NeuroML2ToPOVRay.py

"""
A file for converting NeuroML 2 files (including cells & network structure)
into POVRay files for 3D rendering

Author: Padraig Gleeson & Matteo Farinella

This file has been developed as part of the neuroConstruct project
This work has been funded by the Medical Research Council and Wellcome Trust
"""


import typing
import random
import argparse
import logging

import neuroml
from pyneuroml import pynml

logger = logging.getLogger(__name__)

_WHITE = "<1,1,1,0.55>"  # type: str
_BLACK = "<0,0,0,0.55>"  # type: str
_GREY = "<0.85,0.85,0.85,0.55>"  # type: str

_DUMMY_CELL = "DUMMY_CELL"


logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)


defaults = {
    "split": False,
    "background": _WHITE,
    "movie": False,
    "inputs": False,
    "conns": False,
    "conn_points": False,
    "v": False,
    "frames": 36,
    "posx": 0,
    "posy": 0,
    "posz": 0,
    "viewx": 0,
    "viewy": 0,
    "viewz": 0,
    "scalex": 1,
    "scaley": 1,
    "scalez": 1,
    "mindiam": 0,
    "plane": False,
    "segids": False,
}  # type: typing.Dict[str, typing.Any]


def process_args():
    """
    Parse command-line arguments.
    """
    parser = argparse.ArgumentParser(
        description="A file for converting NeuroML v2 files into POVRay files for 3D rendering"
    )

    parser.add_argument(
        "neuroml_file",
        type=str,
        metavar="<NeuroML file>",
        help="NeuroML (version 2 beta 3+) file to be converted to PovRay format (XML or HDF5 format)",
    )

    parser.add_argument(
        "-split",
        action="store_true",
        default=defaults["split"],
        help="If this is specified, generate separate pov files for cells & network. Default is false",
    )

    parser.add_argument(
        "-background",
        type=str,
        metavar="<background colour>",
        default=defaults["background"],
        help="Colour of background, e.g. <0,0,0,0.55>",
    )

    parser.add_argument(
        "-movie",
        action="store_true",
        default=defaults["movie"],
        help="If this is specified, generate a ini file for generating a sequence of frames for a movie of the 3D structure",
    )

    parser.add_argument(
        "-inputs",
        action="store_true",
        default=defaults["inputs"],
        help="If this is specified, show the locations of (synaptic, current clamp, etc.) inputs into the cells of the network",
    )

    parser.add_argument(
        "-conns",
        action="store_true",
        default=defaults["conns"],
        help="If this is specified, show the connections present in the network with lines",
    )

    parser.add_argument(
        "-conn_points",
        action="store_true",
        default=defaults["conn_points"],
        help="If this is specified, show the end points of the connections present in the network",
    )

    parser.add_argument(
        "-v", action="store_true", default=defaults["v"], help="Verbose output"
    )

    parser.add_argument(
        "-frames",
        type=int,
        metavar="<frames>",
        default=defaults["frames"],
        help="Number of frames in movie",
    )

    parser.add_argument(
        "-posx",
        type=float,
        metavar="<position offset x>",
        default=defaults["posx"],
        help="Offset position in x dir (0 is centre, 1 is top)",
    )
    parser.add_argument(
        "-posy",
        type=float,
        metavar="<position offset y>",
        default=defaults["posy"],
        help="Offset position in y dir (0 is centre, 1 is top)",
    )
    parser.add_argument(
        "-posz",
        type=float,
        metavar="<position offset z>",
        default=defaults["posz"],
        help="Offset position in z dir (0 is centre, 1 is top)",
    )

    parser.add_argument(
        "-viewx",
        type=float,
        metavar="<view offset x>",
        default=defaults["viewx"],
        help="Offset viewing point in x dir (0 is centre, 1 is top)",
    )
    parser.add_argument(
        "-viewy",
        type=float,
        metavar="<view offset y>",
        default=defaults["viewy"],
        help="Offset viewing point in y dir (0 is centre, 1 is top)",
    )
    parser.add_argument(
        "-viewz",
        type=float,
        metavar="<view offset z>",
        default=defaults["viewz"],
        help="Offset viewing point in z dir (0 is centre, 1 is top)",
    )

