def test_traffic_from_casper_file():
# Get the path to the Casper file
file_path = abs_path(
'data/Vluchten Export 2017-11-01 00_00_00 - 2018-11-01 00_00_00_2019-01-29 10_59_35.csv'
)
# Create a traffic object from the data file
traffic = Traffic.read_casper_file(file_path)
# Add day (D), evening (E), night (N)
traffic.add_den()
# Add landing (L) or takeoff (T)
traffic.add_landing_takeoff()
# Add procedure
traffic.add_procedure()
# Get the DEN distribution rounded by 25
distribution = (25 *
(traffic.get_den_distribution() / 25).round()).astype(int)
# Get the procedure distribution
arrivals, departures = traffic.get_procedure_distribution()
# Normalized percentage arrivals per altitude procedure
arrivals_normalized = (100 * arrivals / arrivals.sum()).round(1)
# Normalized percentage departures per procedure
departures_normalized = (100 * departures / departures.sum()).round(1)
def test_traffic_from_nlr_file():
# Get the path to the NLR file
file_path = abs_path('data/20181107_Traffic_2018.xlsx')
# Create a traffic object from the data file
traffic = Traffic.read_nlr_file(file_path)
# Add day (D), evening (E), night (N)
traffic.add_den()
# Add landing (L) or takeoff (T)
traffic.add_landing_takeoff()
# Add procedure
traffic.add_procedure()
# Get the DEN distribution rounded by 5
distribution = (5 *
(traffic.get_den_distribution() / 5).round()).astype(int)
# Get the procedure distribution
arrivals, departures = traffic.get_procedure_distribution()
# Normalized percentage arrivals per altitude procedure
arrivals_normalized = (100 * arrivals / arrivals.sum()).round(1)
# Normalized percentage departures per procedure
departures_normalized = (100 * departures / departures.sum()).round(1)
def example_1():
# Get the path to the sir TAF file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic.csv')
# Set the read_csv arguments for this file
file_kwargs = {
'usecols': [0, 1, 2],
'parse_dates': [1],
'names': ['d_lt', 'd_schedule', 'd_date'],
'skiprows': 1,
'delimiter': ','
}
# Create a new traffic object
aggregate = Traffic.read_taf_file(file_path, **file_kwargs)
# Create a new traffic distribution plot, set the colors
plot = BracketPlot(takeoff_color='#fdbb4b', landing_color='#4a8ab7')
# Add the traffic distribution from the traffic aggregate
plot.add_traffic_bracket(aggregate.get_bracket())
# Add the custom y-labels
plot.add_takeoff_landing_label('starts', 'landingen')
# Add the traffic legend
plot.add_traffic_legend('verkeer')
# Save the figure
plot.save('figures/plot_traffic_per_bracket_example_1.pdf')
# Show the plot
plot.show()
def test_read_taf_file():
# Get the path to the TAF file
file_path = abs_path('data/traffic.csv')
# Set the read_csv arguments for this file
file_kwargs = {
'usecols': [0, 1, 2],
'parse_dates': [1],
'names': ['d_lt', 'd_schedule', 'd_date'],
'skiprows': 1,
'delimiter': ','
}
# Create a traffic object from the data file
aggregate = Traffic.read_taf_file(file_path, **file_kwargs)
assert aggregate.type == 'taf.sir'
# Get the DEN distribution for takeoff and landing rounded by 100
try:
distribution = aggregate.get_den_distribution(
separate_by='d_lt').round(decimals=-2)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_runway_usage('D')
assert False
except TypeError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_runway_usage_statistics('D')
assert False
except TypeError:
assert True
# Get the runway combinations
runway_combinations = pd.read_csv('data/Baancombinaties.txt',
delim_whitespace=1)
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_n_runway_preference_usage(runway_combinations)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_deem_runway_preference_usage(runway_combinations)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
bracket = aggregate.get_bracket()
def example_2():
