I primarily program in python (using jupyter notebooks) but on occasion need to use an R function. I currently do this by using rpy2 and R magic, which works fine. Now I would like to write a function which will summarize part of my analysis procedure into one wrapper function (so I don't always need to run all of the code cells but can simply execute the function once). As part of this procedure I need to call an R function. I adapted my code to import the R function to python using the rpy2.robjects interface with importr. This works but is extremely slow (more than triple the run time for an already lengthy procedure) which makes this simply not feasible analysiswise. I am assuming this has to do with me accessing R through the high-level interface of rpy2 instead of the low-level interface. I am unsure of how to use the low-level interface within a function call though and would need some help adapting my code.
I've tried looking into the rpy2 documentation but am struggling to understand it.
This is my code for executing the R function call from within python using R magic.
Activating rpy2 R magic
%load_ext rpy2.ipython
Load my required libaries
%%R
library(scran)
Actually call the R function
%%R -i data_mat -i input_groups -o size_factors
size_factors = computeSumFactors(data_mat, clusters=input_groups, min.mean=0.1)
This is my alternative code to import the R function using rpy2 importr.
from rpy2.robjects.packages import importr
scran = importr('scran')
computeSumFactors = scran.computeSumFactors
size_factors = computeSumFactors(data_mat, clusters=input_groups, min_mean=0.1)
For some reason this second approach is orders of magnitude slower.
Any help would be much apreciated.
The only difference between the two that I would see have an influence on the observe execution speed is conversion.
When running in an "R magic" code cell (prefixed with %%R), in your example the result of calling computeSumFactors() is an R object bound to the symbol size_factors in R. In the other case, the result of calling the function computeSumFactors() will go through the conversion system (and there what exactly happens depends on what are the active converters you have) before the result is bound to the Python symbol size_factors.
Conversion can be costly: you should consider trying to deactivate numpy / pandas conversion (the localconverter context manager can be a convenient way to temporarily use minimal conversion for a code block).
I'm currently using a python module called petsc4py (https://pypi.org/project/petsc4py/). My main issue is that none of the typical intellisense features seems to work with this module.
I'm guessing it might have something to do with it being a C extension module, but I am not sure exactly why this happens. I initially thought that intellisense was unable to look inside ".so" files, but it seems that numpy is able to do this with the array object, which in my case is inside a file called multiarray.cpython-37m-x86_64-linux-gnu (check example below).
Does anyone know why I see this behaviour in the petsc4py module. Is there anything that I (or the developers of petsc4py) can do to get intellisense to work?
Example:
import sys
import petsc4py
petsc4py.init(sys.argv)
from petsc4py import PETSc
x_p = PETSc.Vec().create()
x_p.setSizes(10)
x_p.setFromOptions()
u_p = x_p.duplicate()
import numpy as np
x_n = np.array([1,2,3])
u_n = x_n.copy()
In this example, when trying to work with a Vec object from petsc4py, doing u_p.duplicate() cannot find the function and the suggestion is simply a repetition of the function immediately before. However, using an array from numpy, doing u_n.copy() works perfectly.
If you're compiling in-place then you're bumping up against https://github.com/microsoft/python-language-server/issues/197.
I'm trying to add cross-references to external API into my documentation but I'm facing three different behaviors.
I am using sphinx(1.3.1) with Python(2.7.3) and my intersphinx mapping is configured as:
{
'python': ('https://docs.python.org/2.7', None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'cv2' : ('http://docs.opencv.org/2.4/', None),
'h5py' : ('http://docs.h5py.org/en/latest/', None)
}
I have no trouble writing a cross-reference to numpy API with :class:`numpy.ndarray` or :func:`numpy.array` which gives me, as expected, something like numpy.ndarray.
However, with h5py, the only way I can have a link generated is if I omit the module name. For example, :class:`Group` (or :class:`h5py:Group`) gives me Group but :class:`h5py.Group` fails to generate a link.
Finally, I cannot find a way to write a working cross-reference to OpenCV API, none of these seems to work:
:func:`cv2.convertScaleAbs`
:func:`cv2:cv2.convertScaleAbs`
:func:`cv2:convertScaleAbs`
:func:`convertScaleAbs`
How to properly write cross-references to external API, or configure intersphinx, to have a generated link as in the numpy case?
