbMYzZakLs3cGwcAuIpfJNrl5eXasmWLpk2bpj/+8Y/q3r27GjVqJJvNJpvNppEjRzao3Xnz5umWW25R69atFRERocTERHXv3l0vvfSSjh8/7twP0QDl5eXavXu3JCk5mQVNAAAEogvOk8KD7cu3eXGivd7B+ACJNgB/EeLpAJzhtttu0+zZs53WXlFRkYYNG6b58+dXKz98+LAOHz6s1atXa9KkSZo5c6Z69OjhtOvW1/Tp03X48GElJiZ6NA4AAOA5IUFS2yZSzqHq5d6887jV+uxmUVKzaPfHAgCu4Dcj2lXFx8froosualBbZWVluvnmm80kOykpSePGjdP777+v1157TT179pQk7d+/XwMGDNDWrVvPLfgG2r17tx566CFJ0gsvvKDw8HCPxAEAADzPavr43uNSUYn7Y6nNiTPS9qP25YxmA/AnfjGi3a1bN7Vr105dunRRly5dlJqaqmnTpumuu+6qd1uTJ0/WkiVLJElpaWn6/PPPlZSUZL6flZWlsWPHauLEiSooKNB9992nlStXWrY1fPhwffPNN/W6/qBBgzR+/Pga6xw/flwDBw7UsWPHdNttt+mee+6p1zUAAIB/aedgnfb2I1JGC/fGUptNuVKFxaPHSLQB+BO/SLQff/xxp7RTXl6uZ5991nw9ffr0akl2pQkTJmjp0qXatGmTVq1apc8++0x9+/a1q7d3717t2LGjXjEcPOhgd5D/Ki4u1k033aScnBxdffXV+te//lWv9gEAgP9Jd7Dz+FYvTLTXsxEagADgF1PHneWLL75Qbm6uJKl3797q3LmzZb3g4GCNHj3afD1jxgzLesuXL5dhGPX6mjZtmsP4zpw5o5tvvlkrVqxQ9+7dNW/ePKaMAwAAXeIg0fbGDdGsEu3wYMej8gDgi0i0q1i0aJF53L9//xrrVn1/4cKFLoupUnl5uX73u99p4cKF6tSpkxYsWKCoqCiXXxcAAHi/6HCpdax9+XdelmhXGNLGXPvyjklSmMXO6QDgq0i0q9i8ebN5nJGRUWPdpKQkpaSkSJIOHTqkw4dd9z+ZYRi655579NFHH6ldu3ZavHix4uLiXHY9AADge9pZjGrvOCqVVbg/Fkd+OCYVWmzQxrRxAP6GRLuKquupU1NTa61ftU5912LXx4MPPqh33nlHF1xwgf7zn/+oaVPmVgEAgOqsdh4vKZd+ynd/LI44XJ/d3L1xAICr+cVmaM5SUFBgHickOFjsVEWTJk0sz3WmefPm6ZVXXpEktW7d2uHGb7/97W/129/+1q68pKREJSW//Om4sLBQklRaWqrS0lLnB+zFKj9voH1ueD/6JrwZ/dN3tI23yepXu825ZUqNsdjm2wPW/Rwsq3Ge9k1KVd8uRt+EN6N/+q+6fk9JtKs4ceKEeRwREVFr/cjISPO4qKjIJTHl5//yZ+hly5Y5rHf++edbJtrjx4/XM888Y1e+ePFiNWrUyCkx+prKx7cB3oa+CW9G//R++WURkuyfgvLpN7sU8sM29wdk4cufr5YUXa2sacgJrVm+tMFt0jfhzeif/ufUqVN1qkei7eVGjhypkSNHNvj8xx57TGPGjDFfFxYWKiUlRdddd51iYmKcEKHvKC0t1ZIlS3TttdcqNDTU0+EAJvomvBn903cYhvS3fxo6XmKrVn465kL173++Z4KqIv+0lPdP+z7U68JG6v+bmjehtULfhDejf/qvyhnCtSHRriIqKsocQT59+nStu3oXFxebx9HR0TXU9Jzw8HDLR4CFhoYG7D/6QP7s8G70TXgz+qdvSGsqfb2/etn2o0EKCQmSzWZ9jrts3m9dntEiSKGhDd82iL4Jb0b/9D91/X6yGVoVVXfyPnLkSK31jx49ankuAACAJ1htiHa0WDpUt5mOLuVwIzR2HAfgh0i0q2jbtq15vGvXrlrrV61T9VxvlJ2drbS0tFofWwYAAHxXuoMHk2zzgudpWyXaUWHSRfHujwUAXI1Eu4r27dubx2vXrq2xbl5envbt2ydJSkxM9PpHbmVlZWnbtm21fi4AAOC7rJ6lLXk+0S6rkL7NtS+/LFkK5rdRAH6IH21V9OvXzzxeuHBhjXUXLFhgHvfvX/8NPAAAAJytTbxktdzZ04n2d0ek4jL7cqaNA/BXJNpV9O7dW8nJyZKk5cuXa8OGDZb1ysvL9eqrr5qvhw4d6pb4AAAAahIWLF3UxL78u9q3nnEp1mcDCDQk2lUEBwfrySefNF8PHz5chw4dsqv36KOPatOmTZKknj17qm9f+2dWehvWaAMAEBjSLaaP/5QvnSp1fyyV1h+wL7NJ6pTs9lAAwC384vFeu3bt0ttvv12tLCcnxzzeuHGjxo0bV+39zp07a/DgwXZtjRo1SnPmzNGSJUu0detWdezYUaNGjVJaWpqOHTumGTNmaOXKlZKk2NhYvfXWWy74RM6XlZWlrKwsFRYWKjY21tPhAAAAF2nXVNJ31csMSduPSJ09NIK8wWJ99sVNpBj7J5ACgF/wi0R7z549ev755x2+n5OTUy3xlqQRI0ZYJtohISGaNWuWhg0bpvnz5ys3N1fPPfecXb2WLVtq5syZSk9PP/cPAAAA4CRWj/iSzk4f90SinXdC2l9oX860cQD+jKnjFqKjo/XJJ59o7ty5Gjx4sFJSUhQeHq6EhARlZmZqwoQJ2rJli3r06OHpUAEAAKpJ87Kdx1mfDSAQ+cWIdp8+fWQYhtPbHThwoAYOHOj0dj0hOztb2dnZKi8v93QoAADAhWIjpJbR0v6i6uVbSbQBwG0Y0Q4QPEcbAIDA0c5i+vj2I1J5hftjsVqfHR8pnR/n9lAAwG1ItAEAAPyM1fTx4jJpz3H3xnG6TNpi/wAXdU6WbDb3xgIA7kSiDQAA4GccbYjm7nXaWw5JZyxWrTFtHIC/I9EGAADwM96SaDtcn93cvXEAgLuRaAeI7OxspaWlKSMjw9OhAAAAF0uJkaLD7MvdvSGaVaIdEiR1SHRvHADgbiTaAYLN0AAACBw2m9TOYp32d0fcF4NhSBssEu20BCky1H1xAIAnkGgDAAD4Iavp43knpSOn3HP9fYXSYYtrMW0cQCAg0QYAAPBDjtZpf+em6eM8PxtAICPRBgAA8ENWj/iSpK1umj7uKNHunOye6wOAJ5FoAwAA+KGLmkjBFs+qdteIttX67OQoqXm0e64PAJ5Eoh0g2HUcAIDAEhEitYm3L9/mhhHtk2esN17r0uzsRm0A4O9ItAMEu44DABB4rNZp/3hMOl3m2utuypMqDPty1mcDCBQk2gAAAH7K6hFf5Yb0/VHXXpeN0AAEOhJtAAAAP5XuYOfxrS5ep221Pjs82PFO6ADgb0i0AQAA/JSjxHabCxPtCsM60e6QJIUFu+66AOBNSLQBAAD8VHyk1CzKvtyVifaP+dLxEvtypo0DCCQk2gAAAH7MalT7uyPWm5U5w/oD1uUk2gACCYl2gODxXgAABCarRPtkqbSnwDXXc7QRWmcSbQABhEQ7QPB4LwAAAlOaxc7jkuuep70h176sdayU0Mg11wMAb0SiDQAA4McuTbQud8XO4wWnpR+O2ZczbRxAoCHRBgAA8GMtY6ToMPtyV2yIZrXbuESiDSDwkGgDAAD4sSCb1M5i+rgrEm3WZwPAWSTaAAAAfs5qQ7S8k9KRU869jtWIduNQqW0T514HALwdiTYAAICfs0q0Jek7J45ql1VIm/Lsyy9LloL5jRNAgOHHHgAAgJ9Ld5BoO3NDtO1HpFOl9uWszwYQiEi0AQAA/NxF8VKIxW99zky0Ha3P7tLcedcAAF9Bog0AAODnwkPOJtu/5sxnaVsl2jZJnZKddw0A8BUk2gEiOztbaWlpysjI8HQoAADAA6zWaf+ULxVbTPduiPUH7MsubiLFhjunfQDwJSTaASIrK0vbtm3T2rVrPR0KAADwgDSLR3xVGNKOo+fedu4JaX+RfTnrswEEKhJtAACAAJCeaF3ujOdpO1yfTaINIECRaAMAAAQAqxFtyTkbollNG5ekrmyEBiBAkWgDAAAEgNgIqWW0fblTEm2LEe0mkVLr2HNvGwB8EYk2AABAgLDaEG37Eam8ouFtni6Ttlgk612aSTZbw9sFAF9Gog0AABAgrBLt4jJp9/GGt5mTJ5VZJOo8PxtAICPRBgAACBDpFom2dG4boq1zsD6bjdAABDISbQAAgABhNaItnVuibbU+OyxYau9gl3MACAQk2gAAAAGiRbQUE25fvuVQw9ozDOtE+9JEKSKkYW0CgD8g0QYAAAgQNpv1qPbmQ2eT5vr6qUDKP21fzrRxAIGORBsAACCAdLCY0p1/WtpfVP+2HD0/m0QbQKAj0QYAAAggHZKsy3Py6t+W1bRxiUQbAEi0A0R2drbS0tKUkZHh6VAAAIAHWY1oS9LmBiTa6ywS7VaxUmLj+rcFAP6ERDtAZGVladu2bVq7dq2nQwEAAB7UKlaKtdgQLaeeG6IVnJZ+OGZfzmg2AJBoAwAABBSbzXr6+OY8qaIeG6Lx/GwAcIxEGwAAIMBYTR8vPCPtKah7G6t/ti7PaN6gkADAr5BoAwAABJj2jjZEq8f08TX77cviIqSLmzQsJgDwJyTaAAAAAcbRhmh13Xm8qETacti+PLOFFGRreFwA4C9ItAEAAAJM82ipSaR9+eY6jmivP2i9nrsb08YBQBKJNgAAQMCx2aT2FqPamw9J5RW1n7/GwfrszJbnFhcA+AsSbR/wj3/8Q0OGDFG7du0UHx+v0NBQJSUl6YYbbtD8+fM9HR4AAPBBVjuPnyqVvj9a+7lWG6FFh0lpCeceFwD4AxJtH/C3v/1Ns2fPVmRkpHr27KnBgwerVatW+vTTT3XjjTdq7Nixng4RAAD4mE7J1uXrDtZ8XnGp9Vrurs2lYH6zBABJUoinA0Dtpk6dqvbt2ysqKqpa+cqVK9WvXz9NnDhRt956qzIzMz0UIQAA8DWOnne97oB0ZwfH560/KJVZTC+/vIVz4gIAf8DfHX1A9+7d7ZJsSerVq5eGDBkiSVq6dKm7wwIAAD4sLkK6KN6+vLYR7a8tHuslSd1ItAHARKLt40JDQyVJ4eHhHo4EAAD4GqtR7f2FUt4Jx+d8ude+rFGo9eZqABCo/CLRLi8v15YtWzRt2jT98Y9/VPfu3dWoUSPZbDbZbDaNHDmyQe3OmzdPt9xyi1q3bq2IiAglJiaqe/fueumll3T8+HHnfogG2LRpk2bOnKng4GD169fP0+EAAAAf09XB47gcjWrnF1uvz+7eUgoNdl5cAODr/GKN9m233abZs2c7rb2ioiINGzbMbkfvw4cP6/Dhw1q9erUmTZqkmTNnqkePHk67bm2mTp2qL774QmfOnNGePXv09ddfKzQ0VG+88YbS09PdFgcAAPAPXR2s0157QBpwkX35yn2SxeOzdUUrp4YFAD7PLxLt8vLyaq/j4+PVpEkT7dy5s95tlZWV6eabb9aSJUskSUlJSRo1apTS0tJ07NgxzZgxQ6tWrdL+/fs1YMAArVy50m1J7qpVq/TOO++Yrxs1aqRXX31Vd911l1uuDwAA/Mv5cVKTSOlocfXylRbTwyVpxR7r8itbOzUsAPB5fpFod+vWTe3atVOXLl3UpUsXpaamatq0aQ1KQCdPnmwm2Wlpafr888+VlPTLgyazsrI0duxYTZw4UQUFBbrvvvu0cuVKy7aGDx+ub775pl7XHzRokMaPH2/53pQpUzRlyhSdPHlSO3fu1KRJk3Tvvfdqzpw5+uijjxQREVGvawEAgMBms0k9UqRPvq9evvOYdLBIahb9S5lhWK/PbhktXRDn0jABwOf4RaL9+OOPO6Wd8vJyPfvss+br6dOnV0uyK02YMEFLly7Vpk2btGrVKn322Wfq27evXb29e/dqx44d9Yrh4MFatvqU1LhxY3Xq1Elvv/22goKCNGXKFE2cOFFPPPFEva4FAABwRSv7RFs6m1TfVmXS3uZD0kGLTdKuaH02YQcA/MIvNkNzli+++EK5ubmSpN69e6tz586W9YKDgzV69Gjz9YwZMyzrLV++XIZh1Otr2rRp9Yr5zjvvlHR24zYAAID6crS++tej1wt/sK7Xm2njAGCHRLuKRYsWmcf9+/evsW7V9xcuXOiymGrTtGlTSWc3agMAAKiv5tFSG4vnaX+5V6r4785nhiEtsNj6JjJE6kOiDQB2SLSr2Lx5s3mckZFRY92kpCSlpKRIkg4dOuSxRHfZsmWSpDZt2njk+gAAwPf1thjVzj8trT9w9nj7EWm3xZNNr06VIkNdGxsA+CIS7SqqrqdOTU2ttX7VOvVdi11X69at0zvvvKOSkhK79+bPn2+uy7733ntdcn0AAOD/HO0aPv+/o9hWa7gl6Xr+zg8AlvxiMzRnKSgoMI8TEhJqrd+kSRPLc51p//79GjlypP70pz+pa9euSkpKUkFBgXbs2KEff/xRkvTQQw9pyJAhlueXlJRUS9ILCwslSaWlpSotLXVJzN6q8vMG2ueG96NvwpvRPwNDRrIUEx6iwpLqu5p9/L2hB7uVaebWEEnV3wsPNtSrRZk81TXom/Bm9E//VdfvKYl2FSdO/LKVZl0elRUZGWkeFxUVuSSmjIwMPfnkk1qxYoV27NihlStXKigoSM2bN9cdd9yh++67T7169XJ4/vjx4/XMM8/YlS9evFiNGjVySczervLxbYC3oW/Cm9E//V+70E5aU1J9aPtYsU23Tc/XkdOJ9vXDD2jFf9a5KzyH6JvwZvRP/3Pq1Kk61SPR9nItWrSwTJTr6rHHHtOYMWPM14WFhUpJSdF1112nmJgYZ4ToM0pLS7VkyRJde+21Cg1lQRm8B30T3oz+GTjif7ZpjcVDTHZYJNmSNOaaJPVoWfPmsa5E34Q3o3/6r8oZwrUh0a4iKipK+fn5kqTTp08rKiqqxvrFxcXmcXR0tEtja6jw8HCFh4fblYeGhgbsP/pA/uzwbvRNeDP6p//r1frs7uM/HKu9bpt46YrzQxTkBc/Ppm/Cm9E//U9dv59shlZFXFyceXzkyJFa6x89etTyXG+UnZ2ttLS0WndTBwAAgclmk+7vUre6f86UVyTZAOCtSLSraNu2rXm8a9euWutXrVP1XG+UlZWlbdu2ae3atZ4OBQAAeKlBl0jpTWuu0625NOAi98QDAL6KRLuK9u3bm8e1JaR5eXnat2+fJCkxMVFNm9byvxIAAICXCw6SXuknxYRZv98kUvpbX0azAaA2JNpV9OvXzzxeuHBhjXUXLFhgHvfv77mNQAAAAJzponjpg1ukC8+rXt4xSfrwViklsPZSBYAGYTO0Knr37q3k5GTl5uZq+fLl2rBhgzp37mxXr7y8XK+++qr5eujQoe4Ms0Gys7OVnZ2t8vJyT4cCAAC8XHpTafEd0tf7pNyTZzc/65R0dh03AKB2jGhXERwcrCeffNJ8PXz4cB06dMiu3qOPPqpNmzZJknr27Km+ffu6K8QGY402AACoj5Ag6YrW0q1p0mXJJNkAUB9+MaK9a9cuvf3229XKcnJyzOONGzdq3Lhx1d7v3LmzBg8ebNfWqFGjNGfOHC1ZskRbt25Vx44dNWrUKKWlpenYsWOaMWOGVq5cKUmKjY3VW2+95YJPBAAAAADwVX6RaO/Zs0fPP/+8w/dzcnKqJd6SNGLECMtEOyQkRLNmzdKwYcM0f/585ebm6rnnnrOr17JlS82cOVPp6enn/gEAAAAAAH6DqeMWoqOj9cknn2ju3LkaPHiwUlJSFB4eroSEBGVmZmrChAnasmWLevTo4elQ64znaAMAAACAe/jFiHafPn1kGIbT2x04cKAGDhzo9HY9ISsrS1lZWSosLFRsbKynwwEAAAAAv8WINgAAAAAATkSiDQAAAACAE5FoAwAAAADgRCTaAYLN0AAAAADAPUi0A0RWVpa2bdumtWvXejoUAAAAAPBrJNoAAAAAADgRiTYAAAAAAE5Eog0AAAAAgBORaAMAAAAA4EQk2gGCXccBAAAAwD1ItAMEu44DAAAAgHuQaAMAAAAA4EQk2gAAAAAAOBGJNgAAAAAATkSiDQAAAACAE5FoAwAAAADgRCTaAAAAAAA4EYl2gOA52gAAAADgHiTaAYLnaAMAAACAe5BoAwAAAADgRCTaAAAAAAA4EYk2AAAAAABOFOLpAOBehmFIkgoLCz0cifuVlpbq1KlTKiwsVGhoqKfDAUz0TXgz+ie8FX0T3oz+6b8q86jKvMoREu0AU1RUJElKSUnxcCQAAAAA4JuKiooUGxvr8H2bUVsqDr9SUVGhAwcOKDo6WjabzdPhuFVhYaFSUlK0b98+xcTEeDocwETfhDejf8Jb0Tfhzeif/sswDBUVFal58+YKCnK8EpsR7QATFBSkli1bejoMj4qJieEHHrwSfRPejP4Jb0XfhDejf/qnmkayK7EZGgAAAAAATkSiDQAAAACAE5FoI2CEh4frqaeeUnh4uKdDAaqhb8Kb0T/hreib8Gb0T7AZGgAAAAAATsSINgAAAAAATkSiDQAAAACAE5FoAwAAAADgRCTaMM2bN0+33HKLWrdurYiICCUmJqp79+566aWXdPz4cZdcc/ny5Ro+fLguuOACRUZGKj4+Xp07d9bTTz+tgwcP1qut48eP66WXXlL37t2VmJioiIgItW7dWrfccos+/vjjOrVhGIa+//57zZgxQw899JD69OmjmJgY2Ww22Ww29enTp14xFRYWavny5Zo4caJuv/12XXzxxQoKCjLbW758eb3aq+TM++YL6Jve3TdPnz6tBQsWaOzYserdu7eSk5MVFhamqKgoXXDBBbrtttv04YcfqrS0tF4x+gL6pnf3zZo89dRTZps2m00jR450SrvehP7pW/1z586deuqpp5SZmWn+HE1OTlbHjh1177336t1339WpU6ca3L43oW/6Rt88dOiQXnzxRV199dVKSkpSeHi4GjVqpFatWmnAgAF68803deLEiXq3GzAMBLzCwkLjhhtuMCQ5/GrZsqWxatUqp12ztLTUuOeee2q85nnnnWfMnTu3Tu19+eWXRosWLWps76abbjJOnDhRYztjxoypsY3evXvX+TMWFBQYNputxvaWLVtW5/YMw/n3zdvRN3/hrX1zxowZRnR0dI1tVX5deumlRk5OTp3j9Gb0zV94a9+sybfffmuEhoZWa3fEiBHn3K63oH/+whf6Z0lJiTF27Fi7Pmn1tXHjxnq3703om7/w9r753nvvGbGxsbX2yebNmzvl57I/ItEOcKWlpca1115r/mNJSkoyxo0bZ7z//vvGa6+9ZvTs2dN8Ly4uztiyZYtTrnvXXXeZ7cbGxhpjxowx3n33XeOtt94yBgwYYL4XFhZmfP755zW2lZOTY8TExJjn9OrVy3jttdeM999/3xg3bpyRlJRkvtevXz+jrKzMYVt/+tOfqv3wiI6ONtLS0hr0Qy8/P79aWzabzWjTpo1x3nnnNfiHnjPvm7ejb1bnrX1z/Pjx5jkJCQnG0KFDjb/97W/GjBkzjOnTpxtjxowxEhISzDpNmjQxtm/fXudYvRF9szpv7ZuOlJWVGV26dDEkGY0bNzbb9ZdEm/5Znbf3z+LiYqN///7m+TExMcbIkSONN954w5g5c6bx+uuvG48//rjRp08fIyQkxKcTbfpmdd7cNz/++ONqifull15q/PWvfzWmT59uTJ482Xj44Yer/d8eERFhbN68uc7tBwoS7QD3+uuvm/9I0tLSjNzcXLs6Dz30kFmnZ8+e53zNBQsWmO01a9bM+P777+3qvPrqq2ad1NRUo6SkxGF7mZmZZt2xY8favZ+bm1vtB9c//vEPh2299dZbxoMPPmi8++67xvbt242Kigpj2bJlDfqhV1RUZAwdOtR4+eWXjc8//9woKCgwDMMwevfu3aAfes6+b96Ovlmdt/bN8ePHG5mZmcasWbOMM2fOWNY5cuRItV+grrrqqjrH6o3om9V5a9905IUXXjB/qX322Wf9LtGmf1bn7f3z97//vXnuDTfcYBw+fNhh3aNHj9Y6SurN6JvVeXPfbNOmjXneuHHjjIqKCrs6J06cMK6++mqz3qBBg+rcfqAg0Q5gZWVlRnJysvkPZP369Q7rderUyay3aNGic7pu165dzbZmzZrlsN6NN95o1nvzzTct68yfP9+s06lTJ4d/OVy3bp1Zr3nz5kZ5eXmd423oDz1HGvpDz5n3zdvRN+vGG/rm0aNH61Tv4MGDRmRkpNn+rl27Gh6oB9E368Yb+qaV7777zggPDzckGZMmTTKmTp3qV4k2/bNuvKV/fv755+Z53bt3N0pLS885Fm9F36wbb+ibO3fuNM9JSkqqcVR+8+bNZt0mTZqcc7z+hs3QAtgXX3yh3NxcSVLv3r3VuXNny3rBwcEaPXq0+XrGjBkNvuZPP/2kdevWSZJSU1M1aNAgh3UffPDBWq85c+ZM8/hPf/qTgoODLet16dJFV155pSTpwIEDWrFiRb1j9yRn3zdvR9/0HfHx8XWql5ycrN69e5uvc3JyXBWSS9E3fVdFRYXuvvtulZSU6PLLL9cf/vAHT4fkdPRP3zJhwgTzeNKkSQoJCfFgNK5F3/Qdhw4dMo8vvPBCh59Tki6++GLzmE3R7JFoB7BFixaZx/3796+xbtX3Fy5c6JRr9uvXTzabzWHdK664QlFRUZKkL7/80vIfsCc+gyc4+755O/qmf4qOjjaPi4uLPRhJw9E3fdcrr7yir7/+WmFhYZoyZYqCgvzvVyD6p+/Yt2+fFi9eLEm67LLL1KVLFw9H5Fr0Td+RlJRkHv/000+qqKhwWHfnzp3m8aWXXurSuHyR//0vgzrbvHmzeZyRkVFj3aSkJKWkpEg6+5euw4cPu/yaISEhuuyyyySdHYn47rvvqr2fl5dnxtGqVSslJibW2F7V623ZsqVecXuaM++bL6Bv+qeq97h169YejKTh6Ju+6ccff9S4ceMkSY8++qjS09M9HJFr0D99x5dffinDMCRJv/nNbyRJ8+fP18CBA9W8eXOFh4crOTlZ1113nV5//XWVlJR4MtxzRt/0HRdeeKHat28vScrNzdVzzz1nWa+4uLjaTICxY8e6JT5fQqIdwHbs2GEep6am1lq/ap2q53rqmp6I31MC6bNK9E1/tHz5cm3fvl2S1LRp01p/6fFW9E3fYxiG7r33Xp06dUrt2rXTE0884emQXIb+6TsqpzRL0kUXXaTbb79dN954oz7++GMdPHhQZ86cUV5enpYsWaKsrCylpaVVSxx9DX3Tt/zjH/8wZ6E9/fTT6tSpk1544QW99957+uc//6lHH31U559/vpYsWaKQkBD93//9n4YOHerhqL2P/y4GQa0KCgrM44SEhFrrN2nSxPJcT13TE/F7SiB9Vom+6W+Ki4t1//33m68fe+yxGtd8eTP6pu954403tHz5ctlsNk2ePFlhYWGeDsll6J++4+DBg+bx//7v/2rnzp0KCgrSbbfdpmuuuUaNGzfW9u3b9fbbb2v//v366aefdNVVV2nDhg1q1aqVByNvGPqmb7n88su1evVq/f73v9eqVav07bff6ttvv61Wx2az6Q9/+INGjx6ttm3beihS78aIdgCruv4kIiKi1vqRkZHmcVFRkcev6Yn4PSWQPqtE3/Q3d911lzmanZGRoQceeMDDETUcfdO37N27V48++qgk6f7771fPnj09HJFr0T99R35+vnm8c+dOhYeH67PPPtOMGTN0zz33aOjQoXr66af13XffmRtJHj161Gc38aNv+p60tDT9/e9/13XXXWf5vmEYmjZtml588UUdPXrUzdH5BhJteExNm1K4oi1nXs+T/OVzeDP6pvM8/vjj5k6tTZo00cyZMxUaGurhqHwXfbN+Ro0apaKiIrVo0ULjx4/3dDh+j/5Zd7/eYOqJJ57QNddcY1cvKipKH3zwgRo1aiRJ+vTTT6ttQIW6oW/Wz6lTpzRs2DBlZGRoxYoV5h99Tp8+raKiIn311Ve68847derUKU2bNk2XX365du3a5emwvQ6JdgCr3F1Rkk6fPl1r/aq7BFfdPbih16zLrsM1XbO+bZ06dcphW97OmffNF9A3/cPzzz9vJjdxcXFavHhxnda2eTP6pu/45z//ae7q/PrrrysmJsbDEbke/dN3/Dre++67z2Hd5ORkDRw40Hy9dOlSl8XlKvRN31FRUaEBAwZoxowZCgsL09KlS/XUU0/pkksuUXh4uKKiotS9e3f961//0ssvvyxJ+uGHH/S73/3Ow5F7HxLtABYXF2ceHzlypNb6VaeFVD3XU9f0RPyeEkifVaJv+oMXX3zR3OU5NjZWn332mcPnpvoS+qZvOHDggB566CFJ0q233qqbbrrJwxG5B/3Td5x33nnmcUpKSq27WFd9/NcPP/zgsrhchb7pO2bPnq3ly5dLkkaOHKkePXo4rPvQQw+Z67O//vprffPNN+4I0WeQaAewqhsX1GW6R9U6Dd30wJnX9ET8nhJIn1Wib/q6CRMm6LHHHpMkxcTE6LPPPlO3bt08HJVz0Dd9w4cffmhuQJSUlKS//vWvll+ffPKJeU5OTo5ZPmXKFA9Ffm7on77jkksuMY/rMtsiNjbWPC4sLHRJTK5E3/QdVX8uOlqfXclms5mPp5OkNWvWuCwuX0SiHcAqn5EnSWvXrq2xbl5envbt2ydJSkxMVNOmTV1+zbKyMm3cuFGSFBQUpLS0tGrvJyUlmXHs3btXhw4dqrG9qte79NJL6xW3pznzvvkC+qbvevHFF83Np6Kjo7Vo0SJlZmZ6OCrnoW/6hsrnE0vSa6+9pv/5n/+x/Jo9e7ZZb+PGjWb5a6+95omwzxn903d07NjRPD5+/Hit9avuXF016fYV9E3fceDAAfO4Ln8EqjpiX3XTOJBoB7R+/fqZxwsXLqyx7oIFC8zj/v37O+WaixYtqvbL0K99+eWX5j/YK6+8Uo0bN66xPXd9Bk9w9n3zdvRN31R1JDsqKkqLFi1S9+7dPRyVc9E34c3on77jiiuuMNfu7t+/X3l5eTXWX79+vXnsayOkEn3Tl1RNrvfu3Vtr/T179pjHVR9rBkkGAlZZWZmRnJxsSDIkGevXr3dYr1OnTma9RYsWndN1MzIyzLZmzZrlsN6NN95o1nvzzTct63z66admnU6dOhllZWWW9datW2fWa9GihVFeXl7neJctW2ae27t37zqf50jv3r3N9pYtW1bn85x537wdfbNuvKVvGoZhTJgwwTw3KirKWLly5TnH443om3XjTX2zJlOnTjXbHTFihNPa9RT6Z914S/8cOXKked4zzzzjsN7BgweNyMhIQ5IRFBRk7Nmz55xjdjf6Zt14Q9986qmnzHOuvvrqGuvm5+cbcXFxZv2NGzeec8z+hEQ7wL3++uvmP4709HQjLy/Prs7YsWPNOj179nTYVtUfDq1bt3ZYb8GCBWa9Zs2aGTt37rSrM2nSJLNOamqqUVJS4rC9zMxMs+7DDz9s935ubq6RlpZm1pk8ebLDtmr7XJ78D9nZ983b0Tdr5y198+WXX66WZH/55ZfnHIs3o2/Wzlv6Zm38LdE2DPpnXXhL/9y1a5cRHh5uSDLCw8ON//znP3Z1ioqKjD59+pjt/+53vzvneD2Fvlk7b+ib27ZtM4KCgszznnzySaOiosKuXmFhoXHdddeZ9Tp06GBZL5DZDKOGeRTwe2VlZerfv7+WLFki6ewjJEaNGqW0tDQdO3ZMM2bM0MqVKyWdXRO0atUqpaenW7a1fPlyXXXVVZKk1q1ba/fu3Q6ve/fdd2vq1Klmu/fee686d+6skydP6uOPP9b8+fMlSWFhYVq0aJHZrpXNmzerV69e5uYgvXr10u233674+Hht27ZNkydPVm5urqSz037mz5+v4OBgy7YKCgr0v//7v9XK9uzZo3fffVeS1KpVK915553V3k9NTdU999xj2d7s2bO1YcOGamXTp083p+Lccccdat26dbX377nnHoePQHLmffN29M3qvLVvTpkyRaNGjTJfP/TQQ+rVq5flNau65JJLqm0G5Evom9V5a9+si2nTpumuu+6SJI0YMULTpk2rdxvehv5Znbf3zzfffFP333+/pLNrg4cMGaJrr71WjRo10vbt2zVlyhTt379f0tnvwbp165SQkGDZlrejb1bnzX3z4YcfrhbbZZddpiFDhig1NVWlpaXKycnR9OnTdfDgQUlSeHi4li5dqp49e1rGFrA8nenD8woLC40bbrjB/IuU1VfLli2NVatW1dhOXf+6aBiGUVpaatx99901XvO8884z5s6dW6fP8OWXXxotWrSosb2bbrrJKCoqqrGdXbt21diG1VdNf3EcMWJEvdur6S+Ozr5v3o6++Qtv7ZsNaUeS8dRTT9Xp/nkr+uYvvLVv1oU/jmgbBv2zKl/on6+99po5NdzRV5cuXYy9e/fW6d55M/rmL7y5b1ZUVBiPP/64ERwcXGsbycnJxuLFi+t07wINiTZMc+fONQYPHmykpKQY4eHhRkJCgpGZmWlMmDDBKCgoqPX8+vzQq3rOHXfcYaSmphoRERFGXFyc0alTJ+PJJ580Dhw4UK/48/PzjQkTJhiZmZlGkyZNjLCwMCMlJcUYPHhwnX94evMPvaqced98AX3Te/tmoCbaleib3ts368JfE+1K9E/f6Z8//vij8cgjjxgdOnQw4uLijLCwMKN58+bGb3/7W2PGjBn1WufrC+ibvtE3d+7caTz22GNGz549jYSEBCM0NNSIiIgwWrZsaQwYMMDIzs42CgsL63HnAgtTxwEAAAAAcCIe7wUAAAAAgBORaAMAAAAA4EQk2gAAAAAAOBGJNgAAAAAATkSiDQAAAACAE5FoAwAAAADgRCTaAAAAAAA4EYk2AAAAAABORKINAAAAAIATkWgDAAAAAOBEJNoAAAAAADgRiTYAAIAb7N69WzabTTabTSNHjvR0OAAAFyLRBgDAQmVCVPk1dOjQOp+7ZMkSu/OnTZvmumD9hK/d84KCAj399NN6+umnNXfuXJdeCwDgW0i0AQCog7lz5yo/P79Odf/5z3+6OJrA4O33vKCgQM8884yeeeYZEm0AQDUk2gAA1CAkJESSVFJSovfee6/W+vn5+WbSVXku6sdf7/n5558vwzBkGAYzHADAz5FoAwBQg6SkJHXu3FmSNHXq1Frrv//++zp9+rQkacCAAS6NzV9xzwEAvo5EGwCAWtx9992SpA0bNujbb7+tsW7lFOaMjAxdeumlLo/NX3HPAQC+jEQbAIBa/O53v1NERISkmtcC5+TkaMOGDZJ+SRTrY8WKFfr973+vdu3aKS4uThEREUpJSdHNN9+sWbNmyTCMGs8vKyvTkiVL9Je//EW9e/dWs2bNFBYWpsaNG+v888/Xrbfeqo8++kgVFRU1tjNt2jS7DcW+//57/fGPf9TFF1+sRo0aKS4uTpdffrn+/ve/q6SkpN6ftTbuuOclJSV644031K9fPzVv3lzh4eGKj4/XZZddpr/85S/atWuX5XmVu4enpqaaZe+8847dZmw2m03Lly+3O68uu45XVFTo3Xff1aBBg5SSkqKIiAjFxcUpPT1dDzzwgHJycmo83+paBQUFeuGFF9S5c2fFxcWpcePGateuncaOHau8vLw63TMAQB0ZAADAjiRDktGiRQvDMAxj6NChhiSjSZMmRklJieU5o0ePNiQZkZGRRkFBgfHEE0+Y7UydOtXhtfLz840bb7zRrOvo68orrzQOHz7ssJ2rrrqq1jYkGT169DByc3MdtjN16tRqcf/rX/8yIiMjHbaXmZlpFBQU1O3G1sCd93z9+vVG69ata7xPYWFhxssvv2x37q5du+p0nyUZy5YtszxvxIgRDmP76aefjA4dOtTYblBQkPHggw8a5eXllm38+lrr1683WrVq5bC9xMRE49tvv3UYEwCgfrx3xxAAALzI3XffrQ8++EBHjx7Vxx9/rFtuuaXa+2fOnDE37ho8eLBiY2Pr1G5hYaF69uypbdu2STq7YdaQIUOUnp6u8PBw7d69WzNmzNCmTZu0YsUKXXPNNVq9erU52lvVqVOn1KhRI/Xp00ddu3ZVamqqoqOjdfLkSX333Xf68MMP9eOPP+qrr77SoEGDtGLFilo3D1u0aJE++ugjRUZG6g9/+IO6deum8PBw5eTk6M0331R+fr7WrFmjMWPG6O23367TZ64rV93zLVu2qHfv3jpx4oQkqW3btrrzzjvVpk0bHT9+XAsWLNC8efN05swZPfzwwyopKdETTzxhnp+YmKg5c+bo0KFDuu+++yRJV111lUaPHm13rfpOZc/NzVXPnj118OBBSVLLli01cuRIpaWlqbi4WMuWLdOMGTNUXl6u//u//1NRUZEmT55cY5v79+/XgAEDdOjQId1888269tprFR8frz179mjKlCnasWOHDh06pCFDhignJ0ehoaH1ihkAYMHTmT4AAN5IvxpdraioMEdAr7/+erv6//73v81zli5dahiGUafR1cpRW0nGmDFjjDNnztjVqaioMB555BGz3hNPPGHZ1pIlS4yTJ086/EylpaVGVlaW2c706dMt61Ud0ZZkpKenG/v27bOrt3PnTiMmJsaQZISEhBgHDx50eO26cMc9r6ioqDZaPGLECMt7Pnv2bCM0NNSQZAQHBxvr1q2zq1PXEer61L/hhhvMOtddd51RVFRkV2fVqlXmfZdkzJ07t8ZrSTKio6ONL774wq7eyZMnja5du5r1Pvroo1o/BwCgdqzRBgCgDqqudV28eLF+/vnnau9XriNOTU3VVVddVac2c3Jy9MEHH0iSBg0apIkTJ1qOJtpsNr344ovq1auXJOm1116zXBd9zTXXqFGjRg6vFxISoldeecVcW/zOO+/UGmNISIhmz56tli1b2r3Xpk0bPfDAA5LOrg//z3/+U2t79eGKe75gwQJzffOll16qKVOmWN7zQYMGady4cZKk8vJyvfTSSw39GHW2ZcsWzZ8/X9LZndf//e9/Kyoqyq5ejx49NGnSJPP1Cy+8UGvbf//733XllVfalTdq1Kja+QsXLmxI6ACAXyHRBgCgju666y7ZbDaVl5frX//6l1n+888/a/HixdXq1EXVRPeRRx6ptf6dd94pSTp+/LjWrFlTn9BNwcHByszMlCR98803tW6wNmDAAF188cUO3//Nb35jHm/durVBMdXE2fd81qxZ5vHYsWNrnDr/5z//2fzDxSeffKLS0tKGfIQ6mz17tnl8//331zgV/o477lCrVq0knf0+7t+/32HdJk2aaPjw4Q7fv/LKK8374IrvIQAEIhJtAADqqHXr1rr66qslVX++87Rp01RRUaGgoKBad5OuasWKFZLOjtzu27dPc+fOrfGr6ohu5ZruXzt16pSmTp2qwYMH66KLLlJMTIyCgoKq7YRdOYpeWFiowsLCGmPs0aNHje+3aNHCPM7Pz6/T564PZ9/z1atXm8d9+/atsW5MTIz5+YuLi2t9zNi5qk9sQUFBuu666yzP/bVu3brV+AeF8PBwJSQkSHLN9xAAAhGboQEAUA933323li5dqp07d+rLL7/UFVdcYT4C65prrlFKSkqd29q9e7ckyTAM3XrrrfWK49ixY3ZlX331lYYOHap9+/bVuZ3CwsIaR04rEzBHwsPDzePTp0/X+br14cx7XrnJWHR0tJKTk2ut37ZtW3NK/IEDB+offD1UxiapxlkEldq2bWse1xRbbd9D6Zfvo6u+hwAQaEi0AQCoh8GDBysuLk4FBQWaOnWqKioq9MMPP0iq/3OcCwoKGhzHmTNnqr3etWuX+vbta+6k3aZNG/Xr108XX3yxEhISFBERYU6vfvXVV7Vs2TJJZ9cf1yQoyPOT35x5z4uKiiRJjRs3rlP9qmukK891lart1yW+usbmDd9DAAg0JNoAANRDRESEbr/9dr3xxhv68MMPdfz4cUlSfHy8fvvb39arraioKBUUFCguLu6cp+y+8MILZpL9yCOPaPz48Q7XLVc+EstXOPOeR0dHq6CgQCdPnqxT/cp7WnmuK1Vt/+TJk9VmC1hxZ2wAgPrhT5wAANRT5SjqiRMnzA2shg0bVmti9GuVO3kXFBTY7ahdX5UbgyUmJur555+vcXOwXbt2ndO1PMFZ97xZs2aSzo4A5+bm1lr/+++/N4+bN29er2vVV2Vsv76uI+6MDQBQPyTaAADUU9euXdWhQ4dqZfWdwixJffr0MY+r7jjdEJVJY2pqqoKDgx3WO3DggMs39XIFZ93zyy+/3Dz+7LPPaqxbVFSkr776SpIUGRmpjh07Vnu/6pTs2nZvd3ZshmGYf1z59bkAAM8j0QYAoAHGjBmjzMxMZWZm6tZbb9Vll11W7zZGjBhhHr/44os6cuRIg+OpXNP7448/1pj0PfvssyorK2vwdTzJGff8lltuMY8nTpxY47145ZVXzCnmN910k93ztquuka7rVPSa3Hzzzebxm2++aU6Rt/Lee+9pz549kqTMzEzL55wDADyHRBsAgAYYMWKEVq9erdWrV+vf//53g9ro2rWrhg4dKunsSHPfvn1rnda9evVqPfzww3blGRkZkqQjR45o4sSJludOnDhRb731VoNi9QbOuOfXX3+9OTK9efNm/f73v7d8PvbHH3+s5557TtLZZ4//5S9/sasTHx9v7ti+adOmcx7VTk9P10033STp7AyFIUOGWCbwa9as0QMPPGC+fvzxx8/pugAA52MzNAAAPGjy5Mn6/vvvtWHDBm3YsEFt27bVwIEDdcUVVyg5OVnl5eU6dOiQNm/erKVLl2r37t268MIL9fLLL1dr589//rM5lfjhhx/WsmXL1K9fPyUlJWnv3r3697//rbVr16pZs2Zq3759tWnHgcRms+m9997T5ZdfrhMnTmjq1Kn6+uuvNXz4cF1wwQUqLCzUwoULNWfOHPOcZ555Rp07d7Zs7ze/+Y1mz56tH3/8Ubfddpu5Q3rlGvlu3bopPj6+zvG99dZbWrt2rQ4ePKjPPvtM7dq101133aV27dqpuLhYy5cv1/vvv2+OxN97771mcg4A8B4k2gAAeFBUVJRWrFihBx54QO+8845KS0v10Ucf6aOPPnJ4jtU04euvv15PP/20nn76aUnSggULtGDBgmp1WrVqpTlz5ujVV1916mfwNenp6Vq+fLluvvlm7dmzR9u3b7ccFQ4LC9Nzzz1nOZpd6amnntKiRYt06tQpy+/bsmXLqq3Fr01ycrJWrVqlgQMHavPmzdq3b5+effZZu3pBQUEaPXq0w9kLAADPItEGAMDDGjdurKlTp+ovf/mLpk2bpuXLl2vXrl3Kz89XWFiYmjZtqrZt26pHjx66/vrr1a1bN8t2nnrqKV155ZV69dVX9fXXX+vYsWOKi4vTBRdcoEGDBum+++5TXFycez+cl+rSpYt27Niht99+W/PmzVNOTo6OHj2qxo0bq3Xr1rr22mv1hz/8QampqTW206FDB23cuFF/+9vftGLFCu3du1enTp06p2nkqamp2rhxo95//3199NFHWr9+vY4cOaKwsDC1bNlSV111le677z67zeEAAN7DZjhjm0wAAAAAACCJzdAAAAAAAHAqEm0AAAAAAJyIRBsAAAAAACci0QYAAAAAwIlItAEAAAAAcCISbQAAAAAAnIhEGwAAAAAAJyLRBgAAAADAiUi0AQAAAABwIhJtAAAAAACciEQbAAAAAAAnItEGAAAAAMCJSLQBAAAAAHAiEm0AAAAAAJyIRBsAAAAAACf6f3Orswq8peuSAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -166,23 +177,23 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "kessler.plot.plot_mix(mix = priors['mean_motion_prior'], \n", - " min_val=min(mean_motions), \n", - " max_val=max(mean_motions), \n", + "kessler.plot.plot_mix(mix = priors['inclination_prior'], \n", + " min_val=0., \n", + " max_val=np.pi, \n", " log_yscale=True,\n", - " xlabel='Mean Motion',\n", + " xlabel='Inclination',\n", " figsize=(10,8),\n", " linewidth=4.,\n", - " color='dodgerblue',\n", + " color='darkorange',\n", " resolution=1000)" ] }, @@ -202,17 +213,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ + "#save to pickle the trace:\n", "with open('trace.pickle', 'rb') as f:\n", " trace = pickle.load(f)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -221,13 +233,13 @@ "" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -237,7 +249,7 @@ } ], "source": [ - "kessler.plot.plot_trace_orbit(trace=trace[0])" + "kessler.plot.plot_trace_orbit(trace=trace)" ] } ], diff --git a/kessler/plot.py b/kessler/plot.py index 105dcfe..6319725 100644 --- a/kessler/plot.py +++ b/kessler/plot.py @@ -1,10 +1,21 @@ +# This code is part of Kessler, a machine learning library for spacecraft collision avoidance. +# +# Copyright (c) 2020- +# Trillium Technologies +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) +# and other contributors, see README in root of repository. +# +# GNU General Public License version 3. See LICENSE in root of repository. + import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.image as mpimg import os import uuid import tempfile -import pyprob +import pyro +import dsgp4 from pyprob.distributions import Empirical import numpy as np import torch @@ -24,7 +35,7 @@ def plot_mix(mix, min_val=-10, max_val=10, resolution=1000, figsize=(10, 5), xla fig, ax = plt.subplots(figsize=figsize) fig.tight_layout() ax.grid() - xvals = np.linspace(min_val, max_val, resolution) + xvals = torch.linspace(min_val, max_val, resolution) ax.plot(xvals, [torch.exp(mix.log_prob(x)) for x in xvals], *args, **kwargs) if log_xscale: ax.set_xscale('log') @@ -188,8 +199,6 @@ def plot_dist(dists, file_name=None, n_bins=30, num_resample=None, trace=None, f if len(marginal_dists[i]['dist_conj']) > 0: marginal_dists[i]['dist_time_cdm'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['time_cdm']) - pyprob.set_verbosity(2) - fig, axs = plt.subplots(8, 4, figsize=figsize) t_color = 'green' @@ -580,41 +589,44 @@ def plot_dist(dists, file_name=None, n_bins=30, num_resample=None, trace=None, f def plot_trace_orbit(trace, time_upsample_factor=100, figsize=(10, 8), file_name=None): t_color, c_color = 'red', 'forestgreen' - time0 = float(trace['time0']) - max_duration_days = float(trace['max_duration_days']) - delta_time = float(trace['delta_time']) + time0 = float(trace.nodes['time0']['value']) + max_duration_days = float(trace.nodes['max_duration_days']['value']) + delta_time = float(trace.nodes['delta_time']['value']) times = np.arange(time0, time0 + max_duration_days, delta_time) - t_mean_motion = float(trace['t_mean_motion']) - t_mean_motion_first_derivative = float(trace['t_mean_motion_first_derivative']) - t_mean_motion_second_derivative= float(trace['t_mean_motion_second_derivative']) - t_eccentricity = float(trace['t_eccentricity']) - t_inclination = float(trace['t_inclination']) - t_argument_of_perigee = float(trace['t_argument_of_perigee']) - t_raan = float(trace['t_raan']) - t_mean_anomaly = float(trace['t_mean_anomaly']) - t_b_star = float(trace['t_b_star']) - - util.lpop_init(trace['t_tle0']) + t_mean_motion = float(trace.nodes['t_mean_motion']['value']) + t_mean_motion_first_derivative = float(trace.nodes['t_mean_motion_first_derivative']['value']) + t_mean_motion_second_derivative= float(trace.nodes['t_mean_motion_second_derivative']['value']) + t_eccentricity = float(trace.nodes['t_eccentricity']['value']) + t_inclination = float(trace.nodes['t_inclination']['value']) + t_argument_of_perigee = float(trace.nodes['t_argument_of_perigee']['value']) + t_raan = float(trace.nodes['t_raan']['value']) + t_mean_anomaly = float(trace.nodes['t_mean_anomaly']['value']) + t_b_star = float(trace.nodes['t_b_star']['value']) + + t_tle=trace.nodes['t_tle']['infer']['t_tle'] try: - t_states = util.lpop_sequence_upsample(times, time_upsample_factor) + dsgp4.initialize_tle(t_tle) + t_states = util.propagate_upsample(tle=t_tle, times_mjd=times, upsample_factor=time_upsample_factor) t_prop_error = False except RuntimeError as e: + print(f'Error during target propagation: {e}') t_prop_error = True - c_mean_motion = float(trace['c_mean_motion']) - c_mean_motion_first_derivative = float(trace['c_mean_motion_first_derivative']) - c_mean_motion_second_derivative= float(trace['c_mean_motion_second_derivative']) - c_eccentricity = float(trace['c_eccentricity']) - c_inclination = float(trace['c_inclination']) - c_argument_of_perigee = float(trace['c_argument_of_perigee']) - c_raan = float(trace['c_raan']) - c_mean_anomaly = float(trace['c_mean_anomaly']) - c_b_star = float(trace['c_b_star']) - - util.lpop_init(trace['c_tle0']) + c_mean_motion = float(trace.nodes['c_mean_motion']['value']) + c_mean_motion_first_derivative = float(trace.nodes['c_mean_motion_first_derivative']['value']) + c_mean_motion_second_derivative= float(trace.nodes['c_mean_motion_second_derivative']['value']) + c_eccentricity = float(trace.nodes['c_eccentricity']['value']) + c_inclination = float(trace.nodes['c_inclination']['value']) + c_argument_of_perigee = float(trace.nodes['c_argument_of_perigee']['value']) + c_raan = float(trace.nodes['c_raan']['value']) + c_mean_anomaly = float(trace.nodes['c_mean_anomaly']['value']) + c_b_star = float(trace.nodes['c_b_star']['value']) + + c_tle=trace.nodes['c_tle']['infer']['c_tle'] try: - c_states = util.lpop_sequence_upsample(times, time_upsample_factor) + dsgp4.initialize_tle(c_tle) + c_states = util.propagate_upsample(tle=c_tle, times_mjd=times, upsample_factor=time_upsample_factor) c_prop_error = False except RuntimeError as e: c_prop_error = True @@ -635,8 +647,8 @@ def plot_trace_orbit(trace, time_upsample_factor=100, figsize=(10, 8), file_name if not c_prop_error: ax.plot(c_states[:,0,0], c_states[:,0,1], c_states[:,0,2], alpha=0.75, color=c_color) # set_axes_equal(ax) - if trace['conj']: - i_conj = int(trace['i_conj']) + if trace.nodes['conj']['value']: + i_conj = int(trace.nodes['i_conj']['value']) if not t_prop_error: t_pos_conj = t_states[i_conj, 0] ax.scatter(t_pos_conj[0], t_pos_conj[1], t_pos_conj[2], s=1e3, marker='*', color='green') diff --git a/kessler/util.py b/kessler/util.py index 28eea84..0200df9 100644 --- a/kessler/util.py +++ b/kessler/util.py @@ -1,9 +1,9 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. @@ -19,7 +19,13 @@ import random import dsgp4 import matplotlib.pyplot as plt +import pyro +import warnings +#for the TruncatedNormal custom distribution +from torch.distributions import constraints +from pyro.distributions.torch_distribution import TorchDistribution +from pyro.distributions import MixtureSameFamily, Categorical, Uniform _print_refresh_rate = 0.25 # @@ -36,6 +42,122 @@ def seed(seed=None): torch.cuda.manual_seed(seed) seed() + +class TruncatedNormal(TorchDistribution): + """ + Truncated Normal distribution with specified lower and upper bounds. + This class inherits from the Pyro Distribution class and implements + the log probability and sampling methods for a truncated normal distribution. + + Args: + loc (``torch.Tensor``): The mean of the normal distribution. + scale (``torch.Tensor``): The standard deviation of the normal distribution. + low (``torch.Tensor``, optional): The lower bound for truncation. Default is None. + high (``torch.Tensor``, optional): The upper bound for truncation. Default is None. + validate_args (bool, optional): Whether to validate the arguments. Default is None. + + Attributes: + loc (``torch.Tensor``): The mean of the normal distribution. + scale (``torch.Tensor``): The standard deviation of the normal distribution. + low (``torch.Tensor``): The lower bound for truncation. + high (``torch.Tensor``): The upper bound for truncation. + base_dist (``Normal``): The base normal distribution. + + Methods: + log_prob(value): Computes the log probability of the given value. + sample(sample_shape): Samples from the truncated normal distribution. + """ + arg_constraints = { + '_loc': constraints.real, + '_scale': constraints.positive, + '_low': constraints.real, + '_high': constraints.real, + } + def __init__(self, loc, scale, low, high):#, clamp_mean_between_low_high=False): + self._loc = torch.as_tensor(loc) + self._scale = torch.as_tensor(scale) + self._low = torch.as_tensor(low) + self._high = torch.as_tensor(high) + # Define batch dimensions + if self._loc.dim() == 0: + self._batch_length = 0 + elif self._loc.dim() in (1, 2): + self._batch_length = self._loc.size(0) + else: + raise RuntimeError('Expecting 1d or 2d (batched) probabilities.') + + # Standard normal distribution for calculations + self._standard_normal_dist = pyro.distributions.Normal( + torch.zeros_like(self._loc), + torch.ones_like(self._scale) + ) + + # Precompute alpha, beta, CDFs, and Z + self._alpha = (self._low - self._loc) / self._scale + self._beta = (self._high - self._loc) / self._scale + self._standard_normal_cdf_alpha = self._standard_normal_dist.cdf(self._alpha) + self._standard_normal_cdf_beta = self._standard_normal_dist.cdf(self._beta) + self._Z = self._standard_normal_cdf_beta - self._standard_normal_cdf_alpha + self._log_stddev_Z = torch.log(self._scale * self._Z) + + # Initialize base class + batch_shape = self._loc.shape + event_shape = torch.Size() + super().