def example_4():
# Collect a Grid
grid_1 = Grid.read_envira(
'../tests/data/MER2019 H_500_doc29_VVR/MER2018 - Doc29 - Lden y1974.dat'
)
# Collect a Grid to compare
grid_2 = Grid.read_envira(
'../tests/data/H_500_00_doc29/MER2015 - Doc29 - Lden y1974.dat')
# Create a figure
plot = GridPlot(grid_1)
# Add the background
plot.add_background('../data/Schiphol_RD900dpi.png')
# Add a scale
plot.add_scale()
# Add the comparison heatmap
plot.add_comparison_heatmap(grid_2, vmin=-3, vmax=3)
# Add a colorbar
plot.add_colorbar()
# Save the figure
plot.save('figures/plot_grid_example_4.pdf')
# And show the plot
plot.show()
def test_statistics_multigrid():
# Get the path to the Envira files
file_paths = abs_path('data/MINIMER2015')
# Set the pattern
pattern = r'[\w\d\s]+\.dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
stats = Grid.statistics(grid)
assert isinstance(stats, dict)
def test_statistics_nan():
# Get the path to the Envira files
file_paths = abs_path('data/MINIMER2015')
# Set the pattern
pattern = r'[\w\d\s]+\.dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
# Modify the data
grid.data = np.nan
# Check if the statistics can be calculated
Grid.statistics(grid)
def example_1():
# Collect a Grid
grid = Grid.read_envira('../tests/data/MER2015 - Doc29 - Lden y1974.dat')
# Create a figure
plot = GridPlot(grid)
# Add the background
plot.add_background('../data/Schiphol_RD900dpi.png')
# Add a scale
plot.add_scale()
# Add the terrain
plot.add_terrain('../data/2013-spl-luchtvaartterrein.shp')
# Add the place names
plot.add_place_names('../data/plaatsnamen.csv')
# Add the 58dB contour
plot.add_contours(58, default['kleuren']['schemergroen'],
default['kleuren']['wolkengrijs_1'])
# Add the 48dB contour
plot.add_contours(48, default['kleuren']['schipholblauw'],
default['kleuren']['middagblauw'])
# Save the figure
plot.save('figures/plot_grid_example_1.pdf')
# And show the plot
plot.show()
def test_read_enviras_inconsistent_list_size():
# Get the path to the Envira files
file_paths = abs_path('data/')
# Set the pattern
pattern = r'GP2018 - Lnight y201[67].dat'
# Get the envira files
file_names = [f for f in os.listdir(file_paths) if re.search(pattern, f)]
# Create info and data lists
cls_info = []
cls_data = []
# Read the envira files
for file_name in file_names:
info, data = read_envira(os.path.join(file_paths, file_name))
cls_info.append(info)
cls_data.append(data)
try:
# Add the data to a Grid object
grid = Grid(data=cls_data, info=cls_info[0], unit='Lnight')
# If the test reaches this point, the method is not working properly
assert False
except TypeError:
# Check if the file names are correct
assert file_names == ['GP2018 - Lnight y2016.dat', 'GP2018 - Lnight y2017.dat']
def test_scale_per_time_interval_apply_night_time_correction():
# Get the path to the Envira files
file_paths = abs_path('data/H_500_00_doc29')
# Create a dict for the meteotoeslag grids
meteotoeslag = {}
for unit in ['Lden', 'Lnight']:
# Set the pattern
pattern = r'[\w\d\s]+{}[\w\d\s]+\.dat'.format(unit)
# Create a meteotoeslag grid object from the data file
meteotoeslag[unit] = Grid.read_enviras(file_paths, pattern).meteotoeslag_grid_from_method('hybride')
# Scale the meteotoeslag 2.1%
meteotoeslag_lden_scaled_with = meteotoeslag['Lden'].copy().scale_per_time_interval(meteotoeslag['Lnight'],
scale_de=1.021)
# Scale the meteotoeslag 2.1% without lnight time correction
meteotoeslag_lden_scaled_without = meteotoeslag['Lden'].copy().scale_per_time_interval(meteotoeslag['Lnight'],
scale_de=1.021,
apply_lnight_time_correction=False)
# Without lnight time correction, the scaled data Lden should be lower
assert (meteotoeslag_lden_scaled_with.data > meteotoeslag_lden_scaled_without.data).all()
def test_interpolation_function_nan():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
grid.data = np.nan
grid.interpolation_function()
def test_read_envira_other_shape():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016h.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
assert grid.data.shape == (grid.info['y_number'], grid.info['x_number'])
def test_refine_value():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
factor = np.nan
grid.refine(factor)
def test_refine_type():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
grid.data = []
grid.refine(2)
def test_to_shapefile():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Refine the grid and export as shapefile
