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last fixes to a few methods
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@tg359 @jamesramsden-bh
tg359 authored and jamesramsden-bh committed Aug 7, 2023
1 parent dd6dc34 commit 941bc69
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15 changes: 9 additions & 6 deletions LadybugTools_Engine/Python/src/ladybugtools_toolkit/categorical/categorical.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -41,22 +41,25 @@ class Categorical:
name: str = field(default="GenericCategories")

def __post_init__(self):
# ensure colors are valid
if len(self.colors) == 0:
self.colors = tuple(
plt.rcParams["axes.prop_cycle"].by_key()["color"][: len(self.bins) - 1]
)

cycle = tuple(plt.rcParams["axes.prop_cycle"].by_key()["color"])
while len(cycle) < len(self.bins):
cycle += cycle
self.colors = cycle[: len(self.bins) - 1]
for color in self.colors:
if not is_color_like(color):
raise ValueError(f"{color} is not a valid color.")
self.colors = tuple([to_hex(i, keep_alpha=True) for i in self.colors])
self.colors = tuple(to_hex(i, keep_alpha=True) for i in self.colors)

# ensure bin names are valid
if len(self.bin_names) == 0:
self.bin_names = [str(i) for i in self.interval_index]

# ensure the number of bins, colors and bin names are consistent
if len(set([len(self.bin_names), len(self.colors), (len(self.bins) - 1)])) > 1:
raise ValueError(
f"The number of colors ({len(self.colors)}) and bin names ({len(self.bin_names)}) must be one less than the number of bins ({len(self.bins)} - 1)."
f"The number of colors ({len(self.colors)}) and bin names ({len(self.bin_names)}) must be one less than the number of bins ({len(self.bins) - 1})."
)

def __repr__(self):
Expand Down
238 changes: 43 additions & 195 deletions LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/external_comfort.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,14 @@
from __future__ import annotations

import calendar
import json
import textwrap
from dataclasses import dataclass, field
from types import FunctionType
from typing import Any, Dict, List, Tuple, Union
from typing import Any, Dict, Tuple, Union

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ladybug.epw import HourlyContinuousCollection
from ladybug_comfort.collection.utci import UTCI
from ladybug_geometry.geometry3d.pointvector import Point3D
from matplotlib.figure import Figure

Expand All @@ -24,16 +21,10 @@
inf_str_to_inf_dtype,
pascalcase,
)
from ..helpers import evaporative_cooling_effect, wind_speed_at_height
from ..ladybug_extension.analysis_period import (
AnalysisPeriod,
analysis_period_to_boolean,
describe_analysis_period,
)
from ..ladybug_extension.datacollection import (
collection_from_series,
collection_to_series,
)
from ..categorical.categories import UTCI_DEFAULT_CATEGORIES, Categorical
from ..helpers import wind_speed_at_height
from ..ladybug_extension.analysis_period import AnalysisPeriod, describe_analysis_period
from ..ladybug_extension.datacollection import collection_to_series
from ..ladybug_extension.epw import epw_to_dataframe
from ..ladybug_extension.location import location_to_string
from ..plot import heatmap
Expand All @@ -48,7 +39,6 @@
from .shelter import Shelter
from .simulate import SimulationResult
from .typology import Typology
from .utci import UTCI_DEFAULT_CATEGORIES, Categorical
from .utci.calculate import utci


Expand Down Expand Up @@ -330,178 +320,6 @@ def to_dataframe(

return df

def feasible_utci_limits(
self, include_additional_moisture: bool = True, as_dataframe: bool = False
) -> Union[pd.DataFrame, List[HourlyContinuousCollection]]:
"""Calculate the absolute min/max collections of UTCI based on possible shade, wind and moisture adjustments to a pre-computed ExternalComfort condition.
Args:
include_additional_moisture (bool):
Include the effect of evaporative cooling on the UTCI limits.
as_dataframe (bool):
Return the output as a dataframe with two columns, instread of two separate collections.
Returns:
List[HourlyContinuousCollection]: The lowest UTCI and highest UTCI temperatures for each hour of the year.
"""

