diff --git a/prepare_userdb.py b/prepare_userdb.py
index c338a9affb25284b0fb3296513ebf8ac1f3df890..21ce0685c86a1bb09b104890769970ed722e523a 100644
--- a/prepare_userdb.py
+++ b/prepare_userdb.py
@@ -59,8 +59,8 @@ conn.execute("INSERT INTO convoc_status (id,job_status) \
conn.commit()
# There is the need for controlled_vocabulary for regions of interest
-# 0: finished
-# 1: active
+# 0: NRW
+# 1: BB
conn.execute('''CREATE TABLE convoc_region
(id INT PRIMARY KEY NOT NULL,
region_name TEXT NOT NULL);''')
@@ -72,6 +72,65 @@ conn.execute("INSERT INTO convoc_region (id,region_name) \
conn.commit()
+# There is the need for controlled_vocabulary for species of interest
+# 0: ozone
+# 1: no
+# 2: no2
+# 3: pm2p5
+conn.execute('''CREATE TABLE convoc_species
+ (id INT PRIMARY KEY NOT NULL,
+ species_name TEXT NOT NULL);''')
+
+conn.execute("INSERT INTO convoc_species(id,species_name) \
+ VALUES (0, 'ozone')")
+conn.execute("INSERT INTO convoc_species(id,species_name) \
+ VALUES (1, 'no')")
+conn.execute("INSERT INTO convoc_species(id,species_name) \
+ VALUES (2, 'no2')")
+conn.execute("INSERT INTO convoc_species (id,species_name) \
+ VALUES (3, 'pm2p5')")
+
+conn.commit()
+
+# There is the need for controlled_vocabulary for output metrics (MLAir)
+# 0: dma8eu
+# 1: mean
+conn.execute('''CREATE TABLE convoc_metric
+ (id INT PRIMARY KEY NOT NULL,
+ metric_name TEXT NOT NULL);''')
+
+conn.execute("INSERT INTO convoc_metric (id,metric_name) \
+ VALUES (0, 'dma8eu')")
+conn.execute("INSERT INTO convoc_metric (id,metric_name) \
+ VALUES (1, 'mean')")
+
+conn.commit()
+
+# There is the need for controlled_vocabulary for emission scenarios
+# 0: standard
+# 1: no-emission
+# 2: home-office and energy saving
+# 3: industry
+# 4: e-traffic
+# 5: fireplace
+conn.execute('''CREATE TABLE convoc_emis_scen
+ (id INT PRIMARY KEY NOT NULL,
+ emis_name TEXT NOT NULL);''')
+
+conn.execute("INSERT INTO convoc_emis_scen (id, emis_name) \
+ VALUES (0, 'standard')")
+conn.execute("INSERT INTO convoc_emis_scen (id, emis_name) \
+ VALUES (1, 'no-emission')")
+conn.execute("INSERT INTO convoc_emis_scen (id, emis_name) \
+ VALUES (2, 'home-office and energy saving')")
+conn.execute("INSERT INTO convoc_emis_scen (id, emis_name) \
+ VALUES (3, 'industry')")
+conn.execute("INSERT INTO convoc_emis_scen (id, emis_name) \
+ VALUES (4, 'e-traffic')")
+conn.execute("INSERT INTO convoc_emis_scen (id, emis_name) \
+ VALUES (5, 'fireplace')")
+
+conn.commit()
# Second, create the dashboard tables
conn.execute('''CREATE TABLE users
@@ -99,40 +158,46 @@ conn.execute('''CREATE TABLE jobs
start_date datetime NOT NULL,
forecast_length INT NOT NULL,
region INT NOT NULL,
+ species INT ,
+ metric INT ,
+ emis_scen INT ,
creation_date datetime NOT NULL,
FOREIGN KEY(user_id) REFERENCES user(id),
FOREIGN KEY(status) REFERENCES convoc_status(id),
FOREIGN KEY(application) REFERENCES convoc_application(id),
- FOREIGN KEY(region) REFERENCES convoc_region(id));''')
-
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('832tgjhingj1', 1, 0, 1, '2018-07-18 00:00', 4, 1, '2024-01-12 16:03')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('jjkl7t3li97m', 1, 1, 0, '2017-01-25 00:00', 3, 0, '2024-01-09 10:14')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('7zhtglaza8ah', 1, 2, 1, '2018-07-18 00:00', 4, 1, '2024-01-15 08:01')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('ji54Fdr0z99m', 1, 0, 0, '2018-07-18 00:00', 1, 0, '2023-12-18 13:57')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('7zknt6702rx5', 1, 0, 3, '2017-01-25 00:00', 3, 0, '2023-12-12 11:27')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('klj836kahg2l', 1, 2, 0, '2018-07-18 00:00', 1, 0, '2023-12-21 17:59')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('a89uj20gxybb', 1, 2, 2, '2017-01-25 00:00', 4, 0, '2024-01-02 14:35')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('832tgjhingj1', 2, 0, 1, '2018-07-18 00:00', 4, 1, '2024-01-12 16:03')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('jjkl7t3li97m', 2, 1, 0, '2017-01-25 00:00', 3, 0, '2024-01-09 10:14')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('7zhtglaza8ah', 2, 2, 1, '2018-07-18 00:00', 4, 1, '2024-01-15 08:01')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('ji54Fdr0z99m', 2, 0, 0, '2018-07-18 00:00', 1, 0, '2023-12-18 13:57')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('7zknt6702rx5', 2, 0, 3, '2017-01-25 00:00', 3, 0, '2023-12-12 11:27')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('klj836kahg2l', 2, 2, 0, '2018-07-18 00:00', 1, 0, '2023-12-21 17:59')")
-conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, creation_date) \
- VALUES ('a89uj20gxybb', 2, 2, 2, '2017-01-25 00:00', 4, 0, '2024-01-02 14:35')")
+ FOREIGN KEY(region) REFERENCES convoc_region(id),
+ FOREIGN KEY(species) REFERENCES convoc_species(id),
+ FOREIGN KEY(metric) REFERENCES convoc_metric(id),
+ FOREIGN KEY(emis_scen) REFERENCES convoc_emis_scen(id));''')
+
+conn.execute("INSERT INTO jobs (id,user_id,application,status,start_date,forecast_length, region, species, metric, emis_scen, creation_date) \
+ VALUES ('832tgjhingj1', 1, 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', 1, 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', 1, 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', 1, 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', 1, 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', 1, 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', 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')")
+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')")
+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')")
+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')")
+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')")
+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')")
+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')")
conn.commit()
diff --git a/src/pages/dashboard.py b/src/pages/dashboard.py
index 37840732f3bc6eb3f107161afb86b28144ea1f60..20fc981ce97da1347a9aeff503212a62fefcd840 100644
--- a/src/pages/dashboard.py
+++ b/src/pages/dashboard.py
@@ -22,6 +22,8 @@ from deployment_settings import (
UNICORE_USER, UNICORE_PASSWORD
)
from mlair.time_series import plot as plot_ml_time_series
+import pages.dashboard_translations as guitr
+import pages.dashboard_constants as guiconst
# the following should be done with static files!
APP_HOME = Path.cwd()
@@ -31,133 +33,6 @@ ASSETS_PATH = APP_HOME.joinpath("assets", "generated_plots")
dash.register_page(__name__, title="DestinE Air Quality Use Case", path="/")
-# settings (can of course better be read from file!)
