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def autocontrast(image):
'Implements Autocontrast function from PIL using TF ops.'
def scale_channel(channel):
'Scale the 2D image using the autocontrast rule.'
lo = tf.cast(tf.reduce_min(channel), tf.float32)
hi = tf.cast(tf.reduce_max(channel), tf.float32)
def scale_values(im):
scale = (255.0 / (hi - lo))
offset = ((- lo) * scale)
im = ((tf.cast(im, tf.float32) * scale) + offset)
return tf.saturate_cast(im, tf.uint8)
result = tf.cond((hi > lo), (lambda : scale_values(channel)), (lambda : channel))
return result
s1 = scale_channel(image[:, :, 0])
s2 = scale_channel(image[:, :, 1])
s3 = scale_channel(image[:, :, 2])
image = tf.stack([s1, s2, s3], 2)
return image | -809,971,772,111,610,000 | Implements Autocontrast function from PIL using TF ops. | third_party/augment_ops.py | autocontrast | google-research/crest | python | def autocontrast(image):
def scale_channel(channel):
'Scale the 2D image using the autocontrast rule.'
lo = tf.cast(tf.reduce_min(channel), tf.float32)
hi = tf.cast(tf.reduce_max(channel), tf.float32)
def scale_values(im):
scale = (255.0 / (hi - lo))
offset = ((- lo) * scale)
im = ((tf.cast(im, tf.float32) * scale) + offset)
return tf.saturate_cast(im, tf.uint8)
result = tf.cond((hi > lo), (lambda : scale_values(channel)), (lambda : channel))
return result
s1 = scale_channel(image[:, :, 0])
s2 = scale_channel(image[:, :, 1])
s3 = scale_channel(image[:, :, 2])
image = tf.stack([s1, s2, s3], 2)
return image |
def autocontrast_blend(image, factor):
'Implements blend of autocontrast with original image.'
return blend(autocontrast(image), image, factor) | 837,949,824,050,951,700 | Implements blend of autocontrast with original image. | third_party/augment_ops.py | autocontrast_blend | google-research/crest | python | def autocontrast_blend(image, factor):
return blend(autocontrast(image), image, factor) |
def sharpness(image, factor):
'Implements Sharpness function from PIL using TF ops.'
orig_im = image
image = tf.cast(image, tf.float32)
image = tf.expand_dims(image, 0)
kernel = (tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0)
kernel = tf.tile(kernel, [1, 1, 3, 1])
strides = [1, 1, 1, 1]
degenerate = tf.nn.depthwise_conv2d(image, kernel, strides, padding='VALID', dilations=[1, 1])
degenerate = tf.squeeze(tf.saturate_cast(degenerate, tf.uint8), [0])
mask = tf.ones_like(degenerate)
padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])
padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])
result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_im)
return blend(result, orig_im, factor) | -2,921,100,399,587,338,000 | Implements Sharpness function from PIL using TF ops. | third_party/augment_ops.py | sharpness | google-research/crest | python | def sharpness(image, factor):
orig_im = image
image = tf.cast(image, tf.float32)
image = tf.expand_dims(image, 0)
kernel = (tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0)
kernel = tf.tile(kernel, [1, 1, 3, 1])
strides = [1, 1, 1, 1]
degenerate = tf.nn.depthwise_conv2d(image, kernel, strides, padding='VALID', dilations=[1, 1])
degenerate = tf.squeeze(tf.saturate_cast(degenerate, tf.uint8), [0])
mask = tf.ones_like(degenerate)
padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])
padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])
result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_im)
return blend(result, orig_im, factor) |
def equalize(image):
'Implements Equalize function from PIL using TF ops.'
def scale_channel(im, c):
'Scale the data in the channel to implement equalize.'
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)])
step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255)
def build_lut(histo, step):
lut = ((tf.cumsum(histo) + (step // 2)) // step)
lut = tf.concat([[0], lut[:(- 1)]], 0)
return tf.clip_by_value(lut, 0, 255)
result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im)))
return tf.cast(result, tf.uint8)
s1 = scale_channel(image, 0)
s2 = scale_channel(image, 1)
s3 = scale_channel(image, 2)
image = tf.stack([s1, s2, s3], 2)
return image | -4,360,158,787,895,066,600 | Implements Equalize function from PIL using TF ops. | third_party/augment_ops.py | equalize | google-research/crest | python | def equalize(image):
def scale_channel(im, c):
'Scale the data in the channel to implement equalize.'
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)])
step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255)
def build_lut(histo, step):
lut = ((tf.cumsum(histo) + (step // 2)) // step)
lut = tf.concat([[0], lut[:(- 1)]], 0)
return tf.clip_by_value(lut, 0, 255)
result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im)))
return tf.cast(result, tf.uint8)
s1 = scale_channel(image, 0)
s2 = scale_channel(image, 1)
s3 = scale_channel(image, 2)
image = tf.stack([s1, s2, s3], 2)
return image |
def equalize_blend(image, factor):
'Implements blend of equalize with original image.'
return blend(equalize(image), image, factor) | -6,565,587,435,892,967,000 | Implements blend of equalize with original image. | third_party/augment_ops.py | equalize_blend | google-research/crest | python | def equalize_blend(image, factor):
return blend(equalize(image), image, factor) |
def blur(image, factor):
'Blur with the same kernel as ImageFilter.BLUR.'
blur_kernel = (tf.constant([[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0]], dtype=tf.float32, shape=[5, 5, 1, 1]) / 16.0)
blurred_im = _convolve_image_with_kernel(image, blur_kernel)
return blend(image, blurred_im, factor) | 1,411,185,370,369,190,700 | Blur with the same kernel as ImageFilter.BLUR. | third_party/augment_ops.py | blur | google-research/crest | python | def blur(image, factor):
blur_kernel = (tf.constant([[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0]], dtype=tf.float32, shape=[5, 5, 1, 1]) / 16.0)
blurred_im = _convolve_image_with_kernel(image, blur_kernel)
return blend(image, blurred_im, factor) |
def smooth(image, factor):
'Smooth with the same kernel as ImageFilter.SMOOTH.'
smooth_kernel = (tf.constant([[1.0, 1.0, 1.0], [1.0, 5.0, 1.0], [1.0, 1.0, 1.0]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0)
smoothed_im = _convolve_image_with_kernel(image, smooth_kernel)
return blend(image, smoothed_im, factor) | -3,157,272,762,969,639,400 | Smooth with the same kernel as ImageFilter.SMOOTH. | third_party/augment_ops.py | smooth | google-research/crest | python | def smooth(image, factor):
smooth_kernel = (tf.constant([[1.0, 1.0, 1.0], [1.0, 5.0, 1.0], [1.0, 1.0, 1.0]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0)
smoothed_im = _convolve_image_with_kernel(image, smooth_kernel)
return blend(image, smoothed_im, factor) |
def rescale(image, level):
'Rescales image and enlarged cornet.'
size = image.shape[:2]
scale = (level * 0.25)
scale_height = tf.cast((scale * size[0]), tf.int32)
scale_width = tf.cast((scale * size[1]), tf.int32)
cropped_image = tf.image.crop_to_bounding_box(image, offset_height=scale_height, offset_width=scale_width, target_height=(size[0] - scale_height), target_width=(size[1] - scale_width))
rescaled = tf.image.resize(cropped_image, size, tf.image.ResizeMethod.BICUBIC)
return tf.saturate_cast(rescaled, tf.uint8) | 6,311,872,618,237,034,000 | Rescales image and enlarged cornet. | third_party/augment_ops.py | rescale | google-research/crest | python | def rescale(image, level):
size = image.shape[:2]
scale = (level * 0.25)
scale_height = tf.cast((scale * size[0]), tf.int32)
scale_width = tf.cast((scale * size[1]), tf.int32)
cropped_image = tf.image.crop_to_bounding_box(image, offset_height=scale_height, offset_width=scale_width, target_height=(size[0] - scale_height), target_width=(size[1] - scale_width))
rescaled = tf.image.resize(cropped_image, size, tf.image.ResizeMethod.BICUBIC)
return tf.saturate_cast(rescaled, tf.uint8) |
def scale_channel(channel):
'Scale the 2D image using the autocontrast rule.'
lo = tf.cast(tf.reduce_min(channel), tf.float32)
hi = tf.cast(tf.reduce_max(channel), tf.float32)
def scale_values(im):
scale = (255.0 / (hi - lo))
offset = ((- lo) * scale)
im = ((tf.cast(im, tf.float32) * scale) + offset)
return tf.saturate_cast(im, tf.uint8)
result = tf.cond((hi > lo), (lambda : scale_values(channel)), (lambda : channel))
return result | 899,198,447,933,424,800 | Scale the 2D image using the autocontrast rule. | third_party/augment_ops.py | scale_channel | google-research/crest | python | def scale_channel(channel):
lo = tf.cast(tf.reduce_min(channel), tf.float32)
hi = tf.cast(tf.reduce_max(channel), tf.float32)
def scale_values(im):
scale = (255.0 / (hi - lo))
offset = ((- lo) * scale)
im = ((tf.cast(im, tf.float32) * scale) + offset)
return tf.saturate_cast(im, tf.uint8)
result = tf.cond((hi > lo), (lambda : scale_values(channel)), (lambda : channel))
return result |
def scale_channel(im, c):
'Scale the data in the channel to implement equalize.'
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)])
step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255)
def build_lut(histo, step):
lut = ((tf.cumsum(histo) + (step // 2)) // step)
lut = tf.concat([[0], lut[:(- 1)]], 0)
return tf.clip_by_value(lut, 0, 255)
result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im)))
return tf.cast(result, tf.uint8) | 6,353,019,026,156,998,000 | Scale the data in the channel to implement equalize. | third_party/augment_ops.py | scale_channel | google-research/crest | python | def scale_channel(im, c):
im = tf.cast(im[:, :, c], tf.int32)
histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
nonzero = tf.where(tf.not_equal(histo, 0))
nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)])
step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255)
def build_lut(histo, step):
lut = ((tf.cumsum(histo) + (step // 2)) // step)
lut = tf.concat([[0], lut[:(- 1)]], 0)
return tf.clip_by_value(lut, 0, 255)
result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im)))
return tf.cast(result, tf.uint8) |
def get_author_name(soup):
"Get the author's name from its main page.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n string: name of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_name(soup)\n J.K. Rowling\n\n "
author_h1 = soup.find('h1', attrs={'class': 'authorName'})
return author_h1.find('span').text | -2,567,605,535,743,929,300 | Get the author's name from its main page.
Args:
soup (bs4.element.Tag): connection to the author page.
Returns:
string: name of the author.
Examples::
>>> from scrapereads import connect
>>> url = 'https://www.goodreads.com/author/show/1077326'
>>> soup = connect(url)
>>> get_author_name(soup)
J.K. Rowling | scrapereads/scrape.py | get_author_name | arthurdjn/scrape-goodreads | python | def get_author_name(soup):
"Get the author's name from its main page.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n string: name of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_name(soup)\n J.K. Rowling\n\n "
author_h1 = soup.find('h1', attrs={'class': 'authorName'})
return author_h1.find('span').text |
def get_author_desc(soup):
"Get the author description / biography.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n str: long description of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_desc(soup)\n See also: Robert Galbraith\n Although she writes under the pen name J.K. Rowling, pronounced like rolling,\n her name when her first Harry Potter book was published was simply Joanne Rowling.\n ...\n\n "
author_info_desc = soup.find('div', attrs={'class': 'aboutAuthorInfo'})
author_info_long = author_info_desc.findAll('span')[(- 1)]
long_desc = ''
for sentence in author_info_long.children:
if isinstance(sentence, bs4.element.Tag):
if (sentence.name == 'br'):
long_desc += '\n'
else:
long_desc += sentence.text
else:
long_desc += sentence
long_desc = long_desc.replace('’', "'")
return long_desc | -7,998,133,341,309,047,000 | Get the author description / biography.
Args:
soup (bs4.element.Tag): connection to the author page.
Returns:
str: long description of the author.
Examples::
>>> from scrapereads import connect
>>> url = 'https://www.goodreads.com/author/show/1077326'
>>> soup = connect(url)
>>> get_author_desc(soup)
See also: Robert Galbraith
Although she writes under the pen name J.K. Rowling, pronounced like rolling,
her name when her first Harry Potter book was published was simply Joanne Rowling.
... | scrapereads/scrape.py | get_author_desc | arthurdjn/scrape-goodreads | python | def get_author_desc(soup):
"Get the author description / biography.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n str: long description of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_desc(soup)\n See also: Robert Galbraith\n Although she writes under the pen name J.K. Rowling, pronounced like rolling,\n her name when her first Harry Potter book was published was simply Joanne Rowling.\n ...\n\n "
author_info_desc = soup.find('div', attrs={'class': 'aboutAuthorInfo'})
author_info_long = author_info_desc.findAll('span')[(- 1)]
long_desc =
for sentence in author_info_long.children:
if isinstance(sentence, bs4.element.Tag):
if (sentence.name == 'br'):
long_desc += '\n'
else:
long_desc += sentence.text
else:
long_desc += sentence
long_desc = long_desc.replace('’', "'")
return long_desc |
def get_author_info(soup):
'Get all information from an author (genres, influences, website etc.).\n\n Args:\n soup (bs4.element.Tag): author page connection.\n\n Returns:\n dict\n\n '
container = soup.find('div', attrs={'class': 'rightContainer'})
author_info = {}
data_div = container.find('br', attrs={'class': 'clear'})
while data_div:
if data_div.name:
data_class = data_div.get('class')[0]
if (data_class == 'aboutAuthorInfo'):
break
elif (data_class == 'dataTitle'):
key = data_div.text.strip()
author_info[key] = []
if (data_div.text == 'Born'):
data_div = data_div.next_sibling
author_info[key].append(data_div.strip())
elif (data_div.text == 'Influences'):
data_div = data_div.next_sibling.next_sibling
data_items = data_div.findAll('span')[(- 1)].findAll('a')
for data_a in data_items:
author_info[key].append(data_a.text.strip())
elif (data_div.text == 'Member Since'):
data_div = data_div.next_sibling.next_sibling
author_info[key].append(data_div.text.strip())
else:
data_items = data_div.findAll('a')
for data_a in data_items:
author_info[key].append(data_a.text.strip())
data_div = data_div.next_sibling
author_info.update({'Description': get_author_desc(soup)})
return author_info | -19,904,277,778,161,580 | Get all information from an author (genres, influences, website etc.).
Args:
soup (bs4.element.Tag): author page connection.
Returns:
dict | scrapereads/scrape.py | get_author_info | arthurdjn/scrape-goodreads | python | def get_author_info(soup):
'Get all information from an author (genres, influences, website etc.).\n\n Args:\n soup (bs4.element.Tag): author page connection.\n\n Returns:\n dict\n\n '
container = soup.find('div', attrs={'class': 'rightContainer'})
author_info = {}
data_div = container.find('br', attrs={'class': 'clear'})
while data_div:
if data_div.name:
data_class = data_div.get('class')[0]
if (data_class == 'aboutAuthorInfo'):
break
elif (data_class == 'dataTitle'):
key = data_div.text.strip()
author_info[key] = []
if (data_div.text == 'Born'):
data_div = data_div.next_sibling
author_info[key].append(data_div.strip())
elif (data_div.text == 'Influences'):
data_div = data_div.next_sibling.next_sibling
data_items = data_div.findAll('span')[(- 1)].findAll('a')
for data_a in data_items:
author_info[key].append(data_a.text.strip())
elif (data_div.text == 'Member Since'):
data_div = data_div.next_sibling.next_sibling
author_info[key].append(data_div.text.strip())
else:
data_items = data_div.findAll('a')
for data_a in data_items:
author_info[key].append(data_a.text.strip())
data_div = data_div.next_sibling
author_info.update({'Description': get_author_desc(soup)})
return author_info |
def scrape_quotes_container(soup):
'Get the quote container from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n bs4.element.Tag\n\n '
return soup.findAll('div', attrs={'class': 'quotes'}) | -2,257,911,790,353,518,800 | Get the quote container from a quote page.
Args:
soup (bs4.element.Tag): connection to the quote page.
Returns:
bs4.element.Tag | scrapereads/scrape.py | scrape_quotes_container | arthurdjn/scrape-goodreads | python | def scrape_quotes_container(soup):
'Get the quote container from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n bs4.element.Tag\n\n '
return soup.findAll('div', attrs={'class': 'quotes'}) |
def scrape_quotes(soup):
'Retrieve all ``<div>`` quote element from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n yield bs4.element.Tag\n\n '
for container_div in scrape_quotes_container(soup):
quote_div = container_div.find('div', attrs={'class': 'quote'})
while quote_div:
if ((quote_div.name == 'div') and quote_div.get('class') and ('quote' in quote_div.get('class'))):
(yield quote_div)
quote_div = quote_div.next_sibling | 3,836,355,887,043,983,400 | Retrieve all ``<div>`` quote element from a quote page.
Args:
soup (bs4.element.Tag): connection to the quote page.
Returns:
yield bs4.element.Tag | scrapereads/scrape.py | scrape_quotes | arthurdjn/scrape-goodreads | python | def scrape_quotes(soup):
'Retrieve all ``<div>`` quote element from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n yield bs4.element.Tag\n\n '
for container_div in scrape_quotes_container(soup):
quote_div = container_div.find('div', attrs={'class': 'quote'})
while quote_div:
if ((quote_div.name == 'div') and quote_div.get('class') and ('quote' in quote_div.get('class'))):
(yield quote_div)
quote_div = quote_div.next_sibling |
def get_quote_text(quote_div):
'Get the text from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element to extract the text.\n\n Returns:\n string\n\n '
quote_text = ''
text_iterator = quote_div.find('div', attrs={'class': 'quoteText'}).children
for text in text_iterator:
if (text.name == 'br'):
quote_text += '\n'
elif (not text.name):
quote_text += text.strip()
quote_text = process_quote_text(quote_text)
return quote_text | 1,677,841,520,926,764,300 | Get the text from a ``<div>`` quote element.
Args:
quote_div (bs4.element.Tag): ``<div>`` quote element to extract the text.
Returns:
string | scrapereads/scrape.py | get_quote_text | arthurdjn/scrape-goodreads | python | def get_quote_text(quote_div):
'Get the text from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element to extract the text.\n\n Returns:\n string\n\n '
quote_text =
text_iterator = quote_div.find('div', attrs={'class': 'quoteText'}).children
for text in text_iterator:
if (text.name == 'br'):
quote_text += '\n'
elif (not text.name):
quote_text += text.strip()
quote_text = process_quote_text(quote_text)
return quote_text |
def scrape_quote_tags(quote_div):
'Scrape tags from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n yield ``<a>`` tags\n\n '
tags_container = quote_div.find('div', attrs={'class': 'greyText smallText left'})
if tags_container:
for tag in tags_container.children:
if (tag.name == 'a'):
(yield tag)
return None | -8,704,284,436,144,329,000 | Scrape tags from a ``<div>`` quote element.
Args:
quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.
Returns:
yield ``<a>`` tags | scrapereads/scrape.py | scrape_quote_tags | arthurdjn/scrape-goodreads | python | def scrape_quote_tags(quote_div):
'Scrape tags from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n yield ``<a>`` tags\n\n '
tags_container = quote_div.find('div', attrs={'class': 'greyText smallText left'})
if tags_container:
for tag in tags_container.children:
if (tag.name == 'a'):
(yield tag)
return None |
def get_quote_book(quote_div):
'Get the reference (book) from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag\n\n '
quote_details = quote_div.find('div', attrs={'class': 'quoteText'})
return quote_details.find('a', attrs={'class': 'authorOrTitle'}) | 325,821,846,411,896,200 | Get the reference (book) from a ``<div>`` quote element.
Args:
quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.
Returns:
bs4.element.Tag | scrapereads/scrape.py | get_quote_book | arthurdjn/scrape-goodreads | python | def get_quote_book(quote_div):
'Get the reference (book) from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag\n\n '
quote_details = quote_div.find('div', attrs={'class': 'quoteText'})
return quote_details.find('a', attrs={'class': 'authorOrTitle'}) |
def get_quote_author_name(quote_div):
"Get the author's name from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n string\n\n "
quote_text = quote_div.find('div', attrs={'class': 'quoteText '})
author_name = quote_text.find('span', attrs={'class': 'authorOrTitle'}).text
return remove_punctuation(author_name).title() | -7,591,282,057,785,029,000 | Get the author's name from a ``<div>`` quote element.
Args:
quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.
Returns:
string | scrapereads/scrape.py | get_quote_author_name | arthurdjn/scrape-goodreads | python | def get_quote_author_name(quote_div):
"Get the author's name from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n string\n\n "
quote_text = quote_div.find('div', attrs={'class': 'quoteText '})
author_name = quote_text.find('span', attrs={'class': 'authorOrTitle'}).text
return remove_punctuation(author_name).title() |
def get_quote_likes(quote_div):
'Get the likes ``<a>`` tag from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag: ``<a>`` tag for likes.\n\n '
quote_footer = quote_div.find('div', attrs={'class': 'quoteFooter'})
return quote_footer.find('a', attrs={'class': 'smallText'}) | 6,157,151,784,991,132,000 | Get the likes ``<a>`` tag from a ``<div>`` quote element.
