index
int64 0
731k
| package
stringlengths 2
98
⌀ | name
stringlengths 1
76
| docstring
stringlengths 0
281k
⌀ | code
stringlengths 4
8.19k
| signature
stringlengths 2
42.8k
⌀ | embed_func_code
listlengths 768
768
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|---|---|---|---|---|---|---|
23,735
|
sphinx.util.nodes
|
_copy_except__document
|
Monkey-patch ```nodes.Element.copy``` to not copy the ``_document``
attribute.
xref: https://github.com/sphinx-doc/sphinx/issues/11116#issuecomment-1376767086
|
def _copy_except__document(el: Element) -> Element:
"""Monkey-patch ```nodes.Element.copy``` to not copy the ``_document``
attribute.
xref: https://github.com/sphinx-doc/sphinx/issues/11116#issuecomment-1376767086
"""
newnode = object.__new__(el.__class__)
# set in Element.__init__()
newnode.children = []
newnode.rawsource = el.rawsource
newnode.tagname = el.tagname
# copied in Element.copy()
newnode.attributes = {k: (v
if k not in {'ids', 'classes', 'names', 'dupnames', 'backrefs'}
else v[:])
for k, v in el.attributes.items()}
newnode.line = el.line
newnode.source = el.source
return newnode
|
(el: 'Element') -> 'Element'
|
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] |
23,740
|
sphinx.util.nodes
|
_deepcopy
|
Monkey-patch ```nodes.Element.deepcopy``` for speed.
|
def _deepcopy(el: Element) -> Element:
"""Monkey-patch ```nodes.Element.deepcopy``` for speed."""
newnode = el.copy()
newnode.children = [child.deepcopy() for child in el.children]
for child in newnode.children:
child.parent = newnode
if el.document:
child.document = el.document
if child.source is None:
child.source = el.document.current_source
if child.line is None:
child.line = el.document.current_line
return newnode
|
(el: 'Element') -> 'Element'
|
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23,752
|
sphinx_thebe
|
html
| null |
def html(self):
text = self["text"]
return (
'<button title="{text}" class="thebelab-button thebe-launch-button" '
'onclick="initThebe()">{text}</button>'.format(text=text)
)
|
(self)
|
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23,780
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sphinx_thebe
|
_bool
| null |
def _bool(b):
if isinstance(b, bool):
return b
else:
return b in ["true", "True"]
|
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|
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] |
23,781
|
sphinx_thebe
|
_do_load_thebe
|
Decide whether to load thebe based on the page's context.
|
def _do_load_thebe(doctree, config_thebe):
"""Decide whether to load thebe based on the page's context."""
# No doctree means there's no page content at all
if not doctree:
return False
# If we aren't properly configured
if not config_thebe:
logger.warning(
"[sphinx-thebe]: Didn't find `thebe_config` in conf.py, add to use thebe"
)
return False
return True
|
(doctree, config_thebe)
|
[
0.0659337043762207,
0.002120387740433216,
0.023914048448204994,
-0.014146093279123306,
-0.0014532015193253756,
0.04119984060525894,
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] |
23,782
|
sphinx_thebe
|
_split_repo_url
|
Split a repository URL into an org / repo combination.
|
def _split_repo_url(url):
"""Split a repository URL into an org / repo combination."""
if "github.com/" in url:
end = url.split("github.com/")[-1]
org, repo = end.split("/")[:2]
else:
logger.warning(
f"[sphinx-thebe]: Currently Thebe repositories must be on GitHub, got {url}"
)
org = repo = None
return org, repo
|
(url)
|
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] |
23,784
|
textwrap
|
dedent
|
Remove any common leading whitespace from every line in `text`.
This can be used to make triple-quoted strings line up with the left
edge of the display, while still presenting them in the source code
in indented form.
Note that tabs and spaces are both treated as whitespace, but they
are not equal: the lines " hello" and "\thello" are
considered to have no common leading whitespace.
Entirely blank lines are normalized to a newline character.
|
def dedent(text):
"""Remove any common leading whitespace from every line in `text`.
This can be used to make triple-quoted strings line up with the left
edge of the display, while still presenting them in the source code
in indented form.
Note that tabs and spaces are both treated as whitespace, but they
are not equal: the lines " hello" and "\\thello" are
considered to have no common leading whitespace.
Entirely blank lines are normalized to a newline character.
"""
# Look for the longest leading string of spaces and tabs common to
# all lines.
margin = None
text = _whitespace_only_re.sub('', text)
indents = _leading_whitespace_re.findall(text)
for indent in indents:
if margin is None:
margin = indent
# Current line more deeply indented than previous winner:
# no change (previous winner is still on top).
elif indent.startswith(margin):
pass
# Current line consistent with and no deeper than previous winner:
# it's the new winner.
elif margin.startswith(indent):
margin = indent
# Find the largest common whitespace between current line and previous
# winner.
else:
for i, (x, y) in enumerate(zip(margin, indent)):
if x != y:
margin = margin[:i]
break
# sanity check (testing/debugging only)
if 0 and margin:
for line in text.split("\n"):
assert not line or line.startswith(margin), \
"line = %r, margin = %r" % (line, margin)
if margin:
text = re.sub(r'(?m)^' + margin, '', text)
return text
|
(text)
|
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] |
23,786
|
sphinx_thebe
|
init_thebe_core
|
Add scripts to configure thebe, and optionally add thebe itself.
By default, defer loading the `thebe` JS bundle until bootstrap is called
in order to speed up page load times.
|
def init_thebe_core(app, env, docnames):
"""Add scripts to configure thebe, and optionally add thebe itself.
By default, defer loading the `thebe` JS bundle until bootstrap is called
in order to speed up page load times.
"""
config_thebe = app.config["thebe_config"]
# Add configuration variables
THEBE_JS_URL = f"https://unpkg.com/thebe@{THEBE_VERSION}/lib/index.js"
thebe_config_lines = [
f"const THEBE_JS_URL = \"{ THEBE_JS_URL }\"",
f"const thebe_selector = \"{ app.config.thebe_config['selector'] }\"",
f"const thebe_selector_input = \"{ app.config.thebe_config['selector_input'] }\"",
f"const thebe_selector_output = \"{ app.config.thebe_config['selector_output'] }\""
]
app.add_js_file(None, body='; '.join(thebe_config_lines))
app.add_js_file(filename="sphinx-thebe.js", **{"async": "async"})
if config_thebe.get("always_load") is True:
# If we've got `always load` on, then load thebe on every page.
app.add_js_file(THEBE_JS_URL, **{"async": "async"})
|
(app, env, docnames)
|
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] |
23,787
|
sphinx_thebe
|
init_thebe_default_config
|
Create a default config for fields that aren't given by the user.
|
def init_thebe_default_config(app, env, docnames):
"""Create a default config for fields that aren't given by the user."""
thebe_config = app.config.thebe_config
defaults = {
"always_load": False,
"selector": ".thebe,.cell",
"selector_input": "pre",
"selector_output": ".output, .cell_output",
}
for key, val in defaults.items():
if key not in thebe_config:
thebe_config[key] = val
# Standardize types for certain values
BOOL_KEYS = ["always_load"]
for key in BOOL_KEYS:
thebe_config[key] = _bool(thebe_config[key])
|
(app, env, docnames)
|
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] |
23,792
|
sphinx_thebe
|
setup
| null |
def setup(app):
logger.verbose("Adding copy buttons to code blocks...")
# Add our static path
app.connect("builder-inited", st_static_path)
# Set default values for the configuration
app.connect("env-before-read-docs", init_thebe_default_config)
# Load the JS/CSS assets for thebe if needed
app.connect("env-before-read-docs", init_thebe_core)
# Update the doctree with thebe-specific information if needed
app.connect("doctree-resolved", update_thebe_context)
# configuration for this tool
app.add_config_value("thebe_config", {}, "html")
# override=True in case Jupyter Sphinx has already been loaded
app.add_directive("thebe-button", ThebeButton, override=True)
# Add relevant code to headers
app.add_css_file("sphinx-thebe.css")
# ThebeButtonNode is the button that activates thebe
# and is only rendered for the HTML builder
app.add_node(
ThebeButtonNode,
html=(visit_element_html, None),
latex=(skip, None),
textinfo=(skip, None),
text=(skip, None),
man=(skip, None),
override=True,
)
return {
"version": __version__,
"parallel_read_safe": True,
"parallel_write_safe": True,
}
|
(app)
|
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23,793
|
sphinx_thebe
|
skip
| null |
def skip(self, node):
raise nodes.SkipNode
|
(self, node)
|
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] |
23,794
|
sphinx_thebe
|
st_static_path
| null |
def st_static_path(app):
static_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "_static"))
app.config.html_static_path.append(static_path)
|
(app)
|
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] |
23,795
|
sphinx_thebe
|
update_thebe_context
|
Add thebe config nodes to this doctree using page-dependent information.
|
def update_thebe_context(app, doctree, docname):
"""Add thebe config nodes to this doctree using page-dependent information."""
config_thebe = app.config["thebe_config"]
# Skip modifying the doctree if we don't need to load thebe
if not _do_load_thebe(doctree, config_thebe):
return
# Thebe configuration
if config_thebe is True:
config_thebe = {}
if not isinstance(config_thebe, dict):
raise ValueError(
"thebe configuration must be `True` or a dictionary for configuration."
)
codemirror_theme = config_thebe.get("codemirror-theme", "abcdef")
# Choose the kernel we'll use
meta = app.env.metadata.get(docname, {})
kernel_name = meta.get("thebe-kernel")
if kernel_name is None:
if meta.get("kernelspec"):
if isinstance(meta.get("kernelspec"), str):
kernel_name = json.loads(meta["kernelspec"]).get("name")
else:
kernel_name = meta["kernelspec"].get("name")
else:
kernel_name = "python3"
# Codemirror syntax
cm_language = kernel_name
if "python" in cm_language:
cm_language = "python"
elif cm_language == "ir":
cm_language = "r"
# Create the URL for the kernel request
repo_url = config_thebe.get(
"repository_url",
"https://github.com/binder-examples/jupyter-stacks-datascience",
)
branch = config_thebe.get("repository_branch", "master")
path_to_docs = config_thebe.get("path_to_docs", ".").strip("/") + "/"
org, repo = _split_repo_url(repo_url)
# Update the doctree with some nodes for the thebe configuration
thebe_html_config = f"""
<script type="text/x-thebe-config">
{{
requestKernel: true,
binderOptions: {{
repo: "{org}/{repo}",
ref: "{branch}",
}},
codeMirrorConfig: {{
theme: "{codemirror_theme}",
mode: "{cm_language}"
}},
kernelOptions: {{
name: "{kernel_name}",
path: "{path_to_docs}{str(Path(docname).parent)}"
}},
predefinedOutput: true
}}
</script>
"""
# Append to the docutils doctree so it makes it into the build outputs
doctree.append(nodes.raw(text=thebe_html_config, format="html"))
doctree.append(
nodes.raw(text=f"<script>kernelName = '{kernel_name}'</script>", format="html")
)
|
(app, doctree, docname)
|
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] |
23,796
|
sphinx_thebe
|
visit_element_html
| null |
def visit_element_html(self, node):
self.body.append(node.html())
raise nodes.SkipNode
|
(self, node)
|
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] |
23,798
|
missingno.missingno
|
bar
|
A bar chart visualization of the nullity of the given DataFrame.
:param df: The input DataFrame.
:param log: Whether or not to display a logarithmic plot. Defaults to False (linear).
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param sort: The column sort order to apply. Can be "ascending", "descending", or None.
:param figsize: The size of the figure to display.
:param fontsize: The figure's font size. This default to 16.
:param labels: Whether or not to display the column names. Would need to be turned off on particularly large
displays. Defaults to True.
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:param color: The color of the filled columns. Default to the RGB multiple `(0.25, 0.25, 0.25)`.
:param orientation: The way the bar plot is oriented. Defaults to vertical if there are less than or equal to 50
columns and horizontal if there are more.
:return: The plot axis.
|
def bar(
df, figsize=None, fontsize=16, labels=None, label_rotation=45, log=False, color='dimgray',
filter=None, n=0, p=0, sort=None, ax=None, orientation=None
):
"""
A bar chart visualization of the nullity of the given DataFrame.
