From 5b1c84eb4c1c1f02ae9ec1b9bd36354f747b9134 Mon Sep 17 00:00:00 2001
From: jebba <root@localhost>
Date: Mon, 24 Jan 2022 16:12:58 -0700
Subject: [PATCH] no tmp notebooks

---
 .gitignore                                    |   3 +-
 .../.ipynb_checkpoints/wut-checkpoint.ipynb   | 774 ------------------
 2 files changed, 2 insertions(+), 775 deletions(-)
 delete mode 100644 notebooks/.ipynb_checkpoints/wut-checkpoint.ipynb

diff --git a/.gitignore b/.gitignore
index 1d65af8..be2b9e5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+.ipynb_checkpoints
 .~lock.*#
-*.swp
 samples
+*.swp
diff --git a/notebooks/.ipynb_checkpoints/wut-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/wut-checkpoint.ipynb
deleted file mode 100644
index 9fba04e..0000000
--- a/notebooks/.ipynb_checkpoints/wut-checkpoint.ipynb
+++ /dev/null
@@ -1,774 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wut --- What U Think? SatNOGS Observation AI.\n",
-    "#\n",
-    "# https://spacecruft.org/spacecruft/satnogs-wut"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# GPLv3+"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Built using Jupyter, Tensorflow, Keras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Other data to consider adding:\n",
-    "# * JSON metadata\n",
-    "# * TLE\n",
-    "# * Audio File (ogg)\n",
-    "#   https://www.tensorflow.org/io/api_docs/python/tfio/ffmpeg/AudioDataset\n",
-    "# * Decoded Data (HEX, ASCII, PNG)\n",
-    "# Data from external sources to consider adding:\n",
-    "# * Weather"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Start\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow.python.keras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.python.keras import Sequential\n",
-    "from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\n",
-    "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.python.keras import optimizers\n",
-    "from tensorflow.python.keras.preprocessing import image\n",
-    "from tensorflow.python.keras.models import load_model\n",
-    "from tensorflow.python.keras.preprocessing.image import load_img\n",
-    "from tensorflow.python.keras.preprocessing.image import img_to_array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.python.keras.models import Model\n",
-    "from tensorflow.python.keras.layers import Input, concatenate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualization\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn.decomposition import PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Seaborn pip dependency\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Interact\n",
-    "# https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n",
-    "from __future__ import print_function\n",
-    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
-    "import ipywidgets as widgets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display Images\n",
-    "from IPython.display import display, Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Python import done\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_dir = os.path.join('data/', 'train')\n",
-    "val_dir = os.path.join('data/', 'val')\n",
-    "test_dir = os.path.join('data/', 'test')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_good_dir = os.path.join(train_dir, 'good')\n",
-    "train_bad_dir = os.path.join(train_dir, 'bad')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val_good_dir = os.path.join(val_dir, 'good')\n",
-    "val_bad_dir = os.path.join(val_dir, 'bad')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_train_good = len(os.listdir(train_good_dir))\n",
-    "num_train_bad = len(os.listdir(train_bad_dir))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_val_good = len(os.listdir(val_good_dir))\n",
-    "num_val_bad = len(os.listdir(val_bad_dir))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_test = len(os.listdir(test_dir))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_train = num_train_good + num_train_bad\n",
-    "total_val = num_val_good + num_val_bad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('total training good images:', num_train_good)\n",
-    "print('total training bad images:', num_train_bad)\n",
-    "print(\"--\")\n",
-    "print(\"Total training images:\", total_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('total validation good images:', num_val_good)\n",
-    "print('total validation bad images:', num_val_bad)\n",
-    "print(\"--\")\n",
-    "print(\"Total validation images:\", total_val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Reduce training and validation set when testing\")\n",
-    "#total_train = 500\n",
-    "#total_val = 500\n",
-    "print(\"Train =\")\n",
-    "print(total_train)\n",
-    "print(\"Validation =\")\n",
-    "print(total_val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Good results\n",
-    "#batch_size = 128\n",
-    "#epochs = 6\n",
-    "#\n",
-    "# Testing, faster more inaccurate results\n",
-    "batch_size = 96\n",
-    "epochs = 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Half size\n",
-    "IMG_HEIGHT = 416\n",
-    "IMG_WIDTH= 804\n",
-    "# Full size, machine barfs probably needs more RAM\n",
-    "#IMG_HEIGHT = 832\n",
-    "#IMG_WIDTH = 1606"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_image_generator = ImageDataGenerator(\n",
-    "    rescale=1./255\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val_image_generator = ImageDataGenerator(\n",
-    "    rescale=1./255\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_image_generator = ImageDataGenerator(\n",
-    "    rescale=1./255\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
-    "                                                           directory=train_dir,\n",
