docs: add usage examples for all models in index.md

README.md

@@ -30,32 +30,28 @@ poetry install
 
 ## 🧪 Example
 
-```python
-from gen_surv.cphm import gen_cphm
-
-df = gen_cphm(
-    n=100,
-    model_cens="uniform",
-    cens_par=1.0,
-    beta=0.5,
-    covar=2.0
-)
-print(df.head())
-```
-
 ```python
 from gen_surv import generate
 
-df = generate(
-    model="cphm",
-    n=100,
-    model_cens="uniform",
-    cens_par=1.0,
-    beta=0.5,
-    covar=2.0
-)
+# CPHM
+generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
+
+# AFT Log-Normal
+generate(model="aft_ln", n=100, beta=[0.5, -0.3], sigma=1.0, model_cens="exponential", cens_par=3.0)
+
+# CMM
+generate(model="cmm", n=100, model_cens="exponential", cens_par=2.0,
+         qmat=[[0, 0.1], [0.05, 0]], p0=[1.0, 0.0])
+
+# TDCM
+generate(model="tdcm", n=100, dist="weibull", corr=0.5,
+         dist_par=[1, 2, 1, 2], model_cens="uniform", cens_par=1.0,
+         beta=[0.1, 0.2, 0.3], lam=1.0)
 
-print(df.head())
+# THMM
+generate(model="thmm", n=100, qmat=[[0, 0.2, 0], [0.1, 0, 0.1], [0, 0.3, 0]],
+         emission_pars={"mu": [0.0, 1.0, 2.0], "sigma": [0.5, 0.5, 0.5]},
+         p0=[1.0, 0.0, 0.0], model_cens="exponential", cens_par=3.0)
 ```
 
 ## 🔧 Available Generators

docs/source/index.md

@@ -8,6 +8,7 @@ It includes generators for:
 - **Continuous-Time Markov Models (CMM)**
 - **Time-Dependent Covariate Models (TDCM)**
 - **Time-Homogeneous Hidden Markov Models (THMM)**
+- **Accelerated Failure Time (AFT) Log-Normal Models**
 
 ---
 
@@ -25,25 +26,27 @@ theory
 # 🚀 Usage Example
 
 ```python
-from gen_surv.cphm import gen_cphm
+from gen_surv import generate
 
-df = gen_cphm(n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
-print(df.head())
-```
+# CPHM
+generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
 
-```python
-from gen_surv import generate
+# AFT Log-Normal
+generate(model="aft_ln", n=100, beta=[0.5, -0.3], sigma=1.0, model_cens="exponential", cens_par=3.0)
+
+# CMM
+generate(model="cmm", n=100, model_cens="exponential", cens_par=2.0,
+         qmat=[[0, 0.1], [0.05, 0]], p0=[1.0, 0.0])
 
-df = generate(
-    model="cphm",
-    n=100,
-    model_cens="uniform",
-    cens_par=1.0,
-    beta=0.5,
-    covar=2.0
-)
+# TDCM
+generate(model="tdcm", n=100, dist="weibull", corr=0.5,
+         dist_par=[1, 2, 1, 2], model_cens="uniform", cens_par=1.0,
+         beta=[0.1, 0.2, 0.3], lam=1.0)
 
-print(df.head())
+# THMM
+generate(model="thmm", n=100, qmat=[[0, 0.2, 0], [0.1, 0, 0.1], [0, 0.3, 0]],
+         emission_pars={"mu": [0.0, 1.0, 2.0], "sigma": [0.5, 0.5, 0.5]},
+         p0=[1.0, 0.0, 0.0], model_cens="exponential", cens_par=3.0)
 ```
 
 ## 🔗 Project Links

examples/run_aft.py

@@ -0,0 +1,17 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from gen_surv.interface import generate
+
+# Generate synthetic survival data using Log-Normal AFT model
+df = generate(
+    model="aft_ln",
+    n=100,
+    beta=[0.5, -0.3],
+    sigma=1.0,
+    model_cens="exponential",
+    cens_par=3.0,
+    seed=123
+)
+
+print(df.head())

examples/run_cmm.py

@@ -0,0 +1,17 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from gen_surv import generate
+
+df = generate(
+    model="cmm",
+    n=100,
+    model_cens="exponential",
+    cens_par=2.0,
+    qmat=[[0, 0.1], [0.05, 0]],
+    p0=[1.0, 0.0],
+    seed=42
+)
+
+print(df.head())

examples/run_cphm.py

@@ -0,0 +1,17 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from gen_surv import generate
+
+df = generate(
+    model="cphm",
+    n=100,
+    model_cens="uniform",
+    cens_par=1.0,
+    beta=0.5,
+    covar=2.0,
+    seed=42
+)
+
+print(df.head())

examples/run_tdcm.py

@@ -0,0 +1,20 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from gen_surv import generate
+
+df = generate(
+    model="tdcm",
+    n=100,
+    dist="weibull",
+    corr=0.5,
+    dist_par=[1, 2, 1, 2],
+    model_cens="uniform",
+    cens_par=1.0,
+    beta=[0.1, 0.2, 0.3],
+    lam=1.0,
+    seed=42
+)
+
+print(df.head())

examples/run_thmm.py

@@ -0,0 +1,18 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from gen_surv import generate
+
+df = generate(
+    model="thmm",
+    n=100,
+    qmat=[[0, 0.2, 0], [0.1, 0, 0.1], [0, 0.3, 0]],
+    emission_pars={"mu": [0.0, 1.0, 2.0], "sigma": [0.5, 0.5, 0.5]},
+    p0=[1.0, 0.0, 0.0],
+    model_cens="exponential",
+    cens_par=3.0,
+    seed=42
+)
+
+print(df.head())

stubs/gen_surv/__init__.pyi

@@ -0,0 +1 @@
+from .interface import generate as generate

stubs/gen_surv/aft.pyi

@@ -0,0 +1,3 @@
+from _typeshed import Incomplete
+
+def gen_aft_log_normal(n, beta, sigma, model_cens, cens_par, seed: Incomplete | None = None): ...