__init__(batch_shape=batch_shape, event_shape=event_shape) + + def log_prob(self, value): + value = torch.as_tensor(value) + + # Ensure the value is within bounds + lb = value.ge(self._low).type_as(self._low) + ub = value.le(self._high).type_as(self._low) + + # Compute log probability + lp = ( + torch.log(lb.mul(ub)) + + self._standard_normal_dist.log_prob((value - self._loc) / self._scale) - + self._log_stddev_Z + ) + + # Handle potential NaN or Inf values + if self._batch_length == 1: + lp = lp.squeeze(0) + + if torch.any(torch.isnan(lp)) or torch.isinf(lp).any(): + warnings.warn('NaN, -Inf, or Inf encountered in TruncatedNormal log_prob.') + print('distribution', self) + print('value', value) + print('log_prob', lp) + + return lp + + def sample(self, sample_shape=torch.Size()): + shape = self._low.expand(sample_shape + self._low.shape) + attempt_count = 0 + ret = torch.full(shape, float('NaN'), dtype=self._low.dtype) + + outside_domain = True + while torch.isnan(ret).any() or outside_domain: + attempt_count += 1 + if attempt_count == 10000: + warnings.warn('Trying to sample from the tail of a truncated normal distribution, which can take a long time. A more efficient implementation is pending.') + + # Sample uniformly between CDF(alpha) and CDF(beta) + rand = torch.rand(shape, dtype=self._low.dtype) + ret = ( + self._standard_normal_dist.icdf( + self._standard_normal_cdf_alpha + rand * (self._standard_normal_cdf_beta - self._standard_normal_cdf_alpha) + ) * self._scale + self._loc + ) + + # Check if the sample is within bounds + lb = ret.ge(self._low).type_as(self._low) + ub = ret.le(self._high).type_as(self._low) + outside_domain = (torch.sum(lb.mul(ub)) == 0) + + if self._batch_length == 1: + ret = ret.squeeze(0) + return ret def fit_mixture(values, *args, **kwargs): """ @@ -617,12 +739,12 @@ def doy_2_date(value, doy, year, idx): doy_2_date - Converts Day of Year (DOY) date format to date format. Args: - - value(``str``): Original date time string with day of year format "YYYY-DDDTHH:MM:SS.ff" - - doy (``str``): The day of year in the DOY format. - - year (``str``): The year. - - idx (``int``): Index of the start of the original "value" string at which characters 'DDD' are found. + value(``str``): Original date time string with day of year format "YYYY-DDDTHH:MM:SS.ff" + doy (``str``): The day of year in the DOY format. + year (``str``): The year. + idx (``int``): Index of the start of the original "value" string at which characters 'DDD' are found. Returns: - -value (``str``): Transformed date in traditional date format. i.e.: "YYYY-mm-ddTHH:MM:SS.ff" + value (``str``): Transformed date in traditional date format. i.e.: "YYYY-mm-ddTHH:MM:SS.ff" ''' # Calculate datetime format @@ -649,13 +771,13 @@ def build_megaconstellation(launch_date, returned. Args: - launch_date (`datetime.datetime`): launch date as a datetime object - constellation_name (`str`) - groups (`str` or `int`): group number as an integer, or 'all' in case all groups shall be selected - mu_earth (`float`): gravitational parameter of the Earth i m^3/s^2 + launch_date (``datetime.datetime``): launch date as a datetime object + constellation_name (``str``) + groups (``str`` or ``int``): group number as an integer, or 'all' in case all groups shall be selected + mu_earth (``float``): gravitational parameter of the Earth i m^3/s^2 Returns: - `list`: list of TLE (`dsgp4.tle.TLE`) objects + ``list``: list of TLE (``dsgp4.tle.TLE``) objects """ from . import TLE @@ -1218,8 +1340,8 @@ def create_path(path, directory=False): This function creates a path if it does not exist. Args: - path (`str`): path to be created - directory (`bool`): if True, the path is a directory, otherwise it is a file + path (``str``): path to be created + directory (``bool``): if True, the path is a directory, otherwise it is a file """ if directory: dir = path @@ -1241,57 +1363,75 @@ def create_priors_from_tles(tles, mixture_components = {'mean_motion': 5, 'eccen by fitting probability density functions to data using the specified number of mixture components for each element. Args: - `list`: list of `dsgp4.tle.TLE` objects - `dict`: dictionary of mixture component numbers (`mean_motion`, `eccentricity`, - `inclination` and `b_star` can be selected). + ``list``: list of ``dsgp4.tle.TLE`` objects + ``dict``: dictionary of mixture component numbers (``mean_motion``, ``eccentricity``, + ``inclination`` and ``b_star`` can be selected). Returns: - `dict`: dictionary of prior distributions. + ``dict``: dictionary of prior distributions. """ - from pyprob.distributions import Mixture, TruncatedNormal, Uniform #I extract the tle elements from the tles: tle_els = tle_elements(tles) mean_motion = tle_els[0] eccentricity = tle_els[1] inclination = tle_els[2] - agument_of_perigee = tle_els[3] - raan = tle_els[4] + #agument_of_perigee = tle_els[3] + #raan = tle_els[4] b_star = tle_els[5] - mean_anomaly = tle_els[6] + #mean_anomaly = tle_els[6] mean_motion_first_derivative = tle_els[7] #mean_motion_second_derivative = tle_els[8] priors_dict = {} + #first the mean motion: m = fit_mixture(np.array(mean_motion)*10000, n_components = mixture_components['mean_motion'], covariance_type = 'diag') - dists = [] + locs=[] + scales=[] for i in range(len(m.means_)): - dists.append(TruncatedNormal(mean_non_truncated = m.means_[i][0]/10000, stddev_non_truncated = np.sqrt(m.covariances_[i][0])/10000, low = min(mean_motion), high = max(mean_motion))) - priors_dict['mean_motion_prior'] = Mixture(distributions = dists, probs = list(m.weights_)) - + locs.append(m.means_[i][0]/10000) + scales.append(np.sqrt(m.covariances_[i][0])/10000) + priors_dict['mean_motion_prior']=MixtureSameFamily(mixture_distribution=Categorical(probs=torch.tensor(m.weights_)), + component_distribution=TruncatedNormal(loc=torch.tensor(locs), + scale=torch.tensor(scales), + low = min(mean_motion), + high = max(mean_motion))) + + #now the eccentricity: m = fit_mixture(values = np.array(eccentricity), n_components = mixture_components['eccentricity'], covariance_type = 'diag') - dists = [] + locs=[] + scales=[] for i in range(len(m.means_)): - dists.append(TruncatedNormal(mean_non_truncated = m.means_[i][0], stddev_non_truncated = np.sqrt(m.covariances_[i][0]), low = 0., high = max(eccentricity) )) - priors_dict['eccentricity_prior'] = Mixture(distributions = dists, probs = list(m.weights_)) - + locs.append(m.means_[i][0]) + scales.append(np.sqrt(m.covariances_[i][0])) + priors_dict['eccentricity_prior']=MixtureSameFamily(mixture_distribution=Categorical(probs=torch.tensor(m.weights_)), + component_distribution=TruncatedNormal(loc=torch.tensor(locs), scale=torch.tensor(scales), low = 0., high = max(eccentricity))) + #now the inclination: m = fit_mixture(values = np.array(inclination), n_components = mixture_components['inclination'], covariance_type = 'diag') - dists = [] + locs=[] + scales=[] for i in range(len(m.means_)): - dists.append(TruncatedNormal(mean_non_truncated = m.means_[i][0], stddev_non_truncated = np.sqrt(m.covariances_[i][0]), low = 0., high = np.pi )) - priors_dict['inclination_prior'] = Mixture(distributions = dists, probs = list(m.weights_)) - + locs.append(m.means_[i][0]) + scales.append(np.sqrt(m.covariances_[i][0])) + priors_dict['inclination_prior']=MixtureSameFamily(mixture_distribution=Categorical(probs=torch.tensor(m.weights_)), + component_distribution=TruncatedNormal(loc=torch.tensor(locs), scale=torch.tensor(scales), low = 0., high = np.pi)) + #now the b_star: m = fit_mixture(values = np.array(b_star)*10000, n_components = mixture_components['b_star'], covariance_type = 'diag') - dists = [] + locs=[] + scales=[] for i in range(len(m.means_)): - dists.append(TruncatedNormal(mean_non_truncated = m.means_[i][0]/10000, stddev_non_truncated = np.sqrt(m.covariances_[i][0]), low = min(b_star), high = max(b_star) )) - priors_dict['b_star_prior'] = Mixture(distributions = dists, probs = list(m.weights_)) -# if plot==True: -# analysis.plot_mix(priors_dict) + locs.append(m.means_[i][0]/10000) + scales.append(np.sqrt(m.covariances_[i][0])/10000) + priors_dict['b_star_prior']=MixtureSameFamily(mixture_distribution=Categorical(probs=torch.tensor(m.weights_)), + component_distribution=TruncatedNormal(loc=torch.tensor(locs), scale=torch.tensor(scales), low = min(b_star), high = max(b_star))) + #now the mean anomaly: priors_dict['mean_anomaly_prior'] = Uniform(low=0.0, high=2*np.pi) + #now the argument of perigee: priors_dict['argument_of_perigee_prior'] = Uniform(low=0.0, high=2*np.pi) + #now the raan: priors_dict['raan_prior'] = Uniform(low=0.0, high=2*np.pi) - priors_dict['mean_motion_first_derivative_prior'] = TruncatedNormal(mean_non_truncated = np.mean(mean_motion_first_derivative), stddev_non_truncated = np.std(mean_motion_first_derivative), low = min(mean_motion_first_derivative), high = max(mean_motion_first_derivative)) + #now the mean motion first derivative: + priors_dict['mean_motion_first_derivative_prior']=TruncatedNormal(loc = np.mean(mean_motion_first_derivative), scale = np.std(mean_motion_first_derivative), low = min(mean_motion_first_derivative), high = max(mean_motion_first_derivative)) return priors_dict @@ -1300,15 +1440,16 @@ def tle_elements(tles): This function takes a list of TLEs as input and extracts their elements as lists. Args: - - tles (`list`): list of `dsgp4.tle.TLE` objects + tles (``list``): list of ``dsgp4.tle.TLE`` objects Returns: - - mean_motion, eccentricity, inclination, argument_of_perigee, raan, b_star, mean_anomaly, mean_motion_first_derivative, mean_motion_second_derivative - Example:: - import matplotlib.pyplot as plt - import kessler - sats = dsgp4.tle.load(file_name = 'path_to_tle.txt') - n, e, i, omega, RAAN, B_star, M, n_dot, n_ddot = dsgp4.tle.tle_elements(sats)#tles is a list of TLEs dictionary - plt.hist(n) + mean_motion, eccentricity, inclination, argument_of_perigee, raan, b_star, mean_anomaly, mean_motion_first_derivative, mean_motion_second_derivative + + Example: + >>> import matplotlib.pyplot as plt + >>> import kessler + >>> sats = dsgp4.tle.load(file_name = 'path_to_tle.txt') + >>> n, e, i, omega, RAAN, B_star, M, n_dot, n_ddot = dsgp4.tle.tle_elements(sats)#tles is a list of TLEs dictionary + >>> plt.hist(n) """ mean_motion, eccentricity, inclination, argument_of_perigee, raan, b_star, mean_anomaly, mean_motion_first_derivative, mean_motion_second_derivative = [], [], [], [], [], [], [], [], [] for tle in tles: @@ -1330,11 +1471,11 @@ def add_megaconstellation_from_file(tles, megaconstellation_file_name): a list of the original TLEs plus the TLEs of the added megaconstellation. Args: - tles (`list`): list of `dsgp4.tle.TLE` objects - megaconstellation_file_name (`str`): megaconstellation file name + tles (``list``): list of ``dsgp4.tle.TLE`` objects + megaconstellation_file_name (``str``): megaconstellation file name Returns: - `list`: list of `dsgp4.tle.TLE` objects + ``list``: list of ``dsgp4.tle.TLE`` objects """ tles_megaconstellation=dsgp4.util.load(file_name=megaconstellation_file_name) return tles+tles_megaconstellation diff --git a/tests/test_util.py b/tests/test_util.py index b36d405..fb65b1f 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -1,14 +1,13 @@ # This code is part of Kessler, a machine learning library for spacecraft collision avoidance. # # Copyright (c) 2020- -# University of Oxford (Atilim Gunes Baydin ) # Trillium Technologies -# Giacomo Acciarini +# University of Oxford +# Giacomo Acciarini (giacomo.acciarini@gmail.com) # and other contributors, see README in root of repository. # # GNU General Public License version 3. See LICENSE in root of repository. - import unittest import numpy as np import datetime @@ -17,7 +16,7 @@ import kessler import kessler.util - +from pyro.distributions import Categorical, MixtureSameFamily class UtilTestCase(unittest.TestCase): def test_from_datetime_to_cdm_datetime_str(self): date = datetime.datetime(2823, 3, 4, 