Plotting interpolated data causes artefacts #6010

huaracheguarache · 2021-11-14T11:50:13Z

huaracheguarache
Nov 14, 2021

What happened:

I'm trying to do some analysis of CMIP6 model data, and I want to plot multi-model ensembles. In order to do that I need to regrid all of the models to a common grid. Whenever I try to plot data from a regridded model there's a white line along the central longitude and the poles. I use the PlateCarree projection and it doesn't matter what I choose as the central longitude; there's always a white line there.

The code I've included below produces 4 plots. The first one is of data that hasn't been interpolated and there's no white line:

The next three are with interpolated data and with different central longitudes. They all have a white line at the central longitude.

central_longitude=0

central_longitude=33

central_longitude=164

What you expected to happen:

No plot artefacts.

Minimal Complete Verifiable Example:

import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

cesm2_waccm = xr.open_dataset('pr_day_CESM2-WACCM_ssp245_r2i1p1f1_gn_20750101-20841231.nc')
mpi = xr.open_dataset('pr_day_MPI-ESM1-2-LR_ssp245_r1i1p1f1_gn_20750101-20941231.nc')

cesm2_waccm_subset = cesm2_waccm.sel(time=slice('2075-01-01', '2075-12-31')).mean(dim='time')
mpi_subset = mpi.sel(time=slice('2075-01-01', '2075-12-31')).mean(dim='time')

map_proj = ccrs.PlateCarree()

# This works.
plot = mpi_subset.pr.plot(subplot_kws={'projection': map_proj})
plot.axes.coastlines()
plt.show()

mpi_interp = mpi_subset.interp(lat=cesm2_waccm_subset['lat'], lon=cesm2_waccm_subset['lon'])

# A randomly chosen set of central longitudes for plots.
longitudes = [0, 33, 164]

for lon in longitudes:
   map_proj = ccrs.PlateCarree(central_longitude=lon)

   # Has a white line at the central longitude.
   plot = mpi_interp.pr.plot(subplot_kws={'projection': map_proj})
   plot.axes.coastlines()
   plt.show()

Anything else we need to know?:

Here's the data I used for plotting:

https://climate.uiogeo-apps.sigma2.no/ESGF/CMIP6/ScenarioMIP/NCAR/CESM2-WACCM/ssp245/r2i1p1f1/day/pr/gn/v20200224/pr_day_CESM2-WACCM_ssp245_r2i1p1f1_gn_20750101-20841231.nc

https://climate.uiogeo-apps.sigma2.no/ESGF/CMIP6/ScenarioMIP/MPI-M/MPI-ESM1-2-LR/ssp245/r1i1p1f1/day/pr/gn/v20190710/pr_day_MPI-ESM1-2-LR_ssp245_r1i1p1f1_gn_20750101-20941231.nc

Environment:

Output of xr.show_versions()

In [3]: xr.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:23:11) 
[GCC 9.4.0]
python-bits: 64
OS: Linux
OS-release: 5.14.16-301.fc35.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: None

xarray: 0.20.1
pandas: 1.3.4
numpy: 1.21.4
scipy: 1.7.2
netCDF4: None
pydap: None
h5netcdf: 0.11.0
h5py: 3.4.0
Nio: None
zarr: None
cftime: 1.5.1.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: 3.3.2
cartopy: 0.20.1
seaborn: None
numbagg: None
fsspec: None
cupy: None
pint: None
sparse: None
setuptools: 58.5.3
pip: 21.3.1
conda: None
pytest: None
IPython: 7.29.0
sphinx: None

huaracheguarache · 2021-11-14T13:28:20Z

huaracheguarache
Nov 14, 2021
Author

I decided to also look at what happens when you plot with contourf. In this case both the plot of the original data and the interpolated data have a white line at the central longitude, but the interpolated data also has white lines at the poles:

Original MPI-ESM

Interpolated MPI-ESM

Here's the code that produced the plots:

import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

cesm2_waccm = xr.open_dataset('pr_day_CESM2-WACCM_ssp245_r2i1p1f1_gn_20750101-20841231.nc')
mpi = xr.open_dataset('pr_day_MPI-ESM1-2-LR_ssp245_r1i1p1f1_gn_20750101-20941231.nc')

cesm2_waccm_subset = cesm2_waccm.sel(time=slice('2075-01-01', '2075-12-31')).mean(dim='time')
mpi_subset = mpi.sel(time=slice('2075-01-01', '2075-12-31')).mean(dim='time')

map_proj = ccrs.PlateCarree()

