v1.5.0

.travis.yml

@@ -9,7 +9,7 @@ env:
   global:
     - TWINE_USERNAME=geostatframework
     - CIBW_BEFORE_BUILD="pip install numpy==1.17.3 scipy==1.3.2 cython==0.29.14 setuptools"
-    - CIBW_TEST_REQUIRES="pytest scikit-learn"
+    - CIBW_TEST_REQUIRES="pytest scikit-learn gstools"
     - CIBW_TEST_COMMAND="pytest -v {project}/tests"
 
 before_install:
@@ -21,9 +21,9 @@ before_install:
         ln -s /c/Python38/python.exe /c/Python38/python3.exe
     fi
 
-script:
-  - python3 -m pip install cibuildwheel==1.3.0
-  - python3 -m cibuildwheel --output-dir dist
+install: python3 -m pip install cibuildwheel==1.3.0
+
+script: python3 -m cibuildwheel --output-dir dist
 
 after_success:
   - |
@@ -40,12 +40,20 @@ notifications:
 
 jobs:
   include:
+    - name: "black check"
+      services: docker
+      install: python3 -m pip install black
+      script: python3 -m black --check pykrige/ examples/ tests/
+      after_success: echo "all files formatted correctly"
+      after_failure: echo "some files not formatted correctly"
+
     - name: "sdist and coverage"
       services: docker
-      script:
-        - python3 -m pip install -U setuptools pytest-cov coveralls scikit-learn
-        - python3 -m pip install -U numpy==1.17.3 cython==0.29.14
+      install:
+        - python3 -m pip install -r requirements_setup.txt
+        - python3 -m pip install -r requirements_test.txt
         - python3 -m pip install -r requirements.txt
+      script:
         - python3 setup.py sdist -d dist
         - python3 setup.py build_ext --inplace
         - python3 -m pytest --cov pykrige --cov-report term-missing -v tests/

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2015-2018, PyKrige Developers
+Copyright (c) 2015-2020, PyKrige Developers
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

docs/source/conf.py

@@ -18,6 +18,7 @@
 import shlex
 import sphinx_rtd_theme
 import matplotlib
+
 matplotlib.use("Agg")
 
 
@@ -61,9 +62,9 @@
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 source_suffix = {
-    '.rst': 'restructuredtext',
-    '.txt': 'restructuredtext',
-    '.md': 'markdown',
+    ".rst": "restructuredtext",
+    ".txt": "restructuredtext",
+    ".md": "markdown",
 }
 
 # The encoding of source files.
@@ -258,13 +259,7 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (
-        master_doc,
-        "PyKrige.tex",
-        "PyKrige Documentation",
-        "PyKrige developers",
-        "manual",
-    )
+    (master_doc, "PyKrige.tex", "PyKrige Documentation", "PyKrige developers", "manual")
 ]
 
 # The name of an image file (relative to this directory) to place at the top of

docs/source/sphinxext/github_link.py

@@ -60,16 +60,12 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision):
     if not fn:
         return
 
-    fn = os.path.relpath(
-        fn, start=os.path.dirname(__import__(package).__file__)
-    )
+    fn = os.path.relpath(fn, start=os.path.dirname(__import__(package).__file__))
     try:
         lineno = inspect.getsourcelines(obj)[1]
     except Exception:
         lineno = ""
-    return url_fmt.format(
-        revision=revision, package=package, path=fn, lineno=lineno
-    )
+    return url_fmt.format(revision=revision, package=package, path=fn, lineno=lineno)
 
 
 def make_linkcode_resolve(package, url_fmt):

examples/gstools_covmodel.py

@@ -10,32 +10,34 @@
 import numpy as np
 from pykrige.ok import OrdinaryKriging
 from matplotlib import pyplot as plt
+
 try:
     from gstools import Gaussian
+
     GS_IMP = True
 except ImportError:
     GS_IMP = False
 
 # 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]])
+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
 if GS_IMP:
-    cov_model = Gaussian(
-        dim=2, len_scale=4, anis=.2, angles=-.5, var=.5, nugget=.1
-    )
+    cov_model = Gaussian(dim=2, len_scale=4, anis=0.2, angles=-0.5, var=0.5, nugget=0.1)
 else:
     cov_model = "gaussian"
 # ordinary kriging with pykrige
 OK1 = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2], cov_model)
-z1, ss1 = OK1.execute('grid', gridx, gridy)
+z1, ss1 = OK1.execute("grid", gridx, gridy)
 plt.imshow(z1, origin="lower")
-if 'CI' not in os.environ:
-    # skip in continous integration
-    plt.show()
+plt.show()

examples/krige_cv.py

@@ -13,11 +13,12 @@
 
 # 2D Kring param opt
 
-param_dict = {"method": ["ordinary", "universal"],
-              "variogram_model": ["linear", "power", "gaussian", "spherical"],
-              # "nlags": [4, 6, 8],
-              # "weight": [True, False]
-              }
+param_dict = {
+    "method": ["ordinary", "universal"],
+    "variogram_model": ["linear", "power", "gaussian", "spherical"],
+    # "nlags": [4, 6, 8],
+    # "weight": [True, False]
+}
 
 estimator = GridSearchCV(Krige(), param_dict, verbose=True)
 