12, 1, 23, 252 ) @@ -130,5 +129,21 @@ def test_from_cartesian_to_keplerian_torch(self): self.assertAlmostEqual(Omega.item(), Omega_poliastro, places=5) self.assertAlmostEqual(omega.item(), omega_poliastro, places=5) self.assertAlmostEqual(M.item(), M_poliastro, places=5) - + + def test_TruncatedNormal(self): + #we check the truncated normal distribution, we do this for a mixture of them: + locs=torch.tensor([6.391167644720491e-05, 0.021593032643530245,0.00089714840255561, 0.004279950440413096, ]) + scales=torch.tensor([9.155414891172675e-05, 0.08825398369676822, 0.0006834100202961423,0.003464680595037937]) + probs=torch.tensor([0.36699734 ,0.02809785 ,0.39047779,0.21442703 ]) + min=-0.73577 + max=0.68639 + + categorical=Categorical(probs=probs) + batched_truncated_normal = kessler.util.TruncatedNormal(loc=locs, scale=scales, min=min, max=max) + mix_truncated=MixtureSameFamily(categorical, batched_truncated_normal) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.0001)).item(), 7.382209300994873, places=8) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.001)).item(), 5.485926151275635, places=8) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.01)).item(), 1.863307237625122, places=8) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.1)).item(), -2.458112955093384, places=8) + From 5bedec50df86808a31d85ce3e8a25fcc845c716e Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 16:31:22 +0200 Subject: [PATCH 06/13] more updates to adapt to pyro [skip ci] --- kessler/plot.py | 908 ++++++++++++++++++++++++------------------------ kessler/util.py | 2 +- 2 files changed, 455 insertions(+), 455 deletions(-) diff --git a/kessler/plot.py b/kessler/plot.py index 6319725..e489f6a 100644 --- a/kessler/plot.py +++ b/kessler/plot.py @@ -158,434 +158,6 @@ def plot_tles(tles, file_name=None, figsize = (36,18), show=True, axs=None, retu if return_axs: return axs - -def plot_dist(dists, file_name=None, n_bins=30, num_resample=None, trace=None, figsize = (16, 18)): - if isinstance(dists, Empirical): - dists = [dists] - - marginal_dists = [{} for _ in range(len(dists))] - pyprob.set_verbosity(0) - for i, dist in enumerate(dists): - if num_resample is not None: - dist = dist.resample(num_resample) - dist = dist.condition(lambda t: not t['prop_error']) - - marginal_dists[i]['dist_time_min'] = dist.map(lambda t:t['time_min']) - marginal_dists[i]['dist_d_min'] = dist.map(lambda t:t['d_min']) - marginal_dists[i]['dist_conj'] = dist.map(lambda t:t['conj']) - marginal_dists[i]['dist_events_with_conjunction'] = dist.condition(lambda t:t['conj']) - marginal_dists[i]['dist_time_conj'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['time_conj']) - marginal_dists[i]['dist_d_conj'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['d_conj']) - - marginal_dists[i]['dist_t_mean_motion'] = dist.map(lambda t:t['t_mean_motion']) - marginal_dists[i]['dist_t_mean_anomaly'] = dist.map(lambda t:t['t_mean_anomaly']) - marginal_dists[i]['dist_t_eccentricity'] = dist.map(lambda t:t['t_eccentricity']) - marginal_dists[i]['dist_t_inclination'] = dist.map(lambda t:t['t_inclination']) - marginal_dists[i]['dist_t_argument_of_perigee'] = dist.map(lambda t:t['t_argument_of_perigee']) - marginal_dists[i]['dist_t_raan'] = dist.map(lambda t:t['t_raan']) - marginal_dists[i]['dist_t_mean_motion_first_derivative'] = dist.map(lambda t:t['t_mean_motion_first_derivative']) - marginal_dists[i]['dist_t_b_star'] = dist.map(lambda t:t['t_b_star']) - - marginal_dists[i]['dist_c_mean_motion'] = dist.map(lambda t:t['c_mean_motion']) - marginal_dists[i]['dist_c_mean_anomaly'] = dist.map(lambda t:t['c_mean_anomaly']) - marginal_dists[i]['dist_c_eccentricity'] = dist.map(lambda t:t['c_eccentricity']) - marginal_dists[i]['dist_c_inclination'] = dist.map(lambda t:t['c_inclination']) - marginal_dists[i]['dist_c_argument_of_perigee'] = dist.map(lambda t:t['c_argument_of_perigee']) - marginal_dists[i]['dist_c_raan'] = dist.map(lambda t:t['c_raan']) - marginal_dists[i]['dist_c_mean_motion_first_derivative'] = dist.map(lambda t:t['c_mean_motion_first_derivative']) - marginal_dists[i]['dist_c_b_star'] = dist.map(lambda t:t['c_b_star']) - - marginal_dists[i]['dist_num_cdms'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['num_cdms']) - if len(marginal_dists[i]['dist_conj']) > 0: - marginal_dists[i]['dist_time_cdm'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['time_cdm']) - - fig, axs = plt.subplots(8, 4, figsize=figsize) - - t_color = 'green' - c_color = 'red' - # Chaser and target - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[0,0].hist(marginal_dists[i]['dist_t_mean_motion'].values_numpy(), bins=n_bins, alpha=0.5, label=label, density=True) - # axs[0,0].legend() - axs[0,0].set_xlabel('mean_motion') - axs[0,0].set_ylabel('Target') - if trace: - axs[0,0].vlines(trace['t_mean_motion'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[0,1].hist(marginal_dists[i]['dist_t_mean_anomaly'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[0,1].legend() - axs[0,1].set_xlabel('mean_anomaly') - if trace: - axs[0,1].vlines(trace['t_mean_anomaly'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[0,2].hist(marginal_dists[i]['dist_t_eccentricity'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[0,2].legend() - axs[0,2].set_xlabel('eccentricity') - if trace: - axs[0,2].vlines(trace['t_eccentricity'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[0,3].hist(marginal_dists[i]['dist_t_inclination'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[0,3].legend() - axs[0,3].set_xlabel('inclination') - if trace: - axs[0,3].vlines(trace['t_inclination'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[1,0].hist(marginal_dists[i]['dist_t_argument_of_perigee'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[1,0].legend() - axs[1,0].set_xlabel('argument_of_perigee') - axs[1,0].set_ylabel('Target') - if trace: - axs[1,0].vlines(trace['t_argument_of_perigee'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[1,1].hist(marginal_dists[i]['dist_t_raan'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[1,1].legend() - axs[1,1].set_xlabel('raan') - if trace: - axs[1,1].vlines(trace['t_raan'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[1,2].hist(marginal_dists[i]['dist_t_mean_motion_first_derivative'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[1,2].legend() - axs[1,2].set_xlabel('mean_motion_first_derivative') - if trace: - axs[1,2].vlines(trace['t_mean_motion_first_derivative'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, bins, _ = axs[1,3].hist(marginal_dists[i]['dist_t_b_star'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[1,3].legend() - axs[1,3].set_xlabel('b_star') - if trace: - axs[1,3].vlines(trace['t_b_star'], 0, np.max(h)*1.05, linestyles='dashed') -# ax.set_xlim(-0.01,0.01) - - - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[2,0].hist(marginal_dists[i]['dist_c_mean_motion'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[2,0].legend() - axs[2,0].set_xlabel('mean_motion') - axs[2,0].set_ylabel('Chaser') - if trace: - axs[2,0].vlines(trace['c_mean_motion'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[2,1].hist(marginal_dists[i]['dist_c_mean_anomaly'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[2,1].legend() - axs[2,1].set_xlabel('mean_anomaly') - if trace: - axs[2,1].vlines(trace['c_mean_anomaly'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[2,2].hist(marginal_dists[i]['dist_c_eccentricity'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[2,2].legend() - axs[2,2].set_xlabel('eccentricity') - if trace: - axs[2,2].vlines(trace['c_eccentricity'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[2,3].hist(marginal_dists[i]['dist_c_inclination'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[2,3].legend() - axs[2,3].set_xlabel('inclination') - if trace: - axs[2,3].vlines(trace['c_inclination'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[3,0].hist(marginal_dists[i]['dist_c_argument_of_perigee'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[3,0].legend() - axs[3,0].set_xlabel('argument_of_perigee') - axs[3,0].set_ylabel('Chaser') - if trace: - axs[3,0].vlines(trace['c_argument_of_perigee'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[3,1].hist(marginal_dists[i]['dist_c_raan'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[3,1].legend() - axs[3,1].set_xlabel('raan') - if trace: - axs[3,1].vlines(trace['c_raan'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[3,2].hist(marginal_dists[i]['dist_c_mean_motion_first_derivative'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[3,2].legend() - axs[3,2].set_xlabel('mean_motion_first_derivative') - if trace: - axs[3,2].vlines(trace['c_mean_motion_first_derivative'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[3,3].hist(marginal_dists[i]['dist_c_b_star'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - # axs[3,3].legend() - axs[3,3].set_xlabel('b_star') - if trace: - axs[3,3].vlines(trace['c_b_star'], 0, np.max(h)*1.05, linestyles='dashed') -# ax.set_xlim(-0.01,0.01) - - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_mean_motion'].values_numpy() - c = marginal_dists[i]['dist_c_mean_motion'].values_numpy() - axs[4,0].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[4,0].set_xlabel('t_mean_motion') - axs[4,0].set_ylabel('c_mean_motion') - if trace: - t = float(trace['t_mean_motion']) - c = float(trace['c_mean_motion']) - axs[4,0].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[4,0].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[4,0].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_mean_anomaly'].values_numpy() - c = marginal_dists[i]['dist_c_mean_anomaly'].values_numpy() - axs[4,1].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[4,1].set_xlabel('t_mean_anomaly') - axs[4,1].set_ylabel('c_mean_anomaly') - if trace: - t = float(trace['t_mean_anomaly']) - c = float(trace['c_mean_anomaly']) - axs[4,1].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[4,1].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[4,1].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_eccentricity'].values_numpy() - c = marginal_dists[i]['dist_c_eccentricity'].values_numpy() - axs[4,2].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[4,2].set_xlabel('t_eccentricity') - axs[4,2].set_ylabel('c_eccentricity') - if trace: - t = float(trace['t_eccentricity']) - c = float(trace['c_eccentricity']) - axs[4,2].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[4,2].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[4,2].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_inclination'].values_numpy() - c = marginal_dists[i]['dist_c_inclination'].values_numpy() - axs[4,3].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[4,3].set_xlabel('t_inclination') - axs[4,3].set_ylabel('c_inclination') - if trace: - t = float(trace['t_inclination']) - c = float(trace['c_inclination']) - axs[4,3].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[4,3].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[4,3].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_argument_of_perigee'].values_numpy() - c = marginal_dists[i]['dist_c_argument_of_perigee'].values_numpy() - axs[5,0].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[5,0].set_xlabel('t_argument_of_perigee') - axs[5,0].set_ylabel('c_argument_of_perigee') - if trace: - t = float(trace['t_argument_of_perigee']) - c = float(trace['c_argument_of_perigee']) - axs[5,0].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[5,0].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[5,0].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_raan'].values_numpy() - c = marginal_dists[i]['dist_c_raan'].values_numpy() - axs[5,1].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[5,1].set_xlabel('t_raan') - axs[5,1].set_ylabel('c_raan') - if trace: - t = float(trace['t_raan']) - c = float(trace['c_raan']) - axs[5,1].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[5,1].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[5,1].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_mean_motion_first_derivative'].values_numpy() - c = marginal_dists[i]['dist_c_mean_motion_first_derivative'].values_numpy() - axs[5,2].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[5,2].set_xlabel('t_mean_motion_first_derivative') - axs[5,2].set_ylabel('c_mean_motion_first_derivative') - if trace: - t = float(trace['t_mean_motion_first_derivative']) - c = float(trace['c_mean_motion_first_derivative']) - axs[5,2].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[5,2].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[5,2].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['dist_t_b_star'].values_numpy() - c = marginal_dists[i]['dist_c_b_star'].values_numpy() - axs[5,3].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[5,3].set_xlabel('t_b_star') - axs[5,3].set_ylabel('c_b_star') - if trace: - t = float(trace['t_b_star']) - c = float(trace['c_b_star']) - axs[5,3].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[5,3].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[5,3].