lint: linted JOS3 model code

CenterForTheBuiltEnvironment · May 2, 2023 · ed25119 · ed25119
1 parent c847e76
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diff --git a/LICENSE b/LICENSE
@@ -1,9 +1,9 @@
 MIT License
 
-Copyright (c) 2019-2020, Federico Tartarini
+Copyright (c) 2019 Federico Tartarini
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice (including the next paragraph) shall be included in all copies or substantial portions of the Software.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/examples/calc_jos3.py b/examples/calc_jos3.py
@@ -21,7 +21,7 @@
 model = JOS3(height=1.7, weight=60, age=30)
 
 # Set the first phase
-model.to = 28  # Operative temperature [oC]
+model.to = 28  # Operative temperature [°C]
 model.rh = 40  # Relative humidity [%]
 model.v = 0.2  # Air velocity [m/s]
 model.par = 1.2  # Physical activity ratio [-]
@@ -34,7 +34,7 @@
 # Show the results
 df = pd.DataFrame(model.dict_results())  # Make pandas.DataFrame
 df.t_skin_mean.plot()  # Plot time series of mean skin temperature.
-plt.ylabel("Mean skin temperature [oC]")  # Set y-label as 'Mean skin temperature [oC]'
+plt.ylabel("Mean skin temperature [°C]")  # Set y-label as 'Mean skin temperature [°C]'
 plt.xlabel("Time [min]")  # Set x-label as 'Time [min]'
 plt.savefig(
     os.path.join(JOS3_EXAMPLE_DIRECTORY, "jos3_example1_mean_skin_temperature.png")
@@ -45,8 +45,8 @@
 model.to_csv(os.path.join(JOS3_EXAMPLE_DIRECTORY, "jos3_example1 (default output).csv"))
 
 # Print the BMR value using the getter
-print('BMR=', model.BMR)
-print('Body name list: ', model.bodynames)
+print("BMR=", model.BMR)
+print("Body name list: ", model.body_names)
 
 # -------------------------------------------
 # EXAMPLE 2 (detail simulation)
@@ -68,10 +68,10 @@
 # Set environmental conditions such as air temperature, mean radiant temperature using the setter methods.
 # Set the first condition
 # Environmental parameters can be input as int, float, list, dict, numpy array format.
-model.tdb = 28  # Air temperature [oC]
-model.tr = 30  # Mean radiant temperature [oC]
+model.tdb = 28  # Air temperature [°C]
+model.tr = 30  # Mean radiant temperature [°C]
 model.rh = 40  # Relative humidity [%]
-model.v = np.array( # Air velocity [m/s]
+model.v = np.array(  # Air velocity [m/s]
     [
         0.2,  # head
         0.4,  # neck
@@ -92,7 +92,9 @@
         0.1,  # right foot
     ]
 )
-model.clo = local_clo_typical_ensembles["briefs, socks, undershirt, work jacket, work pants, safety shoes"]["local_body_part"]
+model.clo = local_clo_typical_ensembles[
+    "briefs, socks, undershirt, work jacket, work pants, safety shoes"
+]["local_body_part"]
 
 # par should be input as int, float.
 model.par = 1.2  # Physical activity ratio [-], assuming a sitting position
@@ -108,34 +110,34 @@
 
 # Set the next condition (You only need to change the parameters that you want to change)
 model.to = 20  # Change operative temperature
-model.v = { # Air velocity [m/s], assuming to use a desk fan
-    'head' : 0.2,
-    'neck' : 0.4,
-    'chest' : 0.4,
-    'back': 0.1,
-    'pelvis' : 0.1,
-    'left_shoulder' : 0.4,
-    'left_arm' : 0.4,
-    'left_hand' : 0.4,
-    'right_shoulder' : 0.4,
-    'right_arm' : 0.4,
-    'right_hand' : 0.4,
-    'left_thigh' : 0.1,
-    'left_leg' : 0.1,
-    'left_foot' : 0.1,
-    'right_thigh' : 0.1,
-    'right_leg' : 0.1,
-    'right_foot' : 0.1
-    }
+model.v = {  # Air velocity [m/s], assuming to use a desk fan
+    "head": 0.2,
+    "neck": 0.4,
+    "chest": 0.4,
+    "back": 0.1,
+    "pelvis": 0.1,
+    "left_shoulder": 0.4,
+    "left_arm": 0.4,
+    "left_hand": 0.4,
+    "right_shoulder": 0.4,
+    "right_arm": 0.4,
+    "right_hand": 0.4,
+    "left_thigh": 0.1,
+    "left_leg": 0.1,
+    "left_foot": 0.1,
+    "right_thigh": 0.1,
+    "right_leg": 0.1,
+    "right_foot": 0.1,
+}
 # Run JOS-3 model
 model.simulate(
     times=60,  # Number of loops of a simulation
     dtime=60,  # Time delta [sec]. The default is 60.
 )  # Additional exposure time = 60 [loops] * 60 [sec] = 60 [min]
 
