refactor dim, dims -> n_dims

examples/uamds.ipynb

@@ -88,9 +88,7 @@
      ]
     }
    ],
-   "source": [
-    "distribs_lo = uamds(distribs_hi, dims=2)"
-   ]
+   "source": "distribs_lo = uamds(distribs_hi, n_dims=2)"
   },
   {
    "cell_type": "markdown",

tests/api_consistency_test.py

@@ -21,8 +21,8 @@ def test_dr_module():
     import numpy as np
     # list of distributions (normal distributions estimated from random data
     distribs = [uadapy.distribution(np.random.rand(10, 3), name='Normal') for _ in range(4)]
-    uadapy.dr.uapca(distributions=distribs, dims=2)
-    uadapy.dr.uamds(distributions=distribs, dims=2)
+    uadapy.dr.uapca(distributions=distribs, n_dims=2)
+    uadapy.dr.uamds(distributions=distribs, n_dims=2)
 
 
 def test_plotting_module():

uadapy/distribution.py

@@ -6,15 +6,15 @@
 
 class distribution:
 
-    def __init__(self, model, name="", dim = 1):
+    def __init__(self, model, name="", n_dims=1):
         """
         Creates a distribution, if samples are passed as the first parameter,
         no assumptions about the distribution are made. For the pdf and the sampling,
         a KDE is used. If the name is "Normal", the samples
         are treated as samples of a normal distribution.
         :param model: A scipy.stats distribution or samples
         :param name: The name of the distribution
-        :param dim: The dimensionality of the distribution
+        :param n_dims: The dimensionality of the distribution
         """
         if name:
             self.name = name
@@ -28,9 +28,9 @@ def __init__(self, model, name="", dim = 1):
             self.model = model
         mean = self.mean()
         if isinstance(mean, np.ndarray):
-            self.dim = len(self.mean())
+            self.n_dims = len(self.mean())
         else:
-            self.dim = 1
+            self.n_dims = 1
         self.kde = None
         if isinstance(self.model, np.ndarray):
             self.kde = stats.gaussian_kde(self.model.T)

uadapy/dr/uamds.py

@@ -521,7 +521,7 @@ def apply_uamds(means: list[np.ndarray], covs: list[np.ndarray], target_dim=2) -
     }
 
 
-def uamds(distributions: list, dims: int=2, seed: int=0):
+def uamds(distributions: list, n_dims: int = 2, seed: int = 0):
     """
     Applies the UAMDS algorithm to the provided distributions and returns the projected distributions
     in lower-dimensional space. It assumes multivariate normal distributions.
@@ -532,7 +532,7 @@ def uamds(distributions: list, dims: int=2, seed: int=0):
     ----------
     distributions : list
         list of input distributions (distribution objects offering mean() and cov() methods)
-    dims : int
+    n_dims : int
         target dimensionality, 2 by default.
     seed : int
         Set the random seed for the initialization, 0 by default
@@ -546,7 +546,7 @@ def uamds(distributions: list, dims: int=2, seed: int=0):
         np.random.seed(seed)
         means = [d.mean() for d in distributions]
         covs = [d.cov() for d in distributions]
-        result = apply_uamds(means, covs, dims)
+        result = apply_uamds(means, covs, n_dims)
         distribs_lo = []
         for (m, c) in zip(result['means'], result['covs']):
             distribs_lo.append(distribution(multivariate_normal(m, c)))

uadapy/dr/uapca.py

@@ -2,20 +2,20 @@
 from uadapy import distribution
 from scipy.stats import multivariate_normal
 
-def uapca(distributions, dims: int):
+def uapca(distributions, n_dims: int = 2):
     """
     Applies UAPCA algorithm to the distribution and returns the distribution
     in lower-dimensional space. It assumes a normal distributions. If you apply
     other distributions that provide mean and covariance, these values would be used
     to approximate a normal distribution
     :param distributions: List of input distributions
-    :param dims: Target dimension
+    :param n_dims: Target dimension
     :return: List of distributions in low-dimensional space
     """
     try:
         means = np.array([d.mean() for d in distributions])
         covs = np.array([d.cov() for d in distributions])
-        means_pca, covs_pca = transform_uapca(means, covs, dims)
+        means_pca, covs_pca = transform_uapca(means, covs, n_dims)
         dist_pca = []
         for (m, c) in zip(means_pca, covs_pca):
             dist_pca.append(distribution(multivariate_normal(m, c)))

uadapy/plotting/plots1D.py

@@ -72,7 +72,7 @@ def setup_plot(distributions, num_samples, seed, fig=None, axs=None, colors=None
 
