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How to use test_median method in pyresttest
Best Python code snippet using pyresttest_python
test_median.py
Source:test_median.py 
1import test_median2import os3import data_select4import gevfit5from datetime import datetime, timedelta6import numpy as np7from map_parameters import polar_stereographic8import matplotlib.pyplot as plt9from shape.basin_boundaries import plot_basin_boundaries_from_shape10import application_properties11application_properties.set_current_directory()12class Test_median():13 def __init__(self, data_path = ''):14 data, times, i_indices, j_indices = data_select.get_data_from_file(data_path)15 self._id, rest = os.path.basename(data_path).split('_', 1)16 self._data = data17 self._times = times18 self._i_indices = i_indices19 self._j_indices = j_indices20 self.data_extremes = None21 self.return_period_years = 222 self.high_flow = True #low_flows are calculated if False 23 self.start_date = None24 self.end_date = None25 self.start_month = 126 self.end_month = 1227 self.event_duration = timedelta(days = 1)28 self.median_field = None29 self.ret_level_2yr = None30 self.longitudes = polar_stereographic.lons[i_indices, j_indices]31 self.latitudes = polar_stereographic.lats[i_indices, j_indices]32 def get_indices_in_2d_grid(self):33 return self._i_indices, self._j_indices34 def select_data_and_calculate(self, save_txt_file = False):35 '''36 Calculate 2 yr return levels and the corresponding median37 '''38 if self.high_flow:39 self.data_extremes = data_select.get_list_of_annual_maximums_for_domain(self._data, self._times,40 start_date = self.start_date, end_date = self.end_date,41 start_month = self.start_month, end_month = self.end_month,42 event_duration = self.event_duration)43 else:44 self.data_extremes = data_select.get_list_of_annual_minimums_for_domain(self._data, self._times,45 start_date = self.start_date, end_date = self.end_date,46 start_month = self.start_month, end_month = self.end_month,47 event_duration = self.event_duration)48 the_type = 'high' if self.high_flow else 'low'49 if save_txt_file:50 save_extremes_to_txt_file('{0}_{1}_values.txt'.format(self._id, the_type),51 self.data_extremes, self._i_indices, self._j_indices)52 53 self._calculate_median_field()54 self._calculate_return_level_field()55 pass56 def _calculate_median_field(self):57 assert self.start_date != None and self.end_date != None, 'start_date and end_date fields should be set.'58 self.median_field = []59 #cycle through the points of the domain60 for pos in range(len(self._i_indices)):61 values = self.data_extremes[pos]62 self.median_field.append(np.median(values))63 self.median_field = np.array(self.median_field)64 pass65 def _calculate_return_level_field(self, save_to_txt_file = False):66 if self.high_flow:67 field = gevfit.get_high_levels_for_id(self._id, return_period = self.return_period_years)68 else:69 field = gevfit.get_low_levels_for_id(self._id, return_period = self.return_period_years)70 the_type = 'high' if self.high_flow else 'low'71 if save_to_txt_file:72 save_ret_levels_to_txt('{0}_{1}yr_{2}_ret_level.txt'.format(self._id,73 self.return_period_years, the_type),74 field, self._i_indices, self._j_indices)75 save_pars_to_txt_file('{0}_{1}_params.txt'.format(self._id, the_type),76 gevfit.get_gevd_params_for_id_and_type(self._id, self.high_flow),77 self._i_indices, self._j_indices)78 self.ret_level_2yr = []79 for k in range(len(self._i_indices)):80 self.ret_level_2yr.append(field[k])81 self.ret_level_2yr = np.array(self.ret_level_2yr)82 def plot(self):83 basemap = polar_stereographic.basemap84 plt.subplot(3,1,1)85 gevfit.plot_data(self.ret_level_2yr, imagefile = None,86 units = 'm**3/s', minmax = (0, None),87 i_list = self._i_indices, j_list = self._j_indices)88 plt.title('2 year return levels')89 plt.subplot(3,1,2)90 gevfit.plot_data(self.median_field, imagefile = None,91 units = 'm**3/s', minmax = (0, None),92 i_list = self._i_indices, j_list = self._j_indices)93 plt.title('median')94 95 96 plt.subplot(3,1,3)97 gevfit.plot_data(np.array(self.median_field) - np.array(self.ret_level_2yr), imagefile = None,98 units = 'm**3/s', minmax = (None, None),99 i_list = self._i_indices, j_list = self._j_indices)100 plt.title('difference: median - 2 year return level')101 plot_basin_boundaries_from_shape(basemap, plotter = plt, linewidth = 0.1)102 basemap.drawcoastlines()103 plt.savefig('median_test.png')104 pass105def save_pars_to_txt_file(filename, pars_dict, i_indices, j_indices):106 f = open(filename, 'w')107 f.