deploy: f2f3322

CONDUCT.html

@@ -241,7 +241,6 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="day3.3a_basic_plotting/readme.html">Plotting Basics</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/01_Introduction_to_Seaborn.html">Introduction to Seaborn</a></li>
 <li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/02_Plotting_distributions.html">Plotting Distributions with Seaborn</a></li>
-<li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/03_BONUS_Multivariate_views.html">Multivariate views</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="day3.3b_advanced_plotting/readme.html">Advanced Plotting</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>

CONTRIBUTING.html

@@ -241,7 +241,6 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="day3.3a_basic_plotting/readme.html">Plotting Basics</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/01_Introduction_to_Seaborn.html">Introduction to Seaborn</a></li>
 <li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/02_Plotting_distributions.html">Plotting Distributions with Seaborn</a></li>
-<li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/03_BONUS_Multivariate_views.html">Multivariate views</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="day3.3b_advanced_plotting/readme.html">Advanced Plotting</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>

README.html

@@ -239,7 +239,6 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="day3.3a_basic_plotting/readme.html">Plotting Basics</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-7"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/01_Introduction_to_Seaborn.html">Introduction to Seaborn</a></li>
 <li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/02_Plotting_distributions.html">Plotting Distributions with Seaborn</a></li>
-<li class="toctree-l2"><a class="reference internal" href="day3.3a_basic_plotting/03_BONUS_Multivariate_views.html">Multivariate views</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="day3.3b_advanced_plotting/readme.html">Advanced Plotting</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>

_sources/day1.3_image_processing_basics/00_images_as_arrays.ipynb

@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# Images are arrays of numbers\n",
     "\n",
@@ -15,7 +19,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -24,7 +32,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Numpy arrays\n",
     "\n",
@@ -34,7 +46,11 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -69,7 +85,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -98,15 +118,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "This output is almost the same as above with the difference that now it is indicated that we are dealing with a Numpy ```array```. Such Numpy arrays can now be treated as a one entity and we can perform the computation that we coudn't before:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "image = np.asarray(raw_image_array)"
@@ -115,7 +143,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -140,14 +172,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Note that these computations are very efficient because they are *vectorized*, i.e. they can in principle be performed in parallel."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Two important properties\n",
     "\n",
@@ -159,7 +199,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -179,7 +223,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -198,7 +246,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Other ways of creating arrays\n",
     "\n",
@@ -208,7 +260,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -236,7 +292,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -265,15 +325,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "As images are just arrays, we just set pixel values as if we were accessing arrays. From this you also learn that the first axis (coordinate 0) is going from top to bottom while the second axis (coordinate 3) goes from left to right."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -304,15 +372,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "For studying noise, we can for example create an image with random values using [```np.random.random```](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.random.html#numpy.random.Generator.random)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -362,4 +438,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}