epw = self.SimulationResult.epw

dbt_evap, rh_evap = np.array(
[
evaporative_cooling_effect(
dry_bulb_temperature=_dbt,
relative_humidity=_rh,
evaporative_cooling_effectiveness=0.5,
atmospheric_pressure=_atm,
)
for _dbt, _rh, _atm in list(
zip(
*[
epw.dry_bulb_temperature,
epw.relative_humidity,
epw.atmospheric_station_pressure,
]
)
)
]
).T
dbt_evap = epw.dry_bulb_temperature.get_aligned_collection(dbt_evap)
rh_evap = epw.relative_humidity.get_aligned_collection(rh_evap)

dbt_rh_options = (
[[dbt_evap, rh_evap], [epw.dry_bulb_temperature, epw.relative_humidity]]
if include_additional_moisture
else [[epw.dry_bulb_temperature, epw.relative_humidity]]
)

utcis = []
for _dbt, _rh in dbt_rh_options:
for _ws in [
self.WindSpeed,
self.WindSpeed.get_aligned_collection(0),
self.WindSpeed * 1.1,
]:
for _mrt in [
self.DryBulbTemperature,
self.MeanRadiantTemperature,
]:
utcis.append(
UTCI(
air_temperature=_dbt,
rad_temperature=_mrt,
rel_humidity=_rh,
wind_speed=_ws,
).universal_thermal_climate_index,
)
df = pd.concat([collection_to_series(i) for i in utcis], axis=1)
min_utci = collection_from_series(
df.min(axis=1).rename("Universal Thermal Climate Index (C)")
)
max_utci = collection_from_series(
df.max(axis=1).rename("Universal Thermal Climate Index (C)")
)

if as_dataframe:
return pd.concat(
[
collection_to_series(min_utci),
collection_to_series(max_utci),
],
axis=1,
keys=["lowest", "highest"],
)

return min_utci, max_utci

def feasible_comfort_category(
self,
include_additional_moisture: bool = True,
analysis_periods: Tuple[AnalysisPeriod] = (AnalysisPeriod()),
simplified: bool = False,
comfort_limits: Tuple = (9, 26),
density: bool = True,
) -> pd.DataFrame:
"""Calculate the feasible comfort categories for each hour of the year.
Args:
include_additional_moisture (bool):
Include the effect of evaporative cooling on the UTCI limits.
analysis_periods (Tuple[AnalysisPeriod]):
A tuple of analysis periods to filter the results by.
simplified (bool):
Set to True to use the simplified comfort categories.
comfort_limits (Tuple):
A tuple of the lower and upper comfort limits.
density (bool):
Set to True to return the density of the comfort category.
Returns:
pd.DataFrame: A dataframe with the comfort categories for each hour of the year.
"""

try:
iter(analysis_periods)
if not all(isinstance(ap, AnalysisPeriod) for ap in analysis_periods):
raise TypeError(
"analysis_periods must be an iterable of AnalysisPeriods"
)
except TypeError as exc:
raise TypeError(
"analysis_periods must be an iterable of AnalysisPeriods"
) from exc

for ap in analysis_periods:
if (ap.st_month != 1) or (ap.end_month != 12):
raise ValueError("Analysis periods must be for the whole year.")

_df = self.feasible_utci_limits(
as_dataframe=True, include_additional_moisture=include_additional_moisture
)

# filter by hours
hours = analysis_period_to_boolean(analysis_periods)
_df_filtered = _df.loc[hours]

cats, _ = utci_comfort_categories(
simplified=simplified,
comfort_limits=comfort_limits,
)

# categorise
_df_cat = categorise(
_df_filtered, simplified=simplified, comfort_limits=comfort_limits
)

# join categories and get low/high lims
temp = pd.concat(
[
_df_cat.groupby(_df_cat.index.month)
.lowest.value_counts(normalize=density)
.unstack()
.reindex(cats, axis=1)
.fillna(0),
_df_cat.groupby(_df_cat.index.month)
.highest.value_counts(normalize=density)
.unstack()
.reindex(cats, axis=1)
.fillna(0),
],
axis=1,
)
columns = pd.MultiIndex.from_product([cats, ["lowest", "highest"]])
temp = pd.concat(
[
temp.groupby(temp.columns, axis=1).min(),
temp.groupby(temp.columns, axis=1).max(),
],
axis=1,
keys=["lowest", "highest"],
).reorder_levels(order=[1, 0], axis=1)[columns]
temp.index = [calendar.month_abbr[i] for i in temp.index]

