PyKrige

Kriging Toolkit for Python.

These details have not been verified by PyPI

Project links

Homepage

Project description

https://zenodo.org/badge/DOI/10.5281/zenodo.3738604.svg

https://img.shields.io/conda/vn/conda-forge/pykrige.svg

https://travis-ci.com/GeoStat-Framework/PyKrige.svg?branch=master

https://coveralls.io/repos/github/GeoStat-Framework/PyKrige/badge.svg?branch=master

https://img.shields.io/badge/code%20style-black-000000.svg

Kriging Toolkit for Python.

Purpose

The code supports 2D and 3D ordinary and universal kriging. Standard variogram models (linear, power, spherical, gaussian, exponential) are built in, but custom variogram models can also be used. The 2D universal kriging code currently supports regional-linear, point-logarithmic, and external drift terms, while the 3D universal kriging code supports a regional-linear drift term in all three spatial dimensions. Both universal kriging classes also support generic ‘specified’ and ‘functional’ drift capabilities. With the ‘specified’ drift capability, the user may manually specify the values of the drift(s) at each data point and all grid points. With the ‘functional’ drift capability, the user may provide callable function(s) of the spatial coordinates that define the drift(s). The package includes a module that contains functions that should be useful in working with ASCII grid files (*.asc).

See the documentation at http://pykrige.readthedocs.io/ for more details.

Installation

PyKrige requires Python 3.5+ as well as numpy, scipy. It can be installed from PyPi with,

pip install pykrige

scikit-learn is an optional dependency needed for parameter tuning and regression kriging. matplotlib is an optional dependency needed for plotting.

If you use conda, PyKrige can be installed from the conda-forge channel with,

conda install -c conda-forge pykrige

Ordinary Kriging Example

First we will create a 2D dataset together with the associated x, y grids,

import numpy as np
import pykrige.kriging_tools as kt
from pykrige.ok import OrdinaryKriging

data = np.array([[0.3, 1.2, 0.47],
                 [1.9, 0.6, 0.56],
                 [1.1, 3.2, 0.74],
                 [3.3, 4.4, 1.47],
                 [4.7, 3.8, 1.74]])

gridx = np.arange(0.0, 5.5, 0.5)
gridy = np.arange(0.0, 5.5, 0.5)

# Create the ordinary kriging object. Required inputs are the X-coordinates of
# the data points, the Y-coordinates of the data points, and the Z-values of the
# data points. If no variogram model is specified, defaults to a linear variogram
# model. If no variogram model parameters are specified, then the code automatically
# calculates the parameters by fitting the variogram model to the binned
# experimental semivariogram. The verbose kwarg controls code talk-back, and
# the enable_plotting kwarg controls the display of the semivariogram.
OK = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2], variogram_model='linear',
                     verbose=False, enable_plotting=False)

# Creates the kriged grid and the variance grid. Allows for kriging on a rectangular
# grid of points, on a masked rectangular grid of points, or with arbitrary points.
# (See OrdinaryKriging.__doc__ for more information.)
z, ss = OK.execute('grid', gridx, gridy)

# Writes the kriged grid to an ASCII grid file.
kt.write_asc_grid(gridx, gridy, z, filename="output.asc")

Universal Kriging Example

from pykrige.uk import UniversalKriging
import numpy as np

data = np.array([[0.3, 1.2, 0.47],
                 [1.9, 0.6, 0.56],
                 [1.1, 3.2, 0.74],
                 [3.3, 4.4, 1.47],
                 [4.7, 3.8, 1.74]])

gridx = np.arange(0.0, 5.5, 0.5)
gridy = np.arange(0.0, 5.5, 0.5)

# Create the ordinary kriging object. Required inputs are the X-coordinates of
# the data points, the Y-coordinates of the data points, and the Z-values of the
# data points. Variogram is handled as in the ordinary kriging case.
# drift_terms is a list of the drift terms to include; currently supported terms
# are 'regional_linear', 'point_log', and 'external_Z'. Refer to
# UniversalKriging.__doc__ for more information.
UK = UniversalKriging(data[:, 0], data[:, 1], data[:, 2], variogram_model='linear',
                      drift_terms=['regional_linear'])

