feat(datatype): Add Data Types for Thermal Comfort

feat(datatype): Add Data Types for Thermal Comfort

ladybug/_datacollectionbase.py

@@ -30,7 +30,7 @@ def __init__(self, header, values, datetimes):
  assert isinstance(header, Header), \
  'header must be a Ladybug Header object. Got {}'.format(type(header))
  assert isinstance(datetimes, Iterable) \
- and not isinstance(datetimes, (str, dict)), \
+ and not isinstance(datetimes, (str, dict, bytes, bytearray)), \
  'datetimes should be a list or tuple. Got {}'.format(type(datetimes))
  datetimes = list(datetimes)
 
@@ -76,7 +76,8 @@ def values(self):
 
  @values.setter
  def values(self, values):
- assert isinstance(values, Iterable) and not isinstance(values, (str, dict)), \
+ assert isinstance(values, Iterable) and not \
+ isinstance(values, (str, dict, bytes, bytearray)), \
  'values should be a list or tuple. Got {}'.format(type(values))
  values = list(values)
  assert len(values) == len(self.datetimes), \
@@ -289,7 +290,8 @@ def get_aligned_collection(self, value=0, data_type=None, unit=None):
  number of values and have matching datetimes.
 
  Args:
- value: The value to be repeated in the aliged collection values.
+ value: A value to be repeated in the aliged collection values or
+ A list of values that has the same length as this collection.
  Default: 0.
  data_type: The data type of the aligned collection. Default is to
  use the data type of this collection.
@@ -305,7 +307,14 @@ def get_aligned_collection(self, value=0, data_type=None, unit=None):
  else:
  data_type = self.header.data_type
  unit = unit or self.header.unit
- values = [value] * len(self._values)
+ if isinstance(value, Iterable) and not isinstance(
+ value, (str, dict, bytes, bytearray)):
+ assert len(value) == len(self._values), "Length of value ({}) must match "\
+ "the length of this collection's values ({})".format(
+ len(value), len(self._values))
+ values = value
+ else:
+ values = [value] * len(self._values)
  header = Header(data_type, unit, self.header.analysis_period,
  self.header.metadata)
  collection = self.__class__(header, values, self.datetimes)
@@ -407,6 +416,65 @@ def are_collections_aligned(data_collections, raise_exception=True):
  return False
  return True
 
+ @staticmethod
+ def compute_function_aligned(funct, data_collections, data_type, unit):
+ """Compute a function with a list of aligned data collections or individual values.
+
+ Args:
+ funct: A function with a single numerical value as output and one or
+ more numerical values as input.
+ data_collections: A list with a length equal to the number of arguments
+ for the function. Items of the list can be either Data Collections
+ or individual values to be used at each datetime of other collections.
+ data_type: An instance of a Ladybug data type that describes the results
+ of the funct.
+ unit: The units of the funct results.
+
+ Return:
+ A Data Collection with the results function. If all items in this list of
+ data_collections are individual values, only a single value will be returned.
+
+ Usage:
+ from ladybug.datacollection import HourlyContinuousCollection
+ from ladybug.epw import EPW
+ from ladybug.psychrometrics import humid_ratio_from_db_rh
+ from ladybug.datatype.percentage import HumidityRatio
+
+ epw_file_path = './epws/denver.epw'
+ denver_epw = EPW(epw_file_path)
+ pressure_at_denver = 85000
+ hr_inputs = [denver_epw.dry_bulb_temperature,
+ denver_epw.relative_humidity,
+ pressure_at_denver]
+ humid_ratio = HourlyContinuousCollection.compute_function_aligned(
+ humid_ratio_from_db_rh, hr_inputs, HumidityRatio(), 'fraction')
+ # humid_ratio will be a Data Colleciton of humidity ratios at Denver
+ """
+ # check that all inputs are either data collections or floats
+ data_colls = []
+ for i, func_input in enumerate(data_collections):
+ if isinstance(func_input, BaseCollection):
+ data_colls.append(func_input)
+ else:
+ try:
+ data_collections[i] = float(func_input)
+ except ValueError:
+ raise TypeError('Expected a number or a Data Colleciton. '
+ 'Got {}'.format(type(func_input)))
+
+ # run the function and return the result
+ if len(data_colls) == 0:
+ return funct(*data_collections)
+ else:
+ BaseCollection.are_collections_aligned(data_colls)
+ val_len = len(data_colls[0].values)
+ for i, col in enumerate(data_collections):
+ data_collections[i] = [col] * val_len if isinstance(col, float) else col
+ result = data_colls[0].get_aligned_collection(data_type=data_type, unit=unit)
+ for i in xrange(val_len):
+ result[i] = funct(*[col[i] for col in data_collections])
+ return result
+
  def is_in_data_type_range(self, raise_exception=True):
  """Check if the Data Collection values are in permissable ranges for the data_type.
 