# Get the path to the Daisy file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic 1971-2016 - mean.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_mean_file(file_path)
# Create a aircraft type plot with the data in it
fig, ax = plot_aircraft_types(aggregate,
align='edge',
width=-0.3,
label='prognose')
plot_aircraft_types(aggregate,
ax=ax,
align='edge',
width=0.3,
label='realisatie')
ax.legend()
# Save the figure
plt.savefig('figures/plot_fleet_to_aircraft_type_example_2.pdf',
dpi=300,
bbox_inches='tight')
# Show the figure
plt.show()
def example_2():
# Get the path to the Casper file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic-Daisy.txt')
# Create a new traffic object
aggregate = Traffic.read_daisy_weekday_file(file_path)
# Create a new traffic distribution plot, set the colors
plot = BracketPlot(takeoff_color='#fdbb4b', landing_color='#4a8ab7')
# Add the traffic distribution from the traffic aggregate
plot.add_traffic_bracket(aggregate.get_bracket())
# Add the custom y-labels
plot.add_takeoff_landing_label('starts', 'landingen')
# Add the traffic legend
plot.add_traffic_legend('verkeer')
# Save the figure
plot.save('figures/plot_traffic_per_bracket_example_2.pdf')
# Show the plot
plot.show()
def test_read_daisy_runway_combination_file_get_preference():
# Get the path to the Daisy file
file_path = abs_path('data/traffic 1971-2016 - pref.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_runway_combination_file(file_path)
# Get the runway combinations
runway_combinations = pd.read_csv('data/Baancombinaties.txt',
delim_whitespace=1)
# Get the night (N) runway preference
night_preference = aggregate.get_n_runway_preference_usage(
runway_combinations)
# Get the day (D), evening (E) and early morning (EM) runway preference
deem_preference = aggregate.get_deem_runway_preference_usage(
runway_combinations)
# Test if the (relative) usage of the night preference is consistent
assert night_preference.index.tolist() == [
'1', '2', '3', '4', 'subtotal', 'other', 'total'
]
np.testing.assert_almost_equal(
night_preference.loc[['1', '2', '3', '4'], 'usage'].sum(),
night_preference.loc['subtotal', 'usage'], 2)
np.testing.assert_almost_equal(
night_preference.loc[['subtotal', 'other'], 'usage'].sum(),
night_preference.loc['total', 'usage'], 2)
np.testing.assert_almost_equal(
night_preference.loc[['1', '2', '3', '4'], 'relative usage'].sum(),
night_preference.loc['subtotal', 'relative usage'], 2)
np.testing.assert_almost_equal(
night_preference.loc[['subtotal', 'other'], 'relative usage'].sum(),
night_preference.loc['total', 'relative usage'], 2)
np.testing.assert_almost_equal(
night_preference.loc['total', 'relative usage'], 100, 2)
# Test if the (relative) usage of the day, evening and early morning preference is consistent
assert deem_preference.index.tolist() == [
'1', '2', '3', '4', '5', '6', 'subtotal', 'other', 'total'
]
np.testing.assert_almost_equal(
deem_preference.loc[['1', '2', '3', '4', '5', '6'], 'usage'].sum(),
deem_preference.loc['subtotal', 'usage'], 2)
np.testing.assert_almost_equal(
deem_preference.loc[['subtotal', 'other'], 'usage'].sum(),
deem_preference.loc['total', 'usage'], 2)
np.testing.assert_almost_equal(
deem_preference.loc[['1', '2', '3', '4', '5', '6'],
'relative usage'].sum(),
deem_preference.loc['subtotal', 'relative usage'], 2)
np.testing.assert_almost_equal(
deem_preference.loc[['subtotal', 'other'], 'relative usage'].sum(),
deem_preference.loc['total', 'relative usage'], 2)
np.testing.assert_almost_equal(
deem_preference.loc['total', 'relative usage'], 100, 2)
def test_read_daisy_phase_file_summer_season():
# Get the path to the Daisy file
file_path = abs_path('data/traffic Zomerseizoen.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_phase_file(file_path)
assert aggregate.type == 'daisy.phase'
# Get the DEN distribution for takeoff and landing rounded by 100
distribution = aggregate.get_den_distribution(separate_by='d_lt')