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format('i', 'j', 'sigma', 'mu', 'ksi'))108 for pos in range(len(i_indices)):109 f.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format(i_indices[pos], j_indices[pos],110 pars_dict[pos][0], pars_dict[pos][1], pars_dict[pos][2]))111 f.close()112 pass113def save_extremes_to_txt_file(filename, data, i_indices, j_indices):114 f = open(filename, 'w')115 for i, j, the_value_list in zip(i_indices, j_indices, data):116 f.write('i = {0},\t j = {1}\n'.format(i, j))117 for value in the_value_list:118 f.write('{0}\n'.format(value))119 f.close()120 pass121def save_ret_levels_to_txt(filename, data, i_indices, j_indices):122 f = open(filename, 'w')123 for i, j, the_value in zip(i_indices, j_indices, data):124 f.write('i = {0},\t j = {1}, \t {2} \n'.format(i, j, the_value))125 f.close()126def main():127 paths = [128 'data/streamflows/hydrosheds_euler9/aet_discharge_1970_01_01_00_00.nc',129 'data/streamflows/hydrosheds_euler9/aeu_discharge_2041_01_01_00_00.nc'130 ]131 path_to_start_date = {paths[0]:datetime(1970,1,1), paths[1]:datetime(2041,1,1)}132 start_to_end = {datetime(1970,1,1): datetime(1999,12, 31), datetime(2041,1,1) : datetime(2070,12, 31)}133 the_types = ['low', 'high']134 type_to_startmonth = {'low':3,'high':4}135 type_to_end_month = {'low':4,'high':6}136 type_to_duration = {'low':timedelta(days = 15),'high':timedelta(days = 1)}137 #low_return_periods = [2, 5]138 high_return_periods = [10]139 the_type = 'high'140 for path in paths:141 for the_period in high_return_periods:142 test_median = Test_median(data_path = path)143 start_date = path_to_start_date[path]144 test_median.start_date = start_date145 test_median.end_date = start_to_end[start_date]146 test_median.start_month = type_to_startmonth[the_type]147 test_median.end_month = type_to_end_month[the_type]148 test_median.return_period_years = the_period149 test_median.high_flow = True150 test_median.event_duration = type_to_duration[the_type]151 print('selecting data and calculating')152 print('path={0}, the_type = {1}, the_period = {2}'.format(path, the_type, the_period))153 test_median.select_data_and_calculate()154 #plot155 test_median.plot()156 return157 the_type = 'low'158 for path in paths:159 for the_period in low_return_periods:160 test_median = Test_median(data_path = path)161 start_date = path_to_start_date[path]162 test_median.start_date = start_date163 test_median.end_date = start_to_end[start_date]164 test_median.start_month = type_to_startmonth[the_type]165 test_median.end_month = type_to_end_month[the_type]166 test_median.return_period_years = the_period167 test_median.high_flow = False168 test_median.event_duration = type_to_duration[the_type]169 print('selecting data and calculating')170 print('path={0}, the_type = {1}, the_period = {2}'.format(path, the_type, the_period))171 test_median.select_data_and_calculate()172# data_path = 'data/streamflows/hydrosheds_euler2_1/aet_discharge_1970_01_01_00_00.nc'173# test_median = Test_median(data_path = data_path)174# test_median.start_date = datetime(1970,1,1)175# test_median.end_date = datetime(1999,12, 31)176# test_median.start_month = 4177# test_median.end_month = 6178# test_median.return_period_years = 2179# test_median.high_flow = True180# test_median.select_data_and_calculate()181# test_median.plot()182# data_path = 'data/streamflows/hydrosheds_euler2/aeu_discharge_2041_01_01_00_00.nc'183# test_median = Test_median(data_path = data_path)184# test_median.start_date = datetime(2041,1,1)185# test_median.end_date = datetime(2070,12, 31)186# test_median.start_month = 4187# test_median.end_month = 6188# test_median.return_period_years = 30189# test_median.select_data_and_calculate()190# test_median.plot()191#192if __name__ == '__main__':193 main()...
