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ff0000/scarlet | scarlet/cms/item.py | PreviewWrapper.get | def get(self, request, *args, **kwargs):
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Method for handling GET requests.
Sets the renderer to be a RenderResponse instance
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ff0000/scarlet | scarlet/cms/item.py | VersionsList.get | def get(self, request, *args, **kwargs):
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ff0000/scarlet | scarlet/cms/item.py | VersionsList.revert | def revert(self, version, url):
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ff0000/scarlet | scarlet/cms/item.py | VersionsList.post | def post(self, request, *args, **kwargs):
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inveniosoftware/invenio-collections | invenio_collections/models.py | Collection.validate_parent_id | def validate_parent_id(self, key, parent_id):
"""Parent has to be different from itself."""
id_ = getattr(self, 'id', None)
if id_ is not None and parent_id is not None:
assert id_ != parent_id, 'Can not be attached to itself.'
return parent_id | python | def validate_parent_id(self, key, parent_id):
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id_ = getattr(self, 'id', None)
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camsci/meteor-pi | src/pythonModules/meteorpi_client/meteorpi_client/__init__.py | _to_encoded_string | def _to_encoded_string(o):
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Build an encoded string suitable for use as a URL component. This includes double-escaping the string to
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camsci/meteor-pi | src/pythonModules/meteorpi_client/meteorpi_client/__init__.py | MeteorClient.list_observatories | def list_observatories(self):
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Get the IDs of all observatories with have stored observations on this server.
:return: a sequence of strings containing observatories IDs
"""
response = requests.get(self.base_url + '/obstories').text
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"""
Get the IDs of all observatories with have stored observations on this server.
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response = requests.get(self.base_url + '/obstories').text
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camsci/meteor-pi | src/pythonModules/meteorpi_client/meteorpi_client/__init__.py | MeteorClient.get_observatory_status | def get_observatory_status(self, observatory_id, status_time=None):
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Get details of the specified camera's status
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camsci/meteor-pi | src/pythonModules/meteorpi_client/meteorpi_client/__init__.py | MeteorClient.search_observations | def search_observations(self, search=None):
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camsci/meteor-pi | src/pythonModules/meteorpi_client/meteorpi_client/__init__.py | MeteorClient._augment_file | def _augment_file(self, f):
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edaniszewski/bison | bison/scheme.py | Scheme.build_defaults | def build_defaults(self):
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Returns:
dict: The default configurations as set by the `Scheme`.
Raises:
errors.InvalidSchemeError: The `Scheme` does not contain
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if self._flat is None:
flat = {}
for a... | python | def flatten(self):
"""Flatten the scheme into a dictionary where the keys are
compound 'dot' notation keys, and the values are the corresponding
options.
Returns:
dict: The flattened `Scheme`.
"""
if self._flat is None:
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"""Validate the given config against the `Scheme`.
Args:
config (dict): The configuration to validate.
Raises:
errors.SchemeValidationError: The configuration fails
validation against the `Schema`.
"""
if not i... | python | def validate(self, config):
"""Validate the given config against the `Scheme`.
Args:
config (dict): The configuration to validate.
Raises:
errors.SchemeValidationError: The configuration fails
validation against the `Schema`.
"""
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edaniszewski/bison | bison/scheme.py | Option.cast | def cast(self, value):
"""Cast a value to the type required by the option, if one is set.
This is used to cast the string values gathered from environment
variable into their required type.
Args:
value: The value to cast.
Returns:
The value casted to th... | python | def cast(self, value):
"""Cast a value to the type required by the option, if one is set.
This is used to cast the string values gathered from environment
variable into their required type.
Args:
value: The value to cast.
Returns:
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | geometricmean | def geometricmean(inlist):
"""
Calculates the geometric mean of the values in the passed list.
That is: n-th root of (x1 * x2 * ... * xn). Assumes a '1D' list.
Usage: lgeometricmean(inlist)
"""
mult = 1.0
one_over_n = 1.0 / len(inlist)
for item in inlist:
mult = mult * pow(item, one_over_n)... | python | def geometricmean(inlist):
"""
Calculates the geometric mean of the values in the passed list.
That is: n-th root of (x1 * x2 * ... * xn). Assumes a '1D' list.
Usage: lgeometricmean(inlist)
"""
mult = 1.0
one_over_n = 1.0 / len(inlist)
for item in inlist:
mult = mult * pow(item, one_over_n)... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | harmonicmean | def harmonicmean(inlist):
"""
Calculates the harmonic mean of the values in the passed list.
That is: n / (1/x1 + 1/x2 + ... + 1/xn). Assumes a '1D' list.