-    "                                                           shuffle=True,\n",
-    "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
-    "                                                           class_mode='binary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val_data_gen = val_image_generator.flow_from_directory(batch_size=batch_size,\n",
-    "                                                       directory=val_dir,\n",
-    "                                                       target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
-    "                                                       class_mode='binary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(test_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,\n",
-    "                                                       directory=test_dir,\n",
-    "                                                       target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
-    "                                                       class_mode='binary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_train_images, _ = next(train_data_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_val_images, _ = next(val_data_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_test_images, _ = next(test_data_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This function will plot images in the form of a grid with 1 row and 3 columns where images are placed in each column.\n",
-    "def plotImages(images_arr):\n",
-    "    fig, axes = plt.subplots(1, 3, figsize=(20,20))\n",
-    "    axes = axes.flatten()\n",
-    "    for img, ax in zip( images_arr, axes):\n",
-    "        ax.imshow(img)\n",
-    "        ax.axis('off')\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotImages(sample_train_images[0:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotImages(sample_val_images[0:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotImages(sample_test_images[0:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = Sequential([\n",
-    "    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),\n",
-    "    MaxPooling2D(),\n",
-    "    Conv2D(32, 3, padding='same', activation='relu'),\n",
-    "    MaxPooling2D(),\n",
-    "    Conv2D(64, 3, padding='same', activation='relu'),\n",
-    "    MaxPooling2D(),\n",
-    "    Flatten(),\n",
-    "    Dense(512, activation='relu'),\n",
-    "    Dense(1, activation='sigmoid')\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam',\n",
-    "              loss='binary_crossentropy',\n",
-    "              metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Image.LOAD_TRUNCATED_IMAGES = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history = model.fit_generator(\n",
-    "    train_data_gen,\n",
-    "    steps_per_epoch=total_train // batch_size,\n",
-    "    epochs=epochs,\n",
-    "    validation_data=val_data_gen,\n",
-    "    validation_steps=total_val // batch_size,\n",
-    "    verbose=1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "acc = history.history['accuracy']\n",
-    "val_acc = history.history['val_accuracy']\n",
-    "\n",
-    "loss = history.history['loss']\n",
-    "val_loss = history.history['val_loss']\n",
-    "\n",
-    "epochs_range = range(epochs)\n",
-    "\n",
-    "plt.figure(figsize=(8, 8))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
-    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
-    "plt.legend(loc='lower right')\n",
-    "plt.title('Training and Validation Accuracy')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(epochs_range, loss, label='Training Loss')\n",
-    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
-    "plt.legend(loc='upper right')\n",
-    "plt.title('Training and Validation Loss')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"TRAINING info\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(train_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(train_good_dir)\n",
-    "print(train_bad_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(train_image_generator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(train_data_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#print(sample_train_images)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save('data/wut-CW.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://keras.io/models/sequential/\n",
-    "print(\"predict\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred=model.predict_generator(test_data_gen,\n",
-    "steps=1,\n",
-    "verbose=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prediction = model.predict(\n",
-    "    x=test_data_gen,\n",
-    "    verbose=2\n",
-    ")\n",
-    "print(\"end predict\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions=[]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Show prediction score\n",
-    "print(prediction)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prediction_bool = (prediction >0.8)\n",
-    "print(prediction_bool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions = prediction_bool.astype(int)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Make final prediction\n",
-    "# XXX, display name, display all of them with mini waterfall, etc.\n",
-    "if prediction_bool[0] == False:\n",
-    "  rating = 'bad'\n",
-    "else:\n",
-    "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if prediction_bool[1] == False:\n",
-    "  rating = 'bad'\n",
-    "else:\n",
-    "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if prediction_bool[2] == False:\n",
-    "  rating = 'bad'\n",
-    "else:\n",
-    "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The End"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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