# -*- coding: utf-8 -*-
"""
======
Rmagic
======

Magic command interface for interactive work with R in ipython. %R and %%R are
the line and cell magics, respectively.

.. note::

  You will need a working copy of R.

Usage
=====

To enable the magics below, execute ``%load_ext rpy2.ipython``.

``%R``

{R_DOC}

``%Rpush``

{RPUSH_DOC}

``%Rpull``

{RPULL_DOC}

``%Rget``

{RGET_DOC}

"""

#-----------------------------------------------------------------------------
#  Copyright (C) 2012 The IPython Development Team
#  Copyright (C) 2013-2017 rpy2 authors
#
#  Distributed under the terms of the BSD License.  The full license is in
#  the file COPYING, distributed as part of this software.
#-----------------------------------------------------------------------------

import sys
import tempfile
from glob import glob
from os import stat
from shutil import rmtree

# numpy and rpy2 imports

import rpy2.rinterface as ri
import rpy2.robjects as ro
import rpy2.robjects.packages as rpacks
from rpy2.robjects.conversion import (Converter,
                                      localconverter)
import warnings
from rpy2.robjects.conversion import converter as template_converter

# Try loading pandas and numpy, emitting a warning if either cannot be
# loaded.
try:
    import numpy
    try:
        import pandas
    except ImportError:
        pandas = None
        warnings.warn(' '.join(("The Python package 'pandas' is strongly"
                                "recommended when using `rpy2.ipython`.",
                                "Unfortunately it could not be loaded,",
                                "but at least we found 'numpy'.")))
except ImportError:
    # Give up on numerics
    numpy = None
    warnings.warn(' '.join(("The Python package 'pandas' is strongly",
                            "recommended when using `rpy2.ipython`.",
                            "Unfortunately it could not be loaded,",
                            "as we did not manage to load 'numpy'",
                            "first.")))
if numpy:
  from rpy2.robjects import numpy2ri
  template_converter += numpy2ri.converter
  if pandas:
    from rpy2.robjects import pandas2ri
    template_converter += pandas2ri.converter


# IPython imports

import IPython
from IPython.core.displaypub import publish_display_data
from IPython.core.magic import (cell_magic,
                                Magics,
                                magics_class,
                                line_cell_magic,
                                line_magic,
                                needs_local_scope)
from IPython.core.magic_arguments import (argument,
                                          argument_group,
                                          magic_arguments,
                                          parse_argstring)

# TODO: drop support for ipython < 5.0
try:
    from IPython.external.simplegeneric import generic
except ImportError:
    # IPython 4.0
    from simplegeneric import generic


class RInterpreterError(ri.RRuntimeError):
    """An error when running R code in a %%R magic cell."""
    
    def __init__(self, line, err, stdout):
        self.line = line
        self.err = err.rstrip()
        self.stdout = stdout.rstrip()

    def __str__(self):
        s = 'Failed to parse and evaluate line %r.\nR error message: %r' % \
                (self.line, self.err)
        if self.stdout and (self.stdout != self.err):
            s += '\nR stdout:\n' + self.stdout
        return s


converter = Converter('ipython conversion',
                      template=template_converter)


# The default conversion for lists is currently to make them an R list. That has
# some advantages, but can be inconvenient (and, it's inconsistent with the way
# python lists are automatically converted by numpy functions), so for
# interactive use in the rmagic, we call unlist, which converts lists to vectors
# **if the list was of uniform (atomic) type**.
@converter.ri2py.register(list)
def ri2py_list(obj):
    # simplify2array is a utility function, but nice for us
    # TODO: use an early binding of the R function
    return ro.r.simplify2array(obj)


# The R magic is opiniated about what the R vectors should become.
@converter.ri2ro.register(ri.SexpVector)
def _(obj):
    if 'data.frame' in obj.rclass:
        # request to turn it to a pandas DataFrame
        res = converter.ri2py(obj)
    else:
        res = ro.sexpvector_to_ro(obj)
    return res        


@magics_class
class RMagics(Magics):
    """A set of magics useful for interactive work with R via rpy2.
    """

    def __init__(self, shell, converter=converter,
                 cache_display_data=False, device='png'):
        """
        Parameters
        ----------

        shell : IPython shell

        converter : rpy2 Converter instance to use. If None,
                    the magic's current converter is used.

        cache_display_data : bool
            If True, the published results of the final call to R are 
            cached in the variable 'display_cache'.

        device : ['png', 'X11', 'svg']
            Device to be used for plotting. 
            Currently only 'png', 'X11' and 'svg' are supported,
            with 'png' and 'svg' being most useful in the notebook,
            and 'X11' allowing interactive plots in the terminal.

