Created using Colaboratory

PA_of_SizeAnalysis.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/VU-CSP/github-classroom-intro-pamlashi/blob/main/PA_of_SizeAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uM3giKLgbwgF"
+      },
+      "source": [
+        "# Analysis of the particle sizes from segmentation output\n",
+        "You should have an output file named `Results.csv` from FIJI/ImageJ segmentation. If you are using Google Colab to run this code, you will need to upload the file manually to Google using the `File` menu to the left.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GTIhuJXYbva8"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBObqOWmh2Zf"
+      },
+      "source": [
+        "Write code below to read the `Results.csv` file as a pandas DataFrame and store it in the object named `measurements`. Show the head of the DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DaYDPgcogyxz"
+      },
+      "outputs": [],
+      "source": [
+        "measurements = pd.read_csv('https://raw.githubusercontent.com/VU-CSP/improc-assignment-pamlashi/main/Results.csv?token=GHSAT0AAAAAACOYLQK6RMTNWYDT6YKESICMZPGUGFQ' , sep = ',')\n",
+        "measurements.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Mz-INH79g5Ks"
+      },
+      "outputs": [],
+      "source": [
+        "# Code to plot a histogram of the Area\n",
+        "sns.histplot(measurements['Area'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZtcMHckuiydC"
+      },
+      "source": [
+        "Calculate the mean and the standard deviation of the distribution and show them below. (HINT: pandas can do this easily)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T9lYcgiBhxUE"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate mean\n",
+        "print(measurements['Area'].mean())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UEgg0XZdi_76"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate standard deviation\n",
+        "print(measurements['Area'].std())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pE62XfF4kgwJ"
+      },
+      "source": [
+        "#### Describe your interpretation of the distribution of the measurements of nuclear area below.\n",
+        "Based on what you see, would you choose different value ranges of particle sizes for counting? Explain why.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jjaN302xvVak"
+      },
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.8.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}