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - # Other variables from simulation - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[6,0].hist(marginal_dists[i]['dist_time_min'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - axs[6,0].set_xlabel('time_min') - if trace: - axs[6,0].vlines(trace['time_min'], 0, np.max(h)*1.05, linestyles='dashed') - - ax = axs[6,1] - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[6,1].hist(marginal_dists[i]['dist_d_min'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - axs[6,1].set_xlabel('d_min') - if trace: - axs[6,1].vlines(trace['d_min'], 0, np.max(h)*1.05, linestyles='dashed') - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - label = dists[i].name - dist_conj = marginal_dists[i]['dist_conj'] - p_conj = sum(dist_conj.values)/len(dist_conj) - axs[6,2].bar(['No conj', 'Conj'], [1-p_conj, p_conj], alpha=0.5) - axs[6,2].set_xlabel('conj') - if trace: - axs[6,2].vlines(trace['conj']==1, 0, 1., linestyles='dashed') - - t_min, t_max = 1e30, -1e30 - c_min, c_max = 1e30, -1e30 - for i in range(len(dists)): - t = marginal_dists[i]['d_conj'].values_numpy() - c = marginal_dists[i]['d_min'].values_numpy() - axs[6,3].scatter(x=t, y=c, alpha=0.5) - t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) - c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) - axs[6,3].set_xlabel('d_conj') - axs[6,3].set_ylabel('d_min') - if trace: - t = float(trace['d_conj']) - c = float(trace['d_min']) - axs[6,3].scatter(x=[t], y=[c], color='black') - t_min, t_max = min(t_min, t), max(t_max, t) - c_min, c_max = min(c_min, c), max(c_max, c) - axs[6,3].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) - axs[6,3].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) - - #axs[6,3].axis('off') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[7,0].hist(marginal_dists[i]['dist_time_conj'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - axs[7,0].set_xlabel('time_conj') - if trace: - if 'time_conj' in trace: - if trace['time_conj'] is not None: - axs[7,0].vlines(trace['time_conj'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[7,1].hist(marginal_dists[i]['dist_d_conj'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - axs[7,1].set_xlabel('d_conj') - if trace: - if 'd_conj' in trace: - if trace['d_conj'] is not None: - axs[7,1].vlines(trace['d_conj'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - h, _, _ = axs[7,2].hist(marginal_dists[i]['dist_num_cdms'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - axs[7,2].set_xlabel('num_cdms') - if trace: - axs[7,2].vlines(trace['num_cdms'], 0, np.max(h)*1.05, linestyles='dashed') - - for i in range(len(dists)): - label = dists[i].name - dist_conj = marginal_dists[i]['dist_conj'] - if len(dist_conj) > 0: - axs[7,3].hist(marginal_dists[i]['dist_time_cdm'].values_numpy(), bins=n_bins, alpha=0.5, density=True) - axs[7,3].set_xlabel('time_cdm') - - plt.tight_layout() - fig.legend() - - if file_name: - print('Plotting to file: {}'.format(file_name)) - plt.savefig(file_name) - return fig, axs - def plot_trace_orbit(trace, time_upsample_factor=100, figsize=(10, 8), file_name=None): t_color, c_color = 'red', 'forestgreen' @@ -693,32 +265,460 @@ def plot_trace_event(trace, *args, **kwargs): return event.plot_features(*args, **kwargs) -def plot_combined(dists, trace, figsize=(20,10), file_name=None): - file_name_1 = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4())) + '.png' - file_name_2 = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4())) + '.png' - file_name_3 = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4())) + '.png' +# def plot_combined(dists, trace, figsize=(20,10), file_name=None): +# file_name_1 = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4())) + '.png' +# file_name_2 = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4())) + '.png' +# file_name_3 = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4())) + '.png' + +# plot_dist(dists, trace=trace, file_name=file_name_1) +# plot_trace_orbit(trace, file_name=file_name_2) +# features = ['MISS_DISTANCE', 'RELATIVE_SPEED', 'RELATIVE_POSITION_R', 'OBJECT1_CR_R', 'OBJECT1_CT_T', 'OBJECT1_CN_N', 'OBJECT1_CRDOT_RDOT', 'OBJECT1_CTDOT_TDOT', 'OBJECT1_CNDOT_NDOT', 'OBJECT2_CR_R', 'OBJECT2_CT_T', 'OBJECT2_CN_N', 'OBJECT2_CRDOT_RDOT', 'OBJECT2_CTDOT_TDOT', 'OBJECT2_CNDOT_NDOT'] +# plot_trace_event(trace, features, file_name=file_name_3) + +# fig = plt.figure(figsize=figsize) +# gs = fig.add_gridspec(2, 2, width_ratios=[2, 1], height_ratios=[1, 1], hspace=0.09, wspace=0.05, left=0, right=1, bottom=0, top=1) + +# ax = fig.add_subplot(gs[:, 0]) +# ax.imshow(mpimg.imread(file_name_1), interpolation='bicubic', aspect='auto') +# ax.axis('off') + +# ax = fig.add_subplot(gs[0, 1]) +# ax.imshow(mpimg.imread(file_name_2), interpolation='bicubic', aspect='auto') +# ax.axis('off') + +# ax = fig.add_subplot(gs[1, 1]) +# ax.imshow(mpimg.imread(file_name_3), interpolation='bicubic', aspect='auto') +# ax.axis('off') +# # plt.tight_layout() + +# if file_name is not None: +# print('Plotting combined plot to file: {}'.format(file_name)) +# fig.savefig(file_name, dpi=150) + +# def plot_dist(dists, file_name=None, n_bins=30, num_resample=None, trace=None, figsize = (16, 18)): +# if isinstance(dists, Empirical): +# dists = [dists] + +# marginal_dists = [{} for _ in range(len(dists))] +# pyprob.set_verbosity(0) +# for i, dist in enumerate(dists): +# if num_resample is not None: +# dist = dist.resample(num_resample) +# dist = dist.condition(lambda t: not t['prop_error']) + +# marginal_dists[i]['dist_time_min'] = dist.map(lambda t:t['time_min']) +# marginal_dists[i]['dist_d_min'] = dist.map(lambda t:t['d_min']) +# marginal_dists[i]['dist_conj'] = dist.map(lambda t:t['conj']) +# marginal_dists[i]['dist_events_with_conjunction'] = dist.condition(lambda t:t['conj']) +# marginal_dists[i]['dist_time_conj'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['time_conj']) +# marginal_dists[i]['dist_d_conj'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['d_conj']) + +# marginal_dists[i]['dist_t_mean_motion'] = dist.map(lambda t:t['t_mean_motion']) +# marginal_dists[i]['dist_t_mean_anomaly'] = dist.map(lambda t:t['t_mean_anomaly']) +# marginal_dists[i]['dist_t_eccentricity'] = dist.map(lambda t:t['t_eccentricity']) +# marginal_dists[i]['dist_t_inclination'] = dist.map(lambda t:t['t_inclination']) +# marginal_dists[i]['dist_t_argument_of_perigee'] = dist.map(lambda t:t['t_argument_of_perigee']) +# marginal_dists[i]['dist_t_raan'] = dist.map(lambda t:t['t_raan']) +# marginal_dists[i]['dist_t_mean_motion_first_derivative'] = dist.map(lambda t:t['t_mean_motion_first_derivative']) +# marginal_dists[i]['dist_t_b_star'] = dist.map(lambda t:t['t_b_star']) + +# marginal_dists[i]['dist_c_mean_motion'] = dist.map(lambda t:t['c_mean_motion']) +# marginal_dists[i]['dist_c_mean_anomaly'] = dist.map(lambda t:t['c_mean_anomaly']) +# marginal_dists[i]['dist_c_eccentricity'] = dist.map(lambda t:t['c_eccentricity']) +# marginal_dists[i]['dist_c_inclination'] = dist.map(lambda t:t['c_inclination']) +# marginal_dists[i]['dist_c_argument_of_perigee'] = dist.map(lambda t:t['c_argument_of_perigee']) +# marginal_dists[i]['dist_c_raan'] = dist.map(lambda t:t['c_raan']) +# marginal_dists[i]['dist_c_mean_motion_first_derivative'] = dist.map(lambda t:t['c_mean_motion_first_derivative']) +# marginal_dists[i]['dist_c_b_star'] = dist.map(lambda t:t['c_b_star']) + +# marginal_dists[i]['dist_num_cdms'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['num_cdms']) +# if len(marginal_dists[i]['dist_conj']) > 0: +# marginal_dists[i]['dist_time_cdm'] = marginal_dists[i]['dist_events_with_conjunction'].map(lambda t:t['time_cdm']) + +# fig, axs = plt.subplots(8, 4, figsize=figsize) + +# t_color = 'green' +# c_color = 'red' +# # Chaser and target +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[0,0].hist(marginal_dists[i]['dist_t_mean_motion'].values_numpy(), bins=n_bins, alpha=0.5, label=label, density=True) +# # axs[0,0].legend() +# axs[0,0].set_xlabel('mean_motion') +# axs[0,0].set_ylabel('Target') +# if trace: +# axs[0,0].vlines(trace['t_mean_motion'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[0,1].hist(marginal_dists[i]['dist_t_mean_anomaly'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[0,1].legend() +# axs[0,1].set_xlabel('mean_anomaly') +# if trace: +# axs[0,1].vlines(trace['t_mean_anomaly'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[0,2].hist(marginal_dists[i]['dist_t_eccentricity'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[0,2].legend() +# axs[0,2].set_xlabel('eccentricity') +# if trace: +# axs[0,2].vlines(trace['t_eccentricity'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[0,3].hist(marginal_dists[i]['dist_t_inclination'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[0,3].legend() +# axs[0,3].set_xlabel('inclination') +# if trace: +# axs[0,3].vlines(trace['t_inclination'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[1,0].hist(marginal_dists[i]['dist_t_argument_of_perigee'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[1,0].legend() +# axs[1,0].set_xlabel('argument_of_perigee') +# axs[1,0].set_ylabel('Target') +# if trace: +# axs[1,0].vlines(trace['t_argument_of_perigee'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[1,1].hist(marginal_dists[i]['dist_t_raan'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[1,1].legend() +# axs[1,1].set_xlabel('raan') +# if trace: +# axs[1,1].vlines(trace['t_raan'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[1,2].hist(marginal_dists[i]['dist_t_mean_motion_first_derivative'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[1,2].legend() +# axs[1,2].set_xlabel('mean_motion_first_derivative') +# if trace: +# axs[1,2].vlines(trace['t_mean_motion_first_derivative'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, bins, _ = axs[1,3].hist(marginal_dists[i]['dist_t_b_star'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[1,3].legend() +# axs[1,3].set_xlabel('b_star') +# if trace: +# axs[1,3].vlines(trace['t_b_star'], 0, np.max(h)*1.05, linestyles='dashed') +# # ax.set_xlim(-0.01,0.01) + + + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[2,0].hist(marginal_dists[i]['dist_c_mean_motion'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[2,0].legend() +# axs[2,0].set_xlabel('mean_motion') +# axs[2,0].set_ylabel('Chaser') +# if trace: +# axs[2,0].vlines(trace['c_mean_motion'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[2,1].hist(marginal_dists[i]['dist_c_mean_anomaly'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[2,1].legend() +# axs[2,1].set_xlabel('mean_anomaly') +# if trace: +# axs[2,1].vlines(trace['c_mean_anomaly'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[2,2].hist(marginal_dists[i]['dist_c_eccentricity'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[2,2].legend() +# axs[2,2].set_xlabel('eccentricity') +# if trace: +# axs[2,2].vlines(trace['c_eccentricity'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[2,3].hist(marginal_dists[i]['dist_c_inclination'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[2,3].legend() +# axs[2,3].set_xlabel('inclination') +# if trace: +# axs[2,3].vlines(trace['c_inclination'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[3,0].hist(marginal_dists[i]['dist_c_argument_of_perigee'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[3,0].legend() +# axs[3,0].set_xlabel('argument_of_perigee') +# axs[3,0].set_ylabel('Chaser') +# if trace: +# axs[3,0].vlines(trace['c_argument_of_perigee'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[3,1].hist(marginal_dists[i]['dist_c_raan'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[3,1].legend() +# axs[3,1].set_xlabel('raan') +# if trace: +# axs[3,1].vlines(trace['c_raan'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[3,2].hist(marginal_dists[i]['dist_c_mean_motion_first_derivative'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[3,2].legend() +# axs[3,2].set_xlabel('mean_motion_first_derivative') +# if trace: +# axs[3,2].vlines(trace['c_mean_motion_first_derivative'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[3,3].hist(marginal_dists[i]['dist_c_b_star'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# # axs[3,3].legend() +# axs[3,3].set_xlabel('b_star') +# if trace: +# axs[3,3].vlines(trace['c_b_star'], 0, np.max(h)*1.05, linestyles='dashed') +# # ax.set_xlim(-0.01,0.01) + + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_mean_motion'].values_numpy() +# c = marginal_dists[i]['dist_c_mean_motion'].values_numpy() +# axs[4,0].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[4,0].set_xlabel('t_mean_motion') +# axs[4,0].set_ylabel('c_mean_motion') +# if trace: +# t = float(trace['t_mean_motion']) +# c = float(trace['c_mean_motion']) +# axs[4,0].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[4,0].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[4,0].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_mean_anomaly'].values_numpy() +# c = marginal_dists[i]['dist_c_mean_anomaly'].values_numpy() +# axs[4,1].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[4,1].set_xlabel('t_mean_anomaly') +# axs[4,1].set_ylabel('c_mean_anomaly') +# if trace: +# t = float(trace['t_mean_anomaly']) +# c = float(trace['c_mean_anomaly']) +# axs[4,1].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[4,1].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[4,1].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_eccentricity'].values_numpy() +# c = marginal_dists[i]['dist_c_eccentricity'].values_numpy() +# axs[4,2].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[4,2].set_xlabel('t_eccentricity') +# axs[4,2].set_ylabel('c_eccentricity') +# if trace: +# t = float(trace['t_eccentricity']) +# c = float(trace['c_eccentricity']) +# axs[4,2].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[4,2].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[4,2].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_inclination'].values_numpy() +# c = marginal_dists[i]['dist_c_inclination'].values_numpy() +# axs[4,3].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[4,3].set_xlabel('t_inclination') +# axs[4,3].set_ylabel('c_inclination') +# if trace: +# t = float(trace['t_inclination']) +# c = float(trace['c_inclination']) +# axs[4,3].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[4,3].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[4,3].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_argument_of_perigee'].values_numpy() +# c = marginal_dists[i]['dist_c_argument_of_perigee'].values_numpy() +# axs[5,0].