# Creates the kriged grid and the variance grid. Allows for kriging on a rectangular
# grid of points, on a masked rectangular grid of points, or with arbitrary points.
# (See UniversalKriging.__doc__ for more information.)
z, ss = UK.execute('grid', gridx, gridy)

Three-Dimensional Kriging Example

from pykrige.ok3d import OrdinaryKriging3D
from pykrige.uk3d import UniversalKriging3D
import numpy as np

data = np.array([[0.1, 0.1, 0.3, 0.9],
                                 [0.2, 0.1, 0.4, 0.8],
                                 [0.1, 0.3, 0.1, 0.9],
                                 [0.5, 0.4, 0.4, 0.5],
                                 [0.3, 0.3, 0.2, 0.7]])

gridx = np.arange(0.0, 0.6, 0.05)
gridy = np.arange(0.0, 0.6, 0.01)
gridz = np.arange(0.0, 0.6, 0.1)

# Create the 3D ordinary kriging object and solves for the three-dimension kriged
# volume and variance. Refer to OrdinaryKriging3D.__doc__ for more information.
ok3d = OrdinaryKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3],
                                                 variogram_model='linear')
k3d, ss3d = ok3d.execute('grid', gridx, gridy, gridz)

# Create the 3D universal kriging object and solves for the three-dimension kriged
# volume and variance. Refer to UniversalKriging3D.__doc__ for more information.
uk3d = UniversalKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3],
                                                  variogram_model='linear', drift_terms=['regional_linear'])
k3d, ss3d = uk3d.execute('grid', gridx, gridy, gridz)

# To use the generic 'specified' drift term, the user must provide the drift values
# at each data point and at every grid point. The following example is equivalent to
# using a linear drift in all three spatial dimensions. Refer to
# UniversalKriging3D.__doc__ for more information.
zg, yg, xg = np.meshgrid(gridz, gridy, gridx, indexing='ij')
uk3d = UniversalKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3],
                                                  variogram_model='linear', drift_terms=['specified'],
                                                  specified_drift=[data[:, 0], data[:, 1]])
k3d, ss3d = uk3d.execute('grid', gridx, gridy, gridz, specified_drift_arrays=[xg, yg, zg])

# To use the generic 'functional' drift term, the user must provide a callable
# function that takes only the spatial dimensions as arguments. The following example
# is equivalent to using a linear drift only in the x-direction. Refer to
# UniversalKriging3D.__doc__ for more information.
func = lambda x, y, z: x
uk3d = UniversalKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3],
                                                  variogram_model='linear', drift_terms=['functional'],
                                                  functional_drift=[func])
k3d, ss3d = uk3d.execute('grid', gridx, gridy, gridz)

# Note that the use of the 'specified' and 'functional' generic drift capabilities is
# essentially identical in the two-dimensional universal kriging class (except for a
# difference in the number of spatial coordinates for the passed drift functions).
# See UniversalKriging.__doc__ for more information.

GSTools covariance models

You can also use GSTools covariance models as input for the variogram_model in the PyKrige routines:

import numpy as np
from gstools import Gaussian
from pykrige.ok import OrdinaryKriging
from matplotlib import pyplot as plt

# conditioning data
data = np.array([[0.3, 1.2, 0.47],
                 [1.9, 0.6, 0.56],
                 [1.1, 3.2, 0.74],
                 [3.3, 4.4, 1.47],
                 [4.7, 3.8, 1.74]])
# grid definition for output field
gridx = np.arange(0.0, 5.5, 0.1)
gridy = np.arange(0.0, 6.5, 0.1)
# a GSTools based covariance model
cov_model = Gaussian(dim=2, len_scale=1, anis=0.2, angles=-0.5, var=0.5, nugget=0.1)
# ordinary kriging with pykrige
OK1 = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2], cov_model)
z1, ss1 = OK1.execute('grid', gridx, gridy)
plt.imshow(z1, origin="lower")
plt.show()

Which gives:

https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/20_pykrige.png

Have a look at the documentation about the Covariance Model of GSTools.

Kriging Parameters Tuning

A scikit-learn compatible API for parameter tuning by cross-validation is exposed in sklearn.model_selection.GridSearchCV. See the Krige CV example for a more practical illustration.

Regression Kriging

Regression kriging can be performed with pykrige.rk.RegressionKriging. This class takes as parameters a scikit-learn regression model, and details of either either the OrdinaryKriging or the UniversalKriging class, and performs a correction steps on the ML regression prediction.

