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Jarrod Millman jarrodmillman UC Berkeley Berkeley, California http://www.jarrodmillman.com/

fperez/reprosw 35

Lecture notes (in IPython Notebook format) on Reproducible Science And Modern Scientific Software

fperez/lacquer 10

A thin layer of convenience on top of useful Python libraries.

fperez/repro-chapter-oss 6

Reproducibility: OSS and scientific research.

executablebooks/myst 2

Myst - Markedly Structured Text

jarrodmillman/brainx 1

Tools for analysis of brain imaging-derived networks, based on NetworkX

jarrodmillman/capstone 1

Lecture notes http://jarrodmillman.github.io/capstone/

jarrodmillman/cse2015 1

Teaching computational thinking and practice minisymposium

jarrodmillman/datarray 1

Prototyping numpy arrays with named axes for data management. Docs are available at URL below

pull request commentnetworkx/networkx

Structured labelled arrays

I'm a little confused by all the pep8 stuff. Shouldn't running black once fix all that? Is it true that black doesn't mess with comments and doc_strings but that flake8 and pycodestyle do? How can we make this easier for submitters?

@dschult We need to set up black to run as part of the CI and remove pep8speaks. @rossbar started looking into how to do this and has a reasonable idea of what to do (I think).

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Pull request review commentnetworkx/networkx

Structured labelled arrays

 def from_pandas_edgelist(     attr_col_headings = []     attribute_data = []     if edge_attr is True:-        attr_col_headings = [c for c in df.columns if c not in reserved_columns]+        attr_col_headings = [+            c for c in df.columns if c not in reserved_columns+        ]     elif isinstance(edge_attr, (list, tuple)):         attr_col_headings = edge_attr     else:         attr_col_headings = [edge_attr]     if len(attr_col_headings) == 0:         raise nx.NetworkXError(-            f"Invalid edge_attr argument: No columns found with name: {attr_col_headings}"+            f"Invalid edge_attr argument: No columns found with name: {attr_col_headings}"  # noqa: E501

If you replace

f"Invalid edge_attr argument: No columns found with name: {attr_col_headings}"

with

f"Invalid edge_attr: No columns found with name: {attr_col_headings}"

it won't get wrapped.

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PR opened ericmjl/networkx

Enable example

@ericmjl This PR runs the example, you may have to merge the PR before you see the result, though.

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PR merged networkx/networkx

Make optional dependencies default Maintenance

Given their central position in the scientific Python ecosystem and the fact that they provide self-contained, easy to install wheels, NumPy, SciPy, Matplotlib, and Pandas are now default dependencies. They don't have pre-built wheels for pypy or 3.9-dev, so they aren't installed by default on those instances via the environment marker:

platform_python_implementation!='PyPy' and python_version<'3.9'

Before we officially add support for Python 3.9, we will change python_version<'3.9' to python_version<'3.10'. If and when the core projects (ie., NumPy, SciPy, Matplotlib, and Pandas) provide self-contained wheels for PyPy, we will remove platform_python_implementation!='PyPy'.

This also makes pyyaml a default dependency. According to https://pypistats.org/top, pyyaml is among the top 15 packages downloaded from pypi and has more conda downloads than networkx. It also easy to install and works on pypy.

It is unlikely that we will add additional new default dependencies. We will also be unlikely to add new extra dependencies. More detail can be found in the new dependency bullet to the Guidelines section of the Contributor Guide (see c9395c8ce1).

This removes code (mostly boiler plate stuff), simplifies our CI system, and should help reduce the number of user installation issues.

@dschult , @rossbar , @stefanv, @MridulS I think this is ready to go. Let me know if you have any questions or suggestions.

+148 -325

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issue commentnetworkx/networkx

Deprecation warning with flatten

Please do. Thanks!

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NXEP 2 — API design of view slices (#4101) * Create nxep-0002.rst * Update NXEP 2 with explanation of an independent slice method * Some suggestions for wording and additions. * DOC: Add NXEP2 to nxeps toctree. Creates link to NXEP on nxeps landing page. * DOC: Beef up abstract. * DOC: Add motivating example. * DOC: Add basic reference impl to nxep * DOC: Added to alternatives section. - Added a bullet on improved error messages. - Reformatted bullet about head/slice method Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

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Cleanup old platforms (#4202) * Remove IronPython code * Remove Jython code

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Fixed "topolgical_sort" typo (#4211) Now reads "topological_sort"

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Pull request review commentnetworkx/networkx

Make optional dependencies default

 Guidelines           # function not for directed graphs *or* for multigraphs           pass 

Here it is rendered: https://305-890377-gh.circle-artifacts.com/0/doc/build/html/developer/contribute.html#guidelines

I wonder if we should rearrange the guidelines to have the dependency thing earlier.

