clean up for doc testing

docs/source/getting_started/code/2_mechanistic_model_1.py

@@ -1,3 +1,97 @@
+import argparse
+
+
+# Set up argument parsing, so plotting and exports can be disabled for
+# testing.
+parser = argparse.ArgumentParser(
+    description='Run example scripts for chi docs.',
+)
+parser.add_argument(
+    '--test',
+    action='store_true',
+    help='Run testing version of script which ignores plotting.',)
+
+# Parse!
+args = parser.parse_args()
+
+# Run testing verion of script
+if args.test:
+    #1
+    import chi
+    import numpy as np
+
+
+    class OneCompPKModel(chi.MechanisticModel):
+        def __init__(self):
+            super().__init__()
+
+        def simulate(self, parameters, times):
+            a0, ke, v = parameters
+            times = np.array(times)
+
+            c = a0 / v * np.exp(-ke * times)
+
+            output = np.empty(shape=(self.n_outputs(), len(times)))
+            output[0] = c
+
+            return output
+
+        def has_sensitivities(self):
+            # We will not implement sensitivities in this part of the tutorial, so
+            # the output of this method is always False
+            return False
+
+        def n_outputs(self):
+            return 1
+
+        def outputs(self):
+            return ['Drug concentration']
+
+        def n_parameters(self):
+            return 3
+
+        def parameters(self):
+            return ['Initial amount', 'Elimination rate', 'Volume of distribution']
+
+
+    import os
+    # 2
+    import plotly.graph_objects as go
+
+
+    # Define model
+    model = OneCompPKModel()
+
+    # Run simulation
+    times = np.linspace(start=0, stop=10, num=200)
+    parameters = [
+        10,        # Initial drug amount
+        1,         # Elimination rate
+        2,         # Volume of distribution
+    ]
+    simulation = model.simulate(parameters=parameters, times=times)[0]
+
+    # Plot drug concentration
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=simulation,
+        mode='lines',
+    ))
+    fig.update_layout(
+        xaxis_title='Time',
+        yaxis_title='Drug concentration',
+        template='plotly_white'
+    )
+    # fig.show()
+
+
+    # directory = os.path.dirname(os.path.dirname(__file__))
+    # fig.write_html(directory + '/images/2_mechanistic_model_1.html')
+
+    # Exit script
+    exit()
+
 #1
 import chi
 import numpy as np

docs/source/getting_started/code/2_mechanistic_model_2.py

@@ -14,6 +14,249 @@
 # Parse!
 args = parser.parse_args()
 