A demonstration of the regression kriging is provided in the corresponding example.

License

PyKrige uses the BSD 3-Clause License.

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

1.7.2

May 27, 2024

1.7.1

Oct 16, 2023

1.7.0

Aug 18, 2022

1.6.1

Sep 2, 2021

1.6.0

Apr 4, 2021

1.6.0rc1 pre-release

Apr 3, 2021

1.5.1

Aug 20, 2020

1.5.1rc1 pre-release

Aug 19, 2020

1.5.0

Apr 4, 2020

This version

1.5.0rc2 pre-release

Apr 4, 2020

1.5.0rc1 pre-release

Apr 2, 2020

1.4.1

Jan 15, 2019

1.4.0

Apr 24, 2018

1.4.0rc1 pre-release

Apr 21, 2018

1.3.2

Oct 8, 2017

1.3.1

Aug 15, 2017

1.3.0

Oct 24, 2015

1.2.0

Aug 2, 2015

1.1.0

May 26, 2015

1.0.3

Feb 16, 2015

1.0.2

Feb 16, 2015

1.0.1

Feb 16, 2015

1.0

Jan 26, 2015

0.2.0

Nov 23, 2014

0.1.2

Oct 27, 2014

0.1.1

Oct 27, 2014

0.1

Oct 27, 2014

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PyKrige-1.5.0rc2.tar.gz (942.9 kB view details)

Uploaded Apr 4, 2020 Source

Built Distributions

PyKrige-1.5.0rc2-cp38-cp38-win_amd64.whl (443.5 kB view details)

Uploaded Apr 4, 2020 CPython 3.8 Windows x86-64

PyKrige-1.5.0rc2-cp38-cp38-win32.whl (413.5 kB view details)

Uploaded Apr 4, 2020 CPython 3.8 Windows x86

PyKrige-1.5.0rc2-cp38-cp38-manylinux2010_x86_64.whl (1.1 MB view details)

Uploaded Apr 4, 2020 CPython 3.8 manylinux: glibc 2.12+ x86-64

PyKrige-1.5.0rc2-cp38-cp38-manylinux2010_i686.whl (866.4 kB view details)

Uploaded Apr 4, 2020 CPython 3.8 manylinux: glibc 2.12+ i686

PyKrige-1.5.0rc2-cp38-cp38-manylinux1_x86_64.whl (1.1 MB view details)

Uploaded Apr 4, 2020 CPython 3.8

PyKrige-1.5.0rc2-cp38-cp38-manylinux1_i686.whl (866.4 kB view details)

Uploaded Apr 4, 2020 CPython 3.8

PyKrige-1.5.0rc2-cp38-cp38-macosx_10_9_x86_64.whl (449.7 kB view details)

Uploaded Apr 4, 2020 CPython 3.8 macOS 10.9+ x86-64

PyKrige-1.5.0rc2-cp37-cp37m-win_amd64.whl (441.1 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m Windows x86-64

PyKrige-1.5.0rc2-cp37-cp37m-win32.whl (410.7 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m Windows x86

PyKrige-1.5.0rc2-cp37-cp37m-manylinux2010_x86_64.whl (968.9 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m manylinux: glibc 2.12+ x86-64

PyKrige-1.5.0rc2-cp37-cp37m-manylinux2010_i686.whl (808.6 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m manylinux: glibc 2.12+ i686

PyKrige-1.5.0rc2-cp37-cp37m-manylinux1_x86_64.whl (968.9 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m

PyKrige-1.5.0rc2-cp37-cp37m-manylinux1_i686.whl (808.6 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m

PyKrige-1.5.0rc2-cp37-cp37m-macosx_10_9_x86_64.whl (447.4 kB view details)

Uploaded Apr 4, 2020 CPython 3.7m macOS 10.9+ x86-64

PyKrige-1.5.0rc2-cp36-cp36m-win_amd64.whl (441.1 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m Windows x86-64

PyKrige-1.5.0rc2-cp36-cp36m-win32.whl (410.7 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m Windows x86

PyKrige-1.5.0rc2-cp36-cp36m-manylinux2010_x86_64.whl (968.8 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m manylinux: glibc 2.12+ x86-64

