diff --git a/src/mlair/time_series.py b/src/mlair/time_series.py
index 149b27684696b6b7fc05fe36c66436246e8958f7..ede43ce74f42e4fc5c5d1191b97b5182c57fa92b 100644
--- a/src/mlair/time_series.py
+++ b/src/mlair/time_series.py
@@ -56,9 +56,7 @@ def plot(station, language_id=0, ispec=0, start_date = "2018-07-18 00:00", forec
ax.errorbar(x=df_ob["ob_time"], y=df_ob["ob_value"], xerr=pd.Timedelta(hours=4), fmt="none", elinewidth=2.0, label=f"{guitr.mlair_legend_obs_label[language_id]} [{statistics}]", color='blue') #plot observations with dma8eu
dummy = ax.legend()
ax.errorbar(x=df_fc["fc_time"], y=df_fc["value"], xerr=pd.Timedelta(hours=4), fmt="none", elinewidth=2.0, label=f'{guitr.mlair_legend_fc_label[language_id]} [{statistics}]', color='orange') #plot fc
- dummy = ax.legend()
- logging.error(f'{df_hourly["hourly_time"][0]=}')
- logging.error(f'{df_fc["fc_time"]=}')
+ dummy = ax.legend(loc='lower right')
# extend x axis since last forecast is not plotted
nticks = 2 * forecast_length + 1
ax.set_xlim(df_hourly["hourly_time"][0], df_fc["fc_time"][forecast_length-1] + pd.Timedelta(hours=12))
diff --git a/src/pages/dashboard.py b/src/pages/dashboard.py
index c7338918877e879a4852e8dae3283af57395adae..3e15fe99b713e25ec864d639aec656e3e4579305 100644
--- a/src/pages/dashboard.py
+++ b/src/pages/dashboard.py
@@ -164,8 +164,6 @@ aai_login = dbc.Row([
def generate_ml_fcast_body(language_id=0):
- disabled_days = [ dt.datetime.strptime('2017-01-26','%Y-%m-%d') + dt.timedelta(days=i) for i in range(13) ]
- disabled_days += [ dt.datetime.strptime('2017-02-18','%Y-%m-%d') + dt.timedelta(days=i) for i in range(512) ]
return [
dbc.Row([
dbc.Col(dbc.Label(guitr.state_label[language_id]), width=3),
@@ -180,20 +178,19 @@ def generate_ml_fcast_body(language_id=0):
], class_name="row mt-3"),
dbc.Row([
dbc.Col(dbc.Label(guitr.date_label[language_id]), width=3),
- dbc.Col(dcc.DatePickerSingle(date=guiconst.min_date_allowed,
+ dbc.Col(dcc.DatePickerSingle(date=dt.date(2018, 7, 18),
display_format=guitr.date_format[language_id],
first_day_of_week=guitr.first_day_of_week[language_id],
- min_date_allowed=guiconst.min_date_allowed,
- max_date_allowed=guiconst.max_date_allowed,
- disabled_days=disabled_days,
- initial_visible_month=guiconst.initial_visible_month,
+ min_date_allowed=dt.date(2018, 7, 18),
+ max_date_allowed=dt.date(2018, 7, 31),
+ initial_visible_month=dt.date(2018, 7, 1),
id="ml_fcast_body_startdate")),
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],
id="ml_fcast_body_forecast_length"))
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(guitr.species_label[language_id]), width=3),
+ dbc.Col(dbc.Label(f"{guitr.species_label[language_id]}:"), width=3),
dbc.Col(
dcc.Dropdown(value=0,
options=[{'label': guitr.species_options[language_id][i],
@@ -204,7 +201,7 @@ def generate_ml_fcast_body(language_id=0):
)
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(guitr.output_metrics_label[language_id]), width=3),
+ dbc.Col(dbc.Label(f"{guitr.output_metrics_label[language_id]}:"), width=3),
dbc.Col(dcc.Dropdown(value=0,
options=[{'label': metrics_info[language_id][' Name'][i],
'value': i,
@@ -227,14 +224,34 @@ def generate_ml_fcast_body(language_id=0):
], class_name="row mt-3")
]
-def generate_ml_fcast_output_body(language_id):
+def generate_ml_fcast_output_body(language_id, jobnr):
stations_list = info.get_available_stations(infile="mlair_stations.csv")
# stations_list = sorted(stations_list)
-
+ if jobnr and (jobnr != 'bla'):
+ job_props = get_db_job_entry(jobnr)
+ start_date = job_props['start_date']
+ ireg = job_props['region']
+ region = guitr.region_text[language_id][ireg]
+ ispec = job_props['species']
