[Matplotlib-users] EXTERNAL: Re: Variability Plots Matplotlib NetCDF4 Data
Hannah
story645 at gmail.com
Fri Mar 20 14:59:29 EDT 2020
This extracts the data from the the netcdf, the sea surface temps,
latitudes, and longitudes respectively
sst = dataset.variables['sst'][0, :, :]
lats = dataset.variables['lat'][:]
lons = dataset.variables['lon'][:]
this creates an axes with a PlateCarree projection
ax = plt.axes(projection=ccrs.PlateCarree())
this line plots the sst at the x (lons) and y(lats) remapping using the
PlateCarree projection
plt.contourf(lons, lats, sst, 60, transform=ccrs.PlateCarree())
xarray basically does the unpacking and the plt.contourf for you in the
stdn.squeeze().plot.contour() call, but you can basically mimic it with
lats = stdna['lat'].data
lons = stdna['lon'].data
slp = stdna['slp'].data
On Fri, Mar 20, 2020 at 12:30 PM Benson, Randall <
Randall.Benson at avangrid.com> wrote:
> Hi Hannah-
>
> Yes, that works!! Thank you! I’ve got one more question –and if I can
> understand this example, then I think I can make my plot display the
> lat/lon lines onto the contour plot. Could you comment on what’s going on
> with this code with the highlight lines below?
> https://scitools.org.uk/cartopy/docs/v0.15/matplotlib/advanced_plotting.html
>
> Then, I should be able to apply it to my plotting problem.
>
> Thanks! Randall
>
>
>
> Here is my image and I’m trying to plot the lat/lon lines and the plotting
> code:
>
>
>
> # Define an axes object with a projection
>
> ax = plt.axes(projection=ccrs.PlateCarree())
>
> # Plot the coastline
>
> ax.coastlines(lw=1)
>
> stdnasq = stdna.squeeze().plot.contourf(levels=12,add_labels=True)
>
> ax.set_xlabel('')
>
> plt.show()
>
>
>
>
>
> *import* *os*
>
> *import* *matplotlib.pyplot* *as* *plt*
>
> *from* *netCDF4* *import* Dataset *as* netcdf_dataset
>
> *import* *numpy* *as* *np*
>
>
>
> *from* *cartopy* *import* config
>
> *import* *cartopy.crs* *as* *ccrs*
>
>
>
>
>
> *# get the path of the file. It can be found in the repo data directory.*
>
> fname = os.path.join(config["repo_data_dir"],
>
> 'netcdf', 'HadISST1_SST_update.nc'
>
> )
>
>
>
> dataset = netcdf_dataset(fname)
>
> sst = dataset.variables['sst'][0, :, :]
>
> lats = dataset.variables['lat'][:]
>
> lons = dataset.variables['lon'][:]
>
>
>
> ax = plt.axes(projection=ccrs.PlateCarree())
>
>
>
> plt.contourf(lons, lats, sst, 60,
>
> transform=ccrs.PlateCarree())
>
>
>
> ax.coastlines()
>
>
>
> plt.show()
>
>
>
>
>
> *From:* Hannah <story645 at gmail.com>
> *Sent:* Thursday, March 19, 2020 3:38 PM
> *To:* Benson, Randall <Randall.Benson at avangrid.com>
> *Cc:* Matplotlib-users <matplotlib-users at python.org>
> *Subject:* Re: EXTERNAL: Re: [Matplotlib-users] Variability Plots
> Matplotlib NetCDF4 Data
>
>
>
> Ok, was able to reproduce when I did stdna.plot.contour() (missed that bit
> the first time!) and the fix was stdna.squeeze().plot.contour()
>
>
>
> On Thu, Mar 19, 2020 at 6:35 PM Hannah <story645 at gmail.com> wrote:
>
> try stdna.squeeze().plot() ?
>
>
>
> I tried to make a minimum reproduction & I can't reproduce the error. Any
> chance you can share a binder or how you're getting your data?
>
>
>
> This is what I tried:
>
> slp = np.random.random(([1, 73,144]))
> stdn = xr.Dataset({'slp': (['month', 'lat', 'lon'], slp)})
> stdna = stdn.to_array()# must convert the xarray "dataset" to
> "dataArray"!!!
>
> print(stdna.shape)
>
> stdna = stdna.rename('Standardized SLP Anomaly')
> ax = plt.axes(projection=ccrs.PlateCarree())
> ax.coastlines(lw=1)
> stdna.plot()
>
>
>
>
>
> On Thu, Mar 19, 2020 at 5:52 PM Benson, Randall <
> Randall.Benson at avangrid.com> wrote:
>
> Hi -
>
> I solved it by converting the datasets to dataarrays using this below with
> my variable a dataArray “stdna” and calling the “plot” command.
