partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
test | get_config_path | Determine the path to the config file. This will return, in this order of
precedence:
- the value of $BUGWARRIORRC if set
- $XDG_CONFIG_HOME/bugwarrior/bugwarriorc if exists
- ~/.bugwarriorrc if exists
- <dir>/bugwarrior/bugwarriorc if exists, for dir in $XDG_CONFIG_DIRS
- $XDG_CONFIG_HOME/bugwa... | bugwarrior/config.py | def get_config_path():
"""
Determine the path to the config file. This will return, in this order of
precedence:
- the value of $BUGWARRIORRC if set
- $XDG_CONFIG_HOME/bugwarrior/bugwarriorc if exists
- ~/.bugwarriorrc if exists
- <dir>/bugwarrior/bugwarriorc if exists, for dir in $XDG_CONFI... | def get_config_path():
"""
Determine the path to the config file. This will return, in this order of
precedence:
- the value of $BUGWARRIORRC if set
- $XDG_CONFIG_HOME/bugwarrior/bugwarriorc if exists
- ~/.bugwarriorrc if exists
- <dir>/bugwarrior/bugwarriorc if exists, for dir in $XDG_CONFI... | [
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"bugwarriorc",... | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/config.py#L186-L210 | [
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test | fix_logging_path | Expand environment variables and user home (~) in the log.file and return
as relative path. | bugwarrior/config.py | def fix_logging_path(config, main_section):
"""
Expand environment variables and user home (~) in the log.file and return
as relative path.
"""
log_file = config.get(main_section, 'log.file')
if log_file:
log_file = os.path.expanduser(os.path.expandvars(log_file))
if os.path.isab... | def fix_logging_path(config, main_section):
"""
Expand environment variables and user home (~) in the log.file and return
as relative path.
"""
log_file = config.get(main_section, 'log.file')
if log_file:
log_file = os.path.expanduser(os.path.expandvars(log_file))
if os.path.isab... | [
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] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/config.py#L213-L223 | [
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test | BugwarriorConfigParser.getint | Accepts both integers and empty values. | bugwarrior/config.py | def getint(self, section, option):
""" Accepts both integers and empty values. """
try:
return super(BugwarriorConfigParser, self).getint(section, option)
except ValueError:
if self.get(section, option) == u'':
return None
else:
... | def getint(self, section, option):
""" Accepts both integers and empty values. """
try:
return super(BugwarriorConfigParser, self).getint(section, option)
except ValueError:
if self.get(section, option) == u'':
return None
else:
... | [
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test | _get_bug_attr | Default longdescs/flags case to [] since they may not be present. | bugwarrior/services/bz.py | def _get_bug_attr(bug, attr):
"""Default longdescs/flags case to [] since they may not be present."""
if attr in ("longdescs", "flags"):
return getattr(bug, attr, [])
return getattr(bug, attr) | def _get_bug_attr(bug, attr):
"""Default longdescs/flags case to [] since they may not be present."""
if attr in ("longdescs", "flags"):
return getattr(bug, attr, [])
return getattr(bug, attr) | [
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] | b2a5108f7b40cb0c437509b64eaa28f941f7ac8b |
test | pull | Pull down tasks from forges and add them to your taskwarrior tasks.
Relies on configuration in bugwarriorrc | bugwarrior/command.py | def pull(dry_run, flavor, interactive, debug):
""" Pull down tasks from forges and add them to your taskwarrior tasks.
Relies on configuration in bugwarriorrc
"""
try:
main_section = _get_section_name(flavor)
config = _try_load_config(main_section, interactive)
lockfile_path =... | def pull(dry_run, flavor, interactive, debug):
""" Pull down tasks from forges and add them to your taskwarrior tasks.
Relies on configuration in bugwarriorrc
"""
try:
main_section = _get_section_name(flavor)
config = _try_load_config(main_section, interactive)
lockfile_path =... | [
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test | BitbucketService.get_data | Perform a request to the fully qualified url and return json. | bugwarrior/services/bitbucket.py | def get_data(self, url):
""" Perform a request to the fully qualified url and return json. """
return self.json_response(requests.get(url, **self.requests_kwargs)) | def get_data(self, url):
""" Perform a request to the fully qualified url and return json. """
return self.json_response(requests.get(url, **self.requests_kwargs)) | [
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test | BitbucketService.get_collection | Pages through an object collection from the bitbucket API.
Returns an iterator that lazily goes through all the 'values'
of all the pages in the collection. | bugwarrior/services/bitbucket.py | def get_collection(self, url):
""" Pages through an object collection from the bitbucket API.
Returns an iterator that lazily goes through all the 'values'
of all the pages in the collection. """
url = self.BASE_API2 + url
while url is not None:
response = self.get_da... | def get_collection(self, url):
""" Pages through an object collection from the bitbucket API.
Returns an iterator that lazily goes through all the 'values'
of all the pages in the collection. """
url = self.BASE_API2 + url
while url is not None:
response = self.get_da... | [
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] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/services/bitbucket.py#L146-L155 | [
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test | hamdist | Count the # of differences between equal length strings str1 and str2 | bugwarrior/db.py | def hamdist(str1, str2):
"""Count the # of differences between equal length strings str1 and str2"""
diffs = 0
for ch1, ch2 in zip(str1, str2):
if ch1 != ch2:
diffs += 1
return diffs | def hamdist(str1, str2):
"""Count the # of differences between equal length strings str1 and str2"""
diffs = 0
for ch1, ch2 in zip(str1, str2):
if ch1 != ch2:
diffs += 1
return diffs | [
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] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/db.py#L91-L97 | [
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] | b2a5108f7b40cb0c437509b64eaa28f941f7ac8b |
test | find_local_uuid | For a given issue issue, find its local UUID.
Assembles a list of task IDs existing in taskwarrior
matching the supplied issue (`issue`) on the combination of any
set of supplied unique identifiers (`keys`) or, optionally,
the task's description field (should `legacy_matching` be `True`).
:params:... | bugwarrior/db.py | def find_local_uuid(tw, keys, issue, legacy_matching=False):
""" For a given issue issue, find its local UUID.
Assembles a list of task IDs existing in taskwarrior
matching the supplied issue (`issue`) on the combination of any
set of supplied unique identifiers (`keys`) or, optionally,
the task's ... | def find_local_uuid(tw, keys, issue, legacy_matching=False):
""" For a given issue issue, find its local UUID.
Assembles a list of task IDs existing in taskwarrior
matching the supplied issue (`issue`) on the combination of any
set of supplied unique identifiers (`keys`) or, optionally,
the task's ... | [
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"UUID",
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] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/db.py#L129-L212 | [
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test | merge_left | Merge array field from the remote_issue into local_task
* Local 'left' entries are preserved without modification
* Remote 'left' are appended to task if not present in local.
:param `field`: Task field to merge.
:param `local_task`: `taskw.task.Task` object into which to merge
remote changes.... | bugwarrior/db.py | def merge_left(field, local_task, remote_issue, hamming=False):
""" Merge array field from the remote_issue into local_task
* Local 'left' entries are preserved without modification
* Remote 'left' are appended to task if not present in local.
:param `field`: Task field to merge.
:param `local_tas... | def merge_left(field, local_task, remote_issue, hamming=False):
""" Merge array field from the remote_issue into local_task
* Local 'left' entries are preserved without modification
* Remote 'left' are appended to task if not present in local.
:param `field`: Task field to merge.
:param `local_tas... | [
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"local_task"
] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/db.py#L215-L263 | [
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test | build_uda_config_overrides | Returns a list of UDAs defined by given targets
For all targets in `targets`, build a dictionary of configuration overrides
representing the UDAs defined by the passed-in services (`targets`).
Given a hypothetical situation in which you have two services, the first
of which defining a UDA named 'servi... | bugwarrior/db.py | def build_uda_config_overrides(targets):
""" Returns a list of UDAs defined by given targets
For all targets in `targets`, build a dictionary of configuration overrides
representing the UDAs defined by the passed-in services (`targets`).
Given a hypothetical situation in which you have two services, t... | def build_uda_config_overrides(targets):
""" Returns a list of UDAs defined by given targets
For all targets in `targets`, build a dictionary of configuration overrides
representing the UDAs defined by the passed-in services (`targets`).
Given a hypothetical situation in which you have two services, t... | [
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test | _parse_sprint_string | Parse the big ugly sprint string stored by JIRA.
They look like:
com.atlassian.greenhopper.service.sprint.Sprint@4c9c41a5[id=2322,rapid
ViewId=1173,state=ACTIVE,name=Sprint 1,startDate=2016-09-06T16:08:07.4
55Z,endDate=2016-09-23T16:08:00.000Z,completeDate=<null>,sequence=2322] | bugwarrior/services/jira.py | def _parse_sprint_string(sprint):
""" Parse the big ugly sprint string stored by JIRA.
They look like:
com.atlassian.greenhopper.service.sprint.Sprint@4c9c41a5[id=2322,rapid
ViewId=1173,state=ACTIVE,name=Sprint 1,startDate=2016-09-06T16:08:07.4
55Z,endDate=2016-09-23T16:08:00.000Z,compl... | def _parse_sprint_string(sprint):
""" Parse the big ugly sprint string stored by JIRA.
They look like:
com.atlassian.greenhopper.service.sprint.Sprint@4c9c41a5[id=2322,rapid
ViewId=1173,state=ACTIVE,name=Sprint 1,startDate=2016-09-06T16:08:07.4
55Z,endDate=2016-09-23T16:08:00.000Z,compl... | [
"Parse",
"the",
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"ugly",
"sprint",
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"by",
"JIRA",
"."
] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/services/jira.py#L43-L53 | [
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test | GmailService.get_credentials | Gets valid user credentials from storage.
If nothing has been stored, or if the stored credentials are invalid,
the OAuth2 flow is completed to obtain the new credentials.
Returns:
Credentials, the obtained credential. | bugwarrior/services/gmail.py | def get_credentials(self):
"""Gets valid user credentials from storage.
If nothing has been stored, or if the stored credentials are invalid,
the OAuth2 flow is completed to obtain the new credentials.
Returns:
Credentials, the obtained credential.
"""
with ... | def get_credentials(self):
"""Gets valid user credentials from storage.
If nothing has been stored, or if the stored credentials are invalid,
the OAuth2 flow is completed to obtain the new credentials.