-layout_cards = [["Description", "Create Forecasts", "My Model Runs", "My Results"],
- ["Beschreibung", "Erstelle Vorhersagen", "Meine Modellläufe", "Meine Auswertungen"]]
-description = [["The DestinE Air Quality Use Case demonstrates interactive triggering of advanced forecasting "
- "and analysis workflows for air quality.",
- html.Br(),
- "The workflows are triggered by clicking on the buttons in the second tab.",
- html.Br(),
- "The results of the workflows are displayed in the third tab.",
- html.Br(),
- "The documentation of the use case can be found ",
- html.A("here", href="https://www.fz-juelich.de/de/iek/iek-8/projekte/"
- + "destination-earth-use-case-fuer-luftqualitaet-de370c")],
- ["Der 'DestinE Air Quality Use Case' (Anwendungsfall) demonstriert die interaktive Auslösung "
- "fortgeschrittener Vorhersage- und Analyse-Workflows für die Luftqualität.",
- html.Br(),
- "Die Workflows werden durch Klicken auf die Schaltflächen im zweiten Reiter ausgelöst.",
- html.Br(),
- "Die Ergebnisse der Workflows werden auf der dritten Registerkarte angezeigt.",
- html.Br(),
- "Die Dokumentation des Anwendungsfalls finden Sie ",
- html.A("hier", href="https://www.fz-juelich.de/de/iek/iek-8/projekte/"
- + "destination-earth-use-case-fuer-luftqualitaet-de370c")]]
-jobs_columns = [["Application", "Status", "Start date", "Forecast Length", "Region", "Creation Date", "Jobnr"],
- ["Anwendung", "Status", "Startdatum", "Vorhersagedauer", "Region", "Erstellungsdatum", "Jobnr"]]
-results_columns = [["Result_ID", "Jobnr", "Application", "Used Options"],
- ["Resultats_ID", "Jobnr", "Anwendung", "Verwendete Optionen"]]
-eurad_scenarios = ["avoid all anthropogenic emissions",
- "home office",
- "industry",
- "traffic"]
-
-# The following lists should be created once from the controlled vocabulary
-status_text = [["finished", "active", "waiting", "aborted"],
- ["beendet", "aktiv", "in der Warteschlange", "abgebrochen"]]
-application_text = [["Field forecasting with EURAD-IM", "Point forecasting with MLAir", "EURAD-IM emission scenarios"],
- ["Gebietsvorhersage mit EURAD-IM", "Stationsvorhersage mit MLAir", "EURAD-IM Emissionsszenarien"]]
-region_text = [["North Rhine-Westphalia", "Berlin-Brandenburg"],
- ["Nordrhein-Westfalen", "Berlin-Brandenburg"]]
-description_text = [["This application produces high-resolution, i.e. 1 km or less, on-demand results over a "
- + "user-selected area for a forecast period of up to 4 days. Here, users can select the forecast "
- + "start date (default today) and the forecast length (shorter runs require less time and "
- + "resources), and the regional domain of the simulation. There are two EURAD-IM model "
- + "configurations applied in the background of this application: i) the default 9 km resolution "
- + "model covering all of Europe, and ii) region-specific 1 km configurations, initially limited "
- + "to North Rhine-Westphalia and Brandenburg. Users can select whether they wish to perform a "
- + "dedicated 1 km EURAD-IM simulation, or whether the 1 km results shall be obtained with ML "
- + "downscaling from the results of a 9 km simulation. For more information about EURAD-IM and ML "
- + "downscaling, please refer to the description tab.",
- "This application concerns point forecasts of air quality at (German) measurement stations "
- + "with the MLAir tool. This workflow makes use of the TOAR database infrastructure that is "
- + "operated at JSC (https://toar-data.fz-juelich.de) and uses data from the continuous Digital "
- + "Twin (DT) as meteorological inputs. Users can select a forecast a start time and a region, and "
- + "the tool will then generate forecasts for all stations in the given region. Users can also "
- + "select the species of interest and the air quality metric that shall be computed. Initial "
- + "settings for these parameters are ozone and 'MDA8', i.e. the maximum daily 8-hour average "
- + "value. For more information about MLAir, please refer to the description tab.",
- "This application, which can be directly applied for decision making, triggers an EURAD-IM "
- + "ensemble simulation with modified emission scenarios. This allows for the assessment of the "
- + "effectiveness of different air pollution mitigation strategies. User choices are similar to "
- + "the field forecast application, except that users will also have to choose the scenario they "
- + "wish to employ. For more information about EURAD-IM emissions, please refer to the description "
- + "tab."],
- ["Diese Anwendung erzeugt hochauflösende, d.h. 1 km oder weniger, On-Demand-Ergebnisse über ein "
- + "vom Benutzer ausgewähltes Gebiet für einen Vorhersagezeitraum von bis zu 4 Tagen. Dabei können "
- + "die Nutzer das Startdatum der Vorhersage (standardmäßig heute) und die Vorhersagelänge "
- + "(kürzere Läufe erfordern weniger Zeit und Ressourcen) sowie den regionalen Bereich der "
- + "Simulation auswählen. Im Hintergrund dieser Anwendung werden zwei "
- + "EURAD-IM-Modellkonfigurationen verwendet: i) das Standardmodell mit 9 km Auflösung, das ganz "
- + "Europa abdeckt, und ii) regionalspezifische 1 km-Konfigurationen, die zunächst auf "
- + "Nordrhein-Westfalen und Brandenburg beschränkt sind. Der Benutzer kann dann wählen, ob er eine "
- + "eigene 1 km EURAD-IM-Simulation durchführen möchte oder ob die 1 km-Ergebnisse durch "
- + "ML-Downscaling aus den Ergebnissen einer 9 km-Simulation gewonnen werden sollen. Weitere Details "
- + "über EURAD-IM finden sich unter 'Beschreibung'.",
- "Diese Anwendung betrifft Punktvorhersagen der Luftqualität an (deutschen) Messstationen mit dem "
- + "MLAir-Tool. Sie nutzt die TOAR-Datenbankinfrastruktur, die beim JSC "
- + "(https://toar-data.fz-juelich.de) betrieben wird, und verwendet Daten aus dem kontinuierlichen "
- + "digitalen Zwilling (Digital Twin -- DT) als meteorologische Eingaben. Der Benutzer kann eine "
- + "Startzeit für die Vorhersage und eine Region auswählen, woraufhin das Tool Vorhersagen für "
- + "alle Stationen in der jeweiligen Region erstellt. Die Benutzer können auch die interessierende "
- + "Spezies und die zu berechnende Luftqualitätsmetrik auswählen. Die Grundeinstellungen für diese "
- + "Parameter sind Ozon und 'MDA8', d. h. der maximale 8-Stunden-Tagesmittelwert. Weitere "
- + "Informationen über MLAir finden sich im Beschreibungs-Tab.",
- "Diese Anwendung löst eine "
- + "EURAD-IM-Ensemblesimulation mit veränderten Emissionsszenarien aus. Dies ermöglicht die "
- + "Bewertung der Wirksamkeit verschiedener Strategien zur Verringerung der Luftverschmutzung. Die "
- + "Auswahlmöglichkeiten des Benutzers sind ähnlich wie bei der Feldvorhersageanwendung, außer "
- + "dass der Benutzer auch das Szenario auswählen muss, das er verwenden möchte. Weitere "
- + "Informationen über EURAD-IM Emissionen finden sich im Beschreibungs-Tab."]]
