texlive[56715] Master: jupynotex (20oct20)
commits+karl at tug.org
commits+karl at tug.org
Tue Oct 20 22:39:10 CEST 2020
Revision: 56715
http://tug.org/svn/texlive?view=revision&revision=56715
Author: karl
Date: 2020-10-20 22:39:10 +0200 (Tue, 20 Oct 2020)
Log Message:
-----------
jupynotex (20oct20)
Modified Paths:
--------------
trunk/Master/tlpkg/bin/tlpkg-ctan-check
trunk/Master/tlpkg/libexec/ctan2tds
trunk/Master/tlpkg/tlpsrc/collection-mathscience.tlpsrc
Added Paths:
-----------
trunk/Master/texmf-dist/doc/latex/jupynotex/
trunk/Master/texmf-dist/doc/latex/jupynotex/LICENSE
trunk/Master/texmf-dist/doc/latex/jupynotex/README.md
trunk/Master/texmf-dist/doc/latex/jupynotex/example/
trunk/Master/texmf-dist/doc/latex/jupynotex/example/build
trunk/Master/texmf-dist/doc/latex/jupynotex/example/example.tex
trunk/Master/texmf-dist/doc/latex/jupynotex/example/notebook.ipynb
trunk/Master/texmf-dist/doc/latex/jupynotex/tests/
trunk/Master/texmf-dist/doc/latex/jupynotex/tests/run
trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_cellparser.py
trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_main.py
trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_notebook.py
trunk/Master/texmf-dist/tex/latex/jupynotex/
trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.py
trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.sty
trunk/Master/tlpkg/tlpsrc/jupynotex.tlpsrc
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/LICENSE
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/LICENSE (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/LICENSE 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,201 @@
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+ http://www.apache.org/licenses/
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Added: trunk/Master/texmf-dist/doc/latex/jupynotex/README.md
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/README.md (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/README.md 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,73 @@
+# What is Jupynotex?
+
+A Jupyter Notebook to LaTeX translator to include whole or partial notebooks in your papers.
+
+## Wait, what?
+
+A TeX package that you can use in your project to include Jupyter Notebooks (all of them, or some specific cells) as part of your text.
+
+It will convert the Jupyter Notebook format to proper LaTeX so it gets included seamless, supporting text, latex, images, etc.
+
+
+# How To Use?
+
+All you need to do is include the `jupynotex.py` and `jupynotex.sty` files in your LaTeX project, and use the package from your any of your `.tex` files:
+
+ \usepackage{jupynotex}
+
+After that, you can include a whole Jupyter Notebook in your file just specifying it's file name:
+
+ \jupynotex{file_name_for_your_notebook.ipynb}
+
+If you do not want to include it completely, you can optionally specify which cells:
+
+ \jupynotex[<which cells>]{sample.ipynb}
+
+The cells specification can be numbers separated by comma, or ranges using dashes (defaulting to first and last if any side is not included).
+
+Examples:
+
+- include the whole *foobar* notebook:
+
+ `\jupynotex{foobar.ipynb}`
+
+- include just the cell #7:
+
+ `\jupynotex[7]{sample.ipynb}`
+
+- include cells 1, 3, and 6, 7, and 8 from the range:
+
+ `\jupynotex[1,3,6-8]{sample.ipynb}`
+
+- include everything up to the fourth cell, and the eigth:
+
+ `\jupynotex[-4,8]{whatever.ipynb}`
+
+- include the cell number 3, and from 12 to the notebook's end
+
+ `\jupynotex[3,12-]{somenote.ipynb}`
+
+
+## Full Example
+
+Check the `example` directory in this project.
+
+There you will find an example `notebook.ipynb`, an `example.tex` file that includes cells from that notebook in different ways, and a `build` script.
+
+Play with it. Enjoy.
+
+
+# Dependencies
+
+You need Python 3 in your system, and the [tcolorbox](https://ctan.org/pkg/tcolorbox) module in your LaTeX toolbox.
+
+
+# Feedback & Development
+
+Please open any issue or ask any question [here](https://github.com/facundobatista/jupynotex/issues/new).
+
+To run the tests (need to have [fades](https://github.com/pyar/fades) installed):
+
+ ./tests/run
+
+This material is subject to the Apache 2.0 license.
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/README.md
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/example/build
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/example/build (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/example/build 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,5 @@
+#!/bin/sh
+
+ln -s ../jupynotex.py .
+ln -s ../jupynotex.sty .
