From b9d9c960313a4f20b47dc7d8eda3005070c047b5 Mon Sep 17 00:00:00 2001
From: valentina-s <vms16@uw.edu>
Date: Fri, 30 Aug 2024 16:06:27 +0000
Subject: [PATCH] notebook

---
 OHW_projects_textmining.ipynb | 1754 +++++++++++++++++++++++++++++++++
 1 file changed, 1754 insertions(+)
 create mode 100644 OHW_projects_textmining.ipynb

diff --git a/OHW_projects_textmining.ipynb b/OHW_projects_textmining.ipynb
new file mode 100644
index 0000000..e6cfeed
--- /dev/null
+++ b/OHW_projects_textmining.ipynb
@@ -0,0 +1,1754 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e60f8f38",
+   "metadata": {},
+   "source": [
+    "## Text Mining of OHW Project Readme's"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0960c7e3",
+   "metadata": {},
+   "source": [
+    "In this notebook we explore the patterns in the text of the OHW Projects READMEs. We will extract topics and low dimensional representations so that we can create a scatter plot and cluster the projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7821392",
+   "metadata": {},
+   "source": [
+    "We use data scraped from the OHW github repos, which is stored in a `.json` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "f8427b79-5196-422a-a0d3-a97f342718f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "8e6b76f7-6ede-437f-b91a-6b85dd63b21a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "b8e3ddfd-457c-4a76-81f4-535892ad96ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "1d89371c-3ff4-4d63-abd6-4aa968f7d83c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = pd.read_csv(\"project_list_final.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "f8e9624a-6c0b-4bc8-8a43-4b830f7d1cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# setting up text mining packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "0efa7213-e04a-4019-8ce1-7c4a6f04641f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: nltk in /home/.pixi/envs/default/lib/python3.12/site-packages (3.9.1)\n",
+      "Requirement already satisfied: click in /home/.pixi/envs/default/lib/python3.12/site-packages (from nltk) (8.1.7)\n",
+      "Requirement already satisfied: joblib in /home/.pixi/envs/default/lib/python3.12/site-packages (from nltk) (1.4.2)\n",
+      "Requirement already satisfied: regex>=2021.8.3 in /home/.pixi/envs/default/lib/python3.12/site-packages (from nltk) (2024.7.24)\n",
+      "Requirement already satisfied: tqdm in /home/.pixi/envs/default/lib/python3.12/site-packages (from nltk) (4.66.4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install nltk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "45444c52-c186-47c8-accd-c6e55cacab7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nltk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "206dfbb3-27a7-4f7b-8ef3-309e7b5430d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.download(\"punkt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "c730c47b-8c99-4dca-aaa4-999770a6097e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.tokenize import sent_tokenize, word_tokenize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "f1959932-eb67-4729-88dc-19c3cc6a40b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.tokenize import RegexpTokenizer\n",
+    "tokenizer = RegexpTokenizer(r'\\w+')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "3de859c0-a805-4d72-b705-a1d0846c64de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import stopwords\n",
+    "stopwords = stopwords.words('english')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "05a2ab7f-df44-46a0-ab41-0264dbf19b14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "# check what is in the json\n",
+    "# from pprint import pprint\n",
+    "\n",
+    "# with open('readmes_proj_ohw.json') as json_data:\n",
+    "#    d = json.load(json_data)\n",
+    "#    json_data.close()\n",
+    "#    #pprint(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "24f3866b-cb7a-4583-a6d7-ba31af075d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data = json.load(\"readmes_proj_ohw.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "cc7e658a-636d-44e8-9784-24e555b4908f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_json(\"readmes_proj_ohw.json\", typ='series')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "0725815a-888b-4614-b9c8-3e785bfdf978",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj_names = [(name.strip('https://github.com/oceanhackweek/')) for name in list(df.index)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49851f2e-e64f-4fc4-adc2-9bab474ffde2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "11b32109-0f51-4f73-a1bd-41dfd9cb2dcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_845/2680116315.py:1: FutureWarning:\n",
+      "\n",
+      "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'## ohw21-proj-model-subsampling\\n## OceanHackWeek21 project to subsample high-resolution model output as if by gliders, ships, or other in situ platforms\\n\\nThe goal of this project is to create a Python package that takes an input trajectory (e.g., the path of an ocean glider), subsamples output from a high-resolution ocean simulation along that trajectory, and returns a set of subsampled variables (e.g., standard physical variables temperature, salinity, velocity; derived physical quantities such as steric height; biogeochemical quantities if available).  We envision this package having two potential uses: 1) designing *in situ* sampling strategies, and 2) interpreting *in situ* data in the context of a highly resolved oceanographic model.\\n\\nTasks:\\n1. Load model data from a specific region (e.g., ROMS output in the California Current from CenOOS; MITgcm regional llc4320 data)\\n2. Create simulated trajectory file\\n3. Obtain real trajectory file\\n4. Interpolate model data to the trajectories\\n5. Compare interpolated data to the full model output via useful visualizations and/or statistics\\n'"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "9043b04f-45ec-427c-98d3-d70a87598037",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to preprocess the readmes\n",