geo_median.py
Source:geo_median.py
1# Code largely taken from: https://gist.github.com/endolith/28371602# with some help from https://github.com/ahwolf/meetup_location/blob/master/code/geo_median.py3# and adapted to support great circle distances over Euclidean.4import csv5from geopy.distance import vincenty6from geopy.distance import great_circle7import numpy8VGI_REPOSITORY = 't51m'9LIMIT_MAD = 30 # acceptable km limit to median absolute deviation of points10LIMIT_POINTS = 5 # acceptable minimum number of GPS points for a user11DISTANCE_THRESHOLD = 1 # distance (meters) between iterations that determines end of search12DATA_POINTS_FILE = 'geo_median/{0}/user_points.csv'.format(VGI_REPOSITORY)13OUTPUT_MEDIANS = 'geo_median/{0}/user_medians.csv'.format(VGI_REPOSITORY)14SNAP_TO_USER_POINTS = False15OUTPUT_ALL_USERS = True16def cand_median(dataPoints):17 """Calculate the first candidate median as the geometric mean."""18 tempLat = 0.019 tempLon = 0.020 for i in range(0, len(dataPoints)):21 tempLat += dataPoints[i][0]22 tempLon += dataPoints[i][1]23 return (tempLat / len(dataPoints), tempLon / len(dataPoints))24def check_median_absolute_deviation(data_points, median):25 """Calculate Median Absolute Deviation of a set of points."""26 distances = []27 for i in range(0, len(data_points)):28 try:29 distances.append(vincenty(median, data_points[i]).kilometers)30 except ValueError:31 # Vincenty doesn't always converge so fall back on great circle distance which is less accurate but always converges32 distances.append(great_circle(median, data_points[i]).kilometers)33 return(numpy.median(distances))34def compute_user_median(data_points, num_iter, csvwriter, current_uid):35 if len(data_points) < LIMIT_POINTS: # Insufficient points for the user - don't record median36 if OUTPUT_ALL_USERS:37 csvwriter.writerow([current_uid, None])38 else:39 if SNAP_TO_USER_POINTS: # ensure median is one of the user's points40 lowest_dev = float("inf")41 for point in data_points:42 tmp_abs_dev = objfunc(point, data_points)43 if tmp_abs_dev < lowest_dev:44 lowest_dev = tmp_abs_dev45 test_median = point46 else:47 test_median = cand_median(data_points) # Calculate centroid more or less as starting point48 if objfunc(test_median, data_points) != 0: # points aren't all the same49 # iterate to find reasonable estimate of median50 for x in range(0, num_iter):51 denom = denomsum(test_median, data_points)52 next_lat = 0.053 next_lon = 0.054 for y in range(0, len(data_points)):55 next_lat += (data_points[y][0] * numersum(test_median, data_points[y])) / denom56 next_lon += (data_points[y][1] * numersum(test_median, data_points[y])) / denom57 prev_median = test_median58 test_median = (next_lat, next_lon)59 try:60 if vincenty(prev_median, test_median).meters < DISTANCE_THRESHOLD:61 break62 except:63 if great_circle(prev_median, test_median).meters < DISTANCE_THRESHOLD:64 break65 if x == num_iter - 1:66 print('{0}: failed to converge. Last change between iterations was {1} meters.'