def get_speaking_language(nb_id):
# gather the markdown cells
md_cells = data.get_md_cells(nb_id)
# if there are no markdown cells, return immediately
if len(md_cells) == 0:
return None
# get the language of the markdown cells
return speaking_language(md_cells)
def frequency(nb_id):
# check if markdown cells exist
if len(data.get_md_cells(nb_id)) == 0:
return None
# get the number of code <-> markdown switches and the number of total switches
cm_switches = count_switches(nb_id)
total_switches = len(data.get_cells(nb_id)) - 1
# calculate proportion
return float(cm_switches) / float(total_switches)
def num_headers(nb_id):
# get the markdown cells
md_cells = data.get_md_cells(nb_id)
# count the number of headers across the cells
num_headers = 0
for cell in md_cells:
num_headers += count_headers(cell)
# return the total
return num_headers
def md_formatting(nb_id):
# gather markdown cells
md_cells = data.get_md_cells(nb_id)
# if there are no markdown cells, return immediately
if len(md_cells) == 0:
return None
# iterate through and check whether they have special formatting
for cell in md_cells:
if has_extra_formatting(cell):
return True
return False
def has_author(nb_id):
# get all the markdown cells and author names
md_cells = data.get_md_cells(nb_id)
author_name = get_author(nb_id)
author_login = get_username(nb_id)
all_names = get_contributors(nb_id) + [author_name, author_login]
# check each cell for an author
for cell in md_cells:
if 'source' in cell.keys():
lines = cell['source']
# scan all the lines in the cell
for line in lines:
# filter out links before searching for author
link = re.search(regex.link, line)
while link != None:
line = line.replace(link.group(0), "")
link = re.search(regex.link, line)
# check all possible authors
for name in all_names:
if name != None and name in line:
return True
# gather comments
comments = data.get_comments(nb_id)
if comments != None:
# search each comment for the author names
for comment in comments:
for name in all_names:
if name != None and name in comment:
return True
# no instances of authors found
return False
def has_equations(nb_id):
# get all the markdown cells
md_cells = data.get_md_cells(nb_id)
# if there are no markdown cells, return immediately
if len(md_cells) == 0:
return None
# search the markdown cells for equations
for cell in md_cells:
# check if markdown cell has source field
if 'source' not in cell.keys():
continue
for line in cell['source']:
if re.search(regex.equation, line):
return True
return False
def is_education(nb_id):
# get the markdown cells
md_cells = data.get_md_cells(nb_id)
# go through the markdown cells and search for the keywords
for cell in md_cells:
if 'source' in cell.keys():
for line in cell['source']:
# make sure to search for each keyword
for pattern in regex.education:
if re.search(pattern, line.lower()):
return True
# check the file path (including the file name) for the keywords as well
path = data.get_path(nb_id)
for pattern in regex.education:
if re.search(pattern, path.lower()):
return True
return False
def markdown_prop(nb_id):
num_cells = len(data.get_cells(nb_id))
num_md = len(data.get_md_cells(nb_id))
return (float(num_md) / float(num_cells))