id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 894753913,MDExOlB1bGxSZXF1ZXN0NjQ3MDcwNjc0,5334,Properly infer intervals of pcolormesh when plotting on logscale,11541317,closed,0,,,6,2021-05-18T20:28:02Z,2021-07-02T17:09:05Z,2021-07-02T12:42:35Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5334,"When inferring the interval breaks, the `coord` and first linearized if `scale=""log""` and then the intervals are brought back to the original scale before being returned to `pcolormesh`. Raise an error if at least one of the elements of the coodinates has a non-positive element when using a logscale. - [x] Closes #5333 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` - [x] New functions/methods are listed in `api.rst` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5334/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 894669073,MDU6SXNzdWU4OTQ2NjkwNzM=,5333,Unexpected behaviour of pcolormesh with logscale in x or y axes,11541317,closed,0,,,1,2021-05-18T18:39:12Z,2021-07-02T12:42:35Z,2021-07-02T12:42:35Z,CONTRIBUTOR,,,," **What happened**: When using `xarray.DataArray.plot.pcolormesh` with `yscale=""log""` the output figure distorts the y axis and thus doesn't plot the data correctly. I think the problem is related to the inference of the intervals through the `xarray.plot.utils._infer_intervals_breaks()` function. **What you expected to happen**: I expect the `yscale=""log""` argument to correctly display the y axis in a logscale while precisley plotting the data. In my opinion, the inference of the intervals for the `pcolormesh` should be also done in log scales. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr import matplotlib.pyplot as plt x = np.linspace(-3, 4, 15) y = np.logspace(-4, 3, 8) xx, yy = np.meshgrid(x, y) z = np.exp(-(xx ** 2) - np.log10(yy) ** 2) - np.exp( -((xx - 1) ** 2) - (np.log10(yy) - 1) ** 2 ) da = xr.DataArray( z, coords={""x"": x, ""y"": y}, dims=(""y"", ""x""), ) da.plot(yscale=""log"") plt.show() ``` Output: ![Figure_1](https://user-images.githubusercontent.com/11541317/118703963-055c4f80-b7ed-11eb-84f6-6e8d55d46607.png) **Anything else we need to know?**: I pass `infer_intervals=False` I get a plot that looks more to the expected one: ```python import numpy as np import xarray as xr import matplotlib.pyplot as plt x = np.linspace(-3, 4, 15) y = np.logspace(-4, 3, 8) xx, yy = np.meshgrid(x, y) z = np.exp(-(xx ** 2) - np.log10(yy) ** 2) - np.exp( -((xx - 1) ** 2) - (np.log10(yy) - 1) ** 2 ) da = xr.DataArray( z, coords={""x"": x, ""y"": y}, dims=(""y"", ""x""), ) da.plot(yscale=""log"", infer_intervals=False) plt.show() ``` ![Figure_3](https://user-images.githubusercontent.com/11541317/118704123-2c1a8600-b7ed-11eb-8a1e-63e409f7b727.png) The problem with deactivating the `infer_intervals` is that matplotlib crops the last row and column of the 2d grid, because we would be selecting`shading=""flat""`, getting the following deprecation warning from matplotlib: ``` /home/santi/git/xarray/xarray/plot/plot.py:1033: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later. primitive = ax.pcolormesh(x, y, z, **kwargs) ``` That's