.format(current_uid, great_circle(prev_median, test_median).meters))67 # Check if user points are under the limit median absolute deviation68 if check_median_absolute_deviation(data_points, test_median) <= LIMIT_MAD:69 csvwriter.writerow([current_uid, (round(test_median[0],6), round(test_median[1],6))])70 else:71 if OUTPUT_ALL_USERS:72 csvwriter.writerow([current_uid, None])73def denomsum(test_median, data_points):74 """Provides the denominator of the weiszfeld algorithm."""75 temp = 0.076 for i in range(0, len(data_points)):77 try:78 temp += 1 / vincenty(test_median, data_points[i]).kilometers79 except ZeroDivisionError:80 continue # filter points that equal the median out (otherwise no convergence)81 except ValueError:82 # Vincenty doesn't always converge so fall back on great circle distance which is less accurate but always converges83 temp += 1 / great_circle(test_median, data_points[i]).kilometers84 return temp85def numersum(test_median, data_point):86 """Provides the denominator of the weiszfeld algorithm depending on whether you are adjusting the candidate x or y."""87 try:88 return 1 / vincenty(test_median, data_point).kilometers89 except ZeroDivisionError:90 return 0 # filter points that equal the median out (otherwise no convergence)91 except ValueError:92 # Vincenty doesn't always converge so fall back on great circle distance which is less accurate but always converges93 return 1 / great_circle(test_median, data_point).kilometers94def objfunc(test_median, data_points):95 """This function calculates the sum of linear distances from the current candidate median to all points96 in the data set, as such it is the objective function that we are minimising.97 """98 temp = 0.099 for i in range(0, len(data_points)):100 try:101 temp += vincenty(test_median, data_points[i]).kilometers102 except ValueError:103 # Vincenty doesn't always converge so fall back on great circle distance which is less accurate but always converges104 temp += great_circle(test_median, data_points[i]).kilometers105 return temp106def main(iterations=1000):107 count = 0108 with open(DATA_POINTS_FILE, 'r') as fin:109 csvreader = csv.reader(fin)110 assert next(csvreader) == ['uid','lat','lon']111 with open(OUTPUT_MEDIANS, 'w') as fout:112 csvwriter = csv.writer(fout)113 csvwriter.writerow(['uid','median'])114 line = next(csvreader)115 data_points = [(float(line[1]), float(line[2]))]116 current_uid = line[0]117 for line in csvreader:118 if line[0] == current_uid:119 data_points.append((float(line[1]), float(line[2])))120 else:121 count += 1122 if count % 2500 == 0:123 print("Processed {0} users.".format(count))124 compute_user_median(data_points, iterations, csvwriter, current_uid)125 # set user and restart array for new current user126 current_uid = line[0]127 data_points = [(float(line[1]), float(line[2]))]128 # compute final user median129 compute_user_median(data_points, iterations, csvwriter, current_uid)130if __name__ == "__main__":...
