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plotting

class cosmic.plotting.Evolve

Bases: object

__annotations__ = {}

__dict__ = mappingproxy({'__module__': 'cosmic.evolve', '__init__': <function Evolve.__init__>, 'evolve': <classmethod(<function Evolve.evolve>)>, '__dict__': <attribute '__dict__' of 'Evolve' objects>, '__weakref__': <attribute '__weakref__' of 'Evolve' objects>, '__doc__': None, '__annotations__': {}})

__doc__ = None

__init__(): initialize Evolve

__module__ = 'cosmic.evolve'

__weakref__: list of weak references to the object (if defined)

classmethod evolve(initialbinarytable, pool=None, **kwargs)

After setting a number of initial conditions we evolve the system.

Parameters:

initialbinarytableDataFrame

Initial conditions of the binary

**kwargs:

There are three ways to tell evolve and thus the fortran what you want all the flags and other BSE specific parameters to be. If you pass both a dictionary of flags and/or a inifile and a table with the BSE parameters in the columns, the column values will be overwritten by what is in the dictionary or ini file.

NUMBER 1: PASS A DICTIONARY OF FLAGS

BSEDict

NUMBER 2: PASS A PANDAS DATA FRAME WITH PARAMS DEFINED AS COLUMNS

All you need is the initialbinarytable if the all the BSE parameters are defined as columns

NUMBER 3: PASS PATH TO A INI FILE WITH THE FLAGS DEFINED

params

randomseedint, optional, default let numpy choose for you

If you would like the random seed that the underlying fortran code uses to be the same for all of the initial conditions you passed then you can send this keyword argument in. It is recommended to just let numpy choose a random number as the Fortran random seed and then this number will be returned as a column in the initial binary table so that you can reproduce the results.

nprocint, optional, default: 1

number of CPUs to use to evolve systems in parallel

idxint, optional, default: 0

initial index of the bcm/bpp arrays

dtpfloat, optional: default: tphysf

timestep size in Myr for bcm output where tphysf is total evolution time in Myr

n_per_blockint, optional, default: -1

number of systems to evolve in a block with _evolve_multi_system, to allow larger multiprocessing queues and reduced overhead. If less than 1 use _evolve_single_system

Returns:

output_bppDataFrame: Evolutionary history of each binary
output_bcmDataFrame: Final state of each binary
initialbinarytableDataFrame: Initial conditions for each binary

class cosmic.plotting.Line2D(xdata, ydata, *, linewidth=None, linestyle=None, color=None, gapcolor=None, marker=None, markersize=None, markeredgewidth=None, markeredgecolor=None, markerfacecolor=None, markerfacecoloralt='none', fillstyle=None, antialiased=None, dash_capstyle=None, solid_capstyle=None, dash_joinstyle=None, solid_joinstyle=None, pickradius=5, drawstyle=None, markevery=None, **kwargs)

Bases: Artist

A line - the line can have both a solid linestyle connecting all the vertices, and a marker at each vertex. Additionally, the drawing of the solid line is influenced by the drawstyle, e.g., one can create “stepped” lines in various styles.

__doc__ = 'A line - the line can have both a solid linestyle connecting all\nthe vertices, and a marker at each vertex. Additionally, the\ndrawing of the solid line is influenced by the drawstyle, e.g., one\ncan create "stepped" lines in various styles.'

__init__(xdata, ydata, *, linewidth=None, linestyle=None, color=None, gapcolor=None, marker=None, markersize=None, markeredgewidth=None, markeredgecolor=None, markerfacecolor=None, markerfacecoloralt='none', fillstyle=None, antialiased=None, dash_capstyle=None, solid_capstyle=None, dash_joinstyle=None, solid_joinstyle=None, pickradius=5, drawstyle=None, markevery=None, **kwargs)

Create a .Line2D instance with x and y data in sequences of xdata, ydata.

