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# install CC
brew install cookiecutter

# make a new library
cookiecutter gh:ionelmc/cookiecutter-pylibrary


# run through config again
cookiecutter --overwrite-if-exists --config-file=<rendered_template>/.cookiecutterrc gh:ionelmc/cookiecutter-pylibrary

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How to convert Linestrings to Polygons in QGIS

1. Import Vector Layer
2. Select Linestring Layer Vector > Geometry Tools > Extract Vertices
3. Install Concave Hull extension
4. Select Points layer, go to Concave Hull extension -> OK
5. Save Polygon layer

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Demo

# Convert notebook to slides
jupyter nbconvert notebook.ipynb --TagRemovePreprocessor.remove_input_tags='{"hide_input"}' --EmbedImagesPreprocessor.resize=small --to slides --post serve

# Set slides to -> Slide
# Set tags to "hide_input"

# Convert mov to mp4
ffmpeg -i demo.mov -vcodec h264 demo.mp4

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# install ffmpeg
brew install ffmpeg

# convert .mov file to .mp4
ffmpeg -i demo.mov -vcodec h264 demo.mp4

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# jupyter notebook import local module

import os
import sys
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
    sys.path.append(nb_dir)

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# 1. Keep train, validation, and test data straight.

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# 1. Apply works with multiple arguments
## instead of this
binary_test["prediction"] = binary_test["sentence"].apply(lambda x: get_prediction(x, PROJECT_ID, BINARY_MODEL_ID))
## do this
binary_test["prediction"] = binary_test["sentence"].apply(get_predictions, project_id=PROJECT_ID, model_id=BINARY_MODEL_ID

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# 1. Use f-strings

#instead of this
name = 'projects/{}/locations/us-central1/models/{}'.format(project_id, model_id)
# do this
name = f'projects/{project_id}/locations/us-central1/models/{model_id}'

# 2. Explicitly name errors:
# instead of this
try:

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import keras

# Integer to categorical
keras.utils.to_categorical(4, num_classes=5, dtype='int')

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# pip cheatsheet

# pip install from requirements
pip install -r requirements.txt