Args:
quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.
Returns:
bs4.element.Tag: ``<a>`` tag for likes. | scrapereads/scrape.py | get_quote_likes | arthurdjn/scrape-goodreads | python | def get_quote_likes(quote_div):
'Get the likes ``<a>`` tag from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag: ``<a>`` tag for likes.\n\n '
quote_footer = quote_div.find('div', attrs={'class': 'quoteFooter'})
return quote_footer.find('a', attrs={'class': 'smallText'}) |
def get_quote_name_id(quote_div):
'Get the name and id of a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n tuple: id and name.\n\n '
quote_href = get_quote_likes(quote_div).get('href')
quote_id = quote_href.split('/')[(- 1)].split('-')[0]
quote_name = '-'.join(quote_href.split('/')[(- 1)].split('-')[1:])
return (quote_id, quote_name) | -2,960,141,701,786,695,700 | Get the name and id of a ``<div>`` quote element.
Args:
quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.
Returns:
tuple: id and name. | scrapereads/scrape.py | get_quote_name_id | arthurdjn/scrape-goodreads | python | def get_quote_name_id(quote_div):
'Get the name and id of a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n tuple: id and name.\n\n '
quote_href = get_quote_likes(quote_div).get('href')
quote_id = quote_href.split('/')[(- 1)].split('-')[0]
quote_name = '-'.join(quote_href.split('/')[(- 1)].split('-')[1:])
return (quote_id, quote_name) |
def scrape_author_books(soup):
"Retrieve books from an author's page.\n\n Args:\n soup (bs4.element.Tag): connection to an author books page.\n\n Returns:\n yield bs4.element.Tag: ``<tr>`` element.\n\n "
table_tr = soup.find('tr')
while table_tr:
if (table_tr.name == 'tr'):
(yield table_tr)
table_tr = table_tr.next_sibling | -1,126,821,855,199,378,000 | Retrieve books from an author's page.
Args:
soup (bs4.element.Tag): connection to an author books page.
Returns:
yield bs4.element.Tag: ``<tr>`` element. | scrapereads/scrape.py | scrape_author_books | arthurdjn/scrape-goodreads | python | def scrape_author_books(soup):
"Retrieve books from an author's page.\n\n Args:\n soup (bs4.element.Tag): connection to an author books page.\n\n Returns:\n yield bs4.element.Tag: ``<tr>`` element.\n\n "
table_tr = soup.find('tr')
while table_tr:
if (table_tr.name == 'tr'):
(yield table_tr)
table_tr = table_tr.next_sibling |
def get_author_book_title(book_tr):
"Get the book title ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book title ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_title = get_author_book_title(book_tr)\n ... print(book_title.text.strip(), book_title.get('href'))\n The Bell Jar /book/show/6514.The_Bell_Jar\n Ariel /book/show/395090.Ariel\n The Collected Poems /book/show/31426.The_Collected_Poems\n The Unabridged Journals of Sylvia Plath /book/show/11623.The_Unabridged_Journals_of_Sylvia_Plath\n\n "
return book_tr.find('a', attrs={'class': 'bookTitle'}) | -1,115,418,599,705,648,800 | Get the book title ``<a>`` element from a table ``<tr>`` element from an author page.
Args:
book_tr (bs4.element.Tag): ``<tr>`` book element.
Returns:
bs4.element.Tag: book title ``<a>`` element.
Examples::
>>> for book_tr in scrape_author_books(soup):
... book_title = get_author_book_title(book_tr)
... print(book_title.text.strip(), book_title.get('href'))
The Bell Jar /book/show/6514.The_Bell_Jar
Ariel /book/show/395090.Ariel
The Collected Poems /book/show/31426.The_Collected_Poems
The Unabridged Journals of Sylvia Plath /book/show/11623.The_Unabridged_Journals_of_Sylvia_Plath | scrapereads/scrape.py | get_author_book_title | arthurdjn/scrape-goodreads | python | def get_author_book_title(book_tr):
"Get the book title ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book title ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_title = get_author_book_title(book_tr)\n ... print(book_title.text.strip(), book_title.get('href'))\n The Bell Jar /book/show/6514.The_Bell_Jar\n Ariel /book/show/395090.Ariel\n The Collected Poems /book/show/31426.The_Collected_Poems\n The Unabridged Journals of Sylvia Plath /book/show/11623.The_Unabridged_Journals_of_Sylvia_Plath\n\n "
return book_tr.find('a', attrs={'class': 'bookTitle'}) |
def get_author_book_author(book_tr):
"Get the author ``<a>`` element from a table ``<tr>`` element.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: author name ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_author = get_author_book_author(book_tr)\n ... print(book_author.text, book_author.get('href'))\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n\n "
return book_tr.find('a', attrs={'class': 'authorName'}) | 4,352,936,690,821,773,300 | Get the author ``<a>`` element from a table ``<tr>`` element.
Args:
book_tr (bs4.element.Tag): ``<tr>`` book element.
Returns:
bs4.element.Tag: author name ``<a>`` element.
Examples::
>>> for book_tr in scrape_author_books(soup):
... book_author = get_author_book_author(book_tr)
... print(book_author.text, book_author.get('href'))
Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath
Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath
Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath
Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath
Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath | scrapereads/scrape.py | get_author_book_author | arthurdjn/scrape-goodreads | python | def get_author_book_author(book_tr):
"Get the author ``<a>`` element from a table ``<tr>`` element.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: author name ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_author = get_author_book_author(book_tr)\n ... print(book_author.text, book_author.get('href'))\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n\n "
return book_tr.find('a', attrs={'class': 'authorName'}) |
def get_author_book_ratings(book_tr):
'Get the ratings ``<span>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: ratings ``<span>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... ratings_span = get_author_book_ratings(book_tr)\n ... print(ratings_span.contents[-1])\n 4.55 avg rating — 2,414 ratings\n 3.77 avg rating — 1,689 ratings\n 4.28 avg rating — 892 ratings\n 4.54 avg rating — 490 ratings\n ...\n\n '
return book_tr.find('span', attrs={'class': 'minirating'}) | -3,227,250,309,905,705,500 | Get the ratings ``<span>`` element from a table ``<tr>`` element from an author page.
Args:
book_tr (bs4.element.Tag): ``<tr>`` book element.
Returns:
bs4.element.Tag: ratings ``<span>`` element.
Examples::
>>> for book_tr in scrape_author_books(soup):
... ratings_span = get_author_book_ratings(book_tr)
... print(ratings_span.contents[-1])
4.55 avg rating — 2,414 ratings
3.77 avg rating — 1,689 ratings
4.28 avg rating — 892 ratings
4.54 avg rating — 490 ratings
... | scrapereads/scrape.py | get_author_book_ratings | arthurdjn/scrape-goodreads | python | def get_author_book_ratings(book_tr):
'Get the ratings ``<span>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: ratings ``<span>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... ratings_span = get_author_book_ratings(book_tr)\n ... print(ratings_span.contents[-1])\n 4.55 avg rating — 2,414 ratings\n 3.77 avg rating — 1,689 ratings\n 4.28 avg rating — 892 ratings\n 4.54 avg rating — 490 ratings\n ...\n\n '
return book_tr.find('span', attrs={'class': 'minirating'}) |
def get_author_book_edition(book_tr):
"Get the edition ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book edition ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_edition = get_author_book_edition(book_tr)\n ... if book_edition:\n ... print(book_edition.text, book_edition.get('href'))\n ... print()\n 493 editions /work/editions/1385044-the-bell-jar\n 80 editions /work/editions/1185316-ariel\n 30 editions /work/editions/1003095-the-collected-poems\n 45 editions /work/editions/3094683-the-unabridged-journals-of-sylvia-plath\n ...\n\n "
book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})
return book_details.find('a', attrs={'class': 'greyText'}) | 8,646,870,124,449,000,000 | Get the edition ``<a>`` element from a table ``<tr>`` element from an author page.
Args:
book_tr (bs4.element.Tag): ``<tr>`` book element.
Returns:
bs4.element.Tag: book edition ``<a>`` element.
Examples::
>>> for book_tr in scrape_author_books(soup):
... book_edition = get_author_book_edition(book_tr)
... if book_edition:
... print(book_edition.text, book_edition.get('href'))
... print()
493 editions /work/editions/1385044-the-bell-jar
80 editions /work/editions/1185316-ariel
30 editions /work/editions/1003095-the-collected-poems
45 editions /work/editions/3094683-the-unabridged-journals-of-sylvia-plath
... | scrapereads/scrape.py | get_author_book_edition | arthurdjn/scrape-goodreads | python | def get_author_book_edition(book_tr):
"Get the edition ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book edition ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_edition = get_author_book_edition(book_tr)\n ... if book_edition:\n ... print(book_edition.text, book_edition.get('href'))\n ... print()\n 493 editions /work/editions/1385044-the-bell-jar\n 80 editions /work/editions/1185316-ariel\n 30 editions /work/editions/1003095-the-collected-poems\n 45 editions /work/editions/3094683-the-unabridged-journals-of-sylvia-plath\n ...\n\n "
book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})
return book_details.find('a', attrs={'class': 'greyText'}) |
def get_author_book_date(book_tr):
'Get the published date from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n int: date of publication\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_date = get_author_book_date(book_tr)\n ... print(book_date)\n None\n None\n 1958\n 2009\n ...\n\n '
book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})
book_publish = book_details.contents[(- 1)].replace('—', '').replace('\n', '')
book_date = book_publish.replace('published', '').strip()
book_date = (eval(book_date) if (book_date != '') else None)
return book_date | 5,196,297,577,235,133,000 | Get the published date from a table ``<tr>`` element from an author page.
Args:
book_tr (bs4.element.Tag): ``<tr>`` book element.
Returns:
int: date of publication
Examples::
>>> for book_tr in scrape_author_books(soup):
... book_date = get_author_book_date(book_tr)
... print(book_date)
None
None
1958
2009
... | scrapereads/scrape.py | get_author_book_date | arthurdjn/scrape-goodreads | python | def get_author_book_date(book_tr):
'Get the published date from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n int: date of publication\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_date = get_author_book_date(book_tr)\n ... print(book_date)\n None\n None\n 1958\n 2009\n ...\n\n '
book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})
book_publish = book_details.contents[(- 1)].replace('—', ).replace('\n', )
book_date = book_publish.replace('published', ).strip()
book_date = (eval(book_date) if (book_date != ) else None)
return book_date |
def get_book_quote_page(soup):
'Find the ``<a>`` element pointing to the quote page of a book.\n\n Args:\n soup (bs4.element.Tag):\n\n Returns:\n\n '
quote_div = soup.findAll('div', attrs={'class': ' clearFloats bigBox'})
if quote_div:
return quote_div[(- 1)].find('a')
return None | -3,858,875,114,353,949,700 | Find the ``<a>`` element pointing to the quote page of a book.
Args:
soup (bs4.element.Tag):
Returns: | scrapereads/scrape.py | get_book_quote_page | arthurdjn/scrape-goodreads | python | def get_book_quote_page(soup):
'Find the ``<a>`` element pointing to the quote page of a book.\n\n Args:\n soup (bs4.element.Tag):\n\n Returns:\n\n '
quote_div = soup.findAll('div', attrs={'class': ' clearFloats bigBox'})
if quote_div:
return quote_div[(- 1)].find('a')
return None |
def find_divisors(x):
'\n This is the "function to find divisors in order to find generators" module.\n This DocTest verifies that the module is correctly calculating all divisors\n of a number x.\n\n >>> find_divisors(10)\n [1, 2, 5, 10]\n\n >>> find_divisors(112)\n [1, 2, 4, 7, 8, 14, 16, 28, 56, 112]\n '
divisors = [i for i in range(1, (x + 1)) if ((x % i) == 0)]
return divisors | -782,998,713,240,490,500 | This is the "function to find divisors in order to find generators" module.
This DocTest verifies that the module is correctly calculating all divisors
of a number x.
>>> find_divisors(10)
[1, 2, 5, 10]
>>> find_divisors(112)
[1, 2, 4, 7, 8, 14, 16, 28, 56, 112] | libsig/FZZ_unique_ring_signature.py | find_divisors | vs-uulm/libsig_pets | python | def find_divisors(x):
'\n This is the "function to find divisors in order to find generators" module.\n This DocTest verifies that the module is correctly calculating all divisors\n of a number x.\n\n >>> find_divisors(10)\n [1, 2, 5, 10]\n\n >>> find_divisors(112)\n [1, 2, 4, 7, 8, 14, 16, 28, 56, 112]\n '
divisors = [i for i in range(1, (x + 1)) if ((x % i) == 0)]
return divisors |
def find_generator(p):
'\n The order of any element in a group can be divided by p-1.\n Step 1: Calculate all Divisors.\n Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.\n if neither is one but e to the power of p-1, a generator is found.\n '
testGen = randint(1, p)
listTested = []
listTested.append(testGen)
divisors = find_divisors(p)
while (len(listTested) < (p - 1)):
if (testGen in listTested):
for div in divisors:
testPotency = (math.pow(testGen, div) % (p + 1))
if ((testPotency == 1.0) and (div != divisors[(- 1)])):
break
elif ((testPotency == 1.0) and (div == divisors[(- 1)])):
return testGen
testGen = randint(1, p)
listTested.append(testGen) | 3,486,228,835,377,068,500 | The order of any element in a group can be divided by p-1.
Step 1: Calculate all Divisors.
Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.
if neither is one but e to the power of p-1, a generator is found. | libsig/FZZ_unique_ring_signature.py | find_generator | vs-uulm/libsig_pets | python | def find_generator(p):
'\n The order of any element in a group can be divided by p-1.\n Step 1: Calculate all Divisors.\n Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.\n if neither is one but e to the power of p-1, a generator is found.\n '
testGen = randint(1, p)
listTested = []
listTested.append(testGen)
divisors = find_divisors(p)
while (len(listTested) < (p - 1)):
if (testGen in listTested):
for div in divisors:
testPotency = (math.pow(testGen, div) % (p + 1))
if ((testPotency == 1.0) and (div != divisors[(- 1)])):
break
elif ((testPotency == 1.0) and (div == divisors[(- 1)])):
return testGen
testGen = randint(1, p)
listTested.append(testGen) |
def list_to_string(input_list):
'\n convert a list into a concatenated string of all its elements\n '
result = ''.join(map(str, input_list))
return result | 3,923,680,371,048,306,000 | convert a list into a concatenated string of all its elements | libsig/FZZ_unique_ring_signature.py | list_to_string | vs-uulm/libsig_pets | python | def list_to_string(input_list):
'\n \n '
result = .join(map(str, input_list))
return result |
@staticmethod
def ringsign(x, pubkey, message, verbose=False):
'\n input: x is the privkey from user i, \n | all public keys: pubkeys,\n | the message\n \n output: (R,m, (H(mR)^xi), c1,t1,...,cn,tn),\n | R: all the pubkeys concatenated,\n | cj,tj: random number within Zq\n '
R = list_to_string(pubkey)
g = UniqueRingSignature.g
q = UniqueRingSignature.q
h1 = UniqueRingSignature.h1
h2 = UniqueRingSignature.h2
mR = (message + str(R))
C = list()
T = list()
A = list()
B = list()
ri = (- 1)
for i in pubkey:
a = 0
b = 0
c = 0
t = 0
if (pow(g, x, q) != i):
(c, t) = (randint(1, q), randint(1, q))
a = ((pow(g, t) * pow(int(i), c)) % q)
b = ((pow(h1(mR), t) * pow(pow(h1(mR), x), c)) % q)
else:
ri = randint(1, q)
a = pow(g, ri, q)
b = pow(h1(mR), ri, q)
c = (- 1)
t = (- 1)
A.append(a)
B.append(b)
C.append(c)
T.append(t)
cj = 0
ab = ''.join(('{}{}'.format(*t) for t in zip(A, B)))
usernr = 0
for i in range(len(pubkey)):
if (pubkey[i] != pow(g, x, q)):
cj = ((cj + C[i]) % q)
else:
usernr = i
ci = (h2(((message + R) + ab)) - (cj % (q - 1)))
C[usernr] = ci
ti = ((ri - (C[usernr] * x)) % (q - 1))
if (ti < 0):
ti = ((q - 1) + ti)
T[usernr] = ti
ct = ','.join(('{},{}'.format(*t) for t in zip(C, T)))
result = ((((((R + ',') + message) + ',') + str(pow(h1(mR), x, q))) + ',') + ct)
if (verbose == True):
print(('RingSign Result: ' + result))
print('---- RingSign Completed ---- \n')
return result | -5,456,669,802,386,871,000 | input: x is the privkey from user i,
| all public keys: pubkeys,
| the message
output: (R,m, (H(mR)^xi), c1,t1,...,cn,tn),
| R: all the pubkeys concatenated,
| cj,tj: random number within Zq | libsig/FZZ_unique_ring_signature.py | ringsign | vs-uulm/libsig_pets | python | @staticmethod
def ringsign(x, pubkey, message, verbose=False):
'\n input: x is the privkey from user i, \n | all public keys: pubkeys,\n | the message\n \n output: (R,m, (H(mR)^xi), c1,t1,...,cn,tn),\n | R: all the pubkeys concatenated,\n | cj,tj: random number within Zq\n '
R = list_to_string(pubkey)
g = UniqueRingSignature.g
q = UniqueRingSignature.q
h1 = UniqueRingSignature.h1
h2 = UniqueRingSignature.h2
mR = (message + str(R))
C = list()
T = list()
A = list()
B = list()
ri = (- 1)
for i in pubkey:
a = 0
b = 0
c = 0
t = 0
if (pow(g, x, q) != i):
(c, t) = (randint(1, q), randint(1, q))
a = ((pow(g, t) * pow(int(i), c)) % q)
b = ((pow(h1(mR), t) * pow(pow(h1(mR), x), c)) % q)
else:
ri = randint(1, q)
a = pow(g, ri, q)
b = pow(h1(mR), ri, q)
c = (- 1)
t = (- 1)
A.append(a)
B.append(b)
C.append(c)
T.append(t)
cj = 0
ab = .join(('{}{}'.format(*t) for t in zip(A, B)))
usernr = 0
for i in range(len(pubkey)):
if (pubkey[i] != pow(g, x, q)):
cj = ((cj + C[i]) % q)
else:
usernr = i
ci = (h2(((message + R) + ab)) - (cj % (q - 1)))
C[usernr] = ci
ti = ((ri - (C[usernr] * x)) % (q - 1))
if (ti < 0):
ti = ((q - 1) + ti)
T[usernr] = ti
ct = ','.join(('{},{}'.format(*t) for t in zip(C, T)))
result = ((((((R + ',') + message) + ',') + str(pow(h1(mR), x, q))) + ',') + ct)
if (verbose == True):
print(('RingSign Result: ' + result))
print('---- RingSign Completed ---- \n')
return result |
@staticmethod
def verify(R, message, signature, verbose=False):
'\n Input: the public keys R\n | the message\n | the signature computed with ringsign\n\n Output: whether the message was signed by R or not\n '
g = UniqueRingSignature.g
q = UniqueRingSignature.q
h1 = UniqueRingSignature.h1
h2 = UniqueRingSignature.h2
parsed = signature.split(',')
tt = int(parsed[2])
cjs = list()
tjs = list()
for i in range(0, int(((len(parsed) / 2) - 1))):
cjs.append(int(parsed[(3 + (2 * i))]))
tjs.append(int(parsed[(4 + (2 * i))]))
mR = list_to_string(R)
val1 = (sum(cjs) % q)
gyh1 = ''
for i in range(len(tjs)):
if (tjs[i] < 0):
tjs[i] = ((q - 1) + tjs[i])
if (cjs[i] < 0):
cjs[i] = ((q - 1) + cjs[i])
gy = ((pow(g, tjs[i], q) * pow(R[i], cjs[i], q)) % q)
h = ((pow(int(h1((message + mR))), int(tjs[i])) * pow(tt, int(cjs[i]))) % q)
gyh1 = ((gyh1 + str(gy)) + str(h))
val2 = str(h2(((message + list_to_string(R)) + gyh1)))
if (int(val1) == int(val2)):
if (verbose == True):
print('Signature is valid!\n')
print(('Common Result: ' + str(val1)))
print('---- Validation Completed ---- \n')
return True
else:
if (verbose == True):
print('Signature is not valid!\n')
print(((str(val1) + ' != ') + str(val2)))
print('---- Validation Completed ---- \n')
return False | -5,893,289,199,147,430,000 | Input: the public keys R
| the message
| the signature computed with ringsign
Output: whether the message was signed by R or not | libsig/FZZ_unique_ring_signature.py | verify | vs-uulm/libsig_pets | python | @staticmethod
def verify(R, message, signature, verbose=False):
'\n Input: the public keys R\n | the message\n | the signature computed with ringsign\n\n Output: whether the message was signed by R or not\n '
g = UniqueRingSignature.g
q = UniqueRingSignature.q
h1 = UniqueRingSignature.h1
h2 = UniqueRingSignature.h2
parsed = signature.split(',')
tt = int(parsed[2])
cjs = list()
tjs = list()
for i in range(0, int(((len(parsed) / 2) - 1))):
cjs.append(int(parsed[(3 + (2 * i))]))
tjs.append(int(parsed[(4 + (2 * i))]))
mR = list_to_string(R)
val1 = (sum(cjs) % q)
gyh1 =
for i in range(len(tjs)):
if (tjs[i] < 0):
tjs[i] = ((q - 1) + tjs[i])
if (cjs[i] < 0):
cjs[i] = ((q - 1) + cjs[i])
gy = ((pow(g, tjs[i], q) * pow(R[i], cjs[i], q)) % q)
h = ((pow(int(h1((message + mR))), int(tjs[i])) * pow(tt, int(cjs[i]))) % q)
gyh1 = ((gyh1 + str(gy)) + str(h))
val2 = str(h2(((message + list_to_string(R)) + gyh1)))
if (int(val1) == int(val2)):
if (verbose == True):
print('Signature is valid!\n')
print(('Common Result: ' + str(val1)))
print('---- Validation Completed ---- \n')
return True
else:
if (verbose == True):
print('Signature is not valid!\n')
print(((str(val1) + ' != ') + str(val2)))
print('---- Validation Completed ---- \n')
return False |
def current_flow_betweenness_centrality_subset(G, sources, targets, normalized=True, weight='weight', dtype=float, solver='lu'):
'Compute current-flow betweenness centrality for subsets of nodes.\n\n Current-flow betweenness centrality uses an electrical current\n model for information spreading in contrast to betweenness\n centrality which uses shortest paths.\n\n Current-flow betweenness centrality is also known as\n random-walk betweenness centrality [2]_.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph \n\n sources: list of nodes\n Nodes to use as sources for current\n\n targets: list of nodes\n Nodes to use as sinks for current\n\n normalized : bool, optional (default=True)\n If True the betweenness values are normalized by b=b/(n-1)(n-2) where\n n is the number of nodes in G.\n\n weight : string or None, optional (default=\'weight\')\n Key for edge data used as the edge weight.\n If None, then use 1 as each edge weight.\n\n dtype: data type (float)\n Default data type for internal matrices.\n Set to np.float32 for lower memory consumption.\n\n solver: string (default=\'lu\')\n Type of linear solver to use for computing the flow matrix.\n Options are "full" (uses most memory), "lu" (recommended), and \n "cg" (uses least memory).\n\n Returns\n -------\n nodes : dictionary\n Dictionary of nodes with betweenness centrality as the value.\n \n See Also\n --------\n approximate_current_flow_betweenness_centrality\n betweenness_centrality\n edge_betweenness_centrality\n edge_current_flow_betweenness_centrality\n\n Notes\n -----\n Current-flow betweenness can be computed in `O(I(n-1)+mn \\log n)`\n time [1]_, where `I(n-1)` is the time needed to compute the \n inverse Laplacian. For a full matrix this is `O(n^3)` but using\n sparse methods you can achieve `O(nm{\\sqrt k})` where `k` is the\n Laplacian matrix condition number. \n\n The space required is `O(nw) where `w` is the width of the sparse\n Laplacian matrix. Worse case is `w=n` for `O(n^2)`.\n\n If the edges have a \'weight\' attribute they will be used as \n weights in this algorithm. Unspecified weights are set to 1.\n\n References\n ----------\n .. [1] Centrality Measures Based on Current Flow. \n Ulrik Brandes and Daniel Fleischer,\n Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS \'05). \n LNCS 3404, pp. 533-544. Springer-Verlag, 2005. \n http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf\n\n .. [2] A measure of betweenness centrality based on random walks,\n M. E. J. Newman, Social Networks 27, 39-54 (2005).\n '
from networkx.utils import reverse_cuthill_mckee_ordering
try:
import numpy as np
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires NumPy ', 'http://scipy.org/')
try:
import scipy
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires SciPy ', 'http://scipy.org/')
if G.is_directed():
raise nx.NetworkXError('current_flow_betweenness_centrality() ', 'not defined for digraphs.')
