Added several plot methods and fixed a few minor bugs/warnings

LadybugTools_Engine/Python/src/ladybugtools_toolkit/external_comfort/utci.py

@@ -6,28 +6,28 @@
 from calendar import month_abbr
 from concurrent.futures import ProcessPoolExecutor
 
-# pylint: enable=E0401
-
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
 from ladybug.datacollection import HourlyContinuousCollection
-from ladybug.datatype.temperature import (
-    UniversalThermalClimateIndex as LB_UniversalThermalClimateIndex,
-)
+from ladybug.datatype.temperature import \
+    UniversalThermalClimateIndex as LB_UniversalThermalClimateIndex
 from ladybug.epw import EPW
 from ladybug_comfort.collection.solarcal import OutdoorSolarCal
 from ladybug_comfort.collection.utci import UTCI
+from python_toolkit.bhom.analytics import bhom_analytics
 from scipy.interpolate import interp1d, interp2d
 from tqdm import tqdm
 
-from python_toolkit.bhom.analytics import bhom_analytics
-from ..categorical.categories import UTCI_DEFAULT_CATEGORIES, CategoricalComfort
+from ..categorical.categories import (UTCI_DEFAULT_CATEGORIES,
+                                      CategoricalComfort)
 from ..helpers import evaporative_cooling_effect, month_hour_binned_series
-from ..ladybug_extension.datacollection import (
-    collection_from_series,
-    collection_to_series,
-)
+from ..ladybug_extension.datacollection import (collection_from_series,
+                                                collection_to_series)
+
+# pylint: enable=E0401
+
+
 
 
 def _saturated_vapor_pressure_hpa(dry_bulb_temperature: np.ndarray):

LadybugTools_Engine/Python/src/ladybugtools_toolkit/helpers.py

@@ -20,22 +20,19 @@
 from caseconverter import snakecase
 from honeybee.config import folders as hb_folders
 from ladybug.datatype.temperature import WetBulbTemperature
-from ladybug.epw import EPW, AnalysisPeriod, HourlyContinuousCollection, Location
+from ladybug.epw import (EPW, AnalysisPeriod, HourlyContinuousCollection,
+                         Location)
 from ladybug.psychrometrics import wet_bulb_from_db_rh
-from ladybug.skymodel import (
-    calc_horizontal_infrared,
-    calc_sky_temperature,
-    estimate_illuminance_from_irradiance,
-    get_extra_radiation,
-    zhang_huang_solar,
-    zhang_huang_solar_split,
-)
+from ladybug.skymodel import (calc_horizontal_infrared, calc_sky_temperature,
+                              estimate_illuminance_from_irradiance,
+                              get_extra_radiation, zhang_huang_solar,
+                              zhang_huang_solar_split)
 from ladybug.sunpath import Sunpath
 from ladybug_geometry.geometry2d import Vector2D
 from meteostat import Hourly, Point
-
 from python_toolkit.bhom.analytics import bhom_analytics
 from python_toolkit.bhom.logging import CONSOLE_LOGGER
+
 from .ladybug_extension.dt import lb_datetime_from_datetime
 
 # pylint: enable=E0401

LadybugTools_Engine/Python/src/ladybugtools_toolkit/ladybug_extension/epw.py

@@ -9,8 +9,6 @@
 from pathlib import Path
 from typing import Any
 
-# pylint: enable=E0401
-
 import numpy as np
 import pandas as pd
 from ladybug.analysisperiod import AnalysisPeriod
@@ -24,32 +22,28 @@
 from ladybug.epw import EPW, EPWFields, MonthlyCollection
 from ladybug.header import Header
 from ladybug.location import Location
-from ladybug.psychrometrics import (
-    dew_point_from_db_wb,
-    enthalpy_from_db_hr,
-    humid_ratio_from_db_rh,
-    rel_humid_from_db_wb,
-    wet_bulb_from_db_rh,
-)
+from ladybug.psychrometrics import (dew_point_from_db_wb, enthalpy_from_db_hr,
+                                    humid_ratio_from_db_rh,
+                                    rel_humid_from_db_wb, wet_bulb_from_db_rh)
 from ladybug.skymodel import clearness_index as lb_ci
 from ladybug.sunpath import Sun, Sunpath
 from ladybug_comfort.degreetime import cooling_degree_time, heating_degree_time
-
 from python_toolkit.bhom.analytics import bhom_analytics
-from ..helpers import (
-    air_pressure_at_height,
-    radiation_at_height,
-    temperature_at_height,
-    timedelta_tostring,
-    wind_speed_at_height,
-)
+
+from ..helpers import (air_pressure_at_height, radiation_at_height,
+                       temperature_at_height, timedelta_tostring,
+                       wind_speed_at_height)
 from .analysisperiod import analysis_period_to_datetimes
 from .datacollection import average as average_collection
 from .datacollection import collection_to_series
 from .groundtemperature import hourly_ground_temperature
 from .header import header_to_string
 from .location import average_location, location_to_string
 