+# Run testing verion of script
+if args.test:
+    # 1
+    import os
+
+    import chi
+
+
+    directory = os.path.dirname(__file__)
+    filename = os.path.join(directory, 'template.xml')
+    model = chi.PKPDModel(sbml_file=filename)
+
+    # print(model._model.name())
+
+
+    # 2
+    import os
+
+    import chi
+
+
+    directory = os.path.dirname(__file__)
+    filename = os.path.join(directory, 'one_compartment_pk_model.xml')
+    model = chi.PKPDModel(sbml_file=filename)
+
+    # print(model._model.code())
+    # print(model.parameters())
+
+    # 3
+    import os
+
+    import chi
+    import numpy as np
+    import plotly.graph_objects as go
+
+
+    # Define model
+    directory = os.path.dirname(__file__)
+    filename = os.path.join(directory, 'one_compartment_pk_model.xml')
+    model = chi.PKPDModel(sbml_file=filename)
+    model.set_outputs(['global.drug_concentration'])
+
+    # Run simulation
+    times = np.linspace(start=0, stop=10, num=200)
+    parameters = [
+        10,        # Initial drug amount
+        1,         # Elimination rate
+        2,         # Volume of distribution
+    ]
+    simulation = model.simulate(parameters=parameters, times=times)[0]
+
+    # Plot drug concentation
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=simulation,
+        mode='lines',
+    ))
+    fig.update_layout(
+        xaxis_title='Time',
+        yaxis_title='Drug concentration',
+        template='plotly_white'
+    )
+    # fig.show()
+
+
+    # directory = os.path.dirname(os.path.dirname(__file__))
+    # fig.write_html(directory + '/images/2_mechanistic_model_2.html')
+
+
+    # 4
+    model.set_administration(compartment='global')
+
+    # print(model._model.code())
+
+    # 5
+    times = np.linspace(start=0, stop=10, num=200)
+    parameters = [0, 1, 2]
+
+    # 2 mg every day
+    model.set_dosing_regimen(dose=2, period=1)
+    sim1 = model.simulate(parameters=parameters, times=times)[0]
+
+    # 4 mg every 2 days
+    model.set_dosing_regimen(dose=4, period=2)
+    sim2 = model.simulate(parameters=parameters, times=times)[0]
+
+    # 2 mg every day with an infusion of 12 hours
+    model.set_dosing_regimen(dose=2, period=1, duration=0.5)
+    sim3 = model.simulate(parameters=parameters, times=times)[0]
+
+    # Plot drug concentations
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=sim1,
+        mode='lines',
+        name='2mg every day'
+    ))
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=sim2,
+        mode='lines',
+        name='4mg every 2 days'
+    ))
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=sim3,
+        mode='lines',
+        name='2mg every day infused for 12h'
+    ))
+    fig.update_layout(
+        xaxis_title='Time',
+        yaxis_title='Drug concentration',
+        template='plotly_white'
+    )
+    # fig.show()
+
+
+    # directory = os.path.dirname(os.path.dirname(__file__))
+    # fig.write_html(directory + '/images/2_mechanistic_model_3.html')
+
+    # 6
+    # Define model
+    directory = os.path.dirname(__file__)
+    filename = os.path.join(directory, 'one_compartment_pk_model.xml')
+    model = chi.PKPDModel(sbml_file=filename)
+    model.set_outputs(['global.drug_concentration'])
+
+    # Set indirect administration
+    model.set_administration(compartment='global', direct=False)
+
+    times = np.linspace(start=0, stop=10, num=200)
+    parameters = [0, 0, 10, 1, 2]
+
+    # 2 mg every day
+    model.set_dosing_regimen(dose=2, period=1)
+    sim1 = model.simulate(parameters=parameters, times=times)[0]
+
+    # 4 mg every 2 days
+    model.set_dosing_regimen(dose=4, period=2)
+    sim2 = model.simulate(parameters=parameters, times=times)[0]
+
+    # 2 mg every day with an infusion of 12 hours
+    model.set_dosing_regimen(dose=2, period=1, duration=0.5)
+    sim3 = model.simulate(parameters=parameters, times=times)[0]
+
+    # Plot drug concentations
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=sim1,
+        mode='lines',
+        name='2mg every day'
+    ))
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=sim2,
+        mode='lines',
+        name='4mg every 2 days'
+    ))
+    fig.add_trace(go.Scatter(
+        x=times,
+        y=sim3,
+        mode='lines',
+        name='2mg every day infused for 12h'
+    ))
+    fig.update_layout(
+        xaxis_title='Time',
+        yaxis_title='Drug concentration',
+        template='plotly_white'
+    )
+    # fig.show()
+
+
+    # directory = os.path.dirname(os.path.dirname(__file__))
+    # fig.write_html(directory + '/images/2_mechanistic_model_4.html')
+
+    # Print model
+    model = chi.PKPDModel(sbml_file=filename)
+    model.set_outputs(['global.drug_concentration'])
+
+    # Set indirect administration
+    model.set_administration(compartment='global', direct=False)
+
+    # print(model._model.code())
+
+    # 7
+    from plotly.subplots import make_subplots
+
+
+    # Simulate concentration and sensitivities
+    model.set_dosing_regimen(dose=2, period=1)
+    model.enable_sensitivities(True)
+    simulation, sensitivities = model.simulate(parameters=parameters, times=times)
+
+    # Plot results
+    fig = make_subplots(rows=3, cols=1, shared_xaxes=True)
+    fig.add_trace(
+        go.Scatter(
+            x=times,
+            y=simulation[0],
+            mode='lines',
+            name='Drug concentration'
+        ),
+        row=1,
+        col=1
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=times,
+            y=sensitivities[:, 0, 1],
+            mode='lines',
+            name='Sensitivity w.r.t. initial amount'
+        ),
+        row=2,
+        col=1
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=times,
+            y=sensitivities[:, 0, 3],
+            mode='lines',
+            name='Sensitivity w.r.t. elim. rate'
+        ),
+        row=3,
+        col=1
+    )
+
+    fig.update_layout(
+        xaxis3_title='Time',
+        yaxis_title='c',
+        yaxis2_title='dc / da_0',
+        yaxis3_title='dc / dk_e',
+        template='plotly_white'
+    )
+    # fig.show()
+
+    # fig.write_html(directory + '/images/2_mechanistic_model_5.html')
+
+    # Exit script
+    exit()
+
 # 1
 import os