:param df: The input DataFrame.
:param log: Whether or not to display a logarithmic plot. Defaults to False (linear).
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param sort: The column sort order to apply. Can be "ascending", "descending", or None.
:param figsize: The size of the figure to display.
:param fontsize: The figure's font size. This default to 16.
:param labels: Whether or not to display the column names. Would need to be turned off on particularly large
displays. Defaults to True.
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:param color: The color of the filled columns. Default to the RGB multiple `(0.25, 0.25, 0.25)`.
:param orientation: The way the bar plot is oriented. Defaults to vertical if there are less than or equal to 50
columns and horizontal if there are more.
:return: The plot axis.
"""
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort, axis='rows')
nullity_counts = len(df) - df.isnull().sum()
if orientation is None:
if len(df.columns) > 50:
orientation = 'left'
else:
orientation = 'bottom'
if ax is None:
ax1 = plt.gca()
if figsize is None:
if len(df.columns) <= 50 or orientation == 'top' or orientation == 'bottom':
figsize = (25, 10)
else:
figsize = (25, (25 + len(df.columns) - 50) * 0.5)
else:
ax1 = ax
figsize = None # for behavioral consistency with other plot types, re-use the given size
plot_args = {'figsize': figsize, 'fontsize': fontsize, 'log': log, 'color': color, 'ax': ax1}
if orientation == 'bottom':
(nullity_counts / len(df)).plot.bar(**plot_args)
else:
(nullity_counts / len(df)).plot.barh(**plot_args)
axes = [ax1]
# Start appending elements, starting with a modified bottom x axis.
if labels or (labels is None and len(df.columns) <= 50):
ax1.set_xticklabels(
ax1.get_xticklabels(), rotation=label_rotation, ha='right', fontsize=fontsize
)
# Create the numerical ticks.
ax2 = ax1.twinx()
axes.append(ax2)
if not log:
ax1.set_ylim([0, 1])
ax2.set_yticks(ax1.get_yticks())
ax2.set_yticklabels([int(n * len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
else:
# For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
# appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
# is used, we have to make it match the `ax1` layout ourselves.
ax2.set_yscale('log')
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticklabels([int(n * len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
# Create the third axis, which displays columnar totals above the rest of the plot.
ax3 = ax1.twiny()
axes.append(ax3)
ax3.set_xticks(ax1.get_xticks())
ax3.set_xlim(ax1.get_xlim())
ax3.set_xticklabels(
nullity_counts.values, fontsize=fontsize, rotation=label_rotation, ha='left'
)
else:
# Create the numerical ticks.
ax2 = ax1.twinx()
axes.append(ax2)
if not log:
# Width
ax1.set_xlim([0, 1])
# Bottom
ax2.set_xticks(ax1.get_xticks())
ax2.set_xticklabels([int(n * len(df)) for n in ax1.get_xticks()], fontsize=fontsize)
# Right
ax2.set_yticks(ax1.get_yticks())
ax2.set_yticklabels(nullity_counts.values, fontsize=fontsize, ha='left')
else:
# For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
# appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
# is used, we have to make it match the `ax1` layout ourselves.
ax1.set_xscale('log')
ax1.set_xlim(ax1.get_xlim())
# Bottom
ax2.set_xticks(ax1.get_xticks())
ax2.set_xticklabels([int(n * len(df)) for n in ax1.get_xticks()], fontsize=fontsize)
# Right
ax2.set_yticks(ax1.get_yticks())
ax2.set_yticklabels(nullity_counts.values, fontsize=fontsize, ha='left')
# Create the third axis, which displays columnar totals above the rest of the plot.
ax3 = ax1.twiny()
axes.append(ax3)
ax3.set_yticks(ax1.get_yticks())
if log:
ax3.set_xscale('log')
ax3.set_xlim(ax1.get_xlim())
ax3.set_ylim(ax1.get_ylim())
ax3.grid(False)
for ax in axes:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
return ax1
|
(df, figsize=None, fontsize=16, labels=None, label_rotation=45, log=False, color='dimgray', filter=None, n=0, p=0, sort=None, ax=None, orientation=None)
|
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] |
23,799
|
missingno.missingno
|
dendrogram
|
Fits a `scipy` hierarchical clustering algorithm to the given DataFrame's variables and visualizes the results as
a `scipy` dendrogram.
The default vertical display will fit up to 50 columns. If more than 50 columns are specified and orientation is
left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.
:param df: The DataFrame whose completeness is being dendrogrammed.
:param method: The distance measure being used for clustering. This is a parameter that is passed to
`scipy.hierarchy`.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to `(25, 10)`.
:param fontsize: The figure's font size.
:param orientation: The way the dendrogram is oriented. Defaults to top-down if there are less than or equal to 50
columns and left-right if there are more.
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:return: The plot axis.
|
def dendrogram(
df, method='average', filter=None, n=0, p=0, orientation=None, figsize=None, fontsize=16,
label_rotation=45, ax=None
):
"""
Fits a `scipy` hierarchical clustering algorithm to the given DataFrame's variables and visualizes the results as
a `scipy` dendrogram.
The default vertical display will fit up to 50 columns. If more than 50 columns are specified and orientation is
left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.
:param df: The DataFrame whose completeness is being dendrogrammed.
:param method: The distance measure being used for clustering. This is a parameter that is passed to
`scipy.hierarchy`.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to `(25, 10)`.
:param fontsize: The figure's font size.
:param orientation: The way the dendrogram is oriented. Defaults to top-down if there are less than or equal to 50
columns and left-right if there are more.
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:return: The plot axis.
"""
if not figsize:
if len(df.columns) <= 50 or orientation == 'top' or orientation == 'bottom':
figsize = (25, 10)
else:
figsize = (25, (25 + len(df.columns) - 50) * 0.5)
if ax is None:
plt.figure(figsize=figsize)
ax0 = plt.gca()
else:
ax0 = ax
df = nullity_filter(df, filter=filter, n=n, p=p)
# Link the hierarchical output matrix, figure out orientation, construct base dendrogram.
x = np.transpose(df.isnull().astype(int).values)
z = hierarchy.linkage(x, method)
if not orientation:
if len(df.columns) > 50:
orientation = 'left'
else:
orientation = 'bottom'
hierarchy.dendrogram(
z,
orientation=orientation,
labels=df.columns.tolist(),
distance_sort='descending',
link_color_func=lambda c: 'black',
leaf_font_size=fontsize,
ax=ax0
)
# Remove extraneous default visual elements.
ax0.set_aspect('auto')
ax0.grid(visible=False)
if orientation == 'bottom':
ax0.xaxis.tick_top()
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
ax0.patch.set_visible(False)
# Set up the categorical axis labels and draw.
if orientation == 'bottom':
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=label_rotation, ha='left')
elif orientation == 'top':
ax0.set_xticklabels(ax0.xaxis.get_majorticklabels(), rotation=label_rotation, ha='right')
if orientation == 'bottom' or orientation == 'top':
ax0.tick_params(axis='y', labelsize=int(fontsize / 16 * 20))
else:
ax0.tick_params(axis='x', labelsize=int(fontsize / 16 * 20))
return ax0
|
(df, method='average', filter=None, n=0, p=0, orientation=None, figsize=None, fontsize=16, label_rotation=45, ax=None)
|
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] |
23,800
|
missingno.missingno
|
heatmap
|
Presents a `seaborn` heatmap visualization of nullity correlation in the given DataFrame.
Note that this visualization has no special support for large datasets. For those, try the dendrogram instead.
:param df: The DataFrame whose completeness is being heatmapped.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default). See
`nullity_filter()` for more information.
:param n: The cap on the number of columns to include in the filtered DataFrame. See `nullity_filter()` for
more information.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame. See `nullity_filter()` for
more information.
:param sort: The column sort order to apply. Can be "ascending", "descending", or None.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to (20, 12).
:param fontsize: The figure's font size.
:param labels: Whether or not to label each matrix entry with its correlation (default is True).
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:param cmap: What `matplotlib` colormap to use. Defaults to `RdBu`.
:param vmin: The normalized colormap threshold. Defaults to -1, e.g. the bottom of the color scale.
:param vmax: The normalized colormap threshold. Defaults to 1, e.g. the bottom of the color scale.
:return: The plot axis.
|
def heatmap(
df, filter=None, n=0, p=0, sort=None, figsize=(20, 12), fontsize=16, labels=True,
label_rotation=45, cmap='RdBu', vmin=-1, vmax=1, cbar=True, ax=None
):
"""
Presents a `seaborn` heatmap visualization of nullity correlation in the given DataFrame.
Note that this visualization has no special support for large datasets. For those, try the dendrogram instead.
:param df: The DataFrame whose completeness is being heatmapped.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default). See
`nullity_filter()` for more information.
:param n: The cap on the number of columns to include in the filtered DataFrame. See `nullity_filter()` for
more information.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame. See `nullity_filter()` for
more information.
:param sort: The column sort order to apply. Can be "ascending", "descending", or None.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to (20, 12).
:param fontsize: The figure's font size.
:param labels: Whether or not to label each matrix entry with its correlation (default is True).
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:param cmap: What `matplotlib` colormap to use. Defaults to `RdBu`.
:param vmin: The normalized colormap threshold. Defaults to -1, e.g. the bottom of the color scale.
:param vmax: The normalized colormap threshold. Defaults to 1, e.g. the bottom of the color scale.
:return: The plot axis.
"""
# Apply filters and sorts, set up the figure.
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort, axis='rows')
if ax is None:
plt.figure(figsize=figsize)
ax0 = plt.gca()
else:
ax0 = ax
# Remove completely filled or completely empty variables.
df = df.iloc[:, [i for i, n in enumerate(np.var(df.isnull(), axis='rows')) if n > 0]]
# Create and mask the correlation matrix. Construct the base heatmap.
corr_mat = df.isnull().corr()
mask = np.zeros_like(corr_mat)
mask[np.triu_indices_from(mask)] = True
if labels:
sns.heatmap(corr_mat, mask=mask, cmap=cmap, ax=ax0, cbar=cbar,
annot=True, annot_kws={'size': fontsize - 2},
vmin=vmin, vmax=vmax)
else:
sns.heatmap(corr_mat, mask=mask, cmap=cmap, ax=ax0, cbar=cbar,
vmin=vmin, vmax=vmax)
# Apply visual corrections and modifications.
ax0.xaxis.tick_bottom()
ax0.set_xticklabels(
ax0.xaxis.get_majorticklabels(), rotation=label_rotation, ha='right', fontsize=fontsize
)
ax0.set_yticklabels(ax0.yaxis.get_majorticklabels(), rotation=0, fontsize=fontsize)
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.patch.set_visible(False)
for text in ax0.texts:
t = float(text.get_text())
if 0.95 <= t < 1:
text.set_text('<1')
elif -1 < t <= -0.95:
text.set_text('>-1')
elif t == 1:
text.set_text('1')
elif t == -1:
text.set_text('-1')
elif -0.05 < t < 0.05:
text.set_text('')
else:
text.set_text(round(t, 1))
return ax0
|
(df, filter=None, n=0, p=0, sort=None, figsize=(20, 12), fontsize=16, labels=True, label_rotation=45, cmap='RdBu', vmin=-1, vmax=1, cbar=True, ax=None)
|
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] |
23,801
|
missingno.missingno
|
matrix
|
A matrix visualization of the nullity of the given DataFrame.
:param df: The `DataFrame` being mapped.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The max number of columns to include in the filtered DataFrame.
:param p: The max percentage fill of the columns in the filtered DataFrame.
:param sort: The row sort order to apply. Can be "ascending", "descending", or None.
:param figsize: The size of the figure to display.
:param fontsize: The figure's font size. Default to 16.
:param labels: Whether or not to display the column names. Defaults to the underlying data labels when there are
50 columns or less, and no labels when there are more than 50 columns.
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:param sparkline: Whether or not to display the sparkline. Defaults to True.
:param width_ratios: The ratio of the width of the matrix to the width of the sparkline. Defaults to `(15, 1)`.
Does nothing if `sparkline=False`.