+xelatex -shell-escape example.tex
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/example/build
___________________________________________________________________
Added: svn:executable
## -0,0 +1 ##
+*
\ No newline at end of property
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/example/example.tex
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/example/example.tex (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/example/example.tex 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,27 @@
+\documentclass{article}
+
+\usepackage{jupynotex}
+
+\begin{document}
+
+One cell:
+
+\jupynotex[1]{notebook.ipynb}
+
+
+A range of cells:
+
+\jupynotex[4-6]{notebook.ipynb}
+
+
+Some specific cells:
+
+\jupynotex[12,17]{notebook.ipynb}
+
+
+The whole notebook:
+
+\jupynotex{notebook.ipynb}
+
+
+\end{document}
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/example/example.tex
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/example/notebook.ipynb
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/example/notebook.ipynb (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/example/notebook.ipynb 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,593 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Non alphanums {'>', '\\t', '[', '\\r', '\\x13', '\\x1d', '<', '^', '\\x04', '\\x08', '\\x17', '\\x1b', '\\\\', '\\x19', '`', '\\x1f', '$', '\\x0b', '\\x14', '\\x12', '\\x18', ')', '=', '?', ' ', \"'\", '\\x1c', '|', ';', '/', '\\x1e', '\\n', '\\x07', '\\x10', '\\x03', '\\x02', '#', '\\x0c', '@', '\\x16', '_', '}', '.', '-', '(', '!', '+', '\\x06', ']', '{', ':', '\\x01', '\\x11', '\\x0f', '\\x05', ',', '~', '\\x15', '\\x00', '\"', '%', '\\x0e', '*', '&', '\\x1a'}\n",
+ "Separators b'([\\\\>\\\\\\t\\\\[\\\\\\r\\\\\\x13\\\\\\x1d\\\\<\\\\^\\\\\\x04\\\\\\x08\\\\\\x17\\\\\\x1b\\\\\\\\\\\\\\x19\\\\`\\\\\\x1f\\\\$\\\\\\x0b\\\\\\x14\\\\\\x12\\\\\\x18\\\\)\\\\=\\\\?\\\\ \\\\\\'\\\\\\x1c\\\\|\\\\;\\\\/\\\\\\x1e\\\\\\n\\\\\\x07\\\\\\x10\\\\\\x03\\\\\\x02\\\\#\\\\\\x0c\\\\@\\\\\\x16\\\\_\\\\}\\\\.\\\\-\\\\(\\\\!\\\\+\\\\\\x06\\\\]\\\\{\\\\:\\\\\\x01\\\\\\x11\\\\\\x0f\\\\\\x05\\\\,\\\\~\\\\\\x15\\\\\\x00\\\\\"\\\\%\\\\\\x0e\\\\*\\\\&\\\\\\x1a])'\n"
+ ]
+ }
+ ],
+ "source": [
+ "import string\n",
+ "\n",
+ "non_alphanums = set(chr(x) for x in range(127)) - set(string.ascii_letters) - set(string.digits)\n",
+ "print(\"Non alphanums\", non_alphanums)\n",
+ "separators = '([' + ''.join('\\\\' + x for x in non_alphanums) + '])'\n",
+ "separators = separators.encode('ascii')\n",
+ "print(\"Separators\", separators)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[b'dlg',\n",
+ " b'=',\n",
+ " b'Resource',\n",
+ " b'.',\n",
+ " b'loadfromresfile',\n",
+ " b'(',\n",
+ " b'filename',\n",
+ " b',',\n",
+ " b'win',\n",
+ " b',',\n",
+ " b'QuoteDialog',\n",
+ " b'.',\n",
+ " b'MyQuoteDialog',\n",
+ " b',',\n",
+ " b\"'\",\n",
+ " b'QuoteDialog',\n",
+ " b\"'\",\n",
+ " b',',\n",
+ " b'win',\n",
+ " b')']"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import re\n",
+ "program_line = b\"\"\"dlg = Resource.loadfromresfile(filename, win, QuoteDialog.MyQuoteDialog, 'QuoteDialog', win)\"\"\"\n",
+ "tokens = [t for x in re.split(separators, program_line) if (t := x.strip())]\n",
+ "tokens"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Checking base dir: HTML document\n",
+ "Checking base dir: C source\n",
+ "Checking base dir: C++ source\n",
+ "Checking base dir: PHP script\n",
+ "Checking base dir: ReStructuredText file\n",
+ "Checking base dir: Python script\n",
+ "Checking base dir: Ruby script\n",
+ "Checking base dir: Java source\n",
+ "Checking base dir: Objective-C source\n",
+ "Checking base dir: Perl5 module source\n",
+ "Checking base dir: XML 1.0 document\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from collections import Counter\n",
+ "\n",
+ "DUMP_BASE = '/home/facundo/devel/ml/dump'\n",
+ "\n",
+ "# directories with 1000 files of each code type, excluding \"just text\" (ascii, utf8, etc)\n",
+ "CODE_TYPES = [\n",
+ " 'HTML document',\n",
+ " 'C source',\n",
+ " 'C++ source',\n",
+ " 'PHP script',\n",
+ " 'ReStructuredText file',\n",
+ " 'Python script',\n",
+ " 'Ruby script',\n",
+ " 'Java source',\n",
+ " 'Objective-C source',\n",
+ " 'Perl5 module source',\n",
+ " 'XML 1.0 document',\n",
+ "]\n",
+ "\n",
+ "# let's collect ALL tokens present in all the program files\n",
+ "tokens = Counter()\n",
+ "for basedir in CODE_TYPES:\n",
+ " print(\"Checking base dir:\", basedir)\n",
+ " for dirpath, dirnames, filenames in os.walk(os.path.join(DUMP_BASE, basedir)):\n",
+ " for fname in filenames:\n",
+ " fpath = os.path.join(dirpath, fname)\n",
+ " with open(fpath, 'rb') as fh:\n",
+ " tokens.update(t for x in re.split(separators, fh.read()) if (t := x.strip()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total different tokens 213400\n",
+ " 870525 b'.'\n",
+ " 756849 b'_'\n",
+ " 730609 b'('\n",
+ " 717433 b'='\n",
+ " 699725 b')'\n",
+ " 688556 b'/'\n",