        """
        super(RMagics, self).__init__(shell)
        self.cache_display_data = cache_display_data

        self.Rstdout_cache = []

        self.converter = converter
        
        self.set_R_plotting_device(device)

    def set_R_plotting_device(self, device):
        """
        Set which device R should use to produce plots.
        If device == 'svg' then the package 'Cairo' 
        must be installed. Because Cairo forces "onefile=TRUE",
        it is not posible to include multiple plots per cell.

        Parameters
        ----------

        device : ['png', 'X11', 'svg']
            Device to be used for plotting. 
            Currently only "png" and "X11" are supported,
            with 'png' and 'svg' being most useful in the notebook,
            and 'X11' allowing interactive plots in the terminal.

        """
        device = device.strip()
        if device not in ['png', 'X11', 'svg']:
            raise ValueError("device must be one of ['png', 'X11' 'svg'], got '%s'", device)
        if device == 'svg':
            try:
                self.cairo = rpacks.importr('Cairo')
            except ri.RRuntimeError as rre:
                if rpacks.isinstalled('Cairo'):
                    msg = "An error occurred when trying to load the R package Cairo'\n%s" % str(rre)
                else:
                    msg = """
The R package 'Cairo' is required but it does not appear to be installed/available. Try:

import rpy2.robjects.packages as rpacks
utils = rpacks.importr('utils')
utils.chooseCRANmirror(ind=1)
utils.install_packages('Cairo')
"""
                raise RInterpreterError(msg)
        self.device = device

    @line_magic
    def Rdevice(self, line):
        """
        Change the plotting device R uses to one of ['png', 'X11', 'svg'].
        """
        self.set_R_plotting_device(line.strip())

    def eval(self, code):
        """
        Parse and evaluate a line of R code with rpy2.
        Returns the output to R's stdout() connection,
        the value generated by evaluating the code, and a
        boolean indicating whether the return value would be
        visible if the line of code were evaluated in an R REPL.

        R Code evaluation and visibility determination are done via an R call of
        the form withVisible(code_string), and this entire expression needs to
        be evaluated in R (we can't use rpy2 function proxies here, as
        withVisible is a LISPy R function).

        """
        old_writeconsole_regular = ri.get_writeconsole_regular()
        ri.set_writeconsole_regular(self.write_console_regular)
        try:
            # Need the newline in case the last line in code is a comment
            value, visible = ro.r("withVisible({%s\n})" % code)
        except (ri.RRuntimeError, ValueError) as exception:
            warning_or_other_msg = self.flush() # otherwise next return seems to have copy of error
            raise RInterpreterError(code, str(exception), warning_or_other_msg)
        text_output = self.flush()
        ri.set_writeconsole_regular(old_writeconsole_regular)
        return text_output, value, visible[0]

    def write_console_regular(self, output):
        """
        A hook to capture R's stdout in a cache.
        """
        self.Rstdout_cache.append(output)

    def flush(self):
        """
        Flush R's stdout cache to a string, returning the string.
        """
        value = ''.join(self.Rstdout_cache)
        self.Rstdout_cache = []
        return value

    # @skip_doctest
    @needs_local_scope
    @line_magic
    def Rpush(self, line, local_ns=None):
        """
        A line-level magic for R that pushes
        variables from python to rpy2. The line should be made up
        of whitespace separated variable names in the IPython
        namespace::

            In [7]: import numpy as np

            In [8]: X = np.array([4.5,6.3,7.9])

            In [9]: X.mean()
            Out[9]: 6.2333333333333343

            In [10]: %Rpush X

            In [11]: %R mean(X)
            Out[11]: array([ 6.23333333])

        """
        if local_ns is None:
            local_ns = {}

        inputs = line.split(' ')
        for input in inputs:
            try:
                val = local_ns[input]
            except KeyError:
                try:
                    val = self.shell.user_ns[input]
                except KeyError:
                    # reraise the KeyError as a NameError so that it looks like
                    # the standard python behavior when you use an unnamed
                    # variable
                    raise NameError("name '%s' is not defined" % input)

            with localconverter(self.converter) as cv:
                ro.r.assign(input, val)

    # @skip_doctest
    @magic_arguments()
    @argument(
        'outputs',
        nargs='*',
        )
    @line_magic
    def Rpull(self, line):
        """
        A line-level magic for R that pulls
        variables from python to rpy2::

            In [18]: _ = %R x = c(3,4,6.7); y = c(4,6,7); z = c('a',3,4)

            In [19]: %Rpull x  y z

            In [20]: x
            Out[20]: array([ 3. ,  4. ,  6.7])

            In [21]: y
            Out[21]: array([ 4.,  6.,  7.])

