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‎tutorials/3-advanced-topics/DTAT387_types.ipynb‎

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@@ -53,7 +53,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -62,32 +62,43 @@
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"def matrix_add(\n",
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" input: ArrayLike[float]\n",
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") -> ArrayLike[float]:\n",
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" \n",
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" \"\"\"Function docsting.\"\"\"\n",
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" return input + input"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And in the context of DeepTrack2, a larger class with several type hints can look something like the following:\n",
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"```python\n",
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"class Augmentation(Feature):\n",
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"And in the context of DeepTrack2, a larger class with several type hints can look something like the following:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import annotations\n",
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"import deeptrack as dt\n",
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"from deeptrack.types import PropertyLike\n",
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"\n",
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"class Augmentation(dt.Feature):\n",
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"\n",
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" def __init__(\n",
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" self,\n",
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" self: Augmentation,\n",
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" time_consistent: bool = False,\n",
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" **kwargs\n",
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" ) -> None:\n",
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" super().__init__(time_consistent=time_consistent, **kwargs)\n",
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"\n",
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" def _image_wrapped_process_and_get (\n",
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" self,\n",
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" image_list: List[Image],\n",
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" def _image_wrapped_process_and_get(\n",
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" self: Augmentation,\n",
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" image_list: list[Image],\n",
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" time_consistent: PropertyLike[bool],\n",
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" **kwargs\n",
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" ) -> List[List]:\n",
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"\n",
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"```"
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" ) -> list[Image]:\n",
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" pass"
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]
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},
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{
@@ -99,33 +110,59 @@
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"We will now declare a new type hint of our own which will represent non-integer numeric datatypes with higher precision than the standard float datatype. We will use `Union` for this, which lets us gather datatypes into a single set."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Declare the new type using Union ..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"typing.Union[numpy.float64, numpy.longdouble, numpy.clongdouble]\n"
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]
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}
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],
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"source": [
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"from typing import Union\n",
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"import numpy as np\n",
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"from typing import Union\n",
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"\n",
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"# Declare type with Union.\n",
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"DoubleLike = Union[np.float64, np.longdouble, np.longcomplex]\n",
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"print(DoubleLike)\n",
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"\n",
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"# Declare function with new type hint.\n",
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"print(DoubleLike)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"... and declare function with new type hint."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def double_add(\n",
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" input: ArrayLike[DoubleLike],\n",
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" number: DoubleLike\n",
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") -> ArrayLike[DoubleLike]:\n",
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" \n",
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" return input + number\n",
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"\n"
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" \"\"\"Function docsting.\"\"\"\n",
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" return input + number"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "py_env_book",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"version": "3.10.15"
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}
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},
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"nbformat": 4,

‎tutorials/3-advanced-topics/DTAT389_elementwise.ipynb‎

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"source": [
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"## 1. What is elementwise?\n",
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"\n",
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"The elementwise module introduces utility functions which lets the user apply Numpy functions to `Feature` objects elementwise.\n",
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"The elementwise module introduces utility functions which lets the user apply Numpy functions to `Feature` objects elementwise, that is element by element.\n",
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"\n",
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"Some functions included in elementwise are:\n",
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"\n",
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"- Trigonometric\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Initialize a `Feature`\n",
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"## 2. Use example\n",
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"\n",
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"We create a feature that subtracts values from an image."
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"Create a feature that subtracts values from an image ..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"import numpy as np\n",
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"from deeptrack.features import Feature\n",
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"\n",
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"\n",
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"class Subtract(Feature):\n",
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" def get(self, image, value, **kwargs):\n",
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" return image - value\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Compute the Absolute value of the feature (In sequence)\n"
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"... and compute the absolute value of the feature (in sequence)."
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]
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"source": [
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"from deeptrack.elementwise import Abs\n",
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"\n",
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"\n",
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"# Sequentially take the absolute value after subtraction.\n",
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"pipeline = Abs(subtract_10)\n",
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"output_image = pipeline(input_image)\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "py_env_book",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"version": "3.10.15"
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}
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},
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"nbformat": 4,

‎tutorials/3-advanced-topics/DTAT391A_sources.base.ipynb‎

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"child_b = node.b\n",
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"\n",
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"# Call child nodes.\n",
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"print (child_a() + child_b())"
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"print(child_a() + child_b())"
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},
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"cell_type": "code",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "py_env_book",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"version": "3.10.15"
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}
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"nbformat": 4,

‎tutorials/3-advanced-topics/DTAT391C_sources.rng.ipynb‎

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{
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"cell_type": "code",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-06-29T20:33:47.187180Z",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Python rng #0 yields a Random Number -> 36\n",
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"Python rng #1 yields a Random Number -> 83\n",
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"Python rng #2 yields a Random Number -> 28\n",
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"Python rng #0 yields a Random Number -> 36\n",
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"Python rng #1 yields a Random Number -> 83\n",
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"Python rng #2 yields a Random Number -> 28\n"
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"Python rng #0 yields a Random Number: 36\n",
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"Python rng #1 yields a Random Number: 83\n",
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"Python rng #2 yields a Random Number: 28\n",
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"Python rng #0 yields a Random Number: 36\n",
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"Python rng #1 yields a Random Number: 83\n",
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"Python rng #2 yields a Random Number: 28\n"
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"text": [
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"Numpy rng #0 yields a Random Number -> 4\n",
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"Numpy rng #1 yields a Random Number -> 88\n",
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"Numpy rng #2 yields a Random Number -> 55\n",
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"Numpy rng #0 yields a Random Number -> 4\n",
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"Numpy rng #1 yields a Random Number -> 88\n",
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"Numpy rng #2 yields a Random Number -> 55\n"
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"Numpy rng #0 yields a Random Number: 4\n",
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"Numpy rng #1 yields a Random Number: 88\n",
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"Numpy rng #2 yields a Random Number: 55\n",
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"Numpy rng #0 yields a Random Number: 4\n",
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"Numpy rng #1 yields a Random Number: 88\n",
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"Numpy rng #2 yields a Random Number: 55\n"
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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"version": "3.10.15"
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"nbformat": 4,

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