@@ -29,23 +30,28 @@
 estimator.fit(X=X, y=y)
 
 
-if hasattr(estimator, 'best_score_'):
-    print('best_score R² = {:.3f}'.format(estimator.best_score_))
-    print('best_params = ', estimator.best_params_)
+if hasattr(estimator, "best_score_"):
+    print("best_score R² = {:.3f}".format(estimator.best_score_))
+    print("best_params = ", estimator.best_params_)
 
-print('\nCV results::')
-if hasattr(estimator, 'cv_results_'):
-    for key in ['mean_test_score', 'mean_train_score',
-                'param_method', 'param_variogram_model']:
-        print(' - {} : {}'.format(key, estimator.cv_results_[key]))
+print("\nCV results::")
+if hasattr(estimator, "cv_results_"):
+    for key in [
+        "mean_test_score",
+        "mean_train_score",
+        "param_method",
+        "param_variogram_model",
+    ]:
+        print(" - {} : {}".format(key, estimator.cv_results_[key]))
 
 # 3D Kring param opt
 
-param_dict3d = {"method": ["ordinary3d", "universal3d"],
-              "variogram_model": ["linear", "power", "gaussian", "spherical"],
-              # "nlags": [4, 6, 8],
-              # "weight": [True, False]
-              }
+param_dict3d = {
+    "method": ["ordinary3d", "universal3d"],
+    "variogram_model": ["linear", "power", "gaussian", "spherical"],
+    # "nlags": [4, 6, 8],
+    # "weight": [True, False]
+}
 
 estimator = GridSearchCV(Krige(), param_dict3d, verbose=True)
 
@@ -57,12 +63,16 @@
 estimator.fit(X=X3, y=y)
 
 
-if hasattr(estimator, 'best_score_'):
-    print('best_score R² = {:.3f}'.format(estimator.best_score_))
-    print('best_params = ', estimator.best_params_)
+if hasattr(estimator, "best_score_"):
+    print("best_score R² = {:.3f}".format(estimator.best_score_))
+    print("best_params = ", estimator.best_params_)
 
-print('\nCV results::')
-if hasattr(estimator, 'cv_results_'):
-    for key in ['mean_test_score', 'mean_train_score',
-                'param_method', 'param_variogram_model']:
-        print(' - {} : {}'.format(key, estimator.cv_results_[key]))
+print("\nCV results::")
+if hasattr(estimator, "cv_results_"):
+    for key in [
+        "mean_test_score",
+        "mean_train_score",
+        "param_method",
+        "param_variogram_model",
+    ]:
+        print(" - {} : {}".format(key, estimator.cv_results_[key]))

examples/krige_geometric.py

@@ -17,42 +17,50 @@
 # of nodes and a uniform distribution of values in the interval
 # [2.0, 5.5]:
 N = 7
-lon = 360.0*np.random.random(N)
-lat = 180.0/np.pi*np.arcsin(2*np.random.random(N)-1)
-z   = 3.5*np.random.rand(N) + 2.0
+lon = 360.0 * np.random.random(N)
+lat = 180.0 / np.pi * np.arcsin(2 * np.random.random(N) - 1)
+z = 3.5 * np.random.rand(N) + 2.0
 
 # Generate a regular grid with 60° longitude and 30° latitude steps:
 grid_lon = np.linspace(0.0, 360.0, 7)
 grid_lat = np.linspace(-90.0, 90.0, 7)
 
 # Create ordinary kriging object:
-OK = OrdinaryKriging(lon, lat, z, variogram_model='linear', verbose=False,
-                     enable_plotting=False, coordinates_type='geographic')
+OK = OrdinaryKriging(
+    lon,
+    lat,
+    z,
+    variogram_model="linear",
+    verbose=False,
+    enable_plotting=False,
+    coordinates_type="geographic",
+)
 
 # Execute on grid:
-z1, ss1 = OK.execute('grid', grid_lon, grid_lat)
+z1, ss1 = OK.execute("grid", grid_lon, grid_lat)
 
 # Create ordinary kriging object ignoring curvature:
-OK = OrdinaryKriging(lon, lat, z, variogram_model='linear', verbose=False,
-                     enable_plotting=False)
+OK = OrdinaryKriging(
+    lon, lat, z, variogram_model="linear", verbose=False, enable_plotting=False
+)
 
