rename coefs to coeffs (#313)

* rename coefs to coeffs * remove unused imports * CHANGELOG
MESMER-group · Sep 29, 2023 · ee3ea68 · ee3ea68
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diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -30,7 +30,9 @@ New Features
      of the standard deviation (`#306 <https://github.com/MESMER-group/mesmer/issues/306>`_).
      By `Mathias Hauser`_.
    - Add ``mesmer.stats.auto_regression._draw_auto_regression_correlated_np``: to draw samples of an
-     auto regression model (`#161 <https://github.com/MESMER-group/mesmer/pull/161>`_).
+     auto regression model (
+     `#161 <https://github.com/MESMER-group/mesmer/pull/161>`_ and
+     `#313 <https://github.com/MESMER-group/mesmer/pull/313>`_).
      By `Mathias Hauser`_.
    - Extract function to select the order of the auto regressive model: ``mesmer.stats.auto_regression._select_ar_order_xr``
      (`#176 <https://github.com/MESMER-group/mesmer/pull/176>`_).

diff --git a/mesmer/create_emulations/create_emus_gv.py b/mesmer/create_emulations/create_emus_gv.py
@@ -147,9 +147,6 @@ def create_emus_gv_AR(params_gv, nr_emus_v, nr_ts_emus_v, seed):
         - [scen] (2d array  (emus, time) of global variability emulation time series)
     """
 
-    # ensure reproducibility
-    np.random.seed(seed)
-
     # buffer so that initial start at 0 does not influence overall result
     # Should this buffer be based on the length of ar_lags instead of hard-coded?
     buffer = 50
@@ -171,8 +168,8 @@ def create_emus_gv_AR(params_gv, nr_emus_v, nr_ts_emus_v, seed):
 
     emus_gv = _draw_auto_regression_correlated_np(
         intercept=ar_int,
-        # reshape to n_coefs x n_cells
-        coefs=ar_coefs[:, np.newaxis],
+        # reshape to n_coefs x n_cells (cell was squeezed in train_gv.train_gv_AR)
+        coeffs=ar_coefs[:, np.newaxis],
         covariance=AR_std_innovs**2,  # pass the (co-)variance!
         n_samples=nr_emus_v,
         n_ts=nr_ts_emus_v,

diff --git a/mesmer/create_emulations/create_emus_lv.py b/mesmer/create_emulations/create_emus_lv.py
@@ -187,7 +187,8 @@ def create_emus_lv_AR1_sci(emus_lv, params_lv, preds_lv, cfg):
             emus_ar = _draw_auto_regression_correlated_np(
                 intercept=params_lv["AR1_int"][targ],
                 # reshape to n_coefs x n_cells
-                coefs=params_lv["AR1_coef"][targ][np.newaxis, :],
+                # (coeffs was squeezed in train_lv.train_lv_AR1_sci)
+                coeffs=params_lv["AR1_coef"][targ][np.newaxis, :],
                 covariance=params_lv["loc_ecov_AR1_innovs"][targ],
                 n_samples=nr_emus_v,
                 n_ts=nr_ts_emus_stoch_v,

diff --git a/mesmer/stats/auto_regression.py b/mesmer/stats/auto_regression.py
@@ -80,26 +80,26 @@ def _select_ar_order_np(data, maxlag, ic="bic"):
 
 
 def _draw_auto_regression_correlated_np(
-    *, intercept, coefs, covariance, n_samples, n_ts, seed, buffer
+    *, intercept, coeffs, covariance, n_samples, n_ts, seed, buffer
 ):
     """
     Draw time series of an auto regression process with possibly spatially-correlated
     innovations
 
     Creates `n_samples` auto-correlated time series of order `ar_order` and length
-    `n_ts` for each set of `n_coefs` coefficients (typically one set for each grid
-    point), the resulting array has shape n_samples x n_ts x n_coefs. The innovations
+    `n_ts` for each set of `n_coeffs` coefficients (typically one set for each grid
+    point), the resulting array has shape n_samples x n_ts x n_coeffs. The innovations
     can be spatially correlated.
 
     Parameters
     ----------
-    intercept : float or ndarray of length n_coefs
+    intercept : float or ndarray of length n_coeffs
         Intercept of the model.
-    coefs : ndarray of shape ar_order x n_coefs
+    coeffs : ndarray of shape ar_order x n_coeffs
         The coefficients of the autoregressive process. Must be a 2D array with the
         autoregressive coefficients along axis=0, while axis=1 contains all independent
         coefficients.
-    covariance : float or ndarray of shape n_coefs x n_coefs
+    covariance : float or ndarray of shape n_coeffs x n_coeffs
         The (co-)variance array. Must be symmetric and positive-semidefinite.
     n_samples : int
         Number of samples to draw for each set of coefficients.
@@ -115,7 +115,7 @@ def _draw_auto_regression_correlated_np(
     -------
     out : ndarray
         Drawn realizations of the specified autoregressive process. The array has shape
-        n_samples x n_ts x n_coefs.
+        n_samples x n_ts x n_coeffs.
 
