provide more flexibility for time periods.

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16

activitysim/abm/models/util/expressions.py

@@ -6,6 +6,7 @@
 
 import os
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -164,13 +165,13 @@ def assign_columns(df, model_settings, locals_dict={}, trace_label=None):
 # helpers
 # ##################################################################################################
 
-def skim_time_period_label(time):
+def skim_time_period_label(time_period):
  """
  convert time period times to skim time period labels (e.g. 9 -> 'AM')
 
  Parameters
  ----------
-time : pandas Series
+time_period : pandas Series
 
  Returns
  -------
@@ -180,12 +181,35 @@ def skim_time_period_label(time):
 
  skim_time_periods = config.setting('skim_time_periods')
 
+ # Default to 60 minute time periods
+ period_minutes = 60
+ if 'period_minutes' in skim_time_periods.keys():
+ period_minutes = skim_time_periods['period_minutes']
+
+ # Default to a day
+ model_time_window_min = 1440
+ if ('time_window') in skim_time_periods.keys():
+ model_time_window_min = skim_time_periods['time_window']
+
+ # Check to make sure the intervals result in no remainder time throught 24 hour day
+ assert 0 == model_time_window_min % period_minutes
+ total_periods = model_time_window_min / period_minutes
+
  # FIXME - eventually test and use np version always?
- if np.isscalar(time):
- bin = np.digitize([time % 24], skim_time_periods['hours'])[0] - 1
+ period_label = 'periods'
+ if 'hours' in skim_time_periods.keys():
+ period_label = 'hours'
+ warnings.warn('`skim_time_periods` key `hours` in settings.yml will be removed in '
+ 'future verions. Use `periods` instead',
+ FutureWarning)
+
+ if np.isscalar(time_period):
+ bin = np.digitize([time_period % total_periods],
+ skim_time_periods[period_label], right=True)[0] - 1
  return skim_time_periods['labels'][bin]
 
- return pd.cut(time, skim_time_periods['hours'], labels=skim_time_periods['labels']).astype(str)
+ return pd.cut(time_period, skim_time_periods[period_label],
+ labels=skim_time_periods['labels'], right=True).astype(str)
 
 
 def annotate_preprocessors(

activitysim/abm/test/configs/settings.yaml

@@ -93,7 +93,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

activitysim/abm/test/configs_test_misc/settings_30_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 30
+ periods:
+ - 0
+ - 12
+ - 22
+ - 32
+ - 40
+ - 48
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/configs_test_misc/settings_60_min.yaml

@@ -0,0 +1,17 @@
+
+skim_time_periods:
+ time_window: 1440
+ period_minutes: 60
+ periods:
+ - 0
+ - 6
+ - 11
+ - 16
+ - 20
+ - 24
+ labels:
+ - EA
+ - AM
+ - MD
+ - PM
+ - EV

activitysim/abm/test/test_expressions.py

@@ -0,0 +1,96 @@
+import os
+
+import pandas as pd
+import pytest
+import yaml
+
+from activitysim.core import inject
+from activitysim.abm.models.util import expressions
+
+
+@pytest.fixture(scope="session")
+def config_path():
+ return os.path.join(os.path.dirname(__file__), 'configs_test_misc')
+
+
+def test_30_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_30_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(16) == 'AM'
+ assert expressions.skim_time_period_label(24) == 'MD'
+ assert expressions.skim_time_period_label(36) == 'PM'
+ assert expressions.skim_time_period_label(46) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 16, 24, 36, 46])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_60_minute_windows(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'EA'
+ assert expressions.skim_time_period_label(8) == 'AM'
+ assert expressions.skim_time_period_label(12) == 'MD'
+ assert expressions.skim_time_period_label(18) == 'PM'
+ assert expressions.skim_time_period_label(23) == 'EV'
+
+ pd.testing.assert_series_equal(
+ expressions.skim_time_period_label(pd.Series([1, 8, 12, 18, 23])),
+ pd.Series(['EA', 'AM', 'MD', 'PM', 'EV']))
+
+
+def test_1_week_time_window():
+ settings = {
+ 'skim_time_periods': {
+ 'time_window': 10080, # One Week
+ 'period_minutes': 1440, # One Day
+ 'periods': [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7
+ ],
+ 'labels': ['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']
+ }
+ }
+
+ inject.add_injectable("settings", settings)
+
+ assert expressions.skim_time_period_label(1) == 'Sunday'
+ assert expressions.skim_time_period_label(2) == 'Monday'
+ assert expressions.skim_time_period_label(3) == 'Tuesday'
+ assert expressions.skim_time_period_label(4) == 'Wednesday'
+ assert expressions.skim_time_period_label(5) == 'Thursday'
+ assert expressions.skim_time_period_label(6) == 'Friday'
+ assert expressions.skim_time_period_label(7) == 'Saturday'
+
+ weekly_series = expressions.skim_time_period_label(pd.Series([1, 2, 3, 4, 5, 6, 7]))
+
+ pd.testing.assert_series_equal(weekly_series,
+ pd.Series(['Sunday', 'Monday', 'Tuesday', 'Wednesday',
+ 'Thursday', 'Friday', 'Saturday']))
+
+
+def test_future_warning(config_path):
+ with open(os.path.join(config_path, 'settings_60_min.yaml')) as f:
+ settings = yaml.load(f, Loader=yaml.SafeLoader)
+
+ settings['skim_time_periods']['hours'] = settings['skim_time_periods'].pop('periods')
+
+ inject.add_injectable("settings", settings)
+
+ with pytest.warns(FutureWarning) as warning_test:
+ expressions.skim_time_period_label(1)

docs/abmexample.rst

@@ -311,6 +311,12 @@ is the main settings file for the model run. This file includes:
  * ``urban_threshold`` - urban threshold area type max value
  * ``county_map`` - mapping of county codes to county names
  * ``skim_time_periods`` - time period upper bound values and labels
+ * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours))
+ * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole 
+ factor of ``time_window``. (Default: 60 minutes)
+ * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment
+ * ``labels`` - Labels to define names for aggregate periods for skims and assignment
+
  * ``household_median_value_of_time`` - various household and person value-of-time model settings
 
 .. _sub-model-spec-files:

example/configs/settings.yaml

@@ -92,7 +92,8 @@ rural_threshold: 6
 #upperPM	Upper limit on time of day for the PM time peak period 19
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 6
  - 11

example_multiple_zone/configs/settings.yaml

@@ -12,7 +12,8 @@ households_sample_size: 1000
 
 
 skim_time_periods:
- hours:
+ period_minutes: 60
+ periods:
  - 0
  - 11
  - 16