From e716a8f7a951d5cb391a577b7478771a8c97c195 Mon Sep 17 00:00:00 2001 From: voetberg Date: Wed, 4 Oct 2023 08:47:53 -0500 Subject: [PATCH] Updated naming of the merged stuff --- DeepSurveySim/Survey/__init__.py | 2 + DeepSurveySim/Survey/cummulative_survey.py | 217 + DeepSurveySim/Survey/observation_variables.py | 45 +- DeepSurveySim/Survey/survey.py | 10 +- DeepSurveySim/Survey/weather.py | 127 + DeepSurveySim/settings/SEO.yaml | 8 +- DeepSurveySim/settings/SEO_weather.csv | 11421 ++++++++++++++++ test/test_cummaltive_survey.py | 4 +- test/test_weather.py | 13 +- 9 files changed, 11823 insertions(+), 24 deletions(-) create mode 100644 DeepSurveySim/Survey/cummulative_survey.py create mode 100644 DeepSurveySim/Survey/weather.py create mode 100644 DeepSurveySim/settings/SEO_weather.csv diff --git a/DeepSurveySim/Survey/__init__.py b/DeepSurveySim/Survey/__init__.py index 46991ef..c01fe34 100644 --- a/DeepSurveySim/Survey/__init__.py +++ b/DeepSurveySim/Survey/__init__.py @@ -2,3 +2,5 @@ from DeepSurveySim.Survey.observation_variables import ( ObservationVariables, ) +from DeepSurveySim.Survey.cummulative_survey import UniformSurvey, LowVisiblitySurvey +from DeepSurveySim.Survey.weather import Weather diff --git a/DeepSurveySim/Survey/cummulative_survey.py b/DeepSurveySim/Survey/cummulative_survey.py new file mode 100644 index 0000000..6ec6255 --- /dev/null +++ b/DeepSurveySim/Survey/cummulative_survey.py @@ -0,0 +1,217 @@ +from DeepSurveySim.Survey.survey import Survey +import numpy as np +import pandas as pd +import json + + +class CummulativeSurvey(Survey): + def __init__( + self, observatory_config: dict, survey_config: dict, *args, **kwargs + ) -> None: + """ + Abstract class for a survey that evaluated across multiple steps + + Args: + observatory_config (dict): _description_ + survey_config (dict): _description_ + """ + super().__init__(observatory_config, survey_config) + self.all_steps = pd.DataFrame() + + def reset(self): + """Return the survey to its initial condition, wipe out all the original steps""" + self.all_steps = pd.DataFrame() + return super().reset() + + def cummulative_reward(self): + """Reward that uses the 'all_steps' class parameter.""" + raise NotImplemented + + def step(self, action: dict): + """ + Move the observator forward with one action and add the reward and stop condition to the returned observation. + Reward is defined with 'cummulative_reward' + + Args: + action (dict): Dictionary containing "time" (array in units Mean Julian Date) "location"(dict with ra, decl, in degrees as arrays) (optional), "band" (str of the represention of the optical filter) (optional) + + Returns: + Tuple : observation (dict, containing survey_config["variables"], vality, Time (in mjd)), reward (array), stop (array), log (dictionary) + """ + observation, reward, stop, log = super().step(action) + observation_pd = {key: observation[key].ravel() for key in observation.keys()} + + observation_pd = pd.DataFrame(observation_pd) + observation_pd["action"] = str( + {"location": action["location"], "band": self.observator.band} + ) + observation_pd["band"] = self.observator.band + observation_pd["location"] = str(action["location"]) + observation_pd["reward"] = reward + + self.all_steps = self.all_steps.append(observation_pd) + + reward = self.cummulative_reward() + return observation, reward, stop, log + + +class UniformSurvey(CummulativeSurvey): + """ + A child survey that instead of evaluating the schedule at every step, evaluates it for a full schedule. + This survey requires a threshold is reached for each observation before it starts recording any sort of reward. + + Args: + obseravtory_config (dict): Setup parameters for Survey.ObservationVariables, the telescope configuration, as read by IO.ReadConfig + survey_config (dict): Parameters for the survey, including the stopping conditions, the validity conditions, the variables to collect, as read by IO.ReadConfig + threshold (float): Threshold the survey must pass to have its quality counted towards the total reward + uniform (str): ["site", "quality"] - If measuring the uniformity of the number of times each site has been visited, or the uniformity of the quality of observations + """ + + def __init__( + self, + observatory_config: dict, + survey_config: dict, + threshold: float = 1.0, + uniform: str = "site", + ) -> None: + super().