Usage: lharmonicmean(inlist)
"""
sum = 0
for item in inlist:
sum = sum + 1.0 / item
return len(inlist) / sum | python | def harmonicmean(inlist):
"""
Calculates the harmonic mean of the values in the passed list.
That is: n / (1/x1 + 1/x2 + ... + 1/xn). Assumes a '1D' list.
Usage: lharmonicmean(inlist)
"""
sum = 0
for item in inlist:
sum = sum + 1.0 / item
return len(inlist) / sum | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | mean | def mean(inlist):
"""
Returns the arithematic mean of the values in the passed list.
Assumes a '1D' list, but will function on the 1st dim of an array(!).
Usage: lmean(inlist)
"""
sum = 0
for item in inlist:
sum = sum + item
return sum / float(len(inlist)) | python | def mean(inlist):
"""
Returns the arithematic mean of the values in the passed list.
Assumes a '1D' list, but will function on the 1st dim of an array(!).
Usage: lmean(inlist)
"""
sum = 0
for item in inlist:
sum = sum + item
return sum / float(len(inlist)) | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | medianscore | def medianscore(inlist):
"""
Returns the 'middle' score of the passed list. If there is an even
number of scores, the mean of the 2 middle scores is returned.
Usage: lmedianscore(inlist)
"""
newlist = copy.deepcopy(inlist)
newlist.sort()
if len(newlist) % 2 == 0: # if even number of scores, avera... | python | def medianscore(inlist):
"""
Returns the 'middle' score of the passed list. If there is an even
number of scores, the mean of the 2 middle scores is returned.
Usage: lmedianscore(inlist)
"""
newlist = copy.deepcopy(inlist)
newlist.sort()
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | mode | def mode(inlist):
"""
Returns a list of the modal (most common) score(s) in the passed
list. If there is more than one such score, all are returned. The
bin-count for the mode(s) is also returned.
Usage: lmode(inlist)
Returns: bin-count for mode(s), a list of modal value(s)
"""
scores = pstat.unique(inlis... | python | def mode(inlist):
"""
Returns a list of the modal (most common) score(s) in the passed
list. If there is more than one such score, all are returned. The
bin-count for the mode(s) is also returned.
Usage: lmode(inlist)
Returns: bin-count for mode(s), a list of modal value(s)
"""
scores = pstat.unique(inlis... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | moment | def moment(inlist, moment=1):
"""
Calculates the nth moment about the mean for a sample (defaults to
the 1st moment). Used to calculate coefficients of skewness and kurtosis.
Usage: lmoment(inlist,moment=1)
Returns: appropriate moment (r) from pyLibrary. 1/n * SUM((inlist(i)-mean)**r)
"""
if moment == 1:
... | python | def moment(inlist, moment=1):
"""
Calculates the nth moment about the mean for a sample (defaults to
the 1st moment). Used to calculate coefficients of skewness and kurtosis.
Usage: lmoment(inlist,moment=1)
Returns: appropriate moment (r) from pyLibrary. 1/n * SUM((inlist(i)-mean)**r)
"""
if moment == 1:
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | describe | def describe(inlist):
"""
Returns some descriptive statistics of the passed list (assumed to be 1D).
Usage: ldescribe(inlist)
Returns: n, mean, standard deviation, skew, kurtosis
"""
n = len(inlist)
mm = (min(inlist), max(inlist))
m = mean(inlist)
sd = stdev(inlist)
sk = skew(inlist)
kurt... | python | def describe(inlist):
"""
Returns some descriptive statistics of the passed list (assumed to be 1D).
Usage: ldescribe(inlist)
Returns: n, mean, standard deviation, skew, kurtosis
"""
n = len(inlist)
mm = (min(inlist), max(inlist))
m = mean(inlist)
sd = stdev(inlist)
sk = skew(inlist)
kurt... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | itemfreq | def itemfreq(inlist):
"""
Returns a list of pairs. Each pair consists of one of the scores in inlist
and it's frequency count. Assumes a 1D list is passed.
Usage: litemfreq(inlist)
Returns: a 2D frequency table (col [0:n-1]=scores, col n=frequencies)
"""
scores = pstat.unique(inlist)
scores.sort()
... | python | def itemfreq(inlist):
"""
Returns a list of pairs. Each pair consists of one of the scores in inlist
and it's frequency count. Assumes a 1D list is passed.
Usage: litemfreq(inlist)
Returns: a 2D frequency table (col [0:n-1]=scores, col n=frequencies)
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scores = pstat.unique(inlist)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | scoreatpercentile | def scoreatpercentile(inlist, percent):
"""
Returns the score at a given percentile relative to the distribution
given by inlist.