why I think that the inference of the breaks should also be done in log scales. **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: 49aa235fc63706dc145128dd13fdf139b9d5bc6e python: 3.9.4 | packaged by conda-forge | (default, May 10 2021, 22:13:33) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 5.12.1-2-MANJARO machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.utf8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.18.1.dev33+g49aa235f pandas: 1.2.4 numpy: 1.20.1 scipy: 1.6.3 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 3.2.1 Nio: None zarr: 2.8.1 cftime: 1.4.1 nc_time_axis: 1.2.0 PseudoNetCDF: installed rasterio: 1.2.3 cfgrib: 0.9.9.0 iris: 2.4.0 bottleneck: 1.3.2 dask: 2021.05.0 distributed: 2021.05.0 matplotlib: 3.4.2 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: installed pint: 0.17 setuptools: 49.6.0.post20210108 pip: 21.1.1 conda: None pytest: 6.2.4 IPython: None sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5333/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 482335669,MDU6SXNzdWU0ODIzMzU2Njk=,3230,Import error when calling open_rasterio,11541317,closed,0,,,2,2019-08-19T14:10:57Z,2019-08-20T12:54:34Z,2019-08-20T12:54:34Z,CONTRIBUTOR,,,,"#### Problem Description When using `xarray.open_rasterio` for loading the [Bedmap2 dataset](https://www.the-cryosphere.net/7/375/2013/) from geoTIFF files we are getting an `ImportError` after `xarray` tries to import `gdal_version` from `rasterio._base`. The error is raised after uploading `rasterio` to `1.0.25`. When using `rasterio` `1.0.24`, the problem does not exist. This error was caught by @ChetGoerzen and originally reported on https://github.com/fatiando/rockhound/issues/53. ```python import xarray as xr xr.open_rasterio(""bedmap2_bed.tif"") ``` ```python-traceback Traceback (most recent call last): File """", line 1, in File ""/home/santi/.miniconda3/envs/rockhound/lib/python3.7/site-packages/xarray/backends/rasterio_.py"", line 214, in open_rasterio import rasterio File ""/home/santi/.miniconda3/envs/rockhound/lib/python3.7/site-packages/rasterio/__init__.py"", line 22, in from rasterio._base import gdal_version ImportError: libtiledb.so.1.6.0: cannot open shared object file: No such file or directory ``` #### Expected Output ```python-traceback [44448889 values with dtype=int16] Coordinates: * band (band) int64 1 * y (y) float64 3.333e+06 3.332e+06 3.331e+06 ... -3.332e+06 -3.333e+06 * x (x) float64 -3.333e+06 -3.332e+06 ... 3.332e+06 3.333e+06 Attributes: transform: (1000.0, 0.0, -3333500.0, 0.0, -1000.0, 3333500.0) crs: +init=epsg:3031 res: (1000.0, 1000.0) is_tiled: 1 nodatavals: (32767.0,) scales: (1.0,) offsets: (0.0,) ``` #### Output of ``xr.show_versions()``
``` INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.19.66-1-MANJARO machine: x86_64 processor: byteorder: little LC_ALL: None LANG: es_AR.utf8 LOCALE: es_AR.UTF-8 libhdf5: None libnetcdf: None xarray: 0.12.3 pandas: 0.25.0 numpy: 1.17.0 scipy: 1.3.1 netCDF4: None pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2.3.0 distributed: 2.3.0 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: None numbagg: None setuptools: 41.0.1 pip: 19.2.2 conda: None pytest: 5.1.0 IPython: None sphinx: 1.8.5 ```
