Merge pull request #113 from silx-kit/numpy-updates

Update the NumPy section

Author: payno

python/numpy/Exercise-HPLC.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
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@@ -11,6 +12,7 @@
    ]
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@@ -19,6 +21,7 @@
    ]
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@@ -29,6 +32,7 @@
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@@ -39,6 +43,7 @@
    ]
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@@ -54,23 +59,18 @@
     "Those **I** values are stored as a 2D dataset of shape (400, 1000) in the `intensities.npy` file.\n",
     "The **q** values are stored in the `q.txt` file.\n",
     "\n",
-    "At first, only the buffer is passing through the capillary, then sample+buffer and finally buffer again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "530ef2d2-5910-447f-831a-b6029e4a1beb",
-   "metadata": {},
-   "source": [
+    "At first, only the buffer is passing through the capillary, then sample + buffer and finally buffer again.\n",
+    "\n",
     "The goal is to extract the intensity contributed by the sample.\n",
     "The steps are:\n",
     "\n",
-    "0. Separate integrated intensities corresponding to buffer+sample from those corresponding to buffer only\n",
-    "0. Estimate the buffer and the sample+buffer intensities by averaging the selected integrated intensities\n",
-    "0. Remove the buffer background from sample+buffer"
+    "1. Separate integrated intensities corresponding to buffer+sample from those corresponding to buffer only\n",
+    "2. Estimate the buffer and the sample + buffer intensities by averaging the selected integrated intensities\n",
+    "3. Remove the buffer background from sample + buffer"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "98afc9af-cdb1-4bf7-8c93-c0d1a7fb2a8e",
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@@ -85,10 +85,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy"
+    "import numpy as np"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d2c55c1c-c588-40e5-ae01-c476a1c2616e",
    "metadata": {},
@@ -110,6 +111,7 @@
    ]
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    "id": "c70d852d-470d-45d1-adc7-a6a23feb5854",
    "metadata": {},
@@ -138,13 +140,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Plot intensities\n",
+    "# Plot the intensities\n",
     "import matplotlib.colors as colors\n",
     "\n",
     "fig = plt.figure()\n",
     "plt.imshow(intensities, norm=colors.LogNorm(), aspect=\"auto\")\n",
-    "\n",
-    "# Note: with latest version of matplotlib:\n",
+    "  \n",
+    "# Note: with latest version of matplotlib:    \n",
     "# plt.imshow(intensities, norm=\"log\", aspect=\"auto\")"
    ]
   },
@@ -162,6 +164,7 @@
    ]
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    "metadata": {},
@@ -170,6 +173,7 @@
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    "id": "4961fad5-fe0b-4d6e-9663-228c630fb60c",
    "metadata": {
@@ -207,14 +211,16 @@
    ]
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    "id": "9c2026c6-a04a-4ad5-92a4-5e473ce3dd3b",
    "metadata": {},
    "source": [
-    "Note: This is not meaningful, the buffer and sample+buffer cases should be separated."
+    "Note: This is not meaningful, the buffer and sample + buffer cases should be separated."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3adff22d-4835-4f3c-935f-a06f71372e01",
    "metadata": {},
@@ -242,12 +248,13 @@
    "outputs": [],
    "source": [
     "fig = plt.figure()\n",
-    "plt.plot(frames_intensities)\n",
+    "plt.plot(intensities_per_frame)\n",
     "plt.xlabel(\"Frame ID\")\n",
     "plt.ylabel(\"I\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "37949d27-0a36-488b-b230-df7765a6f286",
    "metadata": {},
@@ -256,15 +263,16 @@
    ]
   },
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+   "attachments": {},
    "cell_type": "markdown",
    "id": "de3ad40e-97b1-4ef9-aae7-168117aad0a0",
    "metadata": {
     "tags": []
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    "source": [
-    "### Separate sample+buffer from buffer only\n",