    parser.add_argument(
        "-scalex",
        type=float,
        metavar="<scale position x>",
        default=defaults["scalex"],
        help="Scale position from network in x dir",
    )
    parser.add_argument(
        "-scaley",
        type=float,
        metavar="<scale position y>",
        default=defaults["scaley"],
        help="Scale position from network in y dir",
    )
    parser.add_argument(
        "-scalez",
        type=float,
        metavar="<scale position z>",
        default=defaults["scalez"],
        help="Scale position from network in z dir",
    )

    parser.add_argument(
        "-mindiam",
        type=float,
        metavar="<minimum diameter dendrites/axons>",
        default=defaults["mindiam"],
        help="Minimum diameter for dendrites/axons (to improve visualisations)",
    )

    parser.add_argument(
        "-plane",
        action="store_true",
        default=defaults["plane"],
        help="If this is specified, add a 2D plane below cell/network",
    )

    parser.add_argument(
        "-segids",
        action="store_true",
        default=defaults["segids"],
        help="Show segment ids",
    )

    return parser.parse_args()


def define_dummy_cell(pop_id, radius, pov_file):
    dummy_cell_name = "%s_%s" % (_DUMMY_CELL, pop_id)
    pov_file.write(
        """\n/*\n  Defining a dummy cell to use for population %s with radius %s...\n*/\n#declare %s =
union {
    sphere {
        <0.000000, 0.000000, 0.000000>, %s
    }
    pigment { color rgb <1,0,0> }
}\n"""
        % (pop_id, radius, dummy_cell_name, radius)
    )

    return dummy_cell_name


def main():
    args = process_args()

    generate_povray(
        args.neuroml_file,
        args.split,
        args.background,
        args.movie,
        args.inputs,
        args.conns,
        args.conn_points,
        args.v,
        args.frames,
        args.posx,
        args.posy,
        args.posz,
        args.viewx,
        args.viewy,
        args.viewz,
        args.scalex,
        args.scaley,
        args.scalez,
        args.mindiam,
        args.plane,
        args.segids,
    )


def generate_povray(
    neuroml_file: str,
    split: bool = defaults["split"],
    background: str = defaults["background"],
    movie: bool = defaults["movie"],
    inputs: bool = defaults["inputs"],
    conns: bool = defaults["conns"],
    conn_points: bool = defaults["conn_points"],
    v: bool = defaults["v"],
    frames: bool = defaults["frames"],
    posx: float = defaults["posx"],
    posy: float = defaults["posy"],
    posz: float = defaults["posz"],
    viewx: float = defaults["viewx"],
    viewy: float = defaults["viewy"],
    viewz: float = defaults["viewz"],
    scalex: float = defaults["scalex"],
    scaley: float = defaults["scaley"],
    scalez: float = defaults["scalez"],
    mindiam: float = defaults["mindiam"],
    plane: bool = defaults["plane"],
    segids: bool = defaults["segids"],
):
    """Generate a POVRAY image or movie file.

    Please see http://www.povray.org/documentation/ and
    https://wiki.povray.org/content/Main_Page for information on installing and
    using POVRAY.

    This function will generate POVRAY files that you can then run using
    POVRAY.

    :param neuroml_file: path to NeuroML file containing cell/network
    :type neuroml_file: str
    :param split: generate separate files for cells and network
    :type split: bool
    :param background: background for POVRAY rendering
    :type background: str
    :param movie: toggle between image and movie rendering
    :type movie: bool
    :param inputs: show locations of inputs also
    :type inputs: bool
    :param conns: show connections in networks with lines
    :type conns: bool
    :param conn_points: show end points of connections in network
    :type conn_points: bool
    :param v: toggle verbose output
    :type v: bool
    :param frames: number of frames to use in movie
    :type frames: int
    :param posx: offset position in x dir (0 is centre, 1 is top)
    :type posx: float
    :param posy: offset position in y dir (0 is centre, 1 is top)
    :type posy: float
    :param posz: offset position in z dir (0 is centre, 1 is top)
    :type posz: float
    :param viewx: offset viewing point in x dir (0 is centre, 1 is top)
    :type viewx: float
    :param viewy: offset viewing point in y dir (0 is centre, 1 is top)
    :type viewy: float
    :param viewz: offset viewing point in z dir (0 is centre, 1 is top)
    :type viewz: float
    :param scalex: scale position from network in x dir
    :type scalex: float
    :param scaley: scale position from network in y dir
    :type scaley: float
    :param scalez: scale position from network in z dir
    :type scalez: float
    :param mindiam: minimum diameter for dendrites/axons (to improve visualisations)
    :type mindiam: float
    :param plane: add a 2D plane below cell/network
    :type plane: bool
    :param segids: toggle showing segment ids
    :type segids: bool
    """