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[5,0].set_xlabel('t_argument_of_perigee') +# axs[5,0].set_ylabel('c_argument_of_perigee') +# if trace: +# t = float(trace['t_argument_of_perigee']) +# c = float(trace['c_argument_of_perigee']) +# axs[5,0].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[5,0].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[5,0].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_raan'].values_numpy() +# c = marginal_dists[i]['dist_c_raan'].values_numpy() +# axs[5,1].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[5,1].set_xlabel('t_raan') +# axs[5,1].set_ylabel('c_raan') +# if trace: +# t = float(trace['t_raan']) +# c = float(trace['c_raan']) +# axs[5,1].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[5,1].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[5,1].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_mean_motion_first_derivative'].values_numpy() +# c = marginal_dists[i]['dist_c_mean_motion_first_derivative'].values_numpy() +# axs[5,2].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[5,2].set_xlabel('t_mean_motion_first_derivative') +# axs[5,2].set_ylabel('c_mean_motion_first_derivative') +# if trace: +# t = float(trace['t_mean_motion_first_derivative']) +# c = float(trace['c_mean_motion_first_derivative']) +# axs[5,2].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[5,2].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[5,2].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['dist_t_b_star'].values_numpy() +# c = marginal_dists[i]['dist_c_b_star'].values_numpy() +# axs[5,3].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[5,3].set_xlabel('t_b_star') +# axs[5,3].set_ylabel('c_b_star') +# if trace: +# t = float(trace['t_b_star']) +# c = float(trace['c_b_star']) +# axs[5,3].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[5,3].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[5,3].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# # Other variables from simulation +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[6,0].hist(marginal_dists[i]['dist_time_min'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# axs[6,0].set_xlabel('time_min') +# if trace: +# axs[6,0].vlines(trace['time_min'], 0, np.max(h)*1.05, linestyles='dashed') + +# ax = axs[6,1] +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[6,1].hist(marginal_dists[i]['dist_d_min'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# axs[6,1].set_xlabel('d_min') +# if trace: +# axs[6,1].vlines(trace['d_min'], 0, np.max(h)*1.05, linestyles='dashed') + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# label = dists[i].name +# dist_conj = marginal_dists[i]['dist_conj'] +# p_conj = sum(dist_conj.values)/len(dist_conj) +# axs[6,2].bar(['No conj', 'Conj'], [1-p_conj, p_conj], alpha=0.5) +# axs[6,2].set_xlabel('conj') +# if trace: +# axs[6,2].vlines(trace['conj']==1, 0, 1., linestyles='dashed') + +# t_min, t_max = 1e30, -1e30 +# c_min, c_max = 1e30, -1e30 +# for i in range(len(dists)): +# t = marginal_dists[i]['d_conj'].values_numpy() +# c = marginal_dists[i]['d_min'].values_numpy() +# axs[6,3].scatter(x=t, y=c, alpha=0.5) +# t_min, t_max = min(t_min, t.min()), max(t_max, t.max()) +# c_min, c_max = min(c_min, c.min()), max(c_max, c.max()) +# axs[6,3].set_xlabel('d_conj') +# axs[6,3].set_ylabel('d_min') +# if trace: +# t = float(trace['d_conj']) +# c = float(trace['d_min']) +# axs[6,3].scatter(x=[t], y=[c], color='black') +# t_min, t_max = min(t_min, t), max(t_max, t) +# c_min, c_max = min(c_min, c), max(c_max, c) +# axs[6,3].set_xlim(t_min-(t_max-t_min)*0.05, t_max+(t_max-t_min)*0.05) +# axs[6,3].set_ylim(c_min-(c_max-c_min)*0.05, c_max+(c_max-c_min)*0.05) + +# #axs[6,3].axis('off') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[7,0].hist(marginal_dists[i]['dist_time_conj'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# axs[7,0].set_xlabel('time_conj') +# if trace: +# if 'time_conj' in trace: +# if trace['time_conj'] is not None: +# axs[7,0].vlines(trace['time_conj'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[7,1].hist(marginal_dists[i]['dist_d_conj'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# axs[7,1].set_xlabel('d_conj') +# if trace: +# if 'd_conj' in trace: +# if trace['d_conj'] is not None: +# axs[7,1].vlines(trace['d_conj'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# h, _, _ = axs[7,2].hist(marginal_dists[i]['dist_num_cdms'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# axs[7,2].set_xlabel('num_cdms') +# if trace: +# axs[7,2].vlines(trace['num_cdms'], 0, np.max(h)*1.05, linestyles='dashed') + +# for i in range(len(dists)): +# label = dists[i].name +# dist_conj = marginal_dists[i]['dist_conj'] +# if len(dist_conj) > 0: +# axs[7,3].hist(marginal_dists[i]['dist_time_cdm'].values_numpy(), bins=n_bins, alpha=0.5, density=True) +# axs[7,3].set_xlabel('time_cdm') - plot_dist(dists, trace=trace, file_name=file_name_1) - plot_trace_orbit(trace, file_name=file_name_2) - features = ['MISS_DISTANCE', 'RELATIVE_SPEED', 'RELATIVE_POSITION_R', 'OBJECT1_CR_R', 'OBJECT1_CT_T', 'OBJECT1_CN_N', 'OBJECT1_CRDOT_RDOT', 'OBJECT1_CTDOT_TDOT', 'OBJECT1_CNDOT_NDOT', 'OBJECT2_CR_R', 'OBJECT2_CT_T', 'OBJECT2_CN_N', 'OBJECT2_CRDOT_RDOT', 'OBJECT2_CTDOT_TDOT', 'OBJECT2_CNDOT_NDOT'] - plot_trace_event(trace, features, file_name=file_name_3) - - fig = plt.figure(figsize=figsize) - gs = fig.add_gridspec(2, 2, width_ratios=[2, 1], height_ratios=[1, 1], hspace=0.09, wspace=0.05, left=0, right=1, bottom=0, top=1) - - ax = fig.add_subplot(gs[:, 0]) - ax.imshow(mpimg.imread(file_name_1), interpolation='bicubic', aspect='auto') - ax.axis('off') - - ax = fig.add_subplot(gs[0, 1]) - ax.imshow(mpimg.imread(file_name_2), interpolation='bicubic', aspect='auto') - ax.axis('off') - - ax = fig.add_subplot(gs[1, 1]) - ax.imshow(mpimg.imread(file_name_3), interpolation='bicubic', aspect='auto') - ax.axis('off') # plt.tight_layout() +# fig.legend() + +# if file_name: +# print('Plotting to file: {}'.format(file_name)) +# plt.savefig(file_name) +# return fig, axs - if file_name is not None: - print('Plotting combined plot to file: {}'.format(file_name)) - fig.savefig(file_name, dpi=150) diff --git a/kessler/util.py b/kessler/util.py index 0200df9..7ee6b23 100644 --- a/kessler/util.py +++ b/kessler/util.py @@ -597,7 +597,7 @@ def has_nan_or_inf(value): def trace_to_event(trace): from .event import Event - return Event(cdms=trace['cdms']) + return Event(cdms=trace.nodes['cdms']['infer']['cdms']) def dist_to_event_dataset(dist): From 217a85a6b8239cfca3e771c5bd03b0186851dcec Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:14:42 +0200 Subject: [PATCH 07/13] last updates, docstrings and removal of extras --- docs/notebooks/basics.ipynb | 111 +----- .../cdms_analysis_and_plotting.ipynb | 1 - docs/notebooks/kelvins_dataset.ipynb | 4 +- kessler/event.py | 14 +- kessler/util.py | 349 ++++++++---------- setup.py | 2 +- 6 files changed, 180 insertions(+), 301 deletions(-) diff --git a/docs/notebooks/basics.ipynb b/docs/notebooks/basics.ipynb index 762e325..7dad855 100644 --- a/docs/notebooks/basics.ipynb +++ b/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" ] diff --git a/docs/notebooks/cdms_analysis_and_plotting.ipynb b/docs/notebooks/cdms_analysis_and_plotting.ipynb index 3488c17..7fff26b 100644 --- a/docs/notebooks/cdms_analysis_and_plotting.ipynb +++ b/docs/notebooks/cdms_analysis_and_plotting.ipynb @@ -41,7 +41,6 @@ ], "source": [ "import kessler\n", - "from kessler import EventDataset\n", "path_to_cdms_folder='synthetic_cdms'\n", "events=kessler.EventDataset(cdms_dir=path_to_cdms_folder,cdm_extension='.kvn')\n", "#events=EventDataset(path_to_cdms_folder)" diff --git a/docs/notebooks/kelvins_dataset.ipynb b/docs/notebooks/kelvins_dataset.ipynb index a90831e..d4f4f7c 100644 --- a/docs/notebooks/kelvins_dataset.ipynb +++ b/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" ] }, diff --git a/kessler/event.py b/kessler/event.py index 3c22fdc..84a6bf0 100644 --- a/kessler/event.py +++ b/kessler/event.py @@ -13,6 +13,7 @@ import matplotlib as mpl import matplotlib.pyplot as plt from glob import glob +from tqdm import tqdm import copy import os import re @@ -164,7 +165,7 @@ def __len__(self): class EventDataset(): - def __init__(self, cdms_dir=None, cdm_extension='.cdm.kvn.txt', events=None): + def __init__(self, cdms_dir=None, cdm_extension='.kvn', events=None): if events is None: if cdms_dir is None: self._events = [] @@ -398,10 +399,8 @@ def from_pandas(df, cdm_compatible_fields={ df_events = df.groupby(group_events_by).groups print('Grouped into {} event(s)'.format(len(df_events))) events = [] - util.progress_bar_init('Converting DataFrame to EventDataset', len(df_events), 'Events') i = 0 - for k, v in df_events.items(): - util.progress_bar_update(i) + for k, v in tqdm(df_events.items()): i += 1 df_event = df.iloc[v] cdms = [] @@ -416,7 +415,6 @@ def from_pandas(df, cdm_compatible_fields={ cdm[cdm_name] = value cdms.append(cdm) events.append(Event(cdms)) - util.progress_bar_end() event_dataset = EventDataset(events=events) print('\n{}'.format(event_dataset)) return event_dataset @@ -425,12 +423,8 @@ def to_dataframe(self): if len(self) == 0: return pd.DataFrame() event_dataframes = [] - - util.progress_bar_init('Converting EventDataset to DataFrame', len(self._events), 'Events') - for i, event in enumerate(self._events): - util.progress_bar_update(i) + for i, event in enumerate(tqdm(self._events)): event_dataframes.append(event.to_dataframe()) - util.progress_bar_end() return pd.concat(event_dataframes, ignore_index=True) def dates(self): diff --git a/kessler/util.py b/kessler/util.py index 7ee6b23..4fc2de2 100644 --- a/kessler/util.py +++ b/kessler/util.py @@ -161,13 +161,14 @@ def sample(self, sample_shape=torch.Size()): def fit_mixture(values, *args, **kwargs): """ - This function fits a mixture of Gaussians to the provided values. - + Fit a Gaussian Mixture Model to the given data. Args: - values (`numpy.ndarray`): values to fit the mixture to + values (``numpy.ndarray``): The data to fit the model to. + *args: Additional arguments for the GaussianMixture constructor. + **kwargs: Additional keyword arguments for the GaussianMixture constructor. Returns: - tuple: tuple containing: - `numpy.ndarray`: means of the mixture - `numpy.ndarray`: standard deviations of the mixture - `numpy.ndarray`: weights of the mixture + ``GaussianMixture``: The fitted Gaussian Mixture Model. """ from sklearn import mixture values = values.reshape(-1,1) @@ -305,11 +306,11 @@ def rotation_matrix(state): Computes the UVW rotation matrix. Args: - state (`numpy.array`): numpy array of 2 rows and 3 columns, where + state (``numpy.array``): numpy array of 2 rows and 3 columns, where the first row represents position, and the second velocity. Returns: - `numpy.array`: numpy array of the rotation matrix from the cartesian state. + ``numpy.array``: numpy array of the rotation matrix from the cartesian state. """ r, v = state[0], state[1] u = r / np.linalg.norm(r) @@ -324,12 +325,12 @@ def from_cartesian_to_rtn(state, cartesian_to_rtn_rotation_matrix=None): Converts a cartesian state to the RTN frame. Args: - state (`numpy.array`): numpy array of 2 rows and 3 columns, where + state (``numpy.array``): numpy array of 2 rows and 3 columns, where the first row represents position, and the second velocity. - cartesian_to_rtn_rotation_matrix (`numpy.array`): numpy array of the rotation matrix from the cartesian state. If None, it is computed. + cartesian_to_rtn_rotation_matrix (``numpy.array``): numpy array of the rotation matrix from the cartesian state. If None, it is computed. Returns: - `numpy.array`: numpy array of the RTN state. + ``numpy.array``: numpy array of the RTN state. """ # Use the supplied rotation matrix if available, otherwise compute it if cartesian_to_rtn_rotation_matrix is None: @@ -345,12 +346,12 @@ def from_rtn_to_cartesian(state_rtn, rtn_to_cartesian_rotation_matrix): Converts a RTN state to the cartesian frame. Args: - state_rtn (`numpy.array`): numpy array of 2 rows and 3 columns, where + state_rtn (``numpy.array``): numpy array of 2 rows and 3 columns, where the first row represents position, and the second velocity. - rtn_to_cartesian_rotation_matrix (`numpy.array`): numpy array of the rotation matrix from the RTN state. + rtn_to_cartesian_rotation_matrix (``numpy.array``): numpy array of the rotation matrix from the RTN state. Returns: - `numpy.array`: numpy array of the cartesian state. + ``numpy.array``: numpy array of the cartesian state. """ r_rtn, v_rtn = state_rtn[0], state_rtn[1] state_xyz = np.stack([np.matmul(rtn_to_cartesian_rotation_matrix, r_rtn), np.matmul(rtn_to_cartesian_rotation_matrix, v_rtn)]) @@ -386,32 +387,26 @@ def from_TEME_to_ITRF(state, time): state = np.stack([r_new, v_new]) return state -def from_datetime_to_fractional_day(datetime_object): +def from_datetime_to_cdm_datetime_str(date): """ - Converts a datetime object to a fractional day. The fractional day is the number of days since the beginning of the year. For example, January 1st is 0.0, January 2nd is 1.0, etc. - + Converts a datetime object to a string in the format 'yyyy-mm-ddTHH:MM:SS.FFF'. + The date format is compatible with the CCSDS time format. Args: - datetime_object (`datetime.datetime`): datetime object to convert - + date (``datetime.datetime``): datetime object to convert Returns: - `float`: fractional day + ``str``: string in the format 'yyyy-mm-ddTHH:MM:SS.FFF' """ - d = datetime_object-datetime.datetime(datetime_object.year-1, 12, 31) - fractional_day = d.days + d.seconds/60./60./24 + d.microseconds/60./60./24./1e6 - return fractional_day - -def from_datetime_to_cdm_datetime_str(datetime): - return datetime.strftime('%Y-%m-%dT%H:%M:%S.