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import collections
from datetime import datetime
from datasets import DatasetDict, load_dataset
import numpy as np
datasets = {
"stars": load_dataset("open-source-metrics/stars").sort('dates'),
"issues": load_dataset("open-source-metrics/issues").sort('dates'),
"pip": load_dataset("open-source-metrics/pip").sort('day')
}
val = 0
def _range(e):
global val
e['range'] = val
val += 1
current_date = datetime.strptime(e['dates'], "%Y-%m-%dT%H:%M:%SZ")
first_date = datetime.fromtimestamp(1)
week = abs(current_date - first_date).days // 7
e['week'] = week
return e
def _ignore_org_members(e):
global val
e['range_non_org'] = val
if e['type']['authorAssociation'] != 'MEMBER':
val += 1
return e
stars = {}
for k, v in datasets['stars'].items():
stars[k] = v.map(_range)
val = 0
issues = {}
for k, v in datasets['issues'].items():
issues[k] = v.map(_range)
val = 0
issues[k] = issues[k].map(_ignore_org_members)
val = 0
datasets['stars'] = DatasetDict(**stars)
datasets['issues'] = DatasetDict(**issues)
def link_values(library_names, returned_values):
previous_values = {library_name: None for library_name in library_names}
for library_name in library_names:
for i in returned_values.keys():
if library_name not in returned_values[i]:
returned_values[i][library_name] = previous_values[library_name]
else:
previous_values[library_name] = returned_values[i][library_name]
return returned_values
def running_mean(x, N, total_length=-1):
cumsum = np.cumsum(np.insert(x, 0, 0))
to_pad = max(total_length - len(cumsum), 0)
return np.pad(cumsum[N:] - cumsum[:-N], (to_pad, 0)) / float(N)
def retrieve_pip_installs(library_names, cumulated):
if cumulated:
returned_values = {}
for library_name in library_names:
for i in datasets['pip'][library_name]:
if i['day'] in returned_values:
returned_values[i['day']]['Cumulated'] += i['num_downloads']
else:
returned_values[i['day']] = {'Cumulated': i['num_downloads']}
library_names = ['Cumulated']
else:
returned_values = {}
for library_name in library_names:
for i in datasets['pip'][library_name]:
if i['day'] in returned_values:
returned_values[i['day']][library_name] = i['num_downloads']
else:
returned_values[i['day']] = {library_name: i['num_downloads']}
for library_name in library_names:
for i in returned_values.keys():
if library_name not in returned_values[i]:
returned_values[i][library_name] = None
returned_values = collections.OrderedDict(sorted(returned_values.items()))
output = {l: [k[l] for k in returned_values.values()] for l in library_names}
output['day'] = list(returned_values.keys())
return output
def retrieve_stars(libraries, week_over_week):
returned_values = {}
dataset_dict = datasets['stars']
for library_name in libraries:
dataset = dataset_dict[library_name]
last_value = 0
last_week = dataset[0]['week']
for i in dataset:
if week_over_week and last_week == i['week']:
continue
if i['dates'] in returned_values:
returned_values[i['dates']][library_name] = i['range'] - last_value
else:
returned_values[i['dates']] = {library_name: i['range'] - last_value}
last_value = i['range'] if week_over_week else 0
last_week = i['week']
returned_values = collections.OrderedDict(sorted(returned_values.items()))
returned_values = link_values(libraries, returned_values)
output = {l: [k[l] for k in returned_values.values()][::-1] for l in libraries}
output['day'] = list(returned_values.keys())[::-1]
# Trim down to a smaller number of points.
output = {k: [v for i, v in enumerate(value) if i % int(len(value) / 100) == 0] for k, value in output.items()}
return output
def retrieve_issues(libraries, exclude_org_members, week_over_week):
returned_values = {}
dataset_dict = datasets['issues']
range_id = 'range' if not exclude_org_members else 'range_non_org'
for library_name in libraries:
dataset = dataset_dict[library_name]
last_value = 0
last_week = dataset[0]['week']
for i in dataset:
if week_over_week and last_week == i['week']:
continue
if i['dates'] in returned_values:
returned_values[i['dates']][library_name] = i[range_id] - last_value
else:
returned_values[i['dates']] = {library_name: i[range_id] - last_value}
last_value = i[range_id] if week_over_week else 0
last_week = i['week']
returned_values = collections.OrderedDict(sorted(returned_values.items()))
returned_values = link_values(libraries, returned_values)
output = {l: [k[l] for k in returned_values.values()][::-1] for l in libraries}
output['day'] = list(returned_values.keys())[::-1]
# Trim down to a smaller number of points.
output = {
k: [v for i, v in enumerate(value) if i % int(len(value) / 100) == 0] for k, value in output.items()
}
return output
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