Merge pull request #44 from AkihisaNomoto/master

First integration of JOS3 model and pythermalcomfort

examples/calc_jos3.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+from pythermalcomfort.models import JOS3
+from pythermalcomfort.jos3_functions.utilities import local_clo_typical_ensembles
+
+# Make "jos3_example_example" directory in the current directory
+directory_name = "jos3_output_example"
+CURRENT_DIRECTORY = os.getcwd()
+JOS3_EXAMPLE_DIRECTORY = os.path.join(CURRENT_DIRECTORY, directory_name)
+if not os.path.exists(JOS3_EXAMPLE_DIRECTORY):
+    os.makedirs(JOS3_EXAMPLE_DIRECTORY)
+
+# -------------------------------------------
+# EXAMPLE 1 (simple simulation)
+# -------------------------------------------
+
+# Build a model and set a body built
+# Create an instance of the JOS3 class with optional body parameters such as body height, weight, age, sex, etc.
+model = JOS3(height=1.7, weight=60, age=30)
+
+# Set the first phase
+model.to = 28  # Operative temperature [oC]
+model.rh = 40  # Relative humidity [%]
+model.v = 0.2  # Air velocity [m/s]
+model.par = 1.2  # Physical activity ratio [-]
+model.simulate(60)  # Exposure time = 60 [min]
+
+# Set the next condition (You only need to change the parameters that you want to change)
+model.to = 20  # Change only operative temperature
+model.simulate(60)  # Additional exposure time = 60 [min]
+
+# 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.xlabel("Time [min]")  # Set x-label as 'Time [min]'
+plt.savefig(
+    os.path.join(JOS3_EXAMPLE_DIRECTORY, "jos3_example1_mean_skin_temperature.png")
+)  # Save plot at the current directory
+plt.show()  # Show the plot
+
+# Exporting the results as csv
+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)
+
+# -------------------------------------------
+# EXAMPLE 2 (detail simulation)
+# -------------------------------------------
+
+# Build a model and set a body built
+# Create an instance of the JOS3 class with optional body parameters such as body height, weight, age, sex, etc.
+model = JOS3(
+    height=1.7,
+    weight=60,
+    fat=20,
+    age=30,
+    sex="male",
+    bmr_equation="japanese",
+    bsa_equation="fujimoto",
+    ex_output="all",
+)
+
+# 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.rh = 40  # Relative humidity [%]
+model.v = np.array( # Air velocity [m/s]
+    [
+        0.2,  # head
+        0.4,  # neck
+        0.4,  # chest
+        0.1,  # back
+        0.1,  # pelvis
+        0.4,  # left shoulder
+        0.4,  # left arm
+        0.4,  # left hand
+        0.4,  # right shoulder
+        0.4,  # right arm
+        0.4,  # right hand
+        0.1,  # left thigh
+        0.1,  # left leg
+        0.1,  # left foot
+        0.1,  # right thigh
+        0.1,  # right leg
+        0.1,  # right foot
+    ]
+)
+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
+# posture should be input as int (0, 1, or 2) or str ("standing", "sitting" or "lying").
+# (0="standing", 1="sitting" or 2="lying")
+model.posture = "sitting"  # Posture [-], assuming a sitting position
+
+# Run JOS-3 model
+model.simulate(
+    times=30,  # Number of loops of a simulation
+    dtime=60,  # Time delta [sec]. The default is 60.
+)  # Exposure time = 30 [loops] * 60 [sec] = 30 [min]
+
+# 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
+    }
+# 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]
+# Run JOS-3 model
+model.simulate(
+    times=30,  # Number of loops of a simulation
+    dtime=60,  # Time delta [sec]. The default is 60.
+)  # Additional exposure time = 30 [loops] * 60 [sec] = 30 [min]
+
+# 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.
+plt.legend(["Mean", "Head", "Chest", "Left hand"])  # Reset the legends
+plt.ylabel("Skin temperature [oC]")  # Set y-label as 'Skin temperature [oC]'
+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.show()  # Show the plot
+
+# Exporting the results as csv
+model.to_csv(os.path.join(JOS3_EXAMPLE_DIRECTORY, "jos3_example2 (all output).csv"))