+ " 661461 b'\"'\n",
+ " 640989 b','\n",
+ " 625121 b'-'\n",
+ " 594091 b'>'\n"
+ ]
+ }
+ ],
+ "source": [
+ "different_tokens = len(tokens)\n",
+ "print(\"Total different tokens\", different_tokens)\n",
+ "for name, quant in tokens.most_common(10):\n",
+ " print(\"{:8d} {}\".format(quant, name))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total tokens found: 20101050\n",
+ "Total representative: 3324\n",
+ "Last ten...\n",
+ " 289 b'enables'\n",
+ " 289 b'smaller'\n",
+ " 289 b'Creates'\n",
+ " 289 b'cross'\n",
+ " 289 b'GLFW'\n",
+ " 289 b'Os'\n",
+ " 289 b'usb'\n",
+ " 288 b'stylesheets'\n",
+ " 288 b'ad'\n",
+ " 288 b'WIDTH'\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "total_tokens = sum(tokens.values())\n",
+ "print(\"Total tokens found:\", total_tokens)\n",
+ "\n",
+ "most = total_tokens * 0.9\n",
+ "tot = 0\n",
+ "representative_data = []\n",
+ "for name, quant in tokens.most_common():\n",
+ " representative_data.append((name, quant))\n",
+ " tot += quant\n",
+ " if tot > most:\n",
+ " break\n",
+ "\n",
+ "print(\"Total representative:\", len(representative_data))\n",
+ "print(\"Last ten...\")\n",
+ "for name, quant in representative_data[-10:]:\n",
+ " print(\"{:8d} {}\".format(quant, name))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting data from base dir HTML document\n",
+ "Collecting data from base dir C source\n",
+ "Collecting data from base dir C++ source\n",
+ "Collecting data from base dir PHP script\n",
+ "Collecting data from base dir ReStructuredText file\n",
+ "Collecting data from base dir Python script\n",
+ "Collecting data from base dir Ruby script\n",
+ "Collecting data from base dir Java source\n",
+ "Collecting data from base dir Objective-C source\n",
+ "Collecting data from base dir Perl5 module source\n",
+ "Collecting data from base dir XML 1.0 document\n",
+ "Src data samples: 11000\n"
+ ]
+ }
+ ],
+ "source": [
+ "import random\n",
+ "\n",
+ "# real \"ML data\": a list of (code_type, features) (one pair for each file)\n",
+ "# code_type: the *position* of the code type corresponding to the file (needs to be an int)\n",
+ "# features: a list of values, each value corresponds to how many of the tokens of that position the file has\n",
+ "\n",
+ "representative_tokens = [name for name, _ in representative_data]\n",
+ "\n",
+ "all_src_data = []\n",
+ "for idx, basedir in enumerate(CODE_TYPES):\n",
+ " print(\"Collecting data from base dir\", basedir)\n",
+ " for dirpath, dirnames, filenames in os.walk(os.path.join(DUMP_BASE, basedir)):\n",
+ " for fname in filenames:\n",
+ " fpath = os.path.join(dirpath, fname)\n",
+ " with open(fpath, 'rb') as fh:\n",
+ " fcontent = fh.read()\n",
+ " \n",
+ " file_tokens = Counter(t for x in re.split(separators, fcontent) if (t := x.strip()))\n",
+ " token_quantities = [file_tokens.get(t, 0) for t in representative_tokens]\n",
+ "\n",
+ " all_src_data.append((idx, token_quantities))\n",
+ "\n",
+ "print(\"Src data samples:\", len(all_src_data))\n",
+ "\n",
+ "# shuffle, as currently is too much \"per directory\"\n",
+ "random.shuffle(all_src_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import Model, layers\n",
+ "import numpy as np\n",
+ "\n",
+ "# representation of our model\n",
+ "num_classes = len(CODE_TYPES)\n",
+ "num_features = len(representative_tokens)\n",
+ "\n",
+ "# 1st and 2nd layer number of neurons (these numbers are just chamuyo)\n",
+ "n_hidden_1 = 128 \n",
+ "n_hidden_2 = 256\n",
+ "\n",
+ "# training parameters (more chamuyo)\n",
+ "learning_rate = 0.1\n",
+ "\n",
+ "batch_size = 256\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Example code types: (2, 0, 4, 4, 5)\n",
+ "Example token quants: [246, 786, 246, 61, 246] [32, 32, 5, 22, 5]\n",
+ "Token quants shape: (11000, 3324)\n",
+ "Example normalized quants: [0.3129771 1. 0.3129771 0.07760815 0.3129771 ] [0.9411765 0.9411765 0.14705883 0.64705884 0.14705883]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# separate the source data and into two pairing lists\n",
+ "code_types, token_quantities = zip(*all_src_data)\n",
+ "print(\"Example code types:\", code_types[:5])\n",
+ "print(\"Example token quants:\", token_quantities[0][:5], token_quantities[117][:5])\n",
+ "\n",
+ "# convert features to float\n",
+ "float_quantities = np.array(token_quantities, np.float32)\n",
+ "print(\"Token quants shape:\", float_quantities.shape)\n",
+ "\n",
+ "# normalize EACH ONE to [0, 1]\n",
+ "for quants in float_quantities:\n",
+ " quants /= max(quants)\n",
+ "print(\"Example normalized quants:\", float_quantities[0][:5], float_quantities[117][:5])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Real training set: 89.70%\n"
+ ]
+ }
+ ],
+ "source": [
+ "# let's prepare the teching sets, input and output for training first (90% of cases) and\n",
+ "# then testing what's learned (the remaining 10%); \n",
+ "input_training = []\n",
+ "output_training = []\n",
+ "input_testing = []\n",
+ "output_testing = []\n",
