Improved for the 2023 course

Author: Oiangu9

EXERCISES/Exercises_Lecture_04.ipynb

@@ -481,6 +481,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "---\n",
+    "---\n",
     "# Exercise 5: Process the Data Taken in the Lab\n",
     "In this exercise we will simulate the data processing that experimental sciences require after an experiment. We will imagine that you have taken some measurements by hand in an excel file (the attached file `Lab_Data.xlsx`), and a machine has generated some measurements for three replicas of the experiment, each in a text file (the attached files `Replica_1.txt`, `Replica_2.txt`, `Replica_3.txt`). The excel file has two columns, with the variables `x_1,x_2,y_1`, while the text file has its numbers separated by commas in two columns, `x_3,y_3`. \n",
     "\n",
@@ -542,7 +544,7 @@
     "                    $$\n",
     "                    A=1,\\ B=10.5,\\ C=3,\\ D=0.7,\\ E=3$$\n",
     "\n",
-    "*TIP/Example of how to do this!: (a) Use the Function you implemented in the previous lecture's exercises, or (b) use one implemented by somebofy. If you opt for (b): Imagine you had vectors `x_1`, `x_2` and `y` of length `N`, you find in stack-overflow that in order to fit the best coefficients $a,b,c,d$ for the equation $y^3=a e^{x_1}+b x_1^{7}+c \\frac{x_1}{\\sqrt{x_2}}+d$ you can do the following:*\n",
+    "*TIP/Example of how to do this!: (a) Use the Function you implemented in the previous lecture's exercise 3, or (b) use one implemented by somebofy. If you opt for (b): Imagine you had vectors `x_1`, `x_2` and `y` of length `N`, you find in stack-overflow that in order to fit the best coefficients $a,b,c,d$ for the equation $y^3=a e^{x_1}+b x_1^{7}+c \\frac{x_1}{\\sqrt{x_2}}+d$ you can do the following:*\n",
     "\n",
     "        from sklearn import linear_model\n",
     "        \n",
@@ -791,6 +793,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "---\n",
+    "---\n",
     "# Exercise 6: Creating a GIF animation using different Images\n",
     "\n",
     "We acknowledged in the previous lecture's exercises that when simulations got heavy, trying to watch them live was problematic. Instead, what we could do is to save all the frames generated by the simulation in a temporary directory, and only after the simulation has ended, generate an animation (say a `gif` \"moving image\") with a desired frame-rate.\n",
@@ -1259,7 +1263,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1273,7 +1277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

EXERCISES/Exercises_Lecture_05.ipynb

@@ -2617,7 +2617,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2631,7 +2631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
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  "nbformat": 4,

LECTURES/Lecture_01.ipynb

@@ -1537,7 +1537,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1551,7 +1551,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

LECTURES/Lecture_02.ipynb

@@ -2135,7 +2135,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2149,7 +2149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