 # Set the next condition (You only need to change the parameters that you want to change)
-model.tdb = 30  # Change air temperature [oC]
-model.tr = 35  # Change mean radiant temperature [oC]
+model.tdb = 30  # Change air temperature [°C]
+model.tr = 35  # Change mean radiant temperature [°C]
 # Run JOS-3 model
 model.simulate(
     times=30,  # Number of loops of a simulation
@@ -144,11 +146,15 @@
 
 # Show the results
 df = pd.DataFrame(model.dict_results())  # Make pandas.DataFrame
-df[["t_skin_mean", "t_skin_head", "t_skin_chest", "t_skin_left_hand"]].plot()  # Plot time series of local skin temperature.
+df[
+    ["t_skin_mean", "t_skin_head", "t_skin_chest", "t_skin_left_hand"]
+].plot()  # Plot time series of local skin temperature.
 plt.legend(["Mean", "Head", "Chest", "Left hand"])  # Reset the legends
-plt.ylabel("Skin temperature [oC]")  # Set y-label as 'Skin temperature [oC]'
+plt.ylabel("Skin temperature [°C]")  # Set y-label as 'Skin temperature [°C]'
 plt.xlabel("Time [min]")  # Set x-label as 'Time [min]'
-plt.savefig(os.path.join(JOS3_EXAMPLE_DIRECTORY, "jos3_example2_skin_temperatures.png"))  # Save plot at the current directory
+plt.savefig(
+    os.path.join(JOS3_EXAMPLE_DIRECTORY, "jos3_example2_skin_temperatures.png")
+)  # Save plot at the current directory
 plt.show()  # Show the plot
 
 # Exporting the results as csv

diff --git a/src/pythermalcomfort/jos3_functions/construction.py b/src/pythermalcomfort/jos3_functions/construction.py
@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 
-"""
-This code provides functions for calculating the surface area of different body parts, weight ratio,
-basal blood flow ratio, and thermal conductance and thermal capacity.
-
-The values of a NumPy array containing 17 elements correspond to the following order:
-"head", "neck", "chest", "back", "pelvis",
-"left_shoulder", "left_arm", "left_hand", "right_shoulder", "right_arm", "right_hand",
-"left_thigh", "left_leg", "left_hand", "right_thigh", "right_leg" and "right_hand".
+"""This code provides functions for calculating the surface area of different
+body parts, weight ratio, basal blood flow ratio, and thermal conductance and
+thermal capacity.
+
+The values of a NumPy array containing 17 elements correspond to the
+following order: "head", "neck", "chest", "back", "pelvis",
+"left_shoulder", "left_arm", "left_hand", "right_shoulder", "right_arm",
+"right_hand", "left_thigh", "left_leg", "left_hand", "right_thigh",
+"right_leg" and "right_hand".
 """
 
 import numpy as np
@@ -37,9 +38,9 @@
     ]
 )
 
+
 def _to17array(inp):
-    """
-    Create a NumPy array of shape (17,) with the given input.
+    """Create a NumPy array of shape (17,) with the given input.
 
     Parameters
     ----------
@@ -66,19 +67,21 @@ def _to17array(inp):
         if inp.shape == (17,):
             array = inp
         else:
-            ValueError("The input list or ndarray is not of length 17")
+            raise ValueError("The input list or ndarray is not of length 17")
     else:
-        raise ValueError("Unsupported input type. Supported types: int, float, list, dict, ndarray")
+        raise ValueError(
+            "Unsupported input type. Supported types: int, float, list, dict, ndarray"
+        )
 
     return array.copy()
 
+
 def bsa_rate(
     height=1.72,
     weight=74.43,
     formula="dubois",
 ):
-    """
-    Calculate the rate of bsa to standard body.
+    """Calculate the rate of bsa to standard body.
 