if (not nx.is_connected(G)):
raise nx.NetworkXError('Graph not connected.')
n = G.number_of_nodes()
ordering = list(reverse_cuthill_mckee_ordering(G))
mapping = dict(zip(ordering, range(n)))
H = nx.relabel_nodes(G, mapping)
betweenness = dict.fromkeys(H, 0.0)
for (row, (s, t)) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver):
for ss in sources:
i = mapping[ss]
for tt in targets:
j = mapping[tt]
betweenness[s] += (0.5 * np.abs((row[i] - row[j])))
betweenness[t] += (0.5 * np.abs((row[i] - row[j])))
if normalized:
nb = ((n - 1.0) * (n - 2.0))
else:
nb = 2.0
for v in H:
betweenness[v] = ((betweenness[v] / nb) + (1.0 / (2 - n)))
return dict(((ordering[k], v) for (k, v) in betweenness.items())) | 8,374,067,259,139,407,000 | Compute current-flow betweenness centrality for subsets of nodes.
Current-flow betweenness centrality uses an electrical current
model for information spreading in contrast to betweenness
centrality which uses shortest paths.
Current-flow betweenness centrality is also known as
random-walk betweenness centrality [2]_.
Parameters
----------
G : graph
A NetworkX graph
sources: list of nodes
Nodes to use as sources for current
targets: list of nodes
Nodes to use as sinks for current
normalized : bool, optional (default=True)
If True the betweenness values are normalized by b=b/(n-1)(n-2) where
n is the number of nodes in G.
weight : string or None, optional (default='weight')
Key for edge data used as the edge weight.
If None, then use 1 as each edge weight.
dtype: data type (float)
Default data type for internal matrices.
Set to np.float32 for lower memory consumption.
solver: string (default='lu')
Type of linear solver to use for computing the flow matrix.
Options are "full" (uses most memory), "lu" (recommended), and
"cg" (uses least memory).
Returns
-------
nodes : dictionary
Dictionary of nodes with betweenness centrality as the value.
See Also
--------
approximate_current_flow_betweenness_centrality
betweenness_centrality
edge_betweenness_centrality
edge_current_flow_betweenness_centrality
Notes
-----
Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)`
time [1]_, where `I(n-1)` is the time needed to compute the
inverse Laplacian. For a full matrix this is `O(n^3)` but using
sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the
Laplacian matrix condition number.
The space required is `O(nw) where `w` is the width of the sparse
Laplacian matrix. Worse case is `w=n` for `O(n^2)`.
If the edges have a 'weight' attribute they will be used as
weights in this algorithm. Unspecified weights are set to 1.
References
----------
.. [1] Centrality Measures Based on Current Flow.
Ulrik Brandes and Daniel Fleischer,
Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
.. [2] A measure of betweenness centrality based on random walks,
M. E. J. Newman, Social Networks 27, 39-54 (2005). | networkx/algorithms/centrality/current_flow_betweenness_subset.py | current_flow_betweenness_centrality_subset | AllenDowney/networkx | python | def current_flow_betweenness_centrality_subset(G, sources, targets, normalized=True, weight='weight', dtype=float, solver='lu'):
'Compute current-flow betweenness centrality for subsets of nodes.\n\n Current-flow betweenness centrality uses an electrical current\n model for information spreading in contrast to betweenness\n centrality which uses shortest paths.\n\n Current-flow betweenness centrality is also known as\n random-walk betweenness centrality [2]_.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph \n\n sources: list of nodes\n Nodes to use as sources for current\n\n targets: list of nodes\n Nodes to use as sinks for current\n\n normalized : bool, optional (default=True)\n If True the betweenness values are normalized by b=b/(n-1)(n-2) where\n n is the number of nodes in G.\n\n weight : string or None, optional (default=\'weight\')\n Key for edge data used as the edge weight.\n If None, then use 1 as each edge weight.\n\n dtype: data type (float)\n Default data type for internal matrices.\n Set to np.float32 for lower memory consumption.\n\n solver: string (default=\'lu\')\n Type of linear solver to use for computing the flow matrix.\n Options are "full" (uses most memory), "lu" (recommended), and \n "cg" (uses least memory).\n\n Returns\n -------\n nodes : dictionary\n Dictionary of nodes with betweenness centrality as the value.\n \n See Also\n --------\n approximate_current_flow_betweenness_centrality\n betweenness_centrality\n edge_betweenness_centrality\n edge_current_flow_betweenness_centrality\n\n Notes\n -----\n Current-flow betweenness can be computed in `O(I(n-1)+mn \\log n)`\n time [1]_, where `I(n-1)` is the time needed to compute the \n inverse Laplacian. For a full matrix this is `O(n^3)` but using\n sparse methods you can achieve `O(nm{\\sqrt k})` where `k` is the\n Laplacian matrix condition number. \n\n The space required is `O(nw) where `w` is the width of the sparse\n Laplacian matrix. Worse case is `w=n` for `O(n^2)`.\n\n If the edges have a \'weight\' attribute they will be used as \n weights in this algorithm. Unspecified weights are set to 1.\n\n References\n ----------\n .. [1] Centrality Measures Based on Current Flow. \n Ulrik Brandes and Daniel Fleischer,\n Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS \'05). \n LNCS 3404, pp. 533-544. Springer-Verlag, 2005. \n http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf\n\n .. [2] A measure of betweenness centrality based on random walks,\n M. E. J. Newman, Social Networks 27, 39-54 (2005).\n '
from networkx.utils import reverse_cuthill_mckee_ordering
try:
import numpy as np
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires NumPy ', 'http://scipy.org/')
try:
import scipy
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires SciPy ', 'http://scipy.org/')
if G.is_directed():
raise nx.NetworkXError('current_flow_betweenness_centrality() ', 'not defined for digraphs.')
if (not nx.is_connected(G)):
raise nx.NetworkXError('Graph not connected.')
n = G.number_of_nodes()
ordering = list(reverse_cuthill_mckee_ordering(G))
mapping = dict(zip(ordering, range(n)))
H = nx.relabel_nodes(G, mapping)
betweenness = dict.fromkeys(H, 0.0)
for (row, (s, t)) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver):
for ss in sources:
i = mapping[ss]
for tt in targets:
j = mapping[tt]
betweenness[s] += (0.5 * np.abs((row[i] - row[j])))
betweenness[t] += (0.5 * np.abs((row[i] - row[j])))
if normalized:
nb = ((n - 1.0) * (n - 2.0))
else:
nb = 2.0
for v in H:
betweenness[v] = ((betweenness[v] / nb) + (1.0 / (2 - n)))
return dict(((ordering[k], v) for (k, v) in betweenness.items())) |
def edge_current_flow_betweenness_centrality_subset(G, sources, targets, normalized=True, weight='weight', dtype=float, solver='lu'):
'Compute current-flow betweenness centrality for edges using subsets \n of nodes.\n\n Current-flow betweenness centrality uses an electrical current\n model for information spreading in contrast to betweenness\n centrality which uses shortest paths.\n\n Current-flow betweenness centrality is also known as\n random-walk betweenness centrality [2]_.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph \n\n sources: list of nodes\n Nodes to use as sources for current\n\n targets: list of nodes\n Nodes to use as sinks for current\n\n normalized : bool, optional (default=True)\n If True the betweenness values are normalized by b=b/(n-1)(n-2) where\n n is the number of nodes in G.\n\n weight : string or None, optional (default=\'weight\')\n Key for edge data used as the edge weight.\n If None, then use 1 as each edge weight.\n\n dtype: data type (float)\n Default data type for internal matrices.\n Set to np.float32 for lower memory consumption.\n\n solver: string (default=\'lu\')\n Type of linear solver to use for computing the flow matrix.\n Options are "full" (uses most memory), "lu" (recommended), and \n "cg" (uses least memory).\n\n Returns\n -------\n nodes : dictionary\n Dictionary of edge tuples with betweenness centrality as the value.\n \n See Also\n --------\n betweenness_centrality\n edge_betweenness_centrality\n current_flow_betweenness_centrality\n\n Notes\n -----\n Current-flow betweenness can be computed in `O(I(n-1)+mn \\log n)`\n time [1]_, where `I(n-1)` is the time needed to compute the \n inverse Laplacian. For a full matrix this is `O(n^3)` but using\n sparse methods you can achieve `O(nm{\\sqrt k})` where `k` is the\n Laplacian matrix condition number. \n\n The space required is `O(nw) where `w` is the width of the sparse\n Laplacian matrix. Worse case is `w=n` for `O(n^2)`.\n\n If the edges have a \'weight\' attribute they will be used as \n weights in this algorithm. Unspecified weights are set to 1.\n\n References\n ----------\n .. [1] Centrality Measures Based on Current Flow. \n Ulrik Brandes and Daniel Fleischer,\n Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS \'05). \n LNCS 3404, pp. 533-544. Springer-Verlag, 2005. \n http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf\n\n .. [2] A measure of betweenness centrality based on random walks, \n M. E. J. Newman, Social Networks 27, 39-54 (2005).\n '
from networkx.utils import reverse_cuthill_mckee_ordering
try:
import numpy as np
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires NumPy ', 'http://scipy.org/')
try:
import scipy
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires SciPy ', 'http://scipy.org/')
if G.is_directed():
raise nx.NetworkXError('edge_current_flow_betweenness_centrality ', 'not defined for digraphs.')
if (not nx.is_connected(G)):
raise nx.NetworkXError('Graph not connected.')
n = G.number_of_nodes()
ordering = list(reverse_cuthill_mckee_ordering(G))
mapping = dict(zip(ordering, range(n)))
H = nx.relabel_nodes(G, mapping)
betweenness = dict.fromkeys(H.edges(), 0.0)
if normalized:
nb = ((n - 1.0) * (n - 2.0))
else:
nb = 2.0
for (row, e) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver):
for ss in sources:
i = mapping[ss]
for tt in targets:
j = mapping[tt]
betweenness[e] += (0.5 * np.abs((row[i] - row[j])))
betweenness[e] /= nb
return dict((((ordering[s], ordering[t]), v) for ((s, t), v) in betweenness.items())) | -270,612,836,393,175,170 | Compute current-flow betweenness centrality for edges using subsets
of nodes.
Current-flow betweenness centrality uses an electrical current
model for information spreading in contrast to betweenness
centrality which uses shortest paths.
Current-flow betweenness centrality is also known as
random-walk betweenness centrality [2]_.
Parameters
----------
G : graph
A NetworkX graph
sources: list of nodes
Nodes to use as sources for current
targets: list of nodes
Nodes to use as sinks for current
normalized : bool, optional (default=True)
If True the betweenness values are normalized by b=b/(n-1)(n-2) where
n is the number of nodes in G.
weight : string or None, optional (default='weight')
Key for edge data used as the edge weight.
If None, then use 1 as each edge weight.
dtype: data type (float)
Default data type for internal matrices.
Set to np.float32 for lower memory consumption.
solver: string (default='lu')
Type of linear solver to use for computing the flow matrix.
Options are "full" (uses most memory), "lu" (recommended), and
"cg" (uses least memory).
Returns
-------
nodes : dictionary
Dictionary of edge tuples with betweenness centrality as the value.
See Also
--------
betweenness_centrality
edge_betweenness_centrality
current_flow_betweenness_centrality
Notes
-----
Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)`
time [1]_, where `I(n-1)` is the time needed to compute the
inverse Laplacian. For a full matrix this is `O(n^3)` but using
sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the
Laplacian matrix condition number.
The space required is `O(nw) where `w` is the width of the sparse
Laplacian matrix. Worse case is `w=n` for `O(n^2)`.
If the edges have a 'weight' attribute they will be used as
weights in this algorithm. Unspecified weights are set to 1.
References
----------
.. [1] Centrality Measures Based on Current Flow.
Ulrik Brandes and Daniel Fleischer,
Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
.. [2] A measure of betweenness centrality based on random walks,
M. E. J. Newman, Social Networks 27, 39-54 (2005). | networkx/algorithms/centrality/current_flow_betweenness_subset.py | edge_current_flow_betweenness_centrality_subset | AllenDowney/networkx | python | def edge_current_flow_betweenness_centrality_subset(G, sources, targets, normalized=True, weight='weight', dtype=float, solver='lu'):
'Compute current-flow betweenness centrality for edges using subsets \n of nodes.\n\n Current-flow betweenness centrality uses an electrical current\n model for information spreading in contrast to betweenness\n centrality which uses shortest paths.\n\n Current-flow betweenness centrality is also known as\n random-walk betweenness centrality [2]_.\n\n Parameters\n ----------\n G : graph\n A NetworkX graph \n\n sources: list of nodes\n Nodes to use as sources for current\n\n targets: list of nodes\n Nodes to use as sinks for current\n\n normalized : bool, optional (default=True)\n If True the betweenness values are normalized by b=b/(n-1)(n-2) where\n n is the number of nodes in G.\n\n weight : string or None, optional (default=\'weight\')\n Key for edge data used as the edge weight.\n If None, then use 1 as each edge weight.\n\n dtype: data type (float)\n Default data type for internal matrices.\n Set to np.float32 for lower memory consumption.\n\n solver: string (default=\'lu\')\n Type of linear solver to use for computing the flow matrix.\n Options are "full" (uses most memory), "lu" (recommended), and \n "cg" (uses least memory).\n\n Returns\n -------\n nodes : dictionary\n Dictionary of edge tuples with betweenness centrality as the value.\n \n See Also\n --------\n betweenness_centrality\n edge_betweenness_centrality\n current_flow_betweenness_centrality\n\n Notes\n -----\n Current-flow betweenness can be computed in `O(I(n-1)+mn \\log n)`\n time [1]_, where `I(n-1)` is the time needed to compute the \n inverse Laplacian. For a full matrix this is `O(n^3)` but using\n sparse methods you can achieve `O(nm{\\sqrt k})` where `k` is the\n Laplacian matrix condition number. \n\n The space required is `O(nw) where `w` is the width of the sparse\n Laplacian matrix. Worse case is `w=n` for `O(n^2)`.\n\n If the edges have a \'weight\' attribute they will be used as \n weights in this algorithm. Unspecified weights are set to 1.\n\n References\n ----------\n .. [1] Centrality Measures Based on Current Flow. \n Ulrik Brandes and Daniel Fleischer,\n Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS \'05). \n LNCS 3404, pp. 533-544. Springer-Verlag, 2005. \n http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf\n\n .. [2] A measure of betweenness centrality based on random walks, \n M. E. J. Newman, Social Networks 27, 39-54 (2005).\n '
from networkx.utils import reverse_cuthill_mckee_ordering
try:
import numpy as np
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires NumPy ', 'http://scipy.org/')
try:
import scipy
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires SciPy ', 'http://scipy.org/')
if G.is_directed():
raise nx.NetworkXError('edge_current_flow_betweenness_centrality ', 'not defined for digraphs.')
if (not nx.is_connected(G)):
raise nx.NetworkXError('Graph not connected.')
n = G.number_of_nodes()
ordering = list(reverse_cuthill_mckee_ordering(G))
mapping = dict(zip(ordering, range(n)))
H = nx.relabel_nodes(G, mapping)
betweenness = dict.fromkeys(H.edges(), 0.0)
if normalized:
nb = ((n - 1.0) * (n - 2.0))
else:
nb = 2.0
for (row, e) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver):
for ss in sources:
i = mapping[ss]
for tt in targets:
j = mapping[tt]
betweenness[e] += (0.5 * np.abs((row[i] - row[j])))
betweenness[e] /= nb
return dict((((ordering[s], ordering[t]), v) for ((s, t), v) in betweenness.items())) |
def is_inside_offset(inner, outer):
'Checks if the first offset is contained in the second offset\n\n Args:\n inner: inner offset tuple\n outer: outer offset tuple\n\n Returns: bool'
return (outer[0] <= inner[0] <= inner[1] <= outer[1]) | 6,477,983,249,331,503,000 | Checks if the first offset is contained in the second offset
Args:
inner: inner offset tuple
outer: outer offset tuple
Returns: bool | solcast/nodes.py | is_inside_offset | danhper/py-solc-ast | python | def is_inside_offset(inner, outer):
'Checks if the first offset is contained in the second offset\n\n Args:\n inner: inner offset tuple\n outer: outer offset tuple\n\n Returns: bool'
return (outer[0] <= inner[0] <= inner[1] <= outer[1]) |
def children(self, depth=None, include_self=False, include_parents=True, include_children=True, required_offset=None, offset_limits=None, filters=None, exclude_filter=None):
"Get childen nodes of this node.\n\n Arguments:\n depth: Number of levels of children to traverse. 0 returns only this node.\n include_self: Includes this node in the results.\n include_parents: Includes nodes that match in the results, when they also have\n child nodes that match.\n include_children: If True, as soon as a match is found it's children will not\n be included in the search.\n required_offset: Only match nodes with a source offset that contains this offset.\n offset_limits: Only match nodes when their source offset is contained inside\n this source offset.\n filters: Dictionary of {attribute: value} that children must match. Can also\n be given as a list of dicts, children that match one of the dicts\n will be returned.\n exclude_filter: Dictionary of {attribute:value} that children cannot match.\n\n Returns:\n List of node objects."
if (filters is None):
filters = {}
if (exclude_filter is None):
exclude_filter = {}
if isinstance(filters, dict):
filters = [filters]
filter_fn = functools.partial(_check_filters, required_offset, offset_limits, filters, exclude_filter)
find_fn = functools.partial(_find_children, filter_fn, include_parents, include_children)
result = find_fn(find_fn, depth, self)
if (include_self or (not result) or (result[0] != self)):
return result
return result[1:] | 870,645,974,598,897,000 | Get childen nodes of this node.
