From a24c8b2190166713a2894327fa1b7a4a6aa34c64 Mon Sep 17 00:00:00 2001
From: Luigi Sbailo <sbailo@fhi-berlin.mpg.de>
Date: Mon, 4 Jan 2021 18:21:25 +0100
Subject: [PATCH] Add metainfo and setup files

---
 exploratory_analysis.ipynb | 19 ----------------
 metainfo.json              | 46 ++++++++++++++++++++++++++++++++++++++
 setup.py                   | 17 ++++++++++++++
 3 files changed, 63 insertions(+), 19 deletions(-)
 create mode 100644 metainfo.json
 create mode 100644 setup.py

diff --git a/exploratory_analysis.ipynb b/exploratory_analysis.ipynb
index f0ac253..9598019 100644
--- a/exploratory_analysis.ipynb
+++ b/exploratory_analysis.ipynb
@@ -109,10 +109,7 @@
     "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
     "from sklearn.decomposition import PCA\n",
     "from sklearn.manifold import TSNE, MDS\n",
-    "# from sklearn.svm import SVC\n",
-    "# from sklearn.model_selection import train_test_split\n",
     "import hdbscan\n",
-    "# import plotly.express as px\n",
     "import plotly.graph_objects as go\n",
     "import ipywidgets as widgets\n",
     "from IPython.display import display, clear_output\n",
@@ -120,22 +117,6 @@
     "import matplotlib.pyplot as plt"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-01-04T16:28:07.758936Z",
-     "start_time": "2021-01-04T16:28:07.756961Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# import warnings\n",
-    "# warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
-    "# pd.options.mode.chained_assignment = None"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/metainfo.json b/metainfo.json
new file mode 100644
index 0000000..4ab520a
--- /dev/null
+++ b/metainfo.json
@@ -0,0 +1,46 @@
+{
+  "authors": [
+    "Sbailò, Luigi",
+    "Ghiringhelli, Luca M."
+  ],
+  "email": "sbailo@fhi-berlin.mpg.de",
+  "title": "Exploratory analysis of octet-binary compounds ",
+  "description": "Exploratory analyses make use of unsupervised learning techniques to extract information from unknown datasets. In this tutorial, we make use of some of the most popular clustering and dimension reduction algorithms to analyze a dataset composed of 82 octet-binary compounds.",
+  "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-exploratory-analysis",
+  "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/exploratory_analysis_tutorial.ipynb",
+  "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/exploratory_analysis_tutorial.ipynb",
+  "updated": "2021-01-04",
+  "flags":{
+    "featured": true,
+    "top_of_list": false
+  },
+  "labels": {
+    "application_keyword": [
+      "k-means",
+      "Hierarchical clustering",
+      "DBSCAN",
+      "HDBSCAN",
+      "DenPeak",
+      "PCA",
+      "t-SNE",
+      "MDS",
+      "Octet binaries"
+    ],
+    "application_section": [
+      "Tutorials for artificial-intelligence methods"
+    ],
+    "application_system": [
+      "octet-binary materials"
+    ],
+    "category": [
+      "Tutorial"
+    ],
+    "data_analytics_method": [
+      "Clustering",
+      "Dimension reduction"
+    ],
+    "platform": [
+      "jupyter"
+    ]
+  }
+}
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..c356db2
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,17 @@
+import json
+from setuptools import setup, find_packages
+
+with open('metainfo.json') as file:
+    metainfo = json.load(file)
+
+setup(
+    name='exploratory_analysis',
+    version='1.0',
+    author=', '.join(metainfo['authors']),
+    author_email=metainfo['email'],
+    url=metainfo['url'],
+    description=metainfo['title'],
+    long_description=metainfo['description'],
+    packages=find_packages(),
+    install_requires=['numpy', 'pandas', 'matplotlib', 'scikit-learn', 'scipy', 'hdbscan'],
+)
-- 
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