我在python中使用PYCharm和unittest来测试我的类。
逐个运行每个testMethod，所有测试都顺利通过 一起运行所有测试，它会因assertionErrors而失败。 设置了设置和拆解方法。
这是我的testClass的代码：
# -*- coding: utf-8 -*-
import unittest
import numpy as np
import os
import sys
from interpolation.interpolation import Interpolation
from StringIO import StringIO
class InterpolationTests(unittest.TestCase):
_interpolation = None
@classmethod
def setUpClass(cls):
cls._interpolation = Interpolation()
@classmethod
def tearDownClass(cls):
cls._interpolation = None
def test_load_valid_file(self):
self._interpolation.from_file('./testfiles/valid.json')
self.assertEqual('kriging', self._interpolation._method)
self.assertEqual(1.0, self._interpolation._xMin)
self.assertEqual(1.2, self._interpolation._xMax)
self.assertEqual(2.1, self._interpolation._yMin)
self.assertEqual(0.2, self._interpolation._yMax)
self.assertEqual(12, self._interpolation._nX)
self.assertEqual(13, self._interpolation._nY)
self.assertEqual(2, len(self._interpolation._points))
points = self._interpolation._points
self.assertEqual(1.1, points[0]['x'])
self.assertEqual(2.2, points[0]['y'])
self.assertEqual(3.4, points[0]['value'])
self.assertEqual(4.4, points[1]['x'])
self.assertEqual(5.5, points[1]['y'])
self.assertEqual(6.6, points[1]['value'])
def test_load_invalid_JSON_format(self):
self._interpolation.from_file('./testfiles/invalid.json')
saved_stdout = sys.stdout
try:
out = StringIO()
sys.stdout = out
self._interpolation.render_output()
output = out.getvalue().strip()
self.assertEqual('{"error":"Something went wrong with the json decoding"}',
output.encode('ascii', 'ignore'))
finally:
sys.stdout = saved_stdout
def test_load_empty_file(self):
self._interpolation.from_file('./testfiles/empty.json')
saved_stdout = sys.stdout
try:
out = StringIO()
sys.stdout = out
self._interpolation.render_output()
output = out.getvalue().strip()
self.assertEqual('{"error":"Something went wrong with the json decoding"}',
output.encode('ascii', 'ignore'))
finally:
sys.stdout = saved_stdout
def test_idw_with_file(self):
self._interpolation.from_file('./testfiles/valid_idw.json')
self._interpolation.calculate()
self.assertEqual(50, len(self._interpolation._output))
self.assertEqual(50, len(self._interpolation._output[0]))
def test_kriging(self):
self._interpolation.from_file('./testfiles/valid.json')
self._interpolation.calculate()
self.assertEqual(13, len(self._interpolation._output))
self.assertEqual(12, len(self._interpolation._output[0]))
def test_mean_with_file(self):
self._interpolation.from_file('./testfiles/valid_mean.json')
self._interpolation.calculate()
self.assertEqual(60, len(self._interpolation._output))
self.assertEqual(50, len(self._interpolation._output[0]))
def test_mean(self):
mean = Interpolation.mean(10, 20, np.array([1.2, 1.3, 1.4, 1.6, 1.9]))
self.assertEqual(20, len(mean))
self.assertEqual(10, len(mean[1]))
self.assertEqual(1.48, mean[1][2])
def test_gaussian(self):
self._interpolation.from_file('./testfiles/valid_gaussian.json')
self._interpolation.calculate()
self.assertEqual(50, len(self._interpolation._output))
self.assertEqual(50, len(self._interpolation._output[0]))
def test_with_two_points(self):
self._interpolation.from_string(json_input='{"bounding_box":{"x_min":0,"x_max":10,"y_min":0,"y_max":10},"grid_size":{"n_x":10,"n_y":11},"point_values":[{"x":1,"y":5,"value":800},{"x":2,"y":8,"value":3}],"type":"gaussian"}')
self._interpolation.calculate()
self.assertEqual('Exception raised in calculation of method gaussian', self._interpolation._error_message)
if __name__ == '__main__':
unittest.main()
以下是失败测试结果的一部分：
/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/bin/python2.7 "/Applications/PyCharm CE.app/Contents/helpers/pycharm/noserunner.py" /Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py
Testing started at 09:52 ...
..
..