Returns:
Credentials, the obtained credential.
"""
with ... | [
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] | ralphbean/bugwarrior | python | https://github.com/ralphbean/bugwarrior/blob/b2a5108f7b40cb0c437509b64eaa28f941f7ac8b/bugwarrior/services/gmail.py#L120-L140 | [
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test | multi_rouge_n | Efficient way to compute highly repetitive scoring
i.e. sequences are involved multiple time
Args:
sequences(list[str]): list of sequences (either hyp or ref)
scores_ids(list[tuple(int)]): list of pairs (hyp_id, ref_id)
ie. scores[i] = rouge_n(scores_ids[i][0],
... | rouge/rouge_score.py | def multi_rouge_n(sequences, scores_ids, n=2):
"""
Efficient way to compute highly repetitive scoring
i.e. sequences are involved multiple time
Args:
sequences(list[str]): list of sequences (either hyp or ref)
scores_ids(list[tuple(int)]): list of pairs (hyp_id, ref_id)
ie. ... | def multi_rouge_n(sequences, scores_ids, n=2):
"""
Efficient way to compute highly repetitive scoring
i.e. sequences are involved multiple time
Args:
sequences(list[str]): list of sequences (either hyp or ref)
scores_ids(list[tuple(int)]): list of pairs (hyp_id, ref_id)
ie. ... | [
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] | pltrdy/rouge | python | https://github.com/pltrdy/rouge/blob/7bf8a83af5ca5c1677b93620b4e1f85ffd63b377/rouge/rouge_score.py#L140-L174 | [
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... | 7bf8a83af5ca5c1677b93620b4e1f85ffd63b377 |
test | rouge_n | Computes ROUGE-N of two text collections of sentences.
Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
papers/rouge-working-note-v1.3.1.pdf
Args:
evaluated_sentences: The sentences that have been picked by the
summarizer
reference_sentences: The s... | rouge/rouge_score.py | def rouge_n(evaluated_sentences, reference_sentences, n=2):
"""
Computes ROUGE-N of two text collections of sentences.
Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
papers/rouge-working-note-v1.3.1.pdf
Args:
evaluated_sentences: The sentences that have been picked by th... | def rouge_n(evaluated_sentences, reference_sentences, n=2):
"""
Computes ROUGE-N of two text collections of sentences.
Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
papers/rouge-working-note-v1.3.1.pdf
Args:
evaluated_sentences: The sentences that have been picked by th... | [
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test | _union_lcs | Returns LCS_u(r_i, C) which is the LCS score of the union longest common
subsequence between reference sentence ri and candidate summary C.
For example:
if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8
and c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1
... | rouge/rouge_score.py | def _union_lcs(evaluated_sentences, reference_sentence, prev_union=None):
"""
Returns LCS_u(r_i, C) which is the LCS score of the union longest common
subsequence between reference sentence ri and candidate summary C.
For example:
if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w... | def _union_lcs(evaluated_sentences, reference_sentence, prev_union=None):
"""
Returns LCS_u(r_i, C) which is the LCS score of the union longest common
subsequence between reference sentence ri and candidate summary C.
For example:
if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w... | [
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test | rouge_l_summary_level | Computes ROUGE-L (summary level) of two text collections of sentences.
http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Calculated according to:
R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m
P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n
F_lcs = ((1 + beta^2)*R_... | rouge/rouge_score.py | def rouge_l_summary_level(evaluated_sentences, reference_sentences):
"""
Computes ROUGE-L (summary level) of two text collections of sentences.
http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Calculated according to:
R_lcs = SUM(1, u)[LCS<union>(... | def rouge_l_summary_level(evaluated_sentences, reference_sentences):
"""
Computes ROUGE-L (summary level) of two text collections of sentences.
http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Calculated according to:
R_lcs = SUM(1, u)[LCS<union>(... | [
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test | FilesRouge.get_scores | Calculate ROUGE scores between each pair of
lines (hyp_file[i], ref_file[i]).
Args:
* hyp_path: hypothesis file path
* ref_path: references file path
* avg (False): whether to get an average scores or a list | rouge/rouge.py | def get_scores(self, avg=False, ignore_empty=False):
"""Calculate ROUGE scores between each pair of
lines (hyp_file[i], ref_file[i]).
Args:
* hyp_path: hypothesis file path
* ref_path: references file path
* avg (False): whether to get an average scores or a list
... | def get_scores(self, avg=False, ignore_empty=False):
"""Calculate ROUGE scores between each pair of
lines (hyp_file[i], ref_file[i]).
Args:
* hyp_path: hypothesis file path
* ref_path: references file path
* avg (False): whether to get an average scores or a list
... | [
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test | calc_pvalues | calculate pvalues for all categories in the graph
:param set query: set of identifiers for which the p value is calculated
:param dict gene_sets: gmt file dict after background was set
:param set background: total number of genes in your annotated database.
:returns: pvalues
x: overlapped... | gseapy/stats.py | def calc_pvalues(query, gene_sets, background=20000, **kwargs):
""" calculate pvalues for all categories in the graph
:param set query: set of identifiers for which the p value is calculated
:param dict gene_sets: gmt file dict after background was set
:param set background: total number of genes in yo... | def calc_pvalues(query, gene_sets, background=20000, **kwargs):
""" calculate pvalues for all categories in the graph
:param set query: set of identifiers for which the p value is calculated
:param dict gene_sets: gmt file dict after background was set
:param set background: total number of genes in yo... | [
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test | fdrcorrection | benjamini hocheberg fdr correction. inspired by statsmodels | gseapy/stats.py | def fdrcorrection(pvals, alpha=0.05):
""" benjamini hocheberg fdr correction. inspired by statsmodels
"""
# Implement copy from GOATools.
pvals = np.asarray(pvals)
pvals_sortind = np.argsort(pvals)
pvals_sorted = np.take(pvals, pvals_sortind)
ecdffactor = _ecdf(pvals_sorted)
reject = p... | def fdrcorrection(pvals, alpha=0.05):
""" benjamini hocheberg fdr correction. inspired by statsmodels
"""
# Implement copy from GOATools.
pvals = np.asarray(pvals)
pvals_sortind = np.argsort(pvals)
pvals_sorted = np.take(pvals, pvals_sortind)
ecdffactor = _ecdf(pvals_sorted)
reject = p... | [
"benjamini",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/stats.py#L85-L107 | [
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"... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | zscore | Standardize the mean and variance of the data axis Parameters.
:param data2d: DataFrame to normalize.
:param axis: int, Which axis to normalize across. If 0, normalize across rows,
if 1, normalize across columns. If None, don't change data
:Returns: Normalized DataFrame... | gseapy/plot.py | def zscore(data2d, axis=0):
"""Standardize the mean and variance of the data axis Parameters.
:param data2d: DataFrame to normalize.
:param axis: int, Which axis to normalize across. If 0, normalize across rows,
if 1, normalize across columns. If None, don't change data
... | def zscore(data2d, axis=0):
"""Standardize the mean and variance of the data axis Parameters.
:param data2d: DataFrame to normalize.
:param axis: int, Which axis to normalize across. If 0, normalize across rows,
if 1, normalize across columns. If None, don't change data
... | [
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/plot.py#L28-L57 | [
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test | heatmap | Visualize the dataframe.
:param df: DataFrame from expression table.
:param z_score: z_score axis{0, 1}. If None, don't normalize data.
:param title: gene set name.
:param outdir: path to save heatmap.
:param figsize: heatmap figsize.
:param cmap: matplotlib colormap.
:param ofname: output ... | gseapy/plot.py | def heatmap(df, z_score=None, title='', figsize=(5,5), cmap='RdBu_r',
xticklabels=True, yticklabels=True, ofname=None, **kwargs):
"""Visualize the dataframe.
:param df: DataFrame from expression table.
:param z_score: z_score axis{0, 1}. If None, don't normalize data.
:param title: gene se... | def heatmap(df, z_score=None, title='', figsize=(5,5), cmap='RdBu_r',
xticklabels=True, yticklabels=True, ofname=None, **kwargs):
"""Visualize the dataframe.
:param df: DataFrame from expression table.
:param z_score: z_score axis{0, 1}. If None, don't normalize data.
:param title: gene se... | [
"Visualize",
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test | gseaplot | This is the main function for reproducing the gsea plot.
:param rank_metric: pd.Series for rankings, rank_metric.values.
:param term: gene_set name
:param hits_indices: hits indices of rank_metric.index presented in gene set S.
:param nes: Normalized enrichment scores.
:param pval: nominal p-value.... | gseapy/plot.py | def gseaplot(rank_metric, term, hits_indices, nes, pval, fdr, RES,
pheno_pos='', pheno_neg='', figsize=(6,5.5),
cmap='seismic', ofname=None, **kwargs):
"""This is the main function for reproducing the gsea plot.
:param rank_metric: pd.Series for rankings, rank_metric.values.
:p... | def gseaplot(rank_metric, term, hits_indices, nes, pval, fdr, RES,
pheno_pos='', pheno_neg='', figsize=(6,5.5),
cmap='seismic', ofname=None, **kwargs):
"""This is the main function for reproducing the gsea plot.
:param rank_metric: pd.Series for rankings, rank_metric.values.
:p... | [
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test | dotplot | Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame to show. Default: Adjusted P-value
:param title: figure title
:param cutoff: p-adjust cut-off.
:param top_term: number of enriched terms to show.
:param ascending: bool, the order of y axis.... | gseapy/plot.py | def dotplot(df, column='Adjusted P-value', title='', cutoff=0.05, top_term=10,
sizes=None, norm=None, legend=True, figsize=(6, 5.5),
cmap='RdBu_r', ofname=None, **kwargs):
"""Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame t... | def dotplot(df, column='Adjusted P-value', title='', cutoff=0.05, top_term=10,
sizes=None, norm=None, legend=True, figsize=(6, 5.5),
cmap='RdBu_r', ofname=None, **kwargs):
"""Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame t... | [
"Visualize",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/plot.py#L253-L380 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | barplot | Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame to show. Default: Adjusted P-value
:param title: figure title.
:param cutoff: cut-off of the cloumn you've chosen.
:param top_term: number of top enriched terms to show.
