climate_indices | Climate indices for drought monitoring

 by   monocongo Python Version: v0.1.0-beta License: Non-SPDX

kandi X-RAY | climate_indices Summary

kandi X-RAY | climate_indices Summary

climate_indices is a Python library. climate_indices has no bugs, it has no vulnerabilities, it has build file available and it has low support. However climate_indices has a Non-SPDX License. You can install using 'pip install climate_indices' or download it from GitHub, PyPI.

Climate indices for drought monitoring, community reference implementations in Python
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            kandi-support Support

              climate_indices has a low active ecosystem.
              It has 258 star(s) with 121 fork(s). There are 18 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 79 open issues and 192 have been closed. On average issues are closed in 177 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of climate_indices is v0.1.0-beta

            kandi-Quality Quality

              climate_indices has 0 bugs and 0 code smells.

            kandi-Security Security

              climate_indices has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              climate_indices code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              climate_indices has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              climate_indices releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              climate_indices saves you 5155 person hours of effort in developing the same functionality from scratch.
              It has 10835 lines of code, 123 functions and 19 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed climate_indices and discovered the below as its top functions. This is intended to give you an instant insight into climate_indices implemented functionality, and help decide if they suit your requirements.
            • Compute the write index
            • Drops data into shared memory arrays
            • Drop data into shared arrays
            • Builds the function arguments
            • Validate input arguments
            • Compute the spipy ndarray
            • Normalize a dictionary
            • Compute the forecasted time series
            • Calculates the pdsi coefficients from a Z index
            • Assign an X value to a month
            • Assign the backtrack values to the backtrack
            • Calculate the smoothing of a data array
            • Calculates the monthly mean daylight hours for each month
            • Calculate daylight hours
            • Calculate the solar declination
            • Compute the fitted Pearson model for the given values
            • Fit the distribution to a distribution
            • Calculate the water balance accounting for a given time series
            • Convert an array of days to hdfs
            • Compute a fitted gamma distribution
            • Prepare a netCDF file
            • Builds a Dataset containing the data for each forecast
            • Scale values to zero
            • Build the dataset fitting
            • Estimate the Pearson parameters of a timeseries
            • Get a logger
            Get all kandi verified functions for this library.

            climate_indices Key Features

            No Key Features are available at this moment for climate_indices.

            climate_indices Examples and Code Snippets

            No Code Snippets are available at this moment for climate_indices.

            Community Discussions

            QUESTION

            cProfile causes pickling error when running multiprocessing Python code
            Asked 2019-Nov-28 at 00:08

            I have a Python script that runs well when I run it normally:

            $ python script.py

            I am attempting to profile the code using the cProfile module:

            $ python -m cProfile -o script.prof script.py

            When I launch the above command I get an error regarding being unable to pickle a function:

            ...

            ANSWER

            Answered 2019-Nov-28 at 00:08

            The problem you've got here is that, by using -mcProfile, the module __main__ is cProfile (the actual entry point of the code), not your script. cProfile tries to fix this by ensuring that when your script runs, it sees __name__ as "__main__", so it knows it's being run as a script, not imported as a module, but sys.modules['__main__'] remains the cProfile module.

            Problem is, pickle handles pickling functions by just pickling their qualified name (plus some boilerplate to say it's a function in the first place). And to make sure it will survive the round trip, it always double checks that the qualified name can be looked up in sys.modules. So when you do pickle.dumps(_apply_along_axis_palmers) (explicitly, or implicitly in this case by passing it as the mapper function), where _apply_along_axis_palmers is defined in your main script, it double checks that sys.modules['__main__']._apply_along_axis_palmers exists. But it doesn't, because cProfile._apply_along_axis_palmers doesn't exist.

            I don't know of a good solution for this. The best I can come up with is to manually fix up sys.modules to make expose your module and its contents correctly. I haven't tested this completely, so it's possible there will be some quirks, but a solution I've found is to change a module named mymodule.py of the form:

            Source https://stackoverflow.com/questions/53890693

            QUESTION

            xarray.apply_ufunc() with GroupBy: unexpected number of dimensions
            Asked 2019-Nov-22 at 14:43

            I am applying a function to a xarray.DataArray using xarray.apply_ufunc(). It works well with some NetCDFs and fails with others that appear to be comparable in terms of dimensions, coordinates, etc. However there must be something different between the NetCDFs that the code works for and the ones where the code fails, and hopefully someone can comment as to what the problem is after seeing the code and some metadata about the files listed below.

            The code I'm running to perform the computation is this:

            ...

            ANSWER

            Answered 2018-Nov-06 at 15:35

            It turned out that the NetCDF files that were problematic as inputs the latitude coordinate values were in descending order. xarray.apply_ufunc() appears to require that coordinate values be in ascending order, at least in order to avoid this particular issue. This is easily remedied by reversing the offending dimension's coordinate values using NCO's ncpdq command before using the NetCDF file as input to xarray.

            Source https://stackoverflow.com/questions/53108606

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install climate_indices

            You can install using 'pip install climate_indices' or download it from GitHub, PyPI.
            You can use climate_indices like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            CLONE
          • HTTPS

            https://github.com/monocongo/climate_indices.git

          • CLI

            gh repo clone monocongo/climate_indices

          • sshUrl

            git@github.com:monocongo/climate_indices.git

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