%f') + return date.strftime('%Y-%m-%dT%H:%M:%S.%f') def from_mjd_to_jd(mjd_date): """ Converts a Modified Julian Date to a Julian Date. The Julian Date is the number of days since noon on January 1st, 4713 BC. The Modified Julian Date is the number of days since midnight on November 17th, 1858. Args: - mjd_date (`float`): Modified Julian Date + mjd_date (``float``): Modified Julian Date Returns: - `float`: Julian Date + ``float``: Julian Date """ return mjd_date+2400000.5 @@ -420,47 +415,38 @@ def from_jd_to_mjd(jd_date): Converts a Julian Date to a Modified Julian Date. The Julian Date is the number of days since noon on January 1st, 4713 BC. Args: - jd_date (`float`): Julian Date + jd_date (``float``): Julian Date Returns: - `float`: Modified Julian Date + ``float``: Modified Julian Date """ return jd_date-2400000.5 def from_jd_to_cdm_datetime_str(jd_date): - d = dsgp4.util.from_jd_to_datetime(jd_date) - return from_datetime_to_cdm_datetime_str(d) - - -def from_mjd_to_epoch_days_after_1_jan(mjd_date): - d = dsgp4.util.from_mjd_to_datetime(mjd_date) - dd = d - datetime.datetime(d.year, 1, 1) - days = dd.days - days_fraction = (dd.seconds + dd.microseconds/1e6) / (60*60*24) - return days + days_fraction - -def from_mjd_to_datetime_offset_aware(mjd_date): """ - Converts a Modified Julian Date to a datetime object. The Modified Julian Date is the number of days since midnight on November 17, 1858. - + Converts a Julian Date to a string in the format 'yyyy-mm-ddTHH:MM:SS.FFF'. + The date format is compatible with the CCSDS time format. Args: - mjd_date (`float`): Modified Julian Date - + jd_date (``float``): Julian Date to convert Returns: - `datetime.datetime`: datetime object + ``str``: string in the format 'yyyy-mm-ddTHH:MM:SS.FFF' """ - datetime_obj=dsgp4.util.from_mjd_to_datetime(mjd_date) - return datetime_obj.replace(tzinfo = datetime.timezone.utc) + d = dsgp4.util.from_jd_to_datetime(jd_date) + return from_datetime_to_cdm_datetime_str(d) + +# def from_mjd_to_datetime_offset_aware(mjd_date): +# datetime_obj=dsgp4.util.from_mjd_to_datetime(mjd_date) +# return datetime_obj.replace(tzinfo = datetime.timezone.utc) def from_string_to_datetime(string): """ Converts a string to a datetime object. Args: - string (`str`): string to convert + string (``str``): string to convert Returns: - `datetime.datetime`: datetime object + ``datetime.datetime``: datetime object """ if string.find('.')!=-1: return datetime.datetime.strptime(string, '%Y-%m-%d %H:%M:%S.%f') @@ -470,6 +456,19 @@ def from_string_to_datetime(string): @functools.lru_cache(maxsize=None) def from_date_str_to_days(date, date0='2020-05-22T21:41:31.975', date_format='%Y-%m-%dT%H:%M:%S.%f'): + """ + Converts a date string to the number of days since a reference date. + The date string must be in the format YYYY-MM-DDTHH:MM:SS.ssssss. + The date format can be changed by passing a different date_format string. + The date format must be compatible with the strptime function from the datetime module. + + Args: + date (``str``): date string to convert + date0 (``str``, optional): reference date string. Default is '2020-05-22T21:41:31.975'. + date_format (``str``, optional): date format string. Default is '%Y-%m-%dT%H:%M:%S.%f'. + Returns: + ``float``: number of days since the reference date + """ date = datetime.datetime.strptime(date, date_format) date0 = datetime.datetime.strptime(date0, date_format) dd = date-date0 @@ -477,14 +476,32 @@ def from_date_str_to_days(date, date0='2020-05-22T21:41:31.975', date_format='%Y days_fraction = (dd.seconds + dd.microseconds/1e6) / (60*60*24) return days + days_fraction - def add_days_to_date_str(date0, days): + """ + Adds a number of days to a date string. + The date string must be in the format YYYY-MM-DDTHH:MM:SS.ssssss. + + Args: + date0 (``str``): date string to convert + days (``int``): number of days to add + date_format (``str``, optional): date format string. Default is '%Y-%m-%dT%H:%M:%S.%f'. + Returns: + ``str``: date string with the added days + """ date0 = datetime.datetime.strptime(date0, '%Y-%m-%dT%H:%M:%S.%f') date = date0 + datetime.timedelta(days=days) return from_datetime_to_cdm_datetime_str(date) - def is_date(date_string, date_format): + """ + Checks if a string is in a valid date format. + The date format must be compatible with the strptime function from the datetime module. + Args: + date_string (``str``): string to check + date_format (``str``): date format string. Default is '%Y-%m-%dT%H:%M:%S.%f'. + Returns: + ``bool``: True if the string is in a valid date format, False otherwise + """ try: datetime.datetime.strptime(date_string, date_format) return True @@ -493,6 +510,16 @@ def is_date(date_string, date_format): def transform_date_str(date_string, date_format_from, date_format_to): + """ + Transforms a date string from one format to another. + The date format must be compatible with the strptime function from the datetime module. + Args: + date_string (``str``): string to transform + date_format_from (``str``): date format string to transform from. Default is '%Y-%m-%dT%H:%M:%S.%f'. + date_format_to (``str``): date format string to transform to. Default is '%Y-%m-%dT%H:%M:%S.%f'. + Returns: + ``str``: transformed date string + """ date = datetime.datetime.strptime(date_string, date_format_from) return date.strftime(date_format_to) @@ -502,11 +529,11 @@ def find_closest(values, t): Finds the closest value in a list of values to a given value. Args: - values (`list`): list of values - t (`float`): value to find the closest to + values (``list``): list of values + t (``float``): value to find the closest to Returns: - `float`: closest value in the list to the given value + ``float``: closest value in the list to the given value """ indx = np.argmin(abs(values-t)) return indx, values[indx] @@ -516,11 +543,11 @@ def upsample(s, target_resolution): Upsamples a tensor to a given resolution, via linear interpolation. Args: - s (`torch.Tensor`): tensor to upsample - target_resolution (`int`): target resolution + s (``torch.Tensor``): tensor to upsample + target_resolution (``int``): target resolution Returns: - `torch.Tensor`: upsampled tensor + ``torch.Tensor``: upsampled tensor """ s = s.transpose(0, 1) s = torch.nn.functional.interpolate(s.unsqueeze(0), size=(target_resolution), mode='linear', align_corners=True) @@ -529,20 +556,15 @@ def upsample(s, target_resolution): def propagate_upsample(tle, times_mjd, upsample_factor=1): """ - This function is the same as `lpop_sequence`, but it allows to upsample the time, - interpolating in between. The purpose is to reduce computational time. - Caveat: this will reduce the position and velocity prediction accuracy. - + Propagates a TLE object to a set of times, and upsamples the result. + The propagation is done using the dsgp4 library, and the upsampling is done using linear interpolation. + Args: - tle (`dsgp4.tle.TLE`): the two-line element set - times_mjd (`numpy.array`): modified julian dates - upsample_factor (`int`): the state is propagated only every `upsample_factor` times, - and it is performed interpolation in between. - + tle (``dsgp4.TLE``): TLE object to propagate + times_mjd (``list``): list of times in MJD to propagate to + upsample_factor (``int``, optional): factor by which to upsample the result. Default is 1 (no upsampling). Returns: - `numpy.array`: a 3 dimensional array, where in each row, there is a 2x3 - element of position (first row), and velocity (second row), - both expressed in the TEME reference system and SI units. + ``numpy.ndarray``: propagated and upsampled state vector """ if upsample_factor == 1: tsinces=(torch.tensor(times_mjd)-dsgp4.util.from_datetime_to_mjd(tle._epoch))*1440. @@ -557,34 +579,15 @@ def propagate_upsample(tle, times_mjd, upsample_factor=1): ret = ret.view(ret.shape[0], 2, 3).cpu().numpy()*1e3 return ret - -def create_path(path, directory=False): - if directory: - dir = path - else: - dir = os.path.dirname(path) - if not os.path.exists(dir): - print('{} does not exist, creating'.format(dir)) - try: - os.makedirs(dir) - except Exception as e: - print(e) - print('Could not create path, potentially created by another process in the meantime: {}'.format(path)) - - -def tile_rows_cols(num_items): - if num_items < 5: - return 1, num_items - else: - cols = math.ceil(math.sqrt(num_items)) - rows = 0 - while num_items > 0: - rows += 1 - num_items -= cols - return rows, cols - - def has_nan_or_inf(value): + """ + Checks if a value is NaN or Inf. + + Args: + value (``float`` or ``torch.Tensor``): value to check + Returns: + ``bool``: True if the value is NaN or Inf, False otherwise + """ if torch.is_tensor(value): value = torch.sum(value) isnan = int(torch.isnan(value)) > 0 @@ -596,73 +599,24 @@ def has_nan_or_inf(value): def trace_to_event(trace): + """ + Converts a trace object to an Event object. + Args: + trace (``pyro.poutine.trace_struct.Trace``): trace object to convert + Returns: + ``kessler.Event``: Event object + """ from .event import Event return Event(cdms=trace.nodes['cdms']['infer']['cdms']) -def dist_to_event_dataset(dist): - from .event import EventDataset - return EventDataset(events=list(map(trace_to_event, dist))) - - -def days_hours_mins_secs_str(total_seconds): - d, r = divmod(total_seconds, 86400) - h, r = divmod(r, 3600) - m, s = divmod(r, 60) - return '{0}d:{1:02}:{2:02}:{3:02}'.format(int(d), int(h), int(m), int(s)) - - -def progress_bar(i, len): - bar_len = 20 - filled_len = int(round(bar_len * i / len)) - # percents = round(100.0 * i / len, 1) - return '#' * filled_len + '-' * (bar_len - filled_len) - - -progress_bar_num_iters = None -progress_bar_len_str_num_iters = None -progress_bar_time_start = None -progress_bar_prev_duration = None - - -def progress_bar_init(message, num_iters, iter_name='Items'): - global progress_bar_num_iters - global progress_bar_len_str_num_iters - global progress_bar_time_start - global progress_bar_prev_duration - if num_iters < 0: - raise ValueError('num_iters must be a non-negative integer') - progress_bar_num_iters = num_iters - progress_bar_time_start = time.time() - progress_bar_prev_duration = 0 - progress_bar_len_str_num_iters = len(str(progress_bar_num_iters)) - print(message) - sys.stdout.flush() - if progress_bar_num_iters > 0: - print('Time spent | Time remain.| Progress | {} | {}/sec'.format(iter_name.ljust(progress_bar_len_str_num_iters * 2 + 1), iter_name)) - - -def progress_bar_update(iter): - global progress_bar_prev_duration - if progress_bar_num_iters > 0: - duration = time.time() - progress_bar_time_start - if (duration - progress_bar_prev_duration > _print_refresh_rate) or (iter >= progress_bar_num_iters - 1): - progress_bar_prev_duration = duration - traces_per_second = (iter + 1) / duration - print('{} | {} | {} | {}/{} | {:,.2f} '.format(days_hours_mins_secs_str(duration), days_hours_mins_secs_str((progress_bar_num_iters - iter) / traces_per_second), progress_bar(iter, progress_bar_num_iters), str(iter).rjust(progress_bar_len_str_num_iters), progress_bar_num_iters, traces_per_second), end='\r') - sys.stdout.flush() - - -def progress_bar_end(message=None): - progress_bar_update(progress_bar_num_iters) - print() - if message is not None: - print(message) +# def dist_to_event_dataset(dist): +# from .event import EventDataset +# return EventDataset(events=list(map(trace_to_event, dist))) def get_ccsds_time_format(time_string): """ - Adapted by Andrew Ng, 18/3/2022. - Original MATLAB source code: + Original MATLAB source code (adapted by Andrew Ng, 18/3/2022): `NASA CARA Analysis Tools `_ Processes and outputs the format of the time string extracted from the CDM. @@ -684,11 +638,11 @@ def get_ccsds_time_format(time_string): 7. The time string can end with an optional **"Z"** time zone indicator. Args: - time_string (str): Original time string stored in CDM. + time_string (``str``): Original time string stored in CDM. Returns: - str: Outputs the format of the time string. - Must be of the form **yyyy-[mm-dd|ddd]THH:MM:SS[.F*][Z]**, otherwise a `RuntimeError` is raised. + ``str``: Outputs the format of the time string. + Must be of the form **yyyy-[mm-dd|ddd]THH:MM:SS[.F*][Z]**, otherwise a ``RuntimeError`` is raised. """ time_format = [] @@ -733,8 +687,7 @@ def get_ccsds_time_format(time_string): def doy_2_date(value, doy, year, idx): ''' - Written by Andrew Ng, 18/03/2022, - Based on source code @ https://github.com/nasa/CARA_Analysis_Tools + Based on source code @ https://github.com/nasa/CARA_Analysis_Tools (adapted by Andrew Ng, 18/03/2022) Use the datetime python package. doy_2_date - Converts Day of Year (DOY) date format to date format. @@ -803,12 +756,13 @@ def build_megaconstellation(launch_date, if groups not in [1,2]: raise ValueError(f"Only group values of: 1 or 2 are valid; while {groups} provided") if isinstance(launch_date,float): - launch_date=from_mjd_to_datetime(launch_date) + launch_date=dsgp4.util.from_mjd_to_datetime(launch_date) print(f"Launch date: {launch_date}, for constellation: {constellation_name}, group: {groups}") epoch_year=launch_date.year - epoch_days=from_datetime_to_fractional_day(launch_date) + #we transform the datetime in fractional days: + d = launch_date-datetime.datetime(launch_date.year-1, 12, 31) + epoch_days = d.days + d.seconds/60./60./24 + d.microseconds/60./60./24./1e6 tles=[] - if constellation_name=='starlink': starlink_dic={"group_1": {"inclination":np.deg2rad(53), @@ -1335,27 +1289,6 @@ def build_megaconstellation(launch_date, tles.append(tle) return tles -def create_path(path, directory=False): - """ - This function creates a path if it does not exist. - - Args: - path (``str``): path to be created - directory (``bool``): if True, the path is a directory, otherwise it is a file - """ - if directory: - dir = path - else: - dir = os.path.dirname(path) - if not os.path.exists(dir): - print('{} does not exist, creating'.format(dir)) - try: - os.makedirs(dir) - except Exception as e: - print(e) - print('Could not create path, potentially created by another process in the meantime: {}'.format(path)) - - def create_priors_from_tles(tles, mixture_components = {'mean_motion': 5, 'eccentricity': 5, 'inclination': 13, 'b_star': 4}): """ This function takes a list of TLEs and a dictionary of mixture_components numbers, @@ -1479,3 +1412,49 @@ def add_megaconstellation_from_file(tles, megaconstellation_file_name): """ tles_megaconstellation=dsgp4.util.load(file_name=megaconstellation_file_name) return tles+tles_megaconstellation + + +def progress_bar(i, len): + bar_len = 20 + filled_len = int(round(bar_len * i / len)) + # percents = round(100.0 * i / len, 1) + return '#' * filled_len + '-' * (bar_len - filled_len) + + +progress_bar_num_iters = None +progress_bar_len_str_num_iters = None +progress_bar_time_start = None +progress_bar_prev_duration = None + + +def progress_bar_init(message, num_iters, iter_name='Items'): + global progress_bar_num_iters + global progress_bar_len_str_num_iters + global progress_bar_time_start + global progress_bar_prev_duration + if num_iters < 0: + raise ValueError('num_iters must be a non-negative integer') + progress_bar_num_iters = num_iters + progress_bar_time_start = time.time() + progress_bar_prev_duration = 0 + progress_bar_len_str_num_iters = len(str(progress_bar_num_iters)) + print(message) + sys.stdout.flush() + if progress_bar_num_iters > 0: + print('Time spent | Time remain.