ADD:
`generate_data.py` for processing and fitting statistical distributions to data. 'distributions.py' to create new dists to fit 'analysis_data.ipynb" notebook for data analysis
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scripts/generate_data.py
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193
scripts/generate_data.py
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from pathlib import Path
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import sys
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from etc.tools.read_raw_data import load_data, RC_PREFIX, DP_PREFIX
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from etc.fitting import Fitter
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from etc.tools.statistics import aic_statistic
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import pandas as pd
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import plotly.graph_objects as go
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import plotly.io as pio
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import numpy as np
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from plotly.subplots import make_subplots
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import itertools
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import os
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import seaborn as sns
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import matplotlib.pyplot as plt
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from scipy.special import gamma as gamma_func
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from scipy.special import kv as modified_bessel_second_kind
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from scipy.special import gammaln
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from scipy.stats import gamma,gumbel_r,gumbel_l,gompertz ,norm,weibull_min, lognorm, genextreme, genpareto, rayleigh, kstest, rv_continuous, goodness_of_fit
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import statsmodels.api as sm
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from dotenv import load_dotenv
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pio.renderers.default = "browser"
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# LOAD env variables in dir above
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load_dotenv(os.path.join(os.path.dirname(__file__), "..", ".env"))
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DATA_PATH = os.path.join(os.path.dirname(__file__), "..", "data")
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if __name__ == "__main__":
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aberturas = ['12','13','14','15']
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voltas = ['0','2','4','6','8','10']
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ads = ['0','3']
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bursts = ['0','3']
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# if not exists, create the directory to save the dataframes and the figure
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if not os.path.exists(DATA_PATH):
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os.makedirs(DATA_PATH)
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dist_list = [gumbel_r, weibull_min, lognorm, rayleigh]
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dist_list_log = [gumbel_l, weibull_min, genextreme, gamma]
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statistics_dataframe = pd.DataFrame(columns=[dist.name for dist in dist_list_log])
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# create dataframes for each dist in list
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# weibull_dataframe = pd.DataFrame(columns=[f'weibull_{param}' for param in ['shape', 'loc', 'scale']])
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# rayleigh_dataframe = pd.DataFrame(columns=[f'rayleigh_{param}' for param in ['loc', 'scale']])
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# normal_dataframe = pd.DataFrame(columns=[f'normal_{param}' for param in ['loc', 'scale']])
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# gumbel_l_dataframe = pd.DataFrame(columns=[f'gumbel_l_{param}' for param in ['loc','scale']])
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# genextreme_dataframe = pd.DataFrame(columns=[f'genextreme_{param}' for param in ['shape','loc','scale']])
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# gamma_dataframe = pd.DataFrame(columns=[f'gamma_{param}' for param in ['shape','loc','scale']])
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# gompertz_dataframe = pd.DataFrame(columns=[f'gompertz_{param}' for param in ['shape','loc','scale']])
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i = 1
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for abertura, volta, ad, burst in itertools.product(aberturas, voltas, ads, bursts):
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print(f"########### ITERATION {i} ###########")
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print(f"Processing ad{ad} burst{burst} abertura{abertura} volta{volta}")
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_data_rc = load_data(RC_PREFIX, ad_num=ad, burst_num=burst, feixe_num=0, abertura_num=abertura, volta_num=volta)
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_data_rc = np.squeeze(_data_rc)
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_data_dp = load_data(DP_PREFIX, ad_num=ad, burst_num=burst, feixe_num=0, abertura_num=abertura, volta_num=volta)