Usage: lscoreatpercentile(inlist,percent)
"""
if percent > 1:
print("\nDividing percent>1 by 100 in lscoreatpercentile().\n")
percent = percent / 100.0
targetc... | python | def scoreatpercentile(inlist, percent):
"""
Returns the score at a given percentile relative to the distribution
given by inlist.
Usage: lscoreatpercentile(inlist,percent)
"""
if percent > 1:
print("\nDividing percent>1 by 100 in lscoreatpercentile().\n")
percent = percent / 100.0
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | percentileofscore | def percentileofscore(inlist, score, histbins=10, defaultlimits=None):
"""
Returns the percentile value of a score relative to the distribution
given by inlist. Formula depends on the values used to histogram the data(!).
Usage: lpercentileofscore(inlist,score,histbins=10,defaultlimits=None)
"""
h, lrl, bi... | python | def percentileofscore(inlist, score, histbins=10, defaultlimits=None):
"""
Returns the percentile value of a score relative to the distribution
given by inlist. Formula depends on the values used to histogram the data(!).
Usage: lpercentileofscore(inlist,score,histbins=10,defaultlimits=None)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | histogram | def histogram(inlist, numbins=10, defaultreallimits=None, printextras=0):
"""
Returns (i) a list of histogram bin counts, (ii) the smallest value
of the histogram binning, and (iii) the bin width (the last 2 are not
necessarily integers). Default number of bins is 10. If no sequence object
is given for defaultrea... | python | def histogram(inlist, numbins=10, defaultreallimits=None, printextras=0):
"""
Returns (i) a list of histogram bin counts, (ii) the smallest value
of the histogram binning, and (iii) the bin width (the last 2 are not
necessarily integers). Default number of bins is 10. If no sequence object
is given for defaultrea... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | cumfreq | def cumfreq(inlist, numbins=10, defaultreallimits=None):
"""
Returns a cumulative frequency histogram, using the histogram function.
Usage: lcumfreq(inlist,numbins=10,defaultreallimits=None)
Returns: list of cumfreq bin values, lowerreallimit, binsize, extrapoints
"""
h, l, b, e = histogram(inlist, numbins, ... | python | def cumfreq(inlist, numbins=10, defaultreallimits=None):
"""
Returns a cumulative frequency histogram, using the histogram function.
Usage: lcumfreq(inlist,numbins=10,defaultreallimits=None)
Returns: list of cumfreq bin values, lowerreallimit, binsize, extrapoints
"""
h, l, b, e = histogram(inlist, numbins, ... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | relfreq | def relfreq(inlist, numbins=10, defaultreallimits=None):
"""
Returns a relative frequency histogram, using the histogram function.
Usage: lrelfreq(inlist,numbins=10,defaultreallimits=None)
Returns: list of cumfreq bin values, lowerreallimit, binsize, extrapoints
"""
h, l, b, e = histogram(inlist, numbins, de... | python | def relfreq(inlist, numbins=10, defaultreallimits=None):
"""
Returns a relative frequency histogram, using the histogram function.
Usage: lrelfreq(inlist,numbins=10,defaultreallimits=None)
Returns: list of cumfreq bin values, lowerreallimit, binsize, extrapoints
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | obrientransform | def obrientransform(*args):
"""
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test for homogeneity of variance prior to running one-way stats. From
Maxwell and Delaney, p.112.
Usage: lobrientransform(*args)
Returns: transformed data for use in an ANOVA
"""
TINY = 1e-10
k = len(arg... | python | def obrientransform(*args):
"""
Computes a transform on input data (any number of columns). Used to
test for homogeneity of variance prior to running one-way stats. From
Maxwell and Delaney, p.112.
Usage: lobrientransform(*args)
Returns: transformed data for use in an ANOVA
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | samplevar | def samplevar(inlist):
"""
Returns the variance of the values in the passed list using
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Usage: lsamplevar(inlist)
"""
n = len(inlist)
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deviations = []
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deviations.append(item - mn)
ret... | python | def samplevar(inlist):
"""
Returns the variance of the values in the passed list using
N for the denominator (i.e., DESCRIBES the sample variance only).
Usage: lsamplevar(inlist)
"""
n = len(inlist)
mn = mean(inlist)
deviations = []
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | cov | def cov(x, y, keepdims=0):
"""
Returns the estimated covariance of the values in the passed
array (i.e., N-1). Dimension can equal None (ravel array first), an
integer (the dimension over which to operate), or a sequence (operate
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same number of... | python | def cov(x, y, keepdims=0):
"""
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | var | def var(inlist):
"""
Returns the variance of the values in the passed list using N-1
for the denominator (i.e., for estimating population variance).