+ "for token_distribution, code_type in zip(float_quantities, code_types):\n",
+ " if random.random() < .1:\n",
+ " input_testing.append(token_distribution)\n",
+ " output_testing.append(code_type)\n",
+ " else:\n",
+ " input_training.append(token_distribution)\n",
+ " output_training.append(code_type)\n",
+ "print(\"Real training set: {:.2f}%\".format(100 * len(input_training) / len(float_quantities))) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import Model\n",
+ "\n",
+ "class NeuralNet(Model):\n",
+ " \"\"\"Chamuyo al cuadrado.\"\"\"\n",
+ " \n",
+ " def __init__(self):\n",
+ " super(NeuralNet, self).__init__()\n",
+ " self.fc1 = layers.Dense(n_hidden_1, activation=tf.nn.sigmoid) # se puede cambiar a relu\n",
+ " self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.sigmoid) # se puede cambiar a relu\n",
+ " self.out = layers.Dense(num_classes)\n",
+ "\n",
+ " def call(self, x, is_training=False):\n",
+ " x = self.fc1(x)\n",
+ " x = self.fc2(x)\n",
+ " x = self.out(x)\n",
+ " if not is_training:\n",
+ " # tf cross entropy expect logits without softmax, so only\n",
+ " # apply softmax when not training.\n",
+ " x = tf.nn.softmax(x)\n",
+ " return x\n",
+ "\n",
+ "neural_net = NeuralNet()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note that this will apply 'softmax' to the logits.\n",
+ "def cross_entropy_loss(x, y):\n",
+ " # Convert labels to int 64 for tf cross-entropy function.\n",
+ " y = tf.cast(y, tf.int64)\n",
+ " # Apply softmax to logits and compute cross-entropy.\n",
+ " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n",
+ " # Average loss across the batch.\n",
+ " return tf.reduce_mean(loss)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Stochastic gradient descent optimizer.\n",
+ "optimizer = tf.optimizers.SGD(learning_rate)\n",
+ "\n",
+ "# Optimización. \n",
+ "def run_optimization(x, y):\n",
+ " # Funciones para calcular el gradiente\n",
+ " with tf.GradientTape() as g:\n",
+ " # Algoritmo de forward\n",
+ " pred = neural_net(x, is_training=True)\n",
+ " # Computa la función de costo o pérdida utilizando entropía cruzada\n",
+ " loss = cross_entropy_loss(pred, y)\n",
+ " \n",
+ " # Actualiza las variables de entrenamiento.\n",
+ " trainable_variables = neural_net.trainable_variables\n",
+ "\n",
+ " # Computa los gradientes\n",
+ " gradients = g.gradient(loss, trainable_variables)\n",
+ " \n",
+ " # Actualiza los nuevos parámetros W (pesos) y b (bias).\n",
+ " optimizer.apply_gradients(zip(gradients, trainable_variables))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Accuracy metric.\n",
+ "def accuracy(y_pred, y_true):\n",
+ " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n",
+ " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n",
+ " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Step 100: loss=2.379924, accuracy=0.125000\n",
+ "Step 200: loss=2.372284, accuracy=0.128906\n",
+ "Step 300: loss=2.366430, accuracy=0.140625\n",
+ "Step 400: loss=2.360768, accuracy=0.121094\n",
+ "Step 500: loss=2.347394, accuracy=0.109375\n",
+ "Step 600: loss=2.349795, accuracy=0.199219\n",
+ "Step 700: loss=2.312950, accuracy=0.218750\n",
+ "Step 800: loss=2.284508, accuracy=0.289062\n",
+ "Step 900: loss=2.173395, accuracy=0.335938\n",
+ "Step 1000: loss=2.115966, accuracy=0.300781\n",
+ "Step 1100: loss=1.977837, accuracy=0.441406\n",
+ "Step 1200: loss=1.860783, accuracy=0.425781\n",
+ "Step 1300: loss=1.866206, accuracy=0.425781\n",
+ "Step 1400: loss=1.773057, accuracy=0.402344\n",
+ "Step 1500: loss=1.736271, accuracy=0.546875\n",
+ "Step 1600: loss=1.626320, accuracy=0.578125\n",
+ "Step 1700: loss=1.537970, accuracy=0.539062\n",
+ "Step 1800: loss=1.369012, accuracy=0.609375\n",
+ "Step 1900: loss=1.286771, accuracy=0.625000\n",
+ "Step 2000: loss=1.270916, accuracy=0.640625\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_data = tf.data.Dataset.from_tensor_slices((input_training, output_training))\n",
+ "\n",
+ "# NOTE: this doesn't only selectes, it completely transform the structures\n",
+ "# from <TensorSliceDataset shapes: ((3324,), ()), types: (tf.float32, tf.int32)>\n",
+ "# to <PrefetchDataset shapes: ((None, 3324), (None,)), types: (tf.float32, tf.int32)>\n",
+ "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)\n",
+ "\n",
+ "display_step = 100\n",
+ "training_steps = 2000\n",
+ "\n",
+ "# Run training for the given number of steps.\n",
+ "for step, (input_batch, output_batch) in enumerate(train_data.take(training_steps), 1):\n",
+ " # Run the optimization to update W and b values.\n",
+ " run_optimization(input_batch, output_batch)\n",
+ " \n",
+ " if step % display_step == 0:\n",
+ " pred = neural_net(input_batch, is_training=True)\n",
+ " loss = cross_entropy_loss(pred, output_batch)\n",
+ " acc = accuracy(pred, output_batch)\n",
+ " print(\"Step {}: loss={:f}, accuracy={:f}\".format(step, loss, acc))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test Accuracy: 0.578994\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test model on validation set\n",
