README

@@ -26,11 +26,6 @@ sensori_aria_2017.zip - 13M
 sensori_aria_2018.zip - 13M
 sensori_aria_2019.zip - 13M
 
-
-questi dati si trovano qui':
-https://www.dati.lombardia.it/browse?q=dati%20sensori%20aria
-
-
 per visualizzarne i dati e' necessario scaricare l'intero file in una cartella, che di default e' csv/
 ma puo' essere modificata nel file openlamb.py
 lo script se non trova i files necessari nella cartella specificata li scarica in formato .zip
@@ -44,7 +39,7 @@ ogni volta che si lancia lo script viene salvato un file export.csv che contiene
 
 requisiti:
   python3
-  pip3 install sodapy matplotlib pandas tabulate
+  pip3 install sodapy matplotlib pandas
 
 
 sintassi:

openlamb.py

@@ -4,30 +4,20 @@ import argparse
 import traceback
 import sys
 import pandas as pd
-import requests
 import numpy as np
+from sodapy import Socrata
 import matplotlib.pyplot as plt
 import re
-import json
 import glob
 import os
 from os import getcwd, chdir
 
 path_to_csv_files = "csv/"
 
-datasets_ambiente = {"3000": "nicp-bhqi",
-                     "2018": "g2hp-ar79",
-                     "2017": "j8j8-qsb2",
-                     "2010": "nr8w-tj77",
-                     "2000": "cthp-zqrr",
-                     "1999": "evzn-32bs",
-                     }
-# 1999 fino al 1999
-# 2000 dal 2000 al 2009
-# 2010 dal 2010 al 2017
-# 2018 sono i dati dal 2018 ad oggi
-# 3000 sono i dati dell'anno corrente
-
+datasets_ambiente = {"2020": "nicp-bhqi",
+                     "2019": "kujm-kavy",
+                     "2018": "bgqm-yq56",
+                     "2017": "j8j8-qsb2"}
 
 csv_ambiente = {"sensori_aria_1968-1995.zip": "puwt-3xxh",
                 "sensori_aria_1996-2000.zip": "wabv-jucw",
@@ -45,54 +35,14 @@ csv_ambiente = {"sensori_aria_1968-1995.zip": "puwt-3xxh",
                 "sensori_aria_2019.zip": "j2mz-aium"}
 
 
-class SocrataClient:
-    def __init__(self, domain, app_token=None):
-        self.base_url = f"https://{domain}/resource"
-        self.app_token = app_token
-
-    def get(self, resource_id, IdSensore, limit, filters=None):
-        """
-        Retrieve data from a Socrata dataset.
-        :param resource_id: The ID of the Socrata dataset.
-        :param filters: Optional filters to apply to the query.
-        :return: A list of records from the dataset.
-        """
-        url = f"{self.base_url}/{resource_id}.json"
-        params = {"$limit": limit,
-                  "idsensore": IdSensore,
-                 }
-
-        if filters:
-            params.update(filters)
-
-        headers = {}
-        if self.app_token:
-            headers["X-App-Token"] = self.app_token
-
-        response = requests.get(url, params=params, headers=headers)
-        if response.status_code == 200:
-            data = response.json()
-            return data
-
-        raise Exception(f"Failed to retrieve data. Status code: {response.status_code}")
+def _connect():
+    client = Socrata("www.dati.lombardia.it", None)
+    return client
 
 
 def read_data_online(dataset, sensore):
-
-    client = SocrataClient('www.dati.lombardia.it', None)
-    limit = 1000000
-
-    try:
-        data = client.get(dataset, sensore, limit)
-        if data:
-            #print(json.dumps(data, indent=2))
-            return data
-        else:
-            print("No data found for the specified sensor.")
-            sys.exit(-1)
-    except Exception as e:
-        print(f"Error: {e}")
-        sys.exit(-1)
+    client = _connect()
+    return client.get(dataset, IdSensore=sensore)
 
 
 def read_data_from_csv(datafile):
@@ -115,7 +65,7 @@ def process(dati, sensore, csv):
         results_df["data"] = pd.to_datetime(results_df["data"])
         results_df = results_df.replace(-9999, np.nan)
     except:
-        print('\nERRORE: dati non disponibili per il sensore %s\n' % sensore)
+        print('\nERRORE: dati non disponibili per il sensore %s\n') % sensore
         traceback.print_exc()
         sys.exit(-1)
     results_df.sort_values(by=['data'], inplace=True)
@@ -161,7 +111,6 @@ def plot_dataframe(dataframe):
 
 
 def list_of_csv_files(dir_name):
-    """ restituisce la lista dei files .zip presenti in una directory """
     saved = getcwd()
     os.chdir(dir_name)
     filelist = glob.glob('*.zip')
@@ -170,7 +119,6 @@ def list_of_csv_files(dir_name):
 
 
 def parse_range(x):
-    """ espande un range di anni nel formato NNNN-NNNN restituendo una lista dei singoli anni """
     x = x.strip()
     if x.isdigit():
         yield str(x)
@@ -182,7 +130,6 @@ def parse_range(x):
 
 
 def get_csv_dict(dict):
-    """ prende un dizionario dei files csv e assegna ad ogni anno un filename e una sigla id, restituendo un dict """
     d = {}
     for (k, v) in dict.items():
         filename, id = k, v
@@ -193,7 +140,7 @@ def get_csv_dict(dict):
         elif match_single:
             years = [match_single.group()]
         else:
-            print("\nError: no match, the filename does not contain any year")
+            print("no match")
         for year in years:
             d.update({year: [filename, id]})
     return d
@@ -245,39 +192,6 @@ def check_year_range(arg):
     return True
 