test_haralick.py
Source:test_haralick.py
1import unittest2import os.path3import numpy as np4from eolearn.core import EOPatch, FeatureType5from eolearn.features import HaralickTask6class TestHaralick(unittest.TestCase):7 TEST_PATCH_FILENAME = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'TestInputs', 'TestPatch')8 @classmethod9 def setUpClass(cls):10 cls.patch = EOPatch.load(cls.TEST_PATCH_FILENAME)11 cls._prepare_patch(cls.patch)12 HaralickTask((FeatureType.DATA, 'ndvi', 'haralick_contrast'), texture_feature='contrast', distance=1, angle=0,13 levels=255, window_size=3, stride=1).execute(cls.patch)14 HaralickTask((FeatureType.DATA, 'ndvi', 'haralick_sum_of_square_variance'),15 texture_feature='sum_of_square_variance', distance=1, angle=np.pi/2,16 levels=8, window_size=5, stride=1).execute(cls.patch)17 HaralickTask((FeatureType.DATA, 'ndvi', 'haralick_sum_entropy'),18 texture_feature='sum_entropy', distance=1, angle=-np.pi/2,19 levels=8, window_size=7, stride=1).execute(cls.patch)20 cls.initial_patch = EOPatch.load(cls.TEST_PATCH_FILENAME)21 cls._prepare_patch(cls.initial_patch)22 @staticmethod23 def _prepare_patch(patch):24 ndvi = patch.data['ndvi'][:10]25 ndvi[np.isnan(ndvi)] = 026 patch.data['ndvi'] = ndvi27 def test_new_feature(self):28 haralick = self.patch.data['haralick_contrast']29 delta = 1e-430 test_min = np.min(haralick)31 exp_min = 0.032 self.assertAlmostEqual(test_min, exp_min, delta=delta, msg="Expected min {}, got {}".format(exp_min, test_min))33 test_max = np.max(haralick)34 exp_max = 15620.8333335 self.assertAlmostEqual(test_max, exp_max, delta=delta, msg="Expected max {}, got {}".format(exp_max, test_max))36 test_mean = np.mean(haralick)37 exp_mean = 1585.090538 self.assertAlmostEqual(test_mean, exp_mean, delta=delta,39 msg="Expected mean {}, got {}".format(exp_mean, test_mean))40 test_median = np.median(haralick)41 exp_median = 1004.91666642 self.assertAlmostEqual(test_median, exp_median, delta=delta,43 msg="Expected median {}, got {}".format(exp_median, test_median))44 haralick = self.patch.data['haralick_sum_of_square_variance']45 test_min = np.min(haralick)46 exp_min = 7.717447 self.assertAlmostEqual(test_min, exp_min, delta=delta, msg="Expected min {}, got {}".format(exp_min, test_min))48 test_max = np.max(haralick)49 exp_max = 48.781450 self.assertAlmostEqual(test_max, exp_max, delta=delta, msg="Expected max {}, got {}".format(exp_max, test_max))51 test_mean = np.mean(haralick)52 exp_mean = 31.949053 self.assertAlmostEqual(test_mean, exp_mean, delta=delta,54 msg="Expected mean {}, got {}".format(exp_mean, test_mean))55 test_median = np.median(haralick)56 exp_median = 25.035757 self.assertAlmostEqual(test_median, exp_median, delta=delta,58 msg="Expected median {}, got {}".format(exp_median, test_median))59 haralick = self.patch.data['haralick_sum_entropy']60 test_min = np.min(haralick)61 exp_min = 062 self.assertAlmostEqual(test_min, exp_min, delta=delta, msg="Expected min {}, got {}".format(exp_min, test_min))63 test_max = np.max(haralick)64 exp_max = 1.297165 self.assertAlmostEqual(test_max, exp_max, delta=delta, msg="Expected max {}, got {}".format(exp_max, test_max))66 test_mean = np.mean(haralick)67 exp_mean = 0.389868 self.assertAlmostEqual(test_mean, exp_mean, delta=delta,69 msg="Expected mean {}, got {}".format(exp_mean, test_mean))70 test_median = np.median(haralick)71 exp_median = 0.401972 self.assertAlmostEqual(test_median, exp_median, delta=delta,73 msg="Expected median {}, got {}".format(exp_median, test_median))74 def test_unchanged_features(self):75 for feature, value in self.initial_patch.data.items():76 self.assertTrue(np.array_equal(value, self.patch.data[feature]),77 msg="EOPatch data feature '{}' was changed in the process".format(feature))78if __name__ == '__main__':...
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