Like this

  • All code should have tests.

  • All code should be documented, to the same standard <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt#docstring-standard>_ as NumPy and SciPy.

  • All changes are reviewed. Ask on the mailing list <http://groups.google.com/group/networkx-discuss>_ if you get no response to your pull request.

  • Default dependencies are listed in requirements/default.txt and extra (i.e., optional) dependencies are listed in requirements/extra.txt. We don't often add new default and extra dependencies. Typically, new proposed dependencies would first be added as extra dependencies. Extra dependencies should be easy to install on all platforms and widely-used. New default dependencies should be easy to install on all platforms, widely-used in the community, and have demonstrated potential for wide-spread use in NetworkX.

  • Use the following import conventions::

    import numpy as np import scipy as sp import matplotlib as mpl import matplotlib.pyplot as plt import pandas as pd import networkx as nx

  • Use the decorator not_implemented_for in networkx/utils/decorators.py to designate that a function doesn't accept 'directed', 'undirected', 'multigraph' or 'graph'. The first argument of the decorated function should be the graph object to be checked.

    .. code-block:: python

    @nx.not_implemented_for('directed', 'multigraph')
    def function_not_for_MultiDiGraph(G, others):
        # function not for graphs that are directed *and* multigraph
        pass
    
    @nx.not_implemented_for('directed')
    @nx.not_implemented_for('multigraph')
    def function_only_for_Graph(G, others):
        # function not for directed graphs *or* for multigraphs
        pass
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pull request commentnetworkx/networkx

Make optional dependencies default

I wonder whether pyyaml should be a dependency? Here is the code w/out the comments:

from networkx.utils import open_file
import yaml

@open_file(1, mode="w")
def write_yaml(G_to_be_yaml, path_for_yaml_output, **kwds):
    yaml.dump(G_to_be_yaml, path_for_yaml_output, **kwds)

@open_file(0, mode="r")
def read_yaml(path):
    G = yaml.load(path, Loader=yaml.FullLoader)
    return G

If this was being proposed for inclusion now, I think I would -1 on adding it. It seems like folks could just as well import pyyaml themselves. We could remove this in 3.0 and replace it with a gallery example. Thoughts?

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Pull request review commentnetworkx/networkx

Make optional dependencies default

 Guidelines           # function not for directed graphs *or* for multigraphs           pass 

@MridulS , @rossbar I rearranged /edited this section. Take another look to see if you are still OK w/ it.

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Pull request review commentnetworkx/networkx

Make optional dependencies default

 def trophic_levels(G, weight="weight"):     ----------     .. [1] Stephen Levine (1980) J. theor. Biol. 83, 195-207     """-    try:-        import numpy as np-    except ImportError as e:-        raise ImportError("trophic_levels() requires NumPy: http://numpy.org/") from e+    import numpy as np

I will follow-up on this.

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Pull request review commentnetworkx/networkx

Make optional dependencies default

     "networkx.utils": ["tests/*.py"], } -install_requires = ["decorator>=4.3.0"]++def parse_requirements_file(filename):+    with open(filename, encoding="utf-8") as fid:+        requires = [l.strip() for l in fid.readlines() if l]++    return requires+++install_requires = parse_requirements_file("requirements/default.txt") extras_require = {-    "all": [-        "numpy",-        "scipy",-        "pandas",-        "matplotlib",-        "pygraphviz",-        "pydot",-        "pyyaml",-        "lxml",-        "pytest",-    ],-    "gdal": ["gdal"],-    "lxml": ["lxml"],-    "matplotlib": ["matplotlib"],-    "pytest": ["pytest"],-    "numpy": ["numpy"],-    "pandas": ["pandas"],-    "pydot": ["pydot"],-    "pygraphviz": ["pygraphviz"],-    "pyyaml": ["pyyaml"],-    "scipy": ["scipy"],+    dep: parse_requirements_file("requirements/" + dep + ".txt")+    for dep in ["extra", "test"]

Now you can do pip install -e .[developer,doc,extra,test].