PyKrige-1.5.0rc2-cp36-cp36m-manylinux2010_i686.whl (807.3 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m manylinux: glibc 2.12+ i686

PyKrige-1.5.0rc2-cp36-cp36m-manylinux1_x86_64.whl (968.8 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m

PyKrige-1.5.0rc2-cp36-cp36m-manylinux1_i686.whl (807.3 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m

PyKrige-1.5.0rc2-cp36-cp36m-macosx_10_9_x86_64.whl (447.2 kB view details)

Uploaded Apr 4, 2020 CPython 3.6m macOS 10.9+ x86-64

PyKrige-1.5.0rc2-cp35-cp35m-win_amd64.whl (439.6 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m Windows x86-64

PyKrige-1.5.0rc2-cp35-cp35m-win32.whl (409.3 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m Windows x86

PyKrige-1.5.0rc2-cp35-cp35m-manylinux2010_x86_64.whl (957.9 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m manylinux: glibc 2.12+ x86-64

PyKrige-1.5.0rc2-cp35-cp35m-manylinux2010_i686.whl (798.5 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m manylinux: glibc 2.12+ i686

PyKrige-1.5.0rc2-cp35-cp35m-manylinux1_x86_64.whl (957.9 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m

PyKrige-1.5.0rc2-cp35-cp35m-manylinux1_i686.whl (798.5 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m

PyKrige-1.5.0rc2-cp35-cp35m-macosx_10_9_x86_64.whl (443.0 kB view details)

Uploaded Apr 4, 2020 CPython 3.5m macOS 10.9+ x86-64

File details

Details for the file PyKrige-1.5.0rc2.tar.gz.

File metadata

Download URL: PyKrige-1.5.0rc2.tar.gz
Upload date: Apr 4, 2020
Size: 942.9 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.8.0

File hashes

Hashes for PyKrige-1.5.0rc2.tar.gz
Algorithm	Hash digest
SHA256	`d392c01754f6b83db5d80b5c05031021f87f4c8e21fe26197bde1d586d374e35`
MD5	`b63a2b055125e2e56ccdd7921d9c5e3c`
BLAKE2b-256	`10afd933688de06d335bc899c3af6a1e3cc81d7c76ebabf0b0d85e3c3b28536d`

Algorithm	Hash digest
SHA256	`ab967f372040e4c49624092ddfe5124e3c17c0a865002e2d62b7fe5a3dd6c09a`
MD5	`ecd02731a7ff88acabcfe12cd4de94b2`
BLAKE2b-256	`71c1a55c96cb29c83070277069d2543b72d4d05cd2856a2e5b94fb47f2ad0860`

Algorithm	Hash digest
SHA256	`5548e8ef007bb5d5fdbf6e0171026c12a1bb7fb53f3c52711445da1e1cd97357`
MD5	`020c706247035790cadaecdeff4106c4`
BLAKE2b-256	`8deba5e1b1d59a3a78790a5704409138413717d809c9f42da1b98d3e3b63239f`

Algorithm	Hash digest
SHA256	`85798e0554d18dc969d31e3223318ec8eab462086d109cb4436c4f3b51905667`
MD5	`0b62f47ff183e88f6b5349fc2b77e3fe`
BLAKE2b-256	`c83e419eb87e6401f86a632d5385fd06073e53a4f67a355ab239c097b3305868`

Algorithm	Hash digest
SHA256	`9db1b9ed78ac5948bc07a56913da92a5d83d5ac535325e0f9b2f9ed6c299bed3`
MD5	`baeac19601c5b1bf0068926d86d8ee4f`
BLAKE2b-256	`5fe62ab068f3d60373fd0b1bd125cf33a44a3d4d090be84c17805a2b0f7d79bc`

Algorithm	Hash digest
SHA256	`36286a494fb52084a476df6c7e5cf973d8512547d0aad3af4f5725b595829691`
MD5	`92aa125ff466f4735495cd29a01e1020`
BLAKE2b-256	`dc0e837812f9ba2841435cba946efb1ca9a278064f6688ea7d2d544a7923e951`

