add: added all functionality (i hope) for nve model to fit front end

server/ModelFromNVE/icemodellingscripts/getIceThicknessLakes.py

-
 import copy
 import datetime as dt
 import utm
@@ -9,11 +8,41 @@ from server.ModelFromNVE.icemodelling import parameterization as dp, icethicknes
     ice as ice
 from server.ModelFromNVE.utilities import makeplots as pts, getgts as gts, getwsklima as gws
 
-def ice_prognosis_raw_data(to_date = None, sub_div_id = 0, met_stnr=0, x=10.709985478463118, y=60.810991171403316, altitude=0, awt=[], mf=0, icerun_dates=[]):
+
+def ice_prognosis_raw_data(to_date=None, sub_div_id=0, x=10.709985478463118, y=60.810991171403316,
+                           altitude=0, awt=[], mf=0, icerun_dates=[]):
     """
-    lon x
-    lat y
-        :return:
+
+    Args:
+        to_date:    the end date for prognosis season, defaults to seven days into the future
+        sub_div_id:
+        x:              lat
+        y:              lon
+        altitude:       in meters
+        awt:            list of avrage water temperatures
+        mf:             melt factor used by NVE, not much info on this variable
+        icerun_dates:   date for ice of the lake
+
+    Returns:
+        a list of data at this format:
+        [
+            [
+                "Date":             date.strftime("%Y-%m-%d"),
+                "Slush ice (m)":    round(slush, 3),
+                "Black ice (m)":    round(black, 3),
+                "Total ice (m)":    round(total, 3),
+                "Snow depth (m)":   round(snow2, 3),
+                "Total snow (m)":   round(sno_tot2, 3),
+                "Cloud cover":      round(cc2, 3),
+                "Temperature (c)":  round(temp2, 3)
+            ]
+            ...
+            [
+                ...
+            ]
+        ]
+
+
     """
     current_date = dt.datetime.now()
 
@@ -37,6 +66,9 @@ def ice_prognosis_raw_data(to_date = None, sub_div_id = 0, met_stnr=0, x=10.7099
     cords = utm.from_latlon(y, x, 33)
     x, y = int(cords[0]), int(cords[1])
 
+    # check if utm is valid
+    x, y = validate_cords(x, y)
+
     gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
     gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
     gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
@@ -65,9 +97,9 @@ def ice_prognosis_raw_data(to_date = None, sub_div_id = 0, met_stnr=0, x=10.7099
     # cumulated ammount of each ice type at a given date
     slush_ice = []
     black_ice = []
-    total     = []
+    total = []
     total_ice = []
-    dates     = []
+    dates = []
 
     for i in calculated_ice:
         ice_type = -10
@@ -97,20 +129,51 @@ def ice_prognosis_raw_data(to_date = None, sub_div_id = 0, met_stnr=0, x=10.7099
                                                                       sno_tot, cc, temp):
         daily_data = {
             "Date": date.strftime("%Y-%m-%d"),
-            "Slush ice (m)": slush,
-            "Black ice (m)": black,
-            "Total ice (m)": total,
-            "Snow depth (m)": snow2,
-            "Total snow (m)": sno_tot2,
-            "Cloud cover": cc2,
-            "Temperature (c)": temp2
+            "Slush ice (m)": round(slush, 3),
+            "Black ice (m)": round(black, 3),
+            "Total ice (m)": round(total, 3),
+            "Snow depth (m)": round(snow2, 3),
+            "Total snow (m)": round(sno_tot2, 3),
+            "Cloud cover": round(cc2, 3),
+            "Temperature (c)": round(temp2, 3)
         }
         data.append(daily_data)
 
     return data
     # return [sub_div_id, x, y, data]
 
+
+def validate_cords(easting, northing):
+    """
+    Args:
+        easting:
+        northing:
+
+    Returns:
+        if the easting and northing is not acceptable at utm 33 returns middle of mjosa as new easting and northing values
+
+    """
+    default_x, default_y = 266707, 6749365
+
+    if not (100000 <= easting <= 900000) or not (0 <= northing <= 10000000):
+        print("cords given are made default to middle of lake mjosa")
+        easting, northing = default_x, default_y
+    else:
+        print("cords are kept")
+
+    return easting, northing
+
+
 def get_raw_dates(data, from_date=None, to_date=None):
+    """
+    Args:
+        data:       data from ice_prognosis_raw_data
+        from_date:  if not given default to 4 days into future
+        to_date:    if not given default to 3 days into the past
+
+    Returns:
+        returns data for the specified time slot
+    """
     if from_date is None:
         from_date = (dt.datetime.now() - dt.timedelta(days=3)).strftime("%Y-%m-%d")
     if to_date is None:
@@ -119,10 +182,22 @@ def get_raw_dates(data, from_date=None, to_date=None):
     filtred_data = [entry for entry in data if from_date <= entry["Date"] <= to_date]
     return filtred_data
 