-user_label = ["user:", "Benutzer:"]
-run_label = ["run", "Starten"]
-run2_label = ["Run", "Lauf"]
-close_label = ["close", "Schließen"]
-date_label = ["date:", "Datum:"]
-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]
-min_date_allowed = "2017-02-20"
-max_date_allowed = "2018-08-09"
-initial_visible_month = "2017-02-01"
-forecast_length_label = ["forecast length", "Vorhersagedauer"]
-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:"]
-output_metrics_label = ["output metrics:", "Ausgabemetrik:"]
-species_options = [["ozone", "NOx", "PM2.5"],
- ["Ozon", "Stickoxide", "Feinstaub (PM2.5)"]]
-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"]
-time_step_label = ["Time step", "Zeitschritt"]
-start_date_label = ["Start date", "Startdatum"]
-location_label = ["Location", "Ort"]
-day_label = ["day", "Tag"]
-day_plural_label = ["s", "e"]
-save_label = ["Save Results", "Ergebnisse sichern"]
-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"]
-out_option_label = ["output option", "Ausgabe-Option"]
-
# placeholder for time series and map
image_path = IMAGE_PATH.joinpath('placeholder_timeseries.png')
pil_series = Image.open(image_path)
@@ -248,7 +123,7 @@ aai_login = dbc.Row([
def generate_ml_fcast_body(language_id=0):
return [
dbc.Row([
- dbc.Col(dbc.Label(state_label[language_id]), width=3),
+ dbc.Col(dbc.Label(guitr.state_label[language_id]), width=3),
dbc.Col(
dcc.Dropdown(value="Nordrhein Westfalen",
options=["Baden Württemberg", "Bayern", "Berlin", "Brandenburg", "Bremen", "Hamburg", "Hessen",
@@ -257,29 +132,29 @@ def generate_ml_fcast_body(language_id=0):
),
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(date_label[language_id]), width=3),
+ dbc.Col(dbc.Label(guitr.date_label[language_id]), width=3),
dbc.Col(dcc.DatePickerSingle(date=dt.date.today(),
- display_format=date_format[language_id],
- first_day_of_week=first_day_of_week[language_id]))
+ display_format=guitr.date_format[language_id],
+ first_day_of_week=guitr.first_day_of_week[language_id]))
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(species_label[language_id]), width=3),
+ dbc.Col(dbc.Label(guitr.species_label[language_id]), width=3),
dbc.Col(
- dcc.Dropdown(value=species_options[language_id][0], options=species_options[language_id]), width=6
+ dcc.Dropdown(value=guitr.species_options[language_id][0], options=guitr.species_options[language_id]), width=6
)
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(output_metrics_label[language_id]), width=3),
+ dbc.Col(dbc.Label(guitr.output_metrics_label[language_id]), width=3),
dbc.Col(dcc.Dropdown(value=metrics_info[language_id][' Name'][0], options=metrics_info[language_id][' Name'])),
- dbc.Col(dbc.Button(f"{help_metrics_label[language_id]}", id="help_metrics_open", class_name="fzj_style_btn"), width=3),
+ dbc.Col(dbc.Button(f"{guitr.help_metrics_label[language_id]}", id="help_metrics_open", class_name="fzj_style_btn"), width=3),
dbc.Modal([
- dbc.ModalHeader(f"{help_metrics_label[language_id]}"),
+ dbc.ModalHeader(f"{guitr.help_metrics_label[language_id]}"),
dbc.ModalBody(dash_table.DataTable(metrics_info[language_id].to_dict('records'),
[{"name": i, "id": i} for i in metrics_info[language_id].columns],
style_table={"width": "100%"},
style_cell={"textAlign": "left",
"whiteSpace": "pre-line"})),
- dbc.ModalFooter([dbc.Button(f"{close_label[language_id]}", id=f"help_metrics_close", class_name="fzj_style_btn", n_clicks=0)]),
+ dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id=f"help_metrics_close", class_name="fzj_style_btn", n_clicks=0)]),
],
id="help_metrics_modal",
is_open=False,
@@ -292,7 +167,7 @@ def generate_ml_fcast_output_body(language_id):
ts_plot_path = str(APP_HOME.joinpath("assets", "generated_plots", ts_plot_name))
return [
- dbc.Row(dbc.Label(f"{start_date_label[language_id]}: 18.07.2018, Ozon, Nordrhein Westfalen")),
+ 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(
@@ -300,13 +175,13 @@ def generate_ml_fcast_output_body(language_id):
options=station_info,
), width=6
),
- dbc.Col(dbc.Button(f"{map_select_label[language_id]}", class_name="fzj_style_btn"), width=2)
+ 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"{save_label[language_id]}", class_name="fzj_style_btn"), width=6),
+ dbc.Col(dbc.Button(f"{guitr.save_label[language_id]}", class_name="fzj_style_btn"), width=6),
dbc.Col(html.Td([])),
- dbc.Button(f"{download_label[language_id]}", class_name="fzj_style_btn")], width=6),
+ dbc.Button(f"{guitr.download_label[language_id]}", class_name="fzj_style_btn")], width=6),
], class_name="row mt-3"),
]
@@ -315,10 +190,10 @@ def generate_ml_fcast_output_modal(jobnr=None, lisopen=False, language_id=0):
return html.Div([
dbc.Modal(
[
- dbc.ModalTitle(f"{run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
- dbc.ModalHeader(dbc.ModalTitle(application_text[language_id][1])),
+ 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.ModalFooter([dbc.Button(f"{close_label[language_id]}", id="ml_fcast_output_close",
+ dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id="ml_fcast_output_close",
class_name="fzj_style_btn", n_clicks=0)
]),
],
@@ -332,10 +207,10 @@ def generate_ml_fcast_output_modal(jobnr=None, lisopen=False, language_id=0):
def generate_ml_fcast_result_modal(jobnr=None, lisopen=False, language_id=0):
return dbc.Modal(
[
- dbc.ModalTitle(f"{run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
- dbc.ModalHeader(dbc.ModalTitle(application_text[language_id][1])),
+ 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.ModalFooter(dbc.Button(f"{close_label[language_id]}", id="ml_fcast_result_close",
+ dbc.ModalFooter(dbc.Button(f"{guitr.close_label[language_id]}", id="ml_fcast_result_close",