ladybug/datacollection.py

@@ -61,7 +61,7 @@ def __init__(self, header, values, datetimes):
  assert isinstance(header.analysis_period, AnalysisPeriod), \
  'header of {} must have an analysis_period.'.format(self.__class__.__name__)
  assert isinstance(datetimes, Iterable) \
- and not isinstance(datetimes, (str, dict)), \
+ and not isinstance(datetimes, (str, dict, bytes, bytearray)), \
  'datetimes should be a list or tuple. Got {}'.format(type(datetimes))
  datetimes = list(datetimes)
 
@@ -611,7 +611,8 @@ def values(self):
 
  @values.setter
  def values(self, values):
- assert isinstance(values, Iterable) and not isinstance(values, (str, dict)), \
+ assert isinstance(values, Iterable) and not isinstance(
+ values, (str, dict, bytes, bytearray)), \
  'values should be a list or tuple. Got {}'.format(type(values))
  values = list(values)
  if self.header.analysis_period.is_annual:
@@ -867,7 +868,8 @@ def get_aligned_collection(self, value=0, data_type=None, unit=None):
  have the same number of values and have matching datetimes.
 
  Args:
- value: The value to be repeated in the aliged collection values.
+ value: A value to be repeated in the aliged collection values or
+ A list of values that has the same length as this collection.
  Default: 0.
  data_type: The data type of the aligned collection. Default is to
  use the data type of this collection.
@@ -883,7 +885,14 @@ def get_aligned_collection(self, value=0, data_type=None, unit=None):
  else:
  data_type = self.header.data_type
  unit = unit or self.header.unit
- values = [value] * len(self._values)
+ if isinstance(value, Iterable) and not isinstance(
+ value, (str, dict, bytes, bytearray)):
+ assert len(value) == len(self._values), "Length of value ({}) must match "\
+ "the length of this collection's values ({})".format(
+ len(value), len(self._values))
+ values = value
+ else:
+ values = [value] * len(self._values)
  header = Header(data_type, unit, self.header.analysis_period,
  self.header.metadata)
  return self.__class__(header, values)
@@ -991,7 +1000,7 @@ def __init__(self, header, values, datetimes):
  assert isinstance(header.analysis_period, AnalysisPeriod), \
  'header of {} must have an analysis_period.'.format(self.__class__.__name__)
  assert isinstance(datetimes, Iterable) \
- and not isinstance(datetimes, (str, dict)), \
+ and not isinstance(datetimes, (str, dict, bytes, bytearray)), \
  'datetimes should be a list or tuple. Got {}'.format(type(datetimes))
  datetimes = list(datetimes)
 
@@ -1133,7 +1142,7 @@ def __init__(self, header, values, datetimes):
  assert isinstance(header.analysis_period, AnalysisPeriod), \
  'header of {} must have an analysis_period.'.format(self.__class__.__name__)
  assert isinstance(datetimes, Iterable) \
- and not isinstance(datetimes, (str, dict)), \
+ and not isinstance(datetimes, (str, dict, bytes, bytearray)), \
  'datetimes should be a list or tuple. Got {}'.format(type(datetimes))
  datetimes = list(datetimes)
 
