diff --git a/src/mlair/time_series.py b/src/mlair/time_series.py
new file mode 100644
index 0000000000000000000000000000000000000000..c04160ee70f0c0ebcf7ee05126313fcabd8d40e3
--- /dev/null
+++ b/src/mlair/time_series.py
@@ -0,0 +1,55 @@
+import matplotlib.pyplot as plt
+import xarray as xr
+import pandas as pd
+import datetime as dt
+
+def plot(figsize=(6, 3), input_dir="static/data/MLAIR", output_dir="mlair/assets/generated_plots"):
+
+ # get all these from outside!!!
+ species = "O3"
+ station_label = "Bielefeld-Ost"
+ station_code = "DENW067"
+ start_date = "2018-07-18"
+ forecast_length = "4days"
+ units = "ppb"
+
+ # prepare input and output
+ hourly_start_date = (dt.datetime.strptime(start_date, '%Y-%m-%d') - dt.timedelta(days=4)).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
+ # 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) ]
+ 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')
+ file_s = f"{input_dir}/{station_code}_no_no2_o3.nc"
+ dataset = xr.open_dataset(file_s)
+ d4_hourly = dataset['__xarray_dataarray_variable__'].sel(datetime=slice(hourly_start_date,hourly_end_date),variables=species.lower()).data[0]
+ 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"
+
+ # plot
+ plt.figure(figsize=figsize)
+ ax = df_hourly.plot.line(x="hourly_time", y= "value", marker="o", label='Beobachtungen')
+ df_fc.plot.line(x="fc_time", y= "value", linestyle=" ", marker="o", ax=ax, label='Vorhersagen')
+ plt.title(f'{species}: {station_label}')
+ plt.xlabel('Datum')
+ locs, labels = plt.xticks()
+ xticks = [locs[0] + i * 24 for i in range(8) ]
+ xlabels = [(dt.datetime.strptime(hourly_start_date, '%Y-%m-%d') + dt.timedelta(days=i)).strftime('%Y-%m-%d') for i in range(8) ]
+ plt.xticks(xticks, xlabels, rotation=45)
+ plt.ylabel(f'Konzentration ({units})')
+ ax.grid(True)
+ plt.tight_layout()
+ plt.savefig(f"{output_dir}/{output_file_name}")
+
+ return output_file_name
+
+
+if __name__ == "__main__":
+ plot(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 18c87a8c37b50d3431c917d031fa6758456e712e..37840732f3bc6eb3f107161afb86b28144ea1f60 100644
--- a/src/pages/dashboard.py
+++ b/src/pages/dashboard.py
@@ -21,6 +21,7 @@ from deployment_settings import (
UNICORE_BASE, UFTP_BASE,
UNICORE_USER, UNICORE_PASSWORD
)
+from mlair.time_series import plot as plot_ml_time_series
# the following should be done with static files!
APP_HOME = Path.cwd()
@@ -287,12 +288,15 @@ 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))
+
return [
- dbc.Row(dbc.Label(f"{start_date_label[language_id]}: 17 June 2017, ozone, Nordrhein Westfalen")),
+ dbc.Row(dbc.Label(f"{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[0],
+ dcc.Dropdown(value=station_info[87],
options=station_info,
), width=6
),
@@ -345,12 +349,17 @@ ml_fcast_result_modal = html.Div([
], id="ml_fcast_result_modal_container")
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(dcc.DatePickerSingle(date=dt.date.today(),
+ 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])),
+ 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),
+ 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]))
], class_name="row mt-3"),