Failure
Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py", line 77, in test_kriging
self.assertEqual(13, len(self._interpolation._output))
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 513, in assertEqual
assertion_func(first, second, msg=msg)
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 506, in _baseAssertEqual
raise self.failureException(msg)
AssertionError: 13 != 50
-------------------- >> begin captured stdout << ---------------------
<type 'exceptions.Exception'> Matrix is not positive definite
--------------------- >> end captured stdout << ----------------------
F
..
..
..
..
Failure
Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py", line 83, in test_mean_with_file
self.assertEqual(60, len(self._interpolation._output))
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 513, in assertEqual
assertion_func(first, second, msg=msg)
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 506, in _baseAssertEqual
raise self.failureException(msg)
AssertionError: 60 != 50
F
Failure
Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 329, in run
testMethod()
File "/Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py", line 101, in test_with_two_points
self.assertEqual('Exception raised in calculation of method gaussian', self._interpolation._error_message)
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 513, in assertEqual
assertion_func(first, second, msg=msg)
File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py", line 506, in _baseAssertEqual
raise self.failureException(msg)
AssertionError: 'Exception raised in calculation of method gaussian' != 'Something went wrong with the json decoding'
F
======================================================================
FAIL: test_kriging (test_interpolation.InterpolationTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py", line 77, in test_kriging
self.assertEqual(13, len(self._interpolation._output))
AssertionError: 13 != 50
-------------------- >> begin captured stdout << ---------------------
<type 'exceptions.Exception'> Matrix is not positive definite
--------------------- >> end captured stdout << ----------------------
======================================================================
FAIL: test_mean_with_file (test_interpolation.InterpolationTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py", line 83, in test_mean_with_file
self.assertEqual(60, len(self._interpolation._output))
AssertionError: 60 != 50
======================================================================
FAIL: test_render_to_file (test_interpolation.InterpolationTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/Ralf/Projekte/inowas/inowas/py/pyprocessing/tests/test_interpolation.py", line 122, in test_render_to_file
self.assertTrue(os.path.isfile(output_filename))
AssertionError: False is not true
-------------------- >> begin captured stdout << ---------------------
{"error":"Something went wrong with the json decoding"}
--------------------- >> end captured stdout << ----------------------
Ran 11 tests in 0.826s
FAILED (failures=5)
Process finished with exit code 0
最后这里是Interpolation-Class：
#! /usr/env python
from pyKriging.krige import kriging
from sklearn.gaussian_process import GaussianProcess
import demjson
import numpy as np
class Interpolation:
"""The interpolation class"""
_method = ""
_xMin = 0.0
_xMax = 0.0
_yMin = 0.0
_yMax = 0.0
_nX = 0
_nY = 0
_dX = 0.0
_dY = 0.0
_X = []
_Y = []
_points = []
_output = ""
_json_output = ""
_error = False
_error_message = ""
def __init__(self):
pass
def from_file(self, input_file):
try:
_file = open(input_file, 'r')
json_input = _file.read()
except IOError as exc:
self._error = True
self._error_message = str(exc)
return
except Exception as exc:
self._error = True
self._error_message = str(exc)
return
self.decode_json(json_input)
def from_string(self, json_input):
self.decode_json(json_input)
def decode_json(self, json_input):
try:
json_dict = demjson.decode(json_input)
except Exception as exc:
self._error = True
self._error_message = "Something went wrong with the json decoding"
return
if 'type' in json_dict:
self._method = json_dict['type']
if 'bounding_box' in json_dict:
bounding_box = json_dict['bounding_box']
if 'x_min' in bounding_box:
self._xMin = float(bounding_box['x_min'])
if 'x_max' in bounding_box:
self._xMax = float(bounding_box['x_max'])
if 'y_min' in bounding_box:
self._yMin = float(bounding_box['y_min'])
if 'y_max' in bounding_box:
self._yMax = float(bounding_box['y_max'])