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_data_dp = np.squeeze(_data_dp)
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_noise_data_dp = _data_dp[5000:6000,:]
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# values of _noise_data_dp should be greater than 0, take only values greater than 0
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_noise_data_dp = _noise_data_dp[np.abs(_noise_data_dp) > 0]
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_mean__noise_dp = np.abs(_noise_data_dp).mean()
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factor_quartile_99 = 3.424
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factor_quantile_pfa_1e_6 = 4.194
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_data_dp_threshold = _mean__noise_dp * factor_quantile_pfa_1e_6
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_dp_above_noise_idx = np.where(np.abs(_data_dp).mean(axis=1) > _data_dp_threshold)[0]
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# plot dp mean and threshold to check if the threshold is correct, save the figure
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# fig = go.Figure()
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# fig.add_trace(go.Scatter(y=np.abs(_data_dp).mean(axis=1), name='Mean DP Power'))
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# fig.add_trace(go.Scatter(y=[_data_dp_threshold]*len(_data_dp), name='Threshold', line=dict(dash='dash')))
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# fig.update_layout(title=f'Mean DP Power and Threshold for ad{ad} burst{burst} abertura{abertura} volta{volta}', autosize=True)
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# # plot dots on _dp_above_noise_idx
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# fig.add_trace(go.Scatter(x=_dp_above_noise_idx, y=np.abs(_data_dp).mean(axis=1)[_dp_above_noise_idx], mode='markers', name='Above Threshold', marker=dict(color='red', size=5)))
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# fig.show()
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_dp_above_noise_idx = _dp_above_noise_idx[_dp_above_noise_idx >2250] # remove the first 1500 samples to avoid noise
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_rain_data = _data_rc[_dp_above_noise_idx,:]
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# get rain data above zero
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_rain_data = _rain_data[np.abs(_rain_data) > 0]
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# plot rain data to check if it is correct, plot the figure
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# fig = go.Figure()
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# fig.add_trace(go.Scatter(y=np.abs(_data_rc).mean(axis=1), name='Rain Data'))
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# fig.update_layout(title=f'Rain Data for ad{ad} burst{burst} abertura{abertura} volta{volta}', autosize=True)
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# # add dots on _dp_above_noise_idx
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# fig.add_trace(go.Scatter(x=_dp_above_noise_idx, y=np.abs(_data_rc).mean(axis=1)[_dp_above_noise_idx], mode='markers', name='Above Threshold', marker=dict(color='red', size=5)))
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# fig.show()
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# check if _rain_data is empty, if it is, skip this iteration
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if _rain_data.size == 0:
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continue
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### LOG DATA
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_rain_data_log = np.log(_rain_data)
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_rain_data_log = _rain_data_log[np.isfinite(_rain_data_log)]
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fitter = Fitter(dist_list_log, statistic_method=aic_statistic)
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print(f"Fitting distributions for ad{ad} burst{burst} abertura{abertura} volta{volta}")
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fitter.fit(_rain_data_log)
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print(f"Validating fits for ad{ad} burst{burst} abertura{abertura} volta{volta}")
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fitter.validate(n_mc_samples=1,)
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# create a pandas dataframe to store the statistics of the fitted distributions and concatenate it with the existing dataframe
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dist_stats = {dist.name: fitter[dist.name].pvalue for dist in dist_list_log}
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print(f"dist stats: {dist_stats}")
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dist_stats_df = pd.DataFrame(dist_stats,index=[0])
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statistics_dataframe = pd.concat([statistics_dataframe, dist_stats_df], ignore_index=True)
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# check if any p-value is greater than 0.05, if it isnt, break the loop and do not save the dataframes and the figure
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if not any(stat > 0.05 for stat in dist_stats.values()):
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print(f"No distribution passed the GoF test for ad{ad} burst{burst} abertura{abertura} volta{volta}. Skipping saving results.")