class_name="fzj_style_btn", n_clicks=0)),
],
id="ml_fcast_result_modal",
@@ -352,19 +227,19 @@ def generate_eurad_im_body(language_id=0):
disabled_days = [ dt.datetime.strptime('2017-02-21','%Y-%m-%d') + dt.timedelta(days=i) for i in range(510) ]
return [
dbc.Row([
- dbc.Col(dbc.Label(date_label[language_id]), width=3),
+ dbc.Col(dbc.Label(guitr.date_label[language_id]), width=3),
dbc.Col(dcc.DatePickerSingle(date=dt.date(2017, 1, 1),
- display_format=date_format[language_id],
- first_day_of_week=first_day_of_week[language_id],
- min_date_allowed=dt.date(2017, 1, 1),
- max_date_allowed=dt.date(2018, 9, 1),
+ 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=dt.date(2017, 1, 1))),
- dbc.Col(dbc.Label(f"{forecast_length_label[language_id]}:")),
- dbc.Col(dcc.Dropdown(value=forecast_length_options[language_id][0], options=forecast_length_options[language_id]))
+ initial_visible_month=guiconst.initial_visible_month)),
+ 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]))
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(region_label[language_id]), width=3),
+ dbc.Col(dbc.Label(guitr.region_label[language_id]), width=3),
dbc.Col(dcc.RadioItems(['NRW', 'Berlin-Brandenburg'], 'NRW')),
], class_name="row mt-3"),
]
@@ -417,43 +292,43 @@ def generate_eurad_im_output_body(language_id, context):
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(date_format2[language_id])
+ start_date = pd.to_datetime(timestep_list[0]).strftime(guitr.date_format2[language_id])
fc_length = (len(timestep_list)-1) // 24
- fc_length_str = "{} {}{}".format(fc_length, day_label[language_id], day_plural_label[language_id] if fc_length > 1 else "")
+ fc_length_str = "{} {}{}".format(fc_length, guitr.day_label[language_id], guitr.day_plural_label[language_id] if fc_length > 1 else "")
variables_list = info.get_available_variables(infile)
stations_list = info.get_available_stations()
stations_list = sorted(stations_list)
return [
- dbc.Row([dbc.Label(f"{start_date_label[language_id]}: {start_date}, {forecast_length_label[language_id]}: {fc_length_str}")]),
+ 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.Col(dbc.Label(f"{time_step_label[language_id]}:"), width=3),
+ 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)
], class_name="row mt-3"),
dbc.Row([
dbc.Col(dbc.Label("Variable:"), width=3),
dbc.Col(dcc.Dropdown(value=variables_list[0], options=variables_list, id='variable-dropdown-{}'.format(context)), width=3),
- dbc.Col(dbc.Label(f"{location_label[language_id]}:"), width=1),
+ dbc.Col(dbc.Label(f"{guitr.location_label[language_id]}:"), width=1),
dbc.Col(dcc.Dropdown(value=stations_list[0], options=stations_list, id='station-dropdown-{}'.format(context)), width=5)
], class_name="row mt-3"),
dbc.Row([
dbc.Col(html.Div(id='image-container-{}'.format(context)), width=6),
dbc.Col([html.Div(id='image-container-timeseries-{}'.format(context)),
- dbc.Col(dbc.Button(f"{save_label[language_id]}", class_name="fzj_style_btn"), width=6),
+ dbc.Col(dbc.Button(f"{guitr.save_label[language_id]}", class_name="fzj_style_btn"), width=6),
dbc.Col(html.Br()),
- html.Div([dbc.Button(f"{download_label[language_id]}", id="eurad_im_output_download",
+ html.Div([dbc.Button(f"{guitr.download_label[language_id]}", id="eurad_im_output_download",
class_name="fzj_style_btn"),
dcc.Download(id="eurad_im_download_result")]),
dbc.Col(html.Br()),
- html.Div([dbc.Button(f"{im_download_label[language_id]}", id="eurad_im_plots_download",
+ html.Div([dbc.Button(f"{guitr.im_download_label[language_id]}", id="eurad_im_plots_download",
class_name="fzj_style_btn"),
dcc.Download(id="eurad_im_download_plots")])], width=6),
], class_name="row mt-3"),
dbc.Row([
dbc.Col(html.Br(), width=12),
dbc.Col([
- dbc.Checkbox(label=f"{show_downscaling_label[language_id]}", value=0)
+ dbc.Checkbox(label=f"{guitr.show_downscaling_label[language_id]}", value=0)
], style={"display": "flex"}),
], class_name="row mt-3f"),
]
@@ -463,10 +338,10 @@ def generate_eurad_im_output_modal(jobnr=None, lisopen=False, language_id=0):
return html.Div([
dbc.Modal(
[
- dbc.ModalTitle(f"{run2_label[language_id]} {jobnr}", style={"fontSize": 15}, id="eurad_im_output_modal_title"),
- dbc.ModalHeader(dbc.ModalTitle(application_text[language_id][0])),
+ dbc.ModalTitle(f"{guitr.run2_label[language_id]} {jobnr}", style={"fontSize": 15}, id="eurad_im_output_modal_title"),
+ dbc.ModalHeader(dbc.ModalTitle(guitr.application_text[language_id][0])),
dbc.ModalBody(generate_eurad_im_output_body(language_id, "output")),
- dbc.ModalFooter([dbc.Button(f"{close_label[language_id]}", id="eurad_im_output_close",
+ dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id="eurad_im_output_close",
class_name="fzj_style_btn", n_clicks=0)
]),
],
@@ -480,10 +355,10 @@ def generate_eurad_im_output_modal(jobnr=None, lisopen=False, language_id=0):
def generate_eurad_im_result_modal(jobnr=None, lisopen=False, language_id=0):
return dbc.Modal(
[
- dbc.ModalTitle(f"{run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
- dbc.ModalHeader(dbc.ModalTitle(application_text[language_id][0])),
+ dbc.ModalTitle(f"{guitr.run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
+ dbc.ModalHeader(dbc.ModalTitle(guitr.application_text[language_id][0])),
dbc.ModalBody(generate_eurad_im_output_body(language_id, "result")),
- dbc.ModalFooter(dbc.Button(f"{close_label[language_id]}", id="eurad_im_result_close",
+ dbc.ModalFooter(dbc.Button(f"{guitr.close_label[language_id]}", id="eurad_im_result_close",
class_name="fzj_style_btn", n_clicks=0)),
],
id="eurad_im_result_modal",
@@ -501,44 +376,44 @@ def generate_eurad_scen_output_body(language_id):
dbc.Row([
dbc.Col([
dbc.Checklist(options=[