| Progress | {} | {}/sec'.format(iter_name.ljust(progress_bar_len_str_num_iters * 2 + 1), iter_name)) + +def progress_bar_update(iter): + global progress_bar_prev_duration + if progress_bar_num_iters > 0: + duration = time.time() - progress_bar_time_start + if (duration - progress_bar_prev_duration > _print_refresh_rate) or (iter >= progress_bar_num_iters - 1): + progress_bar_prev_duration = duration + traces_per_second = (iter + 1) / duration + print('{} | {} | {} | {}/{} | {:,.2f} '.format(days_hours_mins_secs_str(duration), days_hours_mins_secs_str((progress_bar_num_iters - iter) / traces_per_second), progress_bar(iter, progress_bar_num_iters), str(iter).rjust(progress_bar_len_str_num_iters), progress_bar_num_iters, traces_per_second), end='\r') + sys.stdout.flush() + +def progress_bar_end(message=None): + progress_bar_update(progress_bar_num_iters) + print() + if message is not None: + print(message) diff --git a/setup.py b/setup.py index dde561d..929ee3d 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ def read_package_variable(key): author_email='giacomo.acciarini@gmail.com', packages=find_packages(), install_requires=['pyro', 'numpy', 'matplotlib', 'torch>=1.5.1', 'dsgp4', 'skyfield>=1.26', 'pandas'], - extras_require={'dev': ['pytest', 'coverage', 'pytest-xdist']}, + extras_require={'dev': ['pytest', 'coverage', 'pytest-xdist', 'scikit-learn']}, url='https://kesslerlib.github.io/kessler/', classifiers=['License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3'], license='BSD' From 2445062653c9d4986afafaa62620f294fdb62f51 Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:17:12 +0200 Subject: [PATCH 08/13] syntax fix --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 929ee3d..9e104b8 100644 --- a/setup.py +++ b/setup.py @@ -35,6 +35,6 @@ def read_package_variable(key): extras_require={'dev': ['pytest', 'coverage', 'pytest-xdist', 'scikit-learn']}, url='https://kesslerlib.github.io/kessler/', classifiers=['License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3'], - license='BSD' + license='BSD', keywords='Spacecraft Collision Avoidance Kessler Machine Learning Artificial Intelligence Probabilistic Programming', ) From 36b313e21acc4d5b9b640c0e86e61a39b99ea6a0 Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:22:01 +0200 Subject: [PATCH 09/13] tests --- kessler/model.py | 16 ++++++++-------- tests/test_util.py | 10 +++++----- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/kessler/model.py b/kessler/model.py index 2afc160..2d99957 100644 --- a/kessler/model.py +++ b/kessler/model.py @@ -308,14 +308,14 @@ def make_chaser(self): mean_anomaly = pyro.sample('c_mean_anomaly', self._prior_dict['mean_anomaly_prior']) tle = self._c_tle.copy() tle.update({'mean_anomaly': mean_anomaly}) - pyro.deterministic('c_mean_motion',tle.mean_motion) - pyro.deterministic('c_eccentricity',tle.eccentricity) - pyro.deterministic('c_inclination',tle.inclination) - pyro.deterministic('c_argument_of_perigee',tle.argument_of_perigee) - pyro.deterministic('c_raan',tle.raan) - pyro.deterministic('c_mean_motion_first_derivative',tle.mean_motion_first_derivative) - pyro.deterministic('c_mean_motion_second_derivative',tle.mean_motion_second_derivative) - pyro.deterministic('c_b_star',tle.b_star) + pyro.deterministic('c_mean_motion',torch.tensor(tle.mean_motion)) + pyro.deterministic('c_eccentricity',torch.tensor(tle.eccentricity)) + pyro.deterministic('c_inclination',torch.tensor(tle.inclination)) + pyro.deterministic('c_argument_of_perigee',torch.tensor(tle.argument_of_perigee)) + pyro.deterministic('c_raan',torch.tensor(tle.raan)) + pyro.deterministic('c_mean_motion_first_derivative',torch.tensor(tle.mean_motion_first_derivative)) + pyro.deterministic('c_mean_motion_second_derivative',torch.tensor(tle.mean_motion_second_derivative)) + pyro.deterministic('c_b_star',torch.tensor(tle.b_star)) return tle def generate_cdm(self, diff --git a/tests/test_util.py b/tests/test_util.py index fb65b1f..4d926c5 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -139,11 +139,11 @@ def test_TruncatedNormal(self): max=0.68639 categorical=Categorical(probs=probs) - batched_truncated_normal = kessler.util.TruncatedNormal(loc=locs, scale=scales, min=min, max=max) + batched_truncated_normal = kessler.util.TruncatedNormal(loc=locs, scale=scales, low=min, high=max) mix_truncated=MixtureSameFamily(categorical, batched_truncated_normal) - self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.0001)).item(), 7.382209300994873, places=8) - self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.001)).item(), 5.485926151275635, places=8) - self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.01)).item(), 1.863307237625122, places=8) - self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.1)).item(), -2.458112955093384, places=8) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.0001)).item(), 7.382209300994873, places=6) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.001)).item(), 5.485926151275635, places=6) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.01)).item(), 1.863307237625122, places=6) + self.assertAlmostEqual(mix_truncated.log_prob(torch.tensor(0.1)).item(), -2.458112955093384, places=6) From d7e240d0d7f5357968c2013f0f296ed3663ca8cf Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:24:47 +0200 Subject: [PATCH 10/13] pyro in setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9e104b8..f0717f6 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ def read_package_variable(key): author='Giacomo Acciarini', author_email='giacomo.acciarini@gmail.com', packages=find_packages(), - install_requires=['pyro', 'numpy', 'matplotlib', 'torch>=1.5.1', 'dsgp4', 'skyfield>=1.26', 'pandas'], + install_requires=['pyro-ppl', 'numpy', 'matplotlib', 'torch>=1.5.1', 'dsgp4', 'skyfield>=1.26', 'pandas'], extras_require={'dev': ['pytest', 'coverage', 'pytest-xdist', 'scikit-learn']}, url='https://kesslerlib.github.io/kessler/', classifiers=['License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3'], From 65c79fe3526552d203128c73801433dd633faaff Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:27:02 +0200 Subject: [PATCH 11/13] update plot.py --- kessler/plot.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/kessler/plot.py b/kessler/plot.py index e489f6a..80b6cf2 100644 --- a/kessler/plot.py +++ b/kessler/plot.py @@ -16,7 +16,6 @@ import tempfile import pyro import dsgp4 -from pyprob.distributions import Empirical import numpy as np import torch @@ -25,8 +24,6 @@ mpl.rcParams['axes.unicode_minus'] = False -# I need to re-write this w.r.t. pyprob, since 'nonposy', 'nonposx' are deprecated in favour of 'nonpositive' -# TODO: transform this into a more generic plot_priors, that takes the priors dict, and plots each mixture def plot_mix(mix, min_val=-10, max_val=10, resolution=1000, figsize=(10, 5), xlabel=None, ylabel='Probability', xticks=None, yticks=None, log_xscale=False, log_yscale=False, file_name=None, show=True, fig=None, ax = None, *args, **kwargs): if ax is None: if not show: From 0e904567bbecbdb621011d4c59796985ffca7332 Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:29:10 +0200 Subject: [PATCH 12/13] syntax fix --- kessler/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/kessler/model.py b/kessler/model.py index 2d99957..5e0eaa5 100644 --- a/kessler/model.py +++ b/kessler/model.py @@ -230,11 +230,11 @@ def __init__(self, if t_observing_instruments is None: t_instrument_characteristics={'bias_xyz': np.array([[0., 0., 0.],[0., 0., 0.]]), 'covariance_rtn': np.array([1e-9, 1.115849341564346, 0.059309835843067, 1e-9, 1e-9, 1e-9])**2} t_observing_instruments=[GNSS(t_instrument_characteristics)] - print(f'No observing instruments for target, using default one with diagonal covariance {t_observing_instruments[0]._instrument_characteristics['covariance_rtn']}') + print(f"No observing instruments for target, using default one with diagonal covariance {t_observing_instruments[0]._instrument_characteristics['covariance_rtn']}") if c_observing_instruments is None: c_instrument_characteristics={'bias_xyz': np.array([[0., 0., 0.],[0., 0., 0.]]), 'covariance_rtn': np.array([1.9628939405514678, 2.2307686944695706, 0.9660907831563862, 1e-9, 1e-9, 1e-9])**2} c_observing_instruments=[Radar(c_instrument_characteristics)] - print(f'No observing instruments for chaser, using default one with diagonal covariance {c_observing_instruments[0]._instrument_characteristics['covariance_rtn']}') + print(f"No observing instruments for chaser, using default one with diagonal covariance {c_observing_instruments[0]._instrument_characteristics['covariance_rtn']}") if len(t_observing_instruments) == 0 or len(c_observing_instruments) == 0: raise ValueError("We need at least one observing instrument for target and chaser!") self._t_observing_instruments = t_observing_instruments From f2189be4e552501d9722e3fc3c9fde714d9b9b5a Mon Sep 17 00:00:00 2001 From: "Acciarini, Giacomo (PG/R - Maths & Physics)" Date: Thu, 24 Apr 2025 17:36:03 +0200 Subject: [PATCH 13/13] update missing util --- .../cdms_analysis_and_plotting.ipynb | 42 ++++++++----------- kessler/util.py | 11 +++++ 2 files changed, 28 insertions(+), 25 deletions(-) diff --git a/docs/notebooks/cdms_analysis_and_plotting.ipynb b/docs/notebooks/cdms_analysis_and_plotting.ipynb index 7fff26b..190e9bc 100644 --- a/docs/notebooks/cdms_analysis_and_plotting.ipynb +++ b/docs/notebooks/cdms_analysis_and_plotting.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "broadband-kruger", "metadata": {}, "outputs": [ @@ -33,8 +33,6 @@ "output_type": "stream", "text": [ "Loading CDMS (with extension .kvn) from directory: synthetic_cdms\n", - "['synthetic_cdms/event1_0.kvn', 'synthetic_cdms/event1_1.kvn', 'synthetic_cdms/event1_2.kvn', 'synthetic_cdms/event1_3.kvn', 'synthetic_cdms/event1_4.kvn', 'synthetic_cdms/event1_5.kvn', 'synthetic_cdms/event1_6.kvn', 'synthetic_cdms/event2_0.kvn', 'synthetic_cdms/event2_1.kvn', 'synthetic_cdms/event2_2.kvn', 'synthetic_cdms/event2_3.kvn', 'synthetic_cdms/event2_4.kvn', 'synthetic_cdms/event2_5.kvn', 'synthetic_cdms/event2_6.kvn']\n", - "['synthetic_cdms/event1', 'synthetic_cdms/event2']\n", "Loaded 14 CDMs grouped into 2 events\n" ] } @@ -56,23 +54,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "wrapped-thought", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Converting EventDataset to DataFrame\n" + " 0%| | 0/2 [00:00 2\u001b[0m \u001b[43mevents\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmulti_features_multi_events.pdf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Develop/kessler/kessler/event.py:514\u001b[0m, in \u001b[0;36mEventDataset.plot_features\u001b[0;34m(self, feature_names, figsize, axs, return_axs, file_name, sharex, *args, **kwargs)\u001b[0m\n\u001b[1;32m 512\u001b[0m feature_names \u001b[38;5;241m=\u001b[39m [feature_names]\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 514\u001b[0m rows, cols \u001b[38;5;241m=\u001b[39m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtile_rows_cols\u001b[49m(\u001b[38;5;28mlen\u001b[39m(feature_names))\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m figsize \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 516\u001b[0m figsize \u001b[38;5;241m=\u001b[39m (cols\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m20\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m7\u001b[39m, rows\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m12\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m6\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'kessler.util' has no attribute 'tile_rows_cols'" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ diff --git a/kessler/util.py b/kessler/util.py index 4fc2de2..3b81207 100644 --- a/kessler/util.py +++ b/kessler/util.py @@ -579,6 +579,17 @@ def propagate_upsample(tle, times_mjd, upsample_factor=1): ret = ret.view(ret.shape[0], 2, 3).cpu().numpy()*1e3 return ret +def tile_rows_cols(num_items): + if num_items < 5: + return 1, num_items + else: + cols = math.ceil(math.sqrt(num_items)) + rows = 0 + while num_items > 0: + rows += 1 + num_items -= cols + return rows, cols + def has_nan_or_inf(value): """ Checks if a value is NaN or Inf.