Merge pull request #31 from PumasAI-Labs/dw/updates

Updates for SA workshop

Author: vjd

.gitignore

@@ -1 +1,3 @@
 .DS_Store
+*.jls
+*.png

Exercises/01-pkpd_simulations_exercises.jl

@@ -3,7 +3,7 @@
 # =============================================================================
 
 # Import the previous code that returns the fitted Pumas model
-include(joinpath(@__DIR__, "..","01-TeachingMaterial","02-population_pkpd","03-pkpd_model_fitting.jl") ) # to get warfarin_model and warfarin_model_fit    
+include(joinpath(@__DIR__, "..","TeachingMaterial","population_pkpd","03-pkpd_model_fitting.jl")) # to get warfarin_model and warfarin_model_fit    
 
 # -----------------------------------------------------------------------------
 # 1. PACKAGES 
@@ -29,7 +29,7 @@ Using the warfarin PK-PD model:
 2. Explore the effect on PK and PCA
 """
 
-# Create DosagRegimen object for single dose levels of 50 and 150 at time 0
+# Create DosagRegimen objects for single dose levels of 50 and 150 at time 0
 # your code here
 
 # Create a population of 100 subjects for each new dosing regimen:
@@ -54,7 +54,7 @@ Using the warfarin PK-PD model:
 2. Explore the effect on PK and PCA
 """
 
-# Create DosagRegimen object for daily doses of 50 and 150 for a week:
+# Create DosagRegimen objects for daily doses of 50 and 150 for a week:
 #  your code here
 
 # Create a population for each new dosing regimen:
@@ -79,7 +79,7 @@ Using the warfarin PK-PD model:
 2. Explore the effect on PK and PCA
 """
 
-# Create DosagRegimen object for a loading dose of 400 followed by daily doses of 150 for a week:
+# Create a DosagRegimen object for a loading dose of 400 followed by daily doses of 150 for a week:
 #  your code here
 
 # Create a population with the new dosing regimen:

Exercises/01-variables_exercise.jl

@@ -4,11 +4,11 @@ using Test
 Exercise 1: Variable Declaration and Types
 ----------------------------------------
 Declare variables for the following pharmacometric parameters:
-1. Create a variable 'dose' with value 100 (mg)
-2. Create a variable 'volume_of_distribution' with value 48.2 (L)
-3. Create a variable 'half_life' with value 12.5 (hours)
-4. Create a variable 'is_fasted' as a boolean indicating fasting state
-5. Create a variable 'patient_id' as a string "SUBJ-001"
+1. Create a variable `dose` with value 100 (mg)
+2. Create a variable `volume_of_distribution` with value 48.2 (L)
+3. Create a variable `half_life` with value 12.5 (hours)
+4. Create a variable `is_fasted` as a boolean indicating fasting state
+5. Create a variable `patient_id` as a string "SUBJ-001"
 
 Use the most appropriate type for each variable!
 """
@@ -20,8 +20,8 @@ Use the most appropriate type for each variable!
 Exercise 2: Basic Calculations
 ----------------------------
 Using the variables you created above:
-1. Calculate the elimination rate constant (k) using the formula: k = ln(2)/half_life
-2. Calculate the initial concentration (C0) using: C0 = dose/volume_of_distribution
+1. Calculate the elimination rate constant (k) using the formula: `k` = ln(2)/`half_life`
+2. Calculate the initial concentration (C0) using: `C0` = `dose`/`volume_of_distribution`
 3. Store both results in new variables
 """
 

Exercises/02-syntax_exercise.jl

@@ -4,7 +4,7 @@ using Test
 Exercise 1: Conditional Statements
 --------------------------------
 Write conditional statements to:
-1. Check if a drug concentration (conc = 15.5 μg/mL) is within therapeutic range (10-20 μg/mL)
+1. Check if a drug concentration (`conc` = 15.5 μg/mL) is within therapeutic range (10-20 μg/mL)
 2. Wriate a function that classifies the drug exposure as:
    - "Low" if < 10 μg/mL
    - "Therapeutic" if between 10-20 μg/mL
@@ -18,9 +18,9 @@ Write conditional statements to:
 Exercise 2: Loops
 ----------------
 1. Create an array of hourly time points from 0 to 24 hours
-2. Calculate drug concentrations at each time point using the formula:
-   C(t) = C0 * exp(-k * t)
-   where C0 = 100 μg/mL and k = 0.1 /hour
+2. Calculate drug concentrations at each time point in a loop using the formula:
+   C(t) = `C0` * exp(-`k` * t)
+   where `C0` = 100 μg/mL and `k` = 0.1 /hour
 3. Store results in a new array
 """
 