            In [22]: z
            Out[22]:
            array(['a', '3', '4'],
                  dtype='|S1')


        This is useful when a structured array is desired as output, or
        when the object in R has mixed data types. 
        See the %%R docstring for more examples.

        Notes
        -----

        Beware that R names can have '.' so this is not fool proof.
        To avoid this, don't name your R objects with '.'s...

        """
        args = parse_argstring(self.Rpull, line)
        outputs = args.outputs
        with localconverter(self.converter) as cv:
            for output in outputs:
                robj = ri.globalenv.get(output)
                self.shell.push({output: robj})

    # @skip_doctest
    @magic_arguments()
    @argument(
        'output',
        nargs=1,
        type=str,
        )
    @line_magic
    def Rget(self, line):
        """
        Return an object from rpy2, possibly as a structured array (if possible).
        Similar to Rpull except only one argument is accepted and the value is 
        returned rather than pushed to self.shell.user_ns::

            In [3]: dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')]

            In [4]: datapy = np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5, 'e')], dtype=dtype)

            In [5]: %R -i datapy

            In [6]: %Rget datapy
            Out[6]:
            array([['1', '2', '3', '4'],
                   ['2', '3', '2', '5'],
                   ['a', 'b', 'c', 'e']],
                  dtype='|S1')
        """
        args = parse_argstring(self.Rget, line)
        output = args.output
        # get the R object with the given name, starting from globalenv
        # in the search path
        with localconverter(self.converter) as cv:
            res = ro.globalenv.get(output[0])
        return res


    def setup_graphics(self, args):
        """Setup graphics in preparation for evaluating R code.

        args : argparse bunch (should be whatever the R magic got)."""
        
        if getattr(args, 'units') is not None:
            if args.units != "px" and getattr(args, 'res') is None:
                args.res = 72

        plot_arg_names = ['width', 'height', 'pointsize', 'bg']
        if self.device == 'png':
            plot_arg_names += ['units', 'res']

        argdict = {}
        for name in plot_arg_names:
            val = getattr(args, name)
            if val is not None:
                argdict[name] = val

        tmpd = None
        if self.device in ['png', 'svg']:
            # Create a temporary directory for R graphics output
            # TODO: Do we want to capture file output for other device types other
            # than svg & png?
            tmpd = tempfile.mkdtemp()
            tmpd_fix_slashes = tmpd.replace('\\', '/')

            if self.device == 'png':
                # Note: that %% is to pass into R for interpolation there
                ro.r.png("%s/Rplots%%03d.png" % tmpd_fix_slashes,
                        **argdict)
            elif self.device == 'svg':
                self.cairo.CairoSVG("%s/Rplot.svg" % tmpd_fix_slashes,
                                    **argdict)

        elif self.device == 'X11':
            # Open a new X11 device, except if the current one is already an X11
            # device
            ro.r("""
            if (substr(names(dev.cur()), 1, 3) != "X11") {
                X11()
            }""")

        else:
            # TODO: This isn't actually an R interpreter error...
            raise RInterpreterError("device must be one of ['png', 'X11' 'svg']")

        return tmpd

    def publish_graphics(self, graph_dir, isolate_svgs=True):
        """Wrap graphic file data for presentation in IPython

        graph_dir : str
            Probably provided by some tmpdir call
        isolate_svgs : bool
            Enable SVG namespace isolation in metadata"""
        
        # read in all the saved image files
        images = []
        display_data = []

        # Default empty metadata dictionary
        md = {}

        if self.device == 'png':
            for imgfile in sorted( glob("%s/Rplots*png" % graph_dir) ):
                if stat(imgfile).st_size >= 1000:
                    with open(imgfile, 'rb') as fh_img:
                        images.append(fh_img.read())
        else:
            # as onefile=TRUE, there is only one .svg file
            imgfile = "%s/Rplot.svg" % graph_dir
            # Cairo creates an SVG file every time R is called
            # -- empty ones are not published
            if stat(imgfile).st_size >= 1000:
                with open(imgfile, 'rb') as fh_img:
                    images.append(fh_img.read())

        mimetypes = { 'png' : 'image/png', 'svg' : 'image/svg+xml' }
        mime = mimetypes[self.device]

        # By default, isolate SVG images in the Notebook to avoid garbling
        if images and self.device == "svg" and isolate_svgs:
            md = {'image/svg+xml': dict(isolated=True)}

