Example codes

Single model

Create a model and execute it one time. Plot the results, animate and show the statistics. (Animation doesn’t work with inline backend)

import TCAMpy as tcam

M = tcam.TModel(480, 75, 20, 1, 24, 1/24, 25, 4, 0, 3, 5)
M.run_model(plot = True, animate = True, stats = True)

Multiple executions

Run the model multiple (in this case 5) times and plot the average results.

import TCAMpy as tcam
import matplotlib.pyplot as plt

M = tcam.TModel(480, 75, 20, 1, 24, 1/24, 25, 4, 0, 3, 5)
stats = M.run_multimodel(5, M.cancer, plot = True, stats = True)

# Check visualization for every execution
for i in range(len(M.runs)):
    M.plot_run(i+1)

# Save stats to excel
stats.to_excel("simulations.xlsx")

Modifying initial state

Modify the initial state by defining/changing M.cancer or calling ‘mod_cell()’.

import TCAMpy as tcam
import numpy as np

M = tcam.TModel(480, 75, 20, 1, 24, 1/24, 25, 4, 0, 3, 5)

M.cancer = (M.pmax+1) * np.eye(M.side)
M.cancer[0][0] = 0
M.mod_cell(M.side-1, M.side-1, 0)

M.run_model(plot = True, animate = False, stats = True)

Dashboard

Create a streamlit dashboard. Run the file containing the code with streamlit to open the application.

import TCAMpy as tcam

M = tcam.TModel(480, 75, 20, 1, 24, 1/24, 25, 4, 0, 3, 5)

D = tcam.TDashboard(M)
D.run_dashboard()

streamlit run file_name.py

This dashboard can be used online with Streamlit Community Cloud, without any coding: https://tcampy.streamlit.app/

Machine Learning

Use ML features to generate dataset, train a model and predict tumor size based on given parameters

import TCAMpy as tcam

M = tcam.TModel(480, 75, 20, 1, 24, 1/24, 25, 4, 0, 3, 5)
ml = tcam.TML(M)

# Select parameters to randomize and ranges
randomize = {
    "pmax": (15, 20),
    "PA":   (1, 3),
    "CCT":  (12, 48),
    "PS":   (10, 75),
    "mu":   (2, 10),
    "ad":   (0, 10),
    "M":    (0, 10),
    "I":    (0, 5),
}

# Generate dataset with randomized parameters
df = ml.generate_dataset(
    n=50,
    random_params=randomize,
    output_file="tumor_dataset.csv"
)

# Train a model
model, metrics = ml.train_predictor("tumor_dataset.csv", "Tumor size")

new_params = [480, 75, 15, 1, 24, 1/24, 40, 4, 5, 3, 10]
print ("Predicted Attribute: ", ml.predict_new(new_params))

Example files

For example files please visit: https://github.com/Fetasalyt/TCAMpy/tree/main/examples