 # Execute on grid:
-z2, ss2 = OK.execute('grid', grid_lon, grid_lat)
+z2, ss2 = OK.execute("grid", grid_lon, grid_lat)
 
 # Print data at equator (last longitude index will show periodicity):
 print("Original data:")
-print("Longitude:",lon.astype(int))
-print("Latitude: ",lat.astype(int))
-print("z:        ",np.array_str(z, precision=2))
+print("Longitude:", lon.astype(int))
+print("Latitude: ", lat.astype(int))
+print("z:        ", np.array_str(z, precision=2))
 print("\nKrige at 60° latitude:\n======================")
-print("Longitude:",grid_lon)
-print("Value:    ",np.array_str(z1[5,:], precision=2))
-print("Sigma²:   ",np.array_str(ss1[5,:], precision=2))
+print("Longitude:", grid_lon)
+print("Value:    ", np.array_str(z1[5, :], precision=2))
+print("Sigma²:   ", np.array_str(ss1[5, :], precision=2))
 print("\nIgnoring curvature:\n=====================")
-print("Value:    ",np.array_str(z2[5,:], precision=2))
-print("Sigma²:   ",np.array_str(ss2[5,:], precision=2))
+print("Value:    ", np.array_str(z2[5, :], precision=2))
+print("Sigma²:   ", np.array_str(ss2[5, :], precision=2))
 
-##====================================OUTPUT==================================
+# ====================================OUTPUT==================================
 # >>> Original data:
 # >>> Longitude: [122 166  92 138  86 122 136]
 # >>> Latitude:  [-46 -36 -25 -73 -25  50 -29]

examples/kriging_1D.py

@@ -8,12 +8,15 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
+from pykrige import OrdinaryKriging
 
-plt.style.use('ggplot')
-
-# Data taken from https://blog.dominodatalab.com/fitting-gaussian-process-models-python/
+plt.style.use("ggplot")
 
-X, y = np.array([[-5.01, 1.06], [-4.90, 0.92], [-4.82, 0.35], [-4.69, 0.49], [-4.56, 0.52], 
+# fmt: off
+# Data taken from
+# https://blog.dominodatalab.com/fitting-gaussian-process-models-python/
+X, y = np.array([
+     [-5.01, 1.06], [-4.90, 0.92], [-4.82, 0.35], [-4.69, 0.49], [-4.56, 0.52],
      [-4.52, 0.12], [-4.39, 0.47], [-4.32,-0.19], [-4.19, 0.08], [-4.11,-0.19],
      [-4.00,-0.03], [-3.89,-0.03], [-3.78,-0.05], [-3.67, 0.10], [-3.59, 0.44],
      [-3.50, 0.66], [-3.39,-0.12], [-3.28, 0.45], [-3.20, 0.14], [-3.07,-0.28],
@@ -33,33 +36,36 @@
      [3.52 ,-0.55], [3.63 ,-0.92], [3.72 ,-0.76], [3.80 ,-0.41], [3.91 , 0.12],
      [4.04 , 0.25], [4.13 , 0.16], [4.24 , 0.26], [4.32 , 0.62], [4.44 , 1.69],
      [4.52 , 1.11], [4.65 , 0.36], [4.74 , 0.79], [4.84 , 0.87], [4.93 , 1.01],
-     [5.02 , 0.55]]).T
-
-
-from pykrige import OrdinaryKriging
+     [5.02 , 0.55]
+]).T
+# fmt: on
 
 X_pred = np.linspace(-6, 6, 200)
 
 # pykrige doesn't support 1D data for now, only 2D or 3D
 # adapting the 1D input to 2D
-uk = OrdinaryKriging(X, np.zeros(X.shape), y, variogram_model='gaussian',)
+uk = OrdinaryKriging(X, np.zeros(X.shape), y, variogram_model="gaussian")
 
-y_pred, y_std = uk.execute('grid', X_pred, np.array([0.]))
+y_pred, y_std = uk.execute("grid", X_pred, np.array([0.0]))
 
 y_pred = np.squeeze(y_pred)
 y_std = np.squeeze(y_std)
 
 fig, ax = plt.subplots(1, 1, figsize=(10, 4))
-ax.scatter(X, y, s=40, label='Input data')
+ax.scatter(X, y, s=40, label="Input data")
 
 
-ax.plot(X_pred, y_pred, label='Predicted values')
-ax.fill_between(X_pred, y_pred - 3*y_std, y_pred + 3*y_std, alpha=0.3, label='Confidence interval')
+ax.plot(X_pred, y_pred, label="Predicted values")
+ax.fill_between(
+    X_pred,
+    y_pred - 3 * y_std,
+    y_pred + 3 * y_std,
+    alpha=0.3,
+    label="Confidence interval",
+)
 ax.legend(loc=9)
-ax.set_xlabel('x')
-ax.set_ylabel('y')
+ax.set_xlabel("x")
+ax.set_ylabel("y")
 ax.set_xlim(-6, 6)
 ax.set_ylim(-2.8, 3.5)
-if 'CI' not in os.environ:
-    # skip in continous integration
-    plt.show()
+plt.show()