saved tutorials track data; fixed minor bug section 1

notebooks-examples-trials/TropiDash_backbone.ipynb

@@ -92,7 +92,7 @@
     "# The observed data is saved as a csv file called ibtracs.ACTIVE.list.v04r00.csv\n",
     "url = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.ACTIVE.list.v04r00.csv'\n",
     "r = requests.get(url, allow_redirects=True)\n",
-    "save_file = 'data/ibtracs.ACTIVE.list.v04r00.csv'\n",
+    "save_file = 'data/tracks/ibtracs.ACTIVE.list.v04r00.csv'\n",
     "with open(save_file, 'wb') as f:\n",
     "    f.write(r.content)"
    ]
@@ -110,8 +110,7 @@
     "df_storms_forecast = create_storms_df(start_date)\n",
     "\n",
     "## LOAD THE OBSERVED TRACKS IN A DATAFRAME ## \n",
-    "# df_storms_observed = pd.read_csv('data/ibtracs.ACTIVE.list.v04r00.csv', header=[0,1])\n",
-    "df_storms_observed = pd.read_csv('trials/test_data_access/ibtracs_data.csv', header=[0,1])\n",
+    "df_storms_observed = pd.read_csv('data/tracks/ibtracs.ACTIVE.list.v04r00.csv', header=[0,1])\n",
     "\n",
     "## CREATE THE LIST OF ACTIVE CYCLONES IN THE FORECAST ##\n",
     "cycl = np.array((df_storms_forecast.stormIdentifier.unique(), df_storms_forecast.longStormName.unique()))\n",
@@ -255,12 +254,10 @@
     "            code, name = cyclone.value.split('-')\n",
     "            display(widgets.HTML(value=f\"Creating the dataset for tropical cyclone <b>{name}</b>\")) # clear previous cyclones plots\n",
     "            df_storms_forecast = create_storms_df(start_date_forecast.value)\n",
-    "            # df_storms_observed = pd.read_csv('data/ibtracs.ACTIVE.list.v04r00.csv', header=[0,1])\n",
-    "            df_storms_observed = pd.read_csv('trials/test_data_access/ibtracs_data.csv', header=[0,1])\n",
+    "            df_storms_observed = pd.read_csv('data/tracks/ibtracs.ACTIVE.list.v04r00.csv', header=[0,1])\n",
     "            df_f = df_storms_forecast[df_storms_forecast.stormIdentifier == code]\n",
     "            df_f.reset_index(drop=True, inplace=True)\n",
-    "            # df_o = df_storms_observed[df_storms_observed.NAME.squeeze() == name]\n",
-    "            df_o = df_storms_observed[df_storms_observed.NAME.squeeze() == 'HILARY']\n",
+    "            df_o = df_storms_observed[df_storms_observed.NAME.squeeze() == name]\n",
     "            df_o.reset_index(drop=True, inplace=True)\n",
     "            # Initial and final lat-lon\n",
     "            initial_lat_lon = (df_f.latitude.iloc[0], df_f.longitude.iloc[0])\n",

notebooks-examples-trials/data/atm/2t_20230912_time0_step24_oper_fc.tiff.aux.xml

@@ -0,0 +1,42 @@
+<PAMDataset>
+  <PAMRasterBand band="1">
+    <Description>2 metre temperature</Description>
+    <Metadata>
+      <MDI key="GRIB_cfName">air_temperature</MDI>
+      <MDI key="GRIB_cfVarName">t2m</MDI>
+      <MDI key="GRIB_dataType">fc</MDI>
+      <MDI key="GRIB_gridDefinitionDescription">Latitude/longitude</MDI>
+      <MDI key="GRIB_gridType">regular_ll</MDI>
+      <MDI key="GRIB_iDirectionIncrementInDegrees">0.4</MDI>
+      <MDI key="GRIB_iScansNegatively">0</MDI>
+      <MDI key="GRIB_jDirectionIncrementInDegrees">0.4</MDI>
+      <MDI key="GRIB_jPointsAreConsecutive">0</MDI>
+      <MDI key="GRIB_jScansPositively">0</MDI>
+      <MDI key="GRIB_latitudeOfFirstGridPointInDegrees">90.0</MDI>
+      <MDI key="GRIB_latitudeOfLastGridPointInDegrees">-90.0</MDI>
+      <MDI key="GRIB_longitudeOfFirstGridPointInDegrees">180.0</MDI>
+      <MDI key="GRIB_longitudeOfLastGridPointInDegrees">179.6</MDI>
+      <MDI key="GRIB_missingValue">3.4028234663852886e+38</MDI>
+      <MDI key="GRIB_name">2 metre temperature</MDI>
+      <MDI key="GRIB_numberOfPoints">405900</MDI>
+      <MDI key="GRIB_NV">0</MDI>
+      <MDI key="GRIB_Nx">900</MDI>
+      <MDI key="GRIB_Ny">451</MDI>
+      <MDI key="GRIB_paramId">167</MDI>
+      <MDI key="GRIB_shortName">2t</MDI>
+      <MDI key="GRIB_stepType">instant</MDI>
+      <MDI key="GRIB_stepUnits">1</MDI>
+      <MDI key="GRIB_typeOfLevel">heightAboveGround</MDI>
+      <MDI key="GRIB_units">K</MDI>
+      <MDI key="long_name">2 metre temperature</MDI>
+      <MDI key="standard_name">air_temperature</MDI>
+      <MDI key="STATISTICS_APPROXIMATE">YES</MDI>
+      <MDI key="STATISTICS_MAXIMUM">310.80377197266</MDI>
+      <MDI key="STATISTICS_MEAN">280.61687233108</MDI>
+      <MDI key="STATISTICS_MINIMUM">208.77252197266</MDI>
+      <MDI key="STATISTICS_STDDEV">20.452840550045</MDI>
+      <MDI key="STATISTICS_VALID_PERCENT">100</MDI>
+      <MDI key="units">K</MDI>
+    </Metadata>
+  </PAMRasterBand>
+</PAMDataset>

