Merge May 2025 improvements into main

Course/02_numpy.ipynb

@@ -881,14 +881,16 @@
    "execution_count": null,
    "metadata": {
     "editable": true,
-    "remove_code": "non-comments",
+    "remove_code": "after:# Load non-number columns from file (1,2,3)",
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "outputs": [],
    "source": [
+    "data_path2 = '../Data/presentation/molecule1.xyz'\n",
+    "\n",
     "# Load non-number columns from file (1,2,3)\n"
    ]
   },
@@ -999,7 +1001,7 @@
    "execution_count": null,
    "metadata": {
     "editable": true,
-    "remove_code": "non-comments",
+    "remove_code": "after:# Output matrix multiplication vs elementwise multiplication",
     "slideshow": {
      "slide_type": ""
     },
@@ -1008,6 +1010,10 @@
    "outputs": [],
    "source": [
     "# We define two arrays: A - a 3x3 matrix and B - a 3x1 vector\n",
+    "mat_A = np.array([[1, 0, 3], [-1, 2, 1], [-2, -2, 1]])\n",
+    "print(mat_A)\n",
+    "vec_A = np.array([[1], [0], [-1]])\n",
+    "print(vec_A)\n",
     "\n",
     "# Output matrix multiplication vs elementwise multiplication\n"
    ]

Course/02b_numpy_exercise.ipynb

@@ -179,9 +179,9 @@
     "    - $h$ : Integer, Miller index\n",
     "    - $k$ : Integer, Miller index\n",
     "    - $l$ : Integer, Miller index\n",
-    "    - $F^2_\\text{calc,i}$ : Calculated scaled intensity from the model\n",
-    "    - $F^2_\\text{obs,i}$ : Observed scaled intensity from the model\n",
-    "    - $\\sigma_i$ : Estimated standard deviation of the scaled observed intensity\n",
+    "    - $F^2_\\text{calc,i}$ : Float, Calculated scaled intensity from the model\n",
+    "    - $F^2_\\text{obs,i}$ : Float, Observed scaled intensity from the model\n",
+    "    - $\\sigma_i$ : Float, Estimated standard deviation of the scaled observed intensity\n",
     "\n",
     "Note: Make sure that you take into account that the data file already contains the squared values.\n"
    ]
@@ -320,7 +320,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "teaching_data_analysis",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -334,7 +334,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

Course/04b_pandas_exercise.ipynb

@@ -217,7 +217,7 @@
    "source": [
     "\n",
     "### Task 8: Statistical Testing\n",
-    "- **Conduct a t-test**: To confirm the authors' hypothesis that arguing against one\u2019s own side increases the desire for cleansing products\u2014and to check if the 'Harvard' data points influence this result.\n",
+    "- **Conduct a t-test excluding the suspect rows**: Re-run the same test as Task 3, but filter out the rows flagged as suspect in Task 5. Compare the t-statistic and p-value to your Task 3 result. Does removing the suspect data weaken the effect?\n",
     "- **Commands to use**: Implement `scipy.stats.ttest_ind()` to perform the independent t-tests between groups.\n"
    ]
   },
@@ -258,24 +258,6 @@
    },
    "outputs": [],
    "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "remove_code": "non-comments"
-   },
-   "outputs": [],
-   "source": [
-    "# Perform the independent t-tests between groups\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

Filled_Course/02_numpy.ipynb

@@ -1003,7 +1003,7 @@
    "execution_count": null,
    "metadata": {
     "editable": true,
-    "remove_code": "non-comments",
+    "remove_code": "after:# Load non-number columns from file (1,2,3)",
     "slideshow": {
      "slide_type": ""
     },
@@ -1147,7 +1147,7 @@
    "execution_count": null,
    "metadata": {
     "editable": true,
-    "remove_code": "non-comments",
+    "remove_code": "after:# Output matrix multiplication vs elementwise multiplication",
     "slideshow": {
      "slide_type": ""
     },

Filled_Course/02b_numpy_exercise.ipynb

@@ -168,7 +168,7 @@
    "outputs": [],
    "source": [
     "# reshape elements\n",
-    "elements2d = np.reshape(elements, (6, 2))\n",
+    "elements2d = np.reshape(elements, (2, 6))\n",
     "print(elements2d)\n",
     "\n",
     "# reshape coords to (2, 6, 3)\n",
@@ -193,9 +193,9 @@
     "    - $h$ : Integer, Miller index\n",
     "    - $k$ : Integer, Miller index\n",
     "    - $l$ : Integer, Miller index\n",
-    "    - $F^2_\\text{calc,i}$ : Calculated scaled intensity from the model\n",
-    "    - $F^2_\\text{obs,i}$ : Observed scaled intensity from the model\n",
-    "    - $\\sigma_i$ : Estimated standard deviation of the scaled observed intensity\n",
+    "    - $F^2_\\text{calc,i}$ : Float, Calculated scaled intensity from the model\n",
+    "    - $F^2_\\text{obs,i}$ : Float, Observed scaled intensity from the model\n",
+    "    - $\\sigma_i$ : Float, Estimated standard deviation of the scaled observed intensity\n",
     "\n",
     "Note: Make sure that you take into account that the data file already contains the squared values.\n"
    ]
@@ -344,7 +344,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "teaching_data_analysis",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -358,7 +358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

Filled_Course/03b_matplotlib_exercise.ipynb

@@ -185,7 +185,7 @@
     "fig2, ax2 = plt.subplots(1, 1, figsize=(8, 1))\n",
     "\n",
     "# Create scatter plots for every regression line\n",
-    "for i, ax in enumerate(axs.flat):\n",
+    "for i, ax in enumerate(axs.reshape(4)):\n",
     "    data = data_dict[f'dataset{i+1}']\n",
     "    x = np.array(data['x'])\n",
     "    y = np.array(data['y'])\n",