+ "input_testing = np.array(input_testing)\n",
+ "pred = neural_net(input_testing, is_training=False)\n",
+ "print(\"Test Accuracy: {:f}\".format(accuracy(pred, output_testing)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If we consider the \"more probable\" prediction on each item, how well it goes?\n",
+ "all_guesses = neural_net(input_testing, is_training=False)\n",
+ "guesses_ok = [0] * len(CODE_TYPES)\n",
+ "guesses_bad = [0] * len(CODE_TYPES)\n",
+ "\n",
+ "for guess, real in zip(all_guesses, output_testing):\n",
+ " # guess is an array of len(CODE_TYPES) with a float in each position showing\n",
+ " # which one is the most probable to be real, so we need to get position \n",
+ " # for the max one and check if it matches with the real real :)\n",
+ " position_for_max = np.where(guess == np.amax(guess))[0][0]\n",
+ " if position_for_max == real:\n",
+ " guesses_ok[real] += 1\n",
+ " else:\n",
+ " guesses_bad[real] += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "values_ok = [guesses_ok[i] for i in range(len(CODE_TYPES))]\n",
+ "values_bad = [guesses_bad[i] for i in range(len(CODE_TYPES))]\n",
+ "x_positions = np.arange(len(CODE_TYPES))\n",
+ "\n",
+ "p1 = plt.bar(x_positions, guesses_ok)\n",
+ "p2 = plt.bar(x_positions, guesses_bad, bottom=guesses_ok)\n",
+ "\n",
+ "plt.ylabel('Tipo de código')\n",
+ "plt.title('Aciertos o no, por tipo de código')\n",
+ "plt.xticks(x_positions, CODE_TYPES, rotation=90)\n",
+ "#plt.yticks(np.arange(0, 81, 10))\n",
+ "plt.legend((p1[0], p2[0]), ('Correcto', 'Incorrecto'))\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.8.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/run
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/tests/run (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/tests/run 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,3 @@
+#!/bin/sh
+
+PYTHONPATH=. fades -d pytest -x pytest -sv "$@"
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/run
___________________________________________________________________
Added: svn:executable
## -0,0 +1 ##
+*
\ No newline at end of property
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_cellparser.py
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_cellparser.py (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_cellparser.py 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,79 @@
+# Copyright 2020 Facundo Batista
+# All Rights Reserved
+# Licensed under Apache 2.0
+
+import pytest
+import re
+
+from jupynotex import _parse_cells
+
+
+def test_empty():
+ msg = "Empty cells spec not allowed"
+ with pytest.raises(ValueError, match=re.escape(msg)):
+ _parse_cells('', 100)
+
+
+def test_simple():
+ r = _parse_cells('1', 100)
+ assert r == [1]
+
+
+def test_several_comma():
+ r = _parse_cells('1,3,5,9,7', 100)
+ assert r == [1, 3, 5, 7, 9]
+
+
+def test_several_range():
+ r = _parse_cells('1-9', 100)
+ assert r == [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+
+def test_several_limited():
+ msg = "Notebook loaded of len 3, smaller than requested cells: [1, 2, 3, 4]"
+ with pytest.raises(ValueError, match=re.escape(msg)):
+ _parse_cells('1-4', 3)
+
+
+def test_range_default_start():
+ r = _parse_cells('-3', 8)
+ assert r == [1, 2, 3]
+
+
+def test_range_default_end():
+ r = _parse_cells('5-', 8)
+ assert r == [5, 6, 7, 8]
+
+
+def test_not_int():
+ msg = "Found forbidden characters in cells definition (allowed digits, '-' and ',')"
+ with pytest.raises(ValueError, match=re.escape(msg)):
+ _parse_cells('1,a', 3)
+
+
+def test_not_positive():
+ msg = "Cells need to be >=1"
+ with pytest.raises(ValueError, match=re.escape(msg)):
+ _parse_cells('3,0', 3)
+
+
+def test_several_mixed():
+ r = _parse_cells('1,3,5-7,2,9,11-13', 80)
+ assert r == [1, 2, 3, 5, 6, 7, 9, 11, 12, 13]
+
+
+def test_overlapped():
+ r = _parse_cells('3,5-7,6-9,8', 80)
+ assert r == [3, 5, 6, 7, 8, 9]
+
+
+def test_bad_range_equal():
+ msg = "Range 'from' need to be smaller than 'to' (got '12-12')"
+ with pytest.raises(ValueError, match=re.escape(msg)):
+ _parse_cells('12-12', 80)
+
+
+def test_bad_range_smaller():
+ msg = "Range 'from' need to be smaller than 'to' (got '3-2')"
+ with pytest.raises(ValueError, match=re.escape(msg)):
+ _parse_cells('3-2', 80)
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_cellparser.py
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_main.py
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_main.py (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_main.py 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,111 @@
+# Copyright 2020 Facundo Batista
+# All Rights Reserved
+# Licensed under Apache 2.0
+
+import textwrap
+
+import jupynotex
+from jupynotex import main
+
+
+class FakeNotebook:
+ """Fake notebook.
+
+ The instance supports calling (as it if were instantiated). The .get will return the
+ value in a dict for received key; raise it if exception.
