Update for new version

Author: remykarem

.gitignore

@@ -2,4 +2,5 @@
 .ipynb_checkpoints
 build
 p2j.egg-info
-dist
+dist
+.pyc

README.md

@@ -1,8 +1,34 @@
-# p2j - Python to Jupyter Notebook [![PyPI version](https://badge.fury.io/py/p2j.svg)](https://badge.fury.io/py/p2j)
+# p2j - Python to Jupyter Notebook Parser [![PyPI version](https://badge.fury.io/py/p2j.svg)](https://badge.fury.io/py/p2j)
 
-Convert your Python source code to Jupyter notebook with zero intervention.
+Convert your Python source code to Jupyter notebook with **zero intervention**.
 
-See an example of a [Python source code](p2j/examples/example2.py) and its [Jupyter notebook](p2j/examples/example2.ipynb) after converting.
+Here is an example.
+
+```python
+# Evaluate the model
+model.evaluate()
+
+# Run the model for a while.
+# Then we hide the model.
+run()
+hide()
+
+print(type(data))
+
+# This is considered as a paragraph too
+# It has 2 lines of comments
+
+# The data that we are interested in is made of 8x8 images of digits.
+# Let's have a look at the first 4 images, which is of course
+# stored in the `images` attribute of the dataset.  
+images = list(zip(mnist.images))
+```
+
+which translates to the following:
+
+![example](screenshot.png)
+
+Here's another example of a [Python source code](p2j/examples/example2.py) and its [Jupyter notebook](p2j/examples/example2.ipynb) after converting.
 
 The purpose of this package is to be able to run a code on Jupyter notebook without having to copy each paragraph of the code into every cell. It's also useful if we want to run our code in Google Colab. This parser isn't perfect, but you would be satisfactorily pleased with what you get.
 
@@ -65,7 +91,7 @@ optional arguments:
 
 ## Requirements
 
-- Python 3.6
+- Python >= 3.6
 
 No third party libraries are used.
 