     Notes
     -----
@@ -128,26 +128,26 @@ def _draw_auto_regression_correlated_np(
     intercept = np.asarray(intercept)
     covariance = np.atleast_2d(covariance)
 
-    # coeffs assumed to be ar_order x n_coefs
-    ar_order, n_coefs = coefs.shape
+    # coeffs assumed to be ar_order x n_coeffs
+    ar_order, n_coeffs = coeffs.shape
 
-    # arbitrary lags? see https://github.com/MESMER-group/mesmer/issues/164
+    # arbitrary lags? no, see: https://github.com/MESMER-group/mesmer/issues/164
     ar_lags = np.arange(1, ar_order + 1, dtype=int)
 
     # ensure reproducibility (TODO: clarify approach to this, see #35)
     np.random.seed(seed)
 
-    # innovations has shape (n_samples, n_ts + buffer, n_coefs)
+    # innovations has shape (n_samples, n_ts + buffer, n_coeffs)
     innovations = np.random.multivariate_normal(
-        mean=np.zeros(n_coefs),
+        mean=np.zeros(n_coeffs),
         cov=covariance,
         size=[n_samples, n_ts + buffer],
     )
 
-    out = np.zeros([n_samples, n_ts + buffer, n_coefs])
+    out = np.zeros([n_samples, n_ts + buffer, n_coeffs])
     for t in range(ar_order + 1, n_ts + buffer):
 
-        ar = np.sum(coefs * out[:, t - ar_lags, :], axis=1)
+        ar = np.sum(coeffs * out[:, t - ar_lags, :], axis=1)
 
         out[:, t, :] = intercept + ar + innovations[:, t, :]
 

diff --git a/tests/unit/test_auto_regression.py b/tests/unit/test_auto_regression.py
@@ -85,7 +85,7 @@ def test_draw_auto_regression_correlated_np_shape(ar_order, n_cells, n_samples,
 
     result = mesmer.stats.auto_regression._draw_auto_regression_correlated_np(
         intercept=intercept,
-        coefs=coefs,
+        coeffs=coefs,
         covariance=covariance,
         n_samples=n_samples,
         n_ts=n_ts,
@@ -103,7 +103,7 @@ def test_draw_auto_regression_deterministic_intercept(intercept):
 
     result = mesmer.stats.auto_regression._draw_auto_regression_correlated_np(
         intercept=intercept,
-        coefs=np.array([[0]]),
+        coeffs=np.array([[0]]),
         covariance=[0],
         n_samples=1,
         n_ts=3,
@@ -117,7 +117,7 @@ def test_draw_auto_regression_deterministic_intercept(intercept):
 
     result = mesmer.stats.auto_regression._draw_auto_regression_correlated_np(
         intercept=np.array([[0, intercept]]),
-        coefs=np.array([[0, 0]]),
+        coeffs=np.array([[0, 0]]),
         covariance=np.zeros((2, 2)),
         n_samples=1,
         n_ts=1,
@@ -134,7 +134,7 @@ def test_draw_auto_regression_deterministic_coefs_buffer():
 
     result = mesmer.stats.auto_regression._draw_auto_regression_correlated_np(
         intercept=1,
-        coefs=np.array([[1]]),
+        coeffs=np.array([[1]]),
         covariance=[0],
         n_samples=1,
         n_ts=4,
@@ -151,7 +151,7 @@ def test_draw_auto_regression_deterministic_coefs_buffer():
     for i, buffer in enumerate([1, 2]):
         result = mesmer.stats.auto_regression._draw_auto_regression_correlated_np(
             intercept=1,
-            coefs=np.array([[0.5]]),
+            coeffs=np.array([[0.5]]),
             covariance=[0],
             n_samples=1,
             n_ts=4,
@@ -166,7 +166,7 @@ def test_draw_auto_regression_random():
 
     result = mesmer.stats.auto_regression._draw_auto_regression_correlated_np(
         intercept=1,
-        coefs=np.array([[0.375], [0.125]]),
+        coeffs=np.array([[0.375], [0.125]]),
         covariance=0.5,
         n_samples=1,
         n_ts=4,