LECTURES/Lecture_03.ipynb

@@ -5,12 +5,10 @@
    "metadata": {},
    "source": [
     "# PYTHON COURSE FOR SCIENTIFIC PROGRAMMING \n",
-    "**Lecturer and Main Contributor to this lesson:**\n",
+    "**Main Editor of this Lecture:**\n",
     "\n",
     "Xabier Oianguren Asua: oiangu9@gmail.com\n",
     "\n",
-    "All the course material can be found at: https://llacorp.github.io/Python-Course-for-Scientific-Programming/ \n",
-    "\n",
     "---"
    ]
   },
@@ -19,7 +17,7 @@
    "metadata": {},
    "source": [
     "# LECTURE IV : Numpy and Matplotlib\n",
-    "In this Lecture, we are going to first learn what a **library** is and how to import them in a script, then we will focus on the main library other than the standard one that you will be using: **numpy**. Following this, we will unlock the superpower to access the self-learning skill-tree: **googling**. Finally, we will review some of the options provided by the main plotting library: **matplotlib**.\n",
+    "In this Lecture, we are going to first learn what a **library** is and how to import them in a script, then we will focus on the main library, other than the standard one, that you will be using: **numpy**. Following this, we will unlock the superpower to access the self-learning skill-tree: **googling**. Finally, we will review some of the options provided by the main plotting library: **matplotlib**.\n",
     "\n",
     "### $(A)$ - [LIBRARIES: *Importing and using function packages*](#A)\n",
     "### $(B)$ - [NUMPY: *Your trustworthy numerical algebra library*](#B)\n",
@@ -170,7 +168,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You will be able to call any of the imported stuff this way by its name alone (no need to instantiate the used library). Usually this importing way is not the preferred one as you loose track of where this function comes from (useful if someone else reads your code). Still it allows to avoid importing the whole library if you don't need it"
+    "You will be able to call any of the imported stuff this way by its name alone (no need to make a reference to the used library). Usually this importing way is not the preferred one as you loose track of where this function comes from (useful if someone else reads your code). Still it allows to avoid importing the whole library if you don't need it"
    ]
   },
   {
@@ -212,7 +210,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Still, note that the correct way to go is instantiating the name of the library, or a shortname for it, in order to differentiate them form the self-made functions and the ones from the Standard Library:\n",
+    "Still, note that the correct way to go is referencing the name of the library, or a shortname for it, in order to differentiate them form the self-made functions and the ones from the Standard Library:\n",
     "\n",
     "`import numpy as np`\n",
     "\n",
@@ -1450,7 +1448,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that appart from accessing the elements of an array by slicing in order to see their content or operate with them, we can as well change their values or assign values as if they were variables. Similar to what we could do with lists:"
+    "Note that apart from accessing the elements of an array by slicing in order to see their content or operate with them, we can as well change their values or assign values as if they were variables. Similar to what we could do with lists:"
    ]
   },
   {
@@ -2018,9 +2016,9 @@
     "\n",
     "Luckily there is a very easy solution to this problem: asking the worldwide net! If you search for your problem in **English** on Google, DuckDuckgo or your favourite searching engine and make a little field work I assure you that the solutions will rise on their own! Almost in the first search option actually. With respect to python and very known libraries like numpy or matplotlib in particular, you won't even need to open the third search result before you find what you were looking for. Let us show you the power of googling, by theaching ourselves how to obtain the *mean* and the *standard deviation* of an array of data using numpy and how to get a *linear interpolation*!\n",
     "\n",
-    "And remember: the day you realize that your main visited page is not Youtube but **stackoverflow** or **stackexchange**, you will then notice the good programmer you have turned into!\n",
+    "And remember: the day you realize that your main visited page is **stackoverflow** or **stackexchange**, you will then notice the good programmer you have turned into!\n",
     "\n",
-    "So lets imagine we want to compute the mean of the numbers in an array and their standard deviation. Ofcourse, we could program a function for this ourselves (try it as an exercise, it is easy!), however, we will soon realize that it is already implemented in `numpy`, and that actually it works way faster than our implementation (this will be due to the fact that numpuy routines are in fact implemented in C)."
+    "So lets imagine we want to compute the mean of the numbers in an array and their standard deviation. Of course, we could program a function for this ourselves (try it as an exercise, it is easy!), however, we will soon realize that it is already implemented in `numpy`, and that actually it works way faster than our implementation (this will be due to the fact that numpuy routines are in fact implemented in C)."
    ]
   },
   {
@@ -2057,14 +2055,14 @@
    "source": [
     "Now, imagine we want to do a **linear polynomial fit** with least square errors given some x data \n",
     "array and y data array. I've searched on Google:\n",
-    "\"linear regression numpy python\"\n",
+    "\"linear regression numpy python stack\"\n",
     "And immediately got the answer in an stackoverflow entry:\n",
     "    https://stackoverflow.com/questions/6148207/linear-regression-with-matplotlib-numpy\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -2073,7 +2071,7 @@
        "array([0.52993806, 3.57725396])"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2112,53 +2110,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.107192016517552"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def linearFit( x, fittingCoeffs ):\n",
-    "    return fittingCoeffs[0] + fittingCoeffs[1]*x\n",
-    "\n",
-    "linearFit(2, coefficients)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, the guy answering in stackoverflow recommends creating a 1D polynomial using numpy with the given coefficients, such that we can evaluate whole x data vectors for instance:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[4.37216105 4.63713008 6.01496903 7.97573985]\n",
-      "[3, 4.6, 8.6, 6.8]\n"
+      "7.684445973847216\n",
+      "[[3.40519596 2.21139912 1.04815479]\n",
+      " [3.01472626 2.32271907 3.22249238]\n",
+      " [3.20710188 3.8678744  3.19223183]]\n"
      ]
     }
    ],
    "source": [
-    "fittedPol = np.poly1d(coefficients) # generate the polynomial object\n",
-    "predictedY = fittedPol(x) # evaluate the polynomial\n",
-    "print(predictedY)\n",
-    "print(y)"
+    "def linearFit( x, fittingCoeffs ):\n",
+    "    '''Given an array of points x, computes element-wise line ordinates'''\n",
+    "    return fittingCoeffs[0] + fittingCoeffs[1]*x\n",
+    "\n",
+    "print(linearFit(2, coefficients))\n",
+    "print(linearFit(np.random.rand(3,3), coefficients))"
    ]
   },
   {
@@ -2178,29 +2150,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Slope= 0.529938059187887\n",
-      "Intercept= 3.577253957329664\n",
-      "R-squared= 0.45063755447230586\n"
+      "Slope=0.53\n",
+      "Intercept=3.58\n",
+      "R-squared=0.451\n"
      ]
     }
    ],
    "source": [
-    "from scipy import stats as scipy\n",
+    "import scipy\n",
     "\n",
     "x = [1.5, 2, 4.6, 8.3]\n",
     "y = [3, 4.6, 8.6, 6.8]\n",
     "\n",
     "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)\n",
-    "print('Slope=',slope)\n",
-    "print('Intercept=',intercept)\n",
-    "print(\"R-squared=\", r_value**2)"
+    "print(f'Slope={slope:.3}')\n",
+    "print(f'Intercept={intercept:.3}')\n",
+    "print(f\"R-squared={r_value**2:.3}\")"
    ]
   },
   {
@@ -13648,7 +13620,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -13662,7 +13634,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