+ species = guitr.species_options[language_id][ispec]
+ imetric = job_props['metric']
+ metric = guitr.metrics_options[language_id][imetric]
+ fc_length = job_props['forecast_length']
+ fc_length_str = "{} {}{}".format(fc_length, guitr.day_label[language_id], guitr.day_plural_label[language_id] if fc_length > 1 else "")
+ else:
+ start_date = "2018-07-18"
+ region = "X"
+ species = "X"
+ metric = "X"
+ fc_length_str = "X"
return [
- dbc.Row(dbc.Label(f"{guitr.start_date_label[language_id]}: 18.07.2018, Ozon, Nordrhein Westfalen")),
+ dbc.Row(dbc.Label(f"""{guitr.region_label[language_id]}: {region},
+ {guitr.species_label[language_id]}: {species},
+ {guitr.output_metrics_label[language_id]}: {metric},
+ {guitr.start_date_label[language_id]}: {start_date},
+ {guitr.forecast_length_label[language_id]}: {fc_length_str}""")),
dbc.Row([
- dbc.Col(dbc.Label(f"{guitr.station_label[language_id]}"), width=3),
+ dbc.Col(dbc.Label(f"{guitr.station_label[language_id]}:"), width=3),
dbc.Col(
dcc.Dropdown(id="station-dropdown-ml-output",
value=stations_list[0],
@@ -247,8 +264,14 @@ def generate_ml_fcast_output_body(language_id):
dbc.Col(dbc.Button(f"{guitr.map_select_label[language_id]}", disabled=True, class_name="fzj_style_btn"), width=2)
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(html.Div(id='image-container-ml-output'), width=12),
- dbc.Col(dbc.Button(f"{guitr.download_label[language_id]}", class_name="fzj_style_btn"), width=6)
+ dbc.Col([html.Div(id='image-container-ml-output'),
+ html.Div([dbc.Button(f"{guitr.download_label[language_id]}", id="mlair_download",
+ class_name="fzj_style_btn"),
+ dcc.Download(id="mlair_download_result")]),
+ dbc.Col(html.Br()),
+ html.Div([dbc.Button(f"{guitr.im_download_label[language_id]}", id="mlair_plot_download",
+ class_name="fzj_style_btn"),
+ dcc.Download(id="mlair_download_plot")])], width=12),
], class_name="row mt-3"),
]
@@ -282,7 +305,7 @@ def generate_ml_fcast_output_modal(jobnr=None, lisopen=False, language_id=0):
[
dbc.ModalTitle(f"{guitr.run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
dbc.ModalHeader(dbc.ModalTitle(guitr.application_text[language_id][1])),
- dbc.ModalBody(generate_ml_fcast_output_body(language_id)),
+ dbc.ModalBody(generate_ml_fcast_output_body(language_id, jobnr)),
dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id="ml_fcast_output_close",
class_name="fzj_style_btn", n_clicks=0)
]),
@@ -299,7 +322,7 @@ def generate_ml_fcast_result_modal(jobnr=None, lisopen=False, language_id=0):
[
dbc.ModalTitle(f"{guitr.run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
dbc.ModalHeader(dbc.ModalTitle(guitr.application_text[language_id][1])),
- dbc.ModalBody(generate_ml_fcast_output_body(language_id)),
+ dbc.ModalBody(generate_ml_fcast_output_body(language_id, jobnr)),
dbc.ModalFooter(dbc.Button(f"{guitr.close_label[language_id]}", id="ml_fcast_result_close",
class_name="fzj_style_btn", n_clicks=0)),
],
@@ -333,7 +356,7 @@ def generate_eurad_im_body(language_id=0):
id="eurad_im_forecast_length"))
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(guitr.region_label[language_id]), width=3),
+ dbc.Col(dbc.Label(f"{guitr.region_label[language_id]}:"), width=3),
dbc.Col(dcc.RadioItems(options={
0: 'Nordrhein-Westfalen',
1: 'Berlin-Brandenburg'},
@@ -386,6 +409,7 @@ def generate_eurad_im_output_body(language_id, context, jobnr):
infile = str(DATA_PATH.joinpath(f'{jobnr}.nc'))
job_props = get_db_job_entry(jobnr)
start_date = job_props['start_date']
+ region = job_props['region']
if not os.path.isfile(infile):
trans_filename = transfer_results_from_HPC(jobnr, start_date, 0, DATA_PATH)
@@ -393,7 +417,6 @@ def generate_eurad_im_output_body(language_id, context, jobnr):