Arguments:
depth: Number of levels of children to traverse. 0 returns only this node.
include_self: Includes this node in the results.
include_parents: Includes nodes that match in the results, when they also have
child nodes that match.
include_children: If True, as soon as a match is found it's children will not
be included in the search.
required_offset: Only match nodes with a source offset that contains this offset.
offset_limits: Only match nodes when their source offset is contained inside
this source offset.
filters: Dictionary of {attribute: value} that children must match. Can also
be given as a list of dicts, children that match one of the dicts
will be returned.
exclude_filter: Dictionary of {attribute:value} that children cannot match.
Returns:
List of node objects. | solcast/nodes.py | children | danhper/py-solc-ast | python | def children(self, depth=None, include_self=False, include_parents=True, include_children=True, required_offset=None, offset_limits=None, filters=None, exclude_filter=None):
"Get childen nodes of this node.\n\n Arguments:\n depth: Number of levels of children to traverse. 0 returns only this node.\n include_self: Includes this node in the results.\n include_parents: Includes nodes that match in the results, when they also have\n child nodes that match.\n include_children: If True, as soon as a match is found it's children will not\n be included in the search.\n required_offset: Only match nodes with a source offset that contains this offset.\n offset_limits: Only match nodes when their source offset is contained inside\n this source offset.\n filters: Dictionary of {attribute: value} that children must match. Can also\n be given as a list of dicts, children that match one of the dicts\n will be returned.\n exclude_filter: Dictionary of {attribute:value} that children cannot match.\n\n Returns:\n List of node objects."
if (filters is None):
filters = {}
if (exclude_filter is None):
exclude_filter = {}
if isinstance(filters, dict):
filters = [filters]
filter_fn = functools.partial(_check_filters, required_offset, offset_limits, filters, exclude_filter)
find_fn = functools.partial(_find_children, filter_fn, include_parents, include_children)
result = find_fn(find_fn, depth, self)
if (include_self or (not result) or (result[0] != self)):
return result
return result[1:] |
def parents(self, depth=(- 1), filters=None):
"Get parent nodes of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth.\n filters: Dictionary of {attribute: value} that parents must match.\n\n Returns: list of nodes"
if (filters and (not isinstance(filters, dict))):
raise TypeError('Filters must be a dict')
if (depth < 0):
depth = (self.depth + depth)
if ((depth >= self.depth) or (depth < 0)):
raise IndexError('Given depth exceeds node depth')
node_list = []
parent = self
while True:
parent = parent._parent
if ((not filters) or _check_filter(parent, filters, {})):
node_list.append(parent)
if (parent.depth == depth):
return node_list | -8,765,994,480,727,989,000 | Get parent nodes of this node.
Arguments:
depth: Depth limit. If given as a negative value, it will be subtracted
from this object's depth.
filters: Dictionary of {attribute: value} that parents must match.
Returns: list of nodes | solcast/nodes.py | parents | danhper/py-solc-ast | python | def parents(self, depth=(- 1), filters=None):
"Get parent nodes of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth.\n filters: Dictionary of {attribute: value} that parents must match.\n\n Returns: list of nodes"
if (filters and (not isinstance(filters, dict))):
raise TypeError('Filters must be a dict')
if (depth < 0):
depth = (self.depth + depth)
if ((depth >= self.depth) or (depth < 0)):
raise IndexError('Given depth exceeds node depth')
node_list = []
parent = self
while True:
parent = parent._parent
if ((not filters) or _check_filter(parent, filters, {})):
node_list.append(parent)
if (parent.depth == depth):
return node_list |
def parent(self, depth=(- 1), filters=None):
"Get a parent node of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth. The parent at this exact depth is returned.\n filters: Dictionary of {attribute: value} that the parent must match.\n\n If a filter value is given, will return the first parent that meets the filters\n up to the given depth. If none is found, returns None.\n\n If no filter is given, returns the parent at the given depth."
if (filters and (not isinstance(filters, dict))):
raise TypeError('Filters must be a dict')
if (depth < 0):
depth = (self.depth + depth)
if ((depth >= self.depth) or (depth < 0)):
raise IndexError('Given depth exceeds node depth')
parent = self
while (parent.depth > depth):
parent = parent._parent
if ((parent.depth == depth) and (not filters)):
return parent
if (filters and _check_filter(parent, filters, {})):
return parent
return None | -2,546,208,489,780,907,000 | Get a parent node of this node.
Arguments:
depth: Depth limit. If given as a negative value, it will be subtracted
from this object's depth. The parent at this exact depth is returned.
filters: Dictionary of {attribute: value} that the parent must match.
If a filter value is given, will return the first parent that meets the filters
up to the given depth. If none is found, returns None.
If no filter is given, returns the parent at the given depth. | solcast/nodes.py | parent | danhper/py-solc-ast | python | def parent(self, depth=(- 1), filters=None):
"Get a parent node of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth. The parent at this exact depth is returned.\n filters: Dictionary of {attribute: value} that the parent must match.\n\n If a filter value is given, will return the first parent that meets the filters\n up to the given depth. If none is found, returns None.\n\n If no filter is given, returns the parent at the given depth."
if (filters and (not isinstance(filters, dict))):
raise TypeError('Filters must be a dict')
if (depth < 0):
depth = (self.depth + depth)
if ((depth >= self.depth) or (depth < 0)):
raise IndexError('Given depth exceeds node depth')
parent = self
while (parent.depth > depth):
parent = parent._parent
if ((parent.depth == depth) and (not filters)):
return parent
if (filters and _check_filter(parent, filters, {})):
return parent
return None |
def is_child_of(self, node):
'Checks if this object is a child of the given node object.'
if (node.depth >= self.depth):
return False
return (self.parent(node.depth) == node) | 7,342,769,154,843,425,000 | Checks if this object is a child of the given node object. | solcast/nodes.py | is_child_of | danhper/py-solc-ast | python | def is_child_of(self, node):
if (node.depth >= self.depth):
return False
return (self.parent(node.depth) == node) |
def is_parent_of(self, node):
'Checks if this object is a parent of the given node object.'
if (node.depth <= self.depth):
return False
return (node.parent(self.depth) == self) | 7,078,512,650,039,579,000 | Checks if this object is a parent of the given node object. | solcast/nodes.py | is_parent_of | danhper/py-solc-ast | python | def is_parent_of(self, node):
if (node.depth <= self.depth):
return False
return (node.parent(self.depth) == self) |
def get(self, key, default=None):
'\n Gets an attribute from this node, if that attribute exists.\n\n Arguments:\n key: Field name to return. May contain decimals to return a value\n from a child node.\n default: Default value to return.\n\n Returns: Field value if it exists. Default value if not.\n '
if (key is None):
raise TypeError('Cannot match against None')
obj = self
for k in key.split('.'):
if isinstance(obj, dict):
obj = obj.get(k)
else:
obj = getattr(obj, k, None)
return (obj or default) | 1,159,391,490,741,558,500 | Gets an attribute from this node, if that attribute exists.
Arguments:
key: Field name to return. May contain decimals to return a value
from a child node.
default: Default value to return.
Returns: Field value if it exists. Default value if not. | solcast/nodes.py | get | danhper/py-solc-ast | python | def get(self, key, default=None):
'\n Gets an attribute from this node, if that attribute exists.\n\n Arguments:\n key: Field name to return. May contain decimals to return a value\n from a child node.\n default: Default value to return.\n\n Returns: Field value if it exists. Default value if not.\n '
if (key is None):
raise TypeError('Cannot match against None')
obj = self
for k in key.split('.'):
if isinstance(obj, dict):
obj = obj.get(k)
else:
obj = getattr(obj, k, None)
return (obj or default) |
def pqcost(gencost, ng, on=None):
'Splits the gencost variable into two pieces if costs are given for Qg.\n\n Checks whether C{gencost} has cost information for reactive power\n generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}\n rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves\n C{qcost} empty. Also does some error checking.\n If C{on} is specified (list of indices of generators which are on line)\n it only returns the rows corresponding to these generators.\n\n @author: Ray Zimmerman (PSERC Cornell)\n '
if (on is None):
on = arange(ng)
if (gencost.shape[0] == ng):
pcost = gencost[on, :]
qcost = array([])
elif (gencost.shape[0] == (2 * ng)):
pcost = gencost[on, :]
qcost = gencost[(on + ng), :]
else:
stderr.write('pqcost: gencost has wrong number of rows\n')
return (pcost, qcost) | -4,256,423,973,439,093,000 | Splits the gencost variable into two pieces if costs are given for Qg.
Checks whether C{gencost} has cost information for reactive power
generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}
rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves
C{qcost} empty. Also does some error checking.
If C{on} is specified (list of indices of generators which are on line)
it only returns the rows corresponding to these generators.
@author: Ray Zimmerman (PSERC Cornell) | pandapower/pypower/pqcost.py | pqcost | AdrienGougeon/pandapower | python | def pqcost(gencost, ng, on=None):
'Splits the gencost variable into two pieces if costs are given for Qg.\n\n Checks whether C{gencost} has cost information for reactive power\n generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}\n rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves\n C{qcost} empty. Also does some error checking.\n If C{on} is specified (list of indices of generators which are on line)\n it only returns the rows corresponding to these generators.\n\n @author: Ray Zimmerman (PSERC Cornell)\n '
if (on is None):
on = arange(ng)
if (gencost.shape[0] == ng):
pcost = gencost[on, :]
qcost = array([])
elif (gencost.shape[0] == (2 * ng)):
pcost = gencost[on, :]
qcost = gencost[(on + ng), :]
else:
stderr.write('pqcost: gencost has wrong number of rows\n')
return (pcost, qcost) |
def update_sarsa_table(sarsa, state, action, reward, next_state, next_action, alpha, gamma):
'\n update sarsa state-action pair value, main difference from q learning is that it uses epsilon greedy policy\n return action\n '
next_max = sarsa[(next_state, next_action)]
sarsa[(state, action)] = (sarsa[(state, action)] + (alpha * ((reward + (gamma * next_max)) - sarsa[(state, action)]))) | 5,557,110,078,792,144,000 | update sarsa state-action pair value, main difference from q learning is that it uses epsilon greedy policy
return action | TD/double_q_learning.py | update_sarsa_table | hadleyhzy34/reinforcement_learning | python | def update_sarsa_table(sarsa, state, action, reward, next_state, next_action, alpha, gamma):
'\n update sarsa state-action pair value, main difference from q learning is that it uses epsilon greedy policy\n return action\n '
next_max = sarsa[(next_state, next_action)]
sarsa[(state, action)] = (sarsa[(state, action)] + (alpha * ((reward + (gamma * next_max)) - sarsa[(state, action)]))) |
def epsilon_greedy_policy_sarsa(env, state, sarsa, epsilon):
'\n epsilon greedy policy for q learning to generate actions\n '
if (random.uniform(0, 1) < epsilon):
return env.action_space.sample()
else:
return np.argmax(sarsa[state]) | -8,075,559,200,367,544,000 | epsilon greedy policy for q learning to generate actions | TD/double_q_learning.py | epsilon_greedy_policy_sarsa | hadleyhzy34/reinforcement_learning | python | def epsilon_greedy_policy_sarsa(env, state, sarsa, epsilon):
'\n \n '
if (random.uniform(0, 1) < epsilon):
return env.action_space.sample()
else:
return np.argmax(sarsa[state]) |
def epsilon_greedy_policy(env, state, q, epsilon):
'\n epsilon greedy policy for q learning to generate actions\n '
if (random.uniform(0, 1) < epsilon):
return env.action_space.sample()
else:
return np.argmax(q[state]) | 750,251,734,879,276,300 | epsilon greedy policy for q learning to generate actions | TD/double_q_learning.py | epsilon_greedy_policy | hadleyhzy34/reinforcement_learning | python | def epsilon_greedy_policy(env, state, q, epsilon):
'\n \n '
if (random.uniform(0, 1) < epsilon):
return env.action_space.sample()
else:
return np.argmax(q[state]) |
def extendMarkdown(self, md, md_globals):
' Add MetaPreprocessor to Markdown instance. '
md.preprocessors.add('meta', MetaPreprocessor(md), '>normalize_whitespace') | -7,437,616,332,312,968,000 | Add MetaPreprocessor to Markdown instance. | venv/lib/python3.6/site-packages/markdown/extensions/meta.py | extendMarkdown | AzDan/Sac-Portal | python | def extendMarkdown(self, md, md_globals):
' '
md.preprocessors.add('meta', MetaPreprocessor(md), '>normalize_whitespace') |
def run(self, lines):
' Parse Meta-Data and store in Markdown.Meta. '
meta = {}
key = None
if (lines and BEGIN_RE.match(lines[0])):
lines.pop(0)
while lines:
line = lines.pop(0)
m1 = META_RE.match(line)
if ((line.strip() == '') or END_RE.match(line)):
break
if m1:
key = m1.group('key').lower().strip()
value = m1.group('value').strip()
try:
meta[key].append(value)
except KeyError:
meta[key] = [value]
else:
m2 = META_MORE_RE.match(line)
if (m2 and key):
meta[key].append(m2.group('value').strip())
else:
lines.insert(0, line)
break
self.markdown.Meta = meta
return lines | 4,990,314,545,543,673,000 | Parse Meta-Data and store in Markdown.Meta. | venv/lib/python3.6/site-packages/markdown/extensions/meta.py | run | AzDan/Sac-Portal | python | def run(self, lines):
' '
meta = {}
key = None
if (lines and BEGIN_RE.match(lines[0])):
lines.pop(0)
while lines:
line = lines.pop(0)
m1 = META_RE.match(line)
if ((line.strip() == ) or END_RE.match(line)):
break
if m1:
key = m1.group('key').lower().strip()
value = m1.group('value').strip()
try:
meta[key].append(value)
except KeyError:
meta[key] = [value]
else:
m2 = META_MORE_RE.match(line)
if (m2 and key):
meta[key].append(m2.group('value').strip())
else:
lines.insert(0, line)
break
self.markdown.Meta = meta
return lines |
def thumbIncrementCheck(lmList: list[list[int]]) -> int:
'Checks whether your thumb is up or not.\n No matter what hand you use.\n returns 1 if thumb is up else 0'
count = 0
t_x = lmList[4][1]
p_x = lmList[17][1]
if (t_x > p_x):
if (lmList[4][1] >= lmList[2][1]):
count += 1
elif (lmList[4][1] <= lmList[2][1]):
count += 1
return count | 1,383,995,815,912,717,000 | Checks whether your thumb is up or not.
No matter what hand you use.
returns 1 if thumb is up else 0 | forOutput.py | thumbIncrementCheck | laughingclouds/dt-mst-project | python | def thumbIncrementCheck(lmList: list[list[int]]) -> int:
'Checks whether your thumb is up or not.\n No matter what hand you use.\n returns 1 if thumb is up else 0'
count = 0
t_x = lmList[4][1]
p_x = lmList[17][1]
if (t_x > p_x):
if (lmList[4][1] >= lmList[2][1]):
count += 1
elif (lmList[4][1] <= lmList[2][1]):
count += 1
return count |
def textOutput(count, cc) -> str:
'Returns an appropriate text output depending on\n `count` and `cc`.'
text = 'NOTHING'
if ((count, cc) == (2, 2)):
text = 'SCISSOR'
elif (count == 0):
text = 'ROCK'
elif (count == 5):
text = 'PAPER'
else:
pass
return text | 1,949,855,737,496,309,200 | Returns an appropriate text output depending on
`count` and `cc`. | forOutput.py | textOutput | laughingclouds/dt-mst-project | python | def textOutput(count, cc) -> str:
'Returns an appropriate text output depending on\n `count` and `cc`.'
text = 'NOTHING'
if ((count, cc) == (2, 2)):
text = 'SCISSOR'
elif (count == 0):
text = 'ROCK'
elif (count == 5):
text = 'PAPER'
else:
pass
return text |
def add(self, block):
'Adds block on top of the stack.'
self.register_child(block) | 4,228,037,508,592,754,700 | Adds block on top of the stack. | python/mxnet/gluon/nn/basic_layers.py | add | IIMarch/mxnet | python | def add(self, block):
self.register_child(block) |
def add(self, block):
'Adds block on top of the stack.'
self.register_child(block) | 4,228,037,508,592,754,700 | Adds block on top of the stack. | python/mxnet/gluon/nn/basic_layers.py | add | IIMarch/mxnet | python | def add(self, block):
self.register_child(block) |
def _block_device_mapping_handler(self):
'\n Handles requests which are generated by code similar to:\n\n instance.modify_attribute(\'blockDeviceMapping\', {\'/dev/sda1\': True})\n\n The querystring contains information similar to:\n\n BlockDeviceMapping.1.Ebs.DeleteOnTermination : [\'true\']\n BlockDeviceMapping.1.DeviceName : [\'/dev/sda1\']\n\n For now we only support the "BlockDeviceMapping.1.Ebs.DeleteOnTermination"\n configuration, but it should be trivial to add anything else.\n '
mapping_counter = 1
mapping_device_name_fmt = 'BlockDeviceMapping.%s.DeviceName'
mapping_del_on_term_fmt = 'BlockDeviceMapping.%s.Ebs.DeleteOnTermination'
while True:
mapping_device_name = (mapping_device_name_fmt % mapping_counter)
if (mapping_device_name not in self.querystring.keys()):
break
mapping_del_on_term = (mapping_del_on_term_fmt % mapping_counter)
del_on_term_value_str = self.querystring[mapping_del_on_term][0]
del_on_term_value = (True if ('true' == del_on_term_value_str) else False)
device_name_value = self.querystring[mapping_device_name][0]
instance_ids = instance_ids_from_querystring(self.querystring)
instance_id = instance_ids[0]
instance = self.ec2_backend.get_instance(instance_id)
if self.is_not_dryrun('ModifyInstanceAttribute'):
block_device_type = instance.block_device_mapping[device_name_value]
block_device_type.delete_on_termination = del_on_term_value
mapping_counter += 1
if (mapping_counter > 1):
return EC2_MODIFY_INSTANCE_ATTRIBUTE | -8,310,963,183,193,581,000 | Handles requests which are generated by code similar to:
instance.modify_attribute('blockDeviceMapping', {'/dev/sda1': True})
The querystring contains information similar to:
BlockDeviceMapping.1.Ebs.DeleteOnTermination : ['true']
BlockDeviceMapping.1.DeviceName : ['/dev/sda1']
For now we only support the "BlockDeviceMapping.1.Ebs.DeleteOnTermination"
configuration, but it should be trivial to add anything else. | moto/ec2/responses/instances.py | _block_device_mapping_handler | adtsys-cloud/moto-aws-mock | python | def _block_device_mapping_handler(self):
'\n Handles requests which are generated by code similar to:\n\n instance.modify_attribute(\'blockDeviceMapping\', {\'/dev/sda1\': True})\n\n The querystring contains information similar to:\n\n BlockDeviceMapping.1.Ebs.DeleteOnTermination : [\'true\']\n BlockDeviceMapping.1.DeviceName : [\'/dev/sda1\']\n\n For now we only support the "BlockDeviceMapping.1.Ebs.DeleteOnTermination"\n configuration, but it should be trivial to add anything else.\n '
mapping_counter = 1
mapping_device_name_fmt = 'BlockDeviceMapping.%s.DeviceName'
mapping_del_on_term_fmt = 'BlockDeviceMapping.%s.Ebs.DeleteOnTermination'
while True:
mapping_device_name = (mapping_device_name_fmt % mapping_counter)
if (mapping_device_name not in self.querystring.keys()):
break
mapping_del_on_term = (mapping_del_on_term_fmt % mapping_counter)
del_on_term_value_str = self.querystring[mapping_del_on_term][0]
del_on_term_value = (True if ('true' == del_on_term_value_str) else False)
device_name_value = self.querystring[mapping_device_name][0]
instance_ids = instance_ids_from_querystring(self.querystring)
instance_id = instance_ids[0]
instance = self.ec2_backend.get_instance(instance_id)
if self.is_not_dryrun('ModifyInstanceAttribute'):
block_device_type = instance.block_device_mapping[device_name_value]
block_device_type.delete_on_termination = del_on_term_value
mapping_counter += 1
if (mapping_counter > 1):
return EC2_MODIFY_INSTANCE_ATTRIBUTE |
def get_fs(path, opts=None, rtype='instance'):
"Helper to infer filesystem correctly.\n\n Gets filesystem options from settings and updates them with given `opts`.\n\n Parameters\n ----------\n path: str\n Path for which we want to infer filesystem.\n opts: dict\n Kwargs that will be passed to inferred filesystem instance.\n rtype: str\n Either 'instance' (default) or 'class'.\n "
from drfs.filesystems import FILESYSTEMS
try:
protocol = path.scheme
except AttributeError:
protocol = _get_protocol(path)
try:
cls = FILESYSTEMS[protocol]
if (rtype == 'class'):
return cls
except KeyError:
raise KeyError(f'No filesystem for protocol {protocol}. Try installing it. Available protocols are: {set(FILESYSTEMS.keys())}')
config_scheme_key = (protocol if protocol else 'file')
opts_ = config['fs_opts'][config_scheme_key].get(dict).copy()
if (opts is not None):
opts_.update(opts)
opts_ = _fix_opts_abfs(cls, path, opts_)
return cls(**opts_) | -6,416,019,460,632,777,000 | Helper to infer filesystem correctly.