:param color: The color of the filled columns. Default is `(0.25, 0.25, 0.25)`.
:return: The plot axis.
|
def matrix(
df, filter=None, n=0, p=0, sort=None, figsize=(25, 10), width_ratios=(15, 1),
color=(0.25, 0.25, 0.25), fontsize=16, labels=None, label_rotation=45, sparkline=True,
freq=None, ax=None
):
"""
A matrix visualization of the nullity of the given DataFrame.
:param df: The `DataFrame` being mapped.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The max number of columns to include in the filtered DataFrame.
:param p: The max percentage fill of the columns in the filtered DataFrame.
:param sort: The row sort order to apply. Can be "ascending", "descending", or None.
:param figsize: The size of the figure to display.
:param fontsize: The figure's font size. Default to 16.
:param labels: Whether or not to display the column names. Defaults to the underlying data labels when there are
50 columns or less, and no labels when there are more than 50 columns.
:param label_rotation: What angle to rotate the text labels to. Defaults to 45 degrees.
:param sparkline: Whether or not to display the sparkline. Defaults to True.
:param width_ratios: The ratio of the width of the matrix to the width of the sparkline. Defaults to `(15, 1)`.
Does nothing if `sparkline=False`.
:param color: The color of the filled columns. Default is `(0.25, 0.25, 0.25)`.
:return: The plot axis.
"""
df = nullity_filter(df, filter=filter, n=n, p=p)
df = nullity_sort(df, sort=sort, axis='columns')
height = df.shape[0]
width = df.shape[1]
# z is the color-mask array, g is a NxNx3 matrix. Apply the z color-mask to set the RGB of each pixel.
z = df.notnull().values
g = np.zeros((height, width, 3), dtype=np.float32)
g[z < 0.5] = [1, 1, 1]
g[z > 0.5] = color
# Set up the matplotlib grid layout. A unary subplot if no sparkline, a left-right splot if yes sparkline.
if ax is None:
plt.figure(figsize=figsize)
if sparkline:
gs = gridspec.GridSpec(1, 2, width_ratios=width_ratios)
gs.update(wspace=0.08)
ax1 = plt.subplot(gs[1])
else:
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
else:
if sparkline is not False:
warnings.warn(
"Plotting a sparkline on an existing axis is not currently supported. "
"To remove this warning, set sparkline=False."
)
sparkline = False
ax0 = ax
# Create the nullity plot.
ax0.imshow(g, interpolation='none')
# Remove extraneous default visual elements.
ax0.set_aspect('auto')
ax0.grid(visible=False)
ax0.xaxis.tick_top()
ax0.xaxis.set_ticks_position('none')
ax0.yaxis.set_ticks_position('none')
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
# Set up and rotate the column ticks. The labels argument is set to None by default. If the user specifies it in
# the argument, respect that specification. Otherwise display for <= 50 columns and do not display for > 50.
if labels or (labels is None and len(df.columns) <= 50):
ha = 'left'
ax0.set_xticks(list(range(0, width)))
ax0.set_xticklabels(list(df.columns), rotation=label_rotation, ha=ha, fontsize=fontsize)
else:
ax0.set_xticks([])
# Adds Timestamps ticks if freq is not None, else set up the two top-bottom row ticks.
if freq:
ts_list = []
if type(df.index) == pd.PeriodIndex:
ts_array = pd.date_range(df.index.to_timestamp().date[0],
df.index.to_timestamp().date[-1],
freq=freq).values
ts_ticks = pd.date_range(df.index.to_timestamp().date[0],
df.index.to_timestamp().date[-1],
freq=freq).map(lambda t:
t.strftime('%Y-%m-%d'))
elif type(df.index) == pd.DatetimeIndex:
ts_array = pd.date_range(df.index[0], df.index[-1],
freq=freq).values
ts_ticks = pd.date_range(df.index[0], df.index[-1],
freq=freq).map(lambda t:
t.strftime('%Y-%m-%d'))
else:
raise KeyError('Dataframe index must be PeriodIndex or DatetimeIndex.')
try:
for value in ts_array:
ts_list.append(df.index.get_loc(value))
except KeyError:
raise KeyError('Could not divide time index into desired frequency.')
ax0.set_yticks(ts_list)
ax0.set_yticklabels(ts_ticks, fontsize=int(fontsize / 16 * 20), rotation=0)
else:
ax0.set_yticks([0, df.shape[0] - 1])
ax0.set_yticklabels([1, df.shape[0]], fontsize=int(fontsize / 16 * 20), rotation=0)
# Create the inter-column vertical grid.
in_between_point = [x + 0.5 for x in range(0, width - 1)]
for in_between_point in in_between_point:
ax0.axvline(in_between_point, linestyle='-', color='white')
if sparkline:
# Calculate row-wise completeness for the sparkline.
completeness_srs = df.notnull().astype(bool).sum(axis=1)
x_domain = list(range(0, height))
y_range = list(reversed(completeness_srs.values))
min_completeness = min(y_range)
max_completeness = max(y_range)
min_completeness_index = y_range.index(min_completeness)
max_completeness_index = y_range.index(max_completeness)
# Set up the sparkline, remove the border element.
ax1.grid(visible=False)
ax1.set_aspect('auto')
# GH 25
if int(mpl.__version__[0]) <= 1:
ax1.set_axis_bgcolor((1, 1, 1))
else:
ax1.set_facecolor((1, 1, 1))
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['bottom'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.set_ymargin(0)
# Plot sparkline---plot is sideways so the x and y axis are reversed.
ax1.plot(y_range, x_domain, color=color)
if labels:
# Figure out what case to display the label in: mixed, upper, lower.
label = 'Data Completeness'
if str(df.columns[0]).islower():
label = label.lower()
if str(df.columns[0]).isupper():
label = label.upper()
# Set up and rotate the sparkline label.
ha = 'left'
ax1.set_xticks([min_completeness + (max_completeness - min_completeness) / 2])
ax1.set_xticklabels([label], rotation=label_rotation, ha=ha, fontsize=fontsize)
ax1.xaxis.tick_top()
ax1.set_yticks([])
else:
ax1.set_xticks([])
ax1.set_yticks([])
# Add maximum and minimum labels, circles.
ax1.annotate(max_completeness,
xy=(max_completeness, max_completeness_index),
xytext=(max_completeness + 2, max_completeness_index),
fontsize=int(fontsize / 16 * 14),
va='center',
ha='left')
ax1.annotate(min_completeness,
xy=(min_completeness, min_completeness_index),
xytext=(min_completeness - 2, min_completeness_index),
fontsize=int(fontsize / 16 * 14),
va='center',
ha='right')
ax1.set_xlim([min_completeness - 2, max_completeness + 2]) # Otherwise the circles are cut off.
ax1.plot([min_completeness], [min_completeness_index], '.', color=color, markersize=10.0)
ax1.plot([max_completeness], [max_completeness_index], '.', color=color, markersize=10.0)
# Remove tick mark (only works after plotting).
ax1.xaxis.set_ticks_position('none')
return ax0
|
(df, filter=None, n=0, p=0, sort=None, figsize=(25, 10), width_ratios=(15, 1), color=(0.25, 0.25, 0.25), fontsize=16, labels=None, label_rotation=45, sparkline=True, freq=None, ax=None)
|
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] |
23,803
|
missingno.utils
|
nullity_filter
|
Filters a DataFrame according to its nullity, using some combination of 'top' and 'bottom' numerical and
percentage values. Percentages and numerical thresholds can be specified simultaneously: for example,
to get a DataFrame with columns of at least 75% completeness but with no more than 5 columns, use
`nullity_filter(df, filter='top', p=.75, n=5)`.
:param df: The DataFrame whose columns are being filtered.
:param filter: The orientation of the filter being applied to the DataFrame. One of, "top", "bottom",
or None (default). The filter will simply return the DataFrame if you leave the filter argument unspecified or
as None.
:param p: A completeness ratio cut-off. If non-zero the filter will limit the DataFrame to columns with at least p
completeness. Input should be in the range [0, 1].
:param n: A numerical cut-off. If non-zero no more than this number of columns will be returned.
:return: The nullity-filtered `DataFrame`.
|
def nullity_filter(df, filter=None, p=0, n=0):
"""
Filters a DataFrame according to its nullity, using some combination of 'top' and 'bottom' numerical and
percentage values. Percentages and numerical thresholds can be specified simultaneously: for example,
to get a DataFrame with columns of at least 75% completeness but with no more than 5 columns, use
`nullity_filter(df, filter='top', p=.75, n=5)`.
:param df: The DataFrame whose columns are being filtered.
:param filter: The orientation of the filter being applied to the DataFrame. One of, "top", "bottom",
or None (default). The filter will simply return the DataFrame if you leave the filter argument unspecified or
as None.
:param p: A completeness ratio cut-off. If non-zero the filter will limit the DataFrame to columns with at least p
completeness. Input should be in the range [0, 1].
:param n: A numerical cut-off. If non-zero no more than this number of columns will be returned.
:return: The nullity-filtered `DataFrame`.
"""
if filter == 'top':
if p:
df = df.iloc[:, [c >= p for c in df.count(axis='rows').values / len(df)]]
if n:
df = df.iloc[:, np.sort(np.argsort(df.count(axis='rows').values)[-n:])]
elif filter == 'bottom':
if p:
df = df.iloc[:, [c <= p for c in df.count(axis='rows').values / len(df)]]
if n:
df = df.iloc[:, np.sort(np.argsort(df.count(axis='rows').values)[:n])]
return df
|
(df, filter=None, p=0, n=0)
|
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] |
23,804
|
missingno.utils
|
nullity_sort
|
Sorts a DataFrame according to its nullity, in either ascending or descending order.
:param df: The DataFrame object being sorted.
:param sort: The sorting method: either "ascending", "descending", or None (default).
:return: The nullity-sorted DataFrame.
|
def nullity_sort(df, sort=None, axis='columns'):
"""
Sorts a DataFrame according to its nullity, in either ascending or descending order.
:param df: The DataFrame object being sorted.
:param sort: The sorting method: either "ascending", "descending", or None (default).
:return: The nullity-sorted DataFrame.
"""
if sort is None:
return df
elif sort not in ['ascending', 'descending']:
raise ValueError('The "sort" parameter must be set to "ascending" or "descending".')
if axis not in ['rows', 'columns']:
raise ValueError('The "axis" parameter must be set to "rows" or "columns".')
if axis == 'columns':
if sort == 'ascending':
return df.iloc[np.argsort(df.count(axis='columns').values), :]
elif sort == 'descending':
return df.iloc[np.flipud(np.argsort(df.count(axis='columns').values)), :]
elif axis == 'rows':
if sort == 'ascending':
return df.iloc[:, np.argsort(df.count(axis='rows').values)]
elif sort == 'descending':
return df.iloc[:, np.flipud(np.argsort(df.count(axis='rows').values))]
|
(df, sort=None, axis='columns')
|
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] |
23,820
|
kagglehub.auth
|
login
|
Prompt the user for their Kaggle username and API key and save them globally.
|
def login(validate_credentials: bool = True) -> None: # noqa: FBT002, FBT001
"""Prompt the user for their Kaggle username and API key and save them globally."""
if _is_in_notebook():
_notebook_login(validate_credentials)
return
else:
username = input("Enter your Kaggle username: ")
api_key = input("Enter your Kaggle API key: ")
set_kaggle_credentials(username=username, api_key=api_key)
if not validate_credentials:
return
_validate_credentials_helper()
|
(validate_credentials: bool = True) -> NoneType
|
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] |
23,821
|
kagglehub.models
|
model_download
|
Download model files.
Args:
handle: (string) the model handle.
path: (string) Optional path to a file within the model bundle.
force_download: (bool) Optional flag to force download a model, even if it's cached.
Returns:
A string representing the path to the requested model files.
|
def model_download(handle: str, path: Optional[str] = None, *, force_download: Optional[bool] = False) -> str:
"""Download model files.
Args:
handle: (string) the model handle.
path: (string) Optional path to a file within the model bundle.
force_download: (bool) Optional flag to force download a model, even if it's cached.
Returns:
A string representing the path to the requested model files.
