diff --git a/prepare_userdb.py b/prepare_userdb.py
index 5db8628447f737a469e23bbd5b2a4f55ab3cb6f9..0c1224bb0d94811676e3accb1d77a8a6a93ea86c 100644
--- a/prepare_userdb.py
+++ b/prepare_userdb.py
@@ -230,19 +230,19 @@ conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecas
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
VALUES ('a89uj20gxybb', 1, 2, 2, '2017-01-25 00:00', 4, 0, Null, Null, 5, '2024-01-02 14:35')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('832tgjhingj1', 2, 0, 1, '2018-07-18 00:00', 4, 1, Null, Null, Null, '2024-01-12 16:03')")
+ VALUES ('832tgjhingj2', 2, 0, 1, '2018-07-18 00:00', 4, 1, Null, Null, Null, '2024-01-12 16:03')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('jjkl7t3li97m', 2, 1, 0, '2017-01-25 00:00', 3, 0, 0, 0, Null, '2024-01-09 10:14')")
+ VALUES ('jjkl7t3li97n', 2, 1, 0, '2017-01-25 00:00', 3, 0, 0, 0, Null, '2024-01-09 10:14')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('7zhtglaza8ah', 2, 2, 1, '2018-07-18 00:00', 4, 1, Null, Null, 1, '2024-01-15 08:01')")
+ VALUES ('7zhtglaza8ih', 2, 2, 1, '2018-07-18 00:00', 4, 1, Null, Null, 1, '2024-01-15 08:01')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('ji54Fdr0z99m', 2, 0, 0, '2018-07-18 00:00', 1, 0, Null, Null, Null, '2023-12-18 13:57')")
+ VALUES ('ji54Fdr0z99n', 2, 0, 0, '2018-07-18 00:00', 1, 0, Null, Null, Null, '2023-12-18 13:57')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('7zknt6702rx5', 2, 0, 3, '2017-01-25 00:00', 3, 0, Null, Null, Null, '2023-12-12 11:27')")
+ VALUES ('7zknt6702rx6', 2, 0, 3, '2017-01-25 00:00', 3, 0, Null, Null, Null, '2023-12-12 11:27')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('klj836kahg2l', 2, 2, 0, '2018-07-18 00:00', 1, 0, Null, Null, 2, '2023-12-21 17:59')")
+ VALUES ('klj836kahg2m', 2, 2, 0, '2018-07-18 00:00', 1, 0, Null, Null, 2, '2023-12-21 17:59')")
conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
- VALUES ('a89uj20gxybb', 2, 2, 2, '2017-01-25 00:00', 4, 0, Null, Null, 5, '2024-01-02 14:35')")
+ VALUES ('a89uj20gxybc', 2, 2, 2, '2017-01-25 00:00', 4, 0, Null, Null, 5, '2024-01-02 14:35')")
conn.commit()
diff --git a/src/eurad_plot.py b/src/eurad_plot.py
index 1ec179339ac420ed2f1b7011374c4f135418c98b..f4ea0f2c842dbd1ab7a94eb41a352137e5306e4b 100644
--- a/src/eurad_plot.py
+++ b/src/eurad_plot.py
@@ -9,7 +9,7 @@ def get_euradim_plot(jobnr, timestep, species, station):
# just to make it run
APP_HOME = Path.cwd()
DATA_PATH = APP_HOME.joinpath("static", "data")
- ASSETS_PATH = APP_HOME.joinpath("assets", "generated_plots")
+ ASSETS_PATH = APP_HOME.joinpath("assets", "generated_plots", "eurad")
# Output file is linked to jobnr
infile = str(DATA_PATH.joinpath(f'{jobnr}.nc'))
@@ -25,7 +25,7 @@ def get_euradim_plot(jobnr, timestep, species, station):
# print("plotting map:", jobnr, timestep, species, station)
generate_plot(filename, output_path, species, timestep, station)
- return os.path.join("assets", "generated_plots", output_file)
+ return os.path.join("assets", "generated_plots", "eurad", output_file)
# path relative to website root (src)
@@ -34,7 +34,7 @@ def get_timeseries_plot(jobnr, station, species, timestep):
# just to make it run
APP_HOME = Path.cwd()
DATA_PATH = APP_HOME.joinpath("static", "data")
- ASSETS_PATH = APP_HOME.joinpath("assets", "generated_plots")
+ ASSETS_PATH = APP_HOME.joinpath("assets", "generated_plots", "eurad")
# Output file is linked to jobnr
infile = str(DATA_PATH.joinpath(f'{jobnr}.nc'))
@@ -51,5 +51,5 @@ def get_timeseries_plot(jobnr, station, species, timestep):