Gets filesystem options from settings and updates them with given `opts`.
Parameters
----------
path: str
Path for which we want to infer filesystem.
opts: dict
Kwargs that will be passed to inferred filesystem instance.
rtype: str
Either 'instance' (default) or 'class'. | drfs/filesystems/util.py | get_fs | datarevenue-berlin/drfs | python | def get_fs(path, opts=None, rtype='instance'):
"Helper to infer filesystem correctly.\n\n Gets filesystem options from settings and updates them with given `opts`.\n\n Parameters\n ----------\n path: str\n Path for which we want to infer filesystem.\n opts: dict\n Kwargs that will be passed to inferred filesystem instance.\n rtype: str\n Either 'instance' (default) or 'class'.\n "
from drfs.filesystems import FILESYSTEMS
try:
protocol = path.scheme
except AttributeError:
protocol = _get_protocol(path)
try:
cls = FILESYSTEMS[protocol]
if (rtype == 'class'):
return cls
except KeyError:
raise KeyError(f'No filesystem for protocol {protocol}. Try installing it. Available protocols are: {set(FILESYSTEMS.keys())}')
config_scheme_key = (protocol if protocol else 'file')
opts_ = config['fs_opts'][config_scheme_key].get(dict).copy()
if (opts is not None):
opts_.update(opts)
opts_ = _fix_opts_abfs(cls, path, opts_)
return cls(**opts_) |
def allow_pathlib(func):
'Allow methods to receive pathlib.Path objects.\n\n Parameters\n ----------\n func: callable\n function to decorate must have the following signature\n self, path, *args, **kwargs\n\n Returns\n -------\n wrapper: callable\n '
@wraps(func)
def wrapper(self, path, *args, **kwargs):
from drfs.path import asstr
p = asstr(path)
return func(self, p, *args, **kwargs)
return wrapper | 8,810,367,554,903,184,000 | Allow methods to receive pathlib.Path objects.
Parameters
----------
func: callable
function to decorate must have the following signature
self, path, *args, **kwargs
Returns
-------
wrapper: callable | drfs/filesystems/util.py | allow_pathlib | datarevenue-berlin/drfs | python | def allow_pathlib(func):
'Allow methods to receive pathlib.Path objects.\n\n Parameters\n ----------\n func: callable\n function to decorate must have the following signature\n self, path, *args, **kwargs\n\n Returns\n -------\n wrapper: callable\n '
@wraps(func)
def wrapper(self, path, *args, **kwargs):
from drfs.path import asstr
p = asstr(path)
return func(self, p, *args, **kwargs)
return wrapper |
def return_schemes(func):
'Make sure method returns full path with scheme.'
@wraps(func)
def wrapper(self, path, *args, **kwargs):
res = func(self, path, *args, **kwargs)
try:
res = list(map(partial(prepend_scheme, self.scheme), res))
except TypeError:
res = prepend_scheme(self.scheme, res)
return res
return wrapper | 29,942,895,302,851,876 | Make sure method returns full path with scheme. | drfs/filesystems/util.py | return_schemes | datarevenue-berlin/drfs | python | def return_schemes(func):
@wraps(func)
def wrapper(self, path, *args, **kwargs):
res = func(self, path, *args, **kwargs)
try:
res = list(map(partial(prepend_scheme, self.scheme), res))
except TypeError:
res = prepend_scheme(self.scheme, res)
return res
return wrapper |
def maybe_remove_scheme(func):
'Remove scheme from args and kwargs in case underlying fs does not support it.'
@wraps(func)
def wrapper(self, path, *args, **kwargs):
if (not self.supports_scheme):
path = remove_scheme(path, raise_=False)
args = [remove_scheme(a, raise_=False) for a in args]
kwargs = {k: (remove_scheme(v, raise_=False) if isinstance(v, (Path, str)) else v) for (k, v) in kwargs.items()}
return func(self, path, *args, **kwargs)
return wrapper | 6,853,007,073,676,262,000 | Remove scheme from args and kwargs in case underlying fs does not support it. | drfs/filesystems/util.py | maybe_remove_scheme | datarevenue-berlin/drfs | python | def maybe_remove_scheme(func):
@wraps(func)
def wrapper(self, path, *args, **kwargs):
if (not self.supports_scheme):
path = remove_scheme(path, raise_=False)
args = [remove_scheme(a, raise_=False) for a in args]
kwargs = {k: (remove_scheme(v, raise_=False) if isinstance(v, (Path, str)) else v) for (k, v) in kwargs.items()}
return func(self, path, *args, **kwargs)
return wrapper |
def _default_stim_chs(info):
'Return default stim channels for SQD files.'
return pick_types(info, meg=False, ref_meg=False, misc=True, exclude=[])[:8] | -4,594,179,421,141,542,000 | Return default stim channels for SQD files. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | _default_stim_chs | alexisicte/aviate | python | def _default_stim_chs(info):
return pick_types(info, meg=False, ref_meg=False, misc=True, exclude=[])[:8] |
def _make_stim_channel(trigger_chs, slope, threshold, stim_code, trigger_values):
'Create synthetic stim channel from multiple trigger channels.'
if (slope == '+'):
trig_chs_bin = (trigger_chs > threshold)
elif (slope == '-'):
trig_chs_bin = (trigger_chs < threshold)
else:
raise ValueError("slope needs to be '+' or '-'")
if (stim_code == 'binary'):
trigger_values = (2 ** np.arange(len(trigger_chs)))
elif (stim_code != 'channel'):
raise ValueError(("stim_code must be 'binary' or 'channel', got %s" % repr(stim_code)))
trig_chs = (trig_chs_bin * trigger_values[:, np.newaxis])
return np.array(trig_chs.sum(axis=0), ndmin=2) | 7,044,268,555,867,526,000 | Create synthetic stim channel from multiple trigger channels. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | _make_stim_channel | alexisicte/aviate | python | def _make_stim_channel(trigger_chs, slope, threshold, stim_code, trigger_values):
if (slope == '+'):
trig_chs_bin = (trigger_chs > threshold)
elif (slope == '-'):
trig_chs_bin = (trigger_chs < threshold)
else:
raise ValueError("slope needs to be '+' or '-'")
if (stim_code == 'binary'):
trigger_values = (2 ** np.arange(len(trigger_chs)))
elif (stim_code != 'channel'):
raise ValueError(("stim_code must be 'binary' or 'channel', got %s" % repr(stim_code)))
trig_chs = (trig_chs_bin * trigger_values[:, np.newaxis])
return np.array(trig_chs.sum(axis=0), ndmin=2) |
@verbose
def get_kit_info(rawfile, allow_unknown_format, standardize_names=None, verbose=None):
'Extract all the information from the sqd/con file.\n\n Parameters\n ----------\n rawfile : str\n KIT file to be read.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n info : instance of Info\n An Info for the instance.\n sqd : dict\n A dict containing all the sqd parameter settings.\n '
sqd = dict()
sqd['rawfile'] = rawfile
unsupported_format = False
sqd['dirs'] = dirs = list()
with open(rawfile, 'rb', buffering=0) as fid:
dirs.append(_read_dir(fid))
dirs.extend((_read_dir(fid) for _ in range((dirs[0]['count'] - 1))))
assert (len(dirs) == dirs[KIT.DIR_INDEX_DIR]['count'])
fid.seek(dirs[KIT.DIR_INDEX_SYSTEM]['offset'])
(version, revision) = unpack('2i', fid.read((2 * KIT.INT)))
if ((version < 2) or ((version == 2) and (revision < 3))):
version_string = ('V%iR%03i' % (version, revision))
if allow_unknown_format:
unsupported_format = True
logger.warning('Force loading KIT format %s', version_string)
else:
raise UnsupportedKITFormat(version_string, ('SQD file format %s is not officially supported. Set allow_unknown_format=True to load it anyways.' % (version_string,)))
sysid = unpack('i', fid.read(KIT.INT))[0]
system_name = unpack('128s', fid.read(128))[0].decode()
model_name = unpack('128s', fid.read(128))[0].decode()
sqd['nchan'] = channel_count = unpack('i', fid.read(KIT.INT))[0]
comment = unpack('256s', fid.read(256))[0].decode()
(create_time, last_modified_time) = unpack('2i', fid.read((2 * KIT.INT)))
fid.seek((KIT.INT * 3), SEEK_CUR)
dewar_style = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 3), SEEK_CUR)
fll_type = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 3), SEEK_CUR)
trigger_type = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 3), SEEK_CUR)
adboard_type = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 29), SEEK_CUR)
if ((version < 2) or ((version == 2) and (revision <= 3))):
adc_range = float(unpack('i', fid.read(KIT.INT))[0])
else:
adc_range = unpack('d', fid.read(KIT.DOUBLE))[0]
(adc_polarity, adc_allocated, adc_stored) = unpack('3i', fid.read((3 * KIT.INT)))
system_name = system_name.replace('\x00', '')
system_name = system_name.strip().replace('\n', '/')
model_name = model_name.replace('\x00', '')
model_name = model_name.strip().replace('\n', '/')
full_version = f'V{version:d}R{revision:03d}'
logger.debug('SQD file basic information:')
logger.debug('Meg160 version = %s', full_version)
logger.debug('System ID = %i', sysid)
logger.debug('System name = %s', system_name)
logger.debug('Model name = %s', model_name)
logger.debug('Channel count = %i', channel_count)
logger.debug('Comment = %s', comment)
logger.debug('Dewar style = %i', dewar_style)
logger.debug('FLL type = %i', fll_type)
logger.debug('Trigger type = %i', trigger_type)
logger.debug('A/D board type = %i', adboard_type)
logger.debug('ADC range = +/-%s[V]', (adc_range / 2.0))
logger.debug('ADC allocate = %i[bit]', adc_allocated)
logger.debug('ADC bit = %i[bit]', adc_stored)
description = f'{system_name} ({sysid}) {full_version} {model_name}'
sqd['dtype'] = np.dtype(getattr(np, f'int{adc_allocated}'))
if (fll_type not in KIT.FLL_SETTINGS):
fll_types = sorted(KIT.FLL_SETTINGS.keys())
use_fll_type = fll_types[(np.searchsorted(fll_types, fll_type) - 1)]
warn(('Unknown site filter settings (FLL) for system "%s" model "%s" (ID %s), will assume FLL %d->%d, check your data for correctness, including channel scales and filter settings!' % (system_name, model_name, sysid, fll_type, use_fll_type)))
fll_type = use_fll_type
chan_dir = dirs[KIT.DIR_INDEX_CHANNELS]
(chan_offset, chan_size) = (chan_dir['offset'], chan_dir['size'])
sqd['channels'] = channels = []
exg_gains = list()
for i in range(channel_count):
fid.seek((chan_offset + (chan_size * i)))
(channel_type,) = unpack('i', fid.read(KIT.INT))
if ((sysid == 52) and (i < 160) and (channel_type == KIT.CHANNEL_NULL)):
channel_type = KIT.CHANNEL_MAGNETOMETER_REFERENCE
if (channel_type in KIT.CHANNELS_MEG):
if (channel_type not in KIT.CH_TO_FIFF_COIL):
raise NotImplementedError(('KIT channel type %i can not be read. Please contact the mne-python developers.' % channel_type))
channels.append({'type': channel_type, 'loc': np.fromfile(fid, dtype='d', count=5)})
if (channel_type in KIT.CHANNEL_NAME_NCHAR):
fid.seek(16, SEEK_CUR)
channels[(- 1)]['name'] = _read_name(fid, channel_type)
elif (channel_type in KIT.CHANNELS_MISC):
(channel_no,) = unpack('i', fid.read(KIT.INT))
fid.seek(4, SEEK_CUR)
name = _read_name(fid, channel_type)
channels.append({'type': channel_type, 'no': channel_no, 'name': name})
if (channel_type in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG)):
offset = (6 if (channel_type == KIT.CHANNEL_EEG) else 8)
fid.seek(offset, SEEK_CUR)
exg_gains.append(np.fromfile(fid, 'd', 1)[0])
elif (channel_type == KIT.CHANNEL_NULL):
channels.append({'type': channel_type})
else:
raise IOError(('Unknown KIT channel type: %i' % channel_type))
exg_gains = np.array(exg_gains)
fid.seek(dirs[KIT.DIR_INDEX_CALIBRATION]['offset'])
sensitivity = np.fromfile(fid, dtype='d', count=(channel_count * 2))
sensitivity.shape = (channel_count, 2)
(channel_offset, channel_gain) = sensitivity.T
assert (channel_offset == 0).all()
fid.seek(dirs[KIT.DIR_INDEX_AMP_FILTER]['offset'])
amp_data = unpack('i', fid.read(KIT.INT))[0]
if (fll_type >= 100):
gain1 = ((amp_data & 28672) >> 12)
gain2 = ((amp_data & 1879048192) >> 28)
gain3 = ((amp_data & 117440512) >> 24)
amp_gain = ((KIT.GAINS[gain1] * KIT.GAINS[gain2]) * KIT.GAINS[gain3])
hpf = ((amp_data & 1792) >> 8)
lpf = ((amp_data & 458752) >> 16)
bef = ((amp_data & 3) >> 0)
else:
input_gain = ((amp_data & 6144) >> 11)
output_gain = ((amp_data & 7) >> 0)
amp_gain = (KIT.GAINS[input_gain] * KIT.GAINS[output_gain])
hpf = ((amp_data & 7) >> 4)
lpf = ((amp_data & 1792) >> 8)
bef = ((amp_data & 49152) >> 14)
(hpf_options, lpf_options, bef_options) = KIT.FLL_SETTINGS[fll_type]
sqd['highpass'] = KIT.HPFS[hpf_options][hpf]
sqd['lowpass'] = KIT.LPFS[lpf_options][lpf]
sqd['notch'] = KIT.BEFS[bef_options][bef]
fid.seek(dirs[KIT.DIR_INDEX_ACQ_COND]['offset'])
(sqd['acq_type'],) = (acq_type,) = unpack('i', fid.read(KIT.INT))
(sqd['sfreq'],) = unpack('d', fid.read(KIT.DOUBLE))
if (acq_type == KIT.CONTINUOUS):
fid.seek(KIT.INT, SEEK_CUR)
(sqd['n_samples'],) = unpack('i', fid.read(KIT.INT))
elif ((acq_type == KIT.EVOKED) or (acq_type == KIT.EPOCHS)):
(sqd['frame_length'],) = unpack('i', fid.read(KIT.INT))
(sqd['pretrigger_length'],) = unpack('i', fid.read(KIT.INT))
(sqd['average_count'],) = unpack('i', fid.read(KIT.INT))
(sqd['n_epochs'],) = unpack('i', fid.read(KIT.INT))
if (acq_type == KIT.EVOKED):
sqd['n_samples'] = sqd['frame_length']
else:
sqd['n_samples'] = (sqd['frame_length'] * sqd['n_epochs'])
else:
raise IOError(('Invalid acquisition type: %i. Your file is neither continuous nor epoched data.' % (acq_type,)))
dig_dir = dirs[KIT.DIR_INDEX_DIG_POINTS]
cor_dir = dirs[KIT.DIR_INDEX_COREG]
dig = dict()
hsp = list()
if ((dig_dir['count'] > 0) and (cor_dir['count'] > 0)):
fid.seek(dig_dir['offset'])
for _ in range(dig_dir['count']):
name = _read_name(fid, n=8).strip()
name = name.lower()
rr = np.fromfile(fid, 'd', 3)
if name:
assert (name not in dig)
dig[name] = rr
else:
hsp.append(rr)
elp = [dig.pop(key) for key in ('fidnz', 'fidt9', 'fidt10', 'hpi_1', 'hpi_2', 'hpi_3', 'hpi_4')]
if (('hpi_5' in dig) and dig['hpi_5'].any()):
elp.append(dig.pop('hpi_5'))
elp = np.array(elp)
hsp = np.array(hsp, float).reshape((- 1), 3)
assert (elp.shape in ((7, 3), (8, 3)))
fid.seek(cor_dir['offset'])
mrk = np.zeros(((elp.shape[0] - 3), 3))
for _ in range(cor_dir['count']):
done = np.fromfile(fid, np.int32, 1)[0]
fid.seek(((16 * KIT.DOUBLE) + (16 * KIT.DOUBLE)), SEEK_CUR)
marker_count = np.fromfile(fid, np.int32, 1)[0]
if (not done):
continue
assert (marker_count >= len(mrk))
for mi in range(len(mrk)):
(mri_type, meg_type, mri_done, meg_done) = np.fromfile(fid, np.int32, 4)
assert meg_done
fid.seek((3 * KIT.DOUBLE), SEEK_CUR)
mrk[mi] = np.fromfile(fid, 'd', 3)
fid.seek(256, SEEK_CUR)
sqd.update(hsp=hsp, elp=elp, mrk=mrk)
all_names = set((ch.get('name', '') for ch in channels))
if ((standardize_names is None) and all_names.difference({'', 'EEG'})):
standardize_names = True
warn('standardize_names defaults to True in 0.21 but will change to False in 0.22', DeprecationWarning)
if unsupported_format:
if (sysid not in LEGACY_AMP_PARAMS):
raise IOError(('Legacy parameters for system ID %i unavailable' % (sysid,)))
(adc_range, adc_stored) = LEGACY_AMP_PARAMS[sysid]
is_meg = np.array([(ch['type'] in KIT.CHANNELS_MEG) for ch in channels])
ad_to_volt = (adc_range / (2 ** adc_stored))
ad_to_tesla = ((ad_to_volt / amp_gain) * channel_gain)
conv_factor = np.where(is_meg, ad_to_tesla, ad_to_volt)
is_exg = [(ch['type'] in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG)) for ch in channels]
exg_gains /= (2 ** (adc_stored - 14))
conv_factor[is_exg] = exg_gains
sqd['conv_factor'] = conv_factor[:, np.newaxis]
info = _empty_info(float(sqd['sfreq']))
info.update(meas_date=_stamp_to_dt((create_time, 0)), lowpass=sqd['lowpass'], highpass=sqd['highpass'], kit_system_id=sysid, description=description)
logger.info('Setting channel info structure...')
info['chs'] = fiff_channels = []
channel_index = defaultdict((lambda : 0))
sqd['eeg_dig'] = OrderedDict()
for (idx, ch) in enumerate(channels, 1):
if (ch['type'] in KIT.CHANNELS_MEG):
ch_name = ch.get('name', '')
if ((ch_name == '') or standardize_names):
ch_name = ('MEG %03d' % idx)
(theta, phi) = np.radians(ch['loc'][3:])
x = (sin(theta) * cos(phi))
y = (sin(theta) * sin(phi))
z = cos(theta)
vec_z = np.array([x, y, z])
vec_z /= linalg.norm(vec_z)
vec_x = np.zeros(vec_z.size, dtype=np.float64)
if (vec_z[1] < vec_z[2]):
if (vec_z[0] < vec_z[1]):
vec_x[0] = 1.0
else:
vec_x[1] = 1.0
elif (vec_z[0] < vec_z[2]):
vec_x[0] = 1.0
else:
vec_x[2] = 1.0
vec_x -= (np.sum((vec_x * vec_z)) * vec_z)
vec_x /= linalg.norm(vec_x)
vec_y = np.cross(vec_z, vec_x)
vecs = np.vstack((ch['loc'][:3], vec_x, vec_y, vec_z))
vecs = apply_trans(als_ras_trans, vecs)
unit = FIFF.FIFF_UNIT_T
loc = vecs.ravel()
else:
ch_type_label = KIT.CH_LABEL[ch['type']]
channel_index[ch_type_label] += 1
ch_type_index = channel_index[ch_type_label]
ch_name = ch.get('name', '')
eeg_name = ch_name.lower()
if ((ch_name in ('', 'EEG')) or standardize_names):
ch_name = ('%s %03i' % (ch_type_label, ch_type_index))
unit = FIFF.FIFF_UNIT_V
loc = np.zeros(12)
if (eeg_name and (eeg_name in dig)):
loc[:3] = sqd['eeg_dig'][eeg_name] = dig[eeg_name]
fiff_channels.append(dict(cal=KIT.CALIB_FACTOR, logno=idx, scanno=idx, range=KIT.RANGE, unit=unit, unit_mul=KIT.UNIT_MUL, ch_name=ch_name, coord_frame=FIFF.FIFFV_COORD_DEVICE, coil_type=KIT.CH_TO_FIFF_COIL[ch['type']], kind=KIT.CH_TO_FIFF_KIND[ch['type']], loc=loc))
info._update_redundant()
return (info, sqd) | -8,458,605,956,946,132,000 | Extract all the information from the sqd/con file.
Parameters
----------
rawfile : str
KIT file to be read.
allow_unknown_format : bool
Force reading old data that is not officially supported. Alternatively,
read and re-save the data with the KIT MEG Laboratory application.