"""
h = parse_model_handle(handle)
return registry.model_resolver(h, path, force_download=force_download)
|
(handle: str, path: Optional[str] = None, *, force_download: Optional[bool] = False) -> str
|
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] |
23,822
|
kagglehub.models
|
model_upload
|
Upload model files.
Args:
handle: (string) the model handle.
local_model_dir: (string) path to a file in a local directory.
license_name: (string) model license.
version_notes: (string) Optional to write to model versions.
|
def model_upload(
handle: str, local_model_dir: str, license_name: Optional[str] = None, version_notes: str = ""
) -> None:
"""Upload model files.
Args:
handle: (string) the model handle.
local_model_dir: (string) path to a file in a local directory.
license_name: (string) model license.
version_notes: (string) Optional to write to model versions.
"""
# parse slug
h = parse_model_handle(handle)
if h.is_versioned():
is_versioned_exception = "The model handle should not include the version"
raise ValueError(is_versioned_exception)
# Create the model if it doesn't already exist
create_model_if_missing(h.owner, h.model)
# Upload the model files to GCS
tokens = upload_files_and_directories(local_model_dir, "model")
# Create a model instance if it doesn't exist, and create a new instance version if an instance exists
create_model_instance_or_version(h, tokens, license_name, version_notes)
|
(handle: str, local_model_dir: str, license_name: Optional[str] = None, version_notes: str = '') -> NoneType
|
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] |
23,827
|
kagglehub.auth
|
whoami
|
Return a dictionary with the username of the authenticated Kaggle user or raise an error if unauthenticated.
|
def whoami() -> dict:
"""
Return a dictionary with the username of the authenticated Kaggle user or raise an error if unauthenticated.
"""
try:
credentials = get_kaggle_credentials()
if credentials and credentials.username:
return {"username": credentials.username}
else:
raise UnauthenticatedError()
except Exception as e:
raise UnauthenticatedError() from e
|
() -> dict
|
[
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] |
23,832
|
pytoolconfig.pytoolconfig
|
PyToolConfig
|
Python Tool Configuration Aggregator.
|
class PyToolConfig(Generic[DataclassT]):
"""Python Tool Configuration Aggregator."""
sources: list[Source]
tool: str
working_directory: Path
model: type[DataclassT]
fall_through: bool = False
arg_parser: ArgumentParser | None = None
_config_fields: dict[str, ConfigField]
def __init__( # noqa: PLR0913
self,
tool: str,
working_directory: Path,
model: type[DataclassT],
arg_parser: ArgumentParser | None = None,
custom_sources: Sequence[Source] | None = None,
global_config: bool = False,
global_sources: Sequence[Source] | None = None,
fall_through: bool = False,
*args: Any,
**kwargs: Any,
) -> None:
"""Initialize the configuration object.
:param tool: name of the tool to use.
:param working_directory: working directory in use.
:param model: Model of configuration.
:param arg_parser: Arugument Parser.
:param custom_sources: Custom sources
:param global_config: Enable global configuration
:param global_sources: Custom global sources
:param fall_through: Configuration options should fall through between sources.
:param args: Passed to constructor for PyProject
:param kwargs: Passed to constructor for PyProject
"""
assert is_dataclass(model)
self.model = model
self._config_fields = _gather_config_fields(model)
self.tool = tool
self.sources = [PyProject(working_directory, tool, *args, **kwargs)]
if custom_sources:
self.sources.extend(custom_sources)
if global_config:
self.sources.append(PyTool(tool))
if global_sources:
self.sources.extend(global_sources)
self.arg_parser = arg_parser
self.fall_through = fall_through
self._setup_arg_parser()
def parse(self, args: list[str] | None = None) -> DataclassT:
"""Parse the configuration.
:param args: any additional command line overwrites.
"""
configuration = self._parse_sources()
assert isinstance(self.sources[0], PyProject)
universal: UniversalConfig = self.sources[0].universalconfig()
if self.arg_parser:
if args is None:
args = []
parsed = self.arg_parser.parse_args(args)
for name, value in parsed._get_kwargs():
setattr(configuration, name, value)
for name, field in self._config_fields.items():
if field.universal_config:
universal_value = vars(universal)[field.universal_config.name]
if universal_value is not None:
setattr(
configuration,
name,
universal_value,
)
return configuration
def _setup_arg_parser(self) -> None:
if self.arg_parser:
for name, field in self._config_fields.items():
if field.command_line:
flags = field.command_line
self.arg_parser.add_argument(
*flags,
type=field._type,
help=field.description,
default=SUPPRESS,
metavar=name,
dest=name,
)
def _parse_sources(self) -> DataclassT:
configuration = self.model()
if self.fall_through:
for source in reversed(self.sources):
parsed = source.parse()
if parsed is not None:
configuration = _recursive_merge(configuration, parsed)
else:
for source in self.sources:
parsed = source.parse()
if parsed:
return _dict_to_dataclass(self.model, parsed)
return configuration
|
(tool: str, working_directory: pathlib.Path, model: type[~DataclassT], arg_parser: argparse.ArgumentParser | None = None, custom_sources: 'Sequence[Source] | None' = None, global_config: 'bool' = False, global_sources: 'Sequence[Source] | None' = None, fall_through: bool = False, *args: 'Any', **kwargs: 'Any') -> 'None'
|
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] |
23,833
|
pytoolconfig.pytoolconfig
|
__init__
|
Initialize the configuration object.
:param tool: name of the tool to use.
:param working_directory: working directory in use.
:param model: Model of configuration.
:param arg_parser: Arugument Parser.
:param custom_sources: Custom sources
:param global_config: Enable global configuration
:param global_sources: Custom global sources
:param fall_through: Configuration options should fall through between sources.
:param args: Passed to constructor for PyProject
:param kwargs: Passed to constructor for PyProject
|
def __init__( # noqa: PLR0913
self,
tool: str,
working_directory: Path,
model: type[DataclassT],
arg_parser: ArgumentParser | None = None,
custom_sources: Sequence[Source] | None = None,
global_config: bool = False,
global_sources: Sequence[Source] | None = None,
fall_through: bool = False,
*args: Any,
**kwargs: Any,
) -> None:
"""Initialize the configuration object.
:param tool: name of the tool to use.
:param working_directory: working directory in use.
:param model: Model of configuration.
:param arg_parser: Arugument Parser.
:param custom_sources: Custom sources
:param global_config: Enable global configuration
:param global_sources: Custom global sources
:param fall_through: Configuration options should fall through between sources.
:param args: Passed to constructor for PyProject
:param kwargs: Passed to constructor for PyProject
"""
assert is_dataclass(model)
self.model = model
self._config_fields = _gather_config_fields(model)
self.tool = tool
self.sources = [PyProject(working_directory, tool, *args, **kwargs)]
if custom_sources:
self.sources.extend(custom_sources)
if global_config:
self.sources.append(PyTool(tool))
if global_sources:
self.sources.extend(global_sources)
self.arg_parser = arg_parser
self.fall_through = fall_through
self._setup_arg_parser()
|
(self, tool: str, working_directory: pathlib.Path, model: type[~DataclassT], arg_parser: Optional[argparse.ArgumentParser] = None, custom_sources: Optional[Sequence[pytoolconfig.sources.source.Source]] = None, global_config: bool = False, global_sources: Optional[Sequence[pytoolconfig.sources.source.Source]] = None, fall_through: bool = False, *args: Any, **kwargs: Any) -> NoneType
|
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] |
23,834
|
pytoolconfig.pytoolconfig
|
_parse_sources
| null |
def _parse_sources(self) -> DataclassT:
configuration = self.model()
if self.fall_through:
for source in reversed(self.sources):
parsed = source.parse()
if parsed is not None:
configuration = _recursive_merge(configuration, parsed)
else:
for source in self.sources:
parsed = source.parse()
if parsed:
return _dict_to_dataclass(self.model, parsed)
return configuration
|
(self) -> ~DataclassT
|
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] |
23,835
|
pytoolconfig.pytoolconfig
|
_setup_arg_parser
| null |
def _setup_arg_parser(self) -> None:
if self.arg_parser:
for name, field in self._config_fields.items():
if field.command_line:
flags = field.command_line
self.arg_parser.add_argument(
*flags,
type=field._type,
help=field.description,
default=SUPPRESS,
metavar=name,
dest=name,
)
|
(self) -> NoneType
|
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-0.0165589340031147,
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-0.054609641432762146,
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0.09212855249643326,
0.01958465203642845,
0.004545454867184162,
0.015036906115710735
] |
23,836
|
pytoolconfig.pytoolconfig
|
parse
|
Parse the configuration.
:param args: any additional command line overwrites.
|
def parse(self, args: list[str] | None = None) -> DataclassT:
"""Parse the configuration.
:param args: any additional command line overwrites.
"""
configuration = self._parse_sources()
assert isinstance(self.sources[0], PyProject)
universal: UniversalConfig = self.sources[0].universalconfig()
if self.arg_parser:
if args is None:
args = []
parsed = self.arg_parser.parse_args(args)
for name, value in parsed._get_kwargs():
setattr(configuration, name, value)
for name, field in self._config_fields.items():
if field.universal_config:
universal_value = vars(universal)[field.universal_config.name]
if universal_value is not None:
setattr(
configuration,
name,
universal_value,
)
return configuration
|
(self, args: Optional[list[str]] = None) -> ~DataclassT
|
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] |
23,837
|
pytoolconfig.types
|
UniversalKey
|
See universal config documentation.
|
class UniversalKey(Enum):
"""See universal config documentation."""
formatter = auto()
max_line_length = auto()
min_py_version = auto()
max_py_version = auto()
dependencies = auto()
optional_dependencies = auto()
version = auto()
|
(value, names=None, *, module=None, qualname=None, type=None, start=1)
|
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] |
23,839
|
pytoolconfig.fields
|
field
|
Create a dataclass field with metadata.
|
def field( # noqa: PLR0913
default: T | _MISSINGTYPE = _MISSINGTYPE.MISSING,
description: str | None = None,
command_line: tuple[str] | None = None,
universal_config: UniversalKey | None = None,
default_factory: Callable[[], T] | _MISSINGTYPE = _MISSINGTYPE.MISSING,
init: bool = True,
) -> T:
"""Create a dataclass field with metadata."""
metadata = {
_METADATA_KEY: ConfigField(
description=description,
universal_config=universal_config,
command_line=command_line,
_default=default,
),
}
if default_factory is not MISSING:
metadata[_METADATA_KEY]._default = default_factory()
return dataclasses.field(
default_factory=default_factory,
metadata=metadata,
init=init,
)
assert default is not MISSING
return dataclasses.field(default=default, metadata=metadata, init=init)
|
(default: Union[~T, pytoolconfig.fields._MISSINGTYPE] = <_MISSINGTYPE.MISSING: 1>, description: Optional[str] = None, command_line: Optional[tuple[str]] = None, universal_config: Optional[pytoolconfig.types.UniversalKey] = None, default_factory: Union[Callable[[], ~T], pytoolconfig.fields._MISSINGTYPE] = <_MISSINGTYPE.MISSING: 1>, init: bool = True) -> ~T
|
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0.0565873384475708
] |
23,851
|
astropy
|
astronomical_constants
|
The version of astronomical constants to use.
|
class astronomical_constants(base_constants_version):
"""
The version of astronomical constants to use.