:param figsize: tupl... | gseapy/plot.py | def barplot(df, column='Adjusted P-value', title="", cutoff=0.05, top_term=10,
figsize=(6.5,6), color='salmon', ofname=None, **kwargs):
"""Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame to show. Default: Adjusted P-value
:param title... | def barplot(df, column='Adjusted P-value', title="", cutoff=0.05, top_term=10,
figsize=(6.5,6), color='salmon', ofname=None, **kwargs):
"""Visualize enrichr results.
:param df: GSEApy DataFrame results.
:param column: which column of DataFrame to show. Default: Adjusted P-value
:param title... | [
"Visualize",
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | adjust_spines | function for removing spines and ticks.
:param ax: axes object
:param spines: a list of spines names to keep. e.g [left, right, top, bottom]
if spines = []. remove all spines and ticks. | gseapy/plot.py | def adjust_spines(ax, spines):
"""function for removing spines and ticks.
:param ax: axes object
:param spines: a list of spines names to keep. e.g [left, right, top, bottom]
if spines = []. remove all spines and ticks.
"""
for loc, spine in ax.spines.items():
if loc in... | def adjust_spines(ax, spines):
"""function for removing spines and ticks.
:param ax: axes object
:param spines: a list of spines names to keep. e.g [left, right, top, bottom]
if spines = []. remove all spines and ticks.
"""
for loc, spine in ax.spines.items():
if loc in... | [
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"removing",
"spines",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/plot.py#L436-L463 | [
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test | main | The Main function/pipeline for GSEApy. | gseapy/__main__.py | def main():
"""The Main function/pipeline for GSEApy."""
# Parse options...
argparser = prepare_argparser()
args = argparser.parse_args()
subcommand = args.subcommand_name
if subcommand == "replot":
# reproduce plots using GSEAPY
from .gsea import Replot
rep = Replot(in... | def main():
"""The Main function/pipeline for GSEApy."""
# Parse options...
argparser = prepare_argparser()
args = argparser.parse_args()
subcommand = args.subcommand_name
if subcommand == "replot":
# reproduce plots using GSEAPY
from .gsea import Replot
rep = Replot(in... | [
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/__main__.py#L16-L84 | [
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test | prepare_argparser | Prepare argparser object. New options will be added in this function first. | gseapy/__main__.py | def prepare_argparser():
"""Prepare argparser object. New options will be added in this function first."""
description = "%(prog)s -- Gene Set Enrichment Analysis in Python"
epilog = "For command line options of each command, type: %(prog)s COMMAND -h"
# top-level parser
argparser = ap.ArgumentPars... | def prepare_argparser():
"""Prepare argparser object. New options will be added in this function first."""
description = "%(prog)s -- Gene Set Enrichment Analysis in Python"
epilog = "For command line options of each command, type: %(prog)s COMMAND -h"
# top-level parser
argparser = ap.ArgumentPars... | [
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test | add_output_option | output option | gseapy/__main__.py | def add_output_option(parser):
"""output option"""
parser.add_argument("-o", "--outdir", dest="outdir", type=str, default='GSEApy_reports',
metavar='', action="store",
help="The GSEApy output directory. Default: the current working directory")
parser.add_argu... | def add_output_option(parser):
"""output option"""
parser.add_argument("-o", "--outdir", dest="outdir", type=str, default='GSEApy_reports',
metavar='', action="store",
help="The GSEApy output directory. Default: the current working directory")
parser.add_argu... | [
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/__main__.py#L112-L131 | [
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test | add_output_group | output group | gseapy/__main__.py | def add_output_group(parser, required=True):
"""output group"""
output_group = parser.add_mutually_exclusive_group(required=required)
output_group.add_argument("-o", "--ofile", dest="ofile", type=str, default='GSEApy_reports',
help="Output file name. Mutually exclusive with --... | def add_output_group(parser, required=True):
"""output group"""
output_group = parser.add_mutually_exclusive_group(required=required)
output_group.add_argument("-o", "--ofile", dest="ofile", type=str, default='GSEApy_reports',
help="Output file name. Mutually exclusive with --... | [
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test | add_gsea_parser | Add main function 'gsea' argument parsers. | gseapy/__main__.py | def add_gsea_parser(subparsers):
"""Add main function 'gsea' argument parsers."""
argparser_gsea = subparsers.add_parser("gsea", help="Main GSEApy Function: run GSEApy instead of GSEA.")
# group for input files
group_input = argparser_gsea.add_argument_group("Input files arguments")
group_input.ad... | def add_gsea_parser(subparsers):
"""Add main function 'gsea' argument parsers."""
argparser_gsea = subparsers.add_parser("gsea", help="Main GSEApy Function: run GSEApy instead of GSEA.")
# group for input files
group_input = argparser_gsea.add_argument_group("Input files arguments")
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test | add_prerank_parser | Add function 'prerank' argument parsers. | gseapy/__main__.py | def add_prerank_parser(subparsers):
"""Add function 'prerank' argument parsers."""
argparser_prerank = subparsers.add_parser("prerank", help="Run GSEApy Prerank tool on preranked gene list.")
# group for input files
prerank_input = argparser_prerank.add_argument_group("Input files arguments")
prer... | def add_prerank_parser(subparsers):
"""Add function 'prerank' argument parsers."""
argparser_prerank = subparsers.add_parser("prerank", help="Run GSEApy Prerank tool on preranked gene list.")
# group for input files
prerank_input = argparser_prerank.add_argument_group("Input files arguments")
prer... | [
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test | add_plot_parser | Add function 'plot' argument parsers. | gseapy/__main__.py | def add_plot_parser(subparsers):
"""Add function 'plot' argument parsers."""
argparser_replot = subparsers.add_parser("replot", help="Reproduce GSEA desktop output figures.")
group_replot = argparser_replot.add_argument_group("Input arguments")
group_replot.add_argument("-i", "--indir", action="store... | def add_plot_parser(subparsers):
"""Add function 'plot' argument parsers."""
argparser_replot = subparsers.add_parser("replot", help="Reproduce GSEA desktop output figures.")
group_replot = argparser_replot.add_argument_group("Input arguments")
group_replot.add_argument("-i", "--indir", action="store... | [
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test | add_enrichr_parser | Add function 'enrichr' argument parsers. | gseapy/__main__.py | def add_enrichr_parser(subparsers):
"""Add function 'enrichr' argument parsers."""
argparser_enrichr = subparsers.add_parser("enrichr", help="Using Enrichr API to perform GO analysis.")
# group for required options.
enrichr_opt = argparser_enrichr.add_argument_group("Input arguments")
enrichr_opt.... | def add_enrichr_parser(subparsers):
"""Add function 'enrichr' argument parsers."""
argparser_enrichr = subparsers.add_parser("enrichr", help="Using Enrichr API to perform GO analysis.")
# group for required options.
enrichr_opt = argparser_enrichr.add_argument_group("Input arguments")
enrichr_opt.... | [
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test | add_biomart_parser | Add function 'biomart' argument parsers. | gseapy/__main__.py | def add_biomart_parser(subparsers):
"""Add function 'biomart' argument parsers."""
argparser_biomart = subparsers.add_parser("biomart", help="Using BioMart API to convert gene ids.")
# group for required options.
biomart_opt = argparser_biomart.add_argument_group("Input arguments")
biomart_opt.add... | def add_biomart_parser(subparsers):
"""Add function 'biomart' argument parsers."""
argparser_biomart = subparsers.add_parser("biomart", help="Using BioMart API to convert gene ids.")
# group for required options.
biomart_opt = argparser_biomart.add_argument_group("Input arguments")
biomart_opt.add... | [
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test | enrichment_score | This is the most important function of GSEApy. It has the same algorithm with GSEA and ssGSEA.
:param gene_list: The ordered gene list gene_name_list, rank_metric.index.values
:param gene_set: gene_sets in gmt file, please use gsea_gmt_parser to get gene_set.
:param weighted_score_type: It's ... | gseapy/algorithm.py | def enrichment_score(gene_list, correl_vector, gene_set, weighted_score_type=1,
nperm=1000, rs=np.random.RandomState(), single=False, scale=False):
"""This is the most important function of GSEApy. It has the same algorithm with GSEA and ssGSEA.
:param gene_list: The ordered gene li... | def enrichment_score(gene_list, correl_vector, gene_set, weighted_score_type=1,
nperm=1000, rs=np.random.RandomState(), single=False, scale=False):
"""This is the most important function of GSEApy. It has the same algorithm with GSEA and ssGSEA.
:param gene_list: The ordered gene li... | [
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test | enrichment_score_tensor | Next generation algorithm of GSEA and ssGSEA.
:param gene_mat: the ordered gene list(vector) with or without gene indices matrix.
:param cor_mat: correlation vector or matrix (e.g. signal to noise scores)
corresponding to the genes in the gene list or mat... | gseapy/algorithm.py | def enrichment_score_tensor(gene_mat, cor_mat, gene_sets, weighted_score_type, nperm=1000,
rs=np.random.RandomState(), single=False, scale=False):
"""Next generation algorithm of GSEA and ssGSEA.
:param gene_mat: the ordered gene list(vector) with or without gene indices ... | def enrichment_score_tensor(gene_mat, cor_mat, gene_sets, weighted_score_type, nperm=1000,
rs=np.random.RandomState(), single=False, scale=False):
"""Next generation algorithm of GSEA and ssGSEA.
:param gene_mat: the ordered gene list(vector) with or without gene indices ... | [
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test | ranking_metric_tensor | Build shuffled ranking matrix when permutation_type eq to phenotype.
:param exprs: gene_expression DataFrame, gene_name indexed.
:param str method: calculate correlation or ranking. methods including:
1. 'signal_to_noise'.
2. 't_test'.
... | gseapy/algorithm.py | def ranking_metric_tensor(exprs, method, permutation_num, pos, neg, classes,
ascending, rs=np.random.RandomState()):
"""Build shuffled ranking matrix when permutation_type eq to phenotype.
:param exprs: gene_expression DataFrame, gene_name indexed.
:param str method: calc... | def ranking_metric_tensor(exprs, method, permutation_num, pos, neg, classes,
ascending, rs=np.random.RandomState()):
"""Build shuffled ranking matrix when permutation_type eq to phenotype.
:param exprs: gene_expression DataFrame, gene_name indexed.
:param str method: calc... | [
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test | ranking_metric | The main function to rank an expression table.
:param df: gene_expression DataFrame.
