house cleaning :)

Author: nicrie

tests/integration/test_integration.py

@@ -1,3 +1,5 @@
+import contextlib
+import os
 import unittest
 import warnings
 from os import getcwd
@@ -6,8 +8,8 @@
 
 import numpy as np
 import xarray as xr
-from parameterized import parameterized
 from numpy.testing import assert_allclose, assert_raises
+from parameterized import parameterized
 
 from xmca.xarray import xMCA
 
@@ -16,6 +18,9 @@ class TestIntegration(unittest.TestCase):
 
     @classmethod
     def setUpClass(self):
+        # catch deprecation warnings from cartopy
+        warnings.simplefilter('ignore', category=DeprecationWarning)
+        
         # Load test data
         self.path = 'tests/integration/fixtures'
         # ignore some deprecation warnings of xarray
@@ -401,7 +406,9 @@ def test_summary(self):
         right = self.B
         model = xMCA(left, right)
         model.solve()
-        _ = model.summary()
+
+        with open(os.devnull, "w") as f, contextlib.redirect_stdout(f):
+            model.summary()
 
     @classmethod
     def tearDownClass(self):

xmca/array.py

@@ -13,7 +13,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import yaml
-from numpy.polynomial.polynomial import polyfit
 from scipy.signal import hilbert
 from statsmodels.tsa.forecasting.theta import ThetaModel
 from tqdm import tqdm
@@ -216,30 +215,6 @@ def _get_method_id(self):
             id = 'mca'
         return id
 
-    def _get_complex_id(self):
-        id = int(self._analysis['is_complex'])
-        return 'c{:}'.format(id)
-
-    def _get_rotation_id(self):
-        if self._analysis['is_rotated']:
-            id = self._analysis['n_rot']
-        else:
-            id = 0
-        return 'r{:02}'.format(id)
-
-    def _get_power_id(self):
-        id = self._analysis['power']
-        return 'p{:02}'.format(id)
-
-    def _get_analysis_id(self):
-        method      = self._get_method_id()
-        hilbert     = self._get_complex_id()
-        rotation    = self._get_rotation_id()
-        power       = self._get_power_id()
-
-        analysis    = '_'.join([method, hilbert, rotation, power])
-        return analysis
-
     def _get_analysis_path(self, path=None):
         if path is None:
             name_folder = '_'.join(self._field_names.values())
@@ -1124,7 +1099,6 @@ def predict(
 
         V = self._get_V(original=True)
         fields_mean = self._field_means
-        fields_std = self._field_stds
 
         sqrt_svals = np.sqrt(self._get_svals())
         norm = self._get_norm()
@@ -1414,7 +1388,6 @@ def _create_info_file(self, path):
             'This file contains information neccessary to load stored analysis'
             'data from xmca module.')
 
-        # path_output   = os.path.join(path, self._get_analysis_id())
         path_output = os.path.join(path, 'info.xmca')
 
         file = open(path_output, 'w+')
@@ -1440,7 +1413,6 @@ def _create_info_file(self, path):
         file.close()
 
     def _get_file_names(self, format):
-        # base_name = self._get_analysis_id()
 
         fields  = {}
         eofs    = {}

xmca/tools/array.py

@@ -2,37 +2,14 @@
 # -*- coding: utf-8 -*-
 ''' Collection of tools for numpy.array modifications. '''
 
-import textwrap
 import warnings
 
 import numpy as np
 
+
 # =============================================================================
 # Tools
 # =============================================================================
-
-def arrs_are_equal(arr1 ,arr2):
-    ''' True if arrays are the same. Also works for np.nan entries.'''
-    if arr1.shape == arr2.shape:
-        return ((np.isnan(arr1) & np.isnan(arr2)) | (arr1 == arr2)).all()
-    else:
-        return False
-
-
-def is_arr(data):
-    if (isinstance(data,np.ndarray)):
-        return True
-    else:
-        raise TypeError('Data needs to be np.ndarray.')
-
-
-def check_time_dims(arr1, arr2):
-    if (arr1.shape[0] == arr2.shape[0]):
-        pass
-    else:
-        raise ValueError('Both input fields need to have same time dimensions.')
-
-
 def remove_mean(arr):
     '''Remove the mean of an array along the first dimension.
 
@@ -45,7 +22,7 @@ def remove_mean(arr):
         return arr - arr.mean(axis=0)
 
 
-def get_nan_cols(arr):
+def get_nan_cols(arr: np.ndarray) -> np.ndarray:
     '''Get NaN columns from an array.
 
     Parameters
@@ -64,7 +41,7 @@ def get_nan_cols(arr):
     return nan_index
 
 
-def remove_nan_cols(arr):
+def remove_nan_cols(arr: np.ndarray) -> np.ndarray:
     '''Remove NaN columns in array.
 
     Parameters
@@ -88,19 +65,8 @@ def has_nan_time_steps(array):
     ''' Checks if an array has NaN time steps.
 
     Time is assumed to be on axis=0. The array is then reshaped to be 2D with
-    time along axis 0 and variables along axis 1. A NaN time step is a row which
-    contain NaN only.
+    time along axis 0 and variables along axis 1. A NaN time step is a row
+    which contain NaN only.
     '''
 
     return (np.isnan(array).all(axis=tuple(range(1, array.ndim))).any())
-
-
-def is_not_empty(arr):
-    if (arr.size > 0):
-        pass
-    else:
-        raise ValueError('Input field is empty or contains NaN only.')
-
-
-def norm_to_1(arr, axis):
-    return arr / np.nanmax(abs(arr), axis=axis)

xmca/version.py

@@ -1 +1 @@
-__version__ = '1.0.3'
+__version__ = '1.0.4'