I am trying to create a NetCDF from a CSV file. The idea comes from the fact that the original CSV comes with many columns, three of these representing:
- a specific year ("time")
- the Y coordinate of the observation in EPSG:4326 ("latitude")
- the X coordinate of the observation in EPSG:4326 ("longitude")
As I want to be able to dynamically inspect each variable at a given time as a map, I want to convert it to NetCDF format.
I first import my CSV into a Pandas DataFrame, then I create a MultiIndex with the three dimensions I need, I convert this to an xarray DataSet, I write the CRS with rioxarray, and eventually I export it to a NetCDF nc file.
import pandas as pd
import rioxarray
import xarray as xr
df = pd.read_csv('my_data.csv')
df = df.set_index(['time', 'latitude', 'longitude'])
ds = df.to_xarray()
ds.rio.write_grid_mapping(inplace=True)
ds.rio.write_crs("epsg:4326", inplace=True)
ds.to_netcdf('my_data.nc')
However, when I try to import it as a mesh layer in QGIS, or in Panoply, the coordinates of my NetCDF are not seen correctly (see the pictures below).
I thought ds.rio.write_crs(4326, inplace=True) was enough to wtite the CRS to my dataset in order for it to be recognized by other software, but that does not seem enough.
I really wish to do it in Python to possibly avoid the use of other external software.
Info from ds.info:
Dimensions: (latitude: 144, longitude: 140, time: 9)
Coordinates:
* time (time) datetime64[ns] 1980-01-01 1985-01-01 ... 2020-01-01
* latitude (latitude) float64 -34.75 -34.25 -33.75 ... 36.25 36.75
* longitude (longitude) float64 19.75 19.25 20.25 ... -23.75 -24.75
spatial_ref int64 0
Data variables:
GHS_DENS_5avg (time, latitude, longitude) float64 0.4566 nan ... nan nan
rural (time, latitude, longitude) float64 1.0 nan ... nan nan
cities (time, latitude, longitude) float64 0.0 nan ... nan nan
temp_meanavg (time, latitude, longitude) float64 17.4 nan ... nan nan
temp_anom_5y (time, latitude, longitude) float64 -0.01 nan ... nan nan
prec_meanavg (time, latitude, longitude) float64 37.1 nan ... nan nan
prec_anom_5y (time, latitude, longitude) float64 1.16 nan ... nan nan
best (time, latitude, longitude) float64 nan nan ... nan nan
type_of_violence (time, latitude, longitude) float64 nan nan ... nan nan
n_event (time, latitude, longitude) float64 nan nan ... nan nan
any_event (time, latitude, longitude) float64 0.0 nan ... nan nan>
Info from Panoply:
netcdf file:/home/umberto/Documents/jrc/teleworking/ca/projects/fabrizio/ciclim/data/Conflict%20data/africa_nc_conflict.nc {
dimensions:
time = 9;
latitude = 144;
longitude = 140;
variables:
long time(time=9);
:units = "days since 1980-01-01 00:00:00";
:calendar = "proleptic_gregorian";
double latitude(latitude=144);
:_FillValue = NaN; // double
double longitude(longitude=140);
:_FillValue = NaN; // double
String iso3(time=9, latitude=144, longitude=140);
:coordinates = "";
:grid_mapping = "spatial_ref";
double GHS_DENS_5avg(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:grid_mapping = "spatial_ref";
:coordinates = "";
double rural(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:coordinates = "";
:grid_mapping = "spatial_ref";
double cities(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:coordinates = "";
:grid_mapping = "spatial_ref";
double temp_meanavg(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:long_name = "5yr mean temperature";
:units = "°C";
:coordinates = "";
:grid_mapping = "spatial_ref";
double temp_anom_5y(time=9, latitude=144, longitude=140);
:grid_mapping = "spatial_ref";
:_FillValue = NaN; // double
:coordinates = "";
double prec_meanavg(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:grid_mapping = "spatial_ref";
:coordinates = "";
double prec_anom_5y(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:coordinates = "";
:grid_mapping = "spatial_ref";
double best(time=9, latitude=144, longitude=140);
:grid_mapping = "spatial_ref";
:coordinates = "";
:_FillValue = NaN; // double
:long_name = "Number of deaths";
:units = "count";
double type_of_violence(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:coordinates = "";
:grid_mapping = "spatial_ref";
double n_event(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:long_name = "Number of conflict events";
:coordinates = "";
:grid_mapping = "spatial_ref";
:units = "count";
double any_event(time=9, latitude=144, longitude=140);
:_FillValue = NaN; // double
:long_name = "Conflict events";
:coordinates = "";
:grid_mapping = "spatial_ref";
:units = "Presence of conflict";
long spatial_ref;
:crs_wkt = "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]";
:semi_major_axis = 6378137.0; // double
:semi_minor_axis = 6356752.314245179; // double
:inverse_flattening = 298.257223563; // double
:reference_ellipsoid_name = "WGS 84";
:longitude_of_prime_meridian = 0.0; // double
:prime_meridian_name = "Greenwich";
:geographic_crs_name = "WGS 84";
:grid_mapping_name = "latitude_longitude";
:spatial_ref = "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]";netcdf file:/home/umberto/Documents/jrc/teleworking/ca/projects/fabrizio/ciclim/data/Conflict%20data/africa_nc_conflict.nc {
dimensions:
time = 9;
latitude = 144;
longitude = 140;
// global attributes:
}
