CodeBreak 16/03/2022 - Revision history

C.carouge: Created page with " =
2022-03-22T23:00:48Z

Created page with " = <span style="font-size:13.999999999999998pt; font-family:Arial; color:#434343; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal;..."

New page

= <span style="font-size:13.999999999999998pt; font-family:Arial; color:#434343; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">Copying global attributes from NetCDF</span> =

<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">There was a question where data was converted using a python notebook and xarray, but the global attributes of the input file should be copied over to the output file.</span>

<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">The new file was saved using the line</span> 
<pre>pr.to_dataset(name='pr').to_netcdf(<filename>)</pre>

<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">We recommended to do it in two steps, and add the copying of the attributes in between:</span>  
<pre>ds = pr.to_dataset(name='pr')
ds.attrs = f.attrs
ds.to_netcddf(<filename>)</pre>

= <span style="font-size:13.999999999999998pt; font-family:Arial; color:#434343; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">Calculating differential in xarray</span> =

<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">The question revolved around calculating the differential of the density (⍴) with respect to the depth (</span><span style="font-size:11pt; font-family:'Times New Roman'; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">z</span><span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">). Attempting this calculation using np.gradient led to broadcasting errors. It is much easier to use an xarray aware function like </span>[https://xarray.pydata.org/en/stable/generated/xarray.DataArray.differentiate.html <span style="font-size:11pt; font-family:Arial; color:#1155cc; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:underline; -webkit-text-decoration-skip:none; text-decoration-skip-ink:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">xarray.differentiate</span>]<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">, which knows how to broadcast because of the named coordinates and uses the spacing in the </span><span style="font-size:11pt; font-family:'Times New Roman'; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">z</span><span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">coordinate to properly calculate the differential δ⍴/δ</span><span style="font-size:11pt; font-family:'Times New Roman'; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">z</span><span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">.</span>

= <span style="font-size:13.999999999999998pt; font-family:Arial; color:#434343; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">Regression analysis on spatial data</span> =

<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">The question was about the suitability of using </span>[https://xarray.pydata.org/en/stable/generated/xarray.DataArray.polyfit.html?highlight=polyfit <span style="font-size:11pt; font-family:Arial; color:#1155cc; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:underline; -webkit-text-decoration-skip:none; text-decoration-skip-ink:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">xarray.Dataset.polyfit</span>]<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">to do regression analysis on spatial data. This might not be the correct tool, as it has limited options for the fitting function. If more than a simple fit is required, it might be better to use something like </span>[https://sklearn-xarray.readthedocs.io/en/latest/auto_examples/plot_linear_regression.html <span style="font-size:11pt; font-family:Arial; color:#1155cc; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:underline; -webkit-text-decoration-skip:none; text-decoration-skip-ink:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">sklearn-xarray</span>]<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">or wrap </span>[https://stackoverflow.com/questions/62105673/applying-stats-linregress-to-every-grid-cell-in-a-netcdf-file <span style="font-size:11pt; font-family:Arial; color:#1155cc; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:underline; -webkit-text-decoration-skip:none; text-decoration-skip-ink:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">scipy.stats.lingress</span>]<span style="font-size:11pt; font-family:Arial; color:#000000; background-color:transparent; font-weight:400; font-style:normal; font-variant:normal; text-decoration:none; vertical-align:baseline; white-space:pre; white-space:pre-wrap">in a function and apply this to all points.</span>