In addition to the detailed answer from #gall, I've discovered that intersphinx can also be run as a module:
python -m sphinx.ext.intersphinx 'http://python-eve.org/objects.inv'
This outputs nicely formatted info. For reference: https://github.com/sphinx-doc/sphinx/blob/master/sphinx/ext/intersphinx.py#L390
I gave another try on trying to understand the content of an objects.inv file and hopefully this time I inspected numpy and h5py instead of only OpenCV's one.
How to read an intersphinx inventory file
Despite the fact that I couldn't find anything useful about reading the content of an object.inv file, it is actually very simple with the intersphinx module.
from sphinx.ext import intersphinx
import warnings
def fetch_inventory(uri):
"""Read a Sphinx inventory file into a dictionary."""
class MockConfig(object):
intersphinx_timeout = None # type: int
tls_verify = False
class MockApp(object):
srcdir = ''
config = MockConfig()
def warn(self, msg):
warnings.warn(msg)
return intersphinx.fetch_inventory(MockApp(), '', uri)
uri = 'http://docs.python.org/2.7/objects.inv'
# Read inventory into a dictionary
inv = fetch_inventory(uri)
# Or just print it
intersphinx.debug(['', uri])
File structure (numpy)
After inspecting numpy's one, you can see that keys are domains:
[u'np-c:function',
u'std:label',
u'c:member',
u'np:classmethod',
u'np:data',
u'py:class',
u'np-c:member',
u'c:var',
u'np:class',
u'np:function',
u'py:module',
u'np-c:macro',
u'np:exception',
u'py:method',
u'np:method',
u'np-c:var',
u'py:exception',
u'np:staticmethod',
u'py:staticmethod',
u'c:type',
u'np-c:type',
u'c:macro',
u'c:function',
u'np:module',
u'py:data',
u'np:attribute',
u'std:term',
u'py:function',
u'py:classmethod',
u'py:attribute']
You can see how you can write your cross-reference when you look at the content of a specific domain. For example, py:class:
{u'numpy.DataSource': (u'NumPy',
u'1.9',
u'http://docs.scipy.org/doc/numpy/reference/generated/numpy.DataSource.html#numpy.DataSource',
u'-'),
u'numpy.MachAr': (u'NumPy',
u'1.9',
u'http://docs.scipy.org/doc/numpy/reference/generated/numpy.MachAr.html#numpy.MachAr',
u'-'),
u'numpy.broadcast': (u'NumPy',
u'1.9',
u'http://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast.html#numpy.broadcast',
u'-'),
...}
So here, :class:`numpy.DataSource` will work as expected.
h5py
In the case of h5py, the domains are:
[u'py:attribute', u'std:label', u'py:method', u'py:function', u'py:class']
and if you look at the py:class domain:
{u'AttributeManager': (u'h5py',
u'2.5',
u'http://docs.h5py.org/en/latest/high/attr.html#AttributeManager',
u'-'),
u'Dataset': (u'h5py',
u'2.5',
u'http://docs.h5py.org/en/latest/high/dataset.html#Dataset',
u'-'),
u'ExternalLink': (u'h5py',
u'2.5',
u'http://docs.h5py.org/en/latest/high/group.html#ExternalLink',
u'-'),
...}
That's why I couldn't make it work as numpy references. So a good way to format them would be :class:`h5py:Dataset`.
OpenCV
OpenCV's inventory object seems malformed. Where I would expect to find domains there is actually 902 function signatures:
[u':',
u'AdjusterAdapter::create(const',
u'AdjusterAdapter::good()',
u'AdjusterAdapter::tooFew(int',
u'AdjusterAdapter::tooMany(int',
u'Algorithm::create(const',
u'Algorithm::getList(vector<string>&',
u'Algorithm::name()',
u'Algorithm::read(const',
u'Algorithm::set(const'
...]
and if we take the first one's value:
{u'Ptr<AdjusterAdapter>': (u'OpenCV',
u'2.4',
u'http://docs.opencv.org/2.4/detectorType)',
u'ocv:function 1 modules/features2d/doc/common_interfaces_of_feature_detectors.html#$ -')}
I'm pretty sure it is then impossible to write OpenCV cross-references with this file...
