add: work torwards making the icemodel be more compatible with our system

server/ModelFromNVE/functions.py

@@ -228,9 +228,9 @@ def write_ice_info_for_points_based_on_date(cordinates_and_ids, date, altitude,
         y = i[1][1]
         cords = utm.from_latlon(y, x, 33)
 
-        temp = pl.run_newsite(fdate, ldate, 10, 1, forcing='grid', location_name=location_name,
+        temp = pl.run_newsite(fdate, ldate, 10, 1, forcing='grid', location_name=location_name + f"_sib_div_id_{i[0]}",
                              plot_folder=plot_folder, x=int(cords[0]), y=int(cords[1]), altitude=altitude,
-                             icerun_dates=icerun_dates, make_plots=False, melt_factor=mf, air_water_temp=awt)
+                             icerun_dates=icerun_dates, make_plots=True, melt_factor=mf, air_water_temp=awt)
         ## handle plots ??? how iduno
 
         data.append([i[0], x, y, date, get_ice_thickness_given_date(date, temp[8], temp[9])])
@@ -258,7 +258,8 @@ if __name__ == "__main__":
     print(f"Total ice thiccness for date: {data[8][57]} is {get_icethickness_date_from_csv(data[8][57], location_name, se.plot_folder)} or",
           f"{get_ice_thickness_given_date(data[8][57], data[8], data[9])}")
 
-    temp = [(1, [10.709985478463118, 60.810991171403316]), (2, [10.709985478463118, 60.810991171403316])]
-    write_ice_info_for_points_based_on_date(temp, '2023-01-22', 123, 'Mjøsa')
+    temp = [(1, [10.709985478463118, 60.810991171403316]), (2, [10.709985478463118, 60.850991171403316])]
+    write_ice_info_for_points_based_on_date(temp, '2023-01-22', 123, 'Mjøsa', se.plot_folder)
+
 
     pass
\ No newline at end of file

server/ModelFromNVE/icemodellingscripts/getIceThicknessLakes.py

+
+import copy
+import datetime as dt
+import utm
+import os
+import json
+import server.ModelFromNVE.setenvironment as se
+from server.ModelFromNVE.icemodelling import parameterization as dp, icethickness as it, weatherelement as we, \
+    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=[]):
+    """
+    lon x
+    lat y
+        :return:
+    """
+    current_date = dt.datetime.now()
+
+    if current_date.month < 10:
+        from_date = dt.datetime(current_date.year - 1, 10, 1)
+    else:
+        from_date = dt.datetime(current_date.year, 10, 1)
+
+    if to_date is None or to_date > dt.datetime.now():
+        to_date = dt.datetime.now() + dt.timedelta(days=7)
+
+    tyear, tmonth, tday = from_date.year, from_date.month, from_date.day
+    if tmonth > 7 and tday < 7:
+        tyear += 1
+
+    first_ice = ice.IceColumn(from_date, [])
+    first_ice.add_metadata('LocationName', sub_div_id)  # Using sub_div_id as the location name for metadata
+    observed_ice = [first_ice]
+
+    # make the x and y into utm 33 from lon lat
+    cords = utm.from_latlon(y, x, 33)
+    x, y = int(cords[0]), int(cords[1])
+
+    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)
+
+    # In steep terrain may the grid altitude be different from the wanted height. Adjust for that.
+    gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, altitude)
+
+    temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
+    sno = we.strip_metadata(gridSno)
+    sno_tot = we.strip_metadata(gridSnoTot)
+    cc = dp.clouds_from_precipitation(sno)
+
+    air_temp_date = []
+    air_temp_value = []
+    for i in range(len(gridTemp)):
+        air_temp_date.append(gridTemp[i].Date)
+        air_temp_value.append(gridTemp[i].Value)
+
+    if len(awt) > 0:
+        calculated_ice = None
+    else:
+        calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice), date, temp, sno, cloud_cover=cc,
+                                                         icerun_dates=icerun_dates)
+
+    ###
+    # cumulated ammount of each ice type at a given date
+    slush_ice = []
+    black_ice = []
+    total     = []
+    total_ice = []
+    dates     = []
+
+    for i in calculated_ice:
+        ice_type = -10
+        black_ice_daily = 0
+        slush_ice_daily = 0
+        all_layer_ice_daily = 0
+        all_layer_daily = 0
+        change = False
+        date = i.date
+        for j in i.column:
+            ice_type = j.get_enum()
+            ice_height = j.height
+            if ice_type == 10:
+                black_ice_daily += ice_height
+            elif ice_type == 11:
+                slush_ice_daily += ice_height
+            all_layer_ice_daily += ice_height
+
+        dates.append(date)
+        slush_ice.append(slush_ice_daily)
+        black_ice.append(black_ice_daily)
+        total_ice.append(all_layer_ice_daily)
+
+    data = []
+
+    for date, slush, black, total, snow2, sno_tot2, cc2, temp2 in zip(dates, slush_ice, black_ice, total_ice, sno,
+                                                                      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
+        }
+        data.append(daily_data)
+
+    return data
+    # return [sub_div_id, x, y, data]
+
+def get_raw_dates(data, from_date=None, to_date=None):
+    if from_date is None:
+        from_date = (dt.datetime.now() - dt.timedelta(days=3)).strftime("%Y-%m-%d")
+    if to_date is None:
+        to_date = (dt.datetime.now() + dt.timedelta(days=4)).strftime("%Y-%m-%d")
+
+    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):
+    os.makedirs(location, exist_ok=True)
+    file_path = os.path.join(location, "data_test_test1.json")
+
+    try:
+        with open(file_path, 'w') as json_file:
+            json.dump(data, json_file, indent=4)
+        print(f"Data successfully saved to {file_path}")
+    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):
+    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"
+    file_path = os.path.join(location, filename)
+
+    try:
+        with open(file_path, 'w') as json_file:
+            json.dump(aggregated_data, json_file, indent=4)
+        print(f"All data successfully saved to {file_path}")
+    except Exception as e:
+        print(f"Failed to save data to JSON file. Error: {e}")
+
+def expose_data()
+
+if __name__ == "__main__":
+    data = ice_prognosis_raw_data()
+
+    from_date = "2024-01-10"
+    to_date = "2024-01-20"
+    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]]
+    jsonify_data_sub_div_ids(all_will_be_one)
+
+
+    #ice_prognosis()
+
+    pass
\ No newline at end of file

server/ModelFromNVE/icemodellingscripts/plotlakes.py

@@ -448,7 +448,7 @@ if __name__ == "__main__":
     se.plot_folder += 'Stjørdal-2\\'
 
     # Open an output file for extremes
-    outf = open('{0}{1}_is_ekstremer.txt'.format(se.plot_folder, location_name), 'w')
+    # outf = open('{0}{1}_is_ekstremer.txt'.format(se.plot_folder, location_name), 'w')
 
     # Create csv-file to store solid ice data
     outf4 = open('{0}{1}_is_fast.csv'.format(se.plot_folder, location_name), 'w')