diff --git a/v0.9.2/_examples/delineate_basin.html b/v0.9.2/_examples/delineate_basin.html index 6b992159d..31e02f94f 100644 --- a/v0.9.2/_examples/delineate_basin.html +++ b/v0.9.2/_examples/delineate_basin.html @@ -563,7 +563,7 @@

Import packages
-2024-01-24 10:08:29,496 - basin_delineation - log - INFO - HydroMT version: 0.9.2
+2024-01-25 10:13:25,552 - basin_delineation - log - INFO - HydroMT version: 0.9.2
 
@@ -585,7 +585,7 @@

Read data
-2024-01-24 10:08:29,721 - basin_delineation - data_catalog - INFO - Reading data catalog archive artifact_data v0.0.8
+2024-01-25 10:13:25,582 - basin_delineation - data_catalog - INFO - Reading data catalog archive artifact_data v0.0.8
 
diff --git a/v0.9.2/_examples/doing_extreme_value_analysis.html b/v0.9.2/_examples/doing_extreme_value_analysis.html index 7e60fac63..6b5d0a167 100644 --- a/v0.9.2/_examples/doing_extreme_value_analysis.html +++ b/v0.9.2/_examples/doing_extreme_value_analysis.html @@ -925,17 +925,17 @@

Example: Doing Extreme Value Analysis (EVA) for time series @@ -1371,8 +1371,8 @@

Step 2: fit a EV distribution on these peaks @@ -1890,27 +1890,27 @@

TL;DR#< return_values (stations, rps) float64 1.426e+03 2.252e+03 ... 2.193e+03 Attributes: long_name: discharge - units: m3/s
  • stations
    PandasIndex
    PandasIndex(Index([1, 2], dtype='int32', name='stations'))
  • dparams
    PandasIndex
    PandasIndex(Index(['shape', 'loc', 'scale'], dtype='object', name='dparams'))
  • rps
    PandasIndex
    PandasIndex(Index([2, 5, 25, 100, 500], dtype='int64', name='rps'))
  • long_name :
    discharge
    units :
    m3/s
  • diff --git a/v0.9.2/_examples/export_data.html b/v0.9.2/_examples/export_data.html index a6a62dcf3..61d93ce6d 100644 --- a/v0.9.2/_examples/export_data.html +++ b/v0.9.2/_examples/export_data.html @@ -536,7 +536,7 @@

    Example: Exporting data from a data catalog
    -2024-01-24 10:08:54,198 - export data - log - INFO - HydroMT version: 0.9.2
    +2024-01-25 10:13:48,246 - export data - log - INFO - HydroMT version: 0.9.2
     
    @@ -560,7 +560,7 @@

    Explore the current data catalog
    -2024-01-24 10:08:54,246 - export data - data_catalog - INFO - Reading data catalog archive artifact_data v0.0.8
    +2024-01-25 10:13:48,273 - export data - data_catalog - INFO - Reading data catalog archive artifact_data v0.0.8
     

    The artifact_data catalog is one of the pre-defined available DataCatalog of HydroMT. You can find an overview of pre-defined data catalogs in the online user guide. You can also get an overview of the pre-defined catalogs with their version number from HydroMT.

    @@ -647,7 +647,7 @@

    Explore the current data catalog
    -2024-01-24 10:08:54,310 - export data - rasterdataset - INFO - Reading era5 netcdf data from /home/runner/.hydromt_data/artifact_data/v0.0.8/era5.nc
    +2024-01-25 10:13:48,335 - export data - rasterdataset - INFO - Reading era5 netcdf data from /home/runner/.hydromt_data/artifact_data/v0.0.8/era5.nc
     

    @@ -1821,11 +1821,11 @@

    Open and explore the exported data @@ -1929,7 +1929,7 @@

    Open and explore the exported data
    -2024-01-24 10:08:54,784 - export data - rasterdataset - INFO - Reading merit_hydro raster data from /home/runner/.hydromt_data/artifact_data/v0.0.8/merit_hydro/{variable}.tif
    +2024-01-25 10:13:48,793 - export data - rasterdataset - INFO - Reading merit_hydro raster data from /home/runner/.hydromt_data/artifact_data/v0.0.8/merit_hydro/{variable}.tif
     

    The steps to use your own data within HydroMT are in brief:

    @@ -609,16 +609,16 @@

    Example: Preparing a data catalog
    -merit_hydro_1k
    -rgi.gpkg
    -osm_landareas.gpkg
    -gdp_world.gpkg
    -grwl_tindex.gpkg
    -grdc.csv
    +data_catalog.yml
    +gadm_level1.gpkg
    +era5.nc
    +hydro_reservoirs.gpkg
    +chelsa.tif
    +ghs_pop_2015_54009.tif
    +ghs_pop_2015.tif
     dtu10mdt_egm96.tif
    -worldclim.nc
    -gswo.tif
    -gcn250
    +era5_orography.nc
    +koppen_geiger.tif
     
    @@ -667,7 +667,7 @@

    RasterDataset from raster file
    -<matplotlib.collections.QuadMesh at 0x7fb4cc9aff50>
    +<matplotlib.collections.QuadMesh at 0x7f39a4f41450>
     

    We have here 9 files. When reading tif files, the name of the file is used as the variable name. HydroMT uses data conventions to ensure that certain variables should have the same name and units to be used in automatically in the workflows. For example elevation data should be called elevtn with unit in [m asl]. Check the data conventions and see if you need to rename or change units with unit_add and @@ -1260,7 +1260,7 @@

    RasterDataset from several raster files
    -2024-01-24 10:09:06,164 - prepare data catalog - data_catalog - INFO - Parsing data catalog from tmpdir/merit_hydro.yml
    +2024-01-25 10:13:59,958 - prepare data catalog - data_catalog - INFO - Parsing data catalog from tmpdir/merit_hydro.yml
     

    @@ -4423,12 +4423,12 @@

    GeoDataset from a netcdf file @@ -4484,7 +4484,7 @@

    GeoDataset from a netcdf file
    -2024-01-24 10:09:08,125 - prepare data catalog - data_catalog - INFO - Parsing data catalog from tmpdir/gtsm.yml
    +2024-01-25 10:14:01,896 - prepare data catalog - data_catalog - INFO - Parsing data catalog from tmpdir/gtsm.yml
     

    For this driver to work, the format of the timeseries table is quite strict (see docs). Let’s inspect the two files using pandas in python:

    @@ -5192,7 +5192,7 @@

    GeoDataset from vector files
    -2024-01-24 10:09:08,202 - prepare data catalog - data_catalog - INFO - Parsing data catalog from tmpdir/waterlevel.yml
    +2024-01-25 10:14:01,973 - prepare data catalog - data_catalog - INFO - Parsing data catalog from tmpdir/waterlevel.yml
     
    @@ -993,7 +993,7 @@

    Netcdf or zarr driver + dtype='int32', name='stations'))
  • category :
    ocean
    paper_doi :
    10.24381/cds.8c59054f
    paper_ref :
    Copernicus Climate Change Service 2019
    source_license :
    https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products
    source_url :
    https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.8c59054f?tab=overview
    source_version :
    GTSM v3.0
  • The data can be visualized with the .plot() xarray method. We show the evolution of the water level over time for a specific point location (station).

    @@ -1602,7 +1602,7 @@

    Netcdf or zarr driver