    xmlfile = neuroml_file
    pov_file_name = xmlfile
    endings = [".xml", ".h5", ".nml"]
    for e in endings:
        if pov_file_name.endswith(e):
            pov_file_name.replace(e, ".pov")

    if pov_file_name == xmlfile:
        pov_file_name += ".pov"

    pov_file = open(pov_file_name, "w")

    header = """
/*
POV-Ray file generated from NeuroML network
*/
#version 3.6;

#include "colors.inc"

background {rgbt %s}


    \n"""  # end of header

    pov_file.write(header % (background))

    cells_file = pov_file
    net_file = pov_file
    splitOut = False

    cf = pov_file_name.replace(".pov", "_cells.inc")
    nf = pov_file_name.replace(".pov", "_net.inc")

    if split:
        splitOut = True
        cells_file = open(cf, "w")
        net_file = open(nf, "w")
        logger.info("Saving into %s and %s and %s" % (pov_file_name, cf, nf))

    logger.info("Converting XML file: %s to %s" % (xmlfile, pov_file_name))

    nml_doc = pynml.read_neuroml2_file(
        xmlfile,
        include_includes=True,
        check_validity_pre_include=True,
        verbose=v,
        optimized=True,
    )

    cell_elements = []
    cell_elements.extend(nml_doc.cells)
    cell_elements.extend(nml_doc.cell2_ca_poolses)

    minXc = 1e9
    minYc = 1e9
    minZc = 1e9
    maxXc = -1e9
    maxYc = -1e9
    maxZc = -1e9

    minX = 1e9
    minY = 1e9
    minZ = 1e9
    maxX = -1e9
    maxY = -1e9
    maxZ = -1e9

    declaredcells = {}

    logger.info("There are %i cells in the file" % len(cell_elements))

    cell_id_vs_seg_id_vs_proximal = {}
    cell_id_vs_seg_id_vs_distal = {}
    cell_id_vs_cell = {}

    for cell in cell_elements:
        cellName = cell.id
        cell_id_vs_cell[cell.id] = cell
        logger.info("Handling cell: %s" % cellName)
        cell_id_vs_seg_id_vs_proximal[cell.id] = {}
        cell_id_vs_seg_id_vs_distal[cell.id] = {}

        declaredcell = "cell_" + cellName

        declaredcells[cellName] = declaredcell

        cells_file.write("#declare %s = \n" % declaredcell)
        cells_file.write("union {\n")

        prefix = ""

        segments = cell.morphology.segments

        distpoints = {}
        proxpoints = {}

        for segment in segments:
            id = int(segment.id)
            distal = segment.distal

            x = float(distal.x)
            y = float(distal.y)
            z = float(distal.z)
            r = max(float(distal.diameter) / 2.0, mindiam)

            if (x - r) < minXc:
                minXc = x - r
            if (y - r) < minYc:
                minYc = y - r
            if (z - r) < minZc:
                minZc = z - r

            if (x + r) > maxXc:
                maxXc = x + r
            if (y + r) > maxYc:
                maxYc = y + r
            if (z + r) > maxZc:
                maxZc = z + r

            distalpoint = "<%f, %f, %f>, %f " % (x, y, z, r)

            distpoints[id] = distalpoint
            cell_id_vs_seg_id_vs_distal[cell.id][id] = (x, y, z)