Conclusion
I thought intersphinx generated the objects.inv based on the content of the documentation project in an standard way, which seems not to be the case.
As a result, it seems that the proper way to write cross-references is API dependent and one should inspect a specific inventory object to actually see what's available.
An additional way to inspect the objects.inv file is with the sphobjinv module.
You can search local or even remote inventory files (with fuzzy matching). For instance with scipy:
$ sphobjinv suggest -t 90 -u https://docs.scipy.org/doc/scipy/reference/objects.inv "signal.convolve2d"
Remote inventory found.
:py:function:`scipy.signal.convolve2d`
:std:doc:`generated/scipy.signal.convolve2d`
Note that you may need to use :py:func: and not :py:function: (I'd be happy to know why).
How to use OpenCV 2.4 (cv2) intersphinx
Inspired by #Gall's answer, I wanted to compare the contents of the OpenCV & numpy inventory files. I couldn't get sphinx.ext.intersphinx.fetch_inventory to work from ipython, but the following does work:
curl http://docs.opencv.org/2.4/objects.inv | tail -n +5 | zlib-flate -uncompress > cv2.inv
curl https://docs.scipy.org/doc/numpy/objects.inv | tail -n +5 | zlib-flate -uncompress > numpy.inv
numpy.inv has lines like this:
numpy.ndarray py:class 1 reference/generated/numpy.ndarray.html#$ -
whereas cv2.inv has lines like this:
cv2.imread ocv:pyfunction 1 modules/highgui/doc/reading_and_writing_images_and_video.html#$ -
So presumably you'd link to the OpenCV docs with :ocv:pyfunction:`cv2.imread` instead of :py:function:`cv2.imread`. Sphinx doesn't like it though:
WARNING: Unknown interpreted text role "ocv:pyfunction".
A bit of Googling revealed that the OpenCV project has its own "ocv" sphinx domain: https://github.com/opencv/opencv/blob/2.4/doc/ocv.py -- presumably because they need to document C, C++ and Python APIs all at the same time.
To use it, save ocv.py next to your Sphinx conf.py, and modify your conf.py:
sys.path.insert(0, os.path.abspath('.'))
import ocv
extensions = [
'ocv',
]
intersphinx_mapping = {
'cv2': ('http://docs.opencv.org/2.4/', None),
}
In your rst files you need to say :ocv:pyfunc:`cv2.imread` (not :ocv:pyfunction:).
Sphinx prints some warnings (unparseable C++ definition: u'cv2.imread') but the generated html documentation actually looks ok with a link to http://docs.opencv.org/2.4/modules/highgui/doc/reading_and_writing_images_and_video.html#cv2.imread. You can edit ocv.py and remove the line that prints that warning.
The accepted answer no longer works in the new version (1.5.x) ...
import requests
import posixpath
from sphinx.ext.intersphinx import read_inventory
uri = 'http://docs.python.org/2.7/'
r = requests.get(uri + 'objects.inv', stream=True)
r.raise_for_status()
inv = read_inventory(r.raw, uri, posixpath.join)
Stubborn fool that I am, I used 2to3 and the Sphinx deprecated APIs chart to revive #david-röthlisberger's ocv.py-based answer so it'll work with Sphinx 2.3 on Python 3.5.
The fixed-up version is here:
https://gist.github.com/ssokolow/a230b27b7ea4a31f7fb40621e6461f9a
...and the quick version of what I did was:
Run 2to3 -w ocv.py && rm ocv.py.bak
Cycle back and forth between running Sphinx and renaming functions to their replacements in the chart. I believe these were the only changes I had to make on this step:
Directive now has to be imported from docutils.parsers.rst
Replace calls to l_(...) with calls to _(...) and remove the l_ import.
Replace calls to env.warn with calls to log.warn where log = sphinx.util.logging.getLogger(__name__).
