delete unused functions

pyfixest/utils.py

@@ -222,63 +222,4 @@ def get_data(N=1000, seed=1234, beta_type="1", error_type="1", model="Feols"):
     df["weights"] = rng.uniform(0, 1, N)
     # df["weights"].iloc[]
 
-    return df
-
-
-def get_poisson_data(N=1000, seed=4320):
-    """
-    Generate data following a Poisson regression DGP.
-
-    Parameters
-    ----------
-    N : int, optional
-        Number of observations. Default is 1000.
-    seed : int, optional
-        Seed for the random number generator. Default is 4320.
-
-    Returns
-    -------
-    pandas.DataFrame
-        Generated data with columns 'Y', 'X1', 'X2', 'X3', and 'X4'.
-    """
-    # create data
-    np.random.seed(seed)
-    X1 = np.random.normal(0, 1, N)
-    X2 = np.random.choice([0, 1], N, True)
-    X3 = np.random.choice([0, 1, 2, 3, 4, 5, 6], N, True)
-    X4 = np.random.choice([0, 1], N, True)
-    beta = np.array([1, 0, 1, 0])
-    u = np.random.normal(0, 1, N)
-    mu = np.exp(1 + X1 * beta[0] + X2 * beta[1] + X3 * beta[2] + X4 * beta[3] + u)
-
-    Y = np.random.poisson(mu, N)
-
-    return pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3, "X4": X4})
-
-
-def absolute_diff(x, y, tol=1e-03):
-    """
-
-    Calculate the absolute difference between two values.
-
-    Parameters
-    ----------
-    x : numpy.ndarray
-        Numeric array representing the reference value.
-    y : numpy.ndarray
-        Numeric array representing the value to compare against.
-    tol : float, optional
-        Tolerance value used to determine if two values are different. Default is 1e-03.
-
-    Returns
-    -------
-    bool
-        True if the absolute difference is greater than the tolerance and there
-        is a non-zero value in y, False otherwise.
-    """
-    absolute_diff = (np.abs(x - y) > tol).any()
-    if not any(y == 0):
-        relative_diff = (np.abs(x - y) / np.abs(y) > tol).any()
-        return absolute_diff and relative_diff
-    else:
-        return absolute_diff
+    return df