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Make optional dependencies default

 class _CholeskySolver:     """      def __init__(self, A):-        if not self._cholesky:-            raise nx.NetworkXError("Cholesky solver unavailable.")-        self._chol = self._cholesky(A)--    def solve(self, B, tol=None):-        return self._chol(B)--    try:-        from scikits.sparse.cholmod import cholesky--        _cholesky = cholesky-    except ImportError:-        _cholesky = None+        raise nx.NetworkXError("Cholesky solver deprecated.  Use LU solver instead.")

That works for me. But I will wait to see what @dschult and others think.

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Pull request review commentnetworkx/networkx

Make optional dependencies default

 Guidelines           # function not for directed graphs *or* for multigraphs           pass +* Adding new default and extra dependencies should be done only with careful+  consideration.  New default dependencies should be widely-used in the community+  and have high potential for wide-spread use in NetworkX.  They should also be+  pure Python and easy to install.  Extra dependencies should also be easy to+  install and widely-used, but may have low potential for wide-spread use in+  NetworkX.  Typically, new default dependencies would first be added as new+  extra dependencies.  Default dependencies should be added to

So, I think your language is fine. I will add requirements/extra.txt and requirements/default.txt to make my intention explicit.

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Pull request review commentnetworkx/networkx

Make optional dependencies default

 Guidelines           # function not for directed graphs *or* for multigraphs           pass +* Adding new default and extra dependencies should be done only with careful+  consideration.  New default dependencies should be widely-used in the community+  and have high potential for wide-spread use in NetworkX.  They should also be+  pure Python and easy to install.  Extra dependencies should also be easy to

I wasn't sure about this. I was thinking we might be reluctant to add non-pure Python packages (besides numpy, scipy, etc.), since it would be even more unlikely to work on PyPy or other non-CPython platforms.

That said, I am not committed to this one way or the other. (I was mainly trying to be a bit conservative, since this is already a big break from the last 15 years or so of networkx.) So if Dan, Mridul, and you are happy with taking out pure Python and that is fine with me.

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Pull request review commentnetworkx/networkx

Make optional dependencies default

     "networkx.utils": ["tests/*.py"], } -install_requires = ["decorator>=4.3.0"]++def parse_requirements_file(filename):+    with open(filename, encoding="utf-8") as fid:+        requires = [l.strip() for l in fid.readlines() if l]++    return requires+++install_requires = parse_requirements_file("requirements/default.txt") extras_require = {-    "all": [-        "numpy",-        "scipy",-        "pandas",-        "matplotlib",-        "pygraphviz",-        "pydot",-        "pyyaml",-        "lxml",-        "pytest",-    ],-    "gdal": ["gdal"],-    "lxml": ["lxml"],-    "matplotlib": ["matplotlib"],-    "pytest": ["pytest"],-    "numpy": ["numpy"],-    "pandas": ["pandas"],-    "pydot": ["pydot"],-    "pygraphviz": ["pygraphviz"],-    "pyyaml": ["pyyaml"],-    "scipy": ["scipy"],+    dep: parse_requirements_file("requirements/" + dep + ".txt")+    for dep in ["extra", "test"]

I was imaging that setup.py was mostly for end-users and that developers would continue to use the requirements files. I assume most users are not going to be building the documentation for themselves. And if they are building the documentation, they should probably be working from a git clone of the repo.

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Make optional dependencies default

 def trophic_levels(G, weight="weight"):     ----------     .. [1] Stephen Levine (1980) J. theor. Biol. 83, 195-207     """-    try:-        import numpy as np-    except ImportError as e:-        raise ImportError("trophic_levels() requires NumPy: http://numpy.org/") from e+    import numpy as np

If we move the imports outside the functions, we will need to add come code to avoid failures in pypy (which doesn't have numpy, scipy, etc. installed). I am not sure what the best way to handle this, but this seemed like a good first step. (I.e., it would be easier to see what I removed and how it was replaced.) I am happy to revisit this, but I am not sure how best to keep pypy from complaining. I am hoping that numpy, scipy, etc. will eventually release wheels for pypy.

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 cache: pip matrix:   include:     - os: linux-      python: 3.7+      python: 3.6

Yes, I was thinking of returning to that soon as well.