+ """
+
+ def __init__(self, side_effects):
+ self.side_effects = side_effects
+
+ def __call__(self, path):
+ return self
+
+ def __len__(self):
+ return len(self.side_effects)
+
+ def get(self, key):
+ """Return or raise the stored side effect."""
+ value = self.side_effects[key]
+ if isinstance(value, Exception):
+ raise value
+ else:
+ return value
+
+
+def test_simple_ok(monkeypatch, capsys):
+ fake_notebook = FakeNotebook({
+ 1: ("test cell content up", "test cell content down"),
+ })
+ monkeypatch.setattr(jupynotex, 'Notebook', fake_notebook)
+
+ main('boguspath', '1')
+ expected = textwrap.dedent("""\
+ \\begin{tcolorbox}[title=Cell {01}]
+ test cell content up
+ \\tcblower
+ test cell content down
+ \\end{tcolorbox}
+ """)
+ assert expected == capsys.readouterr().out
+
+
+def test_simple_only_first(monkeypatch, capsys):
+ fake_notebook = FakeNotebook({
+ 1: ("test cell content up", ""),
+ })
+ monkeypatch.setattr(jupynotex, 'Notebook', fake_notebook)
+
+ main('boguspath', '1')
+ expected = textwrap.dedent("""\
+ \\begin{tcolorbox}[title=Cell {01}]
+ test cell content up
+ \\end{tcolorbox}
+ """)
+ assert expected == capsys.readouterr().out
+
+
+def test_simple_error(monkeypatch, capsys):
+ fake_notebook = FakeNotebook({
+ 1: ValueError("test problem"),
+ })
+ monkeypatch.setattr(jupynotex, 'Notebook', fake_notebook)
+
+ main('boguspath', '1')
+
+ # verify the beginning and the end, as the middle part is specific to the environment
+ # where the test runs
+ expected_ini = [
+ r"\begin{tcolorbox}[colback=red!5!white,colframe=red!75!,title={ERROR when parsing cell 1}]", # NOQA
+ r"\begin{verbatim}",
+ r"Traceback (most recent call last):",
+ ]
+ expected_end = [
+ r"ValueError: test problem",
+ r"\end{verbatim}",
+ r"\end{tcolorbox}",
+ ]
+ out = [line for line in capsys.readouterr().out.split('\n') if line]
+ assert expected_ini == out[:3]
+ assert expected_end == out[-3:]
+
+
+def test_multiple(monkeypatch, capsys):
+ fake_notebook = FakeNotebook({
+ 1: ("test cell content up", "test cell content down"),
+ 2: ("test cell content ONLY up", ""),
+ })
+ monkeypatch.setattr(jupynotex, 'Notebook', fake_notebook)
+
+ main('boguspath', '1-2')
+ expected = textwrap.dedent("""\
+ \\begin{tcolorbox}[title=Cell {01}]
+ test cell content up
+ \\tcblower
+ test cell content down
+ \\end{tcolorbox}
+ \\begin{tcolorbox}[title=Cell {02}]
+ test cell content ONLY up
+ \\end{tcolorbox}
+ """)
+ assert expected == capsys.readouterr().out
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_main.py
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Added: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_notebook.py
===================================================================
--- trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_notebook.py (rev 0)
+++ trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_notebook.py 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,237 @@
+# Copyright 2020 Facundo Batista
+# All Rights Reserved
+# Licensed under Apache 2.0
+
+import base64
+import json
+import os
+import pathlib
+import re
+import tempfile
+import textwrap
+
+import pytest
+
+from jupynotex import Notebook
+
+
+ at pytest.fixture
+def notebook():
+ _, name = tempfile.mkstemp()
+
+ def _f(cells):
+ with open(name, 'wt', encoding='utf8') as fh:
+ json.dump({'cells': cells}, fh)
+
+ return Notebook(name)
+
+ yield _f
+ os.unlink(name)
+
+
+def test_empty(notebook):
+ nb = notebook([])
+ assert len(nb) == 0
+
+
+def test_source_code(notebook):
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': ['line1\n', ' line2\n'],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ src, _ = nb.get(1)
+ expected = textwrap.dedent("""\
+ \\begin{verbatim}
+ line1
+ line2
+ \\end{verbatim}
+ """)
+ assert src == expected
+
+
+def test_source_markdown(notebook):
+ rawcell = {
+ 'cell_type': 'markdown',
+ 'source': ['line1\n', ' line2\n'],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ src, _ = nb.get(1)
+ expected = textwrap.dedent("""\
+ \\begin{verbatim}
+ line1
+ line2
+ \\end{verbatim}
+ """)
+ assert src == expected
+
+
+def test_output_missing(notebook):
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ assert out is None
+
+
+def test_output_simple_executeresult_plain(notebook):
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ 'outputs': [
+ {
+ 'output_type': 'execute_result',
+ 'data': {
+ 'text/plain': ['default always present', 'line2'],
+ },
+ },
+ ],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ expected = textwrap.dedent("""\
+ \\begin{verbatim}
+ default always present
+ line2
+ \\end{verbatim}
+ """)
+ assert out == expected
+
+
+def test_output_simple_executeresult_latex(notebook):
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ 'outputs': [
+ {
+ 'output_type': 'execute_result',
+ 'data': {
+ 'text/latex': ['some latex line', 'latex 2'],
+ 'text/plain': ['default always present'],
+ },
+ },
+ ],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ expected = textwrap.dedent("""\
+ some latex line