- {"label": f"{downscaling_label[language_id]}", "value": 1},
+ {"label": f"{guitr.downscaling_label[language_id]}", "value": 1},
])
], style={"display": "flex"}),
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(f"{out_option_label[language_id]}"), width=3),
- dbc.Col(dcc.Dropdown(value=output_format_options[language_id][0],
- options=output_format_options[language_id]), width=6)
+ dbc.Col(dbc.Label(f"{guitr.out_option_label[language_id]}"), width=3),
+ dbc.Col(dcc.Dropdown(value=guitr.output_format_options[language_id][0],
+ options=guitr.output_format_options[language_id]), width=6)
], class_name="row mt-3"),
]
def generate_eurad_scen_body(language_id):
return [
dbc.Row([
- dbc.Col(dbc.Label(f"{date_label[language_id]}"), width=3),
+ dbc.Col(dbc.Label(f"{guitr.date_label[language_id]}"), width=3),
dbc.Col(dcc.DatePickerSingle(date=dt.date.today(),
- display_format=date_format[language_id],
- first_day_of_week=first_day_of_week[language_id])),
- dbc.Col(dbc.Label(f"{forecast_length_label[language_id]}:")),
- dbc.Col(dcc.Dropdown(value=forecast_length_options[language_id][0], options=forecast_length_options[language_id]))
+ display_format=guitr.date_format[language_id],
+ first_day_of_week=guitr.first_day_of_week[language_id])),
+ 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]))
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(f"{region_label[language_id]}"), width=3),
+ dbc.Col(dbc.Label(f"{guitr.region_label[language_id]}"), width=3),
dbc.Col(dcc.RadioItems(['NRW', 'Berlin-Brandenburg'], 'NRW')),
], class_name="row mt-3"),
dbc.Row([
- dbc.Col(dbc.Label(f"{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=emis_info[language_id][" Name"][0], options=emis_info[language_id][" Name"])),
- dbc.Col(dbc.Button(f"{help_emissions_label[language_id]}", id="help_emis_open", class_name="fzj_style_btn"), width=3),
+ dbc.Col(dbc.Button(f"{guitr.help_emissions_label[language_id]}", id="help_emis_open", class_name="fzj_style_btn"), width=3),
dbc.Modal([
- dbc.ModalHeader(f"{help_emissions_label[language_id]}"),
+ dbc.ModalHeader(f"{guitr.help_emissions_label[language_id]}"),
dbc.ModalBody(dash_table.DataTable(emis_info[language_id].to_dict('records'),
[{"name": i, "id": i} for i in emis_info[language_id].columns],
style_table={"width": "100%"},
style_cell={"textAlign": "left",
"whiteSpace": "pre-line"
})),
- dbc.ModalFooter([dbc.Button(f"{close_label[language_id]}", id=f"help_emis_close", class_name="fzj_style_btn", n_clicks=0)]),
+ dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id=f"help_emis_close", class_name="fzj_style_btn", n_clicks=0)]),
],
id="help_emis_modal",
is_open=False,
@@ -552,10 +427,10 @@ def generate_eurad_scen_output_modal(container_id="eurad_scen_output_modal_conta
return html.Div([
dbc.Modal(
[
- dbc.ModalTitle(f"{run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
- dbc.ModalHeader(dbc.ModalTitle(application_text[language_id][2])),
+ dbc.ModalTitle(f"{guitr.run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
+ dbc.ModalHeader(dbc.ModalTitle(guitr.application_text[language_id][2])),
dbc.ModalBody(generate_eurad_scen_output_body(language_id)),
- dbc.ModalFooter([dbc.Button(f"{close_label[language_id]}", id="eurad_scen_output_close",
+ dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id="eurad_scen_output_close",
class_name="fzj_style_btn", n_clicks=0)
]),
],
@@ -570,10 +445,10 @@ def generate_eurad_scen_result_modal(container_id="eurad_scen_result_modal_conta
return html.Div([
dbc.Modal(
[
- dbc.ModalTitle(f"{run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
- dbc.ModalHeader(dbc.ModalTitle(application_text[language_id][2])),
+ dbc.ModalTitle(f"{guitr.run2_label[language_id]} {jobnr}", style={"fontSize": 15}),
+ dbc.ModalHeader(dbc.ModalTitle(guitr.application_text[language_id][2])),
dbc.ModalBody(generate_eurad_scen_output_body(language_id)),
- dbc.ModalFooter([dbc.Button(f"{close_label[language_id]}", id="eurad_scen_result_modal_close",
+ dbc.ModalFooter([dbc.Button(f"{guitr.close_label[language_id]}", id="eurad_scen_result_modal_close",
class_name="fzj_style_btn", n_clicks=0)
]),
],
@@ -586,12 +461,12 @@ def generate_eurad_scen_result_modal(container_id="eurad_scen_result_modal_conta
def create_jobs_tab(language_id=0):
modal_list = [
- {"name": application_text[language_id][0], "id": "eurad_im", "body": generate_eurad_im_body(language_id),
- "description": description_text[language_id][0], "icon": "Icon1_FieldForecasting_small.png"},
- {"name": application_text[language_id][1], "id": "ml_fcast", "body": generate_ml_fcast_body(language_id),
- "description": description_text[language_id][1], "icon": "Icon2_PointForecasting_small.png"},
- {"name": application_text[language_id][2], "id": "eurad_scen", "body": generate_eurad_scen_body(language_id),
- "description": description_text[language_id][2], "icon": "Icon3_EmissionScenarios_small.png"},
+ {"name": guitr.application_text[language_id][0], "id": "eurad_im", "body": generate_eurad_im_body(language_id),
+ "description": guitr.description_text[language_id][0], "icon": "Icon1_FieldForecasting_small.png"},
+ {"name": guitr.application_text[language_id][1], "id": "ml_fcast", "body": generate_ml_fcast_body(language_id),
+ "description": guitr.description_text[language_id][1], "icon": "Icon2_PointForecasting_small.png"},
+ {"name": guitr.application_text[language_id][2], "id": "eurad_scen", "body": generate_eurad_scen_body(language_id),
+ "description": guitr.description_text[language_id][2], "icon": "Icon3_EmissionScenarios_small.png"},
]
return html.Div([
@@ -627,10 +502,10 @@ def create_jobs_tab(language_id=0):
),
dbc.ModalFooter([
dbc.Button(