Usage: lvar(inlist)
"""
n = len(inlist)
mn = mean(inlist)
deviations = [0] * len(inlist)
for i in range(len(inlist)):
deviations[i] = inlist... | python | def var(inlist):
"""
Returns the variance of the values in the passed list using N-1
for the denominator (i.e., for estimating population variance).
Usage: lvar(inlist)
"""
n = len(inlist)
mn = mean(inlist)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | sem | def sem(inlist):
"""
Returns the estimated standard error of the mean (sx-bar) of the
values in the passed list. sem = stdev / sqrt(n)
Usage: lsem(inlist)
"""
sd = stdev(inlist)
n = len(inlist)
return sd / math.sqrt(n) | python | def sem(inlist):
"""
Returns the estimated standard error of the mean (sx-bar) of the
values in the passed list. sem = stdev / sqrt(n)
Usage: lsem(inlist)
"""
sd = stdev(inlist)
n = len(inlist)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | zs | def zs(inlist):
"""
Returns a list of z-scores, one for each score in the passed list.
Usage: lzs(inlist)
"""
zscores = []
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return zscores | python | def zs(inlist):
"""
Returns a list of z-scores, one for each score in the passed list.
Usage: lzs(inlist)
"""
zscores = []
for item in inlist:
zscores.append(z(inlist, item))
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | trimboth | def trimboth(l, proportiontocut):
"""
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10% of scores. Assumes list is sorted by magnitude. Slices off LESS if
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"""
Slices off the passed proportion of items from BOTH ends of the passed
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | trim1 | def trim1(l, proportiontocut, tail='right'):
"""
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"""
Slices off the passed proportion of items from ONE end of the passed
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | paired | def paired(x, y):
"""
Interactively determines the type of data and then runs the
appropriated statistic for paired group data.
Usage: lpaired(x,y)
Returns: appropriate statistic name, value, and probability
"""
samples = ''
while samples not in ['i', 'r', 'I', 'R', 'c', 'C']:
print('\nIndependen... | python | def paired(x, y):
"""
Interactively determines the type of data and then runs the
appropriated statistic for paired group data.
Usage: lpaired(x,y)
Returns: appropriate statistic name, value, and probability
"""
samples = ''
while samples not in ['i', 'r', 'I', 'R', 'c', 'C']:
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | lincc | def lincc(x, y):
"""
Calculates Lin's concordance correlation coefficient.
Usage: alincc(x,y) where x, y are equal-length arrays
Returns: Lin's CC
"""
covar = cov(x, y) * (len(x) - 1) / float(len(x)) # correct denom to n
xvar = var(x) * (len(x) - 1) / float(len(x)) # correct denom to n
yvar = va... | python | def lincc(x, y):
"""
Calculates Lin's concordance correlation coefficient.
Usage: alincc(x,y) where x, y are equal-length arrays
Returns: Lin's CC
"""
covar = cov(x, y) * (len(x) - 1) / float(len(x)) # correct denom to n
xvar = var(x) * (len(x) - 1) / float(len(x)) # correct denom to n
yvar = va... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | pointbiserialr | def pointbiserialr(cats, vals):
"""
Calculates a point-biserial correlation coefficient and the associated
probability value. Taken from Heiman's Basic Statistics for the Behav.
Sci (1st), p.194.
Usage: pointbiserialr(x,y) where x,y are equal-length lists
Returns: Point-biserial r, two-tailed p-value
"""
... | python | def pointbiserialr(cats, vals):
"""
Calculates a point-biserial correlation coefficient and the associated
probability value. Taken from Heiman's Basic Statistics for the Behav.
Sci (1st), p.194.
Usage: pointbiserialr(x,y) where x,y are equal-length lists
Returns: Point-biserial r, two-tailed p-value
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | chisquare | def chisquare(f_obs, f_exp=None):
"""
Calculates a one-way chi square for list of observed frequencies and returns
the result. If no expected frequencies are given, the total N is assumed to
be equally distributed across all groups.
Usage: lchisquare(f_obs, f_exp=None) f_obs = list of observed cell freq.
Retu... | python | def chisquare(f_obs, f_exp=None):
"""
Calculates a one-way chi square for list of observed frequencies and returns
the result. If no expected frequencies are given, the total N is assumed to
be equally distributed across all groups.
Usage: lchisquare(f_obs, f_exp=None) f_obs = list of observed cell freq.
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | wilcoxont | def wilcoxont(x, y):
"""
Calculates the Wilcoxon T-test for related samples and returns the
result. A non-parametric T-test.