%(standardize_names)s
%(verbose)s
Returns
-------
info : instance of Info
An Info for the instance.
sqd : dict
A dict containing all the sqd parameter settings. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | get_kit_info | alexisicte/aviate | python | @verbose
def get_kit_info(rawfile, allow_unknown_format, standardize_names=None, verbose=None):
'Extract all the information from the sqd/con file.\n\n Parameters\n ----------\n rawfile : str\n KIT file to be read.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n info : instance of Info\n An Info for the instance.\n sqd : dict\n A dict containing all the sqd parameter settings.\n '
sqd = dict()
sqd['rawfile'] = rawfile
unsupported_format = False
sqd['dirs'] = dirs = list()
with open(rawfile, 'rb', buffering=0) as fid:
dirs.append(_read_dir(fid))
dirs.extend((_read_dir(fid) for _ in range((dirs[0]['count'] - 1))))
assert (len(dirs) == dirs[KIT.DIR_INDEX_DIR]['count'])
fid.seek(dirs[KIT.DIR_INDEX_SYSTEM]['offset'])
(version, revision) = unpack('2i', fid.read((2 * KIT.INT)))
if ((version < 2) or ((version == 2) and (revision < 3))):
version_string = ('V%iR%03i' % (version, revision))
if allow_unknown_format:
unsupported_format = True
logger.warning('Force loading KIT format %s', version_string)
else:
raise UnsupportedKITFormat(version_string, ('SQD file format %s is not officially supported. Set allow_unknown_format=True to load it anyways.' % (version_string,)))
sysid = unpack('i', fid.read(KIT.INT))[0]
system_name = unpack('128s', fid.read(128))[0].decode()
model_name = unpack('128s', fid.read(128))[0].decode()
sqd['nchan'] = channel_count = unpack('i', fid.read(KIT.INT))[0]
comment = unpack('256s', fid.read(256))[0].decode()
(create_time, last_modified_time) = unpack('2i', fid.read((2 * KIT.INT)))
fid.seek((KIT.INT * 3), SEEK_CUR)
dewar_style = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 3), SEEK_CUR)
fll_type = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 3), SEEK_CUR)
trigger_type = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 3), SEEK_CUR)
adboard_type = unpack('i', fid.read(KIT.INT))[0]
fid.seek((KIT.INT * 29), SEEK_CUR)
if ((version < 2) or ((version == 2) and (revision <= 3))):
adc_range = float(unpack('i', fid.read(KIT.INT))[0])
else:
adc_range = unpack('d', fid.read(KIT.DOUBLE))[0]
(adc_polarity, adc_allocated, adc_stored) = unpack('3i', fid.read((3 * KIT.INT)))
system_name = system_name.replace('\x00', )
system_name = system_name.strip().replace('\n', '/')
model_name = model_name.replace('\x00', )
model_name = model_name.strip().replace('\n', '/')
full_version = f'V{version:d}R{revision:03d}'
logger.debug('SQD file basic information:')
logger.debug('Meg160 version = %s', full_version)
logger.debug('System ID = %i', sysid)
logger.debug('System name = %s', system_name)
logger.debug('Model name = %s', model_name)
logger.debug('Channel count = %i', channel_count)
logger.debug('Comment = %s', comment)
logger.debug('Dewar style = %i', dewar_style)
logger.debug('FLL type = %i', fll_type)
logger.debug('Trigger type = %i', trigger_type)
logger.debug('A/D board type = %i', adboard_type)
logger.debug('ADC range = +/-%s[V]', (adc_range / 2.0))
logger.debug('ADC allocate = %i[bit]', adc_allocated)
logger.debug('ADC bit = %i[bit]', adc_stored)
description = f'{system_name} ({sysid}) {full_version} {model_name}'
sqd['dtype'] = np.dtype(getattr(np, f'int{adc_allocated}'))
if (fll_type not in KIT.FLL_SETTINGS):
fll_types = sorted(KIT.FLL_SETTINGS.keys())
use_fll_type = fll_types[(np.searchsorted(fll_types, fll_type) - 1)]
warn(('Unknown site filter settings (FLL) for system "%s" model "%s" (ID %s), will assume FLL %d->%d, check your data for correctness, including channel scales and filter settings!' % (system_name, model_name, sysid, fll_type, use_fll_type)))
fll_type = use_fll_type
chan_dir = dirs[KIT.DIR_INDEX_CHANNELS]
(chan_offset, chan_size) = (chan_dir['offset'], chan_dir['size'])
sqd['channels'] = channels = []
exg_gains = list()
for i in range(channel_count):
fid.seek((chan_offset + (chan_size * i)))
(channel_type,) = unpack('i', fid.read(KIT.INT))
if ((sysid == 52) and (i < 160) and (channel_type == KIT.CHANNEL_NULL)):
channel_type = KIT.CHANNEL_MAGNETOMETER_REFERENCE
if (channel_type in KIT.CHANNELS_MEG):
if (channel_type not in KIT.CH_TO_FIFF_COIL):
raise NotImplementedError(('KIT channel type %i can not be read. Please contact the mne-python developers.' % channel_type))
channels.append({'type': channel_type, 'loc': np.fromfile(fid, dtype='d', count=5)})
if (channel_type in KIT.CHANNEL_NAME_NCHAR):
fid.seek(16, SEEK_CUR)
channels[(- 1)]['name'] = _read_name(fid, channel_type)
elif (channel_type in KIT.CHANNELS_MISC):
(channel_no,) = unpack('i', fid.read(KIT.INT))
fid.seek(4, SEEK_CUR)
name = _read_name(fid, channel_type)
channels.append({'type': channel_type, 'no': channel_no, 'name': name})
if (channel_type in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG)):
offset = (6 if (channel_type == KIT.CHANNEL_EEG) else 8)
fid.seek(offset, SEEK_CUR)
exg_gains.append(np.fromfile(fid, 'd', 1)[0])
elif (channel_type == KIT.CHANNEL_NULL):
channels.append({'type': channel_type})
else:
raise IOError(('Unknown KIT channel type: %i' % channel_type))
exg_gains = np.array(exg_gains)
fid.seek(dirs[KIT.DIR_INDEX_CALIBRATION]['offset'])
sensitivity = np.fromfile(fid, dtype='d', count=(channel_count * 2))
sensitivity.shape = (channel_count, 2)
(channel_offset, channel_gain) = sensitivity.T
assert (channel_offset == 0).all()
fid.seek(dirs[KIT.DIR_INDEX_AMP_FILTER]['offset'])
amp_data = unpack('i', fid.read(KIT.INT))[0]
if (fll_type >= 100):
gain1 = ((amp_data & 28672) >> 12)
gain2 = ((amp_data & 1879048192) >> 28)
gain3 = ((amp_data & 117440512) >> 24)
amp_gain = ((KIT.GAINS[gain1] * KIT.GAINS[gain2]) * KIT.GAINS[gain3])
hpf = ((amp_data & 1792) >> 8)
lpf = ((amp_data & 458752) >> 16)
bef = ((amp_data & 3) >> 0)
else:
input_gain = ((amp_data & 6144) >> 11)
output_gain = ((amp_data & 7) >> 0)
amp_gain = (KIT.GAINS[input_gain] * KIT.GAINS[output_gain])
hpf = ((amp_data & 7) >> 4)
lpf = ((amp_data & 1792) >> 8)
bef = ((amp_data & 49152) >> 14)
(hpf_options, lpf_options, bef_options) = KIT.FLL_SETTINGS[fll_type]
sqd['highpass'] = KIT.HPFS[hpf_options][hpf]
sqd['lowpass'] = KIT.LPFS[lpf_options][lpf]
sqd['notch'] = KIT.BEFS[bef_options][bef]
fid.seek(dirs[KIT.DIR_INDEX_ACQ_COND]['offset'])
(sqd['acq_type'],) = (acq_type,) = unpack('i', fid.read(KIT.INT))
(sqd['sfreq'],) = unpack('d', fid.read(KIT.DOUBLE))
if (acq_type == KIT.CONTINUOUS):
fid.seek(KIT.INT, SEEK_CUR)
(sqd['n_samples'],) = unpack('i', fid.read(KIT.INT))
elif ((acq_type == KIT.EVOKED) or (acq_type == KIT.EPOCHS)):
(sqd['frame_length'],) = unpack('i', fid.read(KIT.INT))
(sqd['pretrigger_length'],) = unpack('i', fid.read(KIT.INT))
(sqd['average_count'],) = unpack('i', fid.read(KIT.INT))
(sqd['n_epochs'],) = unpack('i', fid.read(KIT.INT))
if (acq_type == KIT.EVOKED):
sqd['n_samples'] = sqd['frame_length']
else:
sqd['n_samples'] = (sqd['frame_length'] * sqd['n_epochs'])
else:
raise IOError(('Invalid acquisition type: %i. Your file is neither continuous nor epoched data.' % (acq_type,)))
dig_dir = dirs[KIT.DIR_INDEX_DIG_POINTS]
cor_dir = dirs[KIT.DIR_INDEX_COREG]
dig = dict()
hsp = list()
if ((dig_dir['count'] > 0) and (cor_dir['count'] > 0)):
fid.seek(dig_dir['offset'])
for _ in range(dig_dir['count']):
name = _read_name(fid, n=8).strip()
name = name.lower()
rr = np.fromfile(fid, 'd', 3)
if name:
assert (name not in dig)
dig[name] = rr
else:
hsp.append(rr)
elp = [dig.pop(key) for key in ('fidnz', 'fidt9', 'fidt10', 'hpi_1', 'hpi_2', 'hpi_3', 'hpi_4')]
if (('hpi_5' in dig) and dig['hpi_5'].any()):
elp.append(dig.pop('hpi_5'))
elp = np.array(elp)
hsp = np.array(hsp, float).reshape((- 1), 3)
assert (elp.shape in ((7, 3), (8, 3)))
fid.seek(cor_dir['offset'])
mrk = np.zeros(((elp.shape[0] - 3), 3))
for _ in range(cor_dir['count']):
done = np.fromfile(fid, np.int32, 1)[0]
fid.seek(((16 * KIT.DOUBLE) + (16 * KIT.DOUBLE)), SEEK_CUR)
marker_count = np.fromfile(fid, np.int32, 1)[0]
if (not done):
continue
assert (marker_count >= len(mrk))
for mi in range(len(mrk)):
(mri_type, meg_type, mri_done, meg_done) = np.fromfile(fid, np.int32, 4)
assert meg_done
fid.seek((3 * KIT.DOUBLE), SEEK_CUR)
mrk[mi] = np.fromfile(fid, 'd', 3)
fid.seek(256, SEEK_CUR)
sqd.update(hsp=hsp, elp=elp, mrk=mrk)
all_names = set((ch.get('name', ) for ch in channels))
if ((standardize_names is None) and all_names.difference({, 'EEG'})):
standardize_names = True
warn('standardize_names defaults to True in 0.21 but will change to False in 0.22', DeprecationWarning)
if unsupported_format:
if (sysid not in LEGACY_AMP_PARAMS):
raise IOError(('Legacy parameters for system ID %i unavailable' % (sysid,)))
(adc_range, adc_stored) = LEGACY_AMP_PARAMS[sysid]
is_meg = np.array([(ch['type'] in KIT.CHANNELS_MEG) for ch in channels])
ad_to_volt = (adc_range / (2 ** adc_stored))
ad_to_tesla = ((ad_to_volt / amp_gain) * channel_gain)
conv_factor = np.where(is_meg, ad_to_tesla, ad_to_volt)
is_exg = [(ch['type'] in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG)) for ch in channels]
exg_gains /= (2 ** (adc_stored - 14))
conv_factor[is_exg] = exg_gains
sqd['conv_factor'] = conv_factor[:, np.newaxis]
info = _empty_info(float(sqd['sfreq']))
info.update(meas_date=_stamp_to_dt((create_time, 0)), lowpass=sqd['lowpass'], highpass=sqd['highpass'], kit_system_id=sysid, description=description)
logger.info('Setting channel info structure...')
info['chs'] = fiff_channels = []
channel_index = defaultdict((lambda : 0))
sqd['eeg_dig'] = OrderedDict()
for (idx, ch) in enumerate(channels, 1):
if (ch['type'] in KIT.CHANNELS_MEG):
ch_name = ch.get('name', )
if ((ch_name == ) or standardize_names):
ch_name = ('MEG %03d' % idx)
(theta, phi) = np.radians(ch['loc'][3:])
x = (sin(theta) * cos(phi))
y = (sin(theta) * sin(phi))
z = cos(theta)
vec_z = np.array([x, y, z])
vec_z /= linalg.norm(vec_z)
vec_x = np.zeros(vec_z.size, dtype=np.float64)
if (vec_z[1] < vec_z[2]):
if (vec_z[0] < vec_z[1]):
vec_x[0] = 1.0
else:
vec_x[1] = 1.0
elif (vec_z[0] < vec_z[2]):
vec_x[0] = 1.0
else:
vec_x[2] = 1.0
vec_x -= (np.sum((vec_x * vec_z)) * vec_z)
vec_x /= linalg.norm(vec_x)
vec_y = np.cross(vec_z, vec_x)
vecs = np.vstack((ch['loc'][:3], vec_x, vec_y, vec_z))
vecs = apply_trans(als_ras_trans, vecs)
unit = FIFF.FIFF_UNIT_T
loc = vecs.ravel()
else:
ch_type_label = KIT.CH_LABEL[ch['type']]
channel_index[ch_type_label] += 1
ch_type_index = channel_index[ch_type_label]
ch_name = ch.get('name', )
eeg_name = ch_name.lower()
if ((ch_name in (, 'EEG')) or standardize_names):
ch_name = ('%s %03i' % (ch_type_label, ch_type_index))
unit = FIFF.FIFF_UNIT_V
loc = np.zeros(12)
if (eeg_name and (eeg_name in dig)):
loc[:3] = sqd['eeg_dig'][eeg_name] = dig[eeg_name]
fiff_channels.append(dict(cal=KIT.CALIB_FACTOR, logno=idx, scanno=idx, range=KIT.RANGE, unit=unit, unit_mul=KIT.UNIT_MUL, ch_name=ch_name, coord_frame=FIFF.FIFFV_COORD_DEVICE, coil_type=KIT.CH_TO_FIFF_COIL[ch['type']], kind=KIT.CH_TO_FIFF_KIND[ch['type']], loc=loc))
info._update_redundant()
return (info, sqd) |
@fill_doc
def read_raw_kit(input_fname, mrk=None, elp=None, hsp=None, stim='>', slope='-', stimthresh=1, preload=False, stim_code='binary', allow_unknown_format=False, standardize_names=None, verbose=None):
"Reader function for Ricoh/KIT conversion to FIF.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n stim : list of int | '<' | '>'\n Channel-value correspondence when converting KIT trigger channels to a\n Neuromag-style stim channel. For '<', the largest values are assigned\n to the first channel (default). For '>', the largest values are\n assigned to the last channel. Can also be specified as a list of\n trigger channel indexes.\n slope : '+' | '-'\n How to interpret values on KIT trigger channels when synthesizing a\n Neuromag-style stim channel. With '+', a positive slope (low-to-high)\n is interpreted as an event. With '-', a negative slope (high-to-low)\n is interpreted as an event.\n stimthresh : float\n The threshold level for accepting voltage changes in KIT trigger\n channels as a trigger event.\n %(preload)s\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n raw : instance of RawKIT\n A Raw object containing KIT data.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n\n Notes\n -----\n If mrk, hsp or elp are array_like inputs, then the numbers in xyz\n coordinates should be in units of meters.\n "
return RawKIT(input_fname=input_fname, mrk=mrk, elp=elp, hsp=hsp, stim=stim, slope=slope, stimthresh=stimthresh, preload=preload, stim_code=stim_code, allow_unknown_format=allow_unknown_format, standardize_names=standardize_names, verbose=verbose) | -419,446,750,408,291,400 | Reader function for Ricoh/KIT conversion to FIF.
Parameters
----------
input_fname : str
Path to the sqd file.
mrk : None | str | array_like, shape (5, 3) | list of str or array_like
Marker points representing the location of the marker coils with
respect to the MEG Sensors, or path to a marker file.
If list, all of the markers will be averaged together.
elp : None | str | array_like, shape (8, 3)
Digitizer points representing the location of the fiducials and the
marker coils with respect to the digitized head shape, or path to a
file containing these points.
hsp : None | str | array, shape (n_points, 3)
Digitizer head shape points, or path to head shape file. If more than
10,000 points are in the head shape, they are automatically decimated.
stim : list of int | '<' | '>'
Channel-value correspondence when converting KIT trigger channels to a
Neuromag-style stim channel. For '<', the largest values are assigned
to the first channel (default). For '>', the largest values are
assigned to the last channel. Can also be specified as a list of
trigger channel indexes.
slope : '+' | '-'
How to interpret values on KIT trigger channels when synthesizing a
Neuromag-style stim channel. With '+', a positive slope (low-to-high)
is interpreted as an event. With '-', a negative slope (high-to-low)
is interpreted as an event.
stimthresh : float
The threshold level for accepting voltage changes in KIT trigger
channels as a trigger event.
%(preload)s
stim_code : 'binary' | 'channel'
How to decode trigger values from stim channels. 'binary' read stim
channel events as binary code, 'channel' encodes channel number.
allow_unknown_format : bool
Force reading old data that is not officially supported. Alternatively,
read and re-save the data with the KIT MEG Laboratory application.
%(standardize_names)s
%(verbose)s
Returns
-------
raw : instance of RawKIT
A Raw object containing KIT data.
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
Notes
-----
If mrk, hsp or elp are array_like inputs, then the numbers in xyz
coordinates should be in units of meters. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | read_raw_kit | alexisicte/aviate | python | @fill_doc
def read_raw_kit(input_fname, mrk=None, elp=None, hsp=None, stim='>', slope='-', stimthresh=1, preload=False, stim_code='binary', allow_unknown_format=False, standardize_names=None, verbose=None):
"Reader function for Ricoh/KIT conversion to FIF.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n stim : list of int | '<' | '>'\n Channel-value correspondence when converting KIT trigger channels to a\n Neuromag-style stim channel. For '<', the largest values are assigned\n to the first channel (default). For '>', the largest values are\n assigned to the last channel. Can also be specified as a list of\n trigger channel indexes.\n slope : '+' | '-'\n How to interpret values on KIT trigger channels when synthesizing a\n Neuromag-style stim channel. With '+', a positive slope (low-to-high)\n is interpreted as an event. With '-', a negative slope (high-to-low)\n is interpreted as an event.\n stimthresh : float\n The threshold level for accepting voltage changes in KIT trigger\n channels as a trigger event.\n %(preload)s\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n raw : instance of RawKIT\n A Raw object containing KIT data.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n\n Notes\n -----\n If mrk, hsp or elp are array_like inputs, then the numbers in xyz\n coordinates should be in units of meters.\n "
return RawKIT(input_fname=input_fname, mrk=mrk, elp=elp, hsp=hsp, stim=stim, slope=slope, stimthresh=stimthresh, preload=preload, stim_code=stim_code, allow_unknown_format=allow_unknown_format, standardize_names=standardize_names, verbose=verbose) |
@fill_doc
def read_epochs_kit(input_fname, events, event_id=None, mrk=None, elp=None, hsp=None, allow_unknown_format=False, standardize_names=None, verbose=None):
"Reader function for Ricoh/KIT epochs files.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n events : array, shape (n_events, 3)\n The events typically returned by the read_events function.\n If some events don't match the events of interest as specified\n by event_id, they will be marked as 'IGNORED' in the drop log.\n event_id : int | list of int | dict | None\n The id of the event to consider. If dict,\n the keys can later be used to access associated events. Example:\n dict(auditory=1, visual=3). If int, a dict will be created with\n the id as string. If a list, all events with the IDs specified\n in the list are used. If None, all events will be used with\n and a dict is created with string integer names corresponding\n to the event id integers.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n epochs : instance of Epochs\n The epochs.\n\n Notes\n -----\n .. versionadded:: 0.9.0\n "
epochs = EpochsKIT(input_fname=input_fname, events=events, event_id=event_id, mrk=mrk, elp=elp, hsp=hsp, allow_unknown_format=allow_unknown_format, standardize_names=standardize_names, verbose=verbose)
return epochs | 2,132,265,082,483,621,600 | Reader function for Ricoh/KIT epochs files.
Parameters
----------
input_fname : str
Path to the sqd file.
events : array, shape (n_events, 3)
The events typically returned by the read_events function.
If some events don't match the events of interest as specified
by event_id, they will be marked as 'IGNORED' in the drop log.
event_id : int | list of int | dict | None
The id of the event to consider. If dict,
the keys can later be used to access associated events. Example:
dict(auditory=1, visual=3). If int, a dict will be created with
the id as string. If a list, all events with the IDs specified
in the list are used. If None, all events will be used with
and a dict is created with string integer names corresponding
to the event id integers.
mrk : None | str | array_like, shape (5, 3) | list of str or array_like
Marker points representing the location of the marker coils with
respect to the MEG Sensors, or path to a marker file.
If list, all of the markers will be averaged together.
elp : None | str | array_like, shape (8, 3)
Digitizer points representing the location of the fiducials and the
marker coils with respect to the digitized head shape, or path to a
file containing these points.
hsp : None | str | array, shape (n_points, 3)
Digitizer head shape points, or path to head shape file. If more than
10,000 points are in the head shape, they are automatically decimated.
allow_unknown_format : bool
Force reading old data that is not officially supported. Alternatively,
read and re-save the data with the KIT MEG Laboratory application.
%(standardize_names)s
%(verbose)s
Returns
-------
epochs : instance of Epochs
The epochs.