Algorithm	Hash digest
SHA256	`5ea289097f6fc99c80da3e1515838053be50b54823939b7d50a8343d6a7643c2`
MD5	`72b8d6c54a9eeac492bbb4279e4c083a`
BLAKE2b-256	`44b6680b700a990b0d20ad36fb039e0aba3181271ec026a88a6d7bc049af283c`

Algorithm	Hash digest
SHA256	`93d98b0bdaa17e8ac2616475c8b74a89d06770f3fd594da7b46e73260f0261ad`
MD5	`23a17af4250acfc88455dfc1696e79c5`
BLAKE2b-256	`1a9a60ad82b9e6ec9923f5a1e4d397b5185b0d27f818b4110a1c7c5326d62fa0`

Algorithm	Hash digest
SHA256	`c698affd9df31d429627101725437159a8a4805c5a10b70593de14064ac03b85`
MD5	`3fd44e3ae0d94351d945a1630d8a2058`
BLAKE2b-256	`c74829c139b99cfe266c45b0065a567b7ad372f81fe4f0b86f11a9413a72ee32`

Algorithm	Hash digest
SHA256	`e5456e0ca4e4aa9390014b306ae75b5e7ad0a54abfe06d5a23d1dd2c99db7366`
MD5	`7b03e4f88da8a1c92aab5d41e7c95065`
BLAKE2b-256	`91074d0139817dc4978399a02414973ab1d6140be648784b9bad1dc556dd0c34`

Algorithm	Hash digest
SHA256	`2dc5b39c474db70fd48186e29ef1de2c1654eb9bdd904f6346febff0e901662c`
MD5	`3f3c4559aec5bb978bfcdae1b0bf90f1`
BLAKE2b-256	`475082f1eb7602454354f9336f44adb9adfc40ab3c26ce0d0257b1269fa9fee9`

Algorithm	Hash digest
SHA256	`96eead935e0f577d127a40a380db9d8fdab03bd6cec979f960c082cf7388384c`
MD5	`ff9d395e813007fd49cf3f2213fb6c15`
BLAKE2b-256	`1fb22811ec54ea99fd53fbfecc6d467680c64ad17890083c500fbf25e017ab47`

Algorithm	Hash digest
SHA256	`942f519f0ff1ea466c4118bf56b3726a5da950ee7661cf5c0948e0518edce604`
MD5	`28a48bd901d00b5e2d793c0af3d2e21f`
BLAKE2b-256	`4c127b595528646fb999eb034dd3daa35bd3dd2cbc3b32d6f35566b38109b187`

Algorithm	Hash digest
SHA256	`14001c81b3f4f82274ac106157f32ee6619e737bfaf4363d916a83d1acdd8c9f`
MD5	`a337c0190dce7f224b70a265cdd3f8c5`
BLAKE2b-256	`70d16eecbb0d6f5f3a071a94a271bdfebfaf98838ba2b7bcce4a777c44841fe6`

Algorithm	Hash digest
SHA256	`ca6e7fb7c6ef90df739b0bfa1dfaf7d6c65b6c7084cefb6c4428e7c8491bdb10`
MD5	`993ebb8c35374c798d7b3acfb31453bc`
BLAKE2b-256	`018b10419905bc316c4022f478321176e78e59378198205f272f4463186907ac`

Algorithm	Hash digest
SHA256	`1aa299357fc60d515e415213d50d379df6505eceb1ca7e2bddb3834284d80a6d`
MD5	`93e4f47a132790bdaeb3ca98a59f23b9`
BLAKE2b-256	`b1218331a4269b866f9dfddaa59799b04b01ad24a6d2a9d4d0119d4b4f878e0c`

Algorithm	Hash digest
SHA256	`18efeadc16bad71ee1ce8fe9c4822f541c559f616f44466e976fbace2f3b9c9e`
MD5	`b824b0f42006110839cdd0beb4d057ef`
BLAKE2b-256	`24b2bbd205924bdf4703143e603e3c8396ebe45bd843d18ea8c9229ac030b658`

Algorithm	Hash digest
SHA256	`773dafa1e450dfe01355ec8e8279ff174e97023b148aaf119bb1d3863f7319fd`
MD5	`40ad2b079f018410f6e9c13d603c6699`
BLAKE2b-256	`7a162f240053b13d53430dcf8cace9bccea00f60b79fcaf9aacae96944e996bf`