+    "\n",
+    "from nltk.tokenize import RegexpTokenizer\n",
+    "from nltk.corpus import stopwords\n",
+    "\n",
+    "def readme_preprocess(readme):\n",
+    "    \n",
+    "    # tokenize\n",
+    "    tokens = tokenizer.tokenize(readme)\n",
+    "    \n",
+    "    # make lower case\n",
+    "    tokens = [tok.lower() for tok in tokens]\n",
+    "    \n",
+    "    # stem\n",
+    "    lancaster = nltk.LancasterStemmer()\n",
+    "    tokens = [lancaster.stem(tok) for tok in tokens]\n",
+    "    \n",
+    "    # remove stop words\n",
+    "    stop_words = stopwords.words('english')\n",
+    "    tokens = [tok for tok in tokens if tok not in stop_words]\n",
+    "    \n",
+    "    return(tokens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "e48bddb8-a1e2-40f4-97b6-89ea4d89a3d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocess each readme\n",
+    "readmes = [readme_preprocess(readme) for readme in list(df)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "6bbdb369-0d70-42c7-8c77-1dfe18df1800",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting to string\n",
+    "final_readmes = []\n",
+    "for readme in readmes:\n",
+    "    for word in readme:\n",
+    "        n = readme.index(word)\n",
+    "        if n == 0:\n",
+    "            string = readme[n]\n",
+    "        else:\n",
+    "            string = string + \" \" + readme[n]\n",
+    "    final_readmes.append(string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff524ee2",
+   "metadata": {},
+   "source": [
+    "## Topic Modeling with Nonnegative Matrix Factorization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59295edc",
+   "metadata": {},
+   "source": [
+    "We will try a simple method to extract topics from `scikit-learn` library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9dc8925",
+   "metadata": {},
+   "source": [
+    "The approach involves creat a Term Frequency - Inverse Document Frequency matrix, which is factorized into topics and weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "a3beecba-8172-431a-9c28-b996a5087798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "2ee71812-9733-4e96-a6bb-5c70e54b3cc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tfidf_vectorizer = TfidfVectorizer(min_df=2, max_df=0.95)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "0fec8f68-26ae-4cce-8137-4f2bc2cb0015",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "tfidf_matrix = tfidf_vectorizer.fit_transform(final_readmes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "4b998368-e676-444f-902d-3c4fd56d9ba0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 675)"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tfidf_matrix.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "03ec4e06-64ad-4eb8-88a4-4c87de0efb00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_topics = 10\n",
+    "\n",
+    "from sklearn.decomposition import NMF\n",
+    "model = NMF(init=\"nndsvd\", n_components=n_topics, random_state=1)\n",
+    "W_matrix = model.fit_transform(tfidf_matrix)\n",
+    "H_matrix = model.components_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "e6a3be8b-1ed9-4905-900f-1a8e0e70663c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Topic 0: shar, ad, structure, particip, dataset, project, link, fold, team, nee\n",
+      "Topic 1: dat, process, grady, model, oc, saildron, thi, may, us, output\n",
+      "Topic 2: prod, ohw22, nutry, clust, templ, process, org, ohw, habit, impact\n",
+      "Topic 3: https, com, github, www, 14, detect, io, spat, copernic, atl\n",
+      "Topic 4: deep, im, satellit, cur, cnn, method, us, estim, lstm, predict\n",
+      "Topic 5: imo, dat, moor, plot, websit, ap, s3, amazonaw, prefix, southeast\n",
+      "Topic 6: acoust, pac, pass, dataset, som, imo, avail, serv, dat, link\n",
+      "Topic 7: de, los, ohw22, prod, en, las, ind, del, github, discuss\n",
+      "Topic 8: point, brazil, coast, sou, sim, transect, sum, reg, model, dispers\n",
+      "Topic 9: gcm, 2021, oceanhackweek, filt, environ, pangeo, readthedoc, latest, ohw, yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print topics and keywords\n",
+    "tfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\n",
+    "for topic_index in range( H_matrix.shape[0] ):\n",
+    "    top_indices = np.argsort( H_matrix[topic_index,:] )[::-1][0:10]  ##show top 10 words associated with each topic\n",
+    "    term_ranking = [tfidf_feature_names[i] for i in top_indices]\n",
+    "    print (\"Topic %d: %s\" % ( topic_index, \", \".join( term_ranking ) ))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74d644bd",
+   "metadata": {},
+   "source": [
+    "### Nonlinear Dimensionality Reduction with T-SNE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89d5cefc",
+   "metadata": {},
+   "source": [
+    "Next we will reduce the dimension from 10 (topics) to 2D so we can plot in a scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "2419c9cc-bf3f-4231-81df-1530ef8a50bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "57329b73-b1ee-44cf-b5c7-9018f259daf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 2)"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "W_embedded = TSNE(n_components=2, learning_rate='auto',\n",