+# pylint: enable=E0401
+
+
+
 
 @bhom_analytics()
 def epw_to_dataframe(
@@ -1054,7 +1048,7 @@ def seasonality_from_temperature_timeseries(
     series = series.loc[~series.index.duplicated()]
 
     # check that series is long enough
-    if max(series.index) - min(series.index) < pd.Timedelta(hours=364):
+    if max(series.index) - min(series.index) < pd.Timedelta(hours=364*24):
         raise ValueError(
             "Input dataset must be at least 365 days long to determine seasonality."
         )
@@ -1217,7 +1211,7 @@ def seasonality_from_temperature_timeseries(
             ).date()
 
     temp = dbt.to_frame()
-    s = pd.Series(index=dbt.index, data=np.nan)
+    s = pd.Series(index=dbt.index, data=np.nan, dtype=str)
     s.loc[spring_start] = "Spring"
     s.loc[summer_start] = "Summer"
     s.loc[autumn_start] = "Autumn"

LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_evaporative_cooling_potential.py

@@ -1,20 +1,25 @@
 """Method for plotting evaporative cooling potential."""
 
-import matplotlib.pyplot as plt
 from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
 from ladybug.epw import EPW
 
-from ._heatmap import heatmap
 from ..ladybug_extension.datacollection import collection_to_series
-from ..ladybug_extension.location import location_to_string
+from ._heatmap import heatmap
 
 
-def evaporative_cooling_potential(epw: EPW, ax: plt.Axes = None, **kwargs) -> plt.Axes:
+def evaporative_cooling_potential(
+    dbt: pd.Series, dpt: pd.Series, ax: plt.Axes = None, agg_year: bool = False, agg: str = "mean", **kwargs
+) -> plt.Axes:
     """Plot evaporative cooling potential (DBT - DPT).
 
     Args:
-        epw (EPW):
-            An EPW object.
+        dbt (pd.Series):
+            A pandas Series containing Dry Bulb Temperature data.
+        dpt (pd.Series):
+            A pandas Series containing Dew Point Temperature data.
         ax (plt.Axes, optional):
             The matplotlib axes to plot the figure on. Defaults to None.
         **kwargs:
@@ -24,27 +29,66 @@ def evaporative_cooling_potential(epw: EPW, ax: plt.Axes = None, **kwargs) -> pl
         plt.Axes: The matplotlib axes.
     """
 
+    if len(dbt) != len(dpt):
+        raise ValueError("The length of the two series must be the same.")
+
+    if any(dbt.index != dpt.index):
+        raise ValueError("The indices of the two series must be the same.")
+
     if ax is None:
         ax = plt.gca()
 
-    dpt = collection_to_series(epw.dew_point_temperature)
-    dbt = collection_to_series(epw.dry_bulb_temperature)
     ecp = (dbt - dpt).clip(lower=0).rename("Evaporative Cooling Potential (C)")
 
+    if agg_year:
+        # check for presence of Feb 29 in ecp index
+        if len(ecp[(ecp.index.month == 2) & (ecp.index.day == 29)]) != 0:
+            idx = pd.date_range(start="2016-01-01", periods=8784, freq="h")
+        else:
+            idx = pd.date_range(start="2017-01-01", periods=8760, freq="h")
+
+        ecp = ecp.groupby([ecp.index.month, ecp.index.day, ecp.index.hour]).agg(agg)
+        ecp.index = idx
+
     if "cmap" not in kwargs:
         kwargs["cmap"] = "GnBu"
 
     heatmap(series=ecp, ax=ax, **kwargs)
     ax.text(
         1,
         1,
-        "*value shown indicate cooling effect from saturating air with moisture (DBT - DPT)",
+        "*values shown indicate cooling effect from saturating air with moisture (DBT - DPT)",
         transform=ax.transAxes,
         ha="right",
         va="top",
         fontsize="small",
     )
 
+    return ax
+
+
+def evaporative_cooling_potential_epw(epw: EPW, ax: plt.Axes = None, **kwargs) -> plt.Axes:
+    """Plot evaporative cooling potential (DBT - DPT).
+
+    Args:
+        epw (EPW):
+            An EPW object.
+        ax (plt.Axes, optional):
+            The matplotlib axes to plot the figure on. Defaults to None.
+        **kwargs:
+            Additional keyword arguments to pass to the heatmap function.
+
+    Returns:
+        plt.Axes: The matplotlib axes.
+    """
+
+    if ax is None:
+        ax = plt.gca()
+
+    evaporative_cooling_potential(
+        dbt=collection_to_series(epw.dry_bulb_temperature), dpt=collection_to_series(epw.dew_point_temperature), ax=ax, **kwargs
+    )
+
     ax.set_title(Path(epw.file_path).name)
 
     return ax

LadybugTools_Engine/Python/src/ladybugtools_toolkit/plot/_misc.py

@@ -1,21 +1,20 @@
 """Miscellaneous plots that don't really fit anywhere else."""
+
 # pylint: disable=line-too-long
 from calendar import month_abbr  # pylint: disable=E0401
 
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mtick
-
 import numpy as np
 import pandas as pd
-
 from ladybug.epw import EPW, Location
 from ladybug.sunpath import Sunpath
 