"""
# Maintainers: update when new constants are added
_value = "iau2015"
_versions = dict(
iau2015="iau2015",
iau2012="iau2012",
astropyconst40="iau2015",
astropyconst20="iau2015",
astropyconst13="iau2012",
)
|
()
|
[
0.029367269948124886,
-0.04660075902938843,
-0.041290029883384705,
0.012327219359576702,
0.04153622314333916,
-0.037878502160310745,
-0.02857593633234501,
-0.05535818263888359,
-0.004475431516766548,
-0.00046628236304968596,
0.003963262774050236,
0.04216929152607918,
0.03093235194683075,
0.0598599947988987,
-0.012001893483102322,
-0.010981951840221882,
0.0038907236885279417,
0.022403534501791,
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-0.05335347354412079,
0.01869305968284607,
0.02001194842159748,
0.07435019314289093,
0.002558645326644182,
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0.0022080405615270138,
-0.11606226861476898,
-0.0598599947988987,
0.009109129197895527,
-0.0014222024474292994,
0.0006424090242944658,
0.009970803745090961,
-0.08623778074979782,
0.038230206817388535,
0.029103493317961693,
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0.033851493149995804,
0.03629583492875099,
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0.01568599045276642,
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0.008133151568472385,
0.03599688783288002,
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0.07245098799467087,
0.04575666785240173,
0.07716382294893265,
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0.027344973757863045,
0.00271911034360528,
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0.007895750924944878,
0.00739457318559289,
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0.07016491889953613,
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0.017813799902796745,
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0.03297223523259163,
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0.008757425472140312,
0.00017021916573867202,
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0.07329507917165756,
0.019185444340109825,
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0.03696407377719879,
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0.05191148445010185,
0.028663862496614456,
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0.010603870265185833,
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0.027380144223570824,
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0.0006632914301007986,
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0.00047507495037280023,
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0.06207572668790817,
0.03457248583436012,
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0.053845856338739395,
-0.02344106137752533,
0.0772341638803482,
0.09165401756763458,
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0.03569794073700905,
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0.01085885614156723,
0.027362558990716934,
0.004616112913936377,
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0.03770264983177185,
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] |
23,852
|
astropy.utils.state
|
__init__
| null |
def __init__(self):
raise RuntimeError("This class is a singleton. Do not instantiate.")
|
(self)
|
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] |
23,855
|
astropy.utils.misc
|
find_api_page
|
Determines the URL of the API page for the specified object, and
optionally open that page in a web browser.
.. note::
You must be connected to the internet for this to function even if
``openinbrowser`` is `False`, unless you provide a local version of
the documentation to ``version`` (e.g., ``file:///path/to/docs``).
Parameters
----------
obj
The object to open the docs for or its fully-qualified name
(as a str).
version : str
The doc version - either a version number like '0.1', 'dev' for
the development/latest docs, or a URL to point to a specific
location that should be the *base* of the documentation. Defaults to
latest if you are on aren't on a release, otherwise, the version you
are on.
openinbrowser : bool
If `True`, the `webbrowser` package will be used to open the doc
page in a new web browser window.
timeout : number, optional
The number of seconds to wait before timing-out the query to
the astropy documentation. If not given, the default python
stdlib timeout will be used.
Returns
-------
url : str
The loaded URL
Raises
------
ValueError
If the documentation can't be found
|
def find_api_page(obj, version=None, openinbrowser=True, timeout=None):
"""
Determines the URL of the API page for the specified object, and
optionally open that page in a web browser.
.. note::
You must be connected to the internet for this to function even if
``openinbrowser`` is `False`, unless you provide a local version of
the documentation to ``version`` (e.g., ``file:///path/to/docs``).
Parameters
----------
obj
The object to open the docs for or its fully-qualified name
(as a str).
version : str
The doc version - either a version number like '0.1', 'dev' for
the development/latest docs, or a URL to point to a specific
location that should be the *base* of the documentation. Defaults to
latest if you are on aren't on a release, otherwise, the version you
are on.
openinbrowser : bool
If `True`, the `webbrowser` package will be used to open the doc
page in a new web browser window.
timeout : number, optional
The number of seconds to wait before timing-out the query to
the astropy documentation. If not given, the default python
stdlib timeout will be used.
Returns
-------
url : str
The loaded URL
Raises
------
ValueError
If the documentation can't be found
"""
import webbrowser
from zlib import decompress
from astropy.utils.data import get_readable_fileobj
if (
not isinstance(obj, str)
and hasattr(obj, "__module__")
and hasattr(obj, "__name__")
):
obj = obj.__module__ + "." + obj.__name__
elif inspect.ismodule(obj):
obj = obj.__name__
if version is None:
from astropy import version
if version.release:
version = "v" + version.version
else:
version = "dev"
if "://" in version:
if version.endswith("index.html"):
baseurl = version[:-10]
elif version.endswith("/"):
baseurl = version
else:
baseurl = version + "/"
elif version == "dev" or version == "latest":
baseurl = "http://devdocs.astropy.org/"
else:
baseurl = f"https://docs.astropy.org/en/{version}/"
# Custom request headers; see
# https://github.com/astropy/astropy/issues/8990
url = baseurl + "objects.inv"
headers = {"User-Agent": f"Astropy/{version}"}
with get_readable_fileobj(
url, encoding="binary", remote_timeout=timeout, http_headers=headers
) as uf:
oiread = uf.read()
# need to first read/remove the first four lines, which have info before
# the compressed section with the actual object inventory
idx = -1
headerlines = []
for _ in range(4):
oldidx = idx
idx = oiread.index(b"\n", oldidx + 1)
headerlines.append(oiread[(oldidx + 1) : idx].decode("utf-8"))
# intersphinx version line, project name, and project version
ivers, proj, vers, compr = headerlines
if "The remainder of this file is compressed using zlib" not in compr:
raise ValueError(
f"The file downloaded from {baseurl}objects.inv does not seem to be"
"the usual Sphinx objects.inv format. Maybe it "
"has changed?"
)
compressed = oiread[(idx + 1) :]
decompressed = decompress(compressed).decode("utf-8")
resurl = None
for l in decompressed.strip().splitlines():
ls = l.split()
name = ls[0]
loc = ls[3]
if loc.endswith("$"):
loc = loc[:-1] + name
if name == obj:
resurl = baseurl + loc
break
if resurl is None:
raise ValueError(f"Could not find the docs for the object {obj}")
elif openinbrowser:
webbrowser.open(resurl)
return resurl
|
(obj, version=None, openinbrowser=True, timeout=None)
|
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] |
23,857
|
astropy
|
online_help
|
Search the online Astropy documentation for the given query.
Opens the results in the default web browser. Requires an active
Internet connection.
Parameters
----------
query : str
The search query.
|
def online_help(query):
"""
Search the online Astropy documentation for the given query.
Opens the results in the default web browser. Requires an active
Internet connection.
Parameters
----------
query : str
The search query.
"""
import webbrowser
from urllib.parse import urlencode
url = online_docs_root + f"search.html?{urlencode({'q': query})}"
webbrowser.open(url)
|
(query)
|
[
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] |
23,858
|
astropy
|
physical_constants
|
The version of physical constants to use.
|
class physical_constants(base_constants_version):
"""
The version of physical constants to use.
"""
# Maintainers: update when new constants are added
_value = "codata2018"
_versions = dict(
codata2018="codata2018",
codata2014="codata2014",
codata2010="codata2010",
astropyconst40="codata2018",
astropyconst20="codata2014",
astropyconst13="codata2010",
)
|
()
|
[
0.0697498619556427,
-0.06206585094332695,
-0.05186707526445389,
0.002824747236445546,
0.033163491636514664,
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-0.03852483630180359,
-0.0511685274541378,
0.026928965002298355,
-0.01209358498454094,
0.018878217786550522,
0.03183625265955925,
0.034700293093919754,
0.027889465913176537,
-0.022370949387550354,
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-0.008395655080676079,
0.00895885843783617,
-0.035678260028362274,
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-0.016756383702158928,
0.022440804168581963,
-0.0481298491358757,
0.026457445695996284,
0.02455390803515911,
0.06555858254432678,
-0.007024757564067841,
-0.011429966427385807,
0.008020186796784401,
-0.12315373867750168,
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0.016206277534365654,
0.0045973085798323154,
0.007849915884435177,
0.0017572809010744095,
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0.06447583436965942,
0.030474089086055756,
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0.04666290059685707,
0.016546819359064102,
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0.013944732956588268,
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0.00590708339586854,
0.0079372338950634,
0.014817916788160801,
0.024920644238591194,
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0.03534644842147827,
0.05088910833001137,
0.08068211376667023,
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0.04558015614748001,
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0.0011646079365164042,
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0.02621295489370823,
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0.006535775028169155,
0.06513945758342743,
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0.01903539150953293,
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0.02265036851167679,
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0.02055472880601883,
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0.03649905323982239,
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0.003612794913351536,
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] |
23,887
|
traitlets.config.configurable
|
Configurable
| null |
class Configurable(HasTraits):
config = Instance(Config, (), {})
parent = Instance("traitlets.config.configurable.Configurable", allow_none=True)
def __init__(self, **kwargs: t.Any) -> None:
"""Create a configurable given a config config.
Parameters
----------
config : Config
If this is empty, default values are used. If config is a
:class:`Config` instance, it will be used to configure the
instance.
parent : Configurable instance, optional
The parent Configurable instance of this object.
Notes
-----
Subclasses of Configurable must call the :meth:`__init__` method of
:class:`Configurable` *before* doing anything else and using
:func:`super`::
class MyConfigurable(Configurable):
def __init__(self, config=None):
super(MyConfigurable, self).__init__(config=config)
# Then any other code you need to finish initialization.
This ensures that instances will be configured properly.
"""
parent = kwargs.pop("parent", None)
if parent is not None:
# config is implied from parent
if kwargs.get("config", None) is None:
kwargs["config"] = parent.config
self.parent = parent
config = kwargs.pop("config", None)
# load kwarg traits, other than config
super().__init__(**kwargs)
# record traits set by config
config_override_names = set()
def notice_config_override(change: Bunch) -> None:
"""Record traits set by both config and kwargs.
They will need to be overridden again after loading config.
"""
if change.name in kwargs:
config_override_names.add(change.name)
self.observe(notice_config_override)
# load config
if config is not None:
# We used to deepcopy, but for now we are trying to just save
# by reference. This *could* have side effects as all components
# will share config. In fact, I did find such a side effect in
# _config_changed below. If a config attribute value was a mutable type
# all instances of a component were getting the same copy, effectively
# making that a class attribute.
# self.config = deepcopy(config)
self.config = config
else:
# allow _config_default to return something
self._load_config(self.config)
self.unobserve(notice_config_override)
for name in config_override_names:
setattr(self, name, kwargs[name])
# -------------------------------------------------------------------------
# Static trait notifications
# -------------------------------------------------------------------------
@classmethod
def section_names(cls) -> list[str]:
"""return section names as a list"""
return [
c.__name__
for c in reversed(cls.__mro__)
if issubclass(c, Configurable) and issubclass(cls, c)
]
def _find_my_config(self, cfg: Config) -> t.Any:
"""extract my config from a global Config object
will construct a Config object of only the config values that apply to me
based on my mro(), as well as those of my parent(s) if they exist.
If I am Bar and my parent is Foo, and their parent is Tim,
this will return merge following config sections, in this order::
[Bar, Foo.Bar, Tim.Foo.Bar]
With the last item being the highest priority.
"""
cfgs = [cfg]
if self.parent:
cfgs.append(self.parent._find_my_config(cfg))
my_config = Config()
for c in cfgs:
for sname in self.section_names():
# Don't do a blind getattr as that would cause the config to
# dynamically create the section with name Class.__name__.
if c._has_section(sname):
my_config.merge(c[sname])
return my_config
def _load_config(
self,
cfg: Config,
section_names: list[str] | None = None,
traits: dict[str, TraitType[t.Any, t.Any]] | None = None,
) -> None:
"""load traits from a Config object"""
if traits is None:
traits = self.traits(config=True)
if section_names is None:
section_names = self.section_names()
my_config = self._find_my_config(cfg)
# hold trait notifications until after all config has been loaded
with self.hold_trait_notifications():
for name, config_value in my_config.items():
if name in traits:
if isinstance(config_value, LazyConfigValue):
# ConfigValue is a wrapper for using append / update on containers
# without having to copy the initial value
initial = getattr(self, name)
config_value = config_value.get_value(initial)
elif isinstance(config_value, DeferredConfig):
# DeferredConfig tends to come from CLI/environment variables
config_value = config_value.get_value(traits[name])
# We have to do a deepcopy here if we don't deepcopy the entire
# config object. If we don't, a mutable config_value will be
# shared by all instances, effectively making it a class attribute.
setattr(self, name, deepcopy(config_value))
elif not _is_section_key(name) and not isinstance(config_value, Config):
from difflib import get_close_matches
if isinstance(self, LoggingConfigurable):
assert self.log is not None
warn = self.log.warning
else:
def warn(msg: t.Any) -> None:
return warnings.warn(msg, UserWarning, stacklevel=9)
matches = get_close_matches(name, traits)
msg = f"Config option `{name}` not recognized by `{self.__class__.__name__}`."
if len(matches) == 1:
msg += f" Did you mean `{matches[0]}`?"
elif len(matches) >= 1:
msg += " Did you mean one of: `{matches}`?".format(
matches=", ".join(sorted(matches))
)
warn(msg)
@observe("config")
@observe_compat
def _config_changed(self, change: Bunch) -> None:
"""Update all the class traits having ``config=True`` in metadata.