Exercises/03-functions_exercise.jl

@@ -3,11 +3,11 @@ using Test
 @info """
 Exercise 1: Basic Function Definition
 ----------------------------------
-1. Create a function calculate_clearance that takes volume (L) and elimination_rate (/hr) as arguments
+1. Create a function `calculate_clearance` that takes `volume` (L) and `elimination_rate` (/hr) as arguments
    and returns clearance (L/hr)
-2. Create a function calculate_half_life that takes elimination_rate (/hr) as argument
+2. Create a function `calculate_half_life` that takes `elimination_rate` (/hr) as argument
    and returns the half-life (hr)
-3. Create a function calculate_auc that takes dose (mg) and clearance (L/hr) as arguments
+3. Create a function `calculate_auc` that takes `dose` (mg) and `clearance` (L/hr) as arguments
    and returns the AUC (mg*hr/L)
 """
 
@@ -17,11 +17,16 @@ Exercise 1: Basic Function Definition
 @info """
 Exercise 2: Multiple Dispatch
 ---------------------------
-1. Create a function calculate_dose that works with different units:
-   - One method that takes weight (kg) and dose_per_kg (mg/kg)
-   - Another method that takes BSA (m²) and dose_per_m2 (mg/m²)
-   Both should return the total dose in mg
-2. Add type annotations to make the function more robust
+1. Create a function `calculate_dose` that works with both
+   weight of a single subject and weights of a population of multiple subjects:
+   - One method that takes `weight` (kg) of a single subject and `dose_per_kg` (mg/kg),
+     and returns the total dose of that subject in mg.
+   - Another method that takes a vector of `weights` (kg) of multiple subjects and
+     `dose_per_kg` (mg/kg), and returns a vector of total doses in mg for each subject.
+   Reuse the method for a single subject in the method for multiple subjects.
+2. In this case, it is actually not necessary to define two different methods.
+   Define a single method `calculate_dose_alt` that works for both cases,
+   without using `if`/`else` statements on the input type.
 """
 
 # Your code here
@@ -43,7 +48,7 @@ Exercise 3: Anonymous Functions
 @info """
 Bonus Challenge: Higher Order Functions
 -----------------------------------
-1. Create a higher-order function simulate_pk that takes:
+1. Create a higher-order function `simulate_pk` that takes:
    - A dosing function (that calculates concentration vs time)
    - Time points array
    - PK parameters dictionary

Exercises/HCV_model/02-HCV_model_exercise.jl

@@ -4,7 +4,7 @@
 # https://pmc.ncbi.nlm.nih.gov/articles/PMC4294071/
 # ==============================================================
 
-using Pumas, CairoMakie, DataFrames, Random, PumasUtilities, Logging
+using Pumas, CairoMakie, DataFrames, Random, PumasUtilities
 
 
 # Model set up
@@ -15,7 +15,7 @@ model_hcv = @model begin
       # fixed effects 
       logθKa ∈ RealDomain()
 
-      # random effects variance parameters, must be posisitive
+      # random effects variance parameters, must be positive
       ω²Ka ∈ RealDomain(lower = 0.0)
 
       # variance parameter in error models

Exercises/TGI_model/01-TGI_model_exercise.jl

@@ -4,7 +4,7 @@
 # https://pubmed.ncbi.nlm.nih.gov/19636014/
 # ==============================================================
 
-using Pumas, CairoMakie, DataFrames, Random, PumasUtilities, Logging
+using Pumas, CairoMakie, DataFrames, Random, PumasUtilities
 
 # Reproduce the model for FU Phase III
 

Solutions/01-pkpd_simulations_solutions.jl

@@ -3,7 +3,7 @@
 # =============================================================================
 