+
 # change to take a list of data with an sub div id first followed by data [sub_div_id, data]
-def jsonify_data(data, location=se.plot_folder):
+def jsonify_data(data, name="temp", location=se.plot_folder):
+    """
+
+    Args:
+        data:       the data to be put into file
+        name:       name of file -> data_{name}.json
+        location:   location, default to se.plot_folder
+
+    Returns:
+        creates a .json file
+
+    """
     os.makedirs(location, exist_ok=True)
-    file_path = os.path.join(location, "data_test_test1.json")
+    file_path = os.path.join(location, f"data_{name}.json")
 
     try:
         with open(file_path, 'w') as json_file:
@@ -131,12 +206,24 @@ def jsonify_data(data, location=se.plot_folder):
     except Exception as e:
         print(f"Failed to save data to JSON file. Error: {e}")
 
-def jsonify_data_sub_div_ids(sub_div_and_data, location=se.plot_folder):
+
+def jsonify_data_sub_div_ids(lake_name, sub_div_and_data, location=se.plot_folder):
+    """
+
+    Args:
+        lake_name:          put as file name {lake_name}_sub_div.json
+        sub_div_and_data:   all data at this format [(id, data) ... (...)]
+        location:           plot folder defaults to se.plot_folder
+
+    Returns:
+        creates a .json file
+
+    """
     aggregated_data = {entry[0]: entry[1] for entry in sub_div_and_data}
 
     os.makedirs(location, exist_ok=True)
 
-    filename = "aggregated_data_test_test.json"
+    filename = f"{lake_name}_sub_div.json"
     file_path = os.path.join(location, filename)
 
     try:
@@ -146,9 +233,32 @@ def jsonify_data_sub_div_ids(sub_div_and_data, location=se.plot_folder):
     except Exception as e:
         print(f"Failed to save data to JSON file. Error: {e}")
 
-def expose_data()
+
+# def expose_data()
+def get_subdiv_ids_n_cords(file_path):
+    """
+
+    Args:
+        file_path: path of the file with ids lat lon values
+
+    Returns:
+        a list of ids lat lon
+
+    """
+    # reads file and gets all ids and cords at this format [(id, x, y), (id, x, y) ... ]
+    id_list = []
+
+    with open(file_path, 'r') as file:
+        for line in file:
+            data = line.strip().split(',')
+            if len(data) == 3:
+                id_list.append((int(data[0]), float(data[1]), float(data[2])))
+
+    return id_list
+
 
 if __name__ == "__main__":
+    '''
     data = ice_prognosis_raw_data()
 
     from_date = "2024-01-10"
@@ -156,10 +266,41 @@ if __name__ == "__main__":
     filtered_dates = get_raw_dates(data, from_date, to_date)
     jsonify_data(filtered_dates)
     filtered_dates2 = get_raw_dates(data)
-    all_will_be_one = [[1, filtered_dates2], [2, filtered_dates]]
+    
+    all_will_be_one = [(1, filtered_dates2), [2, filtered_dates]]
     jsonify_data_sub_div_ids(all_will_be_one)
 
+    '''
 
-    #ice_prognosis()
+    sub_divs = get_subdiv_ids_n_cords('../../lake_relations/skumsjøen_centers.txt')         # lokasjon for txt fil
 
-    pass
\ No newline at end of file
+    from_date = "2024-01-10"
+    to_date = "2024-01-20"
+    #filtered_data_for_dates = [(i[0], get_raw_dates(ice_prognosis_raw_data(sub_div_id=i[0], x=i[1], y=i[2])), from_date, to_date) for i in sub_divs ]
+    filtered_data_for_dates = [(i[0], get_raw_dates(ice_prognosis_raw_data(sub_div_id=i[0], x=i[1], y=i[2]))) for i in sub_divs ]
+
+    jsonify_data_sub_div_ids("skumsjoen", filtered_data_for_dates, location = se.plot_folder)
+    '''
+    filtered_data_for_dates = []
+    
+    # Iterate over each subdivision in the list
+    for subdivision in sub_divs:
+        # Unpack the ID, x-coordinate, and y-coordinate for the subdivision
+        sub_div_id = subdivision[0]
+        x_coordinate = subdivision[1]
+        y_coordinate = subdivision[2]
+
+        # Fetch the raw data for the subdivision
+        raw_data = ice_prognosis_raw_data(sub_div_id=sub_div_id, x=x_coordinate, y=y_coordinate)    # <- problem
+
+        # Filter the raw data based on the specified date range
+        filtered_data = get_raw_dates(raw_data, from_date, to_date)
+
+        # Add the filtered data to the list
+        filtered_data_for_dates.append(filtered_data)
+    '''
+    print("hello world")
+
+    # ice_prognosis()
+
+    pass