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nicolaskruchtennicolaskruchten
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Diff for: ‎px.py

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import plotly.graph_objs as go
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from collections import namedtuple
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default_color_seq = ["#3366cc", "#dc3912", "#ff9900", "#109618",
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"#990099", "#0099c6", "#dd4477", "#66aa00",
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"#b82e2e", "#316395", "#994499", "#22aa99",
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"#aaaa11", "#6633cc", "#e67300", "#8b0707",
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"#651067", "#329262", "#5574a6", "#3b3eac"]
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default_symbol_seq = ["circle", "triangle-down", "square", "x", "cross"]
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default_dash_seq = ["solid", "dot", "dash",
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"longdash", "dashdot", "longdashdot"]
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Mapping = namedtuple('Mapping', ['grouper', 'val_map', 'sequence', 'updater'])
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def make_mapping(variable, parent, args):
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return Mapping(
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grouper=args[variable],
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val_map=args[variable+"_map"].copy(),
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sequence=args[variable+"_sequence"],
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updater=lambda x: {parent: {variable: x}},
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)
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def make_figure(df, constructor, trace_kwargs_by_group, mappings):
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fig = go.FigureWidget(
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{"layout": {'template': 'plotly', 'hovermode': 'closest'}})
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def one_group(x): return ""
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grouper = [x.grouper or one_group for x in mappings] or [one_group]
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for group_name, group in df.groupby(grouper):
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if len(grouper) == 1:
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group_name = [group_name]
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mapping_str = []
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for col, val in zip(grouper, group_name):
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if col != one_group:
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s = "%s=%s" % (col, val)
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if s not in mapping_str:
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mapping_str.append(s)
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trace = constructor(name=", ".join(mapping_str))
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for i, m in enumerate(mappings):
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val = group_name[i]
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if val not in m.val_map:
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m.val_map[val] = m.sequence[len(m.val_map) % len(m.sequence)]
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trace.update(m.updater(m.val_map[val]))
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trace.update(trace_kwargs_by_group(group))
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fig.add_trace(trace)
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return fig
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def cartesian_axes(fig, args):
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fig.layout.xaxis.title = args["x"]
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fig.layout.yaxis.title = args["y"]
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if args["log_x"]:
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fig.layout.xaxis.type = "log"
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if args["log_y"]:
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fig.layout.yaxis.type = "log"
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def scatter(df, x, y, color=None, symbol=None, size=None, name=None,
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color_map={}, symbol_map={},
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color_sequence=default_color_seq,
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symbol_sequence=default_symbol_seq,
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log_x=False, log_y=False):
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if size:
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sizeref = df[size].max() / (45*45)
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fig = make_figure(
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df, go.Scatter,
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lambda g: dict(mode='markers', x=g[x], y=g[y],
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hovertext=name and g[name],
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marker=size and dict(
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size=g[size], sizemode="area", sizeref=sizeref)
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),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("symbol", "marker", locals())
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]
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)
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cartesian_axes(fig, locals())
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return fig
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def density_heatmap(df, x, y, log_x=False, log_y=False):
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fig = make_figure(
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df, go.Histogram2d,
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lambda g: dict(x=g[x], y=g[y]),
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[]
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)
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cartesian_axes(fig, locals())
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return fig
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def density_contour(df, x, y, log_x=False, log_y=False):
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fig = make_figure(
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df, go.Histogram2dContour,
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lambda g: dict(x=g[x], y=g[y]),
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[]
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)
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cartesian_axes(fig, locals())
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return fig
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def line(df, x, y, color=None, dash=None,
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color_map={}, dash_map={},
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color_sequence=default_color_seq,
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dash_sequence=default_dash_seq, log_x=False, log_y=False):
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fig = make_figure(
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df, go.Scatter,
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lambda g: dict(mode='lines', x=g[x], y=g[y]),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("dash", "line", locals())
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]
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)
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cartesian_axes(fig, locals())
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return fig
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def bar(df, x, y, color=None, color_map={}, color_sequence=default_color_seq,
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orientation='v', normalization="", mode="relative", log_x=False, log_y=False):
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fig = make_figure(
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df, go.Bar,
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lambda g: dict(x=g[x], y=g[y], orientation=orientation),
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[make_mapping("color", "marker", locals())]
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)
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cartesian_axes(fig, locals())
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fig.layout.barnorm = normalization
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fig.layout.barmode = mode
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return fig
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def histogram(df, x, y, color=None, color_map={}, color_sequence=default_color_seq,
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orientation='v', mode="stack", normalization=None, log_x=False, log_y=False):
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fig = make_figure(
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df, go.Histogram,
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lambda g: dict(x=g[x], y=g[y], orientation=orientation,
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histnorm=normalization),
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[make_mapping("color", "marker", locals())]
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)
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cartesian_axes(fig, locals())
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fig.layout.barmode = mode
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return fig
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def violin(df, x, y, color=None, color_map={}, color_sequence=default_color_seq,
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orientation='v', mode="group", log_x=False, log_y=False):
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fig = make_figure(