+    "                  init='random', perplexity=3).fit_transform(W_matrix)\n",
+    "W_embedded.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "c903af7e-aa88-4554-8905-961ea1c6dcbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(W_embedded[:,0], W_embedded[:,1], \"bo\")\n",
+    "for i, txt in enumerate(proj_names):\n",
+    "    ax.annotate(txt, (W_embedded[i,0], W_embedded[i,1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f4e0f07",
+   "metadata": {},
+   "source": [
+    "## Plotly Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c006b795",
+   "metadata": {},
+   "source": [
+    "This is clearly not readable! Let's use Plotly to visualize the labels better"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "174644bb-fc48-457f-bfe1-e25cc2169ded",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: plotly in /home/.pixi/envs/default/lib/python3.12/site-packages (5.24.0)\n",
+      "Requirement already satisfied: tenacity>=6.2.0 in /home/.pixi/envs/default/lib/python3.12/site-packages (from plotly) (9.0.0)\n",
+      "Requirement already satisfied: packaging in /home/.pixi/envs/default/lib/python3.12/site-packages (from plotly) (24.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "3b7a66ed-6784-4e38-85c9-a54f382e32e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# organize in a dataframe\n",
+    "d = {'x': W_embedded[:,0], 'y': W_embedded[:,1], 'proj_name': proj_names}\n",
+    "df_2D = pd.DataFrame(data=d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b92659b8-1e2a-4a06-93d0-d95c2963c835",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "3e6bf467-e1d1-4bf1-bcab-21b9449d35f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.io as pio\n",
+    "pio.renderers.default = 'notebook'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c96f40a-a61b-4c9f-a6f0-d6a47dec204f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "def8b5ba-7498-4915-a56d-1955c0edb293",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br><br>x=%{x}<br>y=%{y}<extra></extra>",
+         "hovertext": [
+          "21-proj-model-subsampl",
+          "21-proj-",
+          "21-proj-coastal-radar",
+          "21-proj-deep-curr",
+          "21-proj-radar-q",
+          "21_proj_tag_d",
+          "21-proj-drone-turtl",
+          "21-proj-biological-activity-driven-by-geologic-ev",
+          "22-proj-front-finder",
+          "22-proj-plot_this_and_",
+          "22-proj-biofouled",
+          "22-proj-",
+          "22-proj-lagrange_",
+          "22_proj_matchup_d",
+          "22-proj-gcmfilter",
+          "22-proj-passive-acoustics-data-query",
+          "22-proj-ker",
+          "22-proj_SA_ocean_d",
+          "22-proj-xyz",
+          "22-proj-ENSO_Pred",
+          "22-proj-flow-cytometry",
+          "22-proj-clusters-nutr",
+          "22-proj-biodiversity-indicator",
+          "22-proj-orgs-templ",
+          "22-proj-Extreme_ev",
+          "22-proj-arcticd",
+          "22-proj-video-data-pr",
+          "22-proj-oleaje-coster",
+          "22_proj_mat2py",
+          "23_proj_fancymoor",
+          "23_proj_",
+          "23_proj_SAupwell",
+          "23_proj_oil",
+          "23_proj_argo_ml",
+          "23_proj_sea_ice_oscill",
+          "23-proj-pamproj",
+          "23-proj-",
+          "23_proj_drone_georef",
+          "23_proj_ampl",
+          "yy_proj_templ",
+          "24_proj_extract_cube_data_overlap_poly_",
+          "24_proj_MessageMeWhenItsHot_the_MHW_Vis-Report_app_",
+          "24_proj_pace_",
+          "24_proj_micronekton_img_pipeline_",
+          "24_proj_sdm_",
+          "24_proj_upSSH_",
+          "24_proj_north_atlantic_variability_",
+          "24_proj_big_data_",
+          "24_proj_xarray_load_by_step_",
+          "24_proj_catalog_"
+         ],
+         "legendgroup": "",
+         "marker": {
+          "color": "#636efa",
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "",
+         "orientation": "v",
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Projects\"},\"height\":800},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('fdaf4448-82e0-43d8-932b-4e403531296d');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "fig = px.scatter(df_2D, x=\"x\", y=\"y\",  hover_name=\"proj_name\")\n",
+    "\n",
+    "fig.update_traces(textposition='top center')\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    height=800,\n",
+    "    title_text='OHW Projects'\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2519e543",
+   "metadata": {},
+   "source": [
+    "Q: Why is `21-proj-coastal-radar` an outlier?\n",
+    "\n",
+    "A: They did not fill their README!!!\n",
+    "\n",
+    "https://github.com/oceanhackweek/ohw21-proj-coastal-radar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb96bec",
+   "metadata": {},
+   "source": [
+    "ohw21-proj-coastal-radar\n",
+    "\n",
+    "Participants\n",
+    "\n",
+    "Panagiotis Mitsopoulos\n",
+    "\n",
+    "Filipe Pereira\n",
+    "\n",
+    "Anrijs Abele\n",
+    "\n",
+    "Teresa Updyke\n",
+    "\n",
+    "Mauricio Rocha\n",
+    "\n",
+    "presentation.ipynb is the main notebook, with a description of the project and some preliminary results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "5d8d2687-bf8a-4893-b646-e233a92883ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install -U kaleido"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dff60237-9185-445d-90e3-0eae0f052ca6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4eaf8099-9c59-4949-845d-5eee7e4fca06",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}