def example_5():
# Get the path to the Casper file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic-Daisy.txt')
# Create a new traffic aggregate object
aggregate = Traffic.read_daisy_weekday_file(file_path)
# Add taxi delays
t = pd.to_datetime(aggregate.data['d_schedule'], format="%H:%M")
t[aggregate.data['d_lt'] == 'L'] -= pd.Timedelta(10, unit='minute')
t[aggregate.data['d_lt'] == 'T'] += pd.Timedelta(10, unit='minute')
aggregate.data['d_schedule'] = t.dt.strftime('%H:%M')
# Get periods from a Daisy file and get the scaled capacity
periods = pd.read_excel(
os.path.join(os.path.dirname(__file__),
'../tests/data/periods-Daisy.xls'))
capacity = pd.read_excel(
os.path.join(os.path.dirname(__file__),
'../tests/data/capaciteit.xlsx'))
# Convert the capacity per hour to capacity per 20 minutes
capacity[['Lcap', 'Tcap']] = capacity[['Lcap', 'Tcap']] / 3
# Create a new bracket object
bracket = Bracket.from_periods_and_capacity(periods, capacity)
# Create a new traffic distribution plot, set the colors
plot = BracketPlot(takeoff_color='#fdbb4b', landing_color='#4a8ab7')
# Add the TAF brackets as the capacity
plot.add_capacity_bracket(bracket)
# Add the traffic distribution from the traffic aggregate
plot.add_traffic_bracket(aggregate.get_bracket(percentile=.95))
# Set the ylim
plot.ax.set_ylim([-45, 35])
# Add the custom y-labels
plot.add_takeoff_landing_label('starts', 'landingen')
# Add the traffic legend
plot.add_capacity_legend('baancapaciteit')
# Add the traffic legend
plot.add_traffic_legend('verkeer')
# Save the figure
plot.save('figures/plot_traffic_per_bracket_example_5.pdf')
# Show the plot
plt.show()
def test_read_daisy_phase_file():
# Get the path to the Daisy file
file_path = abs_path('data/traffic Winterseizoen.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_phase_file(file_path)
assert aggregate.type == 'daisy.phase'
# Get the DEN distribution for takeoff and landing rounded by 100
distribution = aggregate.get_den_distribution(separate_by='d_lt').round(
decimals=-2)
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_runway_usage('D')
assert False
except TypeError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_runway_usage_statistics('D')
assert False
except TypeError:
assert True
# Get the runway combinations
runway_combinations = pd.read_csv('data/Baancombinaties.txt',
delim_whitespace=1)
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_n_runway_preference_usage(runway_combinations)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_deem_runway_preference_usage(runway_combinations)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
bracket = aggregate.get_bracket()
def example_4():
# Get the path to the Casper file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic-Daisy.txt')
# Create a new traffic object
aggregate = Traffic.read_daisy_weekday_file(file_path)
# Get periods from a Daisy file and get the scaled capacity
periods = pd.read_excel(
os.path.join(os.path.dirname(__file__),
'../tests/data/periods-Daisy.xls'))
capacity = pd.read_excel(
os.path.join(os.path.dirname(__file__),
'../tests/data/capaciteit.xlsx'))
# Scale the capacity
capacity[['Lcap', 'Tcap']] = capacity[['Lcap', 'Tcap']] / 3
# Create a new bracket object
bracket = Bracket.from_periods_and_capacity(periods, capacity)
# Create a new traffic distribution plot, set the colors
plot = BracketPlot(takeoff_color='#fdbb4b', landing_color='#4a8ab7')
# Add the TAF brackets as the capacity
plot.add_capacity_bracket(bracket)
# Add the traffic distribution from the traffic aggregate
plot.add_traffic_bracket(aggregate.get_bracket())
# Add the custom y-labels
plot.add_takeoff_landing_label('starts', 'landingen')
# Add the traffic legend
plot.add_capacity_legend('baancapaciteit')
# Add the traffic legend
plot.add_traffic_legend('verkeer')
