107 lines
3.3 KiB
Python
Executable File
107 lines
3.3 KiB
Python
Executable File
#!/usr/bin/env python
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# pylint: skip-file
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import argparse
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import traceback
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import sys
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import subprocess
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import pandas as pd
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import numpy as np
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from sodapy import Socrata
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import matplotlib.pyplot as plt
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datasets_ambiente = {"2020": "nicp-bhqi",
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"2019": "kujm-kavy",
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"2018": "bgqm-yq56",
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"2017": "j8j8-qsb2"}
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def _connect():
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client = Socrata("www.dati.lombardia.it", None)
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return client
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def process_dataset(dataset, sensore):
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client = _connect()
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results = client.get(dataset, IdSensore=sensore)
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results_df = pd.DataFrame.from_records(results)
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try:
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results_df = results_df.astype({'valore': 'float64'})
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except:
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print('\nspiacente, dati non disponibili per il sensore %s') % sensore
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sys.exit(-1)
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results_df["data"] = pd.to_datetime(results_df["data"])
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results_df = results_df.replace(-9999, np.nan)
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results_df.sort_values(by=['data'], inplace=True)
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results_df.rename(columns = {'valore':sensore}, inplace = True)
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results_df.drop(columns=['idoperatore', 'idsensore', 'stato'], inplace = True)
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return results_df
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def merge_df(dataframes, sensori):
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df = dataframes[sensori[0]]
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for sensore in sensori[1:]:
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df = pd.merge(df, dataframes[sensore])
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return df
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def get_dataframes(datasets, sensori):
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dataframes = {}
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for sensore in sensori:
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df = process_dataset(datasets[0], sensore)
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for dataset in datasets[1:]:
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df = pd.concat([df, process_dataset(dataset, sensore)], axis=0)
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dataframes[sensore] = df
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return dataframes
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def plot_dataframe(dataframe):
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dataframe.plot(x='data')
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plt.axhline(y=50, color='black', linestyle='-', label='24-hour average EU limit')
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plt.show()
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset", nargs='+', required=True, help="ricerca dei datasets")
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parser.add_argument('--sensori', nargs='+', required=True, help="specifica i sensori")
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args = parser.parse_args()
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try:
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datasets = []
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if "all" in args.dataset:
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for k in datasets_ambiente.keys():
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datasets.append(datasets_ambiente[k])
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else:
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for d in args.dataset:
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datasets.append(datasets_ambiente[d])
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sensori = args.sensori
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dataframes = get_dataframes(datasets, sensori)
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datamerged = merge_df(dataframes, sensori)
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import stazioni
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s = stazioni.get_stazioni()
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for sensore in sensori:
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location = s.loc[s['idsensore'] == sensore, 'nomestazione'].iloc[0]
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print('Valore medio per il sensore %s %s: %s' % (sensore, location, datamerged[sensore].mean().round(1)))
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plot_dataframe(datamerged)
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except KeyError:
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print("\nErrore:")
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#print("Datasets disponibili:")
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#print('\n'.join([str(lst) for lst in sorted(datasets_ambiente.keys())]))
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traceback.print_exc()
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except KeyboardInterrupt:
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print("program terminated by user")
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except SystemExit:
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print("program terminated, bye")
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except:
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print("\nAn unhandled exception occured, here's the traceback!\n")
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traceback.print_exc()
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print("\nReport this to putro@autistici.org")
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sys.exit()
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if __name__ == '__main__':
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main()
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