Additional keyword arguments are .Line2D properties:

Properties:: agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image alpha: scalar or None animated: bool antialiased or aa: bool clip_box: ~matplotlib.transforms.BboxBase or None clip_on: bool clip_path: Patch or (Path, Transform) or None color or c: :mpltype:`color` dash_capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} dash_joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} dashes: sequence of floats (on/off ink in points) or (None, None) data: (2, N) array or two 1D arrays drawstyle or ds: {‘default’, ‘steps’, ‘steps-pre’, ‘steps-mid’, ‘steps-post’}, default: ‘default’ figure: ~matplotlib.figure.Figure fillstyle: {‘full’, ‘left’, ‘right’, ‘bottom’, ‘top’, ‘none’} gapcolor: :mpltype:`color` or None gid: str in_layout: bool label: object linestyle or ls: {‘-’, ‘–’, ‘-.’, ‘:’, ‘’, (offset, on-off-seq), …} linewidth or lw: float marker: marker style string, ~.path.Path or ~.markers.MarkerStyle markeredgecolor or mec: :mpltype:`color` markeredgewidth or mew: float markerfacecolor or mfc: :mpltype:`color` markerfacecoloralt or mfcalt: :mpltype:`color` markersize or ms: float markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool] mouseover: bool path_effects: list of .AbstractPathEffect picker: float or callable[[Artist, Event], tuple[bool, dict]] pickradius: float rasterized: bool sketch_params: (scale: float, length: float, randomness: float) snap: bool or None solid_capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} solid_joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} transform: unknown url: str visible: bool xdata: 1D array ydata: 1D array zorder: float

See set_linestyle() for a description of the line styles, set_marker() for a description of the markers, and set_drawstyle() for a description of the draw styles.

__module__ = 'matplotlib.lines'

__str__(): Return str(self).

_alias_map = {'antialiased': ['aa'], 'color': ['c'], 'drawstyle': ['ds'], 'linestyle': ['ls'], 'linewidth': ['lw'], 'markeredgecolor': ['mec'], 'markeredgewidth': ['mew'], 'markerfacecolor': ['mfc'], 'markerfacecoloralt': ['mfcalt'], 'markersize': ['ms']}

_drawStyles_l = {'default': '_draw_lines', 'steps-mid': '_draw_steps_mid', 'steps-post': '_draw_steps_post', 'steps-pre': '_draw_steps_pre'}

_drawStyles_s = {'steps': '_draw_steps_pre'}

_get_markerfacecolor(alt=False)

_get_transformed_path(): Return this line’s ~matplotlib.transforms.TransformedPath.

_lineStyles = {'': '_draw_nothing', ' ': '_draw_nothing', '-': '_draw_solid', '--': '_draw_dashed', '-.': '_draw_dash_dot', ':': '_draw_dotted', 'None': '_draw_nothing'}

_set_markercolor(name, has_rcdefault, val)

_subslice_optim_min_size = 1000

_transform_path(subslice=None): Put a TransformedPath instance at self._transformed_path; all invalidation of the transform is then handled by the TransformedPath instance.

contains(mouseevent)

Test whether mouseevent occurred on the line.

An event is deemed to have occurred “on” the line if it is less than self.pickradius (default: 5 points) away from it. Use ~.Line2D.get_pickradius or ~.Line2D.set_pickradius to get or set the pick radius.

Parameters:

mouseevent~matplotlib.backend_bases.MouseEvent

Returns:

containsbool

Whether any values are within the radius.

detailsdict

A dictionary {'ind': pointlist}, where pointlist is a list of points of the line that are within the pickradius around the event position.

TODO: sort returned indices by distance

draw(renderer)

Draw the Artist (and its children) using the given renderer.

This has no effect if the artist is not visible (.Artist.get_visible returns False).

Parameters:

renderer~matplotlib.backend_bases.RendererBase subclass.

Notes

This method is overridden in the Artist subclasses.

drawStyleKeys = ['default', 'steps-mid', 'steps-pre', 'steps-post', 'steps']

drawStyles = {'default': '_draw_lines', 'steps': '_draw_steps_pre', 'steps-mid': '_draw_steps_mid', 'steps-post': '_draw_steps_post', 'steps-pre': '_draw_steps_pre'}

fillStyles = ('full', 'left', 'right', 'bottom', 'top', 'none')

filled_markers = ('.', 'o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X')

get_aa(): Alias for get_antialiased.

get_antialiased(): Return whether antialiased rendering is used.

get_bbox(): Get the bounding box of this line.

get_c(): Alias for get_color.

get_color()

Return the line color.

linestyle	description
`'-'` or `'solid'`	solid line
`'--'` or `'dashed'`	dashed line
`'-.'` or `'dashdot'`	dash-dotted line
`':'` or `'dotted'`	dotted line
`'none'`, `'None'`, `' '`, or `''`	draw nothing