# Save the figure
plot.save('figures/plot_traffic_per_bracket_example_4.pdf')
# Show the plot
plt.show()
def test_traffic_add_season():
# Get the path to the Casper file
file_path = abs_path(
'data/Vluchten Export 2017-11-01 00_00_00 - 2018-11-01 00_00_00_2019-01-29 10_59_35.csv'
)
# Create a traffic object from the data file
traffic = Traffic.read_casper_file(file_path)
# Add the seasons to the data
traffic.add_season()
# Check if the season is present in the traffic data
assert 'season' in traffic.data
# Check if it has winter and summer in it
assert ['summer', 'winter'
] == traffic.data['season'].sort_values().unique().tolist()
def example_3():
# Get the path to the Casper file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic-Daisy.txt')
# Create a new traffic object
aggregate = Traffic.read_daisy_weekday_file(file_path)
# Get the path to the TAF bracket file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/bracketlijst.xlsx')
# Create a new bracket object
bracket = Bracket.read_taf_bracket_excel_file(file_path,
sheet_name='106110',
usecols=[0, 5, 6],
names=['bracket', 'L', 'T'])
# Create a new traffic distribution plot, set the colors
plot = BracketPlot(takeoff_color='#fdbb4b', landing_color='#4a8ab7')
# Add the TAF brackets as the capacity
plot.add_capacity_bracket(bracket)
# Add the traffic distribution from the traffic aggregate
plot.add_traffic_bracket(aggregate.get_bracket())
# Add the custom y-labels
plot.add_takeoff_landing_label('starts', 'landingen')
# Add the traffic legend
plot.add_capacity_legend('baancapaciteit')
# Add the traffic legend
plot.add_traffic_legend('verkeer')
# Save the figure
plot.save('figures/plot_traffic_per_bracket_example_3.pdf')
# Show the plot
plt.show()
def example_1():
# Get the path to the Daisy file
file_path = os.path.join(os.path.dirname(__file__),
'../tests/data/traffic 1971-2016 - mean.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_mean_file(file_path)
# Create a aircraft type plot with the data in it
fig, ax = plot_aircraft_types(aggregate,
color='#141251',
align='center',
width=0.5)
# Save the figure
plt.savefig('figures/plot_fleet_to_aircraft_type_example_1.pdf',
dpi=300,
bbox_inches='tight')
# Show the figure
plt.show()
def test_read_daisy_runway_combination_file():
# Get the path to the Daisy file
file_path = abs_path('data/traffic 1971-2016 - pref.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_runway_combination_file(file_path)
assert aggregate.type == 'daisy.runway_combination'
# Get the DEN distribution rounded by 100
distribution = aggregate.get_den_distribution().round(decimals=-2)
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_runway_usage('D')
assert False
except TypeError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_runway_usage_statistics('D')
assert False
except TypeError:
assert True
# Get the runway combinations
runway_combinations = pd.read_csv('data/Baancombinaties.txt',
delim_whitespace=1)
# Since runway data is present, it should be possible to calculate the runway preferences
rp_night = aggregate.get_n_runway_preference_usage(runway_combinations)
rp_deem = aggregate.get_deem_runway_preference_usage(runway_combinations)
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_bracket()
assert False
except TypeError:
assert True
def test_traffic_get_denem_distribution():
# Get the path to the Casper file
file_path = abs_path(
'data/Vluchten Export 2017-11-01 00_00_00 - 2018-11-01 00_00_00_2019-01-29 10_59_35.csv'
)
# Create a traffic object from the data file
traffic = Traffic.read_casper_file(file_path)
# Add the seasons to the data
traffic.add_season()
# Add the departure/arrival to the data
traffic.add_landing_takeoff()
# Add the day (D), evening (E), night (N), and early morning (EM) to the data
traffic.add_denem()