# print("plotting time series:", jobnr, timestep, species, station)
generate_time_series_plot(filename, output_path, station, species, timestamp=timestep, figsize=(5,3))
- return os.path.join("assets", "generated_plots", output_file)
+ return os.path.join("assets", "generated_plots", "eurad", output_file)
# path relative to website root (src)
diff --git a/src/mlair/time_series.py b/src/mlair/time_series.py
index c04160ee70f0c0ebcf7ee05126313fcabd8d40e3..1f5b6a60420b7857ff505a73674f17b9d8c4c2f7 100644
--- a/src/mlair/time_series.py
+++ b/src/mlair/time_series.py
@@ -1,26 +1,32 @@
+import os
import matplotlib.pyplot as plt
import xarray as xr
import pandas as pd
import datetime as dt
+import pages.dashboard_translations as guitr
-def plot(figsize=(6, 3), input_dir="static/data/MLAIR", output_dir="mlair/assets/generated_plots"):
+def plot(station, language_id=0, species=0, start_date = "2018-07-18 00:00", forecast_length = 4, input_dir="static/data/MLAIR", output_dir=None, figsize=(6, 3)):
- # get all these from outside!!!
- species = "O3"
- station_label = "Bielefeld-Ost"
- station_code = "DENW067"
- start_date = "2018-07-18"
- forecast_length = "4days"
+ # get this from outside!!!
units = "ppb"
+ # still needed: translation table from indices to MLAIR abbreviations of species
+ # now a quick hack:
+ if species == 0:
+ species = "O3"
+
# prepare input and output
- hourly_start_date = (dt.datetime.strptime(start_date, '%Y-%m-%d') - dt.timedelta(days=4)).strftime('%Y-%m-%d')
+ station_code, station_label = station.split(',')
+ station_code = station_code.strip()
+ station_label = station_label.strip()
+ start_date = start_date[0:10]
+ hourly_start_date = (dt.datetime.strptime(start_date, '%Y-%m-%d') - dt.timedelta(days=forecast_length)).strftime('%Y-%m-%d')
hourly_end_date = (dt.datetime.strptime(start_date, '%Y-%m-%d') - dt.timedelta(days=1)).strftime('%Y-%m-%d')
file_s = f"{input_dir}/forecasts_{station_code}_test.nc"
dataset = xr.open_dataset(file_s)
- d4_fc = dataset['__xarray_dataarray_variable__'].sel(index=start_date,type='nn').data
+ d4_fc = dataset['__xarray_dataarray_variable__'].sel(index=start_date,type='nn').data[:forecast_length]
# plot the values at the middle of the day --> offset 12:00
- d4_fc_times = [ (dt.datetime.strptime(start_date, '%Y-%m-%d') + dt.timedelta(days=i)).strftime('%Y-%m-%d 12:00') for i in range(4) ]
+ d4_fc_times = [ (dt.datetime.strptime(start_date, '%Y-%m-%d') + dt.timedelta(days=i)).strftime('%Y-%m-%d 12:00') for i in range(forecast_length) ]
df_fc = pd.DataFrame({'fc_time': d4_fc_times, 'value': d4_fc})
# plot the values at the middle of the day --> give an hourly period
df_fc['fc_time'] = pd.to_datetime(df_fc['fc_time']).dt.to_period('H')
@@ -30,7 +36,8 @@ def plot(figsize=(6, 3), input_dir="static/data/MLAIR", output_dir="mlair/assets
d4_hourly_times = dataset['datetime'].sel(datetime=slice(hourly_start_date,hourly_end_date)).data
df_hourly = pd.DataFrame({'hourly_time': d4_hourly_times, 'value': d4_hourly})
df_hourly['hourly_time'] = pd.to_datetime(df_hourly['hourly_time'])
- output_file_name = f"mlair_{species}_{station_code}_{start_date}_{forecast_length}.png"
+ fc_length_str = "{}{}{}".format(forecast_length, guitr.day_label[language_id], guitr.day_plural_label[language_id] if forecast_length > 1 else "")
+ output_file_name = f"mlair_{species}_{station_code}_{start_date}_{fc_length_str}.png"
# plot
plt.figure(figsize=figsize)
@@ -45,11 +52,14 @@ def plot(figsize=(6, 3), input_dir="static/data/MLAIR", output_dir="mlair/assets
plt.ylabel(f'Konzentration ({units})')
ax.grid(True)
plt.tight_layout()
+ if not output_dir:
+ output_dir = str(os.path.join("assets", "generated_plots", "mlair"))
plt.savefig(f"{output_dir}/{output_file_name}")
- return output_file_name
+ return os.path.join("assets", "generated_plots", "mlair", output_file_name)
if __name__ == "__main__":
- plot(input_dir = "/home/s.schroeder/mlworkflowinterface/src/static/data/MLAIR",
+ plot(station = "DENW067, Bielefeld Ost",
+ input_dir = "/home/s.schroeder/mlworkflowinterface/src/static/data/MLAIR",
output_dir = "/home/s.schroeder/mlworkflowinterface/src/mlair/assets/generated_plots")
diff --git a/src/pages/dashboard.py b/src/pages/dashboard.py
index f47286e9864f8693c7b81e0e1b3ff7550781deb7..7547f864f92701324df585284e47017713a4172a 100644
--- a/src/pages/dashboard.py
+++ b/src/pages/dashboard.py
@@ -35,15 +35,7 @@ ASSETS_PATH = APP_HOME.joinpath("assets", "generated_plots")
dash.register_page(__name__, title="DestinE Air Quality Use Case", path="/")
-# placeholder for time series and map
-image_path = IMAGE_PATH.joinpath('placeholder_timeseries.png')
-pil_series = Image.open(image_path)
-image_path = IMAGE_PATH.joinpath('placeholder_pure_timeseries.png')
-pil_pure_series = Image.open(image_path)
-image_path = IMAGE_PATH.joinpath('placeholder_O3_NRW.png')
-pil_img = Image.open(image_path)
-station_info = pd.read_csv(DATA_PATH.joinpath("stations_NRW.csv"))
-station_info = [f"{station[0]} {station[1]}" for station in station_info.values]
+# to be put to correct source code file!
metrics_info_tmp = pd.read_csv(DATA_PATH.joinpath("mlair_metrics.csv"), delimiter=';')
len_met = len(metrics_info_tmp)
metrics_info = [metrics_info_tmp.iloc[:int(len_met/2),:],
@@ -80,6 +72,28 @@ def create_db_job_entry(user_id, job_dict):
conn.close()
+def get_db_job_entry(jobid):
+ conn = sqlite3.connect(DATA_PATH.joinpath('destine_de370c_users.db'))
+ cur = conn.cursor()
+ cur.execute(f"SELECT application, status, start_date, forecast_length, region, species, metric, emis_scen FROM jobs \
+ WHERE id = '{jobid}'")
+# application, status, start_date, forecast_length, region, species, metric, emis_scen = cur.fetchall()[0]
+ jobs = cur.fetchall()
+ conn.close()
+ for job in jobs:
+ application, status, start_date, forecast_length, region, species, metric, emis_scen = job
+ job_dict = {}
+ job_dict['id'] = jobid
+ job_dict['application'] = application
+ job_dict['status'] = status
+ job_dict['start_date'] = start_date
+ job_dict['forecast_length'] = forecast_length
+ job_dict['region'] = region
+ job_dict['species'] = species
+ job_dict['metric'] = metric
+ job_dict['emis_scen'] = emis_scen
+ return job_dict
+
def create_login_button(title="login"):
return html.Div([
@@ -205,28 +219,52 @@ def generate_ml_fcast_body(language_id=0):
]
def generate_ml_fcast_output_body(language_id):
- ts_plot_name = plot_ml_time_series()
- ts_plot_path = str(APP_HOME.joinpath("assets", "generated_plots", ts_plot_name))
+ stations_list = info.get_available_stations(infile="mlair_stations.csv")
+ stations_list = sorted(stations_list)
return [
dbc.Row(dbc.Label(f"{guitr.start_date_label[language_id]}: 18.07.2018, Ozon, Nordrhein Westfalen")),
dbc.Row([
dbc.Col(dbc.Label("station:"), width=3),
dbc.Col(
- dcc.Dropdown(value=station_info[87],
- options=station_info,
+ dcc.Dropdown(id="station-dropdown-ml-output",
+ value=stations_list[0],
+ options=stations_list,
), width=6
),
dbc.Col(dbc.Button(f"{guitr.map_select_label[language_id]}", class_name="fzj_style_btn"), width=2)
], class_name="row mt-3"),
dbc.Row([
- dbc.Col([html.Img(src=pil_pure_series, width=550),
- dbc.Col(dbc.Button(f"{guitr.save_label[language_id]}", class_name="fzj_style_btn"), width=6),
