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 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_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_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_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_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_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_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_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_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_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_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)
def test_resize():
# 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)
# Create an alternative shape
shape = grid.shape.copy()
# Change a few settings
shape.set_x_number(201)
shape.set_y_number(79)
# Resize the grid
grid.resize(shape)
# Check if the shape of the data matches the changed input
assert grid.data.shape == (79, 201)
assert grid.shape.x_number == 201
assert grid.shape.y_number == 79
assert isinstance(grid.info, dict)
def example_3():
# 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 heatmap
plot.add_heatmap(vmin=46, vmax=70)
# Add a colorbar
plot.add_colorbar()
# Save the figure
plot.save('figures/plot_grid_example_3.pdf')
# And show the plot
plot.show()
def test_read_envira_header_incorrect():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016e.dat')
# Create a grid object from the data file
Grid.read_envira(file_path)
def test_read_envira_row_missing():
# Get the path to the Envira file
file_path = abs_path('data/GP2018 - Lnight y2016r.dat')
# Create a grid object from the data file
grid = Grid.read_envira(file_path)