Notes
-----
.. versionadded:: 0.9.0 | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | read_epochs_kit | alexisicte/aviate | python | @fill_doc
def read_epochs_kit(input_fname, events, event_id=None, mrk=None, elp=None, hsp=None, allow_unknown_format=False, standardize_names=None, verbose=None):
"Reader function for Ricoh/KIT epochs files.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n events : array, shape (n_events, 3)\n The events typically returned by the read_events function.\n If some events don't match the events of interest as specified\n by event_id, they will be marked as 'IGNORED' in the drop log.\n event_id : int | list of int | dict | None\n The id of the event to consider. If dict,\n the keys can later be used to access associated events. Example:\n dict(auditory=1, visual=3). If int, a dict will be created with\n the id as string. If a list, all events with the IDs specified\n in the list are used. If None, all events will be used with\n and a dict is created with string integer names corresponding\n to the event id integers.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n epochs : instance of Epochs\n The epochs.\n\n Notes\n -----\n .. versionadded:: 0.9.0\n "
epochs = EpochsKIT(input_fname=input_fname, events=events, event_id=event_id, mrk=mrk, elp=elp, hsp=hsp, allow_unknown_format=allow_unknown_format, standardize_names=standardize_names, verbose=verbose)
return epochs |
def read_stim_ch(self, buffer_size=100000.0):
'Read events from data.\n\n Parameter\n ---------\n buffer_size : int\n The size of chunk to by which the data are scanned.\n\n Returns\n -------\n events : array, [samples]\n The event vector (1 x samples).\n '
buffer_size = int(buffer_size)
start = int(self.first_samp)
stop = int((self.last_samp + 1))
pick = pick_types(self.info, meg=False, ref_meg=False, stim=True, exclude=[])
stim_ch = np.empty((1, stop), dtype=np.int64)
for b_start in range(start, stop, buffer_size):
b_stop = (b_start + buffer_size)
x = self[pick, b_start:b_stop][0]
stim_ch[:, b_start:(b_start + x.shape[1])] = x
return stim_ch | -1,490,638,908,371,606,300 | Read events from data.
Parameter
---------
buffer_size : int
The size of chunk to by which the data are scanned.
Returns
-------
events : array, [samples]
The event vector (1 x samples). | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | read_stim_ch | alexisicte/aviate | python | def read_stim_ch(self, buffer_size=100000.0):
'Read events from data.\n\n Parameter\n ---------\n buffer_size : int\n The size of chunk to by which the data are scanned.\n\n Returns\n -------\n events : array, [samples]\n The event vector (1 x samples).\n '
buffer_size = int(buffer_size)
start = int(self.first_samp)
stop = int((self.last_samp + 1))
pick = pick_types(self.info, meg=False, ref_meg=False, stim=True, exclude=[])
stim_ch = np.empty((1, stop), dtype=np.int64)
for b_start in range(start, stop, buffer_size):
b_stop = (b_start + buffer_size)
x = self[pick, b_start:b_stop][0]
stim_ch[:, b_start:(b_start + x.shape[1])] = x
return stim_ch |
def _set_stimchannels(self, info, stim, stim_code):
"Specify how the trigger channel is synthesized from analog channels.\n\n Has to be done before loading data. For a RawKIT instance that has been\n created with preload=True, this method will raise a\n NotImplementedError.\n\n Parameters\n ----------\n info : instance of MeasInfo\n The measurement info.\n stim : list of int | '<' | '>'\n Can be submitted as list of trigger channels.\n If a list is not specified, the default triggers extracted from\n misc channels will be used with specified directionality.\n '<' means that largest values assigned to the first channel\n in sequence.\n '>' means the largest trigger assigned to the last channel\n in sequence.\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n "
if self.preload:
raise NotImplementedError("Can't change stim channel after loading data")
_check_option('stim_code', stim_code, ['binary', 'channel'])
if (stim is not None):
if isinstance(stim, str):
picks = _default_stim_chs(info)
if (stim == '<'):
stim = picks[::(- 1)]
elif (stim == '>'):
stim = picks
else:
raise ValueError(("stim needs to be list of int, '>' or '<', not %r" % str(stim)))
else:
stim = np.asarray(stim, int)
if (stim.max() >= self._raw_extras[0]['nchan']):
raise ValueError(('Got stim=%s, but sqd file only has %i channels' % (stim, self._raw_extras[0]['nchan'])))
nchan = (self._raw_extras[0]['nchan'] + 1)
info['chs'].append(dict(cal=KIT.CALIB_FACTOR, logno=nchan, scanno=nchan, range=1.0, unit=FIFF.FIFF_UNIT_NONE, unit_mul=FIFF.FIFF_UNITM_NONE, ch_name='STI 014', coil_type=FIFF.FIFFV_COIL_NONE, loc=np.full(12, np.nan), kind=FIFF.FIFFV_STIM_CH, coord_frame=FIFF.FIFFV_COORD_UNKNOWN))
info._update_redundant()
self._raw_extras[0]['stim'] = stim
self._raw_extras[0]['stim_code'] = stim_code | -26,248,256,616,927,200 | Specify how the trigger channel is synthesized from analog channels.
Has to be done before loading data. For a RawKIT instance that has been
created with preload=True, this method will raise a
NotImplementedError.
Parameters
----------
info : instance of MeasInfo
The measurement info.
stim : list of int | '<' | '>'
Can be submitted as list of trigger channels.
If a list is not specified, the default triggers extracted from
misc channels will be used with specified directionality.
'<' means that largest values assigned to the first channel
in sequence.
'>' means the largest trigger assigned to the last channel
in sequence.
stim_code : 'binary' | 'channel'
How to decode trigger values from stim channels. 'binary' read stim
channel events as binary code, 'channel' encodes channel number. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | _set_stimchannels | alexisicte/aviate | python | def _set_stimchannels(self, info, stim, stim_code):
"Specify how the trigger channel is synthesized from analog channels.\n\n Has to be done before loading data. For a RawKIT instance that has been\n created with preload=True, this method will raise a\n NotImplementedError.\n\n Parameters\n ----------\n info : instance of MeasInfo\n The measurement info.\n stim : list of int | '<' | '>'\n Can be submitted as list of trigger channels.\n If a list is not specified, the default triggers extracted from\n misc channels will be used with specified directionality.\n '<' means that largest values assigned to the first channel\n in sequence.\n '>' means the largest trigger assigned to the last channel\n in sequence.\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n "
if self.preload:
raise NotImplementedError("Can't change stim channel after loading data")
_check_option('stim_code', stim_code, ['binary', 'channel'])
if (stim is not None):
if isinstance(stim, str):
picks = _default_stim_chs(info)
if (stim == '<'):
stim = picks[::(- 1)]
elif (stim == '>'):
stim = picks
else:
raise ValueError(("stim needs to be list of int, '>' or '<', not %r" % str(stim)))
else:
stim = np.asarray(stim, int)
if (stim.max() >= self._raw_extras[0]['nchan']):
raise ValueError(('Got stim=%s, but sqd file only has %i channels' % (stim, self._raw_extras[0]['nchan'])))
nchan = (self._raw_extras[0]['nchan'] + 1)
info['chs'].append(dict(cal=KIT.CALIB_FACTOR, logno=nchan, scanno=nchan, range=1.0, unit=FIFF.FIFF_UNIT_NONE, unit_mul=FIFF.FIFF_UNITM_NONE, ch_name='STI 014', coil_type=FIFF.FIFFV_COIL_NONE, loc=np.full(12, np.nan), kind=FIFF.FIFFV_STIM_CH, coord_frame=FIFF.FIFFV_COORD_UNKNOWN))
info._update_redundant()
self._raw_extras[0]['stim'] = stim
self._raw_extras[0]['stim_code'] = stim_code |
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
'Read a chunk of raw data.'
sqd = self._raw_extras[fi]
nchan = sqd['nchan']
data_left = ((stop - start) * nchan)
conv_factor = sqd['conv_factor']
n_bytes = sqd['dtype'].itemsize
assert (n_bytes in (2, 4))
blk_size = min(data_left, (((100000000 // n_bytes) // nchan) * nchan))
with open(self._filenames[fi], 'rb', buffering=0) as fid:
pointer = ((start * nchan) * n_bytes)
fid.seek((sqd['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'] + pointer))
stim = sqd['stim']
for blk_start in (np.arange(0, data_left, blk_size) // nchan):
blk_size = min(blk_size, (data_left - (blk_start * nchan)))
block = np.fromfile(fid, dtype=sqd['dtype'], count=blk_size)
block = block.reshape(nchan, (- 1), order='F').astype(float)
blk_stop = (blk_start + block.shape[1])
data_view = data[:, blk_start:blk_stop]
block *= conv_factor
if (stim is not None):
stim_ch = _make_stim_channel(block[stim, :], sqd['slope'], sqd['stimthresh'], sqd['stim_code'], stim)
block = np.vstack((block, stim_ch))
_mult_cal_one(data_view, block, idx, cals, mult) | -5,310,060,896,194,029,000 | Read a chunk of raw data. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | _read_segment_file | alexisicte/aviate | python | def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
sqd = self._raw_extras[fi]
nchan = sqd['nchan']
data_left = ((stop - start) * nchan)
conv_factor = sqd['conv_factor']
n_bytes = sqd['dtype'].itemsize
assert (n_bytes in (2, 4))
blk_size = min(data_left, (((100000000 // n_bytes) // nchan) * nchan))
with open(self._filenames[fi], 'rb', buffering=0) as fid:
pointer = ((start * nchan) * n_bytes)
fid.seek((sqd['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'] + pointer))
stim = sqd['stim']
for blk_start in (np.arange(0, data_left, blk_size) // nchan):
blk_size = min(blk_size, (data_left - (blk_start * nchan)))
block = np.fromfile(fid, dtype=sqd['dtype'], count=blk_size)
block = block.reshape(nchan, (- 1), order='F').astype(float)
blk_stop = (blk_start + block.shape[1])
data_view = data[:, blk_start:blk_stop]
block *= conv_factor
if (stim is not None):
stim_ch = _make_stim_channel(block[stim, :], sqd['slope'], sqd['stimthresh'], sqd['stim_code'], stim)
block = np.vstack((block, stim_ch))
_mult_cal_one(data_view, block, idx, cals, mult) |
def _read_kit_data(self):
'Read epochs data.\n\n Returns\n -------\n data : array, [channels x samples]\n the data matrix (channels x samples).\n times : array, [samples]\n returns the time values corresponding to the samples.\n '
info = self._raw_extras[0]
epoch_length = info['frame_length']
n_epochs = info['n_epochs']
n_samples = info['n_samples']
filename = info['filename']
dtype = info['dtype']
nchan = info['nchan']
with open(filename, 'rb', buffering=0) as fid:
fid.seek(info['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'])
count = (n_samples * nchan)
data = np.fromfile(fid, dtype=dtype, count=count)
data = data.reshape((n_samples, nchan)).T
data = (data * info['conv_factor'])
data = data.reshape((nchan, n_epochs, epoch_length))
data = data.transpose((1, 0, 2))
return data | 6,968,618,934,094,841,000 | Read epochs data.
Returns
-------
data : array, [channels x samples]
the data matrix (channels x samples).
times : array, [samples]
returns the time values corresponding to the samples. | venv/lib/python3.8/site-packages/mne/io/kit/kit.py | _read_kit_data | alexisicte/aviate | python | def _read_kit_data(self):
'Read epochs data.\n\n Returns\n -------\n data : array, [channels x samples]\n the data matrix (channels x samples).\n times : array, [samples]\n returns the time values corresponding to the samples.\n '
info = self._raw_extras[0]
epoch_length = info['frame_length']
n_epochs = info['n_epochs']
n_samples = info['n_samples']
filename = info['filename']
dtype = info['dtype']
nchan = info['nchan']
with open(filename, 'rb', buffering=0) as fid:
fid.seek(info['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'])
count = (n_samples * nchan)
data = np.fromfile(fid, dtype=dtype, count=count)
data = data.reshape((n_samples, nchan)).T
data = (data * info['conv_factor'])
data = data.reshape((nchan, n_epochs, epoch_length))
data = data.transpose((1, 0, 2))
return data |
@lru_cache()
def get_linux_distribution():
'Compatibility wrapper for {platform,distro}.linux_distribution().\n '
if hasattr(platform, 'linux_distribution'):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
result = platform.linux_distribution()
else:
import distro
result = distro.linux_distribution(full_distribution_name=False)
return result | 2,094,807,504,367,020,000 | Compatibility wrapper for {platform,distro}.linux_distribution(). | datalad/utils.py | get_linux_distribution | AKSoo/datalad | python | @lru_cache()
def get_linux_distribution():
'\n '
if hasattr(platform, 'linux_distribution'):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
result = platform.linux_distribution()
else:
import distro
result = distro.linux_distribution(full_distribution_name=False)
return result |
def getargspec(func, *, include_kwonlyargs=False):
"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n "
f_sign = inspect.signature(func)
args4 = [[], None, None, {}]
kwonlyargs = {}
(args, defaults) = (args4[0], args4[3])
P = inspect.Parameter
for (p_name, p) in f_sign.parameters.items():
if (p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD)):
assert (not kwonlyargs)
args.append(p_name)
if (p.default is not P.empty):
defaults[p_name] = p.default
elif (p.kind == P.VAR_POSITIONAL):
args4[1] = p_name
elif (p.kind == P.VAR_KEYWORD):
args4[2] = p_name
elif (p.kind == P.KEYWORD_ONLY):
assert (p.default is not P.empty)
kwonlyargs[p_name] = p.default
if kwonlyargs:
if (not include_kwonlyargs):
raise ValueError('Function has keyword-only parameters or annotations, either use inspect.signature() API which can support them, or provide include_kwonlyargs=True to this function')
else:
args.extend(list(kwonlyargs))
defaults.update(kwonlyargs)
args4[3] = (None if (not defaults) else tuple(defaults.values()))
return ArgSpecFake(*args4) | -6,180,160,135,232,466,000 | Compat shim for getargspec deprecated in python 3.
The main difference from inspect.getargspec (and inspect.getfullargspec
for that matter) is that by using inspect.signature we are providing
correct args/defaults for functools.wraps'ed functions.
`include_kwonlyargs` option was added to centralize getting all args,
even the ones which are kwonly (follow the ``*,``).
For internal use and not advised for use in 3rd party code.
Please use inspect.signature directly. | datalad/utils.py | getargspec | AKSoo/datalad | python | def getargspec(func, *, include_kwonlyargs=False):
"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n "
f_sign = inspect.signature(func)
args4 = [[], None, None, {}]
kwonlyargs = {}
(args, defaults) = (args4[0], args4[3])
P = inspect.Parameter
for (p_name, p) in f_sign.parameters.items():
if (p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD)):
assert (not kwonlyargs)
args.append(p_name)
if (p.default is not P.empty):
defaults[p_name] = p.default
elif (p.kind == P.VAR_POSITIONAL):
args4[1] = p_name
elif (p.kind == P.VAR_KEYWORD):
args4[2] = p_name
elif (p.kind == P.KEYWORD_ONLY):
assert (p.default is not P.empty)
kwonlyargs[p_name] = p.default
if kwonlyargs:
if (not include_kwonlyargs):
raise ValueError('Function has keyword-only parameters or annotations, either use inspect.signature() API which can support them, or provide include_kwonlyargs=True to this function')
else:
args.extend(list(kwonlyargs))
defaults.update(kwonlyargs)
args4[3] = (None if (not defaults) else tuple(defaults.values()))
return ArgSpecFake(*args4) |
def any_re_search(regexes, value):
'Return if any of regexes (list or str) searches successfully for value'
for regex in ensure_tuple_or_list(regexes):
if re.search(regex, value):
return True
return False | 4,265,866,037,996,960,000 | Return if any of regexes (list or str) searches successfully for value | datalad/utils.py | any_re_search | AKSoo/datalad | python | def any_re_search(regexes, value):
for regex in ensure_tuple_or_list(regexes):
if re.search(regex, value):
return True
return False |
def not_supported_on_windows(msg=None):
'A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n '
if on_windows:
raise NotImplementedError(('This functionality is not yet implemented for Windows OS' + ((': %s' % msg) if msg else ''))) | -9,206,899,521,312,776,000 | A little helper to be invoked to consistently fail whenever functionality is
not supported (yet) on Windows | datalad/utils.py | not_supported_on_windows | AKSoo/datalad | python | def not_supported_on_windows(msg=None):
'A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n '
if on_windows:
raise NotImplementedError(('This functionality is not yet implemented for Windows OS' + ((': %s' % msg) if msg else ))) |
def get_home_envvars(new_home):
'Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS "schema"\n '
new_home = str(new_home)
out = {'HOME': new_home}
if on_windows:
out['USERPROFILE'] = new_home
(out['HOMEDRIVE'], out['HOMEPATH']) = splitdrive(new_home)
return {v: val for (v, val) in out.items() if (v in os.environ)} | 4,516,429,125,876,582,400 | Return dict with env variables to be adjusted for a new HOME
Only variables found in current os.environ are adjusted.
Parameters
----------
new_home: str or Path
New home path, in native to OS "schema" | datalad/utils.py | get_home_envvars | AKSoo/datalad | python | def get_home_envvars(new_home):
'Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS "schema"\n '
new_home = str(new_home)
out = {'HOME': new_home}
if on_windows:
out['USERPROFILE'] = new_home
(out['HOMEDRIVE'], out['HOMEPATH']) = splitdrive(new_home)
return {v: val for (v, val) in out.items() if (v in os.environ)} |
def auto_repr(cls):
'Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n '
cls.__repr__ = __auto_repr__
return cls | -7,868,090,968,649,534,000 | Decorator for a class to assign it an automagic quick and dirty __repr__
It uses public class attributes to prepare repr of a class
Original idea: http://stackoverflow.com/a/27799004/1265472 | datalad/utils.py | auto_repr | AKSoo/datalad | python | def auto_repr(cls):
'Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n '
cls.__repr__ = __auto_repr__
return cls |
def is_interactive():
'Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError("I/O operation on closed file")` exception, this function\n would just return False, since the session cannot be used interactively.\n '
return all((_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))) | 8,509,922,991,414,469,000 | Return True if all in/outs are open and tty.
Note that in a somewhat abnormal case where e.g. stdin is explicitly
closed, and any operation on it would raise a
`ValueError("I/O operation on closed file")` exception, this function
would just return False, since the session cannot be used interactively. | datalad/utils.py | is_interactive | AKSoo/datalad | python | def is_interactive():
'Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError("I/O operation on closed file")` exception, this function\n would just return False, since the session cannot be used interactively.\n '
return all((_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))) |
def get_ipython_shell():
'Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n '
try:
return get_ipython()
except NameError:
return None | 7,239,483,232,310,486,000 | Detect if running within IPython and returns its `ip` (shell) object
Returns None if not under ipython (no `get_ipython` function) | datalad/utils.py | get_ipython_shell | AKSoo/datalad | python | def get_ipython_shell():
'Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n '
try:
return get_ipython()
except NameError:
return None |
def md5sum(filename):
'Compute an MD5 sum for the given file\n '
from datalad.support.digests import Digester
return Digester(digests=['md5'])(filename)['md5'] | -6,846,947,516,037,588,000 | Compute an MD5 sum for the given file | datalad/utils.py | md5sum | AKSoo/datalad | python | def md5sum(filename):
'\n '
from datalad.support.digests import Digester
return Digester(digests=['md5'])(filename)['md5'] |
def sorted_files(path):
'Return a (sorted) list of files under path\n '
return sorted(sum([[op.join(r, f)[(len(path) + 1):] for f in files] for (r, d, files) in os.walk(path) if (not ('.git' in r))], [])) | -8,662,916,188,476,686,000 | Return a (sorted) list of files under path | datalad/utils.py | sorted_files | AKSoo/datalad | python | def sorted_files(path):
'\n '
return sorted(sum([[op.join(r, f)[(len(path) + 1):] for f in files] for (r, d, files) in os.walk(path) if (not ('.git' in r))], [])) |
def find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):
'Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n '
for (dirpath, dirnames, filenames) in os.walk(topdir):
names = ((dirnames + filenames) if dirs else filenames)
paths = (op.join(dirpath, name) for name in names)
for path in filter(re.compile(regex).search, paths):
path = path.rstrip(sep)
if (exclude and re.search(exclude, path)):
continue
if (exclude_vcs and re.search(_VCS_REGEX, path)):
continue
if (exclude_datalad and re.search(_DATALAD_REGEX, path)):
continue
(yield path) | -8,840,877,839,042,412,000 | Generator to find files matching regex
Parameters
----------
regex: basestring
exclude: basestring, optional
Matches to exclude
exclude_vcs:
If True, excludes commonly known VCS subdirectories. If string, used
as regex to exclude those files (regex: `%r`)
exclude_datalad:
If True, excludes files known to be datalad meta-data files (e.g. under
.datalad/ subdirectory) (regex: `%r`)
topdir: basestring, optional
Directory where to search
dirs: bool, optional
Whether to match directories as well as files | datalad/utils.py | find_files | AKSoo/datalad | python | def find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):
'Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n '
for (dirpath, dirnames, filenames) in os.walk(topdir):
names = ((dirnames + filenames) if dirs else filenames)
paths = (op.join(dirpath, name) for name in names)
for path in filter(re.compile(regex).search, paths):
path = path.rstrip(sep)
if (exclude and re.search(exclude, path)):
continue
if (exclude_vcs and re.search(_VCS_REGEX, path)):
continue
if (exclude_datalad and re.search(_DATALAD_REGEX, path)):
continue
(yield path) |
def expandpath(path, force_absolute=True):
'Expand all variables and user handles in a path.\n\n By default return an absolute path\n '
path = expandvars(expanduser(path))
if force_absolute:
path = abspath(path)
return path | -7,795,529,707,431,371,000 | Expand all variables and user handles in a path.