Usage: lwilcoxont(x,y)
Returns: a t-statistic, two-tail probability estimate
"""
if len(x) != len(y):
raise ValueError('Unequal N in wilcoxont. Aborting.')
d = []
for... | python | def wilcoxont(x, y):
"""
Calculates the Wilcoxon T-test for related samples and returns the
result. A non-parametric T-test.
Usage: lwilcoxont(x,y)
Returns: a t-statistic, two-tail probability estimate
"""
if len(x) != len(y):
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d = []
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | friedmanchisquare | def friedmanchisquare(*args):
"""
Friedman Chi-Square is a non-parametric, one-way within-subjects
ANOVA. This function calculates the Friedman Chi-square test for repeated
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value. It assumes 3 or more repeated measures. Only 3 levels requires... | python | def friedmanchisquare(*args):
"""
Friedman Chi-Square is a non-parametric, one-way within-subjects
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | erfcc | def erfcc(x):
"""
Returns the complementary error function erfc(x) with fractional
error everywhere less than 1.2e-7. Adapted from Numerical Recipies.
Usage: lerfcc(x)
"""
z = abs(x)
t = 1.0 / (1.0 + 0.5 * z)
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"""
Returns the complementary error function erfc(x) with fractional
error everywhere less than 1.2e-7. Adapted from Numerical Recipies.
Usage: lerfcc(x)
"""
z = abs(x)
t = 1.0 / (1.0 + 0.5 * z)
ans = t * math.exp(
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | fprob | def fprob(dfnum, dfden, F):
"""
Returns the (1-tailed) significance level (p-value) of an F
statistic given the degrees of freedom for the numerator (dfR-dfF) and
the degrees of freedom for the denominator (dfF).
Usage: lfprob(dfnum, dfden, F) where usually dfnum=dfbn, dfden=dfwn
"""
p = betai(0.5 * dfden,... | python | def fprob(dfnum, dfden, F):
"""
Returns the (1-tailed) significance level (p-value) of an F
statistic given the degrees of freedom for the numerator (dfR-dfF) and
the degrees of freedom for the denominator (dfF).
Usage: lfprob(dfnum, dfden, F) where usually dfnum=dfbn, dfden=dfwn
"""
p = betai(0.5 * dfden,... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | betacf | def betacf(a, b, x):
"""
This function evaluates the continued fraction form of the incomplete
Beta function, betai. (Adapted from: Numerical Recipies in C.)
Usage: lbetacf(a,b,x)
"""
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qam = a - 1.0
... | python | def betacf(a, b, x):
"""
This function evaluates the continued fraction form of the incomplete
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Usage: lbetacf(a,b,x)
"""
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | F_oneway | def F_oneway(*lists):
"""
Performs a 1-way ANOVA, returning an F-value and probability given
any number of groups. From Heiman, pp.394-7.
Usage: F_oneway(*lists) where *lists is any number of lists, one per
treatment group
Returns: F value, one-tailed p-value
"""
a = len... | python | def F_oneway(*lists):
"""
Performs a 1-way ANOVA, returning an F-value and probability given
any number of groups. From Heiman, pp.394-7.
Usage: F_oneway(*lists) where *lists is any number of lists, one per
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Returns: F value, one-tailed p-value
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | F_value | def F_value(ER, EF, dfnum, dfden):
"""
Returns an F-statistic given the following:
ER = error associated with the null hypothesis (the Restricted model)
EF = error associated with the alternate hypothesis (the Full model)
dfR-dfF = degrees of freedom of the numerator
dfF = degrees ... | python | def F_value(ER, EF, dfnum, dfden):
"""
Returns an F-statistic given the following:
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EF = error associated with the alternate hypothesis (the Full model)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | incr | def incr(l, cap): # to increment a list up to a max-list of 'cap'
"""
Simulate a counting system from an n-dimensional list.
Usage: lincr(l,cap) l=list to increment, cap=max values for each list pos'n
Returns: next set of values for list l, OR -1 (if overflow)
"""
l[0] = l[0] + 1 # e.g., [0,0,0]... | python | def incr(l, cap): # to increment a list up to a max-list of 'cap'
"""
Simulate a counting system from an n-dimensional list.
Usage: lincr(l,cap) l=list to increment, cap=max values for each list pos'n
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l[0] = l[0] + 1 # e.g., [0,0,0]... | [
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | cumsum | def cumsum(inlist):
"""
Returns a list consisting of the cumulative sum of the items in the
passed list.