:param method: The method used to calculate a correlation or ranking. Default: 'log2_ratio_of_classes'.
Others methods are:
1. 'signal_to_noise'
... | gseapy/algorithm.py | def ranking_metric(df, method, pos, neg, classes, ascending):
"""The main function to rank an expression table.
:param df: gene_expression DataFrame.
:param method: The method used to calculate a correlation or ranking. Default: 'log2_ratio_of_classes'.
Others methods are... | def ranking_metric(df, method, pos, neg, classes, ascending):
"""The main function to rank an expression table.
:param df: gene_expression DataFrame.
:param method: The method used to calculate a correlation or ranking. Default: 'log2_ratio_of_classes'.
Others methods are... | [
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test | gsea_compute_tensor | compute enrichment scores and enrichment nulls.
:param data: preprocessed expression dataframe or a pre-ranked file if prerank=True.
:param dict gmt: all gene sets in .gmt file. need to call load_gmt() to get results.
:param int n: permutation number. default: 1000.
:param str method: r... | gseapy/algorithm.py | def gsea_compute_tensor(data, gmt, n, weighted_score_type, permutation_type,
method, pheno_pos, pheno_neg, classes, ascending,
processes=1, seed=None, single=False, scale=False):
"""compute enrichment scores and enrichment nulls.
:param data: preprocessed expression datafr... | def gsea_compute_tensor(data, gmt, n, weighted_score_type, permutation_type,
method, pheno_pos, pheno_neg, classes, ascending,
processes=1, seed=None, single=False, scale=False):
"""compute enrichment scores and enrichment nulls.
:param data: preprocessed expression datafr... | [
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test | gsea_compute | compute enrichment scores and enrichment nulls.
:param data: preprocessed expression dataframe or a pre-ranked file if prerank=True.
:param dict gmt: all gene sets in .gmt file. need to call load_gmt() to get results.
:param int n: permutation number. default: 1000.
:param str method: r... | gseapy/algorithm.py | def gsea_compute(data, gmt, n, weighted_score_type, permutation_type,
method, pheno_pos, pheno_neg, classes, ascending,
processes=1, seed=None, single=False, scale=False):
"""compute enrichment scores and enrichment nulls.
:param data: preprocessed expression dataframe or ... | def gsea_compute(data, gmt, n, weighted_score_type, permutation_type,
method, pheno_pos, pheno_neg, classes, ascending,
processes=1, seed=None, single=False, scale=False):
"""compute enrichment scores and enrichment nulls.
:param data: preprocessed expression dataframe or ... | [
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test | gsea_pval | Compute nominal p-value.
From article (PNAS):
estimate nominal p-value for S from esnull by using the positive
or negative portion of the distribution corresponding to the sign
of the observed ES(S). | gseapy/algorithm.py | def gsea_pval(es, esnull):
"""Compute nominal p-value.
From article (PNAS):
estimate nominal p-value for S from esnull by using the positive
or negative portion of the distribution corresponding to the sign
of the observed ES(S).
"""
# to speed up, using numpy function to compute pval in p... | def gsea_pval(es, esnull):
"""Compute nominal p-value.
From article (PNAS):
estimate nominal p-value for S from esnull by using the positive
or negative portion of the distribution corresponding to the sign
of the observed ES(S).
"""
# to speed up, using numpy function to compute pval in p... | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | normalize | normalize the ES(S,pi) and the observed ES(S), separately rescaling
the positive and negative scores by dividing the mean of the ES(S,pi).
return: NES, NESnull | gseapy/algorithm.py | def normalize(es, esnull):
"""normalize the ES(S,pi) and the observed ES(S), separately rescaling
the positive and negative scores by dividing the mean of the ES(S,pi).
return: NES, NESnull
"""
nEnrichmentScores =np.zeros(es.shape)
nEnrichmentNulls=np.zeros(esnull.shape)
esnu... | def normalize(es, esnull):
"""normalize the ES(S,pi) and the observed ES(S), separately rescaling
the positive and negative scores by dividing the mean of the ES(S,pi).
return: NES, NESnull
"""
nEnrichmentScores =np.zeros(es.shape)
nEnrichmentNulls=np.zeros(esnull.shape)
esnu... | [
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test | gsea_significance | Compute nominal pvals, normalized ES, and FDR q value.
For a given NES(S) = NES* >= 0. The FDR is the ratio of the percentage of all (S,pi) with
NES(S,pi) >= 0, whose NES(S,pi) >= NES*, divided by the percentage of
observed S wih NES(S) >= 0, whose NES(S) >= NES*, and similarly if NES(S) = NES*... | gseapy/algorithm.py | def gsea_significance(enrichment_scores, enrichment_nulls):
"""Compute nominal pvals, normalized ES, and FDR q value.
For a given NES(S) = NES* >= 0. The FDR is the ratio of the percentage of all (S,pi) with
NES(S,pi) >= 0, whose NES(S,pi) >= NES*, divided by the percentage of
observed S wi... | def gsea_significance(enrichment_scores, enrichment_nulls):
"""Compute nominal pvals, normalized ES, and FDR q value.
For a given NES(S) = NES* >= 0. The FDR is the ratio of the percentage of all (S,pi) with
NES(S,pi) >= 0, whose NES(S,pi) >= NES*, divided by the percentage of
observed S wi... | [
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test | log_init | logging start | gseapy/utils.py | def log_init(outlog, log_level=logging.INFO):
"""logging start"""
# clear old root logger handlers
logging.getLogger("gseapy").handlers = []
# init a root logger
logging.basicConfig(level = logging.DEBUG,
format = 'LINE %(lineno)-4d: %(asctime)s [%(levelname)-8s] %(mess... | def log_init(outlog, log_level=logging.INFO):
"""logging start"""
# clear old root logger handlers
logging.getLogger("gseapy").handlers = []
# init a root logger
logging.basicConfig(level = logging.DEBUG,
format = 'LINE %(lineno)-4d: %(asctime)s [%(levelname)-8s] %(mess... | [
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test | log_stop | log stop | gseapy/utils.py | def log_stop(logger):
"""log stop"""
handlers = logger.handlers[:]
for handler in handlers:
handler.close()
logger.removeHandler(handler) | def log_stop(logger):
"""log stop"""
handlers = logger.handlers[:]
for handler in handlers:
handler.close()
logger.removeHandler(handler) | [
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test | retry | retry connection.
define max tries num
if the backoff_factor is 0.1, then sleep() will sleep for
[0.1s, 0.2s, 0.4s, ...] between retries.
It will also force a retry if the status code returned is 500, 502, 503 or 504. | gseapy/utils.py | def retry(num=5):
""""retry connection.
define max tries num
if the backoff_factor is 0.1, then sleep() will sleep for
[0.1s, 0.2s, 0.4s, ...] between retries.
It will also force a retry if the status code returned is 500, 502, 503 or 504.
"""
s = requests.Sessi... | def retry(num=5):
""""retry connection.
define max tries num
if the backoff_factor is 0.1, then sleep() will sleep for
[0.1s, 0.2s, 0.4s, ...] between retries.
It will also force a retry if the status code returned is 500, 502, 503 or 504.
"""
s = requests.Sessi... | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | gsea_cls_parser | Extract class(phenotype) name from .cls file.
:param cls: the a class list instance or .cls file which is identical to GSEA input .
:return: phenotype name and a list of class vector. | gseapy/parser.py | def gsea_cls_parser(cls):
"""Extract class(phenotype) name from .cls file.
:param cls: the a class list instance or .cls file which is identical to GSEA input .
:return: phenotype name and a list of class vector.
"""
if isinstance(cls, list) :
classes = cls
sample_name= unique(clas... | def gsea_cls_parser(cls):
"""Extract class(phenotype) name from .cls file.
:param cls: the a class list instance or .cls file which is identical to GSEA input .
:return: phenotype name and a list of class vector.
"""
if isinstance(cls, list) :
classes = cls
sample_name= unique(clas... | [
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test | gsea_edb_parser | Parse results.edb file stored under **edb** file folder.
:param results_path: the .results file located inside edb folder.
:param index: gene_set index of gmt database, used for iterating items.
:return: enrichment_term, hit_index,nes, pval, fdr. | gseapy/parser.py | def gsea_edb_parser(results_path, index=0):
"""Parse results.edb file stored under **edb** file folder.
:param results_path: the .results file located inside edb folder.
:param index: gene_set index of gmt database, used for iterating items.
:return: enrichment_term, hit_index,nes, pval, fdr.
"""
... | def gsea_edb_parser(results_path, index=0):
"""Parse results.edb file stored under **edb** file folder.
:param results_path: the .results file located inside edb folder.
:param index: gene_set index of gmt database, used for iterating items.
:return: enrichment_term, hit_index,nes, pval, fdr.
"""
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test | gsea_gmt_parser | Parse gene_sets.gmt(gene set database) file or download from enrichr server.
:param gmt: the gene_sets.gmt file of GSEA input or an enrichr library name.
checkout full enrichr library name here: http://amp.pharm.mssm.edu/Enrichr/#stats
:param min_size: Minimum allowed number of genes from gene... | gseapy/parser.py | def gsea_gmt_parser(gmt, min_size = 3, max_size = 1000, gene_list=None):
"""Parse gene_sets.gmt(gene set database) file or download from enrichr server.
:param gmt: the gene_sets.gmt file of GSEA input or an enrichr library name.
checkout full enrichr library name here: http://amp.pharm.mssm.ed... | def gsea_gmt_parser(gmt, min_size = 3, max_size = 1000, gene_list=None):
"""Parse gene_sets.gmt(gene set database) file or download from enrichr server.
:param gmt: the gene_sets.gmt file of GSEA input or an enrichr library name.
checkout full enrichr library name here: http://amp.pharm.mssm.ed... | [
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test | get_library_name | return enrichr active enrichr library name.
:param str database: Select one from { 'Human', 'Mouse', 'Yeast', 'Fly', 'Fish', 'Worm' } | gseapy/parser.py | def get_library_name(database='Human'):
"""return enrichr active enrichr library name.
:param str database: Select one from { 'Human', 'Mouse', 'Yeast', 'Fly', 'Fish', 'Worm' }
"""
# make a get request to get the gmt names and meta data from Enrichr
# old code
# response = requests.get(... | def get_library_name(database='Human'):
"""return enrichr active enrichr library name.