>
> However, when I try to plot this variable using -- stdna.plot.contourf() –
> but I get an error saying my data must be 2D. The “stnda” variable looks
> like this –
>
>
>
> stdna
>
> Out[181]:
>
> <xarray.DataArray 'Standardized SLP Anomaly' (variable: 1, month: 1, lat:
> 73, lon: 144)>
>
> dask.array<stack, shape=(1, 1, 73, 144), dtype=float32, chunksize=(1, 1,
> 73, 144), chunktype=numpy.ndarray>
>
> Coordinates:
>
> * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0
> 357.5
>
> * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5
> -90.0
>
> * month (month) int64 1
>
> * variable (variable) <U3 'slp'
>
> --------------------------------------------------------------
>
> This works below….
>
> stdna = stdn.to_array()# must convert the xarray "dataset" to
> "dataArray"!!!
>
> #stdna.rename('Standardized SLP Anomaly')#rename dataarray
>
> stdna = stdna.rename('Standardized SLP Anomaly')#for legend description
> label
>
> # Define an axes object with a projection
>
> ax = plt.axes(projection=ccrs.PlateCarree())
>
> # Plot the coastline
>
> ax.coastlines(lw=1)
>
> stdna.plot()
>
>
>
> #
>
>
>
> *From:* Hannah <story645 at gmail.com>
> *Sent:* Thursday, March 19, 2020 1:32 PM
> *To:* Benson, Randall <Randall.Benson at avangrid.com>
> *Cc:* Matplotlib-users <matplotlib-users at python.org>
> *Subject:* Re: EXTERNAL: Re: [Matplotlib-users] Variability Plots
> Matplotlib NetCDF4 Data
>
>
>
> Hi,
>
> Sorry for the late reply, been swamped. If you haven't sorted this out,
> can you please post at least some of the code that generated this error?
>
> Thanks!
>
>
>
> On Tue, Mar 17, 2020 at 1:06 AM Benson, Randall <
> Randall.Benson at avangrid.com> wrote:
>
> Hi Hannah –
>
>
>
> Are you familiar with this error using xarray to plot gridded netcd4 data?
>
>
>
> This is the out from “stdn.var”
>
>
>
> <bound method ImplementsDatasetReduce._reduce_method.<locals>.wrapped_func
> of <xarray.Dataset>
>
> Dimensions: (lat: 73, lon: 144, month: 1)
>
> Coordinates:
>
> * month (month) int64 2
>
> * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
>
> * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5
> -90.0
>
> Data variables:
>
> slp (month, lat, lon) float32 dask.array<chunksize=(1, 73, 144),
> meta=np.ndarray>>
>
>
>
> I keep getting this error –
>
> - AttributeError: '_Dataset_PlotMethods' object has no attribute 'contourf'
>
>
>
> Thank you,
>
> Randall
>
>
>
> *From:* Hannah <story645 at gmail.com>
> *Sent:* Wednesday, March 11, 2020 4:28 PM
> *To:* Benson, Randall <Randall.Benson at avangrid.com>
> *Cc:* Matplotlib-users <matplotlib-users at python.org>
> *Subject:* EXTERNAL: Re: [Matplotlib-users] Variability Plots Matplotlib
> NetCDF4 Data
>
>
>
> Hi Randall,
>
> I'm not sure what specifically you're asking for. Are you using xarray (
> http://xarray.pydata.org/en/stable/) ? If not, it might help in more
> efficiently computing the anomolies once you read all the files into an
> xarray:
>
>
>
>
> http://xarray.pydata.org/en/stable/examples/weather-data.html#Calculate-monthly-anomalies
>
> On Wed, Mar 11, 2020, 4:17 PM Benson, Randall <Randall.Benson at avangrid.com>
> wrote:
>
> HI-
>
>
>
> I’m trying to develop monthly standardized anomaly plots of Sea Level
> Pressure (SLP) using NCEP reanalysis data in netCDF4 format. I can plot
> netCDF4 data for a single month in plot form like the example shown below.
> However, I need to construct average SLP plots for each month given a set
> of years and use those to plot the standardized anomalies for each month of
> the current year. So, I’m looking too for just examples (python code) of
> creating variability plots using gridded data in Matplotlib. Thank you!
>
>
>
> Randall Benson
>
>
>
> *Randall P. Benson, PhD*
> Wind Asset Meteorologist, Energy Resource - Onshore
>
> 1125 NW Couch Street, Suite 700
>
> Portland, Oregon, USA 97209
> Telephone 503-796-7129
> Cell 971-227-2477
> randall.benson at avangrid.com
>
>
> In the interest of the environment,
> please print only if necessary and recycle.
>
>
>
>
>
>
>
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