@@ -1255,7 +1264,7 @@ def __init__(self, header, values, datetimes):
  assert isinstance(header.analysis_period, AnalysisPeriod), \
  'header of {} must have an analysis_period.'.format(self.__class__.__name__)
  assert isinstance(datetimes, Iterable) \
- and not isinstance(datetimes, (str, dict)), \
+ and not isinstance(datetimes, (str, dict, bytes, bytearray)), \
  'datetimes should be a list or tuple. Got {}'.format(type(datetimes))
  datetimes = list(datetimes)
 

ladybug/datatype/percentage.py

@@ -75,6 +75,12 @@ class RelativeHumidity(Percentage):
  _missing_epw = 999
 
 
+class HumidityRatio(Percentage):
+ _min = 0
+ _max = 100
+ _abbreviation = 'HR'
+
+
 class TotalSkyCover(Percentage):
  # (used if Horizontal IR Intensity missing)
  _min = 0

ladybug/datatype/temperature.py

@@ -93,3 +93,7 @@ class StandardEffectiveTemperature(Temperature):
 
 class UniversalThermalClimateIndex(Temperature):
  _abbreviation = 'UTCI'
+
+
+class PrevailingOutdoorTemperature(Temperature):
+ _abbreviation = 'Tprevail'

ladybug/datatype/temperaturedelta.py

@@ -0,0 +1,60 @@
+# coding=utf-8
+"""Temperature Delta data type."""
+from __future__ import division
+
+from .base import DataTypeBase
+
+
+class TemperatureDelta(DataTypeBase):
+ """Temperature"""
+ _units = ('C', 'F', 'K')
+ _si_units = ('C', 'K')
+ _ip_units = ('F')
+ _abbreviation = 'DeltaT'
+
+ def _C_to_F(self, value):
+ return value * 9. / 5.
+
+ def _C_to_K(self, value):
+ return value
+
+ def _F_to_C(self, value):
+ return value * (5. / 9.)
+
+ def _K_to_C(self, value):
+ return value
+
+ def to_unit(self, values, unit, from_unit):
+ """Return values converted to the unit given the input from_unit."""
+ return self._to_unit_base('C', values, unit, from_unit)
+
+ def to_ip(self, values, from_unit):
+ """Return values in IP and the units to which the values have been converted."""
+ if from_unit == 'F':
+ return values, from_unit
+ else:
+ return self.to_unit(values, 'F', from_unit), 'F'
+
+ def to_si(self, values, from_unit):
+ """Return values in SI and the units to which the values have been converted."""
+ if from_unit == 'C' or from_unit == 'K':
+ return values, from_unit
+ else:
+ return self.to_unit(values, 'C', from_unit), 'C'
+
+ @property
+ def isTemperatureDelta(self):
+ """Return True."""
+ return True
+
+
+class AirTemperatureDelta(TemperatureDelta):
+ _abbreviation = 'DeltaTair'
+
+
+class RadiantTemperatureDelta(TemperatureDelta):
+ _abbreviation = 'DeltaTrad'
+
+
+class OperativeTemperatureDelta(TemperatureDelta):
+ _abbreviation = 'DeltaTo'

ladybug/datatype/thermalcondition.py

@@ -44,10 +44,56 @@ class PredictedMeanVote(ThermalCondition):
  '+1 = Slightly Warm, +2 = Warm, +3 = Hot'
 
