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@jaradc
jaradc / generate-signed-urls-and-cookies-for-cloudfront-with-python-and-golang.md
Created July 12, 2023 21:27 — forked from zh4n7wm/generate-signed-urls-and-cookies-for-cloudfront-with-python-and-golang.md
AWS CloudFront generate signed urls and cookies with Python and Golang
@jaradc
jaradc / Django Unique Slug
Last active April 20, 2022 22:26
Simple-stupid way to guarantee slug uniqueness
from django.db import models
from django.utils.text import slugify
class Content(models.Model):
title = models.CharField(max_length=100)
slug = models.SlugField(max_length=100)
def save(self, *args, **kwargs):
# simple-stupid way to guarantee slug uniqueness
n = 1
@jaradc
jaradc / inlineformset_factory_html_output.html
Created February 16, 2020 17:57
An example of what the output looks like for inlineformset_factory with 3 filled-out module forms containing Title, Description and 2 extra forms in the formset.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Title</title>
</head>
<body>
<form action="" method="post">
<input type="hidden" name="csrfmiddlewaretoken"
@jaradc
jaradc / basic_class_accepts_unlimited_params.py
Created February 13, 2020 07:18
class Jarad:
def __init__(self, **kwargs):
for k,v in kwargs.items():
setattr(self, k, v)
@jaradc
jaradc / changepoint_detection.py
Last active September 13, 2019 22:18
"""
@author: Claudio Bellei
Site: http://www.claudiobellei.com/2016/11/15/changepoint-frequentist/
"""
import numpy as np
import pandas as pd
import csv
import matplotlib
import matplotlib.pyplot as plt
@jaradc
jaradc / sequence_matcher_example.py
Last active September 23, 2019 17:30
import difflib
"""
This simple piece of code can help detect similar strings using SequenceMatcher
"""
data = ['temporary tatoos', 'temporary tatto', 'tempoary tatoo', 'tempoary tattoo', 'temporary tattoos']
for line in data:
for word in line.split():
i = difflib.SequenceMatcher(None, word, 'tattoo').ratio()
@jaradc
jaradc / keyword_grouping_in_python.py
Created July 31, 2019 05:00
Basic Keyword Clustering Example in Python
import pandas as pd
import numpy as np
from nltk.stem import PorterStemmer, WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import cluster
stemmer = PorterStemmer()
@jaradc
jaradc / functiontransformer_example_with_text_data.py
Created August 2, 2018 16:32
A pipeline example showing how to use kwargs in a function, and how to use FunctionTransformer from sklearn to decode URLs
def fit_model(X, y, **kwargs):
print(kwargs)
print(kwargs['max_iter'])
pipeline = Pipeline([
('decode', FunctionTransformer(func=lambda x: x.apply(
lambda url: parse.unquote(parse.unquote(url))), validate=False)),
('cvect', CountVectorizer(binary=True, max_features=1000, stop_words='english',
token_pattern=r'\b\w[\w\.\-\,]+\b')),
('clf', MLPClassifier(verbose=1, solver='sgd', max_iter=kwargs['max_iter'] or 1000,
tol=0.00001, learning_rate='adaptive', learning_rate_init=0.05)),
@jaradc
jaradc / scale_between_two_numbers.py
Created January 5, 2018 21:36
This function scales a numpy array between two values (ex: 0.05, 1.25)
def scale_between(series, min_amt, max_amt):
series_min = series.min()
series_max = series.max()
return (((max_amt - min_amt)*(series - series_min)) / (series_max - series_min)) + min_amt
@jaradc
jaradc / make_moving_avg_plot.py
Last active December 21, 2017 09:44
This lets you create subplots of n rows and 1 column of a metric within a time series.
def ma_subplots(df, window, title=None):
fig, ax = plt.subplots(df.shape[1], 1, figsize=(12, 7))
ax = ax.ravel()
for i,col in enumerate(df):
mean = df[col].mean()
ma = df[col].rolling(window).mean()
mstd = df[col].rolling(window).std()
ax[i].plot(df.index, df[col], color='k', label=col)
ax[i].plot(ma.index, ma, 'b', label='Moving Avg.')
ax[i].fill_between(mstd.index, ma - 2*mstd, ma + 2*mstd, color='b', alpha=0.2)