            proximalpoint = ""
            if segment.proximal is not None:
                proximal = segment.proximal
                proximalpoint = "<%f, %f, %f>, %f " % (
                    float(proximal.x),
                    float(proximal.y),
                    float(proximal.z),
                    max(float(proximal.diameter) / 2.0, mindiam),
                )

                cell_id_vs_seg_id_vs_proximal[cell.id][id] = (
                    float(proximal.x),
                    float(proximal.y),
                    float(proximal.z),
                )
            else:
                parent = int(segment.parent.segments)
                proximalpoint = distpoints[parent]
                cell_id_vs_seg_id_vs_proximal[cell.id][
                    id
                ] = cell_id_vs_seg_id_vs_distal[cell.id][parent]

            proxpoints[id] = proximalpoint

            shape = "cone"
            if proximalpoint == distalpoint:
                shape = "sphere"
                proximalpoint = ""

            if shape == "cone" and (
                proximalpoint.split(">")[0] == distalpoint.split(">")[0]
            ):
                comment = "Ignoring zero length segment (id = %i): %s -> %s\n" % (
                    id,
                    proximalpoint,
                    distalpoint,
                )
                logger.warning(comment)
                cells_file.write("    // " + comment)
            else:
                cells_file.write("    %s {\n" % shape)
                cells_file.write("        %s\n" % distalpoint)
                if len(proximalpoint):
                    cells_file.write("        %s\n" % proximalpoint)
                cells_file.write("        //%s_%s.%s\n" % ("CELL_GROUP_NAME", "0", id))
                cells_file.write("    }\n")

            if segids:
                cells_file.write("    text {\n")
                cells_file.write(
                    '        ttf "timrom.ttf" "------- Segment: %s" .1, 0.01\n'
                    % (segment.id)
                )
                cells_file.write("        pigment { Red }\n")
                cells_file.write("        rotate <0,180,0>\n")
                cells_file.write("        scale <10,10,10>")
                cells_file.write("        translate %s>\n" % distalpoint.split(">")[0])
                cells_file.write("    }\n")

        cells_file.write(
            "    pigment { color rgb <%f,%f,%f> }\n"
            % (random.random(), random.random(), random.random())
        )
        cells_file.write("}\n\n")

    if splitOut:
        pov_file.write('#include "' + cf + '"\n\n')
        pov_file.write('#include "' + nf + '"\n\n')

    pov_file.write(
        """\n/*\n  Defining a dummy cell to use when cell in population is not found in NeuroML file...\n*/\n#declare %s =
union {
    sphere {
        <0.000000, 0.000000, 0.000000>, 5.000000
    }
    pigment { color rgb <1,0,0> }
}\n"""
        % _DUMMY_CELL
    )

    pov_file.write(
        """\n/*\n  Defining the spheres to use for end points of connections...\n*/
    \n#declare conn_start_point =
union {
    sphere {
        <0.000000, 0.000000, 0.000000>, 3.000000
    }
    pigment { color rgb <0,1,0> }
}\n
\n#declare conn_end_point =
union {
    sphere {
        <0.000000, 0.000000, 0.000000>, 3.000000
    }
    pigment { color rgb <1,0,0> }
}\n
\n#declare input_object =
union {
    cone {
        <0, 0, 0>, 0.1    // Center and radius of one end
        <0, -40, 0>, 2.5    // Center and radius of other end
    }
    pigment { color rgb <0.2,0.2,0.8> }
}\n"""
    )

    positions = {}

    if len(nml_doc.networks) > 0:
        popElements = nml_doc.networks[0].populations
    else:
        popElements = []
        nml_doc.networks.append(neuroml.Network(id="dummy_network"))
        for cell in cell_elements:
            pop = neuroml.Population(
                id="dummy_population_%s" % cell.id, size=1, component=cell.id
            )
            nml_doc.networks[0].populations.append(pop)

        popElements = nml_doc.networks[0].populations

    pop_id_vs_cell = {}

    logger.info("There are %i populations in the file" % len(popElements))

    for pop in popElements:
        name = pop.id
        celltype = pop.component
        instances = pop.instances

        if pop.component in cell_id_vs_cell.keys():
            pop_id_vs_cell[pop.id] = cell_id_vs_cell[pop.component]