return temp

def add_insitu_comfort_measures(
self,
overhead_shelter: bool = False,
Expand Down Expand Up @@ -702,7 +520,15 @@ def add_insitu_comfort_measures(
return ec

def plot_title_string(self, analysis_period: AnalysisPeriod = None) -> str:
"""Return the description of this result suitable for use in plotting titles."""
"""Return the description of this result suitable for use in plotting titles.
Args:
analysis_period (AnalysisPeriod, optional): The analysis period to filter the results by. Defaults to None.
Returns:
str: A string describing this result.
"""
typ_str = self.Typology.Name

if self.Typology.sky_exposure() == 1:
Expand Down Expand Up @@ -740,35 +566,54 @@ def plot_utci_day_comfort_metrics(
title=self.plot_title_string(),
)

def plot_utci_heatmap(self, ax: plt.Axes = None) -> plt.Axes:
def plot_utci_heatmap(
self,
ax: plt.Axes = None,
utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
) -> plt.Axes:
"""Create a heatmap showing the annual hourly UTCI values associated with this Typology.
Args:
ax (plt.Axes, optional): A matplotlib Axes object to plot on. Defaults to None.
utci_categories (Categorical, optional): The UTCI categories to use. Defaults to
UTCI_DEFAULT_CATEGORIES.
Returns:
Figure: A matplotlib Figure object.
plt.Axes: A matplotlib Axes object.
"""

return utci_heatmap(
utci_collection=self.UniversalThermalClimateIndex,
ax=ax,
utci_categories=utci_categories,
title=self.plot_title_string(),
)

def plot_utci_heatmap_histogram(self, **kwargs) -> plt.Figure:
def plot_utci_heatmap_histogram(
self, utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES, **kwargs
) -> plt.Figure:
"""Create a heatmap showing the annual hourly UTCI values associated with this Typology.
Args:
utci_categories (Categorical, optional): The UTCI categories to use. Defaults to
UTCI_DEFAULT_CATEGORIES.
**kwargs:
Additional keyword arguments to pass to the heatmap function.
Returns:
Figure: A matplotlib Figure object.
"""

return utci_heatmap_histogram(
utci_collection=self.UniversalThermalClimateIndex,
utci_categories=utci_categories,
title=self.plot_title_string(),
**kwargs,
)

def plot_utci_histogram(
self,
ax: plt.Axes = None,
utci_categories: Categorical = UTCI_DEFAULT_CATEGORIES,
analysis_period: AnalysisPeriod = AnalysisPeriod(),
**kwargs,
) -> plt.Axes:
Expand All @@ -777,6 +622,8 @@ def plot_utci_histogram(
Args:
ax (plt.Axes, optional):
A matplotlib Axes object to plot on. Defaults to None.
utci_categories (Categorical, optional):
The UTCI categories to use. Defaults to UTCI_DEFAULT_CATEGORIES.
analysis_period (AnalysisPeriod, optional):
The analysis period to filter the results by. Defaults to AnalysisPeriod().
**kwargs:
Expand All @@ -791,6 +638,7 @@ def plot_utci_histogram(
analysis_period
),
ax=ax,
utci_categories=utci_categories,
title=self.plot_title_string(analysis_period=analysis_period),
**kwargs,
)
Expand All @@ -799,8 +647,8 @@ def plot_utci_distance_to_comfortable(
self,
ax: plt.Axes = None,
comfort_thresholds: Tuple[float] = (9, 26),
vmin: float = 15,
vmax: float = 25,
vmin: float = -10,
vmax: float = 10,
) -> Figure:
"""Create a heatmap showing the "distance" in C from the "no thermal stress" UTCI comfort
band.
Expand All @@ -810,9 +658,9 @@ def plot_utci_distance_to_comfortable(
comfort_thresholds (List[float], optional): The comfortable band of UTCI temperatures.
Defaults to [9, 26].
vmin (float, optional): The distance from the lower edge of the comfort threshold
to include in the "too cold" part of the heatmap. Defaults to 15.
to include in the "too cold" part of the heatmap. Defaults to -10.
vmax (float, optional): The distance from the upper edge of the comfort threshold
to include in the "too hot" part of the heatmap. Defaults to 25.
to include in the "too hot" part of the heatmap. Defaults to 10.
Returns:
Figure: A matplotlib Figure object.
Expand Down
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