Algorithm	Hash digest
SHA256	`c5b2edc8f57d26eb75c40c8280fee553f3a7af0b4195da72b4e5dac278d83279`
MD5	`086d7e04adade49cf103ee9f07bef158`
BLAKE2b-256	`dafc7e493028be7e6d9dd1395c4772fed58c36e3f1f5d612eeb863aebfe67d35`

Algorithm	Hash digest
SHA256	`36065867ca684fb6ebbe71a989c5449e6cda6ea738ff586f7015b3fee06a4f6a`
MD5	`155e653ad36454a39c8edf5b12fde2fd`
BLAKE2b-256	`9a08028b6e4a9372b49e78c4e7c21b884b5accb54ee07cc107dab83a5165bb4b`

Algorithm	Hash digest
SHA256	`6a83842b8f3d141aef5ae7ad9c6a9809df9391babe4407c458d83ff3ee323fbc`
MD5	`c10ecfa44be6bde58c6d96b2d2760bca`
BLAKE2b-256	`a02f338b2310e0e3987c60a247c13e94986e164d8659fe7cdd458457cbaada8f`

PyKrige 1.5.0rc2

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Purpose

Installation

Ordinary Kriging Example

Universal Kriging Example

Three-Dimensional Kriging Example

GSTools covariance models

Kriging Parameters Tuning

Regression Kriging

License

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

Algorithm	Hash digest
SHA256	`dc31102b86c450d973cc35c60343d8a8b280867de3b30092f757a81b25212f22`
MD5	`e745940f4ad3a9a0537777642d83ee49`
BLAKE2b-256	`a9cc0f4d8f44e0ba8c8aad1bfad4fdee87d56c39b510a52f647c7742390b1baf`

Algorithm	Hash digest
SHA256	`f33f2c6a02cc9fbdb7a4475c5983060b100eed21b01e627f659a052d91a9a433`
MD5	`1f79a821c89c9f2c7cefbdcd5cbc9998`
BLAKE2b-256	`c95982cb69573737d5b943770717e8800df44ba609c25d84fd25ed7f421963ea`

Algorithm	Hash digest
SHA256	`61557fc4514859920fc1fc6f3f9207e4a5c800486896ff43c6a26a2318ee36b0`
MD5	`e8c2394ddbd82a5ffbd245aaa0753e62`
BLAKE2b-256	`cb9e022ea8ec8aa9ed1d4a77fcee2782ea66caa4dd3e5719868ced0f5aab4385`

Algorithm	Hash digest
SHA256	`543ad32ebb88c22502069ca307e6a0b70777808aec37a32e5cb029006fdda1f0`
MD5	`8e04ff05a0a8e3ea1ffdd60c88c53661`
BLAKE2b-256	`dc55eb1ebe801ce25657ee401c4f68878a6208f3b52ba0b89eedc300f9e81d9a`

Algorithm	Hash digest
SHA256	`88f63c6a214a32583ca4c6fcab1c88a70422bb8bfd28c07eb74fd482a195ef2a`
MD5	`6304686a3961cd1c333de633cfa8049c`
BLAKE2b-256	`5bcc1d4fdb6eb9b82417d9a9007df75e1ba234f235d138533184d2368298a71c`

Algorithm	Hash digest
SHA256	`515fbe0490f9ab81f6fcaea8fbd6ae4b68762062ac0ff34afe3075f38b7c9547`
MD5	`ffad4504d9ecb7c4fa4173fe46d93b12`
BLAKE2b-256	`6b148cc3f37af43a58f63385ed06e6ba088a21f13aefd8865b4384680affddb5`

Algorithm	Hash digest
SHA256	`0f3a3635a24f5839668a9a5ba8503bd88fe3e4431b06994169ff821ca0c504f4`
MD5	`0f7df9b684af9b90d61d96e7955356ae`
BLAKE2b-256	`0dc3bbd720b679d76b34ed531c8f75cc97831b4aa5bb47a1c8282c6b145e6d6a`

Algorithm	Hash digest
SHA256	`eeba01e2c07f625f9f342bbc2f32dc40bedb98012c48913c76fc4bb111cae750`
MD5	`b18c11c93cf794e30c6afb2a424257b2`
BLAKE2b-256	`88073174c9e60e477781f08f27ff3341e2e6481aa4c6ddc10f7f7296492f2939`