Maxence maxpv

## README.md

      
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                maxpv
                / README.md
            
            
              Created
              September 4, 2020 13:04
            
              
                SoX bulk wav file processing
              
          
    Based on http://sox.sourceforge.net/sox.html

  
## smart-pip-install.py
# https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/
import sys
!{sys.executable} -m pip install <package>

## log_error.py
from IPython.core.magic import register_cell_magic
import datetime

@register_cell_magic
def log_error(line, cell):
    try:
        exec(cell)
    except Exception as e:
        with open('error.log', 'w+') as f:
            f.write("[{}] Exception ({}): {}".format(datetime.datetime.now(),

## remove_comments.py
import re

def remove_comments(s):
    for x in re.findall(r'("[^\n]*"(?!\\))|(//[^\n]*$|/(?!\\)\*[\s\S]*?\*(?!\\)/)',s,8):s=s.replace(x[1],'')
    s = re.sub(r'(?m) *#.*\n?', '', s)
    return s

## slack_logger.py
import subprocess
from collections import OrderedDict
from collections import Iterable
import six
import time

import numpy as np

from keras.callbacks import Callback

## average_width_file.sh
find folder -type f -name "*.txt" -exec wc -L {} \; | cut -f 1 -d ' '  awk '{{ total+=$NF }} END {{ print total/NR }}'

## soft_binary_metrics.py
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import pandas as pd

def evalutate_metrics(clf, X_test, y_test, threshold=0.5):
    y_pred = clf.predict(X_test)[:,1]
    y_test_l = np.argmax(y_test, axis=1)
    print()
    print('Confusion matrix:')


## list_all_php_functions.sh
curl -s http://php.net/manual/en/indexes.functions.php | grep -o 'class="index">.*</a>' | sed 's/\(class="index">\|<\/a>\)//g'

## days_generator.py
import pandas as pd

def days_generator(start_date, end=pd.Timestamp.today(), step_day=1, format='%Y-%m-%d'):
    """
        Generate a dates in a format from start_date to end
        with a step of step_day
    """
    s = pd.to_datetime(start_date)
    while s <= end:
        yield s.strftime(format)

## getting_threshold_roc.py
from sklearn import metrics
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_raw)

# Just finding point closest to [0,1]
max_indice = np.argmin([((ft[0])**2 + (ft[1]-1)**2) for ft in (zip(fpr, tpr))])
threshold = thresholds[max_indice]

# We can display it on the ROC curve
fig = plt.figure()
plt.plot(fpr,tpr)
	# https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/
	import sys
	!{sys.executable} -m pip install <package>
	from IPython.core.magic import register_cell_magic
	import datetime

	@register_cell_magic
	def log_error(line, cell):
	try:
	exec(cell)
	except Exception as e:
	with open('error.log', 'w+') as f:
	f.write("[{}] Exception ({}): {}".format(datetime.datetime.now(),
	import re

	def remove_comments(s):
	for x in re.findall(r'("[^\n]"(?!\\))\|(//[^\n]$\|/(?!\\)\[\s\S]?\*(?!\\)/)',s,8):s=s.replace(x[1],'')
	s = re.sub(r'(?m) #.\n?', '', s)
	return s
	import subprocess
	from collections import OrderedDict
	from collections import Iterable
	import six
	import time

	import numpy as np

	from keras.callbacks import Callback
	from sklearn.metrics import classification_report
	from sklearn.metrics import confusion_matrix
	import pandas as pd

	def evalutate_metrics(clf, X_test, y_test, threshold=0.5):
	y_pred = clf.predict(X_test)[:,1]
	y_test_l = np.argmax(y_test, axis=1)
	print()
	print('Confusion matrix:')
	import pandas as pd

	def days_generator(start_date, end=pd.Timestamp.today(), step_day=1, format='%Y-%m-%d'):
	"""
	Generate a dates in a format from start_date to end
	with a step of step_day
	"""
	s = pd.to_datetime(start_date)
	while s <= end:
	yield s.strftime(format)
	from sklearn import metrics
	fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_raw)

	# Just finding point closest to [0,1]
	max_indice = np.argmin([((ft[0])2 + (ft[1]-1)2) for ft in (zip(fpr, tpr))])
	threshold = thresholds[max_indice]

	# We can display it on the ROC curve
	fig = plt.figure()
	plt.plot(fpr,tpr)