Frequently Asked Questions#

This contains relevant questions and answers from common workflow issues and questions posted on Zulip.

This page is meant to be a list of FAQ regarding climate datasets, movivated by a variety of employees across UCAR/NCAR

I need help with this!#

Where do I go for help?#

Try one of the following resources.

  1. Xarray’s How Do I do X? page

  2. Xarray Github Discussions

  3. Pangeo Discourse Forum

  4. NCAR Zulip under #python-questions, #python-dev, or #dask.

Avoid personal emails and prefer a public forum.

What do I do if my question is not answered on this page?#

Open an issue here

Someone must have written the function I want. Where do I look?#

See the xarray ecosystem page. Also see the xarray-contrib and pangeo-data organizations. Some NCAR relevant projects include:

  1. GeoCAT-comp

  2. GeoCAT-viz

  3. cf_xarray

  4. climpred

  5. eofs

  6. MetPy

  7. rechunker

  8. xclim

  9. xesmf

  10. xgcm

  11. pop-tools

  12. xskillscore

Conda Environments#

General Advice#

Dealing with Python environments can be tricky… a good place to start is to checkout this guide on dealing with Python environments

Installing conda on NCAR HPC resources#

There are two main steps of installing conda (miniconda in this case) on NCAR HPC resources

  1. Download miniconda within your work directory

  2. Install and activate your installation

There are a few videos which Anderson Banihirwe put together walking through this process - they are embedded below!

Creating and accessing a new conda environment#

You may want to move past just your base environment, and create a new conda environment! There are a few primary steps to this process:

  1. Create the environment If you are creating an environment from scratch, use the following:

    conda create --name
    

    where name is the name of your environment

    if you have an environment file (ex. environment.yml), use the following:

    Note

    Make sure you include the ipykernel package within your environment, which is required for your environment to be available from the JupyterHub

    conda env create -f environment.yml
    
  2. Accessing your conda environment

    This process will change depending on whether you are using an interactive Jupyter environment - I encourage you to check out the video which Anderson Banihirwe put together describing this process on NCAR HPC resources

Conda is taking too long to solve environment: use mamba#

This is a very common issue when installing a new package or trying to update a package in an existing conda environment. This issue is usually manifested in a conda message along these lines:

environment Solving environment: failed with initial frozen solve. Retrying with flexible solve.

One solution to this issue is to use mamba which is a drop-in replacement for conda. Mamba aims to greately speed up and improve conda functionality such as solving environment, installing packages, etc…

  • Installing Mamba

conda install -n base -c conda-forge mamba
  • Set conda-forge and nodefaults channels

conda config --add channels nodefaults
conda config --add channels conda-forge
  • To install a package with mamba, you just run

mamba install package_name
  • To create/update an environment from an environment file, run:

mamba env update -f environment.yml

See mamba documentation for more.

Conda Environments on JupyterHub#

The Computational and Information Systems Lab (CISL) at NCAR put together some good documentation on dealing with environments on Casper/Cheyenne

Activating Your Base Environment Upon Opening a Terminal#

Even after running conda init bash , you may notice that upon opening a terminal on the JupyterHub/NCAR HPC resources, your conda environment is not activated right away. You could call

bash

which would activate your conda environment! A better solution1 would be to ensure that your conda environment is activated upon login.

You can do this using the following snippet:

echo ". ~/.bashrc" >> ~/.bash_profile

Xarray and Dask#

General tips#

  1. Read the xarray documentation on optimizing workflow with dask.

  2. Read the Best practices for dask array

  3. Keep track of chunk sizes throughout your workflow. This is especially important when reading in data using xr.open_mfdataset. Aim for 100-200MB size chunks.

  4. Choose chunking appropriate to your analysis. If you’re working with time series then chunk more in space and less along time.

  5. Avoid indexing with .where as much as possible. In particulate .where(..., drop=True) will trigger a compute since it needs to know where NaNs are present to drop them. Instead see if you can write your statement as a .clip, .sel, .isel, or .query statement.

How do I optimize reading multiple files using Xarray and Dask?#

A good first place to start when reading in multiple files is Xarray’s multi-file documentation.

For example, if you are trying to read in multiple files where you are interested in concatenating over the time dimension, here is an example of the xr.open_dataset line would look like:

ds = xr.open_mfdataset(
        files,
        # Name of the dimension to concatenate along.
        concat_dim="time",

        # Attempt to auto-magically combine the given datasets into one by using dimension coordinates.
        combine="by_coords",

        # Specify chunks for dask - explained later
        chunks={"lev": 1, "time": 500},

        # Only data variables in which the dimension already appears are included.
        data_vars="minimal",

        # Only coordinates in which the dimension already appears are included.
        coords="minimal",

        # Skip comparing and pick variable from first dataset.
        compat="override",
        parallel=True,
    )

Where can I find Xarray tutorials?#

See videos and notebooks.