     Parameters
     ----------
@@ -100,19 +103,18 @@ def bsa_rate(
         weight=weight,
         formula=formula,
     )
-    bsa_rate = bsa_all / _BSAst.sum()  # The bsa ratio to the standard body (1.87m2)
-    return bsa_rate
+    return bsa_all / _BSAst.sum()  # The bsa ratio to the standard body (1.87m2)
 
-def localbsa(
+
+def local_bsa(
     height=1.72,
     weight=74.43,
     formula="dubois",
 ):
-    """
-    Calculate local body surface area (bsa) [m2].
+    """Calculate local body surface area (bsa) [m2].
 
     The local body surface area has been derived from 65MN.
-    The head have been devided to head and neck based on Smith's model.
+    The head have been divided to head and neck based on Smith's model.
         head = 0.1396*0.1117/0.1414 (65MN_Head * Smith_Head / Smith_Head+neck)
         neck = 0.1396*0.0297/0.1414 (65MN_Head * Smith_Neck / Smith_Head+neck)
 
@@ -132,7 +134,6 @@ def localbsa(
         Local body surface area (bsa) [m2].
     bsa_rate : float
         The ratio of bsa to the standard body [-].
-
     """
     _bsa_rate = bsa_rate(
         height=height,
@@ -146,8 +147,7 @@ def localbsa(
 def weight_rate(
     weight=74.43,
 ):
-    """
-    Calculate the ratio of the body weitht to the standard body (74.43 kg).
+    """Calculate the ratio of the body weight to the standard body (74.43 kg).
 
     The standard values of local body weights are as below.
         weight_local = np.array([
@@ -169,8 +169,7 @@ def weight_rate(
         The ratio of the body weight to the standard body (74.43 kg).
         weight_rate = weight / 74.43
     """
-    rate = weight / 74.43
-    return rate
+    return weight / 74.43
 
 
 def bfb_rate(
@@ -180,8 +179,8 @@ def bfb_rate(
     age=20,
     ci=2.59,
 ):
-    """
-    Calculate the ratio of basal blood flow (BFB) of the standard body (290 L/h).
+    """Calculate the ratio of basal blood flow (BFB) of the standard body (290
+    L/h).
 
     Parameters
     ----------
@@ -216,8 +215,7 @@ def bfb_rate(
         ci *= 0.7
 
     bfb_all = ci * bsa_rate(height, weight, equation) * _BSAst.sum()  # [L/h]
-    _bfb_rate = bfb_all / 290
-    return _bfb_rate
+    return bfb_all / 290
 
 
 def conductance(
@@ -226,8 +224,7 @@ def conductance(
     equation="dubois",
     fat=15,
 ):
-    """
-    Calculate thermal conductance between layers [W/K].
+    """Calculate thermal conductance between layers [W/K].
 
     Parameters
     ----------
@@ -375,7 +372,7 @@ def conductance(
     # The shape is a cylinder.
     # It is assumed that the inner is vascular radius, 2.5mm and the outer is
     # stolwijk's core radius.
-    # The heat transer coefficient of the core is assumed as the Michel's
+    # The heat transfer coefficient of the core is assumed as the Michel's
     # counter-flow model 0.66816 [W/(m･K)].
     cdt_ves_cr = np.array(
         [
@@ -423,7 +420,7 @@ def conductance(
 
     # art to vein (counter-flow) [W/K]
     # The data has been derived Mitchell's model.
-    # THe values = 15.869 [W/(m･K)] * the segment lenght [m]
+    # THe values = 15.869 [W/(m･K)] * the segment length [m]
     cdt_art_vein = np.array(
         [
             0,
@@ -468,27 +465,27 @@ def conductance(
 
     cdt_whole = np.zeros((NUM_NODES, NUM_NODES))
     for i, bn in enumerate(BODY_NAMES):
-        # Dictionary of indecies in each body segment
+        # Dictionary of indices in each body segment
         # key = layer name, value = index of matrix
-        indexof = IDICT[bn]
+        index_of = IDICT[bn]
 
