[Matplotlib-users] Variability Plots Matplotlib NetCDF4 Data

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

image005.jpg

image001.jpg

image002.png

···

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@avangrid.com

In the interest of the environment,
please print only if necessary and recycle.

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

image001.jpg

image002.png

image005.jpg

image005.jpg

···

On Wed, Mar 11, 2020, 4:17 PM Benson, Randall Randall.Benson@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

Description: cid:image001.jpg@01D34737.23261CB0

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@avangrid.com

Description: cid:image002.png@01D34737.23261CB0

In the interest of the environment,

please print only if necessary and recycle.


==============================================================
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If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
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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@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..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@gmail.com
Sent: Wednesday, March 11, 2020 4:28 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@avangrid.com

In the interest of the environment,

please print only if necessary and recycle.

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

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==============================================================
Please consider the environment before printing this email.
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
==============================================================

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@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@gmail.com
Sent: Thursday, March 19, 2020 1:32 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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..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@gmail.com
Sent: Wednesday, March 11, 2020 4:28 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@avangrid.com

In the interest of the environment,

please print only if necessary and recycle.

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

Matplotlib-users mailing list
Matplotlib-users@python.org
https://mail.python.org/mailman/listinfo/matplotlib-users

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

==============================================================
Please consider the environment before printing this email.
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
==============================================================

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@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@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@gmail.com
Sent: Thursday, March 19, 2020 1:32 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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..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@gmail.com
Sent: Wednesday, March 11, 2020 4:28 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@avangrid.com

In the interest of the environment,

please print only if necessary and recycle.

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

Matplotlib-users mailing list
Matplotlib-users@python.org
https://mail.python.org/mailman/listinfo/matplotlib-users

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

==============================================================
Please consider the environment before printing this email.
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
==============================================================

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()

image001.jpg

···

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@gmail.com
Sent: Thursday, March 19, 2020 3:38 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@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@gmail.com
Sent: Thursday, March 19, 2020 1:32 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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..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@gmail.com
Sent: Wednesday, March 11, 2020 4:28 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@avangrid.com

In the interest of the environment,

please print only if necessary and recycle.

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The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
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The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
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If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
==============================================================

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

image001.jpg

···

On Fri, Mar 20, 2020 at 12:30 PM Benson, Randall Randall.Benson@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@gmail.com
Sent: Thursday, March 19, 2020 3:38 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@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@gmail.com
Sent: Thursday, March 19, 2020 1:32 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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..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@gmail.com
Sent: Wednesday, March 11, 2020 4:28 PM
To: Benson, Randall Randall.Benson@avangrid.com
Cc: Matplotlib-users matplotlib-users@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@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@avangrid.com

In the interest of the environment,

please print only if necessary and recycle.

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

Matplotlib-users mailing list
Matplotlib-users@python.org
https://mail.python.org/mailman/listinfo/matplotlib-users

==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================
==============================================================
 
Please consider the environment before printing this email.
 
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
 
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
 
 ==============================================================

==============================================================
Please consider the environment before printing this email.
If you have received this message in error, please notify the sender and immediately delete this message and any attachment hereto and/or copy hereof, as such message contains confidential information intended solely for the individual or entity to whom it is addressed. The use or disclosure of such information to third parties is prohibited by law and may give rise to civil or criminal liability.
The views presented in this message are solely those of the author(s) and do not necessarily represent the opinion of Avangrid Renewables, LLC. or any company of its group. Neither Avangrid Renewables, LLC. nor any company of its group guarantees the integrity, security or proper receipt of this message. Likewise, neither Avangrid Renewables, LLC. nor any company of its group accepts any liability whatsoever for any possible damages arising from, or in connection with, data interception, software viruses or manipulation by third parties.
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