+ latex 2
+ """)
+ assert out == expected
+
+
+def test_output_simple_executeresult_image(notebook):
+ raw_content = b"\x01\x02 asdlklda3wudghlaskgdlask"
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ 'outputs': [
+ {
+ 'output_type': 'execute_result',
+ 'data': {
+ 'image/png': base64.b64encode(raw_content).decode('ascii'),
+ 'text/plain': ['default always present'],
+ },
+ },
+ ],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ m = re.match(r'\\includegraphics\{(.+)\}\n', out)
+ assert m
+ (fpath,) = m.groups()
+ assert pathlib.Path(fpath).read_bytes() == raw_content
+
+
+def test_output_simple_stream(notebook):
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ 'outputs': [
+ {
+ 'output_type': 'stream',
+ 'text': ['some text line', 'text 2'],
+ },
+ ],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ expected = textwrap.dedent("""\
+ \\begin{verbatim}
+ some text line
+ text 2
+ \\end{verbatim}
+ """)
+ assert out == expected
+
+
+def test_output_simple_display_data(notebook):
+ raw_content = b"\x01\x02 asdlklda3wudghlaskgdlask"
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ 'outputs': [
+ {
+ 'output_type': 'display_data',
+ 'data': {
+ 'image/png': base64.b64encode(raw_content).decode('ascii'),
+ },
+ },
+ ],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ m = re.match(r'\\includegraphics\{(.+)\}\n', out)
+ assert m
+ (fpath,) = m.groups()
+ assert pathlib.Path(fpath).read_bytes() == raw_content
+
+
+def test_output_multiple(notebook):
+ rawcell = {
+ 'cell_type': 'code',
+ 'source': [],
+ 'outputs': [
+ {
+ 'output_type': 'execute_result',
+ 'data': {
+ 'text/latex': ['some latex line', 'latex 2'],
+ },
+ }, {
+ 'output_type': 'stream',
+ 'text': ['some text line', 'text 2'],
+ },
+ ],
+ }
+ nb = notebook([rawcell])
+ assert len(nb) == 1
+
+ _, out = nb.get(1)
+ expected = textwrap.dedent("""\
+ some latex line
+ latex 2
+ \\begin{verbatim}
+ some text line
+ text 2
+ \\end{verbatim}
+ """)
+ assert out == expected
Property changes on: trunk/Master/texmf-dist/doc/latex/jupynotex/tests/test_notebook.py
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Added: trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.py
===================================================================
--- trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.py (rev 0)
+++ trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.py 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,166 @@
+# Copyright 2020 Facundo Batista
+# All Rights Reserved
+# Licensed under Apache 2.0
+
+"""USAGE: jupynote.py notebook.ipynb cells
+
+ cells is a string with which cells to include, separate groups
+ with comma, ranges with dash (with defaults to start and end.
+"""
+
+import base64
+import json
+import sys
+import tempfile
+import traceback
+
+
+def _verbatimize(lines):
+ """Wrap a series of lines around a verbatim indication."""
+ result = [r"\begin{verbatim}"]
+ for line in lines:
+ result.append(line.rstrip())
+ result.append(r"\end{verbatim}")
+ return result
+
+
+def _save_content(data):
+ """Save the received b64encoded data to a temp file."""
+ _, fname = tempfile.mkstemp(suffix='.png')
+ with open(fname, 'wb') as fh:
+ fh.write(base64.b64decode(data))
+ return fname
+
+
+class Notebook:
+ """The notebook converter to latex."""
+
+ def __init__(self, path):
+ with open(path, 'rt', encoding='utf8') as fh:
+ nb_data = json.load(fh)
+
+ self._cells = nb_data['cells']
+
+ def __len__(self):
+ return len(self._cells)
+
+ def _proc_src(self, content):
+ """Process the source of a cell."""
+ source = content['source']
+ result = []
+ if content['cell_type'] == 'code':
+ result.extend(_verbatimize(source))
+ elif content['cell_type'] == 'markdown':
+ # XXX: maybe we could parse this?
+ result.extend(_verbatimize(source))
+ else:
+ raise ValueError(
+ "Cell type not supported when processing source: {!r}".format(
+ content['cell_type']))
+
+ return '\n'.join(result) + '\n'
+
+ def _proc_out(self, content):
+ """Process the output of a cell."""
+ outputs = content.get('outputs')
+ if not outputs:
+ return
+
+ result = []
+ for item in outputs:
+ output_type = item['output_type']
+ if output_type == 'execute_result':
+ data = item['data']
+ if 'image/png' in data:
+ fname = _save_content(data['image/png'])
+ result.append(r"\includegraphics{{{}}}".format(fname))
+ elif 'text/latex' in data:
+ result.extend(data["text/latex"])
+ else:
+ result.extend(_verbatimize(data["text/plain"]))
+ elif output_type == 'stream':
+ result.extend(_verbatimize(x.rstrip() for x in item["text"]))
+ elif output_type == 'display_data':
+ data = item['data']
+ fname = _save_content(data['image/png'])
+ result.append(r"\includegraphics{{{}}}".format(fname))
+ else:
+ raise ValueError("Output type not supported in item {!r}".format(item))
+
+ return '\n'.join(result) + '\n'
+
+ def get(self, cell_idx):
+ """Return the content from a specific cell in the notebook.
+
+ The content is already splitted in source and output, and converted to latex.