 # Import the previous code that returns the fitted Pumas model
-include(joinpath(@__DIR__, "..","01-TeachingMaterial","02-population_pkpd","03-pkpd_model_fitting.jl") ) # to get warfarin_model and warfarin_model_fit    
+include(joinpath(@__DIR__, "..","TeachingMaterial","population_pkpd","03-pkpd_model_fitting.jl")) # to get warfarin_model and warfarin_model_fit    
 
 # -----------------------------------------------------------------------------
 # 1. PACKAGES 
@@ -51,11 +51,11 @@ sim_150 = simobs(
 )
 
 # Plot:
-sim_plot(warfarin_model, sim_50; observations = [:conc], axis = (; limits = (nothing, (0, 30))))
-sim_plot(warfarin_model, sim_150; observations = [:conc], axis = (; limits = (nothing, (0, 30))))
+sim_plot(warfarin_model, sim_50; observations = [:conc])
+sim_plot(warfarin_model, sim_150; observations = [:conc])
 
-sim_plot(warfarin_model, sim_50; observations = [:pca], axis = (; limits = (nothing, (0, 150))))
-sim_plot(warfarin_model, sim_150; observations = [:pca], axis = (; limits = (nothing, (0, 150))))
+sim_plot(warfarin_model, sim_50; observations = [:pca])
+sim_plot(warfarin_model, sim_150; observations = [:pca])
 
 
 # -----------------------------------------------------------------------------
@@ -70,7 +70,7 @@ Using the warfarin PK-PD model:
 2. Explore the effect on PK and PCA
 """
 
-# Create DosagRegimen object for daily doses of 50 for a week:
+# Create DosagRegimen objects for daily doses of 50 and 150 for a week:
 dose_50qd = DosageRegimen(50; time = 0, ii = 24 , addl = 6)
 dose_150qd = DosageRegimen(150; time = 0, ii = 24 , addl = 6)
 # Create a population for each new dosing regimen:
@@ -91,11 +91,11 @@ sim_150qd = simobs(
     obstimes = 0.0:1:150.0,  # Fine time grid
 )
 # Plot:
-sim_plot(warfarin_model, sim_50qd; observations = [:conc], axis = (; limits = (nothing, (0, 80))))
-sim_plot(warfarin_model, sim_150qd; observations = [:conc], axis = (; limits = (nothing, (0, 80))))
+sim_plot(warfarin_model, sim_50qd; observations = [:conc])
+sim_plot(warfarin_model, sim_150qd; observations = [:conc])
 
-sim_plot(warfarin_model, sim_50qd; observations = [:pca], axis = (; limits = (nothing, (0, 80))))
-sim_plot(warfarin_model, sim_150qd; observations = [:pca], axis = (; limits = (nothing, (0, 80))))
+sim_plot(warfarin_model, sim_50qd; observations = [:pca])
+sim_plot(warfarin_model, sim_150qd; observations = [:pca])
 
 
 # -----------------------------------------------------------------------------
@@ -128,7 +128,3 @@ new_sim = simobs(
 # Plot:
 sim_plot(warfarin_model, new_sim; observations = [:conc])
 sim_plot(warfarin_model, new_sim; observations = [:pca])
-
-
-
-

Solutions/01-variables_solution.jl

@@ -19,8 +19,8 @@ k = log(2)/half_life
 C0 = dose/volume_of_distribution
 
 # Test Exercise 2
-@test isapprox(k, 0.0554, atol=0.001)
-@test isapprox(C0, 2.0747, atol=0.001)
+@test k ≈ 0.0554 atol=0.001
+@test C0 ≈ 2.0747 atol=0.001
 
 # Exercise 3: String Manipulation
 patient_info = "Patient $patient_id received $dose mg"
@@ -47,4 +47,4 @@ patient_params["clearance"] = volume_of_distribution * k
 # Test Bonus Challenge
 @test dose_in_grams == 0.1
 @test haskey(patient_params, "clearance")
-@test isapprox(patient_params["clearance"], 2.67, atol=0.01) 
+@test patient_params["clearance"] ≈ 2.67 atol=0.01

Solutions/02-syntax_solution.jl

@@ -68,4 +68,4 @@ end
 # Test Bonus Challenge
 @test haskey(dosing_recommendations, 60)
 @test dosing_recommendations[60][1] == 60
-@test dosing_recommendations[90][3] == 270  # This should trigger warning 
+@test dosing_recommendations[90][3] == 270