By default return an absolute path | datalad/utils.py | expandpath | AKSoo/datalad | python | def expandpath(path, force_absolute=True):
'Expand all variables and user handles in a path.\n\n By default return an absolute path\n '
path = expandvars(expanduser(path))
if force_absolute:
path = abspath(path)
return path |
def posix_relpath(path, start=None):
'Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.'
return posixpath.join(*split(relpath(path, start=(start if (start is not None) else '')))) | 4,046,002,054,909,217,300 | Behave like os.path.relpath, but always return POSIX paths...
on any platform. | datalad/utils.py | posix_relpath | AKSoo/datalad | python | def posix_relpath(path, start=None):
'Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.'
return posixpath.join(*split(relpath(path, start=(start if (start is not None) else )))) |
def is_explicit_path(path):
"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit."
path = expandpath(path, force_absolute=False)
return (isabs(path) or path.startswith((os.curdir + os.sep)) or path.startswith((os.pardir + os.sep))) | 1,506,708,320,360,227,800 | Return whether a path explicitly points to a location
Any absolute path, or relative path starting with either '../' or
'./' is assumed to indicate a location on the filesystem. Any other
path format is not considered explicit. | datalad/utils.py | is_explicit_path | AKSoo/datalad | python | def is_explicit_path(path):
"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit."
path = expandpath(path, force_absolute=False)
return (isabs(path) or path.startswith((os.curdir + os.sep)) or path.startswith((os.pardir + os.sep))) |
def rotree(path, ro=True, chmod_files=True):
'To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n '
if ro:
chmod = (lambda f: os.chmod(f, (os.stat(f).st_mode & (~ stat.S_IWRITE))))
else:
chmod = (lambda f: os.chmod(f, ((os.stat(f).st_mode | stat.S_IWRITE) | stat.S_IREAD)))
for (root, dirs, files) in os.walk(path, followlinks=False):
if chmod_files:
for f in files:
fullf = op.join(root, f)
if exists(fullf):
chmod(fullf)
chmod(root) | -5,822,748,150,281,047,000 | To make tree read-only or writable
Parameters
----------
path : string
Path to the tree/directory to chmod
ro : bool, optional
Whether to make it R/O (default) or RW
chmod_files : bool, optional
Whether to operate also on files (not just directories) | datalad/utils.py | rotree | AKSoo/datalad | python | def rotree(path, ro=True, chmod_files=True):
'To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n '
if ro:
chmod = (lambda f: os.chmod(f, (os.stat(f).st_mode & (~ stat.S_IWRITE))))
else:
chmod = (lambda f: os.chmod(f, ((os.stat(f).st_mode | stat.S_IWRITE) | stat.S_IREAD)))
for (root, dirs, files) in os.walk(path, followlinks=False):
if chmod_files:
for f in files:
fullf = op.join(root, f)
if exists(fullf):
chmod(fullf)
chmod(root) |
def rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):
"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n "
if (chmod_files == 'auto'):
chmod_files = on_windows
assert_no_open_files(path)
path = str(path)
if children_only:
if (not isdir(path)):
raise ValueError('Can remove children only of directories')
for p in os.listdir(path):
rmtree(op.join(path, p))
return
if (not (islink(path) or (not isdir(path)))):
rotree(path, ro=False, chmod_files=chmod_files)
if on_windows:
path = ('\\\\?\\ '.strip() + path)
_rmtree(path, *args, **kwargs)
else:
unlink(path) | -4,424,460,177,924,723,000 | To remove git-annex .git it is needed to make all files and directories writable again first
Parameters
----------
path: Path or str
Path to remove
chmod_files : string or bool, optional
Whether to make files writable also before removal. Usually it is just
a matter of directories to have write permissions.
If 'auto' it would chmod files on windows by default
children_only : bool, optional
If set, all files and subdirectories would be removed while the path
itself (must be a directory) would be preserved
`*args` :
`**kwargs` :
Passed into shutil.rmtree call | datalad/utils.py | rmtree | AKSoo/datalad | python | def rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):
"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n "
if (chmod_files == 'auto'):
chmod_files = on_windows
assert_no_open_files(path)
path = str(path)
if children_only:
if (not isdir(path)):
raise ValueError('Can remove children only of directories')
for p in os.listdir(path):
rmtree(op.join(path, p))
return
if (not (islink(path) or (not isdir(path)))):
rotree(path, ro=False, chmod_files=chmod_files)
if on_windows:
path = ('\\\\?\\ '.strip() + path)
_rmtree(path, *args, **kwargs)
else:
unlink(path) |
def rmdir(path, *args, **kwargs):
'os.rmdir with our optional checking for open files'
assert_no_open_files(path)
os.rmdir(path) | -3,323,220,695,162,720,000 | os.rmdir with our optional checking for open files | datalad/utils.py | rmdir | AKSoo/datalad | python | def rmdir(path, *args, **kwargs):
assert_no_open_files(path)
os.rmdir(path) |
def get_open_files(path, log_open=False):
'Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n '
import psutil
files = {}
path = str(Path(path).resolve().absolute())
for proc in psutil.process_iter():
try:
open_paths = ([p.path for p in proc.open_files()] + [proc.cwd()])
for p in open_paths:
if path_startswith(p, path):
files[p] = proc
except psutil.NoSuchProcess:
pass
except psutil.AccessDenied:
pass
if (files and log_open):
lgr.log(log_open, 'Open files under %s: %s', path, files)
return files | 5,590,518,783,537,523,000 | Get open files under a path
Note: This function is very slow on Windows.
Parameters
----------
path : str
File or directory to check for open files under
log_open : bool or int
If set - logger level to use
Returns
-------
dict
path : pid | datalad/utils.py | get_open_files | AKSoo/datalad | python | def get_open_files(path, log_open=False):
'Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n '
import psutil
files = {}
path = str(Path(path).resolve().absolute())
for proc in psutil.process_iter():
try:
open_paths = ([p.path for p in proc.open_files()] + [proc.cwd()])
for p in open_paths:
if path_startswith(p, path):
files[p] = proc
except psutil.NoSuchProcess:
pass
except psutil.AccessDenied:
pass
if (files and log_open):
lgr.log(log_open, 'Open files under %s: %s', path, files)
return files |
def rmtemp(f, *args, **kwargs):
'Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n '
if (not os.environ.get('DATALAD_TESTS_TEMP_KEEP')):
if (not os.path.lexists(f)):
lgr.debug("Path %s does not exist, so can't be removed", f)
return
lgr.log(5, 'Removing temp file: %s', f)
if isdir(f):
rmtree(f, *args, **kwargs)
else:
unlink(f)
else:
lgr.info('Keeping temp file: %s', f) | -2,435,413,394,078,092,000 | Wrapper to centralize removing of temp files so we could keep them around
It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP
environment variable is defined | datalad/utils.py | rmtemp | AKSoo/datalad | python | def rmtemp(f, *args, **kwargs):
'Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n '
if (not os.environ.get('DATALAD_TESTS_TEMP_KEEP')):
if (not os.path.lexists(f)):
lgr.debug("Path %s does not exist, so can't be removed", f)
return
lgr.log(5, 'Removing temp file: %s', f)
if isdir(f):
rmtree(f, *args, **kwargs)
else:
unlink(f)
else:
lgr.info('Keeping temp file: %s', f) |
def file_basename(name, return_ext=False):
'\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n '
bname = basename(name)
fbname = re.sub('(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)
if return_ext:
return (fbname, bname[(len(fbname) + 1):])
else:
return fbname | -4,242,195,939,967,012,400 | Strips up to 2 extensions of length up to 4 characters and starting with alpha
not a digit, so we could get rid of .tar.gz etc | datalad/utils.py | file_basename | AKSoo/datalad | python | def file_basename(name, return_ext=False):
'\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n '
bname = basename(name)
fbname = re.sub('(\\.[a-zA-Z_]\\S{1,4}){0,2}$', , bname)
if return_ext:
return (fbname, bname[(len(fbname) + 1):])
else:
return fbname |
def escape_filename(filename):
'Surround filename in "" and escape " in the filename\n '
filename = filename.replace('"', '\\"').replace('`', '\\`')
filename = ('"%s"' % filename)
return filename | -4,358,157,444,839,086,600 | Surround filename in "" and escape " in the filename | datalad/utils.py | escape_filename | AKSoo/datalad | python | def escape_filename(filename):
'\n '
filename = filename.replace('"', '\\"').replace('`', '\\`')
filename = ('"%s"' % filename)
return filename |
def encode_filename(filename):
'Encode unicode filename\n '
if isinstance(filename, str):
return filename.encode(sys.getfilesystemencoding())
else:
return filename | -9,183,947,159,578,037,000 | Encode unicode filename | datalad/utils.py | encode_filename | AKSoo/datalad | python | def encode_filename(filename):
'\n '
if isinstance(filename, str):
return filename.encode(sys.getfilesystemencoding())
else:
return filename |
def decode_input(s):
'Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n '
if isinstance(s, str):
return s
else:
encoding = (sys.stdin.encoding or 'UTF-8')
try:
return s.decode(encoding)
except UnicodeDecodeError as exc:
lgr.warning('Failed to decode input string using %s encoding. Decoding allowing for errors', encoding)
return s.decode(encoding, errors='replace') | -4,714,567,132,680,451,000 | Given input string/bytes, decode according to stdin codepage (or UTF-8)
if not defined
If fails -- issue warning and decode allowing for errors
being replaced | datalad/utils.py | decode_input | AKSoo/datalad | python | def decode_input(s):
'Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n '
if isinstance(s, str):
return s
else:
encoding = (sys.stdin.encoding or 'UTF-8')
try:
return s.decode(encoding)
except UnicodeDecodeError as exc:
lgr.warning('Failed to decode input string using %s encoding. Decoding allowing for errors', encoding)
return s.decode(encoding, errors='replace') |
def ensure_tuple_or_list(obj):
'Given an object, wrap into a tuple if not list or tuple\n '
if isinstance(obj, (list, tuple)):
return obj
return (obj,) | -6,620,389,213,814,764,000 | Given an object, wrap into a tuple if not list or tuple | datalad/utils.py | ensure_tuple_or_list | AKSoo/datalad | python | def ensure_tuple_or_list(obj):
'\n '
if isinstance(obj, (list, tuple)):
return obj
return (obj,) |
def ensure_iter(s, cls, copy=False, iterate=True):
'Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n '
if isinstance(s, cls):
return (s if (not copy) else shallow_copy(s))
elif isinstance(s, str):
return cls((s,))
elif (iterate and hasattr(s, '__iter__')):
return cls(s)
elif (s is None):
return cls()
else:
return cls((s,)) | -8,192,520,584,669,999,000 | Given not a list, would place it into a list. If None - empty list is returned
Parameters
----------
s: list or anything
cls: class
Which iterable class to ensure
copy: bool, optional
If correct iterable is passed, it would generate its shallow copy
iterate: bool, optional
If it is not a list, but something iterable (but not a str)
iterate over it. | datalad/utils.py | ensure_iter | AKSoo/datalad | python | def ensure_iter(s, cls, copy=False, iterate=True):
'Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n '
if isinstance(s, cls):
return (s if (not copy) else shallow_copy(s))
elif isinstance(s, str):
return cls((s,))
elif (iterate and hasattr(s, '__iter__')):
return cls(s)
elif (s is None):
return cls()
else:
return cls((s,)) |
def ensure_list(s, copy=False, iterate=True):
'Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n '
return ensure_iter(s, list, copy=copy, iterate=iterate) | -7,572,995,100,661,384,000 | Given not a list, would place it into a list. If None - empty list is returned
Parameters
----------
s: list or anything
copy: bool, optional
If list is passed, it would generate a shallow copy of the list
iterate: bool, optional
If it is not a list, but something iterable (but not a str)
iterate over it. | datalad/utils.py | ensure_list | AKSoo/datalad | python | def ensure_list(s, copy=False, iterate=True):
'Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n '
return ensure_iter(s, list, copy=copy, iterate=iterate) |
def ensure_list_from_str(s, sep='\n'):
'Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n '
if (not s):
return None
if isinstance(s, list):
return s
return s.split(sep) | -3,554,266,283,958,024,000 | Given a multiline string convert it to a list of return None if empty
Parameters
----------
s: str or list | datalad/utils.py | ensure_list_from_str | AKSoo/datalad | python | def ensure_list_from_str(s, sep='\n'):
'Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n '
if (not s):
return None
if isinstance(s, list):
return s
return s.split(sep) |
def ensure_dict_from_str(s, **kwargs):
'Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n '
if (not s):
return None
if isinstance(s, dict):
return s
out = {}
for value_str in ensure_list_from_str(s, **kwargs):
if ('=' not in value_str):
raise ValueError('{} is not in key=value format'.format(repr(value_str)))
(k, v) = value_str.split('=', 1)
if (k in out):
err = 'key {} was already defined in {}, but new value {} was provided'.format(k, out, v)
raise ValueError(err)
out[k] = v
return out | 3,087,126,313,354,959,400 | Given a multiline string with key=value items convert it to a dictionary
Parameters
----------
s: str or dict
Returns None if input s is empty | datalad/utils.py | ensure_dict_from_str | AKSoo/datalad | python | def ensure_dict_from_str(s, **kwargs):
'Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n '
if (not s):
return None
if isinstance(s, dict):
return s
out = {}
for value_str in ensure_list_from_str(s, **kwargs):
if ('=' not in value_str):
raise ValueError('{} is not in key=value format'.format(repr(value_str)))
(k, v) = value_str.split('=', 1)
if (k in out):
err = 'key {} was already defined in {}, but new value {} was provided'.format(k, out, v)
raise ValueError(err)
out[k] = v
return out |
def ensure_bytes(s, encoding='utf-8'):
'Convert/encode unicode string to bytes.\n\n If `s` isn\'t a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. "utf-8" is the default\n '
if (not isinstance(s, str)):
return s
return s.encode(encoding) | -7,053,226,920,306,413,000 | Convert/encode unicode string to bytes.
If `s` isn't a string, return it as is.
Parameters
----------
encoding: str, optional
Encoding to use. "utf-8" is the default | datalad/utils.py | ensure_bytes | AKSoo/datalad | python | def ensure_bytes(s, encoding='utf-8'):
'Convert/encode unicode string to bytes.\n\n If `s` isn\'t a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. "utf-8" is the default\n '
if (not isinstance(s, str)):
return s
return s.encode(encoding) |
def ensure_unicode(s, encoding=None, confidence=None):
'Convert/decode bytestring to unicode.\n\n If `s` isn\'t a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, "utf-8" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n '
if (not isinstance(s, bytes)):
return s
if (encoding is None):
try:
return s.decode('utf-8')
except UnicodeDecodeError as exc:
lgr.debug('Failed to decode a string as utf-8: %s', CapturedException(exc))
from chardet import detect
enc = detect(s)
denc = enc.get('encoding', None)
if denc:
denc_confidence = enc.get('confidence', 0)
if ((confidence is not None) and (denc_confidence < confidence)):
raise ValueError(('Failed to auto-detect encoding with high enough confidence. Highest confidence was %s for %s' % (denc_confidence, denc)))
lgr.log(5, 'Auto-detected encoding to be %s', denc)
return s.decode(denc)
else:
raise ValueError(('Could not decode value as utf-8, or to guess its encoding: %s' % repr(s)))
else:
return s.decode(encoding) | -1,053,787,349,956,682,400 | Convert/decode bytestring to unicode.
If `s` isn't a bytestring, return it as is.
Parameters
----------
encoding: str, optional
Encoding to use. If None, "utf-8" is tried, and then if not a valid
UTF-8, encoding will be guessed
confidence: float, optional
A value between 0 and 1, so if guessing of encoding is of lower than
specified confidence, ValueError is raised | datalad/utils.py | ensure_unicode | AKSoo/datalad | python | def ensure_unicode(s, encoding=None, confidence=None):
'Convert/decode bytestring to unicode.\n\n If `s` isn\'t a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, "utf-8" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n '
if (not isinstance(s, bytes)):
return s
if (encoding is None):
try:
return s.decode('utf-8')
except UnicodeDecodeError as exc:
lgr.debug('Failed to decode a string as utf-8: %s', CapturedException(exc))
from chardet import detect
enc = detect(s)
denc = enc.get('encoding', None)
if denc:
denc_confidence = enc.get('confidence', 0)
if ((confidence is not None) and (denc_confidence < confidence)):
raise ValueError(('Failed to auto-detect encoding with high enough confidence. Highest confidence was %s for %s' % (denc_confidence, denc)))
lgr.log(5, 'Auto-detected encoding to be %s', denc)
return s.decode(denc)
else:
raise ValueError(('Could not decode value as utf-8, or to guess its encoding: %s' % repr(s)))
else:
return s.decode(encoding) |
def ensure_bool(s):
'Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n '
if isinstance(s, str):
if s.isdigit():
return bool(int(s))
sl = s.lower()
if (sl in {'y', 'yes', 'true', 'on'}):
return True
elif (sl in {'n', 'no', 'false', 'off'}):
return False
else:
raise ValueError(('Do not know how to treat %r as a boolean' % s))
return bool(s) | 5,458,731,529,325,434,000 | Convert value into boolean following convention for strings
to recognize on,True,yes as True, off,False,no as False | datalad/utils.py | ensure_bool | AKSoo/datalad | python | def ensure_bool(s):
'Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n '
if isinstance(s, str):
if s.isdigit():
return bool(int(s))
sl = s.lower()
if (sl in {'y', 'yes', 'true', 'on'}):
return True
elif (sl in {'n', 'no', 'false', 'off'}):
return False
else:
raise ValueError(('Do not know how to treat %r as a boolean' % s))
return bool(s) |
def as_unicode(val, cast_types=object):
'Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n '
if (val is None):
return u''
elif isinstance(val, str):
return val
elif isinstance(val, unicode_srctypes):
return ensure_unicode(val)
elif isinstance(val, cast_types):
return str(val)
else:
raise TypeError(('Value %r is not of any of known or provided %s types' % (val, cast_types))) | -5,259,481,238,356,841,000 | Given an arbitrary value, would try to obtain unicode value of it
For unicode it would return original value, for python2 str or python3
bytes it would use ensure_unicode, for None - an empty (unicode) string,
and for any other type (see `cast_types`) - would apply the unicode
constructor. If value is not an instance of `cast_types`, TypeError
is thrown
Parameters
----------
cast_types: type
Which types to cast to unicode by providing to constructor | datalad/utils.py | as_unicode | AKSoo/datalad | python | def as_unicode(val, cast_types=object):
'Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n '
if (val is None):
return u
elif isinstance(val, str):
return val
elif isinstance(val, unicode_srctypes):
return ensure_unicode(val)
elif isinstance(val, cast_types):
return str(val)
else:
raise TypeError(('Value %r is not of any of known or provided %s types' % (val, cast_types))) |
def unique(seq, key=None, reverse=False):
'Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n '
seen = set()
seen_add = seen.add
trans = (reversed if reverse else (lambda x: x))
if (not key):
out = [x for x in trans(seq) if (not ((x in seen) or seen_add(x)))]
else:
out = [x for x in trans(seq) if (not ((key(x) in seen) or seen_add(key(x))))]
return (out[::(- 1)] if reverse else out) | 4,005,972,471,792,257,000 | Given a sequence return a list only with unique elements while maintaining order
This is the fastest solution. See
https://www.peterbe.com/plog/uniqifiers-benchmark
and
http://stackoverflow.com/a/480227/1265472
for more information.
Enhancement -- added ability to compare for uniqueness using a key function
Parameters
----------
seq:
Sequence to analyze
key: callable, optional
Function to call on each element so we could decide not on a full
element, but on its member etc
reverse: bool, optional
If True, uniqueness checked in the reverse order, so that the later ones
will take the order | datalad/utils.py | unique | AKSoo/datalad | python | def unique(seq, key=None, reverse=False):
'Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n '
seen = set()
seen_add = seen.add
trans = (reversed if reverse else (lambda x: x))
if (not key):
out = [x for x in trans(seq) if (not ((x in seen) or seen_add(x)))]
else:
out = [x for x in trans(seq) if (not ((key(x) in seen) or seen_add(key(x))))]
return (out[::(- 1)] if reverse else out) |
def all_same(items):
'Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n '
first = True
first_item = None
for item in items:
if first:
first = False
first_item = item
elif (item != first_item):
return False
return (not first) | 3,611,848,074,102,730,000 | Quick check if all items are the same.
Identical to a check like len(set(items)) == 1 but
should be more efficient while working on generators, since would
return False as soon as any difference detected thus possibly avoiding
unnecessary evaluations | datalad/utils.py | all_same | AKSoo/datalad | python | def all_same(items):
'Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n '
first = True
first_item = None
for item in items:
if first:
first = False
first_item = item
elif (item != first_item):
return False
return (not first) |
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