timestep_list = info.get_available_time_stamps(infile)
timestep_strings = [np.datetime_as_string(ts, unit="m") for ts in timestep_list]
- # TODO: Zeit in Stunden seit Start
start_date = pd.to_datetime(timestep_list[0]).strftime(guitr.date_format2[language_id])
fc_length = (len(timestep_list)-1) // 24
@@ -405,12 +428,15 @@ def generate_eurad_im_output_body(language_id, context, jobnr):
stations_list = sorted(stations_list)
else:
start_date = '2018-07-19T00:00'
+ region = 0
timestep_strings = [ '2018-07-19 00:00' ]
fc_length_str = '1'
variables_list = [ 'NoneAvailable' ]
stations_list = [ "XX, XX" ]
return [
- dbc.Row([dbc.Label(f"{guitr.start_date_label[language_id]}: {start_date}, {guitr.forecast_length_label[language_id]}: {fc_length_str}")]),
+ dbc.Row([dbc.Label(f"""{guitr.region_label[language_id]}: {guitr.region_text[language_id][region]},
+ {guitr.start_date_label[language_id]}: {start_date},
+ {guitr.forecast_length_label[language_id]}: {fc_length_str}""")]),
dbc.Row([
dbc.Col(dbc.Label(f"{guitr.time_step_label[language_id]}:"), width=3),
dbc.Col(dcc.Dropdown(value=timestep_strings[0], options=timestep_strings, id='time-step-dropdown-{}'.format(context)), width=3)
@@ -435,8 +461,11 @@ def generate_eurad_im_output_body(language_id, context, jobnr):
dbc.Row([
dbc.Col(html.Br(), width=12),
dbc.Col([
- dbc.Checkbox(label=f"{guitr.show_downscaling_label[language_id]}", value=0)
- ], style={"display": "flex"}),
+ dbc.Checkbox(label=f"{guitr.show_target_plot_label[language_id]}", value=0)
+ ], width=12),
+ dbc.Col([
+ dbc.Checkbox(label=f"{guitr.show_downscaling_label[language_id]}", value=0, disabled=True)
+ ], width=12)
], class_name="row mt-3f"),
]
@@ -485,6 +514,8 @@ def generate_eurad_scen_output_body(language_id, context, jobnr):
job_props = get_db_job_entry(jobnr)
start_date = job_props['start_date']
iscen = job_props['emis_scen']
+ ireg = job_props['region']
+ region = guitr.region_text[language_id][ireg]
if not os.path.isfile(infile):
trans_filename = transfer_results_from_HPC(jobnr, start_date, iscen, DATA_PATH)
@@ -509,10 +540,12 @@ def generate_eurad_scen_output_body(language_id, context, jobnr):
variables_list = [ 'NoneAvailable' ]
stations_list = [ "XX, XX" ]
iscen = 0
+ region = "X"
return [
- dbc.Row([dbc.Label(f"{guitr.start_date_label[language_id]}: {start_date},\
- {guitr.forecast_length_label[language_id]}: {fc_length_str},\
- {guitr.emis_scen_label[language_id]} {emis_info[language_id][' Name'][iscen]}")]),
+ dbc.Row([dbc.Label(f"""{guitr.region_label[language_id]}: {region},
+ {guitr.start_date_label[language_id]}: {start_date},
+ {guitr.forecast_length_label[language_id]}: {fc_length_str},
+ {guitr.emis_scen_label[language_id]}: {emis_info[language_id][' Name'][iscen]}""")]),
dbc.Row([
dbc.Col(dbc.Label(f"{guitr.time_step_label[language_id]}:"), width=3),
dbc.Col(dcc.Dropdown(value=timestep_strings[0], options=timestep_strings, id='time-step-dropdown-{}'.format(context)), width=3)
@@ -563,14 +596,14 @@ def generate_eurad_scen_body(language_id):
id="eurad_scen_forecast_length"))
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(f"{guitr.region_label[language_id]}"), width=3),
+ dbc.Col(dbc.Label(f"{guitr.region_label[language_id]}:"), width=3),
dbc.Col(dcc.RadioItems(options={
0: 'Nordrhein-Westfalen',
1: 'Berlin-Brandenburg'},
value=0, id="eurad_scen_region_choice")),
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(f"{guitr.emis_scen_label[language_id]}"), width=3),
+ dbc.Col(dbc.Label(f"{guitr.emis_scen_label[language_id]}:"), width=3),
dbc.Col(
dcc.Dropdown(value=0,
options=[{'label': emis_info[language_id][" Name"][i],
@@ -1143,6 +1176,45 @@ def eurad_scen_job_run(run_button, region, startdate, forecast_length, emi_scen,