LECTURES/Lecture_04.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# PYTHON COURSE FOR SCIENTIFIC PROGRAMMING \n",
-    "**Lecturer and main contributor to this lesson:**\n",
+    "**Main Editor of this Lecture:**\n",
     "\n",
     "Xabier Oianguren Asua: oiangu9@gmail.com\n",
     "\n",
@@ -2078,37 +2078,38 @@
     "\n",
     "Here you have a list of the possible following topics you could attack to deepen your programming skills:\n",
     "\n",
+    "\n",
     "- Using `.py` scripts to code instead of a Jupyter Notebook, running scripts from a terminal/console.\n",
     "\n",
     "\n",
-    "- Object Oriented Programming: the `class` conondrum.\n",
+    "- Data manipulation libraries like `pandas`.\n",
     "\n",
     "\n",
-    "- Assertions and error controls, `try`-`except`-`catch` structures.\n",
+    "- Object Oriented Programming: the `class` conondrum.\n",
     "\n",
     "\n",
     "- `map` and `lambda` functions.\n",
     "\n",
     "\n",
-    "- Manipulating images, videos, sound and other more exotic data types (openCV, sounddevice ...)\n",
+    "- Manipulating images, videos, sound and other more exotic data types (libraries like `openCV, sounddevice` ...)\n",
     "\n",
     "\n",
-    "- Machine Learning and Deep Learning (pytorch, scikit-learn, tensorflow ...)\n",
+    "- Machine Learning and Deep Learning (libraries like `pytorch, scikit-learn, tensorflow` ...)\n",
     "\n",
     "\n",
-    "- Parallel processing using several CPU cores or using the GPU itself (mpi4py, multiprocessing, pytorch ...)\n",
+    "- Parallel processing using several CPU cores or using the GPU itself (`mpi4py, multiprocessing, pytorch` ...)\n",
     "\n",
     "\n",
-    "- Calling C/C++ functions under the hood (ctypes ...)\n",
+    "- Calling C/C++ functions under the hood (`ctypes` ...)\n",
     "\n",
     "\n",
-    "- Graphical user interfaces (pyQt ...)\n",
+    "- Graphical user interfaces (`pyQt` ...)\n",
     "\n",
     "\n",
-    "- Standalone executables (pyinstaller ...)\n",
+    "- Standalone executables (`pyinstaller` ...)\n",
     "\n",
     "\n",
-    "- Web/mobile application development (flask ...)\n",
+    "- Web/mobile application development (`flask` ...)\n",
     "\n",
     "\n",
     "- ..."
@@ -2136,7 +2137,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2150,7 +2151,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
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  "nbformat": 4,