 
-class UTCICondition(ThermalCondition):
- _abbreviation = 'UTCIcond'
- _unit_descr = '-4 = Extreme Cold, -3 = Very Strong Cold, '\
+class DiscomfortReason(ThermalCondition):
+ _abbreviation = 'RDiscomf'
+ _unit_descr = '-2 = Too Dry, -1 = Too Cold, \n' \
+ '0 = Comfortable, \n' \
+ '+1 = Too Hot, +2 = Too Humid'
+
+
+class ThermalConditionFivePoint(ThermalCondition):
+ _abbreviation = 'Tcond-5'
+ _unit_descr = '-2 = Strong/Extreme Cold, -1 = Moderate Cold, \n' \
+ '0 = No Thermal Stress, \n' \
+ '+1 = Moderate Heat, +2 = Strong/Extreme Heat'
+
+
+class ThermalConditionSevenPoint(ThermalCondition):
+ _abbreviation = 'Tcond-7'
+ _unit_descr = '-3 = Very Strong/Extreme Cold, ' \
  '-2 = Strong Cold, -1 = Moderate Cold, \n' \
  '0 = No Thermal Stress, \n' \
- '+1 = Moderate Heat, +2 = Strong Heat, '\
- '+3 = Very Strong Heat, +4 = Extreme Heat'
+ '+1 = Moderate Heat, +2 = Strong Heat, ' \
+ '+3 = Very Strong/Extreme Heat'
+
+
+class ThermalConditionNinePoint(ThermalCondition):
+ _abbreviation = 'Tcond-9'
+ _unit_descr = '-4 = Very Strong/Extreme Cold, ' \
+ '-3 = Strong Cold, -2 = Moderate Cold, -1 = Slight Cold, \n' \
+ '0 = No Thermal Stress, \n' \
+ '+1 = Slight Heat, +2 = Moderate Heat, +3 = Strong Heat, '\
+ '+4 = Very Strong/Extreme Heat'
+
+
+class ThermalConditionElevenPoint(ThermalCondition):
+ _abbreviation = 'Tcond-11'
+ _unit_descr = '-5 = Extreme Cold, -4 = Very Strong Cold, ' \
+ '-3 = Strong Cold, -2 = Moderate Cold, -1 = Slight Cold, \n' \
+ '0 = No Thermal Stress, \n' \
+ '+1 = Slight Heat, +2 = Moderate Heat, +3 = Strong Heat, ' \
+ '+4 = Very Strong Heat, +5 = Extreme Heat'
+
+
+class UTCICategory(ThermalCondition):
+ _abbreviation = 'UTCIcond'
+ _unit_descr = '0 = extreme cold stress' \
+ '1 = very strong cold stress' \
+ '2 = strong cold stress' \
+ '3 = moderate cold stress' \
+ '4 = slight cold stress' \
+ '5 = no thermal stress' \
+ '6 = moderate heat stress' \
+ '7 = strong heat stress' \
+ '8 = strong heat stress' \
+ '9 = extreme heat stress'

ladybug/psychrometrics.py

@@ -5,14 +5,6 @@
 import math
 
 
-def saturated_vapor_pressure_torr(db_temp):
- """Saturated Vapor Pressure (Torr) at temperature (C)
-
- Used frequently throughtout PMV comfort functions.
- """
- return math.exp(18.6686 - 4030.183 / (db_temp + 235.0))
-
-
 def saturated_vapor_pressure(t_kelvin):
  """Saturated Vapor Pressure (Pa) at t_kelvin (K).
 

tests/datacollection_test.py

@@ -10,7 +10,10 @@
 from ladybug.dt import DateTime
 from ladybug.datatype.generic import GenericType
 from ladybug.datatype.temperature import Temperature
-from ladybug.datatype.percentage import RelativeHumidity
+from ladybug.datatype.percentage import RelativeHumidity, HumidityRatio
+
+from ladybug.epw import EPW
+from ladybug.psychrometrics import humid_ratio_from_db_rh
 
 import unittest
 import pytest
@@ -982,6 +985,29 @@ def test_get_aligned_collection_continuous(self):
  assert dc3.header.data_type.name == 'Relative Humidity'
  assert dc3.header.unit == '%'
 
+ def test_compute_function_aligned(self):
+ """Test the method for computing funtions with aligned collections."""
+ epw_file_path = './tests/epw/chicago.epw'
+ chicago_epw = EPW(epw_file_path)
+ pressure_at_chicago = 95000
+ hr_inputs = [chicago_epw.dry_bulb_temperature,
+ chicago_epw.relative_humidity,
+ pressure_at_chicago]
+ humid_ratio = HourlyContinuousCollection.compute_function_aligned(
+ humid_ratio_from_db_rh, hr_inputs, HumidityRatio(), 'fraction')
+ assert isinstance(humid_ratio, HourlyContinuousCollection)
+ assert len(humid_ratio.values) == 8760
+ for i, val in enumerate(humid_ratio.values):
+ assert val == humid_ratio_from_db_rh(chicago_epw.dry_bulb_temperature[i],
+ chicago_epw.relative_humidity[i],
+ pressure_at_chicago)
+
+ hr_inputs = [20, 70, pressure_at_chicago]
+ humid_ratio = HourlyContinuousCollection.compute_function_aligned(
+ humid_ratio_from_db_rh, hr_inputs, HumidityRatio(), 'fraction')
+ assert isinstance(humid_ratio, float)
+ assert humid_ratio == humid_ratio_from_db_rh(20, 70, pressure_at_chicago)
+
  def test_duplicate(self):
  """Test the duplicate method on the discontinuous collections."""
  header = Header(Temperature(), 'C', AnalysisPeriod(end_month=1, end_day=1))