CamelCase convention for class names: distribution -> Distribution

examples/ownData.ipynb

@@ -53,7 +53,7 @@
     "wine = datasets.load_wine()\n",
     "dist = []\n",
     "for c in np.unique(wine.target):\n",
-    "    dist.append(ua.distribution(np.array(wine.data[wine.target == c]), \"Normal\"))"
+    "    dist.append(ua.Distribution(np.array(wine.data[wine.target == c]), \"Normal\"))"
    ]
   },
   {

tests/api_consistency_test.py

@@ -20,7 +20,7 @@ def test_dr_module():
     import uadapy.distribution
     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)]
+    distribs = [uadapy.Distribution(np.random.rand(10, 3), name='Normal') for _ in range(4)]
     uadapy.dr.uapca(distributions=distribs, n_dims=2)
     uadapy.dr.uamds(distributions=distribs, n_dims=2)
 

tests/test_distrib.py

@@ -3,7 +3,7 @@
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import traceback
-from uadapy import distribution
+from uadapy import Distribution
 import numpy as np
 import scipy as sp
 import scipy.stats as st
@@ -71,11 +71,11 @@ def test_distrib_class():
 
     # initialize distribution object for each of the scipy distribs (univariate)
     for scipi_distrib in model_1D:
-        distrib = distribution(scipi_distrib)
+        distrib = Distribution(scipi_distrib)
     # initialize distribution object for each of the scipy distribs (multivariate)
     for scipi_distrib in model_nD:
         try:
-            distrib = distribution(scipi_distrib)
+            distrib = Distribution(scipi_distrib)
             cov = distrib.cov()
             if cov.shape[0] != n or cov.shape[1] != n:
                 raise RuntimeError(f"shape expected to be {n} x {n}, but was {cov.shape}")

uadapy/__init__.py

@@ -1,3 +1,3 @@
-from .distribution import distribution
+from .distribution import Distribution
 
-__all__ = ['distribution']
+__all__ = ['Distribution']

uadapy/data/data.py

@@ -1,6 +1,6 @@
 from sklearn import datasets
 import numpy as np
-from uadapy import distribution
+from uadapy import Distribution
 
 def load_iris_normal():
     """
@@ -10,7 +10,7 @@ def load_iris_normal():
     iris = datasets.load_iris()
     dist = []
     for c in np.unique(iris.target):
-        dist.append(distribution(np.array(iris.data[iris.target == c]), "Normal"))
+        dist.append(Distribution(np.array(iris.data[iris.target == c]), "Normal"))
     return dist
 
 def load_iris():
@@ -21,5 +21,5 @@ def load_iris():
     iris = datasets.load_iris()
     dist = []
     for c in np.unique(iris.target):
-        dist.append(distribution(np.array(iris.data[iris.target == c])))
+        dist.append(Distribution(np.array(iris.data[iris.target == c])))
     return dist

uadapy/distribution.py

@@ -4,7 +4,7 @@
 from scipy.stats import _multivariate as mv
 
 
-class distribution:
+class Distribution:
 
     def __init__(self, model, name="", n_dims=1):
         """

uadapy/dr/uamds.py

@@ -13,7 +13,7 @@
 from scipy.spatial import distance_matrix
 from scipy.optimize import minimize
 from scipy.stats import multivariate_normal
-from uadapy import distribution
+from uadapy import Distribution
 
 
 def precalculate_constants(normal_distr_spec: np.ndarray) -> tuple:
@@ -549,7 +549,7 @@ def uamds(distributions: list, n_dims: int = 2, seed: int = 0):
         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)))
+            distribs_lo.append(Distribution(multivariate_normal(m, c)))
         return distribs_lo
     except Exception as e:
         raise Exception(f'Something went wrong. Did you input normal distributions? Exception:{e}')

uadapy/dr/uapca.py

@@ -1,5 +1,5 @@
 import numpy as np
-from uadapy import distribution
+from uadapy import Distribution
 from scipy.stats import multivariate_normal
 
 def uapca(distributions, n_dims: int = 2):
@@ -18,7 +18,7 @@ def uapca(distributions, n_dims: int = 2):
         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)))
+            dist_pca.append(Distribution(multivariate_normal(m, c)))
         return dist_pca
     except Exception as e:
         raise Exception(f'Something went wrong. Did you input normal distributions? Exception:{e}')

uadapy/plotting/plots1D.py

@@ -1,5 +1,5 @@
 import numpy as np
-from uadapy import distribution
+from uadapy import Distribution
 import matplotlib.pyplot as plt
 from math import ceil, sqrt
 import glasbey as gb
@@ -67,7 +67,7 @@ def setup_plot(distributions, num_samples, seed, fig=None, axs=None, colors=None
 
     samples = []
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
 
     # Calculate the layout of subplots

uadapy/plotting/plots2D.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from uadapy import distribution
+from uadapy import Distribution
 from numpy import ma
 from matplotlib import ticker
 
@@ -35,7 +35,7 @@ def plot_samples(distributions, num_samples, seed=55, **kwargs):
         List of Axes objects used for plotting.
     """
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
     for d in distributions:
         samples = d.sample(num_samples, seed)
@@ -93,7 +93,7 @@ def plot_contour(distributions, resolution=128, ranges=None, quantiles:list=None
         If a quantile is not between 0 and 100 (exclusive), or if a quantile results in an index that is out of bounds.
     """
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
     contour_colors = generate_spectrum_colors(len(distributions))
 
@@ -188,7 +188,7 @@ def plot_contour_bands(distributions, num_samples, resolution=128, ranges=None,
         If a quantile is not between 0 and 100 (exclusive), or if a quantile results in an index that is out of bounds.
     """
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
 
     # Sequential and perceptually uniform colormaps

uadapy/plotting/plotsND.py

@@ -1,6 +1,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from uadapy import distribution
+from uadapy import Distribution
 import uadapy.plotting.utils as utils
 
 def plot_samples(distributions, num_samples, seed=55, **kwargs):
@@ -29,7 +29,7 @@ def plot_samples(distributions, num_samples, seed=55, **kwargs):
         List of Axes objects used for plotting.
     """
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
     # Create matrix
     numvars = distributions[0].n_dims
@@ -110,7 +110,7 @@ def plot_contour(distributions, num_samples, resolution=128, ranges=None, quanti
         If the dimension of the distribution is less than 2.
     """
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
     contour_colors = utils.generate_spectrum_colors(len(distributions))
     # Create matrix
@@ -239,7 +239,7 @@ def plot_contour_samples(distributions, num_samples, resolution=128, ranges=None
         If the dimension of the distribution is less than 2.
     """
 
-    if isinstance(distributions, distribution):
+    if isinstance(distributions, Distribution):
         distributions = [distributions]
     contour_colors = utils.generate_spectrum_colors(len(distributions))
     # Create matrix