grid.refine(20).to_shapefile(abs_path('data/GP2018 - Lnight y2016.shp'), 48)
def test_interpolation_function_nominal():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
interpolation = grid.interpolation_function()
assert isinstance(interpolation, RectBivariateSpline)
def test_hg():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Calculate the Hoeveelheid Geluid
hg = grid.hg()
assert isinstance(hg, float)
def test_meteotoeslag_from_years_doubles():
# Get the path to the Envira files
file_paths = abs_path('data/MINIMER2015')
# Set the pattern
pattern = r'[\w\d\s]+\.dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
# Calculate the meteotoeslag
grid.meteotoeslag_from_years(np.ones((32,), dtype=int) * 1981)
def test_meteotoeslag_from_years():
# Get the path to the Envira files
file_paths = abs_path('data/MINIMER2015')
# Set the pattern
pattern = r'[\w\d\s]+\.dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
# Calculate the meteotoeslag
grid.meteotoeslag_from_years([1981, 1984, 1993, 1994, 1996, 2000, 2002, 2010])
def test_to_envira():
# Get the path from the original Envira file
original_file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(original_file_path)
# Set the path to the new Envira file
new_file_path = abs_path('data/envira-test.dat')
# Export the grid to the new file
grid.to_envira(new_file_path)
# Create a grid object from the new data file
grid_new = Grid.read_envira(new_file_path)
# Test if the headers of the files are the same
assert grid_new.info == grid.info
# Check if the data is still correct
np.testing.assert_equal(grid_new.data, grid.data)
def test_hg_multigrid():
# Get the path to the Envira files
file_paths = abs_path('data/MINIMER2015')
# Set the pattern
pattern = r'[\w\d\s]+\.dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
# Calculate the Hoeveelheid Geluid
grid.hg()
def test_read_enviras():
# Get the path to the Envira files
file_paths = abs_path('data/')
# Set the pattern
pattern = r'GP2018 - Lnight y201[67].dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
# Check if the data is stored correctly
assert isinstance(grid.data, list) and len(grid.data) == 2
assert isinstance(grid.info, list) and len(grid.data) == 2
def test_refine():
"""
Test various use cases for consistency
"""
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid2 = Grid.read_envira(file_path)
# Refine the grid with a factor 2
grid2.refine(2)
# Check if the shape of the data matches the changed input
assert grid2.data.shape == (285, 285)
assert grid2.shape.x_number == 285
assert grid2.shape.y_number == 285
# Create a grid object from the data file
grid05 = Grid.read_envira(file_path)
# Refine the grid with a factor 0.5
grid05.refine(0.5)
# Check if the shape of the data matches the changed input
assert grid05.data.shape == (72, 72)
assert grid05.shape.x_number == 72
assert grid05.shape.y_number == 72
# Create a grid object from the data file
grid35 = Grid.read_envira(file_path)
# Refine the grid with a factor 3.5
grid35.refine(3.5)
assert grid35.data.shape == (498, 498)
assert grid35.shape.x_number == 498
assert grid35.shape.y_number == 498
def test_scale():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Copy the data
d = grid.data.copy()
# Calculate the meteotoeslag
grid.scale(2.)
np.testing.assert_equal(grid.data, d + 10 * np.log10(2))
def test_add_noise_from_grid_lden_lnight():
# Get the path to the WBS file
file_path = abs_path('../data/wbs2005.h5')
# Create a wbs object from the data file
wbs = WBS.read_file(file_path)
# Get the path to the Lden Envira file
file_path = abs_path('data/MER2015 - Doc29 - Lden y1974.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Add the interpolated Lden values from the grid
wbs.add_noise_from_grid(grid)
# Get the path to the Lnight Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Add the interpolated Lnight values from the grid
wbs.add_noise_from_grid(grid)
def test_scale_per_time_interval_incompatible_grids():
# Get the path to the Envira files
file_paths = abs_path('data/H_500_00_doc29')
# Create a dict for the meteotoeslag grids
meteotoeslag = {}
for unit in ['Lden', 'Lnight']:
# Set the pattern
pattern = r'[\w\d\s]+{}[\w\d\s]+\.dat'.format(unit)
# Create a meteotoeslag grid object from the data file
meteotoeslag[unit] = Grid.read_enviras(file_paths, pattern).meteotoeslag_grid_from_method('hybride')
