geocube | Tool to convert geopandas vector data into rasterized xarray data | Map library

by corteva Python Version: 0.5.2 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | geocube Summary

geocube is a Python library typically used in Geo, Map applications. geocube has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install geocube' or download it from GitHub, PyPI.

Tool to convert geopandas vector data into rasterized xarray data.

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Security

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geocube has a low active ecosystem.

It has 258 star(s) with 23 fork(s). There are 9 watchers for this library.

There were 4 major release(s) in the last 12 months.

There are 6 open issues and 40 have been closed. On average issues are closed in 59 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of geocube is 0.5.2

Quality

geocube has 0 bugs and 0 code smells.

Security

geocube has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

geocube code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

geocube is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

geocube releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

geocube saves you 720 person hours of effort in developing the same functionality from scratch.

It has 1718 lines of code, 87 functions and 28 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed geocube and discovered the below as its top functions. This is intended to give you an instant insight into geocube implemented functionality, and help decide if they suit your requirements.

Create a geocube dataset
Gets the gridded grid from a grouped dataframe
Get a dataset from a list of measurements
Get grid coordinates from a dataframe
Format a pandas data series
Get measurement attributes
Update time attributes
Get the logger
Rasterize an image
Return numpy dtype based on fill value
Remove missing data
Check if data values values are numeric
Create geobox from vector data
Load vector data
Create a GeoBox from rio coordinates
Make geocube from vector data
Write coordinates to netCDF file
Rasterize a set of points
Performs rasterization on a grid
Register subcommands

Get all kandi verified functions for this library.

geocube Key Features

No Key Features are available at this moment for geocube.

geocube Examples and Code Snippets

No Code Snippets are available at this moment for geocube.

Community Discussions

Trending Discussions on geocube

Compute polygon mean from raster: rasterstats.zonal_stats versus rio.clip

Selecting value of column based on the values in another column, then applying the value at each row in pandas dataframe

How can I improve the performance of my script?

QUESTION

Compute polygon mean from raster: rasterstats.zonal_stats versus rio.clip

Asked 2021-Aug-17 at 14:56

I have a raster file and a shapefile containing polygons. For each polygon, I want to compute the mean value of all raster cells within that polygon.

I had been doing this with rasterstats.zonal_stats, which works perfectly but is very slow for large rasters. Recently I came across rioxarray's example of instead clipping the raster to the polygon and then computing the mean. From my experiments, the clipping approach is a lot faster than zonal_stats and the results are the same.

I am therefore wondering if there is anything else except for the time difference that would lead to a preference of one over the other method? And why it is that the clipping is so much faster than the zonal_stats?

Below the output and timing of the two methods, and a snippet of the code. The full code can be found here.

It would be great to get insights on this :)

...

ANSWER

Answered 2021-Aug-17 at 14:56

In the view of self-learning, I realized I made a mistake here and I think it is beneficial to report.

It all depends on the order you run the functions in. As I loaded the raster data with rioxarray.open_rasterio, this data is not loaded into memory. Thereafter both the clip and zonal_stats still have to load this into memory. When one of the two is called before the other, the latter makes use of the fact that the data is already loaded.

You can also choose to instead call ds.load() before calling the fuctions. In my tests this didn't change the total computation time.

Source https://stackoverflow.com/questions/68726522

QUESTION

Selecting value of column based on the values in another column, then applying the value at each row in pandas dataframe

Asked 2021-Jun-14 at 09:15

I have to calculate the value of S, which has the formula as: S = (25400/CN) − 254

the CN value which I have to choose will depend on the amc_active condition viz 1, 2 and 3. if amc_active condition at 'index 0 or 1st row' is 1 then I have to choose the CN value from column cn1 i.e 47

and if the amc_active is 3, then I have to choose CN value as 95 from cn3 column in the 4th row and so on..

...

ANSWER

Answered 2021-Jun-14 at 09:15

You can use numpy's fancy indexing:

Source https://stackoverflow.com/questions/67967473

QUESTION

How can I improve the performance of my script?

Asked 2020-Feb-27 at 18:33

I have a "seed" GeoDataFrame (GDF)(RED) which contains a 0.5 arc minutes global grid ((180*2)*(360*2) = 259200). Each cell contains an absolute population estimate. In addition, I have a "leech" GDF (GREEN) with roughly 8250 adjoining non-regular shapes of various sizes (watersheds).

I wrote a script to allocate the population estimates to the geometries in the leech GDF based on the overlapping area between grid cells (seed GDF) and the geometries in the leech GDF. The script works perfectly fine for my sample data (see below). However, once I run it on my actual data, it is very slow. I ran it overnight and the next morning only 27% of the calculations had been performed. I will have to run this script many times and waiting for two days each time, is simply not an option.

After doing a bit of literature research, I already replaced (?) for loops with for index i in df.iterrows() (or is this the same as "conventional" python for loops) but it didn't bring about the performance imporvement I had hoped for.

Any suggestion son how I can speed up my code? In twelve hours, my script only processed only ~30000 rows out of ~200000.

My expected output is the column leech_df['leeched_values'].

...

ANSWER

Answered 2020-Feb-27 at 18:33

Introduction

It might be worthy to profile your code in details to get precise insights of what is your bottleneck.

Bellow some advises to already improve your script performance:

Avoid list.append(1) to count occurrences, use collection.Counter instead;
Avoid pandas.DataFrame.iterrows, use pandas.DataFrame.itertuples instead;
Avoid extra assignation that are not needed, use pandas.DataFrame.fillna instead:

Eg. this line:

Source https://stackoverflow.com/questions/60338228

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

Vulnerabilities

No vulnerabilities reported

Install geocube

You can install using 'pip install geocube' or download it from GitHub, PyPI.
You can use geocube 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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