# Get the day (D), evening (E), night (N), and early morning (EM) distribution for landing (L) and takeoff (T)
distribution = traffic.get_denem_distribution(separate_by='LT')
# Check if it has the requested shape
assert distribution.shape == (4, 2)
import os
import matplotlib.pyplot as plt
from ssdtools.figures import plot_runway_usage
from ssdtools.traffic import Traffic
if __name__ == "__main__":
# Set the branding to use
plt.style.use('../ssdtools/branding/schiphol_default.rc')
# Get the path to the Daisy file
file_path = os.path.join(os.path.dirname(__file__), '../tests/data/traffic 1971-2016 - mean.txt')
# Create a traffic object from the data file
mean_aggregate = Traffic.read_daisy_mean_file(file_path)
# Get the runway usage for the mean traffic aggregate
runway_usage = mean_aggregate.get_runway_usage('D|E')
# Get the path to the Daisy file
file_path = os.path.join(os.path.dirname(__file__), '../tests/data/traffic 1971-2016 - years.txt')
# Create a traffic object from the data file
myear_aggregate = Traffic.read_daisy_meteoyear_file(file_path)
# Get the runway usage statistics for the meteoyear traffic aggregate
runway_usage_statistics = myear_aggregate.get_runway_usage_statistics('D|E')
# Create a aircraft type plot with the data in it
fig, ax = plot_runway_usage(myear_aggregate, labels=['myear aggregate'])
# Save the figure
import os
import matplotlib.pyplot as plt
from ssdtools.figures import plot_season_traffic
from ssdtools.traffic import Traffic
if __name__ == "__main__":
# Get the path to the Casper file
file_path = os.path.join(
os.path.dirname(__file__),
'../tests/data/Vluchten Export 2017-11-01 00_00_00 - 2018-11-01 00_00_00_2019-01-29 10_59_35.csv'
)
# Create a traffic object from the data file
traffic = Traffic.read_casper_file(file_path)
# Add the seasons to the data
traffic.add_season()
# Add the departure/arrival to the data
traffic.add_landing_takeoff()
# Add the part of the day to the data
traffic.add_denem()
# Get the traffic distribution to plot
distribution = traffic.get_season_distribution()
# Rename the labels
distribution = distribution.rename(index={
'summer': 'zomer',
'L': 'landingen',
def test_read_daisy_meteoyear_file():
# Get the path to the Daisy file
file_path = abs_path('data/traffic 1971-2016 - years.txt')
# Create a traffic object from the data file
aggregate = Traffic.read_daisy_meteoyear_file(file_path)
assert aggregate.type == 'daisy.meteoyear'
# Get the runway usage statistics
runway_usage = aggregate.get_runway_usage_statistics('D|E|N')
# Check if the expected shape is returned
assert runway_usage.shape == (20, 8)
# Check if the expected columns are returned
assert runway_usage.columns.tolist() == [
'count',
'mean',
'std',
'min',
'25%',
'50%',
'75%',
'max',
]
# Get the DEN distribution for each meteo year
distribution = aggregate.get_den_distribution(separate_by='d_myear')
# Check if the totals are equal
assert distribution.sum().sum() == aggregate.data['total'].sum()
# In this case the day (D) and evening (E) counts are all equal
assert distribution.loc['D', :].min() == distribution.loc['D', :].max()
assert distribution.loc['E', :].min() == distribution.loc['E', :].max()
# The night distribution varies from 31677 to 31688
assert distribution.loc['N', :].min() == 31677
assert distribution.loc['N', :].max() == 31688
# Test if runway usage can be obtained
assert aggregate.get_runway_usage('D').shape == (2, 12)
# Get the runway combinations
runway_combinations = pd.read_csv('data/Baancombinaties.txt',
delim_whitespace=1)
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_n_runway_preference_usage(runway_combinations)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_deem_runway_preference_usage(runway_combinations)
assert False
except KeyError:
assert True
# Since no runway data is present in this aggregate, the following method should fail
try:
aggregate.get_bracket()
assert False
except TypeError:
assert True