For any class trait with a ``config`` metadata attribute that is
``True``, we update the trait with the value of the corresponding
config entry.
"""
# Get all traits with a config metadata entry that is True
traits = self.traits(config=True)
# We auto-load config section for this class as well as any parent
# classes that are Configurable subclasses. This starts with Configurable
# and works down the mro loading the config for each section.
section_names = self.section_names()
self._load_config(change.new, traits=traits, section_names=section_names)
def update_config(self, config: Config) -> None:
"""Update config and load the new values"""
# traitlets prior to 4.2 created a copy of self.config in order to trigger change events.
# Some projects (IPython < 5) relied upon one side effect of this,
# that self.config prior to update_config was not modified in-place.
# For backward-compatibility, we must ensure that self.config
# is a new object and not modified in-place,
# but config consumers should not rely on this behavior.
self.config = deepcopy(self.config)
# load config
self._load_config(config)
# m
|
(**kwargs: 't.Any') -> 'None'
|
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] |
23,889
|
traitlets.config.configurable
|
__init__
|
Create a configurable given a config config.
Parameters
----------
config : Config
If this is empty, default values are used. If config is a
:class:`Config` instance, it will be used to configure the
instance.
parent : Configurable instance, optional
The parent Configurable instance of this object.
Notes
-----
Subclasses of Configurable must call the :meth:`__init__` method of
:class:`Configurable` *before* doing anything else and using
:func:`super`::
class MyConfigurable(Configurable):
def __init__(self, config=None):
super(MyConfigurable, self).__init__(config=config)
# Then any other code you need to finish initialization.
This ensures that instances will be configured properly.
|
def __init__(self, **kwargs: t.Any) -> None:
"""Create a configurable given a config config.
Parameters
----------
config : Config
If this is empty, default values are used. If config is a
:class:`Config` instance, it will be used to configure the
instance.
parent : Configurable instance, optional
The parent Configurable instance of this object.
Notes
-----
Subclasses of Configurable must call the :meth:`__init__` method of
:class:`Configurable` *before* doing anything else and using
:func:`super`::
class MyConfigurable(Configurable):
def __init__(self, config=None):
super(MyConfigurable, self).__init__(config=config)
# Then any other code you need to finish initialization.
This ensures that instances will be configured properly.
"""
parent = kwargs.pop("parent", None)
if parent is not None:
# config is implied from parent
if kwargs.get("config", None) is None:
kwargs["config"] = parent.config
self.parent = parent
config = kwargs.pop("config", None)
# load kwarg traits, other than config
super().__init__(**kwargs)
# record traits set by config
config_override_names = set()
def notice_config_override(change: Bunch) -> None:
"""Record traits set by both config and kwargs.
They will need to be overridden again after loading config.
"""
if change.name in kwargs:
config_override_names.add(change.name)
self.observe(notice_config_override)
# load config
if config is not None:
# We used to deepcopy, but for now we are trying to just save
# by reference. This *could* have side effects as all components
# will share config. In fact, I did find such a side effect in
# _config_changed below. If a config attribute value was a mutable type
# all instances of a component were getting the same copy, effectively
# making that a class attribute.
# self.config = deepcopy(config)
self.config = config
else:
# allow _config_default to return something
self._load_config(self.config)
self.unobserve(notice_config_override)
for name in config_override_names:
setattr(self, name, kwargs[name])
|
(self, **kwargs: Any) -> NoneType
|
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] |
23,893
|
traitlets.config.configurable
|
_find_my_config
|
extract my config from a global Config object
will construct a Config object of only the config values that apply to me
based on my mro(), as well as those of my parent(s) if they exist.
If I am Bar and my parent is Foo, and their parent is Tim,
this will return merge following config sections, in this order::
[Bar, Foo.Bar, Tim.Foo.Bar]
With the last item being the highest priority.
|
def _find_my_config(self, cfg: Config) -> t.Any:
"""extract my config from a global Config object
will construct a Config object of only the config values that apply to me
based on my mro(), as well as those of my parent(s) if they exist.
If I am Bar and my parent is Foo, and their parent is Tim,
this will return merge following config sections, in this order::
[Bar, Foo.Bar, Tim.Foo.Bar]
With the last item being the highest priority.
"""
cfgs = [cfg]
if self.parent:
cfgs.append(self.parent._find_my_config(cfg))
my_config = Config()
for c in cfgs:
for sname in self.section_names():
# Don't do a blind getattr as that would cause the config to
# dynamically create the section with name Class.__name__.
if c._has_section(sname):
my_config.merge(c[sname])
return my_config
|
(self, cfg: traitlets.config.loader.Config) -> Any
|
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] |
23,895
|
traitlets.config.configurable
|
_load_config
|
load traits from a Config object
|
def _load_config(
self,
cfg: Config,
section_names: list[str] | None = None,
traits: dict[str, TraitType[t.Any, t.Any]] | None = None,
) -> None:
"""load traits from a Config object"""
if traits is None:
traits = self.traits(config=True)
if section_names is None:
section_names = self.section_names()
my_config = self._find_my_config(cfg)
# hold trait notifications until after all config has been loaded
with self.hold_trait_notifications():
for name, config_value in my_config.items():
if name in traits:
if isinstance(config_value, LazyConfigValue):
# ConfigValue is a wrapper for using append / update on containers
# without having to copy the initial value
initial = getattr(self, name)
config_value = config_value.get_value(initial)
elif isinstance(config_value, DeferredConfig):
# DeferredConfig tends to come from CLI/environment variables
config_value = config_value.get_value(traits[name])
# We have to do a deepcopy here if we don't deepcopy the entire
# config object. If we don't, a mutable config_value will be
# shared by all instances, effectively making it a class attribute.
setattr(self, name, deepcopy(config_value))
elif not _is_section_key(name) and not isinstance(config_value, Config):
from difflib import get_close_matches
if isinstance(self, LoggingConfigurable):
assert self.log is not None
warn = self.log.warning
else:
def warn(msg: t.Any) -> None:
return warnings.warn(msg, UserWarning, stacklevel=9)
matches = get_close_matches(name, traits)
msg = f"Config option `{name}` not recognized by `{self.__class__.__name__}`."
if len(matches) == 1:
msg += f" Did you mean `{matches[0]}`?"
elif len(matches) >= 1:
msg += " Did you mean one of: `{matches}`?".format(
matches=", ".join(sorted(matches))
)
warn(msg)
|
(self, cfg: traitlets.config.loader.Config, section_names: Optional[list[str]] = None, traits: Optional[dict[str, traitlets.traitlets.TraitType[Any, Any]]] = None) -> NoneType
|
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] |
23,916
|
traitlets.config.configurable
|
update_config
|
Update config and load the new values
|
def update_config(self, config: Config) -> None:
"""Update config and load the new values"""
# traitlets prior to 4.2 created a copy of self.config in order to trigger change events.
# Some projects (IPython < 5) relied upon one side effect of this,
# that self.config prior to update_config was not modified in-place.
# For backward-compatibility, we must ensure that self.config
# is a new object and not modified in-place,
# but config consumers should not rely on this behavior.
self.config = deepcopy(self.config)
# load config
self._load_config(config)
# merge it into self.config
self.config.merge(config)
# TODO: trigger change event if/when dict-update change events take place
# DO NOT trigger full trait-change
|
(self, config: traitlets.config.loader.Config) -> NoneType
|
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] |
23,947
|
jupyterlab_git.git
|
Git
|
A single parent class containing all of the individual git methods in it.
|
class Git:
"""
A single parent class containing all of the individual git methods in it.
"""
_GIT_CREDENTIAL_CACHE_DAEMON_PROCESS: subprocess.Popen = None
def __init__(self, config=None):
self._config = config
self._execute_timeout = (
20.0 if self._config is None else self._config.git_command_timeout
)
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
async def __execute(
self,
cmdline: "List[str]",
cwd: "str",
env: "Optional[Dict[str, str]]" = None,
username: "Optional[str]" = None,
password: "Optional[str]" = None,
is_binary=False,
) -> "Tuple[int, str, str]":
return await execute(
cmdline,
cwd=cwd,
timeout=self._execute_timeout,
env=env,
username=username,
password=password,
is_binary=is_binary,
)
async def config(self, path, **kwargs):
"""Get or set Git options.
If no kwargs, all options are returned. Otherwise kwargs are set.
"""
response = {"code": 1}
if len(kwargs):
output = []
for k, v in kwargs.items():
cmd = ["git", "config", "--add", k, v]
code, out, err = await self.__execute(cmd, cwd=path)
output.append(out.strip())
response["code"] = code
if code != 0:
response["command"] = " ".join(cmd)
response["message"] = err.strip()
return response
response["message"] = "\n".join(output).strip()
else:
cmd = ["git", "config", "--list"]
code, output, error = await self.__execute(cmd, cwd=path)
response = {"code": code}
if code != 0:
response["command"] = " ".join(cmd)
response["message"] = error.strip()
else:
raw = output.strip()
s = CONFIG_PATTERN.split(raw)
response["options"] = {k: v for k, v in zip(s[1::2], s[2::2])}
return response
async def changed_files(self, path, base=None, remote=None, single_commit=None):
"""Gets the list of changed files between two Git refs, or the files changed in a single commit
There are two reserved "refs" for the base
1. WORKING : Represents the Git working tree
2. INDEX: Represents the Git staging area / index
Keyword Arguments:
single_commit {string} -- The single commit ref
base {string} -- the base Git ref
remote {string} -- the remote Git ref
Returns:
dict -- the response of format {
"code": int, # Command status code
"files": [string, string], # List of files changed.
"message": [string] # Error response
}
"""
if single_commit:
cmd = ["git", "diff", single_commit, "--name-only", "-z"]
elif base and remote:
if base == "WORKING":
cmd = ["git", "diff", remote, "--name-only", "-z"]
elif base == "INDEX":
cmd = ["git", "diff", "--staged", remote, "--name-only", "-z"]
else:
cmd = ["git", "diff", base, remote, "--name-only", "-z"]
else:
raise tornado.web.HTTPError(
400, "Either single_commit or (base and remote) must be provided"
)
response = {}
try:
code, output, error = await self.__execute(cmd, cwd=path)
except subprocess.CalledProcessError as e:
response["code"] = e.returncode
response["message"] = e.output.decode("utf-8")
else:
response["code"] = code
if code != 0:
response["command"] = " ".join(cmd)
response["message"] = error
else:
response["files"] = strip_and_split(output)
return response
async def clone(self, path, repo_url, auth=None, versioning=True, submodules=False):
"""
Execute `git clone`.
When no auth is provided, disables prompts for the password to avoid the terminal hanging.
When auth is provided, await prompts for username/passwords and sends them
:param path: the directory where the clone will be performed.
:param repo_url: the URL of the repository to be cloned.
:param auth: OPTIONAL dictionary with 'username' and 'password' fields
:param versioning: OPTIONAL whether to clone or download a snapshot of the remote repository; default clone
:param submodules: OPTIONAL whether to clone submodules content; default False
:return: response with status code and error message.