- dbc.Col(html.Td([])),
- dbc.Button(f"{guitr.download_label[language_id]}", class_name="fzj_style_btn")], width=6),
+ dbc.Col(html.Div(id='image-container-ml-output'), width=12),
+ dbc.Col(dbc.Button(f"{guitr.save_label[language_id]}", class_name="fzj_style_btn"), width=6),
+ dbc.Col(html.Td([])),
+ dbc.Col(dbc.Button(f"{guitr.download_label[language_id]}", class_name="fzj_style_btn"), width=6)
], class_name="row mt-3"),
]
+@callback(
+ Output('image-container-ml-output', 'children'),
+ Input('station-dropdown-ml-output', 'value'),
+ [State("user-info", "data"),
+ State("job-info", "data")],
+ prevent_initial_call=True
+)
+def update_mlair_image(selected_station, users_dict, job_dict):
+ try:
+ language_id = json.loads(users_dict)["language_id"]
+ except:
+ language_id = 0
+ jobid = json.loads(job_dict)["jobnr"]
+ job_props = get_db_job_entry(jobid)
+
+ image_path = plot_ml_time_series(selected_station, language_id,
+ species=job_props['species'],
+ start_date=job_props['start_date'],
+ forecast_length=job_props['forecast_length'])
+ image = html.Img(src=image_path, className='image-fit-container')
+
+ return image
+
def generate_ml_fcast_output_modal(jobnr=None, lisopen=False, language_id=0):
return html.Div([
@@ -272,8 +310,8 @@ def generate_eurad_im_body(language_id=0):
dbc.Col(dcc.DatePickerSingle(date=dt.date(2018, 7, 22),
display_format=guitr.date_format[language_id],
first_day_of_week=guitr.first_day_of_week[language_id],
- min_date_allowed=dt.date(2018, 7, 22)
- max_date_allowed=dt.date(2018, 7, 31)
+ min_date_allowed=dt.date(2018, 7, 22),
+ max_date_allowed=dt.date(2018, 7, 31),
initial_visible_month=dt.date(2018, 7, 1))),
dbc.Col(dbc.Label(f"{guitr.forecast_length_label[language_id]}:")),
dbc.Col(dcc.Dropdown(value=guitr.forecast_length_options[language_id][0], options=guitr.forecast_length_options[language_id]))
@@ -288,12 +326,13 @@ def generate_eurad_im_body(language_id=0):
@callback(
[Output('image-container-output', 'children'),
Output('image-container-timeseries-output', 'children'),
- Output("plot-info", "data")],
+ Output("plot-info", "data", allow_duplicate=True)],
[Input('time-step-dropdown-output', 'value'),
Input('variable-dropdown-output', 'value'),
Input('station-dropdown-output', 'value')],
[State("user-info", "data"),
- State("job-info", "data")]
+ State("job-info", "data")],
+ prevent_initial_call=True
)
def update_image(selected_time_step, selected_variable, selected_station, users_dict, jobs_dict):
plotinfo_dict = {}
@@ -898,11 +937,11 @@ def eurad_im_plots_download(download_button, job_dict, plot_dict):
infile1 = f'job_{jobid}_station_{station}_species_{species}_time_{timestamp}.png'
infile2 = f'job_{jobid}_time_{timestamp}_species_{species}_station_{station}.png'
files = [infile1, infile2]
- zipname = str(ASSETS_PATH.joinpath(f'job_{jobid}_time_{timestamp}_species_{species}_station_{station}.zip'))
+ zipname = str(ASSETS_PATH.joinpath('eurad', f'job_{jobid}_time_{timestamp}_species_{species}_station_{station}.zip'))
zipf = zipfile.ZipFile(zipname, 'w', zipfile.ZIP_DEFLATED)
for file in files:
- zipf.write(str(ASSETS_PATH.joinpath(file)),file)
+ zipf.write(str(ASSETS_PATH.joinpath('eurad', file)),file)
zipf.close()
return dcc.send_file(
zipname
diff --git a/src/static/images/placeholder_pure_timeseries.png b/src/static/images/placeholder_pure_timeseries.png
deleted file mode 100644
index c9fba795c2d84ebfbd5c1bf07a5ce032141fc740..0000000000000000000000000000000000000000
Binary files a/src/static/images/placeholder_pure_timeseries.png and /dev/null differ