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break
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# create histogram and save .html file with the figure
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fig_1 = fitter.histogram_with_fits()
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fig_1.write_html(f'{DATA_PATH}/histogram_with_fits_ad{ad}_burst{burst}_abertura{abertura}_volta{volta}.html')
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# save qqplot and save .html file with the figure
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fig_2 = fitter.plot_qq_plots(method='filliben')
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fig_2.write_html(f'{DATA_PATH}/qq_plots_ad{ad}_burst{burst}_abertura{abertura}_volta{volta}.html')
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# # create pandas dataframe to store weibull parameters and concatenate it with the existing dataframe
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# weibull_params = {f'weibull_{param}': fitter._dist['weibull_min'].fit_result_params[i] for i, param in enumerate(['shape', 'loc', 'scale'])}
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# weibull_params_df = pd.DataFrame(weibull_params,index=[0])
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# weibull_dataframe = pd.concat([weibull_dataframe, weibull_params_df], ignore_index=True)
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# # create pandas dataframe for rayleigh parameters and concatenate it with the existing dataframe
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# rayleigh_params = {f'rayleigh_{param}': fitter._dist['rayleigh'].fit_result_params[i] for i, param in enumerate(['loc', 'scale'])}
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# rayleigh_params_df = pd.DataFrame(rayleigh_params,index=[0])
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# rayleigh_dataframe = pd.concat([rayleigh_dataframe, rayleigh_params_df], ignore_index=True)
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# # create pandas dataframe for normal parameters and concatenate it with the existing dataframe
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# normal_params = {f'normal_{param}': fitter._dist['norm'].fit_result_params[i] for i, param in enumerate(['loc', 'scale'])}
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# normal_params_df = pd.DataFrame(normal_params,index=[0])
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# normal_dataframe = pd.concat([normal_dataframe, normal_params_df], ignore_index=True)
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# create pandas dataframe for gumbel_r parameters and concatenate it with the existing dataframe
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# gumbel_l_params = {f'gumbel_l_{param}': fitter._dist['gumbel_l'].fit_result_params[i] for i, param in enumerate(['loc', 'scale'])}
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# gumbel_l_params_df = pd.DataFrame(gumbel_l_params,index=[0])
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# gumbel_l_dataframe = pd.concat([gumbel_l_dataframe, gumbel_l_params_df], ignore_index=True)
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# create pandas dataframe for genextreme parameters and concatenate it with the existing dataframe
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# genextreme_params = {f'genextreme_{param}': fitter._dist['genextreme'].fit_result_params[i] for i, param in enumerate(['shape', 'loc', 'scale'])}
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# genextreme_params_df = pd.DataFrame(genextreme_params,index=[0])
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# genextreme_dataframe = pd.concat([genextreme_dataframe, genextreme_params_df], ignore_index=True)
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# create pandas dataframe for gamma parameters and concatenate it with the existing dataframe
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# gamma_params = {f'gamma_{param}': fitter._dist['gamma'].fit_result_params[i] for i, param in enumerate(['shape', 'loc', 'scale'])}
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# gamma_params_df = pd.DataFrame(gamma_params,index=[0])
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# gamma_dataframe = pd.concat([gamma_dataframe, gamma_params_df], ignore_index=True)
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# create pandas dataframe for gompertz parameters and concatenate it with the existing dataframe
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# gompertz_params = {f'gompertz_{param}': fitter._dist['gompertz'].fit_result_params[i] for i, param in enumerate(['shape', 'loc', 'scale'])}
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# gompertz_params_df = pd.DataFrame(gompertz_params,index=[0])
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# gompertz_dataframe = pd.concat([gompertz_dataframe, gompertz_params_df], ignore_index=True)
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i += 1
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# path to save the dataframes and the figure
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statistics_dataframe.to_csv(f'{DATA_PATH}/distribution_fit_pvalue_statistics.csv', index=False)
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# rayleigh_dataframe.to_csv(f'{DATA_PATH}/rayleigh_parameters.csv', index=False)
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#weibull_dataframe.to_csv(f'{DATA_PATH}/weibull_parameters.csv', index=False)
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# normal_dataframe.to_csv(f'{DATA_PATH}/normal_parameters.csv', index=False)
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# gumbel_l_dataframe.to_csv(f'{DATA_PATH}/gumbel_l_parameters.csv', index=False)
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# genextreme_dataframe.to_csv(f'{DATA_PATH}/genextreme_parameters.csv', index=False)
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# gamma_dataframe.to_csv(f'{DATA_PATH}/gamma_parameters.csv', index=False)
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# gompertz_dataframe.to_csv(f'{DATA_PATH}/gompertz_parameters.csv', index=False)
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