:param str database: Select one from { 'Human', 'Mouse', 'Yeast', 'Fly', 'Fish', 'Worm' }
"""
# make a get request to get the gmt names and meta data from Enrichr
# old code
# response = requests.get(... | [
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test | Biomart.get_marts | Get available marts and their names. | gseapy/parser.py | def get_marts(self):
"""Get available marts and their names."""
mart_names = pd.Series(self.names, name="Name")
mart_descriptions = pd.Series(self.displayNames, name="Description")
return pd.concat([mart_names, mart_descriptions], axis=1) | def get_marts(self):
"""Get available marts and their names."""
mart_names = pd.Series(self.names, name="Name")
mart_descriptions = pd.Series(self.displayNames, name="Description")
return pd.concat([mart_names, mart_descriptions], axis=1) | [
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test | Biomart.get_datasets | Get available datasets from mart you've selected | gseapy/parser.py | def get_datasets(self, mart='ENSEMBL_MART_ENSEMBL'):
"""Get available datasets from mart you've selected"""
datasets = self.datasets(mart, raw=True)
return pd.read_csv(StringIO(datasets), header=None, usecols=[1, 2],
names = ["Name", "Description"],sep="\t") | def get_datasets(self, mart='ENSEMBL_MART_ENSEMBL'):
"""Get available datasets from mart you've selected"""
datasets = self.datasets(mart, raw=True)
return pd.read_csv(StringIO(datasets), header=None, usecols=[1, 2],
names = ["Name", "Description"],sep="\t") | [
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test | Biomart.get_attributes | Get available attritbutes from dataset you've selected | gseapy/parser.py | def get_attributes(self, dataset):
"""Get available attritbutes from dataset you've selected"""
attributes = self.attributes(dataset)
attr_ = [ (k, v[0]) for k, v in attributes.items()]
return pd.DataFrame(attr_, columns=["Attribute","Description"]) | def get_attributes(self, dataset):
"""Get available attritbutes from dataset you've selected"""
attributes = self.attributes(dataset)
attr_ = [ (k, v[0]) for k, v in attributes.items()]
return pd.DataFrame(attr_, columns=["Attribute","Description"]) | [
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test | Biomart.get_filters | Get available filters from dataset you've selected | gseapy/parser.py | def get_filters(self, dataset):
"""Get available filters from dataset you've selected"""
filters = self.filters(dataset)
filt_ = [ (k, v[0]) for k, v in filters.items()]
return pd.DataFrame(filt_, columns=["Filter", "Description"]) | def get_filters(self, dataset):
"""Get available filters from dataset you've selected"""
filters = self.filters(dataset)
filt_ = [ (k, v[0]) for k, v in filters.items()]
return pd.DataFrame(filt_, columns=["Filter", "Description"]) | [
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test | Biomart.query | mapping ids using BioMart.
:param dataset: str, default: 'hsapiens_gene_ensembl'
:param attributes: str, list, tuple
:param filters: dict, {'filter name': list(filter value)}
:param host: www.ensembl.org, asia.ensembl.org, useast.ensembl.org
:return: a dataframe contains all a... | gseapy/parser.py | def query(self, dataset='hsapiens_gene_ensembl', attributes=[],
filters={}, filename=None):
"""mapping ids using BioMart.
:param dataset: str, default: 'hsapiens_gene_ensembl'
:param attributes: str, list, tuple
:param filters: dict, {'filter name': list(filter value)}
... | def query(self, dataset='hsapiens_gene_ensembl', attributes=[],
filters={}, filename=None):
"""mapping ids using BioMart.
:param dataset: str, default: 'hsapiens_gene_ensembl'
:param attributes: str, list, tuple
:param filters: dict, {'filter name': list(filter value)}
... | [
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test | gsea | Run Gene Set Enrichment Analysis.
:param data: Gene expression data table, Pandas DataFrame, gct file.
:param gene_sets: Enrichr Library name or .gmt gene sets file or dict of gene sets. Same input with GSEA.
:param cls: A list or a .cls file format required for GSEA.
:param str outdir: Results output ... | gseapy/gsea.py | def gsea(data, gene_sets, cls, outdir='GSEA_', min_size=15, max_size=500, permutation_num=1000,
weighted_score_type=1,permutation_type='gene_set', method='log2_ratio_of_classes',
ascending=False, processes=1, figsize=(6.5,6), format='pdf',
graph_num=20, no_plot=False, seed=None, verbose=False... | def gsea(data, gene_sets, cls, outdir='GSEA_', min_size=15, max_size=500, permutation_num=1000,
weighted_score_type=1,permutation_type='gene_set', method='log2_ratio_of_classes',
ascending=False, processes=1, figsize=(6.5,6), format='pdf',
graph_num=20, no_plot=False, seed=None, verbose=False... | [
"Run",
"Gene",
"Set",
"Enrichment",
"Analysis",
"."
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L858-L933 | [
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"'g... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | ssgsea | Run Gene Set Enrichment Analysis with single sample GSEA tool
:param data: Expression table, pd.Series, pd.DataFrame, GCT file, or .rnk file format.
:param gene_sets: Enrichr Library name or .gmt gene sets file or dict of gene sets. Same input with GSEA.
:param outdir: Results output directory.
:param ... | gseapy/gsea.py | def ssgsea(data, gene_sets, outdir="ssGSEA_", sample_norm_method='rank', min_size=15, max_size=2000,
permutation_num=0, weighted_score_type=0.25, scale=True, ascending=False, processes=1,
figsize=(7,6), format='pdf', graph_num=20, no_plot=False, seed=None, verbose=False):
"""Run Gene Set Enric... | def ssgsea(data, gene_sets, outdir="ssGSEA_", sample_norm_method='rank', min_size=15, max_size=2000,
permutation_num=0, weighted_score_type=0.25, scale=True, ascending=False, processes=1,
figsize=(7,6), format='pdf', graph_num=20, no_plot=False, seed=None, verbose=False):
"""Run Gene Set Enric... | [
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"Analysis",
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"sample",
"GSEA",
"tool"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L936-L988 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | prerank | Run Gene Set Enrichment Analysis with pre-ranked correlation defined by user.
:param rnk: pre-ranked correlation table or pandas DataFrame. Same input with ``GSEA`` .rnk file.
:param gene_sets: Enrichr Library name or .gmt gene sets file or dict of gene sets. Same input with GSEA.
:param outdir: results ou... | gseapy/gsea.py | def prerank(rnk, gene_sets, outdir='GSEA_Prerank', pheno_pos='Pos', pheno_neg='Neg',
min_size=15, max_size=500, permutation_num=1000, weighted_score_type=1,
ascending=False, processes=1, figsize=(6.5,6), format='pdf',
graph_num=20, no_plot=False, seed=None, verbose=False):
""" Ru... | def prerank(rnk, gene_sets, outdir='GSEA_Prerank', pheno_pos='Pos', pheno_neg='Neg',
min_size=15, max_size=500, permutation_num=1000, weighted_score_type=1,
ascending=False, processes=1, figsize=(6.5,6), format='pdf',
graph_num=20, no_plot=False, seed=None, verbose=False):
""" Ru... | [
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"defined",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L991-L1031 | [
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test | replot | The main function to reproduce GSEA desktop outputs.
:param indir: GSEA desktop results directory. In the sub folder, you must contain edb file folder.
:param outdir: Output directory.
:param float weighted_score_type: weighted score type. choose from {0,1,1.5,2}. Default: 1.
:param list figsize: Matpl... | gseapy/gsea.py | def replot(indir, outdir='GSEA_Replot', weighted_score_type=1,
min_size=3, max_size=1000, figsize=(6.5,6), graph_num=20, format='pdf', verbose=False):
"""The main function to reproduce GSEA desktop outputs.
:param indir: GSEA desktop results directory. In the sub folder, you must contain edb file fo... | def replot(indir, outdir='GSEA_Replot', weighted_score_type=1,
min_size=3, max_size=1000, figsize=(6.5,6), graph_num=20, format='pdf', verbose=False):
"""The main function to reproduce GSEA desktop outputs.
:param indir: GSEA desktop results directory. In the sub folder, you must contain edb file fo... | [
"The",
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"function",
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"reproduce",
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"outputs",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L1034-L1056 | [
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test | GSEAbase.prepare_outdir | create temp directory. | gseapy/gsea.py | def prepare_outdir(self):
"""create temp directory."""
self._outdir = self.outdir
if self._outdir is None:
self._tmpdir = TemporaryDirectory()
self.outdir = self._tmpdir.name
elif isinstance(self.outdir, str):
mkdirs(self.outdir)
else:
... | def prepare_outdir(self):
"""create temp directory."""
self._outdir = self.outdir
if self._outdir is None:
self._tmpdir = TemporaryDirectory()
self.outdir = self._tmpdir.name
elif isinstance(self.outdir, str):
mkdirs(self.outdir)
else:
... | [
"create",
"temp",
"directory",
"."
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L34-L54 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | GSEAbase._set_cores | set cpu numbers to be used | gseapy/gsea.py | def _set_cores(self):
"""set cpu numbers to be used"""
cpu_num = cpu_count()-1
if self._processes > cpu_num:
cores = cpu_num
elif self._processes < 1:
cores = 1
else:
cores = self._processes
# have to be int if user input is float
... | def _set_cores(self):
"""set cpu numbers to be used"""
cpu_num = cpu_count()-1
if self._processes > cpu_num:
cores = cpu_num
elif self._processes < 1:
cores = 1
else:
cores = self._processes
# have to be int if user input is float
... | [
"set",
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"numbers",
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"be",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L56-L67 | [
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test | GSEAbase._load_ranking | Parse ranking file. This file contains ranking correlation vector( or expression values)
and gene names or ids.
:param rnk: the .rnk file of GSEA input or a Pandas DataFrame, Series instance.
:return: a Pandas Series with gene name indexed rankings | gseapy/gsea.py | def _load_ranking(self, rnk):
"""Parse ranking file. This file contains ranking correlation vector( or expression values)
and gene names or ids.
:param rnk: the .rnk file of GSEA input or a Pandas DataFrame, Series instance.
:return: a Pandas Series with gene name indexed ran... | def _load_ranking(self, rnk):
"""Parse ranking file. This file contains ranking correlation vector( or expression values)
and gene names or ids.
:param rnk: the .rnk file of GSEA input or a Pandas DataFrame, Series instance.