+    "### Separate sample + buffer from buffer only\n",
     "\n",
-    "Select buffer and sample+buffer intensities by using a threshold over `intensities_summed`"
+    "Select buffer and sample + buffer intensities by using a threshold over `intensities_summed`."
    ]
   },
   {
@@ -298,11 +306,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "51fa7126-33b6-4fb2-8fa9-0c5b427325ba",
    "metadata": {},
    "source": [
-    "### Average sample+buffer and buffer intensities\n",
+    "### Average sample + buffer and buffer intensities\n",
     "\n",
     "Compute the average of azimuthal integrations of `buffer` for each `q`."
    ]
@@ -318,6 +327,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "93b16a5e-466a-4a7f-8da5-6b591bf6c3ce",
    "metadata": {},
@@ -344,13 +354,14 @@
    "source": [
     "fig = plt.figure()\n",
     "plt.plot(q, buffer_mean, 'black', q, sample_buffer_mean, 'red')\n",
-    "plt.title(\"buffer and sample+buffer average\")\n",
+    "plt.title(\"buffer and sample + buffer average\")\n",
     "plt.xlabel(\"q\")\n",
     "plt.ylabel(\"I\")\n",
     "plt.yscale(\"log\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "13476124-7e14-4a6a-a49f-1f3b02dd1a6f",
    "metadata": {},
@@ -383,6 +394,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dbc81e5b-19b4-4aac-8648-7e8b67f3c9e0",
    "metadata": {},
@@ -395,35 +407,35 @@
     "```python\n",
     "# Part I\n",
     "\n",
-    "import numpy\n",
+    "import numpy as np\n",
     "\n",
     "# Load data\n",
-    "intensities = numpy.load(\"intensities.npy\")\n",
-    "q = numpy.loadtxt(\"q.txt\")\n",
+    "intensities = np.load(\"intensities.npy\")\n",
+    "q = np.loadtxt(\"q.txt\")\n",
     "\n",
     "# Part II\n",
     "\n",
     "# Average of all azimuthal integrations\n",
-    "intensities_mean = numpy.mean(intensities, axis=0)\n",
+    "intensities_mean = np.mean(intensities, axis=0)\n",
     "\n",
     "# Summed intensity of each azimuthal integration\n",
-    "frames_intensities = numpy.sum(intensities, axis=1)\n",
+    "intensities_per_frame = np.sum(intensities, axis=1)\n",
     "\n",
     "# Part III\n",
     "\n",
-    "# Separate sample+buffer from buffer only\n",
+    "# Separate sample + buffer from buffer only\n",
     "# 1. with thresholds\n",
-    "buffer_mask = frames_intensities < 32500\n",
+    "buffer_mask = intensities_per_frame < 32500\n",
     "buffer = intensities[buffer_mask]\n",
     "sample_buffer_mask = frames_intensities > 33000\n",
     "sample_buffer = intensities[sample_buffer_mask]\n",
     "# 2. With slicing\n",
     "buffer = intensities[:200]\n",
     "sample_buffer = intensities[270:340]\n",
     "\n",
-    "# Average sample+buffer and buffer intensities\n",
-    "buffer_mean = numpy.mean(buffer, axis=0)\n",
-    "sample_buffer_mean = numpy.mean(sample_buffer, axis=0)\n",
+    "# Average sample + buffer and buffer intensities\n",
+    "buffer_mean = np.mean(buffer, axis=0)\n",
+    "sample_buffer_mean = np.mean(sample_buffer, axis=0)\n",
     "\n",
     "# Remove buffer background\n",
     "sample = sample_buffer_mean - buffer_mean\n",
@@ -434,22 +446,8 @@
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "name": "python"
   }
  },
  "nbformat": 4,

python/numpy/exercicesolution.py

@@ -71,7 +71,7 @@ def ex4_2_alt():
 
 def ex5_inefficient_fill(height=1000, width=1000):
     """Inefficient fill using 2 for loops"""
-    data = numpy.zeros((height, width), dtype=numpy.float)
+    data = numpy.zeros((height, width), dtype=float)
     for row in range(int(height)):
         for col in range(int(width)):
             data[row, col] = numpy.cos(row) * numpy.sin(col)
@@ -82,7 +82,7 @@ def ex5_naive_fill(height=1000, width=1000):
     """Fill using 2 for loops but pre-computing sin and cos"""
     width_sin = numpy.sin(numpy.arange(width))
     height_cos = numpy.cos(numpy.arange(height))
-    data = numpy.zeros((height, width), numpy.float)
+    data = numpy.zeros((height, width), dtype=numpy.float64)
     for row in range(int(height)):
         for col in range(int(width)):
             data[row, col] = height_cos[row] * width_sin[col]