#### Output of ``conda list``
``` # packages in environment at /home/santi/.miniconda3/envs/rockhound: # # Name Version Build Channel _libgcc_mutex 0.1 main affine 2.2.2 py_0 conda-forge alabaster 0.7.12 py_0 conda-forge appdirs 1.4.3 py_1 conda-forge asn1crypto 0.24.0 py37_1003 conda-forge astroid 2.2.5 py37_0 conda-forge atomicwrites 1.3.0 py_0 conda-forge attrs 19.1.0 py_0 conda-forge babel 2.7.0 py_0 conda-forge black 19.3b0 py_0 conda-forge bokeh 1.3.4 py37_0 conda-forge boost-cpp 1.70.0 h8e57a91_2 conda-forge bzip2 1.0.8 h516909a_0 conda-forge ca-certificates 2019.6.16 hecc5488_0 conda-forge cairo 1.16.0 hfb77d84_1002 conda-forge cartopy 0.17.0 py37he1be148_1005 conda-forge certifi 2019.6.16 py37_1 conda-forge cffi 1.12.3 py37h8022711_0 conda-forge cfitsio 3.470 hb60a0a2_2 conda-forge chardet 3.0.4 py37_1003 conda-forge click 7.0 py_0 conda-forge click-plugins 1.1.1 py_0 conda-forge cligj 0.5.0 py_0 conda-forge cloudpickle 1.2.1 py_0 conda-forge cmocean 2.0 py_1 conda-forge colorspacious 1.1.2 pyh24bf2e0_0 conda-forge coverage 4.5.4 py37h516909a_0 conda-forge cryptography 2.7 py37h72c5cf5_0 conda-forge curl 7.65.3 hf8cf82a_0 conda-forge cycler 0.10.0 py_1 conda-forge cytoolz 0.10.0 py37h516909a_0 conda-forge dask 2.3.0 py_0 conda-forge dask-core 2.3.0 py_0 conda-forge dbus 1.13.6 he372182_0 conda-forge distributed 2.3.0 py_0 conda-forge docutils 0.15.2 py37_0 conda-forge entrypoints 0.3 py37_1000 conda-forge expat 2.2.5 he1b5a44_1003 conda-forge flake8 3.7.8 py37_1 conda-forge fontconfig 2.13.1 h86ecdb6_1001 conda-forge freetype 2.10.0 he983fc9_1 conda-forge freexl 1.0.5 h14c3975_1002 conda-forge fsspec 0.4.1 py_0 conda-forge geos 3.7.2 he1b5a44_1 conda-forge geotiff 1.5.1 h560c3f3_2 conda-forge gettext 0.19.8.1 hc5be6a0_1002 conda-forge giflib 5.1.7 h516909a_1 conda-forge glib 2.58.3 h6f030ca_1002 conda-forge gst-plugins-base 1.14.5 h0935bb2_0 conda-forge gstreamer 1.14.5 h36ae1b5_0 conda-forge hdf4 4.2.13 h9a582f1_1002 conda-forge hdf5 1.10.5 nompi_h3c11f04_1100 conda-forge heapdict 1.0.0 py37_1000 conda-forge icu 64.2 he1b5a44_0 conda-forge idna 2.8 py37_1000 conda-forge imagesize 1.1.0 py_0 conda-forge importlib_metadata 0.18 py37_0 conda-forge isort 4.3.21 py37_0 conda-forge jinja2 2.10.1 py_0 conda-forge jpeg 9c h14c3975_1001 conda-forge json-c 0.13.1 h14c3975_1001 conda-forge kealib 1.4.10 h58c409b_1005 conda-forge kiwisolver 1.1.0 py37hc9558a2_0 conda-forge krb5 1.16.3 h05b26f9_1001 conda-forge lazy-object-proxy 1.4.1 py37h516909a_0 conda-forge libblas 3.8.0 12_openblas conda-forge libcblas 3.8.0 12_openblas conda-forge libcurl 7.65.3 hda55be3_0 conda-forge libdap4 3.20.2 hd48c02d_1000 conda-forge libedit 3.1.20170329 hf8c457e_1001 conda-forge libffi 3.2.1 he1b5a44_1006 conda-forge libgcc-ng 9.1.0 hdf63c60_0 libgdal 3.0.1 hbef8c27_6 conda-forge libgfortran-ng 7.3.0 hdf63c60_0 libiconv 1.15 h516909a_1005 conda-forge libkml 1.3.0 h4fcabce_1010 conda-forge liblapack 3.8.0 12_openblas conda-forge libnetcdf 4.6.2 h056eaf5_1002 conda-forge libopenblas 0.3.7 h6e990d7_1 