__init__(observatory_config, survey_config) + + self.threshold = threshold + reward_function = { + "site": self.site_reward, + "quality": self.quality_reward, + } + assert uniform in reward_function + + self.reward_function = reward_function[uniform] + + def site_reward(self): + """ + Calculate the reward for all sites, assuming it is required for a threshold on the number of times each site is visited. + Follows the equation: + + $$R_{S_{n}} = \frac{1}{||T||* Var(\\{||(s_i)||: s_i \\in S_n\\})}* \\Sigma^{S_{n}}_{i=0} \begin{cases} \\Sigma^{t_{n}}_{j=0} \tau_{eff}(s_{i,j}) & ||s_i|| \\geq N \\ 0 & ||s_i|| < N \\ \\end{cases}$$ + + Returns: + float: reward based on if number of sites a site was visited reached a threshold + """ + counts = self.all_steps["action"].value_counts() + reward_scale = 1 / (len(self.all_steps) * np.var(counts)) + + current_steps = self.all_steps.copy() + # Replace reward if it doesn't make it pass the threshold + current_steps.loc[ + self.all_steps["action"].isin(counts.index[counts < self.threshold]), + "reward", + ] = 0 + reward_sum = ( + current_steps.groupby(["mjd", "action", "band"])["reward"].sum().sum() + ) + + return reward_scale * reward_sum + + def quality_reward(self): + """Cummulitive reward based on if a per site reward threshold is reached. + Defined as: + + $$R_{S_{n}} = \frac{1}{||T||* Var(\\{\tau_{eff}(s_i): s_i \\in S_n)\\}}* \\Sigma^{t_{n}}_{i=0} \begin{cases} \tau_{eff}(s_i) & T_{eff}(s_i) \\geq \theta \\ 0 & \tau_{eff}(s_i) < \theta \\ \\end{cases}$$ + + Returns: + float: reward based on if a threshold in reward per site is reached. + """ + reward_scale = 1 / (len(self.all_steps)) * np.var(self.all_steps["reward"]) + + current_steps = self.all_steps.copy() + # Replace reward if it doesn't make it pass the threshold + current_steps.loc[current_steps["reward"] < self.threshold, "reward"] = 0 + reward_sum = current_steps["reward"].sum() + return reward_scale * reward_sum + + def cummulative_reward(self, *args, **kwargs): + """ + Quality thresholded or visit thresholded reward + + Returns: + float: all sites reward + """ + if len(self.all_steps) != 0: + reward = self.reward_function() + reward = reward if not (pd.isnull(reward) or reward == -np.inf) else 0 + + return reward + else: + return 0 + + +class LowVisiblitySurvey(CummulativeSurvey): + def __init__( + self, + observatory_config: dict, + survey_config: dict, + required_sites: list = [], + other_site_weight: float = 0.6, + time_tolerance: float = 0.01388, + ) -> None: + """ + Survey that evaluates how many times a "required site" has been visited, and defines the reward of a site based on this. + + Args: + obseravtory_config (dict): Setup parameters for Survey.ObservationVariables, the telescope configuration, as read by IO.ReadConfig + survey_config (dict): Parameters for the survey, including the stopping conditions, the validity conditions, the variables to collect, as read by IO.ReadConfig + required_sites (list, optional): Sites that must be visited to achieve reward. Same format as an action. Defaults to []. + other_site_weight (float, optional): Weighting factor applied to reward gained from visiting sites not in the "required_sites" list. Defaults to 0.6. + time_tolerance (float, optional): Tolerance given to a required time. Defaults to 0.01388 (seconds, 20 minutes). + """ + super().