+ """
+ content = self._cells[cell_idx - 1]
+ source = self._proc_src(content)
+ output = self._proc_out(content)
+ return source, output
+
+
+def _parse_cells(spec, maxlen):
+ """Convert the cells spec to a range of ints."""
+ if not spec:
+ raise ValueError("Empty cells spec not allowed")
+ if set(spec) - set('0123456789-,'):
+ raise ValueError(
+ "Found forbidden characters in cells definition (allowed digits, '-' and ',')")
+
+ cells = set()
+ groups = spec.split(',')
+ for group in groups:
+ if '-' in group:
+ cfrom, cto = group.split('-')
+ cfrom = 1 if cfrom == '' else int(cfrom)
+ cto = maxlen if cto == '' else int(cto)
+ if cfrom >= cto:
+ raise ValueError(
+ "Range 'from' need to be smaller than 'to' (got {!r})".format(group))
+ cells.update(range(cfrom, cto + 1))
+ else:
+ cells.add(int(group))
+ cells = sorted(cells)
+
+ if any(x < 1 for x in cells):
+ raise ValueError("Cells need to be >=1")
+ if maxlen < cells[-1]:
+ raise ValueError(
+ "Notebook loaded of len {}, smaller than requested cells: {}".format(maxlen, cells))
+
+ return cells
+
+
+def main(notebook_path, cells_spec):
+ """Main entry point."""
+ nb = Notebook(notebook_path)
+ cells = _parse_cells(cells_spec, len(nb))
+
+ for cell in cells:
+ try:
+ src, out = nb.get(cell)
+ except Exception:
+ title = "ERROR when parsing cell {}".format(cell)
+ print(
+ r"\begin{{tcolorbox}}"
+ r"[colback=red!5!white,colframe=red!75!,title={{{}}}]".format(title))
+ tb = traceback.format_exc()
+ print('\n'.join(_verbatimize(tb.split('\n'))))
+ print(r"\end{tcolorbox}")
+ continue
+
+ print(r"\begin{{tcolorbox}}[title=Cell {{{:02d}}}]".format(cell))
+ print(src)
+ if out:
+ print(r"\tcblower")
+ print(out)
+ print(r"\end{tcolorbox}")
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 3:
+ print(__doc__)
+ exit()
+
+ main(*sys.argv[1:3])
Property changes on: trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.py
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Added: trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.sty
===================================================================
--- trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.sty (rev 0)
+++ trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.sty 2020-10-20 20:39:10 UTC (rev 56715)
@@ -0,0 +1,7 @@
+\ProvidesPackage{jupynotex}[0.1]
+
+\usepackage{tcolorbox}
+
+\newcommand{\jupynotex}[2][-]{
+ \input|"python3 jupynotex.py #2 #1"
+}
Property changes on: trunk/Master/texmf-dist/tex/latex/jupynotex/jupynotex.sty
___________________________________________________________________
Added: svn:eol-style
## -0,0 +1 ##
+native
\ No newline at end of property
Modified: trunk/Master/tlpkg/bin/tlpkg-ctan-check
===================================================================
--- trunk/Master/tlpkg/bin/tlpkg-ctan-check 2020-10-20 20:37:29 UTC (rev 56714)
+++ trunk/Master/tlpkg/bin/tlpkg-ctan-check 2020-10-20 20:39:10 UTC (rev 56715)
@@ -402,7 +402,7 @@
jbact jfmutil jigsaw
jknapltx jkmath jlabels jlreq jlreq-deluxe
jmb jmlr jneurosci jnuexam josefin jpsj jsclasses
- jslectureplanner jumplines junicode
+ jslectureplanner jumplines junicode jupynotex
jura juraabbrev jurabib juramisc jurarsp js-misc jvlisting
kalendarium kanaparser kantlipsum karnaugh karnaugh-map karnaughmap kastrup
kblocks kdgdocs kerkis kerntest ketcindy
Modified: trunk/Master/tlpkg/libexec/ctan2tds
===================================================================
--- trunk/Master/tlpkg/libexec/ctan2tds 2020-10-20 20:37:29 UTC (rev 56714)
+++ trunk/Master/tlpkg/libexec/ctan2tds 2020-10-20 20:39:10 UTC (rev 56715)
@@ -1934,6 +1934,7 @@
'jadetex', '\.ltx|\.def|\.tex|\.ini|\.sty|\.fd',
'js-misc', '(cassette|idverb|js-misc|schild|sperr|xfig)\.tex',
'jslectureplanner', '\.lps|' . $standardtex,
+ 'jupynotex', '\.py|' . $standardtex,
'kanaparser', 'kanaparser.(tex|lua)$',
'karnaugh', 'kvmacros.tex',
'kastrup', 'binhex.tex|' . $standardtex,
Modified: trunk/Master/tlpkg/tlpsrc/collection-mathscience.tlpsrc
===================================================================
--- trunk/Master/tlpkg/tlpsrc/collection-mathscience.tlpsrc 2020-10-20 20:37:29 UTC (rev 56714)
+++ trunk/Master/tlpkg/tlpsrc/collection-mathscience.tlpsrc 2020-10-20 20:39:10 UTC (rev 56715)
@@ -109,6 +109,7 @@
depend ionumbers
depend isomath
depend jkmath
+depend jupynotex
depend karnaugh
depend karnaugh-map
depend karnaughmap
Added: trunk/Master/tlpkg/tlpsrc/jupynotex.tlpsrc
===================================================================
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