     # Calculate the layout of subplots
     if axs is None:
-        num_plots = distributions[0].dim
+        num_plots = distributions[0].n_dims
         num_rows = ceil(sqrt(num_plots))
         num_cols = ceil(num_plots / num_rows)
         fig, axs = plt.subplots(num_rows, num_cols)

uadapy/plotting/plotsND.py

@@ -32,7 +32,7 @@ def plot_samples(distributions, num_samples, seed=55, **kwargs):
     if isinstance(distributions, distribution):
         distributions = [distributions]
     # Create matrix
-    numvars = distributions[0].dim
+    numvars = distributions[0].n_dims
     fig, axes = plt.subplots(nrows=numvars, ncols=numvars)
     contour_colors = utils.generate_spectrum_colors(len(distributions))
     for ax in axes.flat:
@@ -42,7 +42,7 @@ def plot_samples(distributions, num_samples, seed=55, **kwargs):
 
     # Fill matrix with data
     for k, d in enumerate(distributions):
-        if d.dim < 2:
+        if d.n_dims < 2:
             raise Exception('Wrong dimension of distribution')
         samples = d.sample(num_samples, seed)
         for i, j in zip(*np.triu_indices_from(axes, k=1)):
@@ -114,7 +114,7 @@ def plot_contour(distributions, num_samples, resolution=128, ranges=None, quanti
         distributions = [distributions]
     contour_colors = utils.generate_spectrum_colors(len(distributions))
     # Create matrix
-    numvars = distributions[0].dim
+    numvars = distributions[0].n_dims
     if ranges is None:
         min_val = np.zeros(distributions[0].mean().shape)+1000
         max_val = np.zeros(distributions[0].mean().shape)-1000
@@ -133,11 +133,11 @@ def plot_contour(distributions, num_samples, resolution=128, ranges=None, quanti
 
     # Fill matrix with data
     for k, d in enumerate(distributions):
-        if d.dim < 2:
+        if d.n_dims < 2:
             raise Exception('Wrong dimension of distribution')
         dims = ()
         test = ()
-        for i in range(d.dim):
+        for i in range(d.n_dims):
             test = (*test, i)
             x = np.linspace(ranges[i][0], ranges[i][1], resolution)
             dims = (*dims, x)
@@ -167,7 +167,7 @@ def plot_contour(distributions, num_samples, resolution=128, ranges=None, quanti
         for i, j in zip(*np.triu_indices_from(axes, k=1)):
             for x, y in [(i, j), (j, i)]:
                 color = contour_colors[k]
-                indices = list(np.arange(d.dim))
+                indices = list(np.arange(d.n_dims))
                 indices.remove(x)
                 indices.remove(y)
                 pdf_agg = np.sum(pdf, axis=tuple(indices))
@@ -177,7 +177,7 @@ def plot_contour(distributions, num_samples, resolution=128, ranges=None, quanti
 
         # Fill diagonal
         for i in range(numvars):
-            indices = list(np.arange(d.dim))
+            indices = list(np.arange(d.n_dims))
             indices.remove(i)
             axes[i,i].plot(dims[i], np.sum(pdf, axis=tuple(indices)), color=color)
             axes[i,i].xaxis.set_visible(True)
@@ -243,7 +243,7 @@ def plot_contour_samples(distributions, num_samples, resolution=128, ranges=None
         distributions = [distributions]
     contour_colors = utils.generate_spectrum_colors(len(distributions))
     # Create matrix
-    numvars = distributions[0].dim
+    numvars = distributions[0].n_dims
     if ranges is None:
         min_val = np.zeros(distributions[0].mean().shape)+1000
         max_val = np.zeros(distributions[0].mean().shape)-1000
@@ -263,10 +263,10 @@ def plot_contour_samples(distributions, num_samples, resolution=128, ranges=None
     # Fill matrix with data
     for k, d in enumerate(distributions):
         samples = d.sample(num_samples, seed)
-        if d.dim < 2:
+        if d.n_dims < 2:
             raise Exception('Wrong dimension of distribution')
         dims = ()
-        for i in range(d.dim):
+        for i in range(d.n_dims):
             x = np.linspace(ranges[i][0], ranges[i][1], resolution)
             dims = (*dims, x)
         coordinates = np.array(np.meshgrid(*dims)).transpose(tuple(range(1, numvars+1)) + (0,))
@@ -295,7 +295,7 @@ def plot_contour_samples(distributions, num_samples, resolution=128, ranges=None
         for i, j in zip(*np.triu_indices_from(axes, k=1)):
             for x, y in [(i, j), (j, i)]:
                 color = contour_colors[k]
-                indices = list(np.arange(d.dim))
+                indices = list(np.arange(d.n_dims))
                 indices.remove(x)
                 indices.remove(y)
                 pdf_agg = np.sum(pdf, axis=tuple(indices))
@@ -308,7 +308,7 @@ def plot_contour_samples(distributions, num_samples, resolution=128, ranges=None
 
         # Fill diagonal
         for i in range(numvars):
-            indices = list(np.arange(d.dim))
+            indices = list(np.arange(d.n_dims))
             indices.remove(i)
             axes[i,i].plot(dims[i], np.sum(pdf, axis=tuple(indices)), color=color)
             axes[i,i].xaxis.set_visible(True)