if 'grid_size' in json_dict:
grid_size = json_dict['grid_size']
if 'n_x' in grid_size:
self._nX = grid_size['n_x']
if 'n_y' in grid_size:
self._nY = grid_size['n_y']
self._dX = (self._xMax - self._xMin) / self._nX
self._dY = (self._yMax - self._yMin) / self._nY
if 'point_values' in json_dict:
self._points = json_dict['point_values']
for point in self._points:
if 'x' in point and 'y' in point:
self._X.append([point['x'], point['y']])
if 'value' in point:
self._Y.append(point['value'])
def calculate(self):
if not self._error:
try:
if self._method == 'kriging':
self._output = self.kriging(self._nX, self._nY, self._X, self._Y, self._xMin, self._yMin, self._dX, self._dY)
elif self._method == 'mean':
self._output = self.mean(self._nX, self._nY, self._Y)
elif self._method == 'gaussian':
self._output = self.gaussian_process(self._nX, self._nY, self._X, self._Y, self._xMin, self._yMin, self._dX, self._dY)
elif self._method == 'idw':
self._output = self.inverse_distance_weighting(self._nX, self._nY, self._X, self._Y, self._xMin, self._yMin, self._dX, self._dY)
else:
self._error = True
self._error_message = 'Method %s is not supported' % self._method
except:
self._error = True
self._error_message = 'Exception raised in calculation of method %s' % self._method
def render_output(self, output_file=''):
if self._error:
self.render_error()
return
output = self.render(self._method, self._output)
if output_file == '':
print output
else:
try:
output_file = open(output_file, 'w')
output_file.truncate()
output_file.write(output)
output_file.close()
except IOError as exc:
self._error = True
self._error_message = str(exc)
self.render_error()
return
self.render_success()
def render_success(self):
result = {"success": self._method}
print demjson.encode(result)
def render_error(self):
result = {"error": self._error_message}
print demjson.encode(result)
@staticmethod
def render(method, output):
if (method == 'kriging') or (method == 'mean') or (method == 'gaussian') or (method == 'idw'):
result = {"raster": output, "method": method}
return demjson.encode(result)
@staticmethod
def kriging(nx, ny, x, y, x_min, y_min, dx, dy):
grid = np.zeros((ny, nx))
k = kriging(np.array(x), np.array(y))
k.train()
for i in range(ny):
for j in range(nx):
cell = np.array([y_min + dy * j + .5 * dy, x_min + dx * i + .5 * dx])
grid[i][j] = k.predict(cell)
return grid
@staticmethod
def mean(nx, ny, values):
mean_value = np.mean(values)
grid = mean_value * np.ones((ny, nx))
return grid
@staticmethod
def gaussian_process(nx, ny, X, y, x_min, y_min, dx, dy):
"""
Gausian process method. To replace kriging.
Description:
http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcess.html#sklearn.gaussian_process.GaussianProcess.predict
The scikit learn python library should be installed
Should be tested
"""
# Prediction is very sensetive to the parameters below, method to be used carefully!
gp = GaussianProcess(regr='quadratic', corr='cubic', theta0=0.1, thetaL=.001, thetaU=1., nugget=0.01)
gp.fit(X, y)
X_grid_x = np.linspace(x_min, x_min+dx*nx, nx)
X_grid_y = np.linspace(y_min, y_min+dy*ny, ny)
xv, yv = np.meshgrid(X_grid_x, X_grid_y)
X_grid = np.dstack(( xv.flatten(), yv.flatten()))[0]
grid = np.reshape(gp.predict(X_grid, eval_MSE=False, batch_size=None), (ny, nx))
return grid
@staticmethod
def inverse_distance_weighting(nx, ny, X, y, x_min, y_min, dx, dy):
"""
Inverse-distance weighting interpolation method
"""
def pointValue(x, y, power, smoothing, xv, yv, values):
""" This function is used inside the inverse_distance_weighting method. """
from math import pow
from math import sqrt
nominator=0
denominator=0
for i in range(0,len(values)):
dist = sqrt((x-xv[i])*(x-xv[i])+(y-yv[i])*(y-yv[i])+smoothing*smoothing)
#If the point is really close to one of the data points, return the data point value to avoid singularities
if(dist<0.0000000001):
return values[i]
nominator=nominator+(values[i]/pow(dist,power))
denominator=denominator+(1/pow(dist,power))
#Return NODATA if the denominator is zero
if denominator > 0:
value = nominator/denominator
else:
value = -9999
return value
power, smoothing = 5, 0
xv = [i[0] for i in X]
yv = [i[1] for i in X]
grid = np.zeros((ny, nx))
for i in range(nx):
for j in range(ny):
grid[j][i] = pointValue((x_min + dx/2)+dx*i, (y_min + dy/2)+dy*j, power, smoothing, xv, yv, y)
return grid
答案 0 :(得分：1)
您使用的是setUpClass()和tearDownClass()，它们只为整个班级调用一次。这会导致您的不同测试方法通过更改_interpolation属性来相互干扰。
您可以使用setUp()和tearDown()代替，在每种测试方法之前/之后调用一次。
更好的是，由于您在每个测试中从文件初始化此对象，只需让每个测试方法在开头创建自己的Interpolation对象。