"""
env = os.environ.copy()
cmd = ["git", "clone"]
if not versioning:
cmd.append("--depth=1")
current_content = set(os.listdir(path))
if submodules:
cmd.append("--recurse-submodules")
cmd.append(unquote(repo_url))
if auth:
if auth.get("cache_credentials"):
await self.ensure_credential_helper(path)
env["GIT_TERMINAL_PROMPT"] = "1"
cmd.append("-q")
code, output, error = await self.__execute(
cmd,
username=auth["username"],
password=auth["password"],
cwd=path,
env=env,
)
else:
env["GIT_TERMINAL_PROMPT"] = "0"
code, output, error = await self.__execute(
cmd,
cwd=path,
env=env,
)
if not versioning:
new_content = set(os.listdir(path))
directory = (new_content - current_content).pop()
shutil.rmtree(f"{path}/{directory}/.git")
response = {"code": code, "message": output.strip()}
if code != 0:
response["message"] = error.strip()
return response
async def fetch(self, path, auth=None):
"""
Execute git fetch command
"""
cwd = path
# Start by fetching to get accurate ahead/behind status
cmd = [
"git",
"fetch",
"--all",
"--prune",
] # Run prune by default to help beginners
env = os.environ.copy()
if auth:
if auth.get("cache_credentials"):
await self.ensure_credential_helper(path)
env["GIT_TERMINAL_PROMPT"] = "1"
code, _, fetch_error = await self.__execute(
cmd,
cwd=cwd,
username=auth["username"],
password=auth["password"],
env=env,
)
else:
env["GIT_TERMINAL_PROMPT"] = "0"
code, _, fetch_error = await self.__execute(cmd, cwd=cwd, env=env)
result = {
"code": code,
}
if code != 0:
result["command"] = " ".join(cmd)
result["error"] = fetch_error
result["message"] = fetch_error
return result
async def get_nbdiff(
self, prev_content: str, curr_content: str, base_content=None
) -> dict:
"""Compute the diff between two notebooks.
Args:
prev_content: Notebook previous content
curr_content: Notebook current content
base_content: Notebook base content - only passed during a merge conflict
Returns:
if not base_content:
{"base": Dict, "diff": Dict}
else:
{"base": Dict, "merge_decisions": Dict}
"""
def read_notebook(content):
if not content:
return nbformat.versions[nbformat.current_nbformat].new_notebook()
if isinstance(content, dict):
# Content may come from model as a dict directly
|
(config=None)
|
[
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] |
23,948
|
jupyterlab_git.git
|
__execute
| null |
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
|
(self, cmdline: List[str], cwd: str, env: Optional[Dict[str, str]] = None, username: Optional[str] = None, password: Optional[str] = None, is_binary=False) -> Tuple[int, str, str]
|
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] |
23,950
|
jupyterlab_git.git
|
__init__
| null |
def __init__(self, config=None):
self._config = config
self._execute_timeout = (
20.0 if self._config is None else self._config.git_command_timeout
)
|
(self, config=None)
|
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] |
23,951
|
jupyterlab_git.git
|
_get_base_ref
|
Get the object reference for an unmerged ``filename`` at base stage.
Execute git ls-files -u -z <filename>
Returns:
The object reference or None
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, path, filename)
|
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] |
23,952
|
jupyterlab_git.git
|
_get_branch_reference
|
Execute git rev-parse --symbolic-full-name <branch-name> and return the result (or None).
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, branchname, path)
|
[
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] |
23,953
|
jupyterlab_git.git
|
_get_current_branch_detached
|
Execute 'git branch -a' to get current branch details in case of dirty state (rebasing, detached head,...).
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, path)
|
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] |
23,954
|
jupyterlab_git.git
|
_get_tag
|
Execute 'git describe commit_sha' to get
nearest tag associated with latest commit in branch.
Reference : https://git-scm.com/docs/git-describe#git-describe-ltcommit-ishgt82308203
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, path, commit_sha)
|
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] |
23,955
|
jupyterlab_git.git
|
_is_binary
|
Determine whether Git handles a file as binary or text.
## References
- <https://stackoverflow.com/questions/6119956/how-to-determine-if-git-handles-a-file-as-binary-or-as-text/6134127#6134127>
- <https://git-scm.com/docs/git-diff#Documentation/git-diff.txt---numstat>
- <https://git-scm.com/docs/git-diff#_other_diff_formats>
Args:
filename (str): Filename (relative to the git repository)
ref (str): Commit reference or "INDEX" if file is staged
path (str): Git repository filepath
Returns:
bool: Is file binary?
Raises:
HTTPError: if git command failed
|
lambda n: n.lower(),
|
(self, filename, ref, path)
|
[
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] |
23,956
|
jupyterlab_git.git
|
_is_remote_branch
|
Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, branch_reference)
|
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] |
23,958
|
jupyterlab_git.git
|
add
|
Execute git add<filename> command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, filename, path)
|
[
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] |
23,959
|
jupyterlab_git.git
|
add_all
|
Execute git add all command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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] |
23,960
|
jupyterlab_git.git
|
add_all_unstaged
|
Execute git add all unstaged command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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] |
23,961
|
jupyterlab_git.git
|
add_all_untracked
|
Find all untracked files, execute git add & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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0.009813785552978516,
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] |
23,962
|
jupyterlab_git.git
|
apply_stash
|
Execute git stash apply to apply a single stash entry to the repository.
If not stash_index is provided, apply the latest stash.
path: str
Git path repository
stash_index: number
Index of the stash list is applied to the repository.
|
def ensure_git_credential_cache_daemon(
self,
socket: Optional[pathlib.Path] = None,
debug: bool = False,
force: bool = False,
cwd: Optional[str] = None,
env: Dict[str, str] = None,
) -> None:
"""
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
"""
if not socket:
socket = pathlib.Path.home() / ".git-credential-cache" / "socket"
if socket.exists():
return
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS is None or force:
if force and self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
if not socket.parent.exists():
socket.parent.mkdir(parents=True, exist_ok=True)
socket.parent.chmod(0o700)
args: List[str] = ["git", "credential-cache--daemon"]
if debug:
args.append("--debug")
args.append(socket)
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS = subprocess.Popen(
args,
cwd=cwd,
env=env,
)
get_logger().debug(
"A credential cache daemon has been spawned with PID %d",
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.pid,
)
elif self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.poll():
self.ensure_git_credential_cache_daemon(socket, debug, True, cwd, env)
|
(self, path: str, stash_index: Optional[int] = None) -> dict
|
[
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] |
23,963
|
jupyterlab_git.git
|
branch
|
Execute 'git for-each-ref' command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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] |
23,964
|
jupyterlab_git.git
|
branch_delete
|
Execute 'git branch -D <branchname>'
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path, branch)
|
[
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] |
23,965
|
jupyterlab_git.git
|
branch_heads
|
Execute 'git for-each-ref' command on refs/heads & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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0.009822018444538116,
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] |
23,967
|
jupyterlab_git.git
|
changed_files
|
Gets the list of changed files between two Git refs, or the files changed in a single commit
There are two reserved "refs" for the base
1. WORKING : Represents the Git working tree
2. INDEX: Represents the Git staging area / index
Keyword Arguments:
single_commit {string} -- The single commit ref
base {string} -- the base Git ref
remote {string} -- the remote Git ref
Returns:
dict -- the response of format {
"code": int, # Command status code
"files": [string, string], # List of files changed.
"message": [string] # Error response
}
|
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
|
(self, path, base=None, remote=None, single_commit=None)
|
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] |
23,968
|
jupyterlab_git.git
|
check_credential_helper
|
Check if the credential helper exists, and whether we need to setup a Git credential cache daemon in case the credential helper is Git credential cache.
path: str
Git path repository
Return None if the credential helper is not set.
Otherwise, return True if we need to setup a Git credential cache daemon, else False.
Raise an exception if `git config` errored.
|
def read_file(self, path):
"""
Reads file content located at path and returns it as a string
path: str
The path of the file
"""
try:
file = pathlib.Path(path)
content = file.read_text()
return {"code": 0, "content": content}
except BaseException as error:
return {"code": -1, "content": ""}
|
(self, path: str) -> Optional[bool]
|
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] |
23,969
|
jupyterlab_git.git
|
checkout
|
Execute git checkout command for the filename & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, filename, path)
|
[
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] |
23,970
|
jupyterlab_git.git
|
checkout_all
|
Execute git checkout command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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0.009822018444538116,
-0.009012225084006786,
-0.013548812828958035
] |
23,971
|
jupyterlab_git.git
|
checkout_branch
|
Execute git checkout <branch-name> command & return the result.
Use the --track parameter for a remote branch.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, branchname, path)
|
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] |
23,972
|
jupyterlab_git.git
|
checkout_new_branch
|
Execute git checkout <make-branch> command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, branchname, startpoint, path)
|
[
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] |
23,973
|
jupyterlab_git.git
|
clone
|
Execute `git clone`.
When no auth is provided, disables prompts for the password to avoid the terminal hanging.
When auth is provided, await prompts for username/passwords and sends them
:param path: the directory where the clone will be performed.
:param repo_url: the URL of the repository to be cloned.
:param auth: OPTIONAL dictionary with 'username' and 'password' fields
:param versioning: OPTIONAL whether to clone or download a snapshot of the remote repository; default clone
:param submodules: OPTIONAL whether to clone submodules content; default False
:return: response with status code and error message.
|
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
|
(self, path, repo_url, auth=None, versioning=True, submodules=False)
|
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] |
23,974
|
jupyterlab_git.git
|
commit
|
Execute git commit <filename> command & return the result.
If the amend argument is true, amend the commit instead of creating a new one.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, commit_msg, amend, path, author=None)
|
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] |
23,975
|
jupyterlab_git.git
|
config
|
Get or set Git options.
If no kwargs, all options are returned. Otherwise kwargs are set.
|
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
|
(self, path, **kwargs)
|
[
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23,976
|
jupyterlab_git.git
|
delete_commit
|
Delete a specified commit from the repository.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, commit_id, path)
|
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] |
23,977
|
jupyterlab_git.git
|
detailed_log
|
Execute git log -m --cc -1 --numstat --oneline -z command (used to get
insertions & deletions per file) & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, selected_hash, path)
|
[
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] |
23,978
|
jupyterlab_git.git
|
diff
|
Execute git diff command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path, previous=None, current=None)
|
[
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] |
23,979
|
jupyterlab_git.git
|
drop_stash
|
Execute git stash drop to delete a single stash entry.
If not stash_index is provided, delete the entire stash.
path: Git path repository
stash_index: number or None
Index of the stash list to remove from the stash.
If None, the entire stash is removed.
|
def ensure_git_credential_cache_daemon(
self,
socket: Optional[pathlib.Path] = None,
debug: bool = False,
force: bool = False,
cwd: Optional[str] = None,
env: Dict[str, str] = None,
) -> None:
"""
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
"""
if not socket:
socket = pathlib.Path.home() / ".git-credential-cache" / "socket"
if socket.exists():
return
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS is None or force:
if force and self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
if not socket.parent.exists():
socket.parent.mkdir(parents=True, exist_ok=True)
socket.parent.chmod(0o700)
args: List[str] = ["git", "credential-cache--daemon"]
if debug:
args.append("--debug")
args.append(socket)
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS = subprocess.Popen(
args,
cwd=cwd,
env=env,
)
get_logger().debug(
"A credential cache daemon has been spawned with PID %d",
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.pid,
)
elif self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.poll():
self.ensure_git_credential_cache_daemon(socket, debug, True, cwd, env)
|
(self, path, stash_index: Optional[int] = None) -> dict
|
[
0.08462470769882202,
0.0038266461342573166,
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0.0663616880774498,
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0.030161114409565926,
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] |
23,980
|
jupyterlab_git.git
|
ensure_credential_helper
|
Check whether `git config --list` contains `credential.helper`.