How do I debug my code when using dask?#

An option is to use .compute(scheduler="single-threaded"). This will run your code as a serial for loop. When an error is raised you can use the %debug magic to drop in to the stack and debug from there. See this post for more debugging tips in a serial context.

KilledWorker X{. What do I do?#

Keep an eye on the dask dashboard.

  1. If a lot of the bars in the Memory tab are orange, that means your workers are running out of memory. Reduce your chunk size.

Help, my analysis is slow!#

  1. Try subsetting for just the variable(s) you need for example, if you are reading in a dataset with ~25 variables, and you only need temperature, just read in temperature. You can specificy which variables to read in by using the following syntax, following the example of the temperature variable.

ds = xr.open_dataset(file, data_vars=['temperature'])
  1. Take a look at your chunk size, it might not be optimized. When reading a file in using Xarray with Dask, a “general rule of thumb” is to keep your chunk size down to around 100 mb.

For example, let’s say you trying to read in multiple files, each with ~600 time steps. This is case where each file is very large (several 10s of GB) and using Dask to help with data processing is essential.

You can check the size of each chunk by subsetting a single DataArray (ex. ds['temperature'])

If you have very large chunks, try modifying the number of chunks you specify within xr.open_mfdataset(files, ..., chunks={'lev':1, "time": 500}) where lev and time are vertical and time dimensions respectively.

Check to see how large each chunk is after modifying the chunk size, and modify as necessary.

  1. You do not have enough dask workers

If you have a few large files, having the number of workers equal to to the number of input files read in using xr.open_mfdataset would be a good practice

If you have a large number of smaller files, you may not run into this issue, and it is suggest you look at the other potential solutions.

I have to do lots of rechunking, but the rechunk step uses too much memory and kills my workers.#

Try the rechunker package.

Writing to files in parallel#

Distributed writes to netCDF are hard.

  1. Try writing to zarr using Dataset.to_zarr.

  2. If you need to write to netCDF and your final dataset can fit in memory then use dataset.load().to_netcdf(...).

  3. If you really must write a big dataset to netCDF try using save_mfdataset (see here).

My Dask workers are taking a long time to start. How can I monitor them?#

Dask worker requests are added to the job queues on Casper and Cheyenne with the cluster.scale() method. After this method is called, you can verify that they are waiting in the queue with this command:

  • qstat -u <my_username> on Cheyenne, and the same command will work on Casper after April 2021.

If you see no pending worker jobs, then verify that you have called cluster.scale().

Github#

Setting up Github Authentication#

Beginning August 13, 2021, Github will no longer accept account passwords when authenticating git operations. There are essentially two options, which Github provides proper documentation for getting setup:

  1. Setup two-factor authentication

  2. Connect to Github via SSH

CESM Data#

Dealing with CESM monthly output - is there something wrong with time#

A well known issue of CESM data is that timestamps for fields saved as averages are placed at the end of the averaging period. For instance, in the following example, the January/1920 average has a timestamp of February/1920:

In [25]: filename = '/glade/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly/TS/b.e11.B20TRC5CNBDRD.f09_g16.011.cam.h0.TS.192001-200512.nc'

In [33]: ds = xr.open_dataset(filename)

In [34]: ds.time
Out[34]:
<xarray.DataArray 'time' (time: 1032)>
array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,
       cftime.DatetimeNoLeap(2005, 11, 1, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2005, 12, 1, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2006, 1, 1, 0, 0, 0, 0)], dtype=object)
Coordinates:
  * time     (time) object 1920-02-01 00:00:00 ... 2006-01-01 00:00:00
Attributes:
    long_name:  time
    bounds:     time_bnds

A temporary workaround is to fix the issue ourselves by computing new time axis by averaging the time bounds:

In [29]: import xarray as xr

In [30]: import cf_xarray # use cf-xarray so that we can use CF attributes

In [31]: filename = '/glade/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly/TS/b.e11.B20TRC5CNBDRD.f09_g16.011.cam.h0.TS.192001-200512.nc'

In [32]: ds = xr.open_dataset(filename)

In [34]: attrs, encoding = ds.time.attrs.copy(), ds.time.encoding.copy()

In [36]: time_bounds = ds.cf.get_bounds('time')

In [37]: time_bounds_dim_name = ds.cf.get_bounds_dim_name('time')

In [38]: ds = ds.assign_coords(time=time_bounds.mean(time_bounds_dim_name))

In [39]: ds.time.attrs, ds.time.encoding = attrs, encoding

In [40]: ds.time
Out[40]:
<xarray.DataArray 'time' (time: 1032)>
array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,
       cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0),
       cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0),
       cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0)], dtype=object)
Coordinates:
  * time     (time) object 1920-01-16 12:00:00 ... 2005-12-16 12:00:00
Attributes:
    long_name:  time
    bounds:     time_bnds

Note

cf-xarray can be installed via pip or conda. cf-xarray docs are available here.


1

Assuming you are using a bash terminal, which is the default on NCAR HPC