Usage: lcumsum(inlist)
"""
newlist = copy.deepcopy(inlist)
for i in range(1, len(newlist)):
newlist[i] = newlist[i] + newlist[i - 1]
return newlist | python | def cumsum(inlist):
"""
Returns a list consisting of the cumulative sum of the items in the
passed list.
Usage: lcumsum(inlist)
"""
newlist = copy.deepcopy(inlist)
for i in range(1, len(newlist)):
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | ss | def ss(inlist):
"""
Squares each value in the passed list, adds up these squares and
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Usage: lss(inlist)
"""
ss = 0
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ss = ss + item * item
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"""
Squares each value in the passed list, adds up these squares and
returns the result.
Usage: lss(inlist)
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ss = 0
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | summult | def summult(list1, list2):
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Multiplies elements in list1 and list2, element by element, and
returns the sum of all resulting multiplications. Must provide equal
length lists.
Usage: lsummult(list1,list2)
"""
if len(list1) != len(list2):
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s... | python | def summult(list1, list2):
"""
Multiplies elements in list1 and list2, element by element, and
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Usage: lsummult(list1,list2)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | sumdiffsquared | def sumdiffsquared(x, y):
"""
Takes pairwise differences of the values in lists x and y, squares
these differences, and returns the sum of these squares.
Usage: lsumdiffsquared(x,y)
Returns: sum[(x[i]-y[i])**2]
"""
sds = 0
for i in range(len(x)):
sds = sds + (x[i] - y[i]) ** 2
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"""
Takes pairwise differences of the values in lists x and y, squares
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Usage: lsumdiffsquared(x,y)
Returns: sum[(x[i]-y[i])**2]
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | shellsort | def shellsort(inlist):
"""
Shellsort algorithm. Sorts a 1D-list.
Usage: lshellsort(inlist)
Returns: sorted-inlist, sorting-index-vector (for original list)
"""
n = len(inlist)
svec = copy.deepcopy(inlist)
ivec = range(n)
gap = n / 2 # integer division needed
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"""
Shellsort algorithm. Sorts a 1D-list.
Usage: lshellsort(inlist)
Returns: sorted-inlist, sorting-index-vector (for original list)
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n = len(inlist)
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | rankdata | def rankdata(inlist):
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Usage: rankdata(inlist)
Returns: a list of length equal to inlist, containing rank scores
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n = len(inlist)
svec, ivec = shellsort(inlist)
sumranks ... | python | def rankdata(inlist):
"""
Ranks the data in inlist, dealing with ties appropritely. Assumes
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klahnakoski/pyLibrary | mo_math/vendor/strangman/stats.py | findwithin | def findwithin(data):
"""
Returns an integer representing a binary vector, where 1=within-
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column 0=random factor, column -1=measured values (those two are skipped).
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"""
Returns an integer representing a binary vector, where 1=within-
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.register_model | def register_model(self, model, bundle):
"""
Registers a bundle as the main bundle for a
model. Used when we need to lookup urls by
a model.
"""
if model in self._model_registry:
raise AlreadyRegistered('The model %s is already registered' \
... | python | def register_model(self, model, bundle):
"""
Registers a bundle as the main bundle for a
model. Used when we need to lookup urls by
a model.
"""
if model in self._model_registry:
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.unregister_model | def unregister_model(self, model):
"""
Unregisters the given model.
"""
if model not in self._model_registry:
raise NotRegistered('The model %s is not registered' % model)
del self._model_registry[model] | python | def unregister_model(self, model):
"""
Unregisters the given model.
"""
if model not in self._model_registry:
raise NotRegistered('The model %s is not registered' % model)
del self._model_registry[model] | [
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.register | def register(self, slug, bundle, order=1, title=None):
"""
Registers the bundle for a certain slug.
If a slug is already registered, this will raise AlreadyRegistered.
:param slug: The slug to register.
:param bundle: The bundle instance being registered.
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"""
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:param slug: The slug to register.
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.unregister | def unregister(self, slug):
"""
Unregisters the given url.
If a slug isn't already registered, this will raise NotRegistered.
"""
if slug not in self._registry:
raise NotRegistered('The slug %s is not registered' % slug)
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"""
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raise NotRegistered('The slug %s is not registered' % slug)
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.password_change | def password_change(self, request):
"""
Handles the "change password" task -- both form display and validation.
Uses the default auth views.
"""
from django.contrib.auth.views import password_change
url = reverse('admin:cms_password_change_done')
defaults = {
... | python | def password_change(self, request):
"""
Handles the "change password" task -- both form display and validation.
Uses the default auth views.