conda-forge libpng 1.6.37 hed695b0_0 conda-forge libpq 11.5 hd9ab2ff_0 conda-forge libspatialite 4.3.0a he1bb1e1_1029 conda-forge libssh2 1.8.2 h22169c7_2 conda-forge libstdcxx-ng 9.1.0 hdf63c60_0 libtiff 4.0.10 h57b8799_1003 conda-forge libuuid 2.32.1 h14c3975_1000 conda-forge libxcb 1.13 h14c3975_1002 conda-forge libxml2 2.9.9 hee79883_2 conda-forge locket 0.2.0 py_2 conda-forge lz4-c 1.8.3 he1b5a44_1001 conda-forge markupsafe 1.1.1 py37h14c3975_0 conda-forge matplotlib 3.1.1 py37_1 conda-forge matplotlib-base 3.1.1 py37he7580a8_1 conda-forge mccabe 0.6.1 py_1 conda-forge more-itertools 7.2.0 py_0 conda-forge msgpack-python 0.6.1 py37h6bb024c_0 conda-forge ncurses 6.1 hf484d3e_1002 conda-forge numpy 1.17.0 py37h95a1406_0 conda-forge numpydoc 0.9.1 py_0 conda-forge olefile 0.46 py_0 conda-forge openjpeg 2.3.1 h58a6597_0 conda-forge openssl 1.1.1c h516909a_0 conda-forge owslib 0.18.0 py_0 conda-forge packaging 19.0 py_0 conda-forge pandas 0.25.0 py37hb3f55d8_0 conda-forge 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conda-forge python 3.7.3 h33d41f4_1 conda-forge python-dateutil 2.8.0 py_0 conda-forge pytz 2019.2 py_0 conda-forge pyyaml 5.1.2 py37h516909a_0 conda-forge qt 5.9.7 h0c104cb_3 conda-forge rasterio 1.0.25 py37h900e953_2 conda-forge readline 8.0 hf8c457e_0 conda-forge requests 2.22.0 py37_1 conda-forge rockhound 0.1.0+5.g39043fa dev_0 scipy 1.3.1 py37h921218d_2 conda-forge setuptools 41.0.1 py37_0 conda-forge shapely 1.6.4 py37hec07ddf_1006 conda-forge sip 4.19.8 py37hf484d3e_1000 conda-forge six 1.12.0 py37_1000 conda-forge snowballstemmer 1.9.0 py_0 conda-forge snuggs 1.4.6 py_0 conda-forge sortedcontainers 2.1.0 py_0 conda-forge sphinx 1.8.5 py37_0 conda-forge sphinx-gallery 0.4.0 py37_0 conda-forge sphinx_rtd_theme 0.4.3 py_0 conda-forge sphinxcontrib-websupport 1.1.2 py_0 conda-forge sqlite 3.29.0 hcee41ef_0 conda-forge tbb 2018.0.5 h2d50403_0 conda-forge tblib 1.4.0 py_0 conda-forge tiledb 1.6.2 h69c774e_0 conda-forge tk 8.6.9 hed695b0_1002 conda-forge toml 0.10.0 py_0 conda-forge toolz 0.10.0 py_0 conda-forge tornado 6.0.3 py37h516909a_0 conda-forge tzcode 2019a h516909a_1002 conda-forge urllib3 1.25.3 py37_0 conda-forge viscm 0.7 pyh24bf2e0_0 conda-forge wcwidth 0.1.7 py_1 conda-forge wheel 0.33.4 py37_0 conda-forge wrapt 1.11.2 py37h516909a_0 conda-forge xarray 0.12.3 py_0 conda-forge xerces-c 3.2.2 h8412b87_1004 conda-forge xorg-kbproto 1.0.7 h14c3975_1002 conda-forge xorg-libice 1.0.10 h516909a_0 conda-forge xorg-libsm 1.2.3 h84519dc_1000 conda-forge xorg-libx11 1.6.8 h516909a_0 conda-forge xorg-libxau 1.0.9 h14c3975_0 conda-forge xorg-libxdmcp 1.1.3 h516909a_0 conda-forge xorg-libxext 1.3.4 h516909a_0 conda-forge xorg-libxrender 0.9.10 h516909a_1002 conda-forge xorg-renderproto 0.11.1 h14c3975_1002 conda-forge xorg-xextproto 7.3.0 h14c3975_1002 conda-forge xorg-xproto 7.0.31 h14c3975_1007 conda-forge xz 5.2.4 h14c3975_1001 conda-forge yaml 0.1.7 h14c3975_1001 conda-forge zict 1.0.0 py_0 conda-forge zipp 0.5.2 py_0 conda-forge zlib 1.2.11 h516909a_1005 conda-forge zstd 1.4.0 h3b9ef0a_0 conda-forge ```
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