# Scale the meteotoeslag without actually scaling
meteotoeslag['Lden'].copy().scale_per_time_interval(meteotoeslag['Lnight'].refine(.5))
def test_meteotoeslag_from_method():
# Get the path to the Envira files
file_paths = abs_path('data/MINIMER2015')
# Set the pattern
pattern = r'[\w\d\s]+\.dat'
# Create a grid object from the data file
grid = Grid.read_enviras(file_paths, pattern)
# Calculate the meteotoeslag
meteotoeslag, meteo_years = grid.meteotoeslag_from_method('hybride')
# Check if the data is processed correctly
assert meteo_years.shape == (32,)
assert grid.data[0].shape == meteotoeslag.shape
assert np.all(grid.data[0] <= meteotoeslag)
def example_2():
# Collect a Grid
grid = Grid.read_enviras('../tests/data/MER2019 H_500_doc29_VVR',
r'[\w\d\s]+{}[\w\d\s]+\.dat'.format('Lden'))
# Create a figure
plot = GridPlot(grid)
# Add the 58dB contour
plot.add_contours(48, default['kleuren']['schipholblauw'],
default['kleuren']['middagblauw'])
# Save the figure
plot.save('figures/plot_grid_example_2.pdf')
# And show the plot
plot.show()
def test_select_above_wrong_unit():
# Get the path to the WBS file
file_path = abs_path('../data/wbs2005.h5')
# Create a wbs object from the data file
wbs = WBS.read_file(file_path)
# Get the path to the Envira file
file_path = abs_path('data/MER2015 - Doc29 - Lden y1974.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Add the interpolated Lden values from the grid
wbs.add_noise_from_grid(grid)
# Select the Lnight values above 48
wbs.select_above('Lnight', 40)
def test_scale_per_time_interval_no_scale():
# Get the path to the Envira files
file_paths = abs_path('data/H_500_00_doc29')
# Create a dict for the meteotoeslag grids
meteotoeslag = {}
for unit in ['Lden', 'Lnight']:
# Set the pattern
pattern = r'[\w\d\s]+{}[\w\d\s]+\.dat'.format(unit)
# Create a meteotoeslag grid object from the data file
meteotoeslag[unit] = Grid.read_enviras(file_paths, pattern).meteotoeslag_grid_from_method('hybride')
# Scale the meteotoeslag without actually scaling
meteotoeslag_lden_scaled = meteotoeslag['Lden'].copy().scale_per_time_interval(meteotoeslag['Lnight'])
# Check if the values are still the same
np.testing.assert_almost_equal(meteotoeslag['Lden'].data, meteotoeslag_lden_scaled.data, 12)
def test_scale_per_time_interval_decrease():
# Get the path to the Envira files
file_paths = abs_path('data/H_500_00_doc29')
# Create a dict for the meteotoeslag grids
meteotoeslag = {}
for unit in ['Lden', 'Lnight']:
# Set the pattern
pattern = r'[\w\d\s]+{}[\w\d\s]+\.dat'.format(unit)
# Create a meteotoeslag grid object from the data file
meteotoeslag[unit] = Grid.read_enviras(file_paths, pattern).meteotoeslag_grid_from_method('hybride')
# Scale the meteotoeslag with a scaling factor below 1
meteotoeslag_lden_scaled = meteotoeslag['Lden'].copy().scale_per_time_interval(meteotoeslag['Lnight'], scale_de=.9)
# The Lden data should decrease due to the decrease of Lde
assert (meteotoeslag_lden_scaled.data < meteotoeslag['Lden'].data).all()
def test_select_above():
# Get the path to the WBS file
file_path = abs_path('../data/wbs2005.h5')
# Create a wbs object from the data file
wbs = WBS.read_file(file_path)
# Get the path to the Envira file
file_path = abs_path('data/MER2015 - Doc29 - Lden y1974.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Add the interpolated Lden values from the grid
wbs.add_noise_from_grid(grid)
# Select the Lden values above 40
s = wbs.select_above(40, 'Lden').values
assert isinstance(s, np.ndarray)
def test_count_sleep_disturbed_people():
# Get the path to the WBS file
file_path = abs_path('../data/wbs2005.h5')
# Create a wbs object from the data file
wbs = WBS.read_file(file_path)
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Add the interpolated Lnight values from the grid
wbs.add_noise_from_grid(grid)
# Count the sleep disturbed people
s = wbs.count_sleep_disturbed_people()
assert isinstance(s, float)
def test_count_annoyed_people():
# Get the path to the WBS file
file_path = abs_path('../data/wbs2005.h5')
# Create a wbs object from the data file
wbs = WBS.read_file(file_path)
# Get the path to the Envira file
file_path = abs_path('data/MER2015 - Doc29 - Lden y1974.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
# Add the interpolated Lden values from the grid
wbs.add_noise_from_grid(grid)
# Count the annoyed people
s = wbs.count_annoyed_people()
assert isinstance(s, float)