- f"{run_label[language_id]}", id=f"{modal['id']}_run", class_name="fzj_style_btn", n_clicks=0
+ f"{guitr.run_label[language_id]}", id=f"{modal['id']}_run", class_name="fzj_style_btn", n_clicks=0
),
dbc.Button(
- f"{close_label[language_id]}", id=f"{modal['id']}_close", class_name="fzj_style_btn", n_clicks=0
+ f"{guitr.close_label[language_id]}", id=f"{modal['id']}_close", class_name="fzj_style_btn", n_clicks=0
)
]),
],
@@ -651,7 +526,7 @@ def create_jobs_tab(language_id=0):
def create_description_content(language_id):
return dbc.Card(
dbc.CardBody(
- description[language_id]
+ guitr.description[language_id]
),
class_name="mt-3",
)
@@ -662,17 +537,20 @@ def get_my_jobs_from_db(user_id=None, language_id=0):
if user_id:
conn = sqlite3.connect(DATA_PATH.joinpath('destine_de370c_users.db'))
cur = conn.cursor()
- cur.execute(f"SELECT application, status, start_date, forecast_length, region, creation_date, id FROM jobs WHERE user_id={user_id}")
+ cur.execute(f"SELECT application, status, start_date, forecast_length, region, species, metric, emis_scen, creation_date, id FROM jobs WHERE user_id={user_id}")
data_rows_from_db = cur.fetchall()
for job in data_rows_from_db:
- data_from_db.append({jobs_columns[language_id][0]: application_text[language_id][job[0]],
- jobs_columns[language_id][1]: status_text[language_id][job[1]],
- jobs_columns[language_id][2]: dt.datetime.strptime(job[2],'%Y-%m-%d %H:%M').strftime(date_format2[language_id]),
- jobs_columns[language_id][3]: "{} {}{}".format(job[3], day_label[language_id], day_plural_label[language_id] if job[3] > 1 else ""),
- jobs_columns[language_id][4]: region_text[language_id][job[4]],
- jobs_columns[language_id][5]: dt.datetime.strptime(job[5],'%Y-%m-%d %H:%M').strftime(date_format2[language_id]),
- jobs_columns[language_id][6]: job[6]})
+ data_from_db.append({guitr.jobs_columns[language_id][0]: guitr.application_text[language_id][job[0]],
+ guitr.jobs_columns[language_id][1]: guitr.status_text[language_id][job[1]],
+ guitr.jobs_columns[language_id][2]: dt.datetime.strptime(job[2],'%Y-%m-%d %H:%M').strftime(guitr.date_format2[language_id]),
+ guitr.jobs_columns[language_id][3]: "{} {}{}".format(job[3], guitr.day_label[language_id], guitr.day_plural_label[language_id] if job[3] > 1 else ""),
+ guitr.jobs_columns[language_id][4]: guitr.region_text[language_id][job[4]],
+ guitr.jobs_columns[language_id][5]: "{}".format(guitr.species_options[language_id][job[5]] if job[5] is not None else ""),
+ guitr.jobs_columns[language_id][6]: "{}".format(guitr.metrics_options[language_id][job[6]] if job[6] is not None else ""),
+ guitr.jobs_columns[language_id][7]: "{}".format(emis_info[language_id][" Name"][job[7]] if job[7] is not None else ""),
+ guitr.jobs_columns[language_id][8]: dt.datetime.strptime(job[8],'%Y-%m-%d %H:%M').strftime(guitr.date_format2[language_id]),
+ guitr.jobs_columns[language_id][9]: job[9]})
conn.close()
return data_from_db
@@ -684,7 +562,7 @@ def generate_jobs_table(user_id=None, language_id=0):
dash_table.DataTable(
id='tbl-interactive',
columns=[
- {"name": i, "id": i} for i in jobs_columns[language_id]
+ {"name": i, "id": i} for i in guitr.jobs_columns[language_id]
],
data=data_from_db,
filter_action="native",
@@ -726,10 +604,10 @@ def get_results_from_db(user_id=None, language_id=0):
data_rows_from_db = cur.fetchall()
for result in data_rows_from_db:
- data_from_db.append({results_columns[language_id][0]: result[0],
- results_columns[language_id][1]: result[2],
- results_columns[language_id][2]: application_text[language_id][result[3]],
- results_columns[language_id][3]: result[4]})
+ data_from_db.append({guitr.results_columns[language_id][0]: result[0],
+ guitr.results_columns[language_id][1]: result[2],
+ guitr.results_columns[language_id][2]: guitr.application_text[language_id][result[3]],
+ guitr.results_columns[language_id][3]: result[4]})
conn.close()
return data_from_db
@@ -741,7 +619,7 @@ def generate_results_table(user_id=None, language_id=0):
dash_table.DataTable(
id='results-tbl-interactive',
columns=[
- {"name": i, "id": i} for i in results_columns[language_id]],
+ {"name": i, "id": i} for i in guitr.results_columns[language_id]],
data=data_from_db,
style_header={"fontWeight": "bold"},
filter_action="native",
@@ -759,22 +637,22 @@ def create_tabs_layout(user_id=None, language_id=0):
lhide = False
return dbc.Tabs([
# tab for "Description"
- dbc.Tab(label=layout_cards[language_id][0], id="description_tab", children=[
+ dbc.Tab(label=guitr.layout_cards[language_id][0], id="description_tab", children=[
create_description_content(language_id)
], tab_style={"font-size":"40px"}),
# tab for "Create Jobs"
- dbc.Tab(label=layout_cards[language_id][1], id="create_jobs_tab", children=[
+ dbc.Tab(label=guitr.layout_cards[language_id][1], id="create_jobs_tab", children=[
create_jobs_tab(language_id)
], tab_style={"font-size":"40px"}, disabled=lhide),
# tab for "My Jobs"
- dbc.Tab(label=layout_cards[language_id][2], id="my_jobs_tab", children=[
+ dbc.Tab(label=guitr.layout_cards[language_id][2], id="my_jobs_tab", children=[
generate_jobs_table(user_id=user_id, language_id=language_id),
generate_eurad_im_output_modal(language_id=language_id),
generate_ml_fcast_output_modal(language_id=language_id),
generate_eurad_scen_output_modal(container_id="eurad_scen_output_modal_container", language_id=language_id)
], tab_style={"font-size":"40px"}, disabled=lhide),
# tab for "Results"
- dbc.Tab(label=layout_cards[language_id][3], id="results_tab", children=[
+ dbc.Tab(label=guitr.layout_cards[language_id][3], id="results_tab", children=[
generate_results_table(user_id=user_id, language_id=language_id),
eurad_im_result_modal,
ml_fcast_result_modal,