Then, you just pair it with this intersphinx definition and you get something still new enough to be relevant for most use cases:
'cv2': ('https://docs.opencv.org/3.0-last-rst/', None)
For convenience, I made a small extension for aliasing intersphinx cross references. This is useful as sometimes the object inventory gets confused when an object from a submodule is imported from a package's __init__.py.
See also https://github.com/sphinx-doc/sphinx/issues/5603
###
# Workaround of
# Intersphinx references to objects imported at package level can"t be mapped.
#
# See https://github.com/sphinx-doc/sphinx/issues/5603
intersphinx_aliases = {
("py:class", "click.core.Group"):
("py:class", "click.Group"),
("py:class", "click.core.Command"):
("py:class", "click.Command"),
}
def add_intersphinx_aliases_to_inv(app):
from sphinx.ext.intersphinx import InventoryAdapter
inventories = InventoryAdapter(app.builder.env)
for alias, target in app.config.intersphinx_aliases.items():
alias_domain, alias_name = alias
target_domain, target_name = target
try:
found = inventories.main_inventory[target_domain][target_name]
try:
inventories.main_inventory[alias_domain][alias_name] = found
except KeyError:
print("could not add to inv")
continue
except KeyError:
print("missed :(")
continue
def setup(app):
app.add_config_value("intersphinx_aliases", {}, "env")
app.connect("builder-inited", add_intersphinx_aliases_to_inv)
To use this, I paste the above code in my conf.py and add aliases to the intersphinx_aliases dictionary.
This question concerns Matlab 2014b, Python 3.4 and Mac OS 10.10.
I have the following Python file tmp.py:
from statsmodels.tsa.arima_process import ArmaProcess
import numpy as np
def generate_AR_time_series():
arparams = np.array([-0.8])
maparams = np.array([])
ar = np.r_[1, -arparams]
ma = np.r_[1, maparams]
arma_process = ArmaProcess(ar, ma)
return arma_process.generate_sample(100)
I want to call generate_AR_time_series from Matlab so I used:
py.tmp.generate_AR_time_series()
which gave a vague error message
Undefined variable "py" or class "py.tmp.generate_AR_time_series".
To look into the problem further, I tried
tmp = py.eval('__import__(''tmp'')', struct);
which gave me a detailed but still obscured error message:
Python Error:
dlopen(/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/special/_ufuncs.so, 2): Symbol
not found: __gfortran_stop_numeric_f08
Referenced from: /opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/special/_ufuncs.so
Expected in: /Applications/MATLAB_R2014b.app/sys/os/maci64/libgfortran.3.dylib
in /opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/special/_ufuncs.so
I can call the function within Python just fine, so I guess the problem is with Matlab. From the detailed message, it seems that the problem lies in something is expected in the Matlab installation path, but of course Matlab installation path does not contain those things since they are third-party libraries for Python.
How to solve this problem?
Edit 1:
libgfortran.3.dylib can be found in a lot of places:
/Applications/MATLAB_R2014a.app/sys/os/maci64/libgfortran.3.dylib
/Applications/MATLAB_R2014b.app/sys/os/maci64/libgfortran.3.dylib
/opt/local/lib/gcc48/libgfortran.3.dylib
/opt/local/lib/gcc49/libgfortran.3.dylib
/opt/local/lib/libgcc/libgfortran.3.dylib
/Users/wdg/Documents/MATLAB/mcode/nativelibs/macosx/bin/libgfortran.3.dylib
Try:
setenv('DYLD_LIBRARY_PATH', '/usr/local/bin/');
For me, using the setenv approach from within MATLAB did not work. Also, MATLAB modifies the DYLD_LIBRARY_PATH variable during startup to include necessary libraries.
First, you have to make sure which version of gfortran scipy was linked against: in Terminal.app, enter otool -L /opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/special/_ufuncs.so and look for 'libgfortran' in the output.
It worked for me to copy $(MATLABROOT)/bin/.matlab7rc.sh to my home directory and change the line LDPATH_PREFIX='' in the mac section (around line 195 in my case) to LDPATH_PREFIX='/opt/local/lib/gcc49', or whatever path to libgfortran you found above.
This ensures that /opt/local/lib/gcc49/libgfortran.3.dylib is found before the MATLAB version, but leaves other paths intact.