:return: a Pandas Series with gene name indexed ran... | [
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L69-L110 | [
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test | GSEAbase.load_gmt | load gene set dict | gseapy/gsea.py | def load_gmt(self, gene_list, gmt):
"""load gene set dict"""
if isinstance(gmt, dict):
genesets_dict = gmt
elif isinstance(gmt, str):
genesets_dict = self.parse_gmt(gmt)
else:
raise Exception("Error parsing gmt parameter for gene sets")
... | def load_gmt(self, gene_list, gmt):
"""load gene set dict"""
if isinstance(gmt, dict):
genesets_dict = gmt
elif isinstance(gmt, str):
genesets_dict = self.parse_gmt(gmt)
else:
raise Exception("Error parsing gmt parameter for gene sets")
... | [
"load",
"gene",
"set",
"dict"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L112-L143 | [
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test | GSEAbase.parse_gmt | gmt parser | gseapy/gsea.py | def parse_gmt(self, gmt):
"""gmt parser"""
if gmt.lower().endswith(".gmt"):
with open(gmt) as genesets:
genesets_dict = { line.strip().split("\t")[0]: line.strip().split("\t")[2:]
for line in genesets.readlines()}
return geneset... | def parse_gmt(self, gmt):
"""gmt parser"""
if gmt.lower().endswith(".gmt"):
with open(gmt) as genesets:
genesets_dict = { line.strip().split("\t")[0]: line.strip().split("\t")[2:]
for line in genesets.readlines()}
return geneset... | [
"gmt",
"parser"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L145-L169 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | GSEAbase.get_libraries | return active enrichr library name.Offical API | gseapy/gsea.py | def get_libraries(self, database=''):
"""return active enrichr library name.Offical API """
lib_url='http://amp.pharm.mssm.edu/%sEnrichr/datasetStatistics'%database
libs_json = json.loads(requests.get(lib_url).text)
libs = [lib['libraryName'] for lib in libs_json['statistics']]
... | def get_libraries(self, database=''):
"""return active enrichr library name.Offical API """
lib_url='http://amp.pharm.mssm.edu/%sEnrichr/datasetStatistics'%database
libs_json = json.loads(requests.get(lib_url).text)
libs = [lib['libraryName'] for lib in libs_json['statistics']]
... | [
"return",
"active",
"enrichr",
"library",
"name",
".",
"Offical",
"API"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L171-L177 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | GSEAbase._download_libraries | download enrichr libraries. | gseapy/gsea.py | def _download_libraries(self, libname):
""" download enrichr libraries."""
self._logger.info("Downloading and generating Enrichr library gene sets......")
s = retry(5)
# queery string
ENRICHR_URL = 'http://amp.pharm.mssm.edu/Enrichr/geneSetLibrary'
query_string = '?mode=t... | def _download_libraries(self, libname):
""" download enrichr libraries."""
self._logger.info("Downloading and generating Enrichr library gene sets......")
s = retry(5)
# queery string
ENRICHR_URL = 'http://amp.pharm.mssm.edu/Enrichr/geneSetLibrary'
query_string = '?mode=t... | [
"download",
"enrichr",
"libraries",
"."
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L179-L204 | [
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test | GSEAbase._heatmat | only use for gsea heatmap | gseapy/gsea.py | def _heatmat(self, df, classes, pheno_pos, pheno_neg):
"""only use for gsea heatmap"""
width = len(classes) if len(classes) >= 6 else 5
cls_booA =list(map(lambda x: True if x == pheno_pos else False, classes))
cls_booB =list(map(lambda x: True if x == pheno_neg else False, classes))
... | def _heatmat(self, df, classes, pheno_pos, pheno_neg):
"""only use for gsea heatmap"""
width = len(classes) if len(classes) >= 6 else 5
cls_booA =list(map(lambda x: True if x == pheno_pos else False, classes))
cls_booB =list(map(lambda x: True if x == pheno_neg else False, classes))
... | [
"only",
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"gsea",
"heatmap"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L206-L216 | [
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test | GSEAbase._plotting | Plotting API.
:param rank_metric: sorted pd.Series with rankings values.
:param results: self.results
:param data: preprocessed expression table | gseapy/gsea.py | def _plotting(self, rank_metric, results, graph_num, outdir,
format, figsize, pheno_pos='', pheno_neg=''):
""" Plotting API.
:param rank_metric: sorted pd.Series with rankings values.
:param results: self.results
:param data: preprocessed expression table
... | def _plotting(self, rank_metric, results, graph_num, outdir,
format, figsize, pheno_pos='', pheno_neg=''):
""" Plotting API.
:param rank_metric: sorted pd.Series with rankings values.
:param results: self.results
:param data: preprocessed expression table
... | [
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test | GSEAbase._save_results | reformat gsea results, and save to txt | gseapy/gsea.py | def _save_results(self, zipdata, outdir, module, gmt, rank_metric, permutation_type):
"""reformat gsea results, and save to txt"""
res = OrderedDict()
for gs, gseale, ind, RES in zipdata:
rdict = OrderedDict()
rdict['es'] = gseale[0]
rdict['nes'] = gseale[1]
... | def _save_results(self, zipdata, outdir, module, gmt, rank_metric, permutation_type):
"""reformat gsea results, and save to txt"""
res = OrderedDict()
for gs, gseale, ind, RES in zipdata:
rdict = OrderedDict()
rdict['es'] = gseale[0]
rdict['nes'] = gseale[1]
... | [
"reformat",
"gsea",
"results",
"and",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L259-L312 | [
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test | GSEA.load_data | pre-processed the data frame.new filtering methods will be implement here. | gseapy/gsea.py | def load_data(self, cls_vec):
"""pre-processed the data frame.new filtering methods will be implement here.
"""
# read data in
if isinstance(self.data, pd.DataFrame) :
exprs = self.data.copy()
# handle index is gene_names
if exprs.index.dtype == 'O':
... | def load_data(self, cls_vec):
"""pre-processed the data frame.new filtering methods will be implement here.
"""
# read data in
if isinstance(self.data, pd.DataFrame) :
exprs = self.data.copy()
# handle index is gene_names
if exprs.index.dtype == 'O':
... | [
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test | GSEA.run | GSEA main procedure | gseapy/gsea.py | def run(self):
"""GSEA main procedure"""
assert self.permutation_type in ["phenotype", "gene_set"]
assert self.min_size <= self.max_size
# Start Analysis
self._logger.info("Parsing data files for GSEA.............................")
# phenotype labels parsing
phe... | def run(self):
"""GSEA main procedure"""
assert self.permutation_type in ["phenotype", "gene_set"]
assert self.min_size <= self.max_size
# Start Analysis
self._logger.info("Parsing data files for GSEA.............................")
# phenotype labels parsing
phe... | [
"GSEA",
"main",
"procedure"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L384-L438 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | Prerank.run | GSEA prerank workflow | gseapy/gsea.py | def run(self):
"""GSEA prerank workflow"""
assert self.min_size <= self.max_size
# parsing rankings
dat2 = self._load_ranking(self.rnk)
assert len(dat2) > 1
# cpu numbers
self._set_cores()
# Start Analysis
self._logger.info("Parsing data files f... | def run(self):
"""GSEA prerank workflow"""
assert self.min_size <= self.max_size
# parsing rankings
dat2 = self._load_ranking(self.rnk)
assert len(dat2) > 1
# cpu numbers
self._set_cores()
# Start Analysis
self._logger.info("Parsing data files f... | [
"GSEA",
"prerank",
"workflow"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L474-L515 | [
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test | SingleSampleGSEA.norm_samples | normalization samples
see here: http://rowley.mit.edu/caw_web/ssGSEAProjection/ssGSEAProjection.Library.R | gseapy/gsea.py | def norm_samples(self, dat):
"""normalization samples
see here: http://rowley.mit.edu/caw_web/ssGSEAProjection/ssGSEAProjection.Library.R
"""
if self.sample_norm_method == 'rank':
data = dat.rank(axis=0, method='average', na_option='bottom')
data = 10000*data ... | def norm_samples(self, dat):
"""normalization samples
see here: http://rowley.mit.edu/caw_web/ssGSEAProjection/ssGSEAProjection.Library.R
"""
if self.sample_norm_method == 'rank':
data = dat.rank(axis=0, method='average', na_option='bottom')
data = 10000*data ... | [
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test | SingleSampleGSEA.run | run entry | gseapy/gsea.py | def run(self):
"""run entry"""
self._logger.info("Parsing data files for ssGSEA...........................")
# load data
data = self.load_data()
# normalized samples, and rank
normdat = self.norm_samples(data)
# filtering out gene sets and build gene sets dictiona... | def run(self):
"""run entry"""
self._logger.info("Parsing data files for ssGSEA...........................")
# load data
data = self.load_data()
# normalized samples, and rank
normdat = self.norm_samples(data)
# filtering out gene sets and build gene sets dictiona... | [
"run",
"entry"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L625-L648 | [
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test | SingleSampleGSEA.runSamplesPermu | Single Sample GSEA workflow with permutation procedure | gseapy/gsea.py | def runSamplesPermu(self, df, gmt=None):
"""Single Sample GSEA workflow with permutation procedure"""
assert self.min_size <= self.max_size
mkdirs(self.outdir)
self.resultsOnSamples = OrderedDict()
outdir = self.outdir
# iter throught each sample
for name, ser in... | def runSamplesPermu(self, df, gmt=None):
"""Single Sample GSEA workflow with permutation procedure"""
assert self.min_size <= self.max_size
mkdirs(self.outdir)
self.resultsOnSamples = OrderedDict()
outdir = self.outdir
# iter throught each sample
for name, ser in... | [
"Single",
"Sample",
"GSEA",
"workflow",
"with",
"permutation",
"procedure"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L650-L691 | [
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test | SingleSampleGSEA.runSamples | Single Sample GSEA workflow.
multiprocessing utility on samples. | gseapy/gsea.py | def runSamples(self, df, gmt=None):
"""Single Sample GSEA workflow.
multiprocessing utility on samples.
"""
# df.index.values are gene_names
# Save each sample results to odict
self.resultsOnSamples = OrderedDict()
outdir = self.outdir
# run ssgsea for... | def runSamples(self, df, gmt=None):
"""Single Sample GSEA workflow.
multiprocessing utility on samples.