@@ -824,7 +702,7 @@ def login_open(lb_click, lc_click, name, password):
db_password = fernet.decrypt(user[1]).decode()
if db_password == password:
language_id=user[2]
- return create_login_button(f"{user_label[language_id]} {name}"), \
+ return create_login_button(f"{guitr.user_label[language_id]} {name}"), \
'{"user_id": ' + f'{user[0]}, "language_id": {user[2]}' +'}', \
create_tabs_layout(user_id=user[0], language_id=language_id),\
False, no_update
@@ -1035,13 +913,13 @@ def postprocess_job(rows, derived_virtual_selected_rows, users_dict):
ml_fcast_output_modal_isopen = False
eurad_scen_output_modal_isopen = False
if derived_virtual_selected_rows != []:
- status = rows[derived_virtual_selected_rows[0]][jobs_columns[language_id][1]]
- application = rows[derived_virtual_selected_rows[0]][jobs_columns[language_id][0]]
- jobnr = rows[derived_virtual_selected_rows[0]][jobs_columns[language_id][6]]
- if status == status_text[language_id][0]:
- eurad_im_output_modal_isopen = (application == application_text[language_id][0])
- ml_fcast_output_modal_isopen = (application == application_text[language_id][1])
- eurad_scen_output_modal_isopen = (application == application_text[language_id][2])
+ status = rows[derived_virtual_selected_rows[0]][guitr.jobs_columns[language_id][1]]
+ application = rows[derived_virtual_selected_rows[0]][guitr.jobs_columns[language_id][0]]
+ jobnr = rows[derived_virtual_selected_rows[0]][guitr.jobs_columns[language_id][9]]
+ if status == guitr.status_text[language_id][0]:
+ eurad_im_output_modal_isopen = (application == guitr.application_text[language_id][0])
+ ml_fcast_output_modal_isopen = (application == guitr.application_text[language_id][1])
+ eurad_scen_output_modal_isopen = (application == guitr.application_text[language_id][2])
jobinfo_dict["jobnr"] = jobnr
jobinfo_dict["eurad_im_output_modal_isopen"] = eurad_im_output_modal_isopen
jobinfo_dict["ml_fcast_output_modal_isopen"] = ml_fcast_output_modal_isopen
@@ -1120,11 +998,11 @@ def postprocess_result(rows, derived_virtual_selected_rows, user_dict):
eurad_scen_output_modal_isopen = False
jobnr = "bla"
if derived_virtual_selected_rows != []:
- application = rows[derived_virtual_selected_rows[0]][results_columns[language_id][2]]
+ application = rows[derived_virtual_selected_rows[0]][guitr.results_columns[language_id][2]]
jobnr = rows[derived_virtual_selected_rows[0]]["Jobnr"]
- eurad_im_output_modal_isopen = (application == application_text[language_id][0])
- ml_fcast_output_modal_isopen = (application == application_text[language_id][1])
- eurad_scen_output_modal_isopen = (application == application_text[language_id][2])
+ eurad_im_output_modal_isopen = (application == guitr.application_text[language_id][0])
+ ml_fcast_output_modal_isopen = (application == guitr.application_text[language_id][1])
+ eurad_scen_output_modal_isopen = (application == guitr.application_text[language_id][2])
resultinfo_dict["jobnr"] = jobnr
resultinfo_dict["eurad_im_output_modal_isopen"] = eurad_im_output_modal_isopen
resultinfo_dict["ml_fcast_output_modal_isopen"] = ml_fcast_output_modal_isopen
diff --git a/src/pages/dashboard_constants.py b/src/pages/dashboard_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bf027e9b84aa32cbb42e8ab94a1d006e7ea1146
--- /dev/null
+++ b/src/pages/dashboard_constants.py
@@ -0,0 +1,5 @@
+import datetime as dt
+
+min_date_allowed=dt.date(2017, 1, 1)
+max_date_allowed=dt.date(2018, 9, 1)
+initial_visible_month=dt.date(2017, 1, 1)
diff --git a/src/pages/dashboard_translations.py b/src/pages/dashboard_translations.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f3ad926c64b4cfc8ea2278ccb698749647d05b9
--- /dev/null
+++ b/src/pages/dashboard_translations.py
@@ -0,0 +1,123 @@
+from dash import html
+
+layout_cards = [["Description", "Create Forecasts", "My Model Runs", "My Results"],
+ ["Beschreibung", "Erstelle Vorhersagen", "Meine Modellläufe", "Meine Auswertungen"]]
+description = [["The DestinE Air Quality Use Case demonstrates interactive triggering of advanced forecasting "
+ "and analysis workflows for air quality.",
+ html.Br(),
+ "The workflows are triggered by clicking on the buttons in the second tab.",
+ html.Br(),
+ "The results of the workflows are displayed in the third tab.",
+ html.Br(),
+ "The documentation of the use case can be found ",
+ html.A("here", href="https://www.fz-juelich.de/de/iek/iek-8/projekte/"
+ + "destination-earth-use-case-fuer-luftqualitaet-de370c")],
+ ["Der 'DestinE Air Quality Use Case' (Anwendungsfall) demonstriert die interaktive Auslösung "
+ "fortgeschrittener Vorhersage- und Analyse-Workflows für die Luftqualität.",
+ html.Br(),
+ "Die Workflows werden durch Klicken auf die Schaltflächen im zweiten Reiter ausgelöst.",
+ html.Br(),
+ "Die Ergebnisse der Workflows werden auf der dritten Registerkarte angezeigt.",
+ html.Br(),
+ "Die Dokumentation des Anwendungsfalls finden Sie ",
+ html.A("hier", href="https://www.fz-juelich.de/de/iek/iek-8/projekte/"
+ + "destination-earth-use-case-fuer-luftqualitaet-de370c")]]
+jobs_columns = [["Application", "Status", "Start date", "Forecast Length", "Region", "Species", "Metric", "Emission Scenario", "Creation Date", "Jobnr"],
+ ["Anwendung", "Status", "Startdatum", "Vorhersagedauer", "Region", "Spezies", "Metrik", "Emissionsszenario", "Erstellungsdatum", "Jobnr"]]
+results_columns = [["Result_ID", "Jobnr", "Application", "Used Options"],
+ ["Resultats_ID", "Jobnr", "Anwendung", "Verwendete Optionen"]]
+
+# The following lists should be created once from the controlled vocabulary
+status_text = [["finished", "active", "waiting", "aborted"],
+ ["beendet", "aktiv", "in der Warteschlange", "abgebrochen"]]