"""
from django.contrib.auth.views import password_change
url = reverse('admin:cms_password_change_done')
defaults = {
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.password_change_done | def password_change_done(self, request, extra_context=None):
"""
Displays the "success" page after a password change.
"""
from django.contrib.auth.views import password_change_done
defaults = {
'extra_context': extra_context or {},
'template_name': 'cms/pa... | python | def password_change_done(self, request, extra_context=None):
"""
Displays the "success" page after a password change.
"""
from django.contrib.auth.views import password_change_done
defaults = {
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.logout | def logout(self, request, extra_context=None):
"""
Logs out the user for the given HttpRequest.
This should *not* assume the user is already logged in.
"""
from django.contrib.auth.views import logout
defaults = {
'extra_context': extra_context or {},
... | python | def logout(self, request, extra_context=None):
"""
Logs out the user for the given HttpRequest.
This should *not* assume the user is already logged in.
"""
from django.contrib.auth.views import logout
defaults = {
'extra_context': extra_context or {},
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.login | def login(self, request, extra_context=None):
"""
Displays the login form for the given HttpRequest.
"""
from django.contrib.auth.views import login
context = {
'title': _('Log in'),
'app_path': request.get_full_path(),
REDIRECT_FIELD_NAME: req... | python | def login(self, request, extra_context=None):
"""
Displays the login form for the given HttpRequest.
"""
from django.contrib.auth.views import login
context = {
'title': _('Log in'),
'app_path': request.get_full_path(),
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite._get_allowed_sections | def _get_allowed_sections(self, dashboard):
"""
Get the sections to display based on dashboard
"""
allowed_titles = [x[0] for x in dashboard]
allowed_sections = [x[2] for x in dashboard]
return tuple(allowed_sections), tuple(allowed_titles) | python | def _get_allowed_sections(self, dashboard):
"""
Get the sections to display based on dashboard
"""
allowed_titles = [x[0] for x in dashboard]
allowed_sections = [x[2] for x in dashboard]
return tuple(allowed_sections), tuple(allowed_titles) | [
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ff0000/scarlet | scarlet/cms/sites.py | AdminSite.index | def index(self, request, extra_context=None):
"""
Displays the dashboard. Includes the main
navigation that the user has permission for as well
as the cms log for those sections. The log list can
be filtered by those same sections
and is paginated.
"""
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"""
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ff0000/scarlet | scarlet/cache/groups.py | CacheGroup.register_models | def register_models(self, *models, **kwargs):
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Calls register for each argument passed along with
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Register multiple models with the same
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camsci/meteor-pi | src/pythonModules/meteorpi_server/meteorpi_server/query_api.py | add_routes | def add_routes(meteor_app, url_path=''):
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Adds search and retrieval routes to a :class:`meteorpi_server.MeteorServer` instance
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Adds search and retrieval routes to a :class:`meteorpi_server.MeteorServer` instance
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klahnakoski/pyLibrary | jx_elasticsearch/es52/util.py | es_query_template | def es_query_template(path):
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:return: (es_query, es_filters) TUPLE
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... | python | def es_query_template(path):
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RETURN TEMPLATE AND PATH-TO-FILTER AS A 2-TUPLE
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inveniosoftware/invenio-collections | invenio_collections/receivers.py | _ancestors | def _ancestors(collection):
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yield c.name
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"""Get the ancestors of the collection."""
for index, c in enumerate(collection.path_to_root()):
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inveniosoftware/invenio-collections | invenio_collections/receivers.py | _find_matching_collections_internally | def _find_matching_collections_internally(collections, record):
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"""
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klahnakoski/pyLibrary | mo_graphs/algorithms.py | dfs | def dfs(graph, func, head, reverse=None):
"""
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klahnakoski/pyLibrary | mo_graphs/algorithms.py | dominator_tree | def dominator_tree(graph):
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"""
todo = Queue()
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dominat... | python | def dominator_tree(graph):
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RETURN DOMINATOR FOREST
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klahnakoski/pyLibrary | jx_python/meta.py | get_schema_from_list | def get_schema_from_list(table_name, frum):
"""
SCAN THE LIST FOR COLUMN TYPES
"""
columns = UniqueIndex(keys=("name",))
_get_schema_from_list(frum, ".", parent=".", nested_path=ROOT_PATH, columns=columns)
return Schema(table_name=table_name, columns=list(columns)) | python | def get_schema_from_list(table_name, frum):
"""
SCAN THE LIST FOR COLUMN TYPES
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columns = UniqueIndex(keys=("name",))
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klahnakoski/pyLibrary | jx_python/meta.py | _get_schema_from_list | def _get_schema_from_list(frum, table_name, parent, nested_path, columns):
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CONVERT SORT PARAMETERS TO A NORMAL FORM SO EASIER TO USE
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"""
CONVERT SORT PARAMETERS TO A NORMAL FORM SO EASIER TO USE
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Galarzaa90/tibia.py | tibiapy/utils.py | parse_tibia_datetime | def parse_tibia_datetime(datetime_str) -> Optional[datetime.datetime]:
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Accepted format:
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-------------
datetime_str: :class:`str`
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"""Parses date and time from the format used in Tibia.com
Accepted format:
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Galarzaa90/tibia.py | tibiapy/utils.py | parse_tibia_date | def parse_tibia_date(date_str) -> Optional[datetime.date]:
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The date as represented in Tibia.com
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Galarzaa90/tibia.py | tibiapy/utils.py | try_datetime | def try_datetime(obj) -> Optional[datetime.datetime]:
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Galarzaa90/tibia.py | tibiapy/utils.py | parse_tibiacom_content | def parse_tibiacom_content(content, *, html_class="BoxContent", tag="div", builder="lxml"):
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The raw HTML content from Tibia.com
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The enum to convert to.