 
-def create_folder_if_not_exists(folder_path):
-    if not os.path.exists(folder_path):
-        try:
-            os.makedirs(folder_path)
-            print(f"Folder '{folder_path}' created successfully.")
-        except OSError as e:
-            print(f"Error creating folder '{folder_path}': {e}")
-    else:
-        print(f"Folder '{folder_path}' already exists.")
-
-
-def is_graphical_environment_active():
-    value = os.environ.get("DISPLAY")
-    if value is not None and value != "":
-        return True
-    else:
-        return False
-
-
-def is_remote_tty():
-    """
-    Check if the script is executed on a remote TTY.
-
-    Returns:
-        bool: True if running on a remote TTY, False if running locally.
-    """
-    term = os.environ.get('TERM')
-    if term is not None and (term.startswith('xterm') or term == 'ssh'):
-        return True
-    else:
-        return False
-
-
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--dataset", nargs='+', required=False,
@@ -289,7 +203,6 @@ def main():
     args = parser.parse_args()
 
     try:
-        create_folder_if_not_exists(path_to_csv_files)
         csv_dict = get_csv_dict(csv_ambiente)
         csv_files = list_of_csv_files(path_to_csv_files)
         dati_csv = []
@@ -313,26 +226,21 @@ def main():
         s = stazioni.get_stazioni()
         for sensore in datamerged.columns[1:]:
             location = s.loc[s['idsensore'] == sensore.split("-")[0], 'nomestazione'].iloc[0]
-            sensor_type = s.loc[s['idsensore'] == sensore.split("-")[0], 'nometiposensore'].iloc[0]
-            print('Valore medio per il sensore %s %s %s: %s' % (sensore, sensor_type, location, datamerged[sensore].mean().round(1)))
-        if is_graphical_environment_active() and not is_remote_tty():
-            plot_dataframe(datamerged)
+            print('Valore medio per il sensore %s %s: %s' % (sensore, location, datamerged[sensore].mean().round(1)))
+        plot_dataframe(datamerged)
     except KeyError:
         print("\nKeyError: forse hai specificato un dataset che non esiste ?\n"
               "i dataset sono disponibili per gli anni %s\n " % list(datasets_ambiente.keys()))
-        #traceback.print_exc()
-        sys.exit(-1)
+        traceback.print_exc()
     except KeyboardInterrupt:
         print("program terminated by user")
-        sys.exit(-1)
     except SystemExit:
         print("program terminated, bye")
-        sys.exit(-1)
     except:
         print("\nAn unhandled exception occured, here's the traceback!\n")
         traceback.print_exc()
         print("\nReport this to putro@autistici.org")
-        sys.exit(-1)
+        sys.exit()
 
 
 if __name__ == '__main__':

stazioni.py

@@ -3,39 +3,12 @@
 import os.path
 import tabulate
 import pandas as pd
-import requests
-
-
-class SocrataClient:
-    def __init__(self, domain, app_token=None):
-        self.domain = domain
-        self.app_token = app_token
-
-    def get_data(self, resource_id, query=None, limit=None):
-        base_url = f"https://{self.domain}/resource/{resource_id}.json"
-        params = {'$limit': limit} if limit else {}
-        if query:
-            params['$query'] = query
-
-        headers = {}
-        if self.app_token:
-            headers['X-App-Token'] = self.app_token
-
-        response = requests.get(base_url, params=params, headers=headers)
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            print(f"Error {response.status_code}: {response.text}")
-            return []
-
-    def get_dataframe(self, resource_id, query=None, limit=None):
-        data = self.get_data(resource_id, query=query, limit=limit)
-        df = pd.DataFrame(data)
-        return df
+from sodapy import Socrata
 
 
+def _connect():
+    client = Socrata("www.dati.lombardia.it", None)
+    return client
 
 
 def get_stazioni(offline=False):
@@ -47,8 +20,8 @@ def get_stazioni(offline=False):
                                   "Limiti amministrativi 2015 delle province di Regione Lombardia"], inplace=True)
         stazioni_df.columns = [x.lower() for x in stazioni_df.columns]
     else:
-        client = SocrataClient("www.dati.lombardia.it", None)
-        stazioni = client.get_data("ib47-atvt")
+        client = _connect()
+        stazioni = client.get("ib47-atvt")
         stazioni_df = pd.DataFrame.from_records(stazioni)
         stazioni_df.drop(columns=[":@computed_region_6hky_swhk", ":@computed_region_ttgh_9sm5", "utm_est",
                                   "utm_nord", "storico", "idstazione", "lat", "lng", "location"], inplace=True)