generate_eurad_scen_output_modal(container_id="eurad_scen_output_modal_container", language_id=language_id)]
+@callback(
+ Output("mlair_download_result", "data"),
+ Input("mlair_output_download", "n_clicks"),
+ State("job-info", "data"),
+ prevent_initial_call=True
+)
+def mlair_output_download(download_button, job_dict):
+# jobid = json.loads(job_dict)["jobnr"]
+ # infile = str(DATA_PATH.joinpath('MLAIR', f'{jobid}.nc'))
+ # demonstrator
+ infile = str(DATA_PATH.joinpath('MLAIR', 'forecasts_DENW067_test.nc'))
+ return dcc.send_file(
+ infile
+ )
+
+
+@callback(
+ Output("mlair_download_plot", "data"),
+ Input("mlair_plot_download", "n_clicks"),
+ [State("job-info", "data"),
+ State("plot-info", "data")],
+ prevent_initial_call=True
+)
+def mlair_plots_download(download_button, job_dict, plot_dict):
+# jobid = json.loads(job_dict)["jobnr"]
+# species = json.loads(job_dict)["variable"]
+# start_date = json.loads(job_dict)["start_date"]
+# station = json.loads(plot_dict)["station"]
+
+ # file name according to users selection
+# output_file_name = f"mlair_{ml_species}_{station_code}_{start_date}_{fc_length_str}.png"
+ # just to test
+ output_file_name = "mlair_O3_DENW067_2018-07-18_4Tage.png"
+ infile = str(ASSETS_PATH.joinpath('mlair', output_file_name))
+ return dcc.send_file(
+ infile
+ )
+
+
@callback(
Output("eurad_im_download_result", "data"),
Input("eurad_im_output_download", "n_clicks"),
diff --git a/src/pages/dashboard_translations.py b/src/pages/dashboard_translations.py
index 5975d77670457a00b0d70646cad1637ecc2c7188..4ef6b89b0ee6001638b61a7a8a1f9ee631a97a45 100644
--- a/src/pages/dashboard_translations.py
+++ b/src/pages/dashboard_translations.py
@@ -99,20 +99,20 @@ date_format = ["M/D/Y", "D.M.Y"]
date_format2 = ['%Y-%m-%d %H:%M', '%d.%m.%Y %H:%M']
first_day_of_week = [0, 1]
forecast_length_label = ["forecast length", "Vorhersagedauer"]
-region_label = ["region:", "Region:"]
+region_label = ["region", "Region"]
forecast_length_options = [["4 days", "3 days", "2 days", "1 day"],
["4 Tage", "3 Tage", "2 Tage", "1 Tag"]]
output_format_options = [["raw model output", "2D plots", "time series"],
["Rohmodellausgabe", "2D-Diagramme", "Zeitreihen"]]
state_label = ["state:", "Bundesland:"]
-species_label = ["species:", "Spezies:"]
-station_label = ["station:", "Station:"]
-output_metrics_label = ["output metrics:", "Ausgabemetrik:"]
+species_label = ["species", "Spezies"]
+station_label = ["station", "Station"]
+output_metrics_label = ["output metrics", "Ausgabemetrik"]
species_options = [["ozone", "NO", "NO2", "PM2.5"],
["Ozon", "Stickstoffmonoxid", "Stickstoffdioxid", "Feinstaub (PM2.5)"]]
metrics_options = [["dma8eu", "mean"],
["dma8eu", "Mittelwert"]]
-emis_scen_label = ["emission scenario:", "Emissionsszenario:"]
+emis_scen_label = ["emission scenario", "Emissionsszenario"]
help_metrics_label = ["Help on metrics", "Hilfe zu Ausgabemetriken"]
help_emissions_label = ["Help on emission scenarios", "Hilfe zu Emissions-Szenarien"]
map_select_label = ["Select on map", "auf Karte auswählen"]
@@ -125,6 +125,7 @@ download_label = ["Download Data", "Daten herunterladen"]
im_download_label = ["Download Plots", "Plots herunterladen"]
downscaling_label = ["Postprocessing with ML-Downscaling", "Postprocessing mit ML-Downscaling"]
show_downscaling_label = ["Show results with ML downscaling", "Ergebnisse mit ML-Downscaling anzeigen"]
+show_target_plot_label = ["Show target plot", "Ergebnisse als Schwellwertplot anzeigen"]
out_option_label = ["output option", "Ausgabe-Option"]
mlair_legend_obs_label = ["measurements", "Beobachtungen"]
mlair_legend_fc_label = ["forecasts", "Vorhersagen"]