@@ -175,6 +201,10 @@ print(type(data))
 images = list(zip(mnist.images))
 ```
 
+which translates to the following:
+
+![example](screenshot.png)
+
 ### 4. Indentation
 
 Any line of code or comment that is indented by a multiple of 4 spaces is considered code, and will stay in the same code cell as the previous non-empty line. This ensures that function and class definitions, loops and multi-line code stay in one cell.

p2j/examples/example.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["\nTrains a denoising autoencoder on MNIST dataset.<br>\n", "Denoising is one of the classic applications of autoencoders.<br>\n", "The denoising process removes unwanted noise that corrupted the<br>\n", "true signal.<br>\n", "Noise + Data ---> Denoising Autoencoder ---> Data<br>\n", "Given a training dataset of corrupted data as input and<br>\n", "true signal as output, a denoising autoencoder can recover the<br>\n", "hidden structure to generate clean data.<br>\n", "This example has modular design. The encoder, decoder and autoencoder<br>\n", "are 3 models that share weights. For example, after training the<br>\n", "autoencoder, the encoder can be used to  generate latent vectors<br>\n", "of input data for low-dim visualization like PCA or TSNE.<br>\n", ""]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "import keras\n", "from keras.layers import Activation, Dense, Input\n", "from keras.layers import Conv2D, Flatten\n", "from keras.layers import Reshape, Conv2DTranspose\n", "from keras.models import Model\n", "from keras import backend as K\n", "from keras.datasets import mnist\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from PIL import Image"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["np.random.seed(1337)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["MNIST dataset"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["(x_train, _), (x_test, _) = mnist.load_data()"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["image_size = x_train.shape[1]\n", "x_train = np.reshape(x_train, [-1, image_size, image_size, 1])\n", "x_test = np.reshape(x_test, [-1, image_size, image_size, 1])\n", "x_train = x_train.astype('float32') / 255\n", "x_test = x_test.astype('float32') / 255"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Generate corrupted MNIST images by adding noise with normal dist<br>\n", "centered at 0.5 and std=0.5"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["noise = np.random.normal(loc=0.5, scale=0.5, size=x_train.shape)\n", "x_train_noisy = x_train + noise\n", "noise = np.random.normal(loc=0.5, scale=0.5, size=x_test.shape)\n", "x_test_noisy = x_test + noise"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n", "x_test_noisy = np.clip(x_test_noisy, 0., 1.)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Network parameters"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["input_shape = (image_size, image_size, 1)\n", "batch_size = 128\n", "kernel_size = 3\n", "latent_dim = 16\n", "# Encoder/Decoder number of CNN layers and filters per layer\n", "layer_filters = [32, 64]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Build the Autoencoder Model<br>\n", "First build the Encoder Model"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["inputs = Input(shape=input_shape, name='encoder_input')\n", "x = inputs\n", "# Stack of Conv2D blocks\n", "# Notes:\n", "# 1) Use Batch Normalization before ReLU on deep networks\n", "# 2) Use MaxPooling2D as alternative to strides>1\n", "# - faster but not as good as strides>1\n", "for filters in layer_filters:\n", "    x = Conv2D(filters=filters,\n", "               kernel_size=kernel_size,\n", "               strides=2,\n", "               activation='relu',\n", "               padding='same')(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Shape info needed to build Decoder Model"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["shape = K.int_shape(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Generate the latent vector"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["x = Flatten()(x)\n", "latent = Dense(latent_dim, name='latent_vector')(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Instantiate Encoder Model"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["encoder = Model(inputs, latent, name='encoder')\n", "encoder.summary()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Build the Decoder Model"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["latent_inputs = Input(shape=(latent_dim,), name='decoder_input')\n", "x = Dense(shape[1] * shape[2] * shape[3])(latent_inputs)\n", "x = Reshape((shape[1], shape[2], shape[3]))(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Stack of Transposed Conv2D blocks<br>\n", "Notes:<br>\n", "1) Use Batch Normalization before ReLU on deep networks<br>\n", "2) Use UpSampling2D as alternative to strides>1<br>\n", "- faster but not as good as strides>1"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["for filters in layer_filters[::-1]:\n", "    x = Conv2DTranspose(filters=filters,\n", "                        kernel_size=kernel_size,\n", "                        strides=2,\n", "                        activation='relu',\n", "                        padding='same')(x)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["x = Conv2DTranspose(filters=1,\n", "                    kernel_size=kernel_size,\n", "                    padding='same')(x)"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["outputs = Activation('sigmoid', name='decoder_output')(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Instantiate Decoder Model"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["decoder = Model(latent_inputs, outputs, name='decoder')\n", "decoder.summary()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Autoencoder = Encoder + Decoder<br>\n", "Instantiate Autoencoder Model"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')\n", "autoencoder.summary()"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["autoencoder.compile(loss='mse', optimizer='adam')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Train the autoencoder"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["autoencoder.fit(x_train_noisy,\n", "                x_train,\n", "                validation_data=(x_test_noisy, x_test),\n", "                epochs=30,\n", "                batch_size=batch_size)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Predict the Autoencoder output from corrupted test images"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["x_decoded = autoencoder.predict(x_test_noisy)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Display the 1st 8 corrupted and denoised images"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["rows, cols = 10, 30\n", "num = rows * cols\n", "imgs = np.concatenate([x_test[:num], x_test_noisy[:num], x_decoded[:num]])\n", "imgs = imgs.reshape((rows * 3, cols, image_size, image_size))\n", "imgs = np.vstack(np.split(imgs, rows, axis=1))\n", "imgs = imgs.reshape((rows * 3, -1, image_size, image_size))\n", "imgs = np.vstack([np.hstack(i) for i in imgs])\n", "imgs = (imgs * 255).astype(np.uint8)\n", "plt.figure()\n", "plt.axis('off')\n", "plt.title('Original images: top rows, '\n", "          'Corrupted Input: middle rows, '\n", "          'Denoised Input:  third rows')\n", "plt.imshow(imgs, interpolation='none', cmap='gray')\n", "Image.fromarray(imgs).save('corrupted_and_denoised.png')\n", "plt.show()"]}], "metadata": {"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": "3.6.4"}}, "nbformat": 4, "nbformat_minor": 2}

p2j/p2j.py

@@ -1,5 +1,5 @@
 """
-This code translate .py files to .ipynb
+This code translates .py files to .ipynb
 """
 # Standard imports for file handling and JSON files
 import argparse

screenshot.png

@@ -6,7 +6,7 @@
 
 # Package meta-data.
 NAME = "p2j"
-VERSION = "1.2.1"
+VERSION = "1.2.2"
 DESCRIPTION = "p2j: Convert Python scripts to Jupyter notebook with minimal intervention"
 URL = "https://github.com/raibosome/python2jupyter"
 AUTHOR = "Raimi bin Karim"
@@ -18,16 +18,16 @@
 HERE = os.path.abspath(os.path.dirname(__file__))
 try:
     with io.open(os.path.join(HERE, 'README.md'), encoding='utf-8') as f:
-        long_description = '\n' + f.read()
+        LONG_DESCRIPTION = '\n' + f.read()
 except FileNotFoundError:
-    long_description = DESCRIPTION
+    LONG_DESCRIPTION = DESCRIPTION
 
 # This call to setup() does all the work
 setup(
     name=NAME,
     version=VERSION,
     description=DESCRIPTION,
-    long_description=long_description,
+    long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",
     url=URL,
     author=AUTHOR,