swc_workshop_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The jupyter notebook we used in the swc-Workshop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Header with all used and needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We started with assigning values of the data types integer, float and string to variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg = 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('the weight in kilogramm is',weight_kg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduce the numpy library array object class to handle nummerical data arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = numpy.array([[1,2,3],[4,5,6]]) #2D[[a,b],[c,d]] #[[[]]] #3D[[[],[]],[[],[]]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load data from a directory here the inflammation-01.csv data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn about function attributes and slicing arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[30,20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_data= data[0:6,3:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[4:8,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use numpy inbuild function to calculate metrics on the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('maximum inflammation:', maxval)\n",
    "print('minimum inflammation:', minval)\n",
    "print('standard deviation:', stdval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_3 = data[2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxval, minval, stdval = numpy.max(patient_3), numpy.min(patient_3), numpy.std(patient_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('maximum inflammation patient_3:', numpy.max(data[2,:]))\n",
    "print('minimum inflammation patient_3:', minval)\n",
    "print('standard deviation patient_3:', stdval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_inflammation_ap = numpy.max(data, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(numpy.mean(data, axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.mean(data,axis=0).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Addressing elements in collection/ensembles like strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "element = 'oxygen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "element[-5:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start to create the first simple python plots and figures / based on the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ave_inflammation = numpy.mean(data,axis = 0)\n",
    "ave_plot = matplotlib.pyplot.plot(ave_inflammation,label = 'mean')\n",
    "matplotlib.pyplot.xlabel('days')\n",
    "matplotlib.pyplot.legend()\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_plot = matplotlib.pyplot.plot(numpy.max(data,axis = 0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_plot = matplotlib.pyplot.plot(numpy.min(data, axis = 0))\n",
    "max_plot = matplotlib.pyplot.plot(numpy.max(data,axis = 0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a phton matplotlib.pyplot figure with three subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = matplotlib.pyplot.figure(figsize=(10.0,3.0))\n",
    "\n",
    "axes1 = fig.add_subplot(1,3,1)\n",
    "axes2 = fig.add_subplot(1,3,2)\n",
    "axes3 = fig.add_subplot(1,3,3)\n",
    "\n",
    "axes1.set_ylabel('average')\n",
    "axes1.plot(numpy.mean(data,axis =0))\n",
    "\n",
    "axes2.set_ylabel('max')\n",
    "axes2.plot(numpy.max(data,axis = 0))\n",
    "\n",
    "axes3.set_ylabel('min')\n",
    "axes3.plot(numpy.min(data,axis = 0))\n",
    "axes3.set_ylim(0,6)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start to introduce the python loop mechanics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "element = 'lead' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "element[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(element[0])\n",
    "print(element[1])\n",
    "print(element[2])\n",
    "print(element[3])\n",
    "print(element[4])\n",
    "print(element[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = 'oxygen'\n",
    "for char in word:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 0\n",
    "for char in word:\n",
    "    length = length +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "letter = 'z'\n",
    "print(letter)\n",
    "length = 0\n",
    "for letter in 'abcd':\n",
    "    print(letter)\n",
    "    length = length + 1\n",
    "print(letter)\n",
    "print(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(letter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for int in range(4,10,2):\n",
    "    print(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(5**3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a loop that calculates the same result as 5 ** 3 using multiplication (and without exponentiation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "value = 1\n",
    "for element in range(0,3):\n",
    "    value = value * 5\n",
    "    print('during the loop', value)\n",
    "print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = 'abc' + 'd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "string_old = 'Newton'\n",
    "new_string = 'notweN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_string = ''\n",
    "for char in string_old:\n",
    "    new_string = char + new_string\n",
    "    print(new_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(new_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing that two strings can be concatenated using the + operator, write a loop that takes a string and produces a new string with the characters in reverse order, so 'Newton' becomes 'notweN'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "string_old[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx,char in enumerate(string_old):\n",
    "    print(idx,char)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introducing the list concept to store collectibles like strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_char = ['a','b','c','d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(list_char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_char[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for char in list_char:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_names = ['Newton', 'Curie', 'Darwin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in list_names:\n",
    "    print(name)\n",
    "    for char in name:\n",
    "        print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_names = ['Newton', 'Curie', 'Dargin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_names[2] = 'Darwin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use of the glob library to collect matching filemanes and store them into a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_file_names = glob.glob('inflammation*.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sorted(list_file_names))\n",
    "len(list_file_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "import glob\n",
    "\n",
    "file_names = sorted(glob.glob('inflammation*.csv'))\n",
    "file_names = file_names[:3]\n",
    "\n",
    "#print(file_names)\n",
    "\n",
    "for filename in file_names:\n",
    "    #print(filename)\n",
    "    data = numpy.loadtxt(fname= filename, delimiter = ',')\n",
    "    \n",
    "    fig = matplotlib.pyplot.figure(figsize=(10.0,3.0))\n",
    "    \n",
    "    axes1 = fig.add_subplot(1,3,1)\n",
    "    axes2 = fig.add_subplot(1,3,2)\n",
    "    axes3 = fig.add_subplot(1,3,3)\n",
    "    \n",
    "    axes1.set_ylabel('average')\n",
    "    axes1.plot(numpy.mean(data,axis=0))\n",
    "    \n",
    "    axes2.set_ylabel('max')\n",
    "    axes2.plot(numpy.max(data,axis=0))\n",
    "    \n",
    "    axes3.set_ylabel('min')\n",
    "    axes3.plot(numpy.min(data,axis = 0))\n",
    "    \n",
    "    matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the difference between the average inflammation in the first and third and second and third data set. Create two plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = numpy.array([1,2,3,4])\n",
    "a2 = numpy.array([5,6,7,8])\n",
    "a3 = a1-a2\n",
    "print(a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = numpy.loadtxt(fname=file_names[0], delimiter = ',')\n",
    "data2 = numpy.loadtxt(fname=file_names[1], delimiter = ',')\n",
    "data3 = numpy.loadtxt(fname=file_names[2], delimiter = ',')\n",
    "\n",
    "fig = matplotlib.pyplot.figure(figsize=(10.0,3.0))\n",
    "\n",
    "axes1 = fig.add_subplot(2,1,1)\n",
    "axes2 = fig.add_subplot(2,1,2)\n",
    "\n",
    "axes1.set_ylabel('average')\n",
    "axes1.plot(numpy.mean(numpy.loadtxt(fname=file_names[0], delimiter = ','),axis=0)-numpy.mean(data3,axis=0))\n",
    "\n",
    "axes2.set_ylabel('average')\n",
    "axes2.plot(numpy.mean(data2,axis=0)-numpy.mean(data3,axis=0))\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names = sorted(glob.glob('inflammation*.csv'))\n",
    "for i in file_names:\n",
    "    print(numpy.loadtxt(fname=i, delimiter = ',').shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = (data1+data2+data3+...+data12)/12\n",
    "file_names = sorted(glob.glob('inflammation*.csv'))\n",
    "empty_array = numpy.zeros((60,40))\n",
    "for data_set in file_names:\n",
    "    empty_array = empty_array + numpy.loadtxt(fname=data_set, delimiter = ',') \n",
    "    #data = numpy.loadtxt(fname=data_set, delimiter = ',')\n",
    "data_sum = empty_array/len(file_names)    \n",
    "\n",
    "matplotlib.pyplot.imshow(data_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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