        info = "Population: %s has %i positioned cells of type: %s" % (
            name,
            len(instances),
            celltype,
        )
        logger.info(info)

        colour = "1"
        substitute_radius = None

        for prop in pop.properties:
            if prop.tag == "color":
                colour = prop.value
                colour = colour.replace(" ", ",")
                # print "Colour determined to be: "+colour
            if prop.tag == "radius":
                substitute_radius = float(prop.value)

        net_file.write("\n\n/* " + info + " */\n\n")

        pop_positions = {}

        if celltype not in declaredcells:
            minXc = 0
            minYc = 0
            minZc = 0
            maxXc = 0
            maxYc = 0
            maxZc = 0
            if substitute_radius:
                dummy_cell_name = define_dummy_cell(name, substitute_radius, pov_file)
                cell_definition = dummy_cell_name
            else:
                cell_definition = _DUMMY_CELL
        else:
            cell_definition = declaredcells[celltype]

        for instance in instances:
            location = instance.location
            id = int(instance.id)
            net_file.write("object {\n")
            net_file.write("    %s\n" % cell_definition)
            x = float(location.x)
            y = float(location.y)
            z = float(location.z)
            pop_positions[id] = (x, y, z)

            if x + minXc < minX:
                minX = x + minXc
            if y + minYc < minY:
                minY = y + minYc
            if z + minZc < minZ:
                minZ = z + minZc

            if x + maxXc > maxX:
                maxX = x + maxXc
            if y + maxYc > maxY:
                maxY = y + maxYc
            if z + maxZc > maxZ:
                maxZ = z + maxZc

            net_file.write("    translate <%s, %s, %s>\n" % (x, y, z))

            if colour == "1":
                colour = "%f,%f,%f" % (
                    random.random(),
                    random.random(),
                    random.random(),
                )

            if colour is not None:
                net_file.write("    pigment { color rgb <%s> }" % (colour))

            net_file.write("\n    //%s_%s\n" % (name, id))

            net_file.write("}\n")

        positions[name] = pop_positions

        if len(instances) == 0 and int(pop.size > 0):
            info = "Population: %s has %i unpositioned cells of type: %s" % (
                name,
                pop.size,
                celltype,
            )
            logger.info(info)

            colour = "1"
            """
            if pop.annotation:
                print dir(pop.annotation)
                print pop.annotation.anytypeobjs_
                print pop.annotation.member_data_items_[0].name
                print dir(pop.annotation.member_data_items_[0])
                for prop in pop.annotation.anytypeobjs_:
                    print prop

                    if len(prop.getElementsByTagName('meta:tag'))>0 and prop.getElementsByTagName('meta:tag')[0].childNodes[0].data == 'color':
                        #print prop.getElementsByTagName('meta:tag')[0].childNodes
                        colour = prop.getElementsByTagName('meta:value')[0].childNodes[0].data
                        colour = colour.replace(" ", ",")
                    elif prop.hasAttribute('tag') and prop.getAttribute('tag') == 'color':
                        colour = prop.getAttribute('value')
                        colour = colour.replace(" ", ",")
                    print "Colour determined to be: "+colour
            """

            net_file.write("\n\n/* " + info + " */\n\n")
            net_file.write("object {\n")
            net_file.write("    %s\n" % cell_definition)
            x = 0
            y = 0
            z = 0

            if x + minXc < minX:
                minX = x + minXc
            if y + minYc < minY:
                minY = y + minYc
            if z + minZc < minZ:
                minZ = z + minZc

            if x + maxXc > maxX:
                maxX = x + maxXc
            if y + maxYc > maxY:
                maxY = y + maxYc
            if z + maxZc > maxZ:
                maxZ = z + maxZc

            net_file.write("    translate <%s, %s, %s>\n" % (x, y, z))
            if colour == "1":
                colour = "%f,%f,%f" % (
                    random.random(),
                    random.random(),
                    random.random(),
                )

            if colour is not None:
                net_file.write("    pigment { color rgb <%s> }" % (colour))

            net_file.write("\n    //%s_%s\n" % (name, id))
            net_file.write("}\n")