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Pull request review commentnetworkx/networkx

Make optional dependencies default

 Guidelines           # function not for directed graphs *or* for multigraphs           pass +* Adding new default and extra dependencies should be done only with careful+  consideration.  New default dependencies should be widely-used in the community+  and have high potential for wide-spread use in NetworkX.  They should also be+  pure Python and easy to install.  Extra dependencies should also be easy to+  install and widely-used, but may have low potential for wide-spread use in+  NetworkX.  Typically, new default dependencies would first be added as new+  extra dependencies.  Default dependencies should be added to

I was thinking that, in general, new packages would be first added as an extra dependency (i.e., added to requirements/extra.txt). If it was used more generally, it might eventually be changed to a default dependency (i.e., moved from requirements/extra.txt to requirements/default.txt).

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issue commentpygraphviz/pygraphviz

Wheels for PyGraphviz - advice?

In response to an issue we filed upstream (https://gitlab.com/graphviz/graphviz/-/issues/1808), we got the following comment:

"This is unlikely to be a response you want to hear, but my recommendation is to continue to call command line tools. There are numerous known unfixed out-of-bounds memory accesses (grep the issue tracker for segfault or anything filed by Google Autofuzz) and I suspect there are further latent issues like that fixed in !1489 (merged). Expecting Graphviz libraries to be memory safe or re-entrant is unwise. We are working on fixing the situation, but there is too much work and too few hands.

Sincerely, any and all help would be greatly appreciated."

So, we may need to help improve the Graphviz libraries before we can rely on them for pygraphviz.

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pull request commentnetworkx/networkx

Algos for max ordered common subtree embedding/isomorphism

We currently don't use Cython and will need to make a decision whether we will start before merging this. Historically, our focus has been on simple implementations over performance. But, now is a good time for us to revisit this.

We have plenty of time before the next release, so let's leave it in the PR until we revisit whether or not to change our Cython policy. We may need to create a NXEP to decide what we are going to do about optimizing code for performance. Thanks for your patience.

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pull request commentnetworkx/networkx

debugging

@mehdinemo Thanks for writing the tests. I pushed them to the patch-1 branch on mehdinemo/networkx so that they are visible here now. I also moved them from the top-level tests directory (networkx/tests/test_kernigan_lin.py) to the existing tests for this module (networkx/algorithms/community/tests/test_kernighan_lin.py).

It looks like your tests check that the code has the previous error. Please change them to confirm that your change fixed this problem (i.e., these tests should pass).

In order for these tests to show up here, please push your changes to the patch-1 branch on mehdinemo/networkx. You will need to pull the changes I made first.

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MAINT: Refactor nx.info

I prefer DiGraph named "asdf" with 3 nodes and 2 edges and DiGraph with 3 nodes and 2 edges over DiGraph(name:"asdf",nodes:3,edges:2) as well.

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Speed up transitivity, remove redundant call (#4198)

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MAINT: Speed up transitivity, remove redundant call Enhancement

Remove multiple calls to _triangles_and_degree_iter. This cuts the transitivity calculation time by half.

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DOC: Update docstrings in cytoscape module (#4180) * DOC: Add sections to cytoscape_data docstring * DOC: Add docstring to cytoscape_graph * DOC: Fix incorrect dict in parameter spec

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DOC: Update docstrings in cytoscape module Documentation

Add missing parameter and additional sections to cytoscape_data docstring, and add a docstring to the cytoscape_graph function.

Related to discussion in #4173

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graphml: re-add graph attribute type 'long' after 857aa81 removed it (#4189) closes: #4188

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Test mac osx via actions Enhancement Maintenance

This moves our testing to GH actions from Travis. Travis was running too much. The mac tests were particularly slow (sometimes taking over 30 minutes just to run 3.7 with dependencies). This will make are CI system faster and also slightly reduce the lines of code.

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Partitions for quotient graphs (#4183) * Update `algorithms/minors.py` - make `equivalence_classes` a public method * Update `algorithms/minors.py` (update docstring for `equivalence_classes`) + add `equivalence_relation` to module doc source * Update `algorithms/minors.py` - tweak `quotient_graph` to accept partitions as dicts keyed by block labels and block lists/tuples/sets as values * Fix `equivalence_classes` docstring in `algorithms/minors.py` * Update `quotient_graph` docstring in `algorithms/minors.py` * Docstring tweaks in `algorithms/minors.py` * Tweak `equivalence_classes` docstring in `algorithms/minors.py` + update tests

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graphml: re-add graph attribute type 'long' after 857aa81 removed it (#4189) closes: #4188

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graphml: re-add graph attribute type 'long' after 857aa81 removed it Defect

closes: #4188

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