notebooks-examples-trials/data/atm/msl_20230912_time0_step24_oper_fc.tiff.aux.xml

@@ -0,0 +1,13 @@
+<PAMDataset>
+  <PAMRasterBand band="1">
+    <Description>msl</Description>
+    <Metadata>
+      <MDI key="STATISTICS_APPROXIMATE">YES</MDI>
+      <MDI key="STATISTICS_MAXIMUM">104.59562683105</MDI>
+      <MDI key="STATISTICS_MEAN">100.96308049192</MDI>
+      <MDI key="STATISTICS_MINIMUM">96.25562286377</MDI>
+      <MDI key="STATISTICS_STDDEV">1.2182982426252</MDI>
+      <MDI key="STATISTICS_VALID_PERCENT">100</MDI>
+    </Metadata>
+  </PAMRasterBand>
+</PAMDataset>

notebooks-examples-trials/data/atm/tp_20230912_time0_step24_oper_fc.tiff.aux.xml

@@ -0,0 +1,13 @@
+<PAMDataset>
+  <PAMRasterBand band="1">
+    <Description>Total precipitation</Description>
+    <Metadata>
+      <MDI key="STATISTICS_APPROXIMATE">YES</MDI>
+      <MDI key="STATISTICS_MAXIMUM">0.14429473876953</MDI>
+      <MDI key="STATISTICS_MEAN">0.0028296943035604</MDI>
+      <MDI key="STATISTICS_MINIMUM">0</MDI>
+      <MDI key="STATISTICS_STDDEV">0.0066340284669862</MDI>
+      <MDI key="STATISTICS_VALID_PERCENT">100</MDI>
+    </Metadata>
+  </PAMRasterBand>
+</PAMDataset>

notebooks-examples-trials/data/tracks/pts_raster_13L_20230912.tif.aux.xml

@@ -0,0 +1,12 @@
+<PAMDataset>
+  <PAMRasterBand band="1">
+    <Metadata>
+      <MDI key="STATISTICS_APPROXIMATE">YES</MDI>
+      <MDI key="STATISTICS_MAXIMUM">100</MDI>
+      <MDI key="STATISTICS_MEAN">5.4612016908213</MDI>
+      <MDI key="STATISTICS_MINIMUM">0</MDI>
+      <MDI key="STATISTICS_STDDEV">19.26469297149</MDI>
+      <MDI key="STATISTICS_VALID_PERCENT">100</MDI>
+    </Metadata>
+  </PAMRasterBand>
+</PAMDataset>

notebooks-examples-trials/tracks_utils.py

@@ -194,7 +194,7 @@ def mean_forecast_track(df_storm):
         date = dates[t].strftime("%d-%m-%Y %H:%M")
         if len(lat) > 0:
             mean_lat_lon = meanposit(len(lat), lat, lon)
-            pressures.append(prs.mean())
+            pressures.append(prs.mean() * 10**-2) # Pa to hPa
             winds.append(wds.mean())
             mean_track_coord.append(mean_lat_lon)
             timesteps.append(date)
@@ -325,7 +325,7 @@ def storm_df_reorganization(df):
 def strike_probability_map(df_storm_forecast):
     """
     Computes the strike probability map of a cyclone and saves it to raster file
-    "data/pts_raster.tif"
+    "data/tracks/pts_raster.tif"
     
     df_storm_forecast: pandas.DataFrame
         Dataframe containing the cyclone forecast tracks data
@@ -341,6 +341,7 @@ def strike_probability_map(df_storm_forecast):
     df = storm_df_reorganization(df_storm_forecast.copy())
     df.dropna(subset = ['lat', 'lon'], inplace=True)
     df["id"] = df.number
+    forecast_date = min(df.date)
 
     # Set of general parameters for the pts algorithm
     distance = 200.0e3
@@ -461,7 +462,7 @@ def ranges(i):
     strike_map = val.reshape((Nj, Ni))
     strike_map_xr = xr.DataArray(strike_map, dims=('latitude', 'longitude'), coords={'latitude': lats, 'longitude': lons})
 
-    tif_path = f"data/pts_raster{storm_code}.tif"
+    tif_path = f"data/tracks/pts_raster_{storm_code}_{forecast_date}.tif"
     strike_map_xr = strike_map_xr.rio.write_crs("epsg:4326")
     strike_map_xr.rio.to_raster(tif_path)