add examples

example.py

@@ -19,7 +19,7 @@ def example_uamds():
     #plotsND.plot_contour_samples(distribs_lo, 10000, 128, None, [5, 25, 50, 75, 95])
 
 def example_kde():
-    samples = np.random.randn(1000,2)
+    samples = np.random.randn(1000, 2)
     distr = ua.distribution.distribution(samples)
     plots2D.plot_contour(distr)
 

examples/dimensionalityReduction.ipynb → examples/ownData.ipynb

@@ -5,23 +5,12 @@
    "execution_count": null,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
-    "is_executing": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "import uadapy"
+    ""
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   },
-   "id": "11bfe50ff5bc1eb0"
   }
  ],
  "metadata": {

examples/uamds.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-07-02T14:56:08.629731500Z",
+     "start_time": "2024-07-02T14:56:08.622215400Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import uadapy.data as data\n",
+    "import uadapy.dr.uamds as uamds\n",
+    "import uadapy.plotting.plots2D as plots2D\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Loading data\n",
+    "We provide ready-to-use toy datasets for getting started with the analysis quickly. In this example, we fit normal distributions to the points of the iris-dataset."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "7d50ba4e72fe8896"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [],
+   "source": [
+    "distribs_hi = data.load_iris_normal()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-07-02T14:56:09.122543800Z",
+     "start_time": "2024-07-02T14:56:09.107284800Z"
+    }
+   },
+   "id": "399ccab20e87f833"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Applying UAMDS\n",
+    "Uncertainty-aware multi-dimensional scaling can be applied directly to reduce the data to a user-defined target dimension."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "6a4094383bfc23a6"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [],
+   "source": [
+    "distribs_lo = uamds.uamds(distribs_hi, dims=2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-07-02T14:56:09.660688100Z",
+     "start_time": "2024-07-02T14:56:09.644078700Z"
+    }
+   },
+   "id": "17d410f46ac143ad"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Visualizing data\n",
+    "The data can be directly visualized, for example as a contour plot."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "b90713040349a03b"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "plots2D.plot_contour(distribs_lo, ranges=[[5,13],[8,15]])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true
+   },
+   "id": "36652dfe9d1204d4"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "8215650467252202"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

uadapy/plotting/plots1D.py

@@ -156,7 +156,11 @@ def plot_1d_distribution(distributions, num_samples, plot_types:list, seed=55, f
 
     fig, axs, samples, palette, num_plots, num_cols = setup_plot(distributions, num_samples, seed, fig, axs, colors, **kwargs)
 
-    num_attributes = np.shape(samples)[2]
+    # Check number of attributes
+    num_attributes = 1
+    if np.ndim(samples) > 2:
+        num_attributes = np.shape(samples)[2]
+
     if labels:
         ticks = range(len(labels))
     else:
@@ -169,6 +173,8 @@ def plot_1d_distribution(distributions, num_samples, plot_types:list, seed=55, f
                 y_min = 9999
                 y_max = -9999
                 for k, sample in enumerate(samples):
+                    if np.ndim(sample) == 1:
+                        sample = np.array(sample).reshape(1,-1)
                     if 'boxplot' in plot_types:
                         boxprops = dict(facecolor=palette[k % len(palette)], edgecolor='black')
                         whiskerprops = dict(color='black', linestyle='--')