__init__(observatory_config, survey_config) + + self.required_sites = required_sites + self.time_tolerance = time_tolerance + self.weight = other_site_weight + + def sites_hit(self): + """Count the number of times a required site was visited. Does not follow a thresholding rule. + + Returns: + int: times the requires sites were visited. + """ + + # TODO do this with sets and arrays instead of a loop + hit_counter = 0 + for site in self.required_sites: + + subset = self.all_steps.copy() + if "time" in site.keys(): + subset = subset[ + (subset["mjd"] < site["time"][0] + self.time_tolerance) + & (subset["mjd"] > site["time"][0] - self.time_tolerance) + ] + + if "band" in site.keys(): + subset = subset[subset["band"] == site["band"]] + + subset = subset[subset["location"] == str(site["location"])] + hit_counter += len(subset) + + return hit_counter + + def cummulative_reward(self): + """ + Reward for all visited sites as defined as + $$ R_{s_{n}} = \frac{1}{||T||} (||\\{s_i: s_i \\in S_{interest}\\}|| + \\lambda \\Sigma^{t_{n}}_{t=0} T_{eff_t}(s_t)) $$ + + Returns: + float: reward calculted as a result of all current sites visited in the schedule + """ + if len(self.all_steps) != 0: + + reward_scale = 1 / len(self.all_steps) + weighted_term = self.weight * self.all_steps["reward"].sum() + number_of_interest_hit = self.sites_hit() + + reward = reward_scale * (weighted_term + number_of_interest_hit) + reward = reward if not (pd.isnull(reward) or reward == -np.inf) else 0 + + return reward + + else: + return 0.0 diff --git a/DeepSurveySim/Survey/observation_variables.py b/DeepSurveySim/Survey/observation_variables.py index 954eb18..6df35a3 100644 --- a/DeepSurveySim/Survey/observation_variables.py +++ b/DeepSurveySim/Survey/observation_variables.py @@ -83,6 +83,8 @@ def __init__(self, observator_configuration: dict): self.seeing = observator_configuration["seeing"] self.clouds = observator_configuration["cloud_extinction"] + if observator_configuration["weather_sim"]: + self._init_weather(observator_configuration["weather_config"]) self.optics_fwhm = observator_configuration["fwhm"] @@ -103,27 +105,37 @@ def __init__(self, observator_configuration: dict): self.readout_seconds = observator_configuration["readout_seconds"] def _init_skybright(self, skybright_config): + from configparser import ConfigParser + try: from skybright import skybright - from configparser import ConfigParser - - default_config_path = f"{os.path.dirname(__file__).rstrip('/')}/../settings/skybright_config.conf" - - config_path = ( - default_config_path - if skybright_config["config"] == "default" - else skybright_config["config"] - ) - skybright_config_file = ConfigParser() - skybright_config_file.read(config_path) - - self.skybright = skybright.MoonSkyModel(skybright_config_file) except ModuleNotFoundError: - print( + raise ModuleNotFoundError( "ERROR: skybright module not found, please install it from https://github.com/ehneilsen/skybright.git" ) + default_config_path = ( + f"{os.path.dirname(__file__).rstrip('/')}/../settings/skybright_config.conf" + ) + + config_path = ( + default_config_path + if skybright_config["config"] == "default" + else skybright_config["config"] + ) + skybright_config_file = ConfigParser() + skybright_config_file.read(config_path) + + self.skybright = skybright.MoonSkyModel(skybright_config_file) + + def _init_weather(self, weather_config): + from DeepSurveySim.Survey import Weather + + self.weather = Weather( + base_seeing=self.seeing, base_clouds=self.clouds, **weather_config + ) + def update( self, time: Union[float, list[float]], @@ -157,6 +169,11 @@ def update( self.band = band if band is not None else self.band self.location = location + if hasattr(self, "weather"): + conditions = self.weather.condition(self.time) + self.seeing = self.weather.seeing(conditions) + self.clouds = self.weather.clouds(conditions) + def _angular_distance(self, location): ra1, ra2 = np.array( (self.location.ra.value * np.pi / 180) + 10**-9 diff --git