If it is not set, then it will be set to the value string for `credential.helper`
defined in the server settings.
path: str
Git path repository
env: Dict[str, str]
Environment variables
|
def read_file(self, path):
"""
Reads file content located at path and returns it as a string
path: str
The path of the file
"""
try:
file = pathlib.Path(path)
content = file.read_text()
return {"code": 0, "content": content}
except BaseException as error:
return {"code": -1, "content": ""}
|
(self, path: str, env: Optional[Dict[str, str]] = None) -> NoneType
|
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] |
23,981
|
jupyterlab_git.git
|
ensure_git_credential_cache_daemon
|
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
|
def ensure_git_credential_cache_daemon(
self,
socket: Optional[pathlib.Path] = None,
debug: bool = False,
force: bool = False,
cwd: Optional[str] = None,
env: Dict[str, str] = None,
) -> None:
"""
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
"""
if not socket:
socket = pathlib.Path.home() / ".git-credential-cache" / "socket"
if socket.exists():
return
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS is None or force:
if force and self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
if not socket.parent.exists():
socket.parent.mkdir(parents=True, exist_ok=True)
socket.parent.chmod(0o700)
args: List[str] = ["git", "credential-cache--daemon"]
if debug:
args.append("--debug")
args.append(socket)
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS = subprocess.Popen(
args,
cwd=cwd,
env=env,
)
get_logger().debug(
"A credential cache daemon has been spawned with PID %d",
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.pid,
)
elif self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.poll():
self.ensure_git_credential_cache_daemon(socket, debug, True, cwd, env)
|
(self, socket: Optional[pathlib.Path] = None, debug: bool = False, force: bool = False, cwd: Optional[str] = None, env: Optional[Dict[str, str]] = None) -> NoneType
|
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] |
23,982
|
jupyterlab_git.git
|
ensure_gitignore
|
Handle call to ensure .gitignore file exists and the
next append will be on a new line (this means an empty file
or a file ending with
).
path: str
Top Git repository path
|
def read_file(self, path):
"""
Reads file content located at path and returns it as a string
path: str
The path of the file
"""
try:
file = pathlib.Path(path)
content = file.read_text()
return {"code": 0, "content": content}
except BaseException as error:
return {"code": -1, "content": ""}
|
(self, path)
|
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] |
23,983
|
jupyterlab_git.git
|
fetch
|
Execute git fetch command
|
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
|
(self, path, auth=None)
|
[
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] |
23,984
|
jupyterlab_git.git
|
get_content
|
Get the file content of filename.
|
lambda n: n.lower(),
|
(self, contents_manager, filename, path)
|
[
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] |
23,985
|
jupyterlab_git.git
|
get_content_at_reference
|
Collect get content of the file at the git reference.
|
lambda n: n.lower(),
|
(self, filename, reference, path, contents_manager)
|
[
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] |
23,986
|
jupyterlab_git.git
|
get_current_branch
|
Use `symbolic-ref` to get the current branch name. In case of
failure, assume that the HEAD is currently detached or rebasing, and fall back
to the `branch` command to get the name.
See https://git-blame.blogspot.com/2013/06/checking-current-branch-programatically.html
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, path)
|
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] |
23,987
|
jupyterlab_git.git
|
get_nbdiff
|
Compute the diff between two notebooks.
Args:
prev_content: Notebook previous content
curr_content: Notebook current content
base_content: Notebook base content - only passed during a merge conflict
Returns:
if not base_content:
{"base": Dict, "diff": Dict}
else:
{"base": Dict, "merge_decisions": Dict}
|
def __del__(self):
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
|
(self, prev_content: str, curr_content: str, base_content=None) -> dict
|
[
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0.042925603687763214,
0.014170491136610508,
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0.06435239315032959,
0.01590804196894169,
0.013000119477510452,
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0.02897118218243122,
0.0071527669206261635,
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] |
23,988
|
jupyterlab_git.git
|
get_upstream_branch
|
Execute 'git rev-parse --abbrev-ref branch_name@{upstream}' to get
upstream branch name tracked by given local branch.
Reference : https://git-scm.com/docs/git-rev-parse#git-rev-parse-emltbranchnamegtupstreamemegemmasterupstreamememuem
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, path, branch_name)
|
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] |
23,989
|
jupyterlab_git.git
|
ignore
|
Handle call to add an entry in .gitignore.
path: str
Top Git repository path
file_path: str
The path of the file in .gitignore
|
def read_file(self, path):
"""
Reads file content located at path and returns it as a string
path: str
The path of the file
"""
try:
file = pathlib.Path(path)
content = file.read_text()
return {"code": 0, "content": content}
except BaseException as error:
return {"code": -1, "content": ""}
|
(self, path, file_path)
|
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] |
23,990
|
jupyterlab_git.git
|
init
|
Execute git init command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
[
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] |
23,991
|
jupyterlab_git.git
|
log
|
Execute git log command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path, history_count=10, follow_path=None)
|
[
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] |
23,992
|
jupyterlab_git.git
|
merge
|
Execute git merge command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, branch: str, path: str) -> dict
|
[
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] |
23,993
|
jupyterlab_git.git
|
pop_stash
|
Execute git stash pop for a certain index of the stash list. If no index is provided, it will
path: str
Git path repository
stash_index: number
Index of the stash list is first applied to the current branch, then removed from the stash.
If the index is not provided, the most recent stash (index=0) will be removed from the stash.
|
def ensure_git_credential_cache_daemon(
self,
socket: Optional[pathlib.Path] = None,
debug: bool = False,
force: bool = False,
cwd: Optional[str] = None,
env: Dict[str, str] = None,
) -> None:
"""
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
"""
if not socket:
socket = pathlib.Path.home() / ".git-credential-cache" / "socket"
if socket.exists():
return
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS is None or force:
if force and self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
if not socket.parent.exists():
socket.parent.mkdir(parents=True, exist_ok=True)
socket.parent.chmod(0o700)
args: List[str] = ["git", "credential-cache--daemon"]
if debug:
args.append("--debug")
args.append(socket)
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS = subprocess.Popen(
args,
cwd=cwd,
env=env,
)
get_logger().debug(
"A credential cache daemon has been spawned with PID %d",
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.pid,
)
elif self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.poll():
self.ensure_git_credential_cache_daemon(socket, debug, True, cwd, env)
|
(self, path: str, stash_index: Optional[int] = None) -> dict
|
[
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] |
23,994
|
jupyterlab_git.git
|
pull
|
Execute git pull --no-commit. Disables prompts for the password to avoid the terminal hanging while waiting
for auth.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path, auth=None, cancel_on_conflict=False)
|
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] |
23,995
|
jupyterlab_git.git
|
push
|
Execute `git push $UPSTREAM $BRANCH`. The choice of upstream and branch is up to the caller.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, remote, branch, path, auth=None, set_upstream=False, force=False, tags=True)
|
[
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] |
23,996
|
jupyterlab_git.git
|
read_file
|
Reads file content located at path and returns it as a string
path: str
The path of the file
|
def read_file(self, path):
"""
Reads file content located at path and returns it as a string
path: str
The path of the file
"""
try:
file = pathlib.Path(path)
content = file.read_text()
return {"code": 0, "content": content}
except BaseException as error:
return {"code": -1, "content": ""}
|
(self, path)
|
[
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0.041305139660835266,
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0.0010298071429133415,
0.02142374962568283,
0.08298646658658981,
-0.04416414722800255,
0.08719974011182785,
0.0478883795440197,
-0.003618431743234396,
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0.019119614735245705,
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] |
23,997
|
jupyterlab_git.git
|
rebase
|
Execute git rebase command & return the result.
Args:
branch: Branch to rebase onto
path: Git repository path
|
def ensure_git_credential_cache_daemon(
self,
socket: Optional[pathlib.Path] = None,
debug: bool = False,
force: bool = False,
cwd: Optional[str] = None,
env: Dict[str, str] = None,
) -> None:
"""
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
"""
if not socket:
socket = pathlib.Path.home() / ".git-credential-cache" / "socket"
if socket.exists():
return
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS is None or force:
if force and self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
if not socket.parent.exists():
socket.parent.mkdir(parents=True, exist_ok=True)
socket.parent.chmod(0o700)
args: List[str] = ["git", "credential-cache--daemon"]
if debug:
args.append("--debug")
args.append(socket)
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS = subprocess.Popen(
args,
cwd=cwd,
env=env,
)
get_logger().debug(
"A credential cache daemon has been spawned with PID %d",
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.pid,
)
elif self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.poll():
self.ensure_git_credential_cache_daemon(socket, debug, True, cwd, env)
|
(self, branch: str, path: str) -> dict
|
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] |
23,998
|
jupyterlab_git.git
|
remote_add
|
Handle call to `git remote add` command.
path: str
Top Git repository path
url: str
Git remote url
name: str
Remote name; default "origin"
|
lambda n: n.lower(),
|
(self, path, url, name='origin')
|
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] |
23,999
|
jupyterlab_git.git
|
remote_remove
|
Handle call to `git remote remove <name>` command.
Args:
path (str): Git repository path
name (str): Remote name
|
lambda n: n.lower(),
|
(self, path, name)
|
[
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] |
24,000
|
jupyterlab_git.git
|
remote_show
|
Handle call to `git remote show` command.
Args:
path (str): Git repository path
verbose (bool): true if details are needed, otherwise, false
Returns:
if not verbose: List[str]: Known remotes
if verbose: List[ { name: str, url: str } ]: Known remotes
|
lambda n: n.lower(),
|
(self, path, verbose=False)
|
[
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] |
24,001
|
jupyterlab_git.git
|
reset
|
Execute git reset <filename> command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, filename, path)
|
[
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] |
24,002
|
jupyterlab_git.git
|
reset_all
|
Execute git reset command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
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] |
24,003
|
jupyterlab_git.git
|
reset_to_commit
|
Reset the current branch to a specific past commit.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, commit_id, path)
|
[
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] |
24,004
|
jupyterlab_git.git
|
resolve_rebase
|
Execute git rebase --<action> command & return the result.
Args:
path: Git repository path
|
def ensure_git_credential_cache_daemon(
self,
socket: Optional[pathlib.Path] = None,
debug: bool = False,
force: bool = False,
cwd: Optional[str] = None,
env: Dict[str, str] = None,
) -> None:
"""
Spawn a Git credential cache daemon with the socket file being `socket` if it does not exist.
If `debug` is `True`, the daemon will be spawned with `--debug` flag.
If `socket` is empty, it is set to `~/.git-credential-cache-daemon`.
If `force` is `True`, a daemon will be spawned, and if the daemon process is accessible,
the existing daemon process will be terminated before spawning a new one.
Otherwise, if `force` is `False`, the PID of the existing daemon process is returned.
If the daemon process is not accessible, `-1` is returned.
`cwd` and `env` are passed to the process that spawns the daemon.
"""
if not socket:
socket = pathlib.Path.home() / ".git-credential-cache" / "socket"
if socket.exists():
return
if self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS is None or force:
if force and self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS:
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.terminate()
if not socket.parent.exists():
socket.parent.mkdir(parents=True, exist_ok=True)
socket.parent.chmod(0o700)
args: List[str] = ["git", "credential-cache--daemon"]
if debug:
args.append("--debug")
args.append(socket)
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS = subprocess.Popen(
args,
cwd=cwd,
env=env,
)
get_logger().debug(
"A credential cache daemon has been spawned with PID %d",
self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.pid,
)
elif self._GIT_CREDENTIAL_CACHE_DAEMON_PROCESS.poll():
self.ensure_git_credential_cache_daemon(socket, debug, True, cwd, env)
|
(self, path: str, action: jupyterlab_git.git.RebaseAction) -> dict
|
[
0.08455966413021088,
0.0038314969278872013,
-0.014919103123247623,
0.0662950724363327,
0.05302158743143082,
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-0.024955591186881065,
0.030163714662194252,
0.008757061325013638,
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] |
24,005
|
jupyterlab_git.git
|
set_tag
|
Set a git tag pointing to a specific commit.
path: str
Git path repository
tag : str
Name of new tag.
commitId:
Identifier of commit tag is pointing to.
|
def read_file(self, path):
"""
Reads file content located at path and returns it as a string
path: str
The path of the file
"""
try:
file = pathlib.Path(path)
content = file.read_text()
return {"code": 0, "content": content}
except BaseException as error:
return {"code": -1, "content": ""}
|
(self, path, tag, commitId)
|
[
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] |
24,006
|
jupyterlab_git.git
|
show
|
Execute
git show <ref:filename>
Or
git show <ref>
Return the file content
|
def _is_remote_branch(self, branch_reference):
"""Check if given branch is remote branch by comparing with 'remotes/',
TODO : Consider a better way to check remote branch
"""
return branch_reference.startswith("refs/remotes/")
|
(self, path, ref, filename=None, is_binary=False)
|
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] |
24,007
|
jupyterlab_git.git
|
show_prefix
|
Execute git --show-prefix command & return the result.
|
for line in filter(lambda l: len(l) > 0, strip_and_split(text_output)):
|
(self, path)
|
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