"""
# df.index.values are gene_names
# Save each sample results to odict
self.resultsOnSamples = OrderedDict()
outdir = self.outdir
# run ssgsea for... | [
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L693-L747 | [
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test | SingleSampleGSEA._save | save es and stats | gseapy/gsea.py | def _save(self, outdir):
"""save es and stats"""
# save raw ES to one csv file
samplesRawES = pd.DataFrame(self.resultsOnSamples)
samplesRawES.index.name = 'Term|ES'
# normalize enrichment scores by using the entire data set, as indicated
# by Barbie et al., 2009, online ... | def _save(self, outdir):
"""save es and stats"""
# save raw ES to one csv file
samplesRawES = pd.DataFrame(self.resultsOnSamples)
samplesRawES.index.name = 'Term|ES'
# normalize enrichment scores by using the entire data set, as indicated
# by Barbie et al., 2009, online ... | [
"save",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L749-L779 | [
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test | Replot.run | main replot function | gseapy/gsea.py | def run(self):
"""main replot function"""
assert self.min_size <= self.max_size
assert self.fignum > 0
import glob
from bs4 import BeautifulSoup
# parsing files.......
try:
results_path = glob.glob(self.indir+'*/edb/results.edb')[0]
rank_p... | def run(self):
"""main replot function"""
assert self.min_size <= self.max_size
assert self.fignum > 0
import glob
from bs4 import BeautifulSoup
# parsing files.......
try:
results_path = glob.glob(self.indir+'*/edb/results.edb')[0]
rank_p... | [
"main",
"replot",
"function"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/gsea.py#L802-L854 | [
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test | enrichr | Enrichr API.
:param gene_list: Flat file with list of genes, one gene id per row, or a python list object
:param gene_sets: Enrichr Library to query. Required enrichr library name(s). Separate each name by comma.
:param organism: Enrichr supported organism. Select from (human, mouse, yeast, fly, fish, worm... | gseapy/enrichr.py | def enrichr(gene_list, gene_sets, organism='human', description='',
outdir='Enrichr', background='hsapiens_gene_ensembl', cutoff=0.05,
format='pdf', figsize=(8,6), top_term=10, no_plot=False, verbose=False):
"""Enrichr API.
:param gene_list: Flat file with list of genes, one gene id per... | def enrichr(gene_list, gene_sets, organism='human', description='',
outdir='Enrichr', background='hsapiens_gene_ensembl', cutoff=0.05,
format='pdf', figsize=(8,6), top_term=10, no_plot=False, verbose=False):
"""Enrichr API.
:param gene_list: Flat file with list of genes, one gene id per... | [
"Enrichr",
"API",
"."
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L355-L393 | [
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test | Enrichr.prepare_outdir | create temp directory. | gseapy/enrichr.py | def prepare_outdir(self):
"""create temp directory."""
self._outdir = self.outdir
if self._outdir is None:
self._tmpdir = TemporaryDirectory()
self.outdir = self._tmpdir.name
elif isinstance(self.outdir, str):
mkdirs(self.outdir)
else:
... | def prepare_outdir(self):
"""create temp directory."""
self._outdir = self.outdir
if self._outdir is None:
self._tmpdir = TemporaryDirectory()
self.outdir = self._tmpdir.name
elif isinstance(self.outdir, str):
mkdirs(self.outdir)
else:
... | [
"create",
"temp",
"directory",
"."
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L50-L63 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | Enrichr.parse_genesets | parse gene_sets input file type | gseapy/enrichr.py | def parse_genesets(self):
"""parse gene_sets input file type"""
enrichr_library = self.get_libraries()
if isinstance(self.gene_sets, list):
gss = self.gene_sets
elif isinstance(self.gene_sets, str):
gss = [ g.strip() for g in self.gene_sets.strip().split(",") ]
... | def parse_genesets(self):
"""parse gene_sets input file type"""
enrichr_library = self.get_libraries()
if isinstance(self.gene_sets, list):
gss = self.gene_sets
elif isinstance(self.gene_sets, str):
gss = [ g.strip() for g in self.gene_sets.strip().split(",") ]
... | [
"parse",
"gene_sets",
"input",
"file",
"type"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L65-L96 | [
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test | Enrichr.parse_genelists | parse gene list | gseapy/enrichr.py | def parse_genelists(self):
"""parse gene list"""
if isinstance(self.gene_list, list):
genes = self.gene_list
elif isinstance(self.gene_list, pd.DataFrame):
# input type is bed file
if self.gene_list.shape[1] >=3:
genes= self.gene_list.iloc[:,:3... | def parse_genelists(self):
"""parse gene list"""
if isinstance(self.gene_list, list):
genes = self.gene_list
elif isinstance(self.gene_list, pd.DataFrame):
# input type is bed file
if self.gene_list.shape[1] >=3:
genes= self.gene_list.iloc[:,:3... | [
"parse",
"gene",
"list"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L98-L126 | [
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test | Enrichr.send_genes | send gene list to enrichr server | gseapy/enrichr.py | def send_genes(self, gene_list, url):
""" send gene list to enrichr server"""
payload = {
'list': (None, gene_list),
'description': (None, self.descriptions)
}
# response
response = requests.post(url, files=payload)
if not response.ok:
r... | def send_genes(self, gene_list, url):
""" send gene list to enrichr server"""
payload = {
'list': (None, gene_list),
'description': (None, self.descriptions)
}
# response
response = requests.post(url, files=payload)
if not response.ok:
r... | [
"send",
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"to",
"enrichr",
"server"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L128-L141 | [
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test | Enrichr.check_genes | Compare the genes sent and received to get successfully recognized genes | gseapy/enrichr.py | def check_genes(self, gene_list, usr_list_id):
'''
Compare the genes sent and received to get successfully recognized genes
'''
response = requests.get('http://amp.pharm.mssm.edu/Enrichr/view?userListId=%s' % usr_list_id)
if not response.ok:
raise Exception('Error get... | def check_genes(self, gene_list, usr_list_id):
'''
Compare the genes sent and received to get successfully recognized genes
'''
response = requests.get('http://amp.pharm.mssm.edu/Enrichr/view?userListId=%s' % usr_list_id)
if not response.ok:
raise Exception('Error get... | [
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test | Enrichr.get_results | Enrichr API | gseapy/enrichr.py | def get_results(self, gene_list):
"""Enrichr API"""
ADDLIST_URL = 'http://amp.pharm.mssm.edu/%sEnrichr/addList'%self._organism
job_id = self.send_genes(gene_list, ADDLIST_URL)
user_list_id = job_id['userListId']
RESULTS_URL = 'http://amp.pharm.mssm.edu/%sEnrichr/export'%self._or... | def get_results(self, gene_list):
"""Enrichr API"""
ADDLIST_URL = 'http://amp.pharm.mssm.edu/%sEnrichr/addList'%self._organism
job_id = self.send_genes(gene_list, ADDLIST_URL)
user_list_id = job_id['userListId']
RESULTS_URL = 'http://amp.pharm.mssm.edu/%sEnrichr/export'%self._or... | [
"Enrichr",
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] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L154-L170 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | Enrichr.get_background | get background gene | gseapy/enrichr.py | def get_background(self):
"""get background gene"""
# input is a file
if os.path.isfile(self.background):
with open(self.background) as b:
bg2 = b.readlines()
bg = [g.strip() for g in bg2]
return set(bg)
# package included ... | def get_background(self):
"""get background gene"""
# input is a file
if os.path.isfile(self.background):
with open(self.background) as b:
bg2 = b.readlines()
bg = [g.strip() for g in bg2]
return set(bg)
# package included ... | [
"get",
"background",
"gene"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L186-L217 | [
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"readlin... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | Enrichr.get_organism | Select Enrichr organism from below:
Human & Mouse: H. sapiens & M. musculus
Fly: D. melanogaster
Yeast: S. cerevisiae
Worm: C. elegans
Fish: D. rerio | gseapy/enrichr.py | def get_organism(self):
"""Select Enrichr organism from below:
Human & Mouse: H. sapiens & M. musculus
Fly: D. melanogaster
Yeast: S. cerevisiae
Worm: C. elegans
Fish: D. rerio
"""
organism = {'default': ['', 'hs', 'mm', 'human','mouse',
... | def get_organism(self):
"""Select Enrichr organism from below:
Human & Mouse: H. sapiens & M. musculus
Fly: D. melanogaster
Yeast: S. cerevisiae
Worm: C. elegans
Fish: D. rerio
"""
organism = {'default': ['', 'hs', 'mm', 'human','mouse',
... | [
"Select",
"Enrichr",
"organism",
"from",
"below",
":"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L219-L248 | [
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... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | Enrichr.enrich | use local mode
p = p-value computed using the Fisher exact test (Hypergeometric test)
Not implemented here:
combine score = log(p)·z
see here: http://amp.pharm.mssm.edu/Enrichr/help#background&q=4
columns contain:
Term Overlap ... | gseapy/enrichr.py | def enrich(self, gmt):
"""use local mode
p = p-value computed using the Fisher exact test (Hypergeometric test)
Not implemented here:
combine score = log(p)·z
see here: http://amp.pharm.mssm.edu/Enrichr/help#background&q=4
columns contain:
... | def enrich(self, gmt):
"""use local mode
p = p-value computed using the Fisher exact test (Hypergeometric test)
Not implemented here:
combine score = log(p)·z
see here: http://amp.pharm.mssm.edu/Enrichr/help#background&q=4
columns contain:
... | [
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"computed",
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"test",
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"test",
")"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L250-L300 | [
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test | Enrichr.run | run enrichr for one sample gene list but multi-libraries | gseapy/enrichr.py | def run(self):
"""run enrichr for one sample gene list but multi-libraries"""
# set organism
self.get_organism()
# read input file
genes_list = self.parse_genelists()
gss = self.parse_genesets()
# if gmt
self._logger.info("Connecting to Enrichr Server to ... | def run(self):
"""run enrichr for one sample gene list but multi-libraries"""
# set organism
self.get_organism()
# read input file
genes_list = self.parse_genelists()
gss = self.parse_genesets()
# if gmt
self._logger.info("Connecting to Enrichr Server to ... | [
"run",
"enrichr",
"for",
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"sample",
"gene",
"list",
"but",
"multi",
"-",
"libraries"
] | zqfang/GSEApy | python | https://github.com/zqfang/GSEApy/blob/673e9ec1391e3b14d3e8a4353117151fd2cb9345/gseapy/enrichr.py#L302-L352 | [
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"_... | 673e9ec1391e3b14d3e8a4353117151fd2cb9345 |
test | cube | Create a cube primitive
Note that this is made of 6 quads, not triangles | meshlabxml/create.py | def cube(script, size=1.0, center=False, color=None):
"""Create a cube primitive
Note that this is made of 6 quads, not triangles
"""
"""# Convert size to list if it isn't already
if not isinstance(size, list):
size = list(size)
# If a single value was supplied use it for all 3 axes
... | def cube(script, size=1.0, center=False, color=None):
"""Create a cube primitive
Note that this is made of 6 quads, not triangles
"""
"""# Convert size to list if it isn't already
if not isinstance(size, list):
size = list(size)
# If a single value was supplied use it for all 3 axes
... | [
"Create",
"a",
"cube",
"primitive"
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L13-L47 | [
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"\"\"\"# Convert size to list if it isn't already\n if not isinstance(size, list):\n size = list(size)\n # If a single value was supplied use it f... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | cylinder | Create a cylinder or cone primitive. Usage is based on OpenSCAD.