# Now this also produces a white line.
plot = mpi_subset.pr.plot.contourf(subplot_kws={'projection': map_proj})
plot.axes.coastlines()
plt.show()

mpi_interp = mpi_subset.interp(lat=cesm2_waccm_subset['lat'], lon=cesm2_waccm_subset['lon'])

# Has a white line at the central longitude.
plot = mpi_interp.pr.plot.contourf(subplot_kws={'projection': map_proj})
plot.axes.coastlines()
plt.show()

0 replies

spencerkclark · 2021-11-14T14:13:07Z

spencerkclark
Nov 14, 2021
Maintainer

Xarray's built-in interpolation method does not know how to handle periodic coordinates, which is why you see the blank center line. I would recommend using xESMF, which is an xarray wrapper of some code specifically written to interpolate from one geospatial grid to another, and knows how to handle periodic coordinates (see the periodic flag in the constructor of the Regridder object). It also provides multiple different interpolating methods which, depending on your application, might be more applicable than xarray's options.

0 replies

huaracheguarache · 2021-11-14T22:56:32Z

huaracheguarache
Nov 14, 2021
Author

@spencerkclark Thanks for the suggestion! I haven't made any serious tests yet, but my initial tests worked fine =)

0 replies

mathause · 2021-11-15T13:20:04Z

mathause
Nov 15, 2021
Maintainer

If this solves your issue all is fine. However, I am a bit surprised that the pcolormesh creates a white line - can you print the lon coords? For the contourf you may need to add a cyclic point.

3 replies

huaracheguarache Nov 23, 2021
Author

I finally have some time to look at this again. So, here are the lon coordinates of the original MPI dataset:

In [29]: mpi_subset['lon'].values
Out[29]: 
array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,
        13.125,  15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,
        26.25 ,  28.125,  30.   ,  31.875,  33.75 ,  35.625,  37.5  ,
        39.375,  41.25 ,  43.125,  45.   ,  46.875,  48.75 ,  50.625,
        52.5  ,  54.375,  56.25 ,  58.125,  60.   ,  61.875,  63.75 ,
        65.625,  67.5  ,  69.375,  71.25 ,  73.125,  75.   ,  76.875,
        78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,  90.   ,
        91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,
       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 ,
       118.125, 120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375,
       131.25 , 133.125, 135.   , 136.875, 138.75 , 140.625, 142.5  ,
       144.375, 146.25 , 148.125, 150.   , 151.875, 153.75 , 155.625,
       157.5  , 159.375, 161.25 , 163.125, 165.   , 166.875, 168.75 ,
       170.625, 172.5  , 174.375, 176.25 , 178.125, 180.   , 181.875,
       183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125, 195.   ,
       196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,
       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 ,
       223.125, 225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375,
       236.25 , 238.125, 240.   , 241.875, 243.75 , 245.625, 247.5  ,
       249.375, 251.25 , 253.125, 255.   , 256.875, 258.75 , 260.625,
       262.5  , 264.375, 266.25 , 268.125, 270.   , 271.875, 273.75 ,
       275.625, 277.5  , 279.375, 281.25 , 283.125, 285.   , 286.875,
       288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125, 300.   ,
       301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,
       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 ,
       328.125, 330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375,
       341.25 , 343.125, 345.   , 346.875, 348.75 , 350.625, 352.5  ,
       354.375, 356.25 , 358.125])

These are the coordinates of the CESM2-WACCM dataset:

In [30]: cesm2_waccm['lon'].values
Out[30]: 
array([  0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,
        10.  ,  11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,
        20.  ,  21.25,  22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,
        30.  ,  31.25,  32.5 ,  33.75,  35.  ,  36.25,  37.5 ,  38.75,
        40.  ,  41.25,  42.5 ,  43.75,  45.  ,  46.25,  47.5 ,  48.75,
        50.  ,  51.25,  52.5 ,  53.75,  55.  ,  56.25,  57.5 ,  58.75,
        60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,  67.5 ,  68.75,
        70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,  78.75,
        80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,
        90.  ,  91.25,  92.5 ,  93.75,  95.  ,  96.25,  97.5 ,  98.75,
       100.  , 101.25, 102.5 , 103.75, 105.  , 106.25, 107.5 , 108.75,
       110.  , 111.25, 112.5 , 113.75, 115.  , 116.25, 117.5 , 118.75,
       120.  , 121.25, 122.5 , 123.75, 125.  , 126.25, 127.5 , 128.75,
       130.  , 131.25, 132.5 , 133.75, 135.  , 136.25, 137.5 , 138.75,
       140.  , 141.25, 142.5 , 143.75, 145.  , 146.25, 147.5 , 148.75,
       150.  , 151.25, 152.5 , 153.75, 155.  , 156.25, 157.5 , 158.75,
       160.  , 161.25, 162.5 , 163.75, 165.  , 166.25, 167.5 , 168.75,
       170.  , 171.25, 172.5 , 173.75, 175.  , 176.25, 177.5 , 178.75,
       180.  , 181.25, 182.5 , 183.75, 185.  , 186.25, 187.5 , 188.75,
       190.  , 191.25, 192.5 , 193.75, 195.  , 196.25, 197.5 , 198.75,
       200.  , 201.25, 202.5 , 203.75, 205.  , 206.25, 207.5 , 208.75,
       210.  , 211.25, 212.5 , 213.75, 215.  , 216.25, 217.5 , 218.75,
       220.  , 221.25, 222.5 , 223.75, 225.  , 226.25, 227.5 , 228.75,
       230.  , 231.25, 232.5 , 233.75, 235.  , 236.25, 237.5 , 238.75,
       240.  , 241.25, 242.5 , 243.75, 245.  , 246.25, 247.5 , 248.75,
       250.  , 251.25, 252.5 , 253.75, 255.  , 256.25, 257.5 , 258.75,
       260.  , 261.25, 262.5 , 263.75, 265.  , 266.25, 267.5 , 268.75,
       270.  , 271.25, 272.5 , 273.75, 275.  , 276.25, 277.5 , 278.75,
       280.  , 281.25, 282.5 , 283.75, 285.  , 286.25, 287.5 , 288.75,
       290.  , 291.25, 292.5 , 293.75, 295.  , 296.25, 297.5 , 298.75,
       300.  , 301.25, 302.5 , 303.75, 305.  , 306.25, 307.5 , 308.75,
       310.  , 311.25, 312.5 , 313.75, 315.  , 316.25, 317.5 , 318.75,
       320.  , 321.25, 322.5 , 323.75, 325.  , 326.25, 327.5 , 328.75,
       330.  , 331.25, 332.5 , 333.75, 335.  , 336.25, 337.5 , 338.75,
       340.  , 341.25, 342.5 , 343.75, 345.  , 346.25, 347.5 , 348.75,
       350.  , 351.25, 352.5 , 353.75, 355.  , 356.25, 357.5 , 358.75])

And these are the resulting coordinates of the interpolated MPI dataset:

In [32]: mpi_interp['lon'].values
Out[32]: 
array([  0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,
        10.  ,  11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,
        20.  ,  21.25,  22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,
        30.  ,  31.25,  32.5 ,  33.75,  35.  ,  36.25,  37.5 ,  38.75,
        40.  ,  41.25,  42.5 ,  43.75,  45.  ,  46.25,  47.5 ,  48.75,
        50.  ,  51.25,  52.5 ,  53.75,  55.  ,  56.25,  57.5 ,  58.75,
        60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,  67.5 ,  68.75,
        70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,  78.75,
        80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,
        90.  ,  91.25,  92.5 ,  93.75,  95.  ,  96.25,  97.5 ,  98.75,
       100.  , 101.25, 102.5 , 103.75, 105.  , 106.25, 107.5 , 108.75,
       110.  , 111.25, 112.5 , 113.75, 115.  , 116.25, 117.5 , 118.75,
       120.  , 121.25, 122.5 , 123.75, 125.  , 126.25, 127.5 , 128.75,
       130.  , 131.25, 132.5 , 133.75, 135.  , 136.25, 137.5 , 138.75,
       140.  , 141.25, 142.5 , 143.75, 145.  , 146.25, 147.5 , 148.75,
       150.  , 151.25, 152.5 , 153.75, 155.  , 156.25, 157.5 , 158.75,
       160.  , 161.25, 162.5 , 163.75, 165.  , 166.25, 167.5 , 168.75,
       170.  , 171.25, 172.5 , 173.75, 175.  , 176.25, 177.5 , 178.75,
       180.  , 181.25, 182.5 , 183.75, 185.  , 186.25, 187.5 , 188.75,
       190.  , 191.25, 192.5 , 193.75, 195.  , 196.25, 197.5 , 198.75,
       200.  , 201.25, 202.5 , 203.75, 205.  , 206.25, 207.5 , 208.75,
       210.  , 211.25, 212.5 , 213.75, 215.  , 216.25, 217.5 , 218.75,
       220.  , 221.25, 222.5 , 223.75, 225.  , 226.25, 227.5 , 228.75,
       230.  , 231.25, 232.5 , 233.75, 235.  , 236.25, 237.5 , 238.75,
       240.  , 241.25, 242.5 , 243.75, 245.  , 246.25, 247.5 , 248.75,
       250.  , 251.25, 252.5 , 253.75, 255.  , 256.25, 257.5 , 258.75,
       260.  , 261.25, 262.5 , 263.75, 265.  , 266.25, 267.5 , 268.75,
       270.  , 271.25, 272.5 , 273.75, 275.  , 276.25, 277.5 , 278.75,
       280.  , 281.25, 282.5 , 283.75, 285.  , 286.25, 287.5 , 288.75,
       290.  , 291.25, 292.5 , 293.75, 295.  , 296.25, 297.5 , 298.75,
       300.  , 301.25, 302.5 , 303.75, 305.  , 306.25, 307.5 , 308.75,
       310.  , 311.25, 312.5 , 313.75, 315.  , 316.25, 317.5 , 318.75,
       320.  , 321.25, 322.5 , 323.75, 325.  , 326.25, 327.5 , 328.75,
       330.  , 331.25, 332.5 , 333.75, 335.  , 336.25, 337.5 , 338.75,
       340.  , 341.25, 342.5 , 343.75, 345.  , 346.25, 347.5 , 348.75,
       350.  , 351.25, 352.5 , 353.75, 355.  , 356.25, 357.5 , 358.75])

mathause Nov 24, 2021
Maintainer

As @spencerkclark mentioned this might be an interpolation issue. Is

mpi_interp.pr.sel(lon=358.75)

a column of NaNs?

huaracheguarache Nov 24, 2021
Author

Yup, that's the case:

In [10]: mpi_interp.pr.sel(lon=358.75)
Out[10]: 
<xarray.DataArray 'pr' (lat: 192)>
array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
Coordinates:
  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
    lon      float64 358.8

It's also the case for the first and last lat coordinate which explains the whites lines at the poles.

In [13]: mpi_interp.pr.isel(lat=0)
Out[13]: 
<xarray.DataArray 'pr' (lon: 288)>
array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan])
Coordinates:
    lat      float64 -90.0
  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8

In [12]: mpi_interp.pr.isel(lat=-1)
Out[12]: 
<xarray.DataArray 'pr' (lon: 288)>
array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan])
Coordinates:
    lat      float64 90.0
  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8

spencerkclark · 2021-11-15T13:29:01Z

spencerkclark
Nov 15, 2021
Maintainer

@mathause I think the issue is that non-periodic interpolation introduces NaNs into the dataset, which get plotted as a white line.

0 replies

TomNicholas · 2021-11-16T04:15:41Z

TomNicholas
Nov 16, 2021
Maintainer

As an aside, this is something that we might want to try and fix via a PeriodicIndex, once the explicit indexes refactor is complete.

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Plotting interpolated data causes artefacts #6010

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huaracheguarache Nov 14, 2021

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huaracheguarache
Nov 14, 2021

Replies: 8 comments 3 replies

huaracheguarache
Nov 14, 2021
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spencerkclark
Nov 14, 2021
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huaracheguarache
Nov 14, 2021
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mathause
Nov 15, 2021
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huaracheguarache Nov 23, 2021
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mathause Nov 24, 2021
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spencerkclark
Nov 15, 2021
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TomNicholas
Nov 16, 2021
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