-from ..ladybug_extension.datacollection import collection_to_series
+from ..categorical.categories import Categorical
 from ..helpers import sunrise_sunset
+from ..ladybug_extension.datacollection import collection_to_series
 from ._heatmap import heatmap
-from ..categorical.categories import Categorical
 
 
 def cloud_cover_categories(epw: EPW, ax: plt.Axes = None) -> plt.Axes:
@@ -49,9 +48,7 @@ def cloud_cover_categories(epw: EPW, ax: plt.Axes = None) -> plt.Axes:
         9: "overcast",
         10: "overcast",
     }
-    mtx = mtx.T.groupby(mapper).sum()[
-        ["clear", "mostly clear", "partly cloudy", "mostly cloudy", "overcast"]
-    ]
+    mtx = mtx.T.groupby(mapper).sum()[["clear", "mostly clear", "partly cloudy", "mostly cloudy", "overcast"]]
     mtx = (mtx.T / mtx.sum(axis=1)).T
 
     mtx.plot.area(ax=ax, color=["#95B5DF", "#B1C4DD", "#C9D0D9", "#ACB0B6", "#989CA1"])
@@ -95,37 +92,20 @@ def hours_sunlight(location: Location, ax: plt.Axes = None) -> plt.Axes:
         index=srss_df.index,
     )
     civil_twilight = (
-        [
-            i.seconds / (60 * 60)
-            for i in (srss_df["civil twilight end"] - srss_df["civil twilight start"])
-        ]
-        - daylight
+        [i.seconds / (60 * 60) for i in (srss_df["civil twilight end"] - srss_df["civil twilight start"])] - daylight
     ).rename("civil twilight")
     nautical_twilight = (
-        [
-            i.seconds / (60 * 60)
-            for i in (
-                srss_df["nautical twilight end"] - srss_df["nautical twilight start"]
-            )
-        ]
+        [i.seconds / (60 * 60) for i in (srss_df["nautical twilight end"] - srss_df["nautical twilight start"])]
         - daylight
         - civil_twilight
     ).rename("nautical twilight")
     astronomical_twilight = (
-        [
-            i.seconds / (60 * 60)
-            for i in (
-                srss_df["astronomical twilight end"]
-                - srss_df["astronomical twilight start"]
-            )
-        ]
+        [i.seconds / (60 * 60) for i in (srss_df["astronomical twilight end"] - srss_df["astronomical twilight start"])]
         - daylight
         - civil_twilight
         - nautical_twilight
     ).rename("astronomical twilight")
-    night = (
-        24 - (daylight + civil_twilight + nautical_twilight + astronomical_twilight)
-    ).rename("night")
+    night = (24 - (daylight + civil_twilight + nautical_twilight + astronomical_twilight)).rename("night")
 
     temp = pd.concat(
         [daylight, civil_twilight, nautical_twilight, astronomical_twilight, night],
@@ -223,37 +203,20 @@ def hours_sunrise_sunset(location: Location, ax: plt.Axes = None) -> plt.Axes:
         index=srss_df.index,
     )
     civil_twilight = (
-        [
-            i.seconds / (60 * 60)
-            for i in (srss_df["civil twilight end"] - srss_df["civil twilight start"])
-        ]
-        - daylight
+        [i.seconds / (60 * 60) for i in (srss_df["civil twilight end"] - srss_df["civil twilight start"])] - daylight
     ).rename("civil twilight")
     nautical_twilight = (
-        [
-            i.seconds / (60 * 60)
-            for i in (
-                srss_df["nautical twilight end"] - srss_df["nautical twilight start"]
-            )
-        ]
+        [i.seconds / (60 * 60) for i in (srss_df["nautical twilight end"] - srss_df["nautical twilight start"])]
         - daylight
         - civil_twilight
     ).rename("nautical twilight")
     astronomical_twilight = (
-        [
-            i.seconds / (60 * 60)
-            for i in (
-                srss_df["astronomical twilight end"]
-                - srss_df["astronomical twilight start"]
-            )
-        ]
+        [i.seconds / (60 * 60) for i in (srss_df["astronomical twilight end"] - srss_df["astronomical twilight start"])]
         - daylight
         - civil_twilight
         - nautical_twilight
     ).rename("astronomical twilight")
-    night = (
-        24 - (daylight + civil_twilight + nautical_twilight + astronomical_twilight)
-    ).rename("night")
+    night = (24 - (daylight + civil_twilight + nautical_twilight + astronomical_twilight)).rename("night")
 
     temp = pd.concat(
         [daylight, civil_twilight, nautical_twilight, astronomical_twilight, night],
@@ -289,9 +252,7 @@ def hours_sunrise_sunset(location: Location, ax: plt.Axes = None) -> plt.Axes:
         fc="#B9AC86",
         label="Civil twilight",
     )
-    ax.fill_between(
-        hours.index, hours["sunrise"], hours["sunset"], fc="#FCE49D", label="Day"
-    )
+    ax.fill_between(hours.index, hours["sunrise"], hours["sunset"], fc="#FCE49D", label="Day")
     ax.fill_between(
         hours.index,
         hours["sunset"],