+application_text = [["Field forecasting with EURAD-IM", "Point forecasting with MLAir", "EURAD-IM emission scenarios"],
+ ["Gebietsvorhersage mit EURAD-IM", "Stationsvorhersage mit MLAir", "EURAD-IM Emissionsszenarien"]]
+region_text = [["North Rhine-Westphalia", "Berlin-Brandenburg"],
+ ["Nordrhein-Westfalen", "Berlin-Brandenburg"]]
+description_text = [["This application produces high-resolution, i.e. 1 km or less, on-demand results over a "
+ + "user-selected area for a forecast period of up to 4 days. Here, users can select the forecast "
+ + "start date (default today) and the forecast length (shorter runs require less time and "
+ + "resources), and the regional domain of the simulation. There are two EURAD-IM model "
+ + "configurations applied in the background of this application: i) the default 9 km resolution "
+ + "model covering all of Europe, and ii) region-specific 1 km configurations, initially limited "
+ + "to North Rhine-Westphalia and Brandenburg. Users can select whether they wish to perform a "
+ + "dedicated 1 km EURAD-IM simulation, or whether the 1 km results shall be obtained with ML "
+ + "downscaling from the results of a 9 km simulation. For more information about EURAD-IM and ML "
+ + "downscaling, please refer to the description tab.",
+ "This application concerns point forecasts of air quality at (German) measurement stations "
+ + "with the MLAir tool. This workflow makes use of the TOAR database infrastructure that is "
+ + "operated at JSC (https://toar-data.fz-juelich.de) and uses data from the continuous Digital "
+ + "Twin (DT) as meteorological inputs. Users can select a forecast a start time and a region, and "
+ + "the tool will then generate forecasts for all stations in the given region. Users can also "
+ + "select the species of interest and the air quality metric that shall be computed. Initial "
+ + "settings for these parameters are ozone and 'MDA8', i.e. the maximum daily 8-hour average "
+ + "value. For more information about MLAir, please refer to the description tab.",
+ "This application, which can be directly applied for decision making, triggers an EURAD-IM "
+ + "ensemble simulation with modified emission scenarios. This allows for the assessment of the "
+ + "effectiveness of different air pollution mitigation strategies. User choices are similar to "
+ + "the field forecast application, except that users will also have to choose the scenario they "
+ + "wish to employ. For more information about EURAD-IM emissions, please refer to the description "
+ + "tab."],
+ ["Diese Anwendung erzeugt hochauflösende, d.h. 1 km oder weniger, On-Demand-Ergebnisse über ein "
+ + "vom Benutzer ausgewähltes Gebiet für einen Vorhersagezeitraum von bis zu 4 Tagen. Dabei können "
+ + "die Nutzer das Startdatum der Vorhersage (standardmäßig heute) und die Vorhersagelänge "
+ + "(kürzere Läufe erfordern weniger Zeit und Ressourcen) sowie den regionalen Bereich der "
+ + "Simulation auswählen. Im Hintergrund dieser Anwendung werden zwei "
+ + "EURAD-IM-Modellkonfigurationen verwendet: i) das Standardmodell mit 9 km Auflösung, das ganz "
+ + "Europa abdeckt, und ii) regionalspezifische 1 km-Konfigurationen, die zunächst auf "
+ + "Nordrhein-Westfalen und Brandenburg beschränkt sind. Der Benutzer kann dann wählen, ob er eine "
+ + "eigene 1 km EURAD-IM-Simulation durchführen möchte oder ob die 1 km-Ergebnisse durch "
+ + "ML-Downscaling aus den Ergebnissen einer 9 km-Simulation gewonnen werden sollen. Weitere Details "
+ + "über EURAD-IM finden sich unter 'Beschreibung'.",
+ "Diese Anwendung betrifft Punktvorhersagen der Luftqualität an (deutschen) Messstationen mit dem "
+ + "MLAir-Tool. Sie nutzt die TOAR-Datenbankinfrastruktur, die beim JSC "
+ + "(https://toar-data.fz-juelich.de) betrieben wird, und verwendet Daten aus dem kontinuierlichen "
+ + "digitalen Zwilling (Digital Twin -- DT) als meteorologische Eingaben. Der Benutzer kann eine "
+ + "Startzeit für die Vorhersage und eine Region auswählen, woraufhin das Tool Vorhersagen für "
+ + "alle Stationen in der jeweiligen Region erstellt. Die Benutzer können auch die interessierende "
+ + "Spezies und die zu berechnende Luftqualitätsmetrik auswählen. Die Grundeinstellungen für diese "
+ + "Parameter sind Ozon und 'MDA8', d. h. der maximale 8-Stunden-Tagesmittelwert. Weitere "
+ + "Informationen über MLAir finden sich im Beschreibungs-Tab.",
+ "Diese Anwendung löst eine "
+ + "EURAD-IM-Ensemblesimulation mit veränderten Emissionsszenarien aus. Dies ermöglicht die "
+ + "Bewertung der Wirksamkeit verschiedener Strategien zur Verringerung der Luftverschmutzung. Die "
+ + "Auswahlmöglichkeiten des Benutzers sind ähnlich wie bei der Feldvorhersageanwendung, außer "
+ + "dass der Benutzer auch das Szenario auswählen muss, das er verwenden möchte. Weitere "
+ + "Informationen über EURAD-IM Emissionen finden sich im Beschreibungs-Tab."]]
+user_label = ["user:", "Benutzer:"]
+run_label = ["run", "Starten"]
+run2_label = ["Run", "Lauf"]
+close_label = ["close", "Schließen"]
+date_label = ["date:", "Datum:"]
+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:"]
+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:"]
+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:"]
+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"]
+time_step_label = ["Time step", "Zeitschritt"]
+start_date_label = ["Start date", "Startdatum"]
+location_label = ["Location", "Ort"]
+day_label = ["day", "Tag"]
+day_plural_label = ["s", "e"]
+save_label = ["Save Results", "Ergebnisse sichern"]
+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"]
+out_option_label = ["output option", "Ausgabe-Option"]
+