📊📨 -> Add api route for classifier using Cuttle in #1

Ansible/Generator/Detection/TransitTimeVariation.ipynb

@@ -0,0 +1,25 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def odd_even_phase(lc, period, t0, plot_size = False, same_axes = False, binning = False):\n",
+    "    lc = lc.normalise()\n",
+    "\n",
+    "    if binning != False:\n",
+    "        lc = lc.bin(binning/60/24)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Ansible/Generator/Star Generation/StellarVariability.ipynb

@@ -0,0 +1,30 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Ansible/docs/API/cuttle-process.md

@@ -0,0 +1,31 @@
+Import data
+Split data into training and testing set
+Train model on training set
+Evaluate model on testing set
+
+
+Hide data from cuttle:
+```py
+#cuttle-environment-disable sailor-api
+```
+
+API Route config:
+```py
+#cuttle-environment-set-config sailor-api route=/api/predict method=POST
+# (Flask Transformer)
+
+file = open("./images/mnist3.png", "rb") #cuttle-environment-get-config sailor-apiu request.files['files']
+
+# Return variable as api response
+c = str(digit) # cuttle-environment-set-config sailor-api response
+```
+
+Transform notebook:
+```bash
+cuttle transform sailor-api
+```
+Note: make sure to comment out `%matplotlib inline` / `matplotlib notebook`
+
+# Flask configuration
+Users will import TIC data into flask using the POST method
+This will then be processed by the notebook function and passed into the other blueprints inside Flask

server/api/Views/TIC.py

@@ -0,0 +1,6 @@
+from flask import Flask, request, Blueprint
+
+TIC_Page = Blueprint('TIC_Page', __name__, template_folder='templates')
+@TIC_Page.route('/tic_planet')
+def show(page):
+    pass

server/api/main.py

@@ -0,0 +1,5 @@
+from flask import Flask, request, Blueprint, render_template
+#from .TIC_Page import TIC_page
+
+app = Flask(__name__)
+app.register_blueprint(TIC_Page)

server/notebook/data/PlanetStats.ipynb

@@ -143,13 +143,13 @@
     }
    ],
    "source": [
-    "%matplotlib notebook\n",
+    "%matplotlib inline\n",
     "\n",
     "import sys\n",
-    "!{sys.executable} -m pip install numpy\n",
-    "!{sys.executable} -m pip install lightkurve\n",
-    "!{sys.executable} -m pip install matplotlib\n",
-    "!{sys.executable} -m pip install astropy\n",
+    "#!{sys.executable} -m pip install numpy\n",
+    "#!{sys.executable} -m pip install lightkurve\n",
+    "#!{sys.executable} -m pip install matplotlib\n",
+    "#!{sys.executable} -m pip install astropy\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import lightkurve as lk\n",

server/notebook/data/cuttle.json

@@ -0,0 +1,9 @@
+{
+    "environments": {
+        "sailor-api": {
+            "platform": "linux",
+            "transformer": "flask"
+        }
+    },
+    "notebook": "PlanetStats.ipynb"
+}

server/notebook/data/mnist.ipynb

@@ -0,0 +1,25 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

server/notebook/data/output/sailor-api/main.py

@@ -0,0 +1,75 @@
+
+from flask import Flask
+from flask import request
+app = Flask(__name__)
+
+#%matplotlib inline
+
+import sys
+#!{sys.executable} -m pip install numpy
+#!{sys.executable} -m pip install lightkurve
+#!{sys.executable} -m pip install matplotlib
+#!{sys.executable} -m pip install astropy
+
+import matplotlib.pyplot as plt
+import lightkurve as lk
+import astropy
+import numpy as np
+
+TIC = 'TIC 284475976' # TIC Star ID
+sector_data = lk.search_lightcurve(TIC, author = 'SPOC', sector = 23) # can remove each arg if needed
+sector_data
+
+lc = sector_data.download_all()
+lc.plot()
+
+lc = sector_data.download_all()
+lc.plot()
+sector_data = lk.search_lightcurve(TIC, author = 'SPOC', sector=23)
+sector_data
+lc = sector_data.download_all()
+lc.plot(linewidth = 0, marker = '.')
+lc.plot(linewidth = 0, marker = '.', color = 'lightcyan', alpha = 0.3)
+
+TIC_2 = 'TIC 55525572'
+available_data_all = lk.search_lightcurve(TIC_2, author = 'SPOC')
+available_data_all
+
+select_sector = available_data_all[0:4]
+select_sector
+
+lc_collection = select_sector.download_all() # download all the sectors ([0:4])
+lc_collection
+
+lc_collection.plot(linewidth = 0, marker = '.')
+
+lc_collection_stitched = lc_collection.stitch()
+lc_collection_stitched.plot(linewidth = 0, marker = '.', color = 'red')
+lc.normalise().plot()
+
+bin_time = 15/24/60 # LK uses day units, this is 15 minutes over 24 hours
+lc_collection_binned = lc_collection.bin(bin_time)
+lc_collection_binned.plot()
+
+available_data_select = lk.search_lightcurve(TIC, author = 'SPOC')[0:9] # Query data, select data from mutliple sectors (e.g. all availablke first year TESS data)
+lc_collection = available_data_select.download_all().stitch()
+fig, ax = plt.subplots(figsize = (8,4))
+lc_collection.plot(ax = ax, linewidth = 0, marker = 'o', color = 'purple', markersize = 1, alpha = 0.7)
+
+period = 83.8979
+t0 = 2125.847
+
+lc_phased = lc_collection.fold(period = period, epoch_time = t0)
+lc_phased.plot(linewidth = 0, color = 'gold', marker = '.', markersize = 1, alpha = 0.7)
+
+lc_phased_binned = lc_phased.bin(15/24/60)
+fig, ax = plt.subplots(figsize = (8,5)) # defines a plotting region to plot multiple data sets
+lc_phased.plot(ax = ax, marker = '.', linewidth = 0, color = 'blue', alpha = 0.4, markersize = 3, label = 'unbinned')
+lc_phased_binned.plot(ax = ax, marker = 'o', linewidth = 0, color = 'purple', alpha = 0.8, markersize = 6, label = 'binned')
+
+plt.xlim(-2, 2) # upper and lower limit for x-axis, zooming in from -40, 40 wide to -2, 2 wide
+plt.ylim(0.996, 1.004) 
+
+
+if __name__ == '__main__':
+    app.run()