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df, go.Violin,
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lambda g: dict(x=g[x], y=g[y], orientation=orientation),
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[make_mapping("color", "marker", locals())]
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)
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cartesian_axes(fig, locals())
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fig.layout.violinmode = mode
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return fig
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def box(df, x, y, color=None, color_map={}, color_sequence=default_color_seq,
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orientation='v', mode="group", log_x=False, log_y=False):
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fig = make_figure(
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df, go.Box,
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lambda g: dict(x=g[x], y=g[y], orientation=orientation),
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[make_mapping("color", "marker", locals())]
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)
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cartesian_axes(fig, locals())
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fig.layout.boxmode = mode
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return fig
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def scatter_ternary(df, a, b, c, color=None, symbol=None, size=None,
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color_map={}, symbol_map={},
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color_sequence=default_color_seq,
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symbol_sequence=default_symbol_seq):
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if size:
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sizeref = df[size].max() / (45*45)
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fig = make_figure(
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df, go.Scatterternary,
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lambda g: dict(mode='markers', a=g[a], b=g[b], c=g[c],
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marker=size and dict(
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size=g[size], sizemode="area", sizeref=sizeref)
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),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("symbol", "marker", locals())
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]
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)
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fig.layout.ternary.aaxis.title = a
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fig.layout.ternary.baxis.title = b
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fig.layout.ternary.caxis.title = c
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return fig
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def line_ternary(df, a, b, c, color=None, dash=None,
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color_map={}, dash_map={},
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color_sequence=default_color_seq,
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dash_sequence=default_dash_seq):
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fig = make_figure(
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df, go.Scatter,
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lambda g: dict(mode='lines', a=g[a], b=g[b], c=g[c]),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("dash", "line", locals())
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]
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)
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fig.layout.ternary.aaxis.title = a
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fig.layout.ternary.baxis.title = b
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fig.layout.ternary.caxis.title = c
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return fig
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def scatter_polar(df, r, theta, color=None, symbol=None, size=None,
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color_map={}, symbol_map={},
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color_sequence=default_color_seq,
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symbol_sequence=default_symbol_seq):
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if size:
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sizeref = df[size].max() / (45*45)
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fig = make_figure(
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df, go.Scatterpolar,
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lambda g: dict(mode='markers', r=g[r], theta=g[theta],
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marker=size and dict(
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size=g[size], sizemode="area", sizeref=sizeref)
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),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("symbol", "marker", locals())
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]
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)
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return fig
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def line_polar(df, r, theta, color=None, dash=None,
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color_map={}, dash_map={},
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color_sequence=default_color_seq,
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dash_sequence=default_dash_seq):
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fig = make_figure(
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df, go.Scatter,
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lambda g: dict(mode='lines', r=g[r], theta=g[theta]),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("dash", "line", locals())
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]
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)
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return fig
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def bar_polar(df, r, theta, color=None, color_map={}, color_sequence=default_color_seq,
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normalization="", mode="relative"):
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fig = make_figure(
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df, go.Barpolar,
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lambda g: dict(r=g[r], theta=g[theta]),
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[
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make_mapping("color", "marker", locals())
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]
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)
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fig.layout.barnorm = normalization
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fig.layout.barmode = mode
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return fig
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def splom(df, dimensions=None, color=None, symbol=None,
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color_map={}, symbol_map={},
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color_sequence=default_color_seq,
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symbol_sequence=default_symbol_seq):
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fig = make_figure(
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df, go.Splom,
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lambda g: dict(dimensions=[
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dict(label=name, values=column.values)
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for name, column in g.iteritems()
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if (
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(dimensions and name in dimensions) or
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(not dimensions and column.dtype in ["int64", "float64"])
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)
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]),
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[
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make_mapping("color", "marker", locals()),
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make_mapping("symbol", "marker", locals())
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]
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)
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return fig
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# TODO canonical examples ... needed now!
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# TODO test with none, all, any mappings
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# TODO test each plot
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# TODO test defaults
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# TODO histogram weights and calcs
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# TODO various box and violin options
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# TODO log scales in SPLOM and generally in facets
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# TODO check on dates
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# TODO facets
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# TODO marginals
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# TODO validate inputs
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# TODO name / hover labels
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# TODO opacity
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# TODO continuous color
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# TODO color splits in densities
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# TODO groupby ignores NaN ... ?
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# TODO suppress plotly.py errors... don't show our programming errors?
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# TODO parcoords, parcats

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