`get_gridded_data`

The get_gridded_data method returns a numpy ndarray of the information identified by the filter attributes passed to the function.

import hf_hydrodata as hf

options = {
      "dataset": "NLDAS2", "variable": "precipitation", "temporal_resolution": "hourly",
      "start_time": "2005-10-1", "end_time": "2005-10-2", "grid_bounds": [100, 100, 200, 200]
}
data = hf.get_gridded_data(options)

`get_gridded_files`

The get_gridded_files method calls get_gridded_data() in multiple threads and saves data into multiple files. This allows you to perform large downloads using multiple threads into multiple files with one function call. It can save files as PFB, NetCDF, or GeoTiff based on the extension in the specified filename template.

import hf_hydrodata as hf

variables = ["air_temp", "precipitation"]
options = {
      "dataset": "CW3E", "temporal_resolution": "hourly",
      "start_time": "2005-10-1", "end_time": "2005-10-4", "grid_bounds": [100, 100, 200, 200]
}
hf.get_gridded_files(options, variables=variables)

By default this creates PFB files. This creates daily files with the hourly data:

CW3E.Temp.000000_to_000024.pfb
CW3E.Temp.000025_to_000048.pfb
CW3E.Temp.000048_to_000072.pfb
CW3E.APCP.000000_to_000024.pfb
CW3E.APCP.000025_to_000048.pfb
CW3E.APCP.000048_to_000072.pfb

import hf_hydrodata as hf

variables = ["air_temp", "precipitation"]
huc_id = "1019000404"
options = {
      "dataset": "CW3E", "temporal_resolution": "hourly",
      "start_time": "2005-09-20", "end_time": "2005-10-4", "huc_id": huc_id
}
hf.get_gridded_files(options, variables=variables, filename_template="{dataset}_{wy}.nc")

This creates two NetCDF files

CW3E_2005.nc
CW3E_2006.nc

Each containing variables "air_temp" and "precipitation" and a time dimension coordinates in the time range of the water year. It used the huc_id option to specify the grid_bounds using the bounding box of a comma seperated list of HUC id.

import hf_hydrodata as hf

variables = ["air_temp", "precipitation"]
options = {
      "dataset": "CW3E", "temporal_resolution": "hourly",
      "start_time": "2005-09-20"
}
hf.get_gridded_files(options, variables=variables, filename_template="{dataset}_{variable}.tiff")

This creates two Geotiff files

CW3E_air_temp.tiff
CW3E_precipitation.tiff

Each containing projection information suiteable to view with GIS.

`get_raw_file`

The get_raw_file method returns the raw file from the server that is idenfied by the filter attributes.

import hf_hydrodata as hf

options = {
    "dataset": "huc_mapping", "grid": "conus2", "level": "4"}
}
hf.get_raw_file("huc4.tiff", options)

`get_date_range`

The get_date_range method returns an array of [start_date, end_date] of the range of dates available for the dataset identified by the same filter attributes passed to get_gridded_data.

import hf_hydrodata as hf
options = {"dataset": "NLDAS2", "temporal_resolution": "daily", "variable": "precipitation",
        "start_time":"2005-09-30", "end_time":"2005-10-03",
        "grid_bounds":[200, 200, 300, 250]
}
range = hf.get_date_range(options)

`get_catalog_entry`

The get_catalog_entry method returns a python dict with attributes describing the data identified by the same filter attributes passed to get_gridded_data.

import hf_hydrodata as hf

options = {
    "dataset": "NLDAS2", "temporal_resolution": "daily",
    "variable": "precipitation", "start_time": "2005-7-1"
}
entry = hf.get_catalog_entry(options)

`get_catalog_enties`

The get_catalog_enties method returns an array of catalog entries filtered by the attributes passed to the function.

import hf_hydrodata as hf

entries = hf.get_catalog_entries(dataset="NLDAS2", temporal_resolution="daily")

options = {"dataset": "NLDAS2", "temporal_resolution": "daily"}
entries = hf.get_catalog_entries(options)
assert len(entries) == 20
entry = entries[0]
assert entry["dataset"] == "NLDAS2"

`get_citations`

Get a citations string about a dataset.

import hf_hydrodata as hf

citations = hf.get_citations("NLDAS2")
print(citations)

citations = hf.get_citations(dataset = "NLDAS2")
print(citations)

options = {"dataset": "NLDAS2", "temporal_resolution": "daily"}
citations = hf.get_citations(options)

`get_datasets`

The get_datasets method returns an array of dataset names available to be specified as the "dataset" filter attribute. Any filter attribute can be passed as arguments to return only datasets with data matching the filter attributes.

import hf_hydrodata as hf

datasets = hf.get_datasets()
assert len(datasets) == 13
assert datasets[0] == "CW3E"

datasets = hf.get_datasets(variable = "air_temp")
assert len(datasets) == 5
assert datasets[0] == "CW3E"

datasets = hf.get_datasets(grid = "conus2")
assert len(datasets) == 5
assert datasets[0] == "CW3E"

options = {"variable": "air_temp", "grid": "conus1"}
datasets = hf.get_datasets(options)
assert len(datasets) == 3
assert datasets[0] == "NLDAS2"

`get_variables`

The get_datasets method returns an array of variable names available to be specified as the "variable" filter attribute. Any filter attribute can be passed as arguments to return only variables with data matching the filter attributes.

import hf_hydrodata as hf

variables = hf.get_variables()
assert len(variables) == 63
assert variables[0] == "air_temp"

variables = hf.get_variables(dataset = "CW3E")
assert len(variables) == 8
assert variables[0] == "air_temp"

variables = hf.get_variables(grid = "conus2")
assert len(variables) == 30
assert variables[0] == "air_temp"

options = {"dataset": "NLDAS2", "grid": "conus1"}
variables = hf.get_variables(options)
assert len(variables) == 8
assert variables[0] == "air_temp"

`from_latlon`

This converts a lat/lon point or an array of lat/lon points to grid coordiates.

import hf_hydrodata as hf

(x, y) = hf.from_latlon("conus1", 31.759219, -115.902573)
latlon_bounds = hf.from_latlon("conus1", *[31.651836, -115.982367, 31.759219, -115.902573])

`to_latlon`

This converts an x,y grid point or an array of x,y grid points to lat/lon coordinates.

import hf_hydrodata as hf

(lat, lon) = hf.to_latlon("conus1", 10, 10)
latlon_bounds = hf.to_latlon("conus1", *[0, 0, 20, 20])
(lat, lon) = hf.to_latlon("conus1", 10.5, 10.5)

`get_huc_from_xy`

This returns a HUC id that contains an x,y grid point.

import hf_hydrodata as hf

huc_id = hf.get_huc_from_xy("conus1", 6, 300, 100)
assert huc_id == "181001"

`get_huc_from_latlon`

This returns a HUC id from a lat/lon coordinate.

import hf_hydrodata as hf

huc_id = hf.get_huc_from_latlon("conus1", 6, 34.48, -115.63)
assert huc_id == "181001"

`get_huc_bbox`

This returns the bounding box of a list of HUC ids in grid coordinates.

import hf_hydrodata as hf

bbox = hf.get_huc_bbox("conus1", ["181001"])
assert bbox == (1, 167, 180, 378)

get_gridded_data

get_gridded_files

get_raw_file

get_date_range

get_catalog_entry

get_catalog_enties

get_citations

get_datasets

get_variables

from_latlon

to_latlon

get_huc_from_xy

get_huc_from_latlon

get_huc_bbox