Guillermo Blanco geblanco

## extract_ipython_code.py
import os
import sys
import json

languages = dict(
  python=dict(ext='py', comment='#'),
  javascript=dict(ext='js', comment='//')
)

if len(sys.argv) < 2:

## evaluate-squad-v2.0.py
"""Official evaluation script for SQuAD version 2.0.

In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import argparse
import collections
import json

## generateSquareIcon.js
// requires canvas, npm i canvas

var Canvas = require('canvas')
var fs     = require('fs')

// Constants
var DIM = { width : 24, border : 1.3, font : 14 }
var COLOR = { light: '#ec9a97', text:'#3189cc' , border:'#e13d35' }

function paintCircularText(filePath=null, text=null, fill=false, border=true, callback=null){

## dvc-getting-started-train-checkpoint.patch
5a6,8
> import json
>
> import sklearn.metrics as metrics
6a10,11
> from sklearn.metrics import precision_recall_curve
> from dvc.api import make_checkpoint
10c15
< if len(sys.argv) != 3:
---

## sklearn.classification_report.py

# extracted from: https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/metrics/_classification.py#L1825
def classification_report(data_dict):
    """Build a text report showing the main classification metrics.
    Read more in the :ref:`User Guide <classification_report>`.
    Parameters
    ----------
    report : string
        Text summary of the precision, recall, F1 score for each class.
        Dictionary returned if output_dict is True. Dictionary has the

## interactive_map_from_addresses.py
# !pip install requests urllib3 geopy pandas folium
# partially taken from:
# https://towardsdatascience.com/pythons-geocoding-convert-a-list-of-addresses-into-a-map-f522ef513fd6\

import requests
import json
import urllib
from geopy.geocoders import Nominatim, GoogleV3
import pandas as pd
import folium

## process_singleton.py
# requires: zmq

import sys
import zmq
import threading

from time import sleep
from filelock import FileLock, Timeout


## obs_project_scaffold.py
#!/bin/env python
import argparse
from pathlib import Path
from shutil import rmtree
from typing import Any, List, Optional

# You must override this value!
PROJS_BASE = None
DEF_FILES = ["index", "readme", "todo", "meetings"]
FILES_W_CONTEXT = ["todo", "meetings"]

## upgrade_py_pkgs.sh
#!/bin/bash

# If a requirements.txt file is supplied, it will be updated with the latest versions of each package.
# If a pyproject.toml is required, package upgrades will just be printed.

echo "Careful:"
echo " - Conda is required"
echo " - If any of your PYPY packages contain the strin 'http', it is possible that it gets astray, manually inspect it"

env_name="pkgs_upgrade"

## gen_init.py
"""Generate __init__.py for a given folder
Very naive generator, just parses the AST of every subfile in the provided folder to get the list of exported variables.

ToDo :=
- Recursively parse subfolders and previous __init__.py files
- Improve pkg base adding
"""

import os
import ast
	import os
	import sys
	import json

	languages = dict(
	python=dict(ext='py', comment='#'),
	javascript=dict(ext='js', comment='//')
	)

	if len(sys.argv) < 2:
	"""Official evaluation script for SQuAD version 2.0.

	In addition to basic functionality, we also compute additional statistics and
	plot precision-recall curves if an additional na_prob.json file is provided.
	This file is expected to map question ID's to the model's predicted probability
	that a question is unanswerable.
	"""
	import argparse
	import collections
	import json
	// requires canvas, npm i canvas

	var Canvas = require('canvas')
	var fs = require('fs')

	// Constants
	var DIM = { width : 24, border : 1.3, font : 14 }
	var COLOR = { light: '#ec9a97', text:'#3189cc' , border:'#e13d35' }

	function paintCircularText(filePath=null, text=null, fill=false, border=true, callback=null){
	5a6,8
	> import json
	>
	> import sklearn.metrics as metrics
	6a10,11
	> from sklearn.metrics import precision_recall_curve
	> from dvc.api import make_checkpoint
	10c15
	< if len(sys.argv) != 3:
	---

	# extracted from: https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/metrics/_classification.py#L1825
	def classification_report(data_dict):
	"""Build a text report showing the main classification metrics.
	Read more in the :ref:`User Guide <classification_report>`.
	Parameters
	----------
	report : string
	Text summary of the precision, recall, F1 score for each class.
	Dictionary returned if output_dict is True. Dictionary has the
	# !pip install requests urllib3 geopy pandas folium
	# partially taken from:
	# https://towardsdatascience.com/pythons-geocoding-convert-a-list-of-addresses-into-a-map-f522ef513fd6\

	import requests
	import json
	import urllib
	from geopy.geocoders import Nominatim, GoogleV3
	import pandas as pd
	import folium
	# requires: zmq

	import sys
	import zmq
	import threading

	from time import sleep
	from filelock import FileLock, Timeout
	#!/bin/env python
	import argparse
	from pathlib import Path
	from shutil import rmtree
	from typing import Any, List, Optional

	# You must override this value!
	PROJS_BASE = None
	DEF_FILES = ["index", "readme", "todo", "meetings"]
	FILES_W_CONTEXT = ["todo", "meetings"]
	#!/bin/bash

	# If a requirements.txt file is supplied, it will be updated with the latest versions of each package.
	# If a pyproject.toml is required, package upgrades will just be printed.

	echo "Careful:"
	echo " - Conda is required"
	echo " - If any of your PYPY packages contain the strin 'http', it is possible that it gets astray, manually inspect it"

	env_name="pkgs_upgrade"
	"""Generate __init__.py for a given folder
	Very naive generator, just parses the AST of every subfile in the provided folder to get the list of exported variables.

	ToDo :=
	- Recursively parse subfolders and previous __init__.py files
	- Improve pkg base adding
	"""

	import os
	import ast