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ninapavlich/django-share-me-share-me | share_me_share_me/templatetags/share_me_share_me_tags.py | get_social_share_link | def get_social_share_link(context, share_link, object_url, object_title):
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oblalex/django-candv-choices | candv_x/django/choices/admin.py | ChoicesFieldListFilter.choices | def choices(self, cl):
"""
Take choices from field's 'choices' attribute for 'ChoicesField' and
use 'flatchoices' as usual for other fields.
"""
#: Just tidy up standard implementation for the sake of DRY principle.
def _choice_item(is_selected, query_string, title):
... | python | def choices(self, cl):
"""
Take choices from field's 'choices' attribute for 'ChoicesField' and
use 'flatchoices' as usual for other fields.
"""
#: Just tidy up standard implementation for the sake of DRY principle.
def _choice_item(is_selected, query_string, title):
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uralbash/pyramid_pages | pyramid_pages/routes.py | page_factory | def page_factory(request):
""" Page factory.
Config models example:
.. code-block:: python
models = {
'': [WebPage, CatalogResource],
'catalogue': CatalogResource,
'news': NewsResource,
}
"""
prefix = request.matchdict['prefix'] # /{prefix}/pag... | python | def page_factory(request):
""" Page factory.
Config models example:
.. code-block:: python
models = {
'': [WebPage, CatalogResource],
'catalogue': CatalogResource,
'news': NewsResource,
}
"""
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uralbash/pyramid_pages | pyramid_pages/routes.py | register_views | def register_views(*args):
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"""
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pages_config = settings[CONFIG_MODELS]
resources = resources_of_config(pages_config)
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... | python | def register_views(*args):
""" Registration view for each resource from config.
"""
config = args[0]
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pages_config = settings[CONFIG_MODELS]
resources = resources_of_config(pages_config)
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klahnakoski/pyLibrary | jx_elasticsearch/es52/setop.py | accumulate_nested_doc | def accumulate_nested_doc(nested_path, expr=IDENTITY):
"""
:param nested_path: THE PATH USED TO EXTRACT THE NESTED RECORDS
:param expr: FUNCTION USED ON THE NESTED OBJECT TO GET SPECIFIC VALUE
:return: THE DE_TYPED NESTED OBJECT ARRAY
"""
name = literal_field(nested_path)
def output(doc):
... | python | def accumulate_nested_doc(nested_path, expr=IDENTITY):
"""
:param nested_path: THE PATH USED TO EXTRACT THE NESTED RECORDS
:param expr: FUNCTION USED ON THE NESTED OBJECT TO GET SPECIFIC VALUE
:return: THE DE_TYPED NESTED OBJECT ARRAY
"""
name = literal_field(nested_path)
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klahnakoski/pyLibrary | jx_elasticsearch/es52/setop.py | es_query_proto | def es_query_proto(path, selects, wheres, schema):
"""
RETURN TEMPLATE AND PATH-TO-FILTER AS A 2-TUPLE
:param path: THE NESTED PATH (NOT INCLUDING TABLE NAME)
:param wheres: MAP FROM path TO LIST OF WHERE CONDITIONS
:return: (es_query, filters_map) TUPLE
"""
output = None
last_where = MA... | python | def es_query_proto(path, selects, wheres, schema):
"""
RETURN TEMPLATE AND PATH-TO-FILTER AS A 2-TUPLE
:param path: THE NESTED PATH (NOT INCLUDING TABLE NAME)
:param wheres: MAP FROM path TO LIST OF WHERE CONDITIONS
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