# height = height of the cylinder
# radius1 = radius of the cone on bottom end
# radius2 = radius of the cone on top end
# center = If true will center the height of the cone/cylinder around
# the origin. Default is false, placing the ... | meshlabxml/create.py | def cylinder(script, up='z', height=1.0, radius=None, radius1=None,
radius2=None, diameter=None, diameter1=None, diameter2=None,
center=False, cir_segments=32, color=None):
"""Create a cylinder or cone primitive. Usage is based on OpenSCAD.
# height = height of the cylinder
# radiu... | def cylinder(script, up='z', height=1.0, radius=None, radius1=None,
radius2=None, diameter=None, diameter1=None, diameter2=None,
center=False, cir_segments=32, color=None):
"""Create a cylinder or cone primitive. Usage is based on OpenSCAD.
# height = height of the cylinder
# radiu... | [
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... | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L63-L131 | [
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"=",
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... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | icosphere | create an icosphere mesh
radius Radius of the sphere
# subdivisions = Subdivision level; Number of the recursive subdivision of the
# surface. Default is 3 (a sphere approximation composed by 1280 faces).
# Admitted values are in the range 0 (an icosahedron) to 8 (a 1.3 MegaTris
# approximation of ... | meshlabxml/create.py | def icosphere(script, radius=1.0, diameter=None, subdivisions=3, color=None):
"""create an icosphere mesh
radius Radius of the sphere
# subdivisions = Subdivision level; Number of the recursive subdivision of the
# surface. Default is 3 (a sphere approximation composed by 1280 faces).
# Admitted va... | def icosphere(script, radius=1.0, diameter=None, subdivisions=3, color=None):
"""create an icosphere mesh
radius Radius of the sphere
# subdivisions = Subdivision level; Number of the recursive subdivision of the
# surface. Default is 3 (a sphere approximation composed by 1280 faces).
# Admitted va... | [
"create",
"an",
"icosphere",
"mesh"
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L134-L164 | [
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":",
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"/",
"2",
"filter_x... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | sphere_cap | # angle = Angle of the cone subtending the cap. It must be <180 less than 180
# subdivisions = Subdivision level; Number of the recursive subdivision of the
# surface. Default is 3 (a sphere approximation composed by 1280 faces).
# Admitted values are in the range 0 (an icosahedron) to 8 (a 1.3 MegaTris
... | meshlabxml/create.py | def sphere_cap(script, angle=1.0, subdivisions=3, color=None):
"""# angle = Angle of the cone subtending the cap. It must be <180 less than 180
# subdivisions = Subdivision level; Number of the recursive subdivision of the
# surface. Default is 3 (a sphere approximation composed by 1280 faces).
# Admitt... | def sphere_cap(script, angle=1.0, subdivisions=3, color=None):
"""# angle = Angle of the cone subtending the cap. It must be <180 less than 180
# subdivisions = Subdivision level; Number of the recursive subdivision of the
# surface. Default is 3 (a sphere approximation composed by 1280 faces).
# Admitt... | [
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"#... | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L167-L193 | [
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",",
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",",
... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | torus | Create a torus mesh
Args:
major_radius (float, (optional)): radius from the origin to the
center of the cross sections
minor_radius (float, (optional)): radius of the torus cross
section
inner_diameter (float, (optional)): inner diameter of torus. If
both... | meshlabxml/create.py | def torus(script, major_radius=3.0, minor_radius=1.0, inner_diameter=None,
outer_diameter=None, major_segments=48, minor_segments=12,
color=None):
"""Create a torus mesh
Args:
major_radius (float, (optional)): radius from the origin to the
center of the cross sections
... | def torus(script, major_radius=3.0, minor_radius=1.0, inner_diameter=None,
outer_diameter=None, major_segments=48, minor_segments=12,
color=None):
"""Create a torus mesh
Args:
major_radius (float, (optional)): radius from the origin to the
center of the cross sections
... | [
"Create",
"a",
"torus",
"mesh"
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L196-L256 | [
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"minor_segments",
"=",
"12",
",",
"color",
"=",
... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | grid | 2D square/plane/grid created on XY plane
x_segments # Number of segments in the X direction.
y_segments # Number of segments in the Y direction.
center="false" # If true square will be centered on origin;
otherwise it is place in the positive XY quadrant. | meshlabxml/create.py | def grid(script, size=1.0, x_segments=1, y_segments=1, center=False,
color=None):
"""2D square/plane/grid created on XY plane
x_segments # Number of segments in the X direction.
y_segments # Number of segments in the Y direction.
center="false" # If true square will be centered on origin;
... | def grid(script, size=1.0, x_segments=1, y_segments=1, center=False,
color=None):
"""2D square/plane/grid created on XY plane
x_segments # Number of segments in the X direction.
y_segments # Number of segments in the Y direction.
center="false" # If true square will be centered on origin;
... | [
"2D",
"square",
"/",
"plane",
"/",
"grid",
"created",
"on",
"XY",
"plane"
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L259-L318 | [
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"(",
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... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | annulus | Create a 2D (surface) circle or annulus
radius1=1 # Outer radius of the circle
radius2=0 # Inner radius of the circle (if non-zero it creates an annulus)
color="" # specify a color name to apply vertex colors to the newly created mesh
OpenSCAD: parameters: diameter overrides radius, radius1 & radius2 o... | meshlabxml/create.py | def annulus(script, radius=None, radius1=None, radius2=None, diameter=None,
diameter1=None, diameter2=None, cir_segments=32, color=None):
"""Create a 2D (surface) circle or annulus
radius1=1 # Outer radius of the circle
radius2=0 # Inner radius of the circle (if non-zero it creates an annulus)
... | def annulus(script, radius=None, radius1=None, radius2=None, diameter=None,
diameter1=None, diameter2=None, cir_segments=32, color=None):
"""Create a 2D (surface) circle or annulus
radius1=1 # Outer radius of the circle
radius2=0 # Inner radius of the circle (if non-zero it creates an annulus)
... | [
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"creates... | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L321-L373 | [
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"cir_segments",
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"32",
",",
"c... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | cylinder_open_hires | Creates a round open tube, e.g. a cylinder with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus.
invert_normals (bool (optional)): if True normals point outward; in false normals point inward. | meshlabxml/create.py | def cylinder_open_hires(script, height=1.0, radius=1, diameter=None,
cir_segments=48, height_segments=1,
invert_normals=False, center=False, color=None):
""" Creates a round open tube, e.g. a cylinder with no top or bottom.
Useful if you want to wrap it around an... | def cylinder_open_hires(script, height=1.0, radius=1, diameter=None,
cir_segments=48, height_segments=1,
invert_normals=False, center=False, color=None):
""" Creates a round open tube, e.g. a cylinder with no top or bottom.
Useful if you want to wrap it around an... | [
"Creates",
"a",
"round",
"open",
"tube",
"e",
".",
"g",
".",
"a",
"cylinder",
"with",
"no",
"top",
"or",
"bottom",
"."
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L376-L405 | [
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"=",
"False",
",",
"center",
"=",
"Fal... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | cube_open_hires_old | Creates a square open tube, e.g. a box with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus. | meshlabxml/create.py | def cube_open_hires_old(script, size=1.0, x_segments=1, y_segments=1, z_segments=1,
center=False, color=None):
""" Creates a square open tube, e.g. a box with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus.
"""
"""# Convert si... | def cube_open_hires_old(script, size=1.0, x_segments=1, y_segments=1, z_segments=1,
center=False, color=None):
""" Creates a square open tube, e.g. a box with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus.
"""
"""# Convert si... | [
"Creates",
"a",
"square",
"open",
"tube",
"e",
".",
"g",
".",
"a",
"box",
"with",
"no",
"top",
"or",
"bottom",
"."
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L408-L452 | [
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"None",
")",
":",
"\"\"\"# Convert size to list if ... | 177cce21e92baca500f56a932d66bd9a33257af8 |
test | cube_open_hires | Creates a square open tube, e.g. a box with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus. | meshlabxml/create.py | def cube_open_hires(script, size=1.0, x_segments=1, y_segments=1, z_segments=1,
center=False, color=None):
""" Creates a square open tube, e.g. a box with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus.
"""
"""# Convert size t... | def cube_open_hires(script, size=1.0, x_segments=1, y_segments=1, z_segments=1,
center=False, color=None):
""" Creates a square open tube, e.g. a box with no top or bottom.
Useful if you want to wrap it around and join the open ends together, forming a torus.
"""
"""# Convert size t... | [
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"square",
"open",
"tube",
"e",
".",
"g",
".",
"a",
"box",
"with",
"no",
"top",
"or",
"bottom",
"."
] | 3DLIRIOUS/MeshLabXML | python | https://github.com/3DLIRIOUS/MeshLabXML/blob/177cce21e92baca500f56a932d66bd9a33257af8/meshlabxml/create.py#L455-L507 | [
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"script",
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"1.0",
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"x_segments",
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"=",
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"center",
"=",
"False",
",",
"color",
"=",
"None",
")",
":",
"\"\"\"# Convert size to list if it i... | 177cce21e92baca500f56a932d66bd9a33257af8 |
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