gkit

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Gkit is a suite of utilities for processing geo-dataset allowing you to manipulate raster datasets like np.MaskedArray.

Gkit supports Python3 and all OS which could install numpy, matplotlib, gdal.

In Python2, may have unpredictable bugs.

Documents: https://titorx.github.io/gkit/html/

Simple Example

Here is examples of some basic features that Gkit provides.

import numpy as np
import gkit as gk

# Read the first band from .tif
r = gk.read("lst.tif")

# You could also specific point out which band you want to load
r = gk.read("lst.tif", 2)

# gk.read returns a Raster class
type(r)
# Output:
# gkit.core.Raster

# Open an interactive window display raster using matplotlib(call plt.show)
r.show()

The screenshot:

https://raw.githubusercontent.com/TitorX/gkit/master/docs/imgs/lst_plot.png

# Only draw raster without calling plt.show.
import matplotlib.pylab as plt
r.plot()
plt.xlabel("Lon")
plt.ylabel("Lat")
plt.title("LST(K)")
plt.colorbar()
plt.savefig("raster_plot.png")

The screenshot:

https://raw.githubusercontent.com/TitorX/gkit/master/docs/imgs/raster_plot.png

# Raster class inherits from np.ma.MaskedArray.
# It has all features which MaskedArray has.
r
# Output:
# masked_array(data =
#  [[-- -- -- ..., -- -- --]
#  [-- -- -- ..., -- -- --]
#  [-- -- -- ..., -- -- --]
#  ...,
#  [242.5966339111328 242.6825408935547 242.79612731933594 ...,
#   243.512451171875 243.46498107910156 243.45751953125]
#  [241.1952667236328 241.18592834472656 241.19235229492188 ...,
#   241.02757263183594 241.04196166992188 241.0919189453125]
#  [241.97023010253906 242.03948974609375 242.05393981933594 ...,
#   241.8543243408203 241.85800170898438 241.80813598632812]],
#              mask =
#  [[ True  True  True ...,  True  True  True]
#  [ True  True  True ...,  True  True  True]
#  [ True  True  True ...,  True  True  True]
#  ...,
#  [False False False ..., False False False]
#  [False False False ..., False False False]
#  [False False False ..., False False False]],
#        fill_value = 1e+20)

# Doing operation like common numpy masked array.
tmp = (r - 273.15)**3 / 4
tmp = np.cos(r)
tmp = np.abs(r)
tmp = np.sqrt(r)

print(r.shape)
print(r.mean())
print(r.max())
print(r.min())

# convert data type
tmp = r.astype(np.float64)

# Save to file
r.save("out_file.tif")

# Create a raster from numpy array
import numpy as np
x, y = np.mgrid[-1:1:100j, -2:2:200j]
array = np.sqrt(x**2 + y**2)

print(array.shape)
# Output:
# (100, 200)

transform = [-100, 0.1, 0, 0, 0, -0.1]

raster = gk.Raster(array, transform)
raster.show()

The screenshot:

https://raw.githubusercontent.com/TitorX/gkit/master/docs/imgs/array_plot.png

Metadata

Release files for gkit 0.6.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for gkit 0.6.4
File	Interpreter	ABI	Platform	Reset
gkit-0.6.4-py3-none-any.whl 12.7 kB May 1, 2019	Python 3	none	any	Details

Release files / gkit-0.6.4-py3-none-any.whl

Download URL	gkit-0.6.4-py3-none-any.whl
Size	12.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`2698fee87976e354d2483eaaedcaeb3d7e301aee45f6f6d08148b25e61b57e71`
BLAKE2b-256 checksum How to use checksums	`42de7f17d08a85362513bf4f6e42e5fee2db0139732440afe9392a24f6051db4`
Upload date	May 1, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.20.1 setuptools/40.6.3 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.0`