         # Common
-        cdt_whole[indexof["artery"], indexof["vein"]] = cdt_art_vein[i]  # art to vein
-        cdt_whole[indexof["artery"], indexof["core"]] = cdt_ves_cr[i]  # art to cr
-        cdt_whole[indexof["vein"], indexof["core"]] = cdt_ves_cr[i]  # vein to cr
+        cdt_whole[index_of["artery"], index_of["vein"]] = cdt_art_vein[i]  # art to vein
+        cdt_whole[index_of["artery"], index_of["core"]] = cdt_ves_cr[i]  # art to cr
+        cdt_whole[index_of["vein"], index_of["core"]] = cdt_ves_cr[i]  # vein to cr
 
         # Only limbs
         if i >= 5:
-            cdt_whole[indexof["sfvein"], indexof["skin"]] = cdt_sfv_sk[i]  # sfv to sk
+            cdt_whole[index_of["sfvein"], index_of["skin"]] = cdt_sfv_sk[i]  # sfv to sk
 
         # If the segment has a muscle or fat layer
-        if not indexof["muscle"] is None:  # or not indexof["fat"] is None
-            cdt_whole[indexof["core"], indexof["muscle"]] = cdt_cr_ms[i]  # cr to ms
-            cdt_whole[indexof["muscle"], indexof["fat"]] = cdt_ms_fat[i]  # ms to fat
-            cdt_whole[indexof["fat"], indexof["skin"]] = cdt_fat_sk[i]  # fat to sk
+        if index_of["muscle"] is not None:  # or not indexof["fat"] is None
+            cdt_whole[index_of["core"], index_of["muscle"]] = cdt_cr_ms[i]  # cr to ms
+            cdt_whole[index_of["muscle"], index_of["fat"]] = cdt_ms_fat[i]  # ms to fat
+            cdt_whole[index_of["fat"], index_of["skin"]] = cdt_fat_sk[i]  # fat to sk
 
         else:
-            cdt_whole[indexof["core"], indexof["skin"]] = cdt_cr_sk[i]  # cr to sk
+            cdt_whole[index_of["core"], index_of["skin"]] = cdt_cr_sk[i]  # cr to sk
 
     # Creates a symmetrical matrix
     cdt_whole = cdt_whole + cdt_whole.T
@@ -497,8 +494,7 @@ def conductance(
 
 
 def capacity(height=1.72, weight=74.43, equation="dubois", age=20, ci=2.59):
-    """
-    Calculate the thermal capacity [J/K].
+    """Calculate the thermal capacity [J/K].
 
     The values of vascular and central blood capacity have been derived from
     Yokoyama's model.
@@ -704,24 +700,24 @@ def capacity(height=1.72, weight=74.43, equation="dubois", age=20, ci=2.59):
     cap_whole[0] = cap_cb
 
     for i, bn in enumerate(BODY_NAMES):
-        # Dictionary of indecies in each body segment
+        # Dictionary of indices in each body segment
         # key = layer name, value = index of matrix
-        indexof = IDICT[bn]
+        index_of = IDICT[bn]
 
         # Common
-        cap_whole[indexof["artery"]] = cap_art[i]
-        cap_whole[indexof["vein"]] = cap_vein[i]
-        cap_whole[indexof["core"]] = cap_cr[i]
-        cap_whole[indexof["skin"]] = cap_sk[i]
+        cap_whole[index_of["artery"]] = cap_art[i]
+        cap_whole[index_of["vein"]] = cap_vein[i]
+        cap_whole[index_of["core"]] = cap_cr[i]
+        cap_whole[index_of["skin"]] = cap_sk[i]
 
         # Only limbs
         if i >= 5:
-            cap_whole[indexof["sfvein"]] = cap_sfv[i]
+            cap_whole[index_of["sfvein"]] = cap_sfv[i]
 
         # If the segment has a muscle or fat layer
-        if not indexof["muscle"] is None:  # or not indexof["fat"] is None
-            cap_whole[indexof["muscle"]] = cap_ms[i]
-            cap_whole[indexof["fat"]] = cap_fat[i]
+        if index_of["muscle"] is not None:  # or not indexof["fat"] is None
+            cap_whole[index_of["muscle"]] = cap_ms[i]
+            cap_whole[index_of["fat"]] = cap_fat[i]
 
     cap_whole *= 3600  # Changes unit [Wh/K] to [J/K]
     return cap_whole