    if conns or conn_points:
        projections = (
            nml_doc.networks[0].projections
            + nml_doc.networks[0].electrical_projections
            + nml_doc.networks[0].continuous_projections
        )
        for projection in projections:
            pre = projection.presynaptic_population
            post = projection.postsynaptic_population

            if isinstance(projection, neuroml.Projection):
                connections = []
                for c in projection.connection_wds:
                    connections.append(c)
                for c in projection.connections:
                    connections.append(c)
                color = "Grey"
            elif isinstance(projection, neuroml.ElectricalProjection):
                connections = (
                    projection.electrical_connections
                    + projection.electrical_connection_instances
                    + projection.electrical_connection_instance_ws
                )
                color = "Yellow"
            elif isinstance(projection, neuroml.ContinuousProjection):
                connections = (
                    projection.continuous_connections
                    + projection.continuous_connection_instances
                    + projection.continuous_connection_instance_ws
                )
                color = "Blue"

            logger.info(
                "Adding %i connections for %s: %s -> %s "
                % (len(connections), projection.id, pre, post)
            )
            # print cell_id_vs_seg_id_vs_distal
            # print cell_id_vs_seg_id_vs_proximal
            for connection in connections:
                pre_cell_id = connection.get_pre_cell_id()
                post_cell_id = connection.get_post_cell_id()

                pre_loc = (0, 0, 0)
                if pre in positions.keys():
                    if len(positions[pre]) > 0:
                        pre_loc = positions[pre][pre_cell_id]
                post_loc = (0, 0, 0)
                if post in positions.keys():
                    post_loc = positions[post][post_cell_id]

                if projection.presynaptic_population in pop_id_vs_cell.keys():
                    pre_cell = pop_id_vs_cell[projection.presynaptic_population]
                    d = cell_id_vs_seg_id_vs_distal[pre_cell.id][
                        connection.get_pre_segment_id()
                    ]
                    p = cell_id_vs_seg_id_vs_proximal[pre_cell.id][
                        connection.get_pre_segment_id()
                    ]
                    m = [
                        p[i] + connection.get_pre_fraction_along() * (d[i] - p[i])
                        for i in [0, 1, 2]
                    ]
                    logger.info(
                        "Pre point is %s, %s between %s and %s"
                        % (m, connection.get_pre_fraction_along(), p, d)
                    )
                    pre_loc = [pre_loc[i] + m[i] for i in [0, 1, 2]]

                if projection.postsynaptic_population in pop_id_vs_cell.keys():
                    post_cell = pop_id_vs_cell[projection.postsynaptic_population]
                    d = cell_id_vs_seg_id_vs_distal[post_cell.id][
                        connection.get_post_segment_id()
                    ]
                    p = cell_id_vs_seg_id_vs_proximal[post_cell.id][
                        connection.get_post_segment_id()
                    ]
                    m = [
                        p[i] + connection.get_post_fraction_along() * (d[i] - p[i])
                        for i in [0, 1, 2]
                    ]
                    logger.info(
                        "Post point is %s, %s between %s and %s"
                        % (m, connection.get_post_fraction_along(), p, d)
                    )
                    post_loc = [post_loc[i] + m[i] for i in [0, 1, 2]]

                if post_loc != pre_loc:
                    info = "// Connection from %s:%s %s -> %s:%s %s\n" % (
                        pre,
                        pre_cell_id,
                        pre_loc,
                        post,
                        post_cell_id,
                        post_loc,
                    )

                    logger.info(info)
                    net_file.write("// %s" % info)
                    if conns:
                        net_file.write(
                            "cylinder { <%s,%s,%s>, <%s,%s,%s>, .5  pigment{color %s}}\n"
                            % (
                                pre_loc[0],
                                pre_loc[1],
                                pre_loc[2],
                                post_loc[0],
                                post_loc[1],
                                post_loc[2],
                                color,
                            )
                        )
                    if conn_points:
                        net_file.write(
                            "object { conn_start_point translate <%s,%s,%s> }\n"
                            % (pre_loc[0], pre_loc[1], pre_loc[2])
                        )
                        net_file.write(
                            "object { conn_end_point translate <%s,%s,%s> }\n"
                            % (post_loc[0], post_loc[1], post_loc[2])
                        )