stubs/gen_surv/bivariate.pyi

@@ -0,0 +1 @@
+def sample_bivariate_distribution(n, dist, corr, dist_par): ...

stubs/gen_surv/censoring.pyi

@@ -0,0 +1,4 @@
+import numpy as np
+
+def runifcens(size: int, cens_par: float) -> np.ndarray: ...
+def rexpocens(size: int, cens_par: float) -> np.ndarray: ...

stubs/gen_surv/cmm.pyi

@@ -0,0 +1,5 @@
+from gen_surv.censoring import rexpocens as rexpocens, runifcens as runifcens
+from gen_surv.validate import validate_gen_cmm_inputs as validate_gen_cmm_inputs
+
+def generate_event_times(z1: float, beta: list, rate: list) -> dict: ...
+def gen_cmm(n, model_cens, cens_par, beta, covar, rate): ...

stubs/gen_surv/cphm.pyi

@@ -0,0 +1,6 @@
+import pandas as pd
+from gen_surv.censoring import rexpocens as rexpocens, runifcens as runifcens
+from gen_surv.validate import validate_gen_cphm_inputs as validate_gen_cphm_inputs
+
+def generate_cphm_data(n, rfunc, cens_par, beta, covariate_range): ...
+def gen_cphm(n: int, model_cens: str, cens_par: float, beta: float, covar: float) -> pd.DataFrame: ...

stubs/gen_surv/interface.pyi

@@ -0,0 +1,7 @@
+from gen_surv.aft import gen_aft_log_normal as gen_aft_log_normal
+from gen_surv.cmm import gen_cmm as gen_cmm
+from gen_surv.cphm import gen_cphm as gen_cphm
+from gen_surv.tdcm import gen_tdcm as gen_tdcm
+from gen_surv.thmm import gen_thmm as gen_thmm
+
+def generate(model: str, **kwargs): ...

stubs/gen_surv/tdcm.pyi

@@ -0,0 +1,6 @@
+from gen_surv.bivariate import sample_bivariate_distribution as sample_bivariate_distribution
+from gen_surv.censoring import rexpocens as rexpocens, runifcens as runifcens
+from gen_surv.validate import validate_gen_tdcm_inputs as validate_gen_tdcm_inputs
+
+def generate_censored_observations(n, dist_par, model_cens, cens_par, beta, lam, b): ...
+def gen_tdcm(n, dist, corr, dist_par, model_cens, cens_par, beta, lam): ...

stubs/gen_surv/thmm.pyi

@@ -0,0 +1,5 @@
+from gen_surv.censoring import rexpocens as rexpocens, runifcens as runifcens
+from gen_surv.validate import validate_gen_thmm_inputs as validate_gen_thmm_inputs
+
+def calculate_transitions(z1: float, cens_par: float, beta: list, rate: list, rfunc) -> dict: ...
+def gen_thmm(n, model_cens, cens_par, beta, covar, rate): ...

stubs/gen_surv/validate.pyi

@@ -0,0 +1,6 @@
+def validate_gen_cphm_inputs(n: int, model_cens: str, cens_par: float, covar: float): ...
+def validate_gen_cmm_inputs(n: int, model_cens: str, cens_par: float, beta: list, covar: float, rate: list): ...
+def validate_gen_tdcm_inputs(n: int, dist: str, corr: float, dist_par: list, model_cens: str, cens_par: float, beta: list, lam: float): ...
+def validate_gen_thmm_inputs(n: int, model_cens: str, cens_par: float, beta: list, covar: float, rate: list): ...
+def validate_dg_biv_inputs(n: int, dist: str, corr: float, dist_par: list): ...
+def validate_gen_aft_log_normal_inputs(n, beta, sigma, model_cens, cens_par) -> None: ...

tasks.py

@@ -23,3 +23,25 @@ def publish(c):
 @task
 def clean(c):
     c.run("rm -rf dist build docs/build .pytest_cache .mypy_cache coverage.xml .coverage stubs")
+
+@task
+def git_push(c):
+    """
+    Stage all changes, prompt for a commit message, create a signed commit, and push.
+    """
+    import getpass
+
+    c.run("git add .")
+
+    try:
+        # Prompt for a commit message
+        message = input("Enter commit message: ").strip()
+        if not message:
+            print("Aborting: empty commit message.")
+            return
+
+        c.run(f'git commit -S -m "{message}"')
+        c.run("git push")
+    except KeyboardInterrupt:
+        print("\nAborted by user.")
+

tests/test_aft.py

@@ -0,0 +1,19 @@
+import pandas as pd
+from gen_surv.aft import gen_aft_log_normal
+
+def test_gen_aft_log_normal_runs():
+    df = gen_aft_log_normal(
+        n=10,
+        beta=[0.5, -0.2],
+        sigma=1.0,
+        model_cens="uniform",
+        cens_par=5.0,
+        seed=42
+    )
+    assert isinstance(df, pd.DataFrame)
+    assert not df.empty
+    assert "time" in df.columns
+    assert "status" in df.columns
+    assert "X0" in df.columns
+    assert "X1" in df.columns
+    assert set(df["status"].unique()).issubset({0, 1})