        # flush text streams before sending figures, helps a little with output
        for image in images:
            # synchronization in the console (though it's a bandaid, not a real sln)
            sys.stdout.flush(); sys.stderr.flush()
            display_data.append(('RMagic.R', {mime: image}))

        return display_data, md


    # @skip_doctest
    @magic_arguments()
    @argument(
        '-i', '--input', action='append',
        help=('Names of input variable from shell.user_ns to be assigned to R variables'
              ' of the same names after using the Converter self.converter. Multiple names can be'
              ' passed separated only by commas with no whitespace.')
    )
    @argument(
        '-o', '--output', action='append',
        help=("Names of variables to be pushed from rpy2 to shell.user_ns after executing"
              " cell body (rpy2's internal facilities will apply ri2ro as appropriate)."
              " Multiple names can be passed separated only by commas with no whitespace.")
    )
    @argument(
        '-n', '--noreturn',
        help='Force the magic to not return anything.',
        action='store_true',
        default=False
        )
    @argument_group("Plot", "Arguments to plotting device")
    @argument(
        '-w', '--width', type=float,
        help='Width of plotting device in R.'
        )
    @argument(
        '-h', '--height', type=float,
        help='Height of plotting device in R.'
        )
    @argument(
        '-p', '--pointsize', type=int,
        help='Pointsize of plotting device in R.'
        )
    @argument(
        '-b', '--bg',
        help='Background of plotting device in R.'
        )
    @argument_group("SVG", "SVG specific arguments")
    @argument(
        '--noisolation',
        help=('Disable SVG isolation in the Notebook. By default, SVGs are isolated to'
              ' avoid namespace collisions between figures.'
              ' Disabling SVG isolation allows to reference previous figures or share'
              ' CSS rules across a set of SVGs.'),
        action='store_false',
        default=True,
        dest='isolate_svgs'
        )
    @argument_group("PNG", "PNG specific arguments")
    @argument(
        '-u', '--units', type=str, choices=["px", "in", "cm", "mm"],
        help=('Units of png plotting device sent as an argument to *png* in R. One of'
              ' ["px", "in", "cm", "mm"].')
        )
    @argument(
        '-r', '--res', type=int,
        help=('Resolution of png plotting device sent as an argument to *png* in R.'
              ' Defaults to 72 if *units* is one of ["in", "cm", "mm"].')
        )
    @argument(
        '-c', '--converter',
        default=None,
        help=('Name of local converter to use. A converter contains the rules to convert objects'
              ' back and forth between Python and R. If not specified/None, the defaut converter'
              ' for the magic\'s module is used'
              ' (that is rpy2\'s default converter + numpy converter + pandas converter'
              ' if all three are available).')
        )
    @argument(
        'code',
        nargs='*',
        )
    @needs_local_scope
    @line_cell_magic
    def R(self, line, cell=None, local_ns=None):
        """
        Execute code in R, optionally returning results to the Python runtime.

        In line mode, this will evaluate an expression and convert the returned
        value to a Python object.  The return value is determined by rpy2's
        behaviour of returning the result of evaluating the final expression.

        Multiple R expressions can be executed by joining them with semicolons::

            In [9]: %R X=c(1,4,5,7); sd(X); mean(X)
            Out[9]: array([ 4.25])

        In cell mode, this will run a block of R code. The resulting value
        is printed if it would be printed when evaluating the same code
        within a standard R REPL.

        Nothing is returned to python by default in cell mode::

            In [10]: %%R
               ....: Y = c(2,4,3,9)
               ....: summary(lm(Y~X))

            Call:
            lm(formula = Y ~ X)

            Residuals:
                1     2     3     4
             0.88 -0.24 -2.28  1.64

            Coefficients:
                        Estimate Std. Error t value Pr(>|t|)
            (Intercept)   0.0800     2.3000   0.035    0.975
            X             1.0400     0.4822   2.157    0.164

            Residual standard error: 2.088 on 2 degrees of freedom
            Multiple R-squared: 0.6993,Adjusted R-squared: 0.549
            F-statistic: 4.651 on 1 and 2 DF,  p-value: 0.1638

        In the notebook, plots are published as the output of the cell::

            %R plot(X, Y)

        will create a scatter plot of X bs Y.

        If cell is not None and line has some R code, it is prepended to
        the R code in cell.

        Objects can be passed back and forth between rpy2 and python via the -i -o flags in line::

            In [14]: Z = np.array([1,4,5,10])

            In [15]: %R -i Z mean(Z)
            Out[15]: array([ 5.])


            In [16]: %R -o W W=Z*mean(Z)
            Out[16]: array([  5.,  20.,  25.,  50.])