    if inputs:
        for il in nml_doc.networks[0].input_lists:
            for input in il.input:
                popi = il.populations
                cell_id = input.get_target_cell_id()
                cell = pop_id_vs_cell[popi]

                loc = (0, 0, 0)
                if popi in positions.keys():
                    if len(positions[popi]) > 0:
                        loc = positions[popi][cell_id]

                d = cell_id_vs_seg_id_vs_distal[cell.id][input.get_segment_id()]
                p = cell_id_vs_seg_id_vs_proximal[cell.id][input.get_segment_id()]
                m = [
                    p[i] + input.get_fraction_along() * (d[i] - p[i]) for i in [0, 1, 2]
                ]

                input_info = (
                    "Input on cell %s:%s at %s; point %s along (%s -> %s): %s"
                    % (popi, cell_id, loc, input.get_fraction_along(), d, p, m)
                )

                loc = [loc[i] + m[i] for i in [0, 1, 2]]

                net_file.write("/* %s */\n" % input_info)
                net_file.write(
                    "object { input_object translate <%s,%s,%s> }\n\n"
                    % (loc[0], loc[1], loc[2])
                )

    plane_ = """
plane {
   y, vv(-1)
   pigment {checker color rgb 1.0, color rgb 0.8 scale 20}
}
"""

    footer = """

#declare minX = %f;
#declare minY = %f;
#declare minZ = %f;

#declare maxX = %f;
#declare maxY = %f;
#declare maxZ = %f;

#macro uu(xx)
    0.5 * (maxX *(1+xx) + minX*(1-xx))
#end

#macro vv(xx)
    0.5 * (maxY *(1+xx) + minY*(1-xx))
#end

#macro ww(xx)
    0.5 * (maxZ *(1+xx) + minZ*(1-xx))
#end

light_source {
  <uu(5),uu(2),uu(5)>
  color rgb <1,1,1>

}
light_source {
  <uu(-5),uu(2),uu(-5)>
  color rgb <1,1,1>

}
light_source {
  <uu(5),uu(-2),uu(-5)>
  color rgb <1,1,1>

}
light_source {
  <uu(-5),uu(-2),uu(5)>
  color rgb <1,1,1>
}


// Trying to view box
camera {
  location < uu(%s + %s * sin (clock * 2 * 3.141)) , vv(%s + %s * sin (clock * 2 * 3.141)) , ww(%s + %s * cos (clock * 2 * 3.141)) >
  look_at < uu(%s + 0) , vv(%s + 0.05+0.3*sin (clock * 2 * 3.141)) , ww(%s + 0)>
}

%s
    \n""" % (
        minX,
        minY,
        minZ,
        maxX,
        maxY,
        maxZ,
        posx,
        scalex,
        posy,
        scaley,
        posz,
        scalez,
        viewx,
        viewy,
        viewz,
        (plane_ if plane else ""),
    )  # end of footer

    pov_file.write(footer)
    pov_file.close()

    if movie:
        ini_file_name = pov_file_name.replace(".pov", "_movie.ini")
        ini_movie = """
Antialias=On

+W800 +H600

Antialias_Threshold=0.3
Antialias_Depth=4

Input_File_Name=%s

Initial_Frame=1
Final_Frame=%i
Initial_Clock=0
Final_Clock=1

Cyclic_Animation=on
Pause_when_Done=off

        """
        ini_file = open(ini_file_name, "w")
        ini_file.write(ini_movie % (pov_file_name, frames))
        ini_file.close()

        logger.info(
            "Created file for generating %i movie frames at: %s. To run this type:\n\n    povray %s\n"
            % (frames, ini_file_name, ini_file_name)
        )
    else:
        logger.info(
            "Created file for generating image of network. To run this type:\n\n    povray %s\n"
            % (pov_file_name)
        )
        logger.info(
            "Or for higher resolution:\n\n    povray Antialias=On Antialias_Depth=10 Antialias_Threshold=0.1 +W1200 +H900 %s\n"
            % (pov_file_name)
        )


if __name__ == "__main__":
    main()