a/DeepSurveySim/Survey/survey.py b/DeepSurveySim/Survey/survey.py index b026ca5..d4e4094 100644 --- a/DeepSurveySim/Survey/survey.py +++ b/DeepSurveySim/Survey/survey.py @@ -139,7 +139,9 @@ def step(self, action: dict): Returns: Tuple : observation (dict, containing survey_config["variables"], vality, Time (in mjd)), reward (array), stop (array), log (dictionary) """ - print(action) + if "time" not in action: + action["time"] = np.array(self.time) + self.observator.update(**action) self.time = self.observator.time.mjd.mean() observation = self._observation_calculation() @@ -164,6 +166,12 @@ def _observation_calculation(self): return observation def __call__(self): + """ + Run the survey with the initial location until the stopping condition is met, return the completed survey + + Returns: + dict: Evaluated survey in the form of time:{"variable_name":[variable_value]} + """ stop = False results = {} while not stop: diff --git a/DeepSurveySim/Survey/weather.py b/DeepSurveySim/Survey/weather.py new file mode 100644 index 0000000..a9a0232 --- /dev/null +++ b/DeepSurveySim/Survey/weather.py @@ -0,0 +1,127 @@ +import pandas as pd +from astropy.time import Time + + +class Weather: + def __init__( + self, + weather_source_file: str, + seeing_tolerance: float = 0.5, + cloud_tolerance: float = 0.5, + base_seeing: float = 0.9, + base_clouds: float = 0, + csv_configuration: dict = {}, + **kwargs + ) -> None: + """Run a deteriministic weather simulation based on historical data. + Assumes all weather conditions are a function of the month. + Update either "clouds" or "seeing" based on given tolerances. + + Args: + weather_source_file (str): Path to csv file containing historical weather data. + seeing_tolerance (float, optional): Allowed percent of cloudiness before seeing is impacted. Defaults to 0.5. + cloud_tolerance (float, optional): Allowed percent of cloudiness before clouds are set to full extiniction. Defaults to 0.5. + base_seeing (float, optional): Best allowed seeing conditions. Defaults to 0.9. + base_clouds (float, optional): Best allowed cloud conditions. Defaults to 0. + csv_configuration (dict, optional): Instructions on how to read the csv, add in any parameters to the configuration dictionary with this argument. Defaults to {"seeing": "HourlySkyConditions","date": "DATE", "allowed_conditions": ["FEW", "CLR"]} + } + . + """ + + csv_configuration = {**csv_configuration, **self._default_configuration()} + self.seeing_name = csv_configuration["seeing"] + self.date_name = csv_configuration["date"] + + self.seeing_tolerance = seeing_tolerance + self.clouds_tolerance = cloud_tolerance + + self.reference_seeing = base_seeing + self.reference_clouds = base_clouds + + self.weather_source = pd.read_csv(weather_source_file)[ + [self.seeing_name, self.date_name] + ] + self._format_source(csv_configuration) + + def _default_configuration(self): + return { + "seeing": "HourlySkyConditions", + "date": "DATE", + "allowed_conditions": ["FEW", "CLR"], + } + + def _format_source(self, configuration): + + self.weather_source.dropna(inplace=True) + + allowed = "|".join(configuration["allowed_conditions"]) + + self.weather_source[self.seeing_name] = ( + self.weather_source[self.seeing_name].str.contains(allowed).astype(int) + ) + + self.weather_source[self.date_name] = pd.to_datetime( + self.weather_source[self.date_name], infer_datetime_format=True + ) + + def _find_date(self, mjd): + date = Time(mjd, format="mjd") + month = date.datetime.month + day = date.datetime.day + return month, day + + def condition(self, mjd): + """ + Return the sky conditions for all data with the same month as the supplied mjd. + + Args: + mjd (Union(int, float)): Date in MJD to get sky conditions for. + + Returns: + pd.Series: Conditions of the sky with the same month as the supplied mjd. + """ + month, day = self._find_date(mjd) + matching = self.weather_source[ + self.weather_source[self.date_name].dt.month == month + ] + matching = matching[ + matching[self.date_name].dt.day.isin([day, day + 1, day + 2]) + ] + return matching + + def seeing(self, condition): + """ + Approximate seeing conditions. If the seeing condition is above the tolerance level, seeing is scaled by the percent removed from perfect seeing + + Args: + condition (pd.Series): Series to arggergate into a seeing score + + Returns: + float: Updated seeing conditions + """ + seeing_conditions = 1 - condition[self.seeing_name].mean() + + if seeing_conditions >= self.seeing_tolerance: + seeing = self.reference_seeing * (1 - round(seeing_conditions, 1)) + else: + seeing = self.reference_seeing + + return seeing + + def clouds(self, condition): + """ + Approximate cloud extiction. If the cloud conditions are above the tolerance levels, clouds are set to 1. Else returns them to base levels. + + Args: + condition (pd.Series): Series to arggergate into a cloud score + + Returns: + float: clouds for the passed condition + """ + cloud_condition = 1 - condition[self.seeing_name].mean() + if cloud_condition >= self.clouds_tolerance: + clouds = 1 + else: + clouds = self.reference_clouds + + return clouds diff --git a/DeepSurveySim/settings/SEO.yaml b/DeepSurveySim/settings/SEO.yaml index f0b62e8..c7ea47c 100644 --- a/DeepSurveySim/settings/SEO.yaml +++ b/DeepSurveySim/settings/SEO.yaml @@ -25,6 +25,10 @@ fwhm: 0.45 # weather seeing: 0.9 cloud_extinction: 0.0 +weather_sim: False +weather_config: { + 'weather_source_file': "./telescope_positioning_simulation/settings/SEO_weather.csv" +} # camera shutter_seconds: 0.0 @@ -34,4 +38,6 @@ location : {'n_sites': 10} # skybright options use_skybright: False -skybright: {"config":'default'} \ No newline at end of file +skybright: {"config":'default'} + +# weather options: diff --git 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+18893,2022-07-03T16:53:00,SCT:04 200 +16504,2022-04-15T21:00:00,BKN:07 20 +14408,2022-02-09T12:53:00,FEW:02 150 +14120,2022-02-01T00:53:00,CLR:00 +21283,2022-09-17T18:53:00,CLR:00 +23682,2022-11-23T06:53:00,BKN:07 1 +19432,2022-07-21T01:53:00,OVC:08 2 diff --git a/test/test_cummaltive_survey.py b/test/test_cummaltive_survey.py index 3aa31d6..9f299f1 100644 --- a/test/test_cummaltive_survey.py +++ b/test/test_cummaltive_survey.py @@ -1,7 +1,7 @@ import pytest -from telescope_positioning_simulation.Survey import UniformSurvey, LowVisiblitySurvey -from telescope_positioning_simulation.IO import ReadConfig +from DeepSurveySim.Survey import UniformSurvey, LowVisiblitySurvey +from DeepSurveySim.IO import ReadConfig action = {"location": {"ra": [0], "decl": [0]}, "band": "g"} action_2 = {"location": {"ra": [1], "decl": [1]}, "band": "g"} diff --git a/test/test_weather.py b/test/test_weather.py index b860cf5..d5c17d1 100644 --- a/test/test_weather.py +++ b/test/test_weather.py @@ -1,11 +1,11 @@ import pytest import numpy as np -from telescope_positioning_simulation.Survey import Weather -from telescope_positioning_simulation.Survey import ObservationVariables -from telescope_positioning_simulation.IO import ReadConfig +from DeepSurveySim.Survey import Weather +from DeepSurveySim.Survey import ObservationVariables +from DeepSurveySim.IO import ReadConfig -weather_source_file = "./telescope_positioning_simulation/settings/SEO_weather.csv" +weather_source_file = "./DeepSurveySim/settings/SEO_weather.csv" @pytest.fixture @@ -29,6 +29,7 @@ def test_find_date_summer(weather): assert month == 7 assert day == 1 + def test_find_date_winter(weather): mjd = 58119 # Jan 01 2018 month, day = weather._find_date(mjd) @@ -42,8 +43,8 @@ def test_find_condition(weather): condition = weather.condition(mjd) assert np.all(condition["DATE"].dt.month == 7) - assert np.all(condition['DATE'].dt.day.isin([1,2,3])) - + assert np.all(condition["DATE"].dt.day.isin([1, 2, 3])) + def test_find_seeing(weather): mjd = 58320