            In [17]: W
            Out[17]: array([  5.,  20.,  25.,  50.])

        The return value is determined by these rules:

        * If the cell is not None (i.e., has contents), the magic returns None.

        * If the final line results in a NULL value when evaluated
          by rpy2, then None is returned.

        * No attempt is made to convert the final value to a structured array.
          Use %Rget to push a structured array.

        * If the -n flag is present, there is no return value.

        * A trailing ';' will also result in no return value as the last
          value in the line is an empty string.
        """

        args = parse_argstring(self.R, line)

        # arguments 'code' in line are prepended to
        # the cell lines

        if cell is None:
            code = ''
            return_output = True
            line_mode = True
        else:
            code = cell
            return_output = False
            line_mode = False

        code = ' '.join(args.code) + code

        # if there is no local namespace then default to an empty dict
        if local_ns is None:
            local_ns = {}

        if args.converter is None:
            converter = self.converter
        else:
            try:
                converter = local_ns[args.converter]
            except KeyError:
                try:
                    converter = self.shell.user_ns[args.converter]
                except KeyError:
                    raise NameError("name '%s' is not defined" % args.converter)
            if not isinstance(converter, Converter):
                raise ValueError("'%s' must be a %s object (but it is a %s)."
                                 % (args.converter, Converter, type(localconverter)))
            
        if args.input:
            for input in ','.join(args.input).split(','):
                try:
                    val = local_ns[input]
                except KeyError:
                    try:
                        val = self.shell.user_ns[input]
                    except KeyError:
                        raise NameError("name '%s' is not defined" % input)
                with localconverter(converter) as cv:
                    ro.r.assign(input, val)

        tmpd = self.setup_graphics(args)

        text_output = ''
        try:
            if line_mode:
                for line in code.split(';'):
                    text_result, result, visible = self.eval(line)
                    text_output += text_result
                if text_result:
                    # the last line printed something to the console so we won't return it
                    return_output = False
            else:
                text_result, result, visible = self.eval(code)
                text_output += text_result
                if visible:
                    old_writeconsole_regular = ri.get_writeconsole_regular()
                    ri.set_writeconsole_regular(self.write_console_regular)
                    ro.r.show(result)
                    text_output += self.flush()
                    ri.set_writeconsole_regular(old_writeconsole_regular)

        except RInterpreterError as e:
            # TODO: Maybe we should make this red or something?
            print(e.stdout)
            if not e.stdout.endswith(e.err):
                print(e.err)
            if tmpd:
                rmtree(tmpd)
            return
        finally:
            if self.device in ['png', 'svg']:
                ro.r('dev.off()')

        if text_output:
            # display_data.append(('RMagic.R', {'text/plain':text_output}))
            publish_display_data(data={'text/plain':text_output}, source='RMagic.R')
        # publish the R images
        if self.device in ['png', 'svg']:
            display_data, md = self.publish_graphics(tmpd, args.isolate_svgs)

            for tag, disp_d in display_data:
                publish_display_data(data=disp_d, source=tag, metadata=md)

            # kill the temporary directory - currently created only for "svg"
            # and "png" (else it's None)
            rmtree(tmpd)

        if args.output:
            with localconverter(converter) as cv:
                for output in ','.join(args.output).split(','):
                    output_ipy = ro.globalenv.get(output)
                    self.shell.push({output: output_ipy })

        # this will keep a reference to the display_data
        # which might be useful to other objects who happen to use
        # this method

        if self.cache_display_data:
            self.display_cache = display_data

        # We're in line mode and return_output is still True, 
        # so return the converted result
        if return_output and not args.noreturn:
            if result is not ri.NULL:
                with localconverter(converter) as cv:
                    res = cv.ri2py(result)
                return res


__doc__ = __doc__.format(
                R_DOC = ' '*8 + RMagics.R.__doc__,
                RPUSH_DOC = ' '*8 + RMagics.Rpush.__doc__,
                RPULL_DOC = ' '*8 + RMagics.Rpull.__doc__,
                RGET_DOC = ' '*8 + RMagics.Rget.__doc__
)


def load_ipython_extension(ip):
    """Load the extension in IPython."""

    ip.register_magics(RMagics)

    # TODO: drop support for ipython < 5.0
    # Initialising rpy2 interferes with readline. Since, at this point, we've
    # probably just loaded rpy2, we reset the delimiters. See issue gh-2759.
    if hasattr(ip, 'has_readline'):
        if ip.has_readline:
            ip.readline.set_completer_delims(ip.readline_delims)
    else:
        # ipython >= 5.0.0
        # not doing anything (for now)
        pass

