Read Data
netCDF
We recommend using xarray’s open_dataset
For a single file
import xarray as xr
ds = xr.open_dataset(filename)For multiple files
import xarray as xr
ds = xr.open_mfdataset(list_of_filenames)R code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!HDF5
For HDF5 files, there are two methods we can use: xarray’s open_dataset and h5py
import xarray as xr
ds = xr.open_dataset(filename,
group=path_to_H5netCDF_group)# add in directions for h5pyR code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!GeoTIFF
For GeoTIFF files, we recommend using rioxarray’s open_rasterio
import rioxarray
xds = rioxarray.open_rasterio("my.tif")R code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!Shapefiles & GeoJSON
To open shapefiles or GeoJSON, we recommend using geopandas’s read_file.
import geopandas as gpd
gdf = gpd.read_file(filename)R code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!CSV
To open CSV files, we recommend using pandas’s read_csv.
import pandas as pd
df = pd.read_csv(filename)R code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!Excel
To open Excel files, we recommend using pandas’s read_excel
import pandas as pd
df = pd.read_excel(filename)R code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!.mat Files
#coming soon! scipy & .matR code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!Binary Files
To open binary files, we recommend using numpy’s from_file. You will need to know the dimensions and data type of the binary file and if there is an offset or header information in the binary file.
import numpy as np
arr = np.from_file(filepath, dtype=data_type).reshape(nrows, ncols)R code coming soon!
# Coming soon!Matlab code coming soon!
#| echo: true
# Coming soon!