plot_run_metric

rubin_sim.maf.run_comparison.plot_run_metric(summary, baseline_run=None, vertical_quantity='run', horizontal_quantity='value', run_label_map=None, metric_label_map=None, metric_set=None, ax=None, cmap=[[0.188235, 0.635294, 0.854902], [0.988235, 0.309804, 0.188235], [0.898039, 0.682353, 0.219608], [0.427451, 0.564706, 0.309804], [0.545098, 0.545098, 0.545098], [0.090196, 0.745098, 0.811765], [0.580392, 0.403922, 0.741176], [0.839216, 0.152941, 0.156863], [0.121569, 0.466667, 0.705882], [0.890196, 0.466667, 0.760784], [0.54902, 0.337255, 0.294118], [0.737255, 0.741176, 0.133333], [0.227451, 0.003922, 0.513725], [0.0, 0.262745, 0.0], [0.058824, 1.0, 0.662745], [0.368627, 0.0, 0.25098], [0.776471, 0.741176, 1.0], [0.258824, 0.313725, 0.321569], [0.721569, 0.0, 0.501961], [1.0, 0.717647, 0.701961], [0.490196, 0.007843, 0.0], [0.380392, 0.14902, 1.0], [1.0, 1.0, 0.603922], [0.682353, 0.788235, 0.670588], [0.0, 0.52549, 0.486275], [0.333333, 0.227451, 0.0], [0.580392, 0.988235, 1.0], [0.0, 0.74902, 0.0], [0.490196, 0.0, 0.627451], [0.670588, 0.447059, 0.0], [0.568627, 1.0, 0.0], [0.003922, 0.745098, 0.541176], [0.0, 0.270588, 0.482353], [0.784314, 0.509804, 0.435294], [1.0, 0.121569, 0.513725], [0.866667, 0.0, 1.0], [0.019608, 0.454902, 0.0], [0.392157, 0.266667, 0.380392], [0.533333, 0.560784, 1.0], [1.0, 0.713725, 0.956863], [0.32549, 0.384314, 0.215686], [0.807843, 0.521569, 1.0], [0.407843, 0.415686, 0.517647], [0.745098, 0.705882, 0.745098], [0.647059, 0.376471, 0.537255], [0.584314, 0.827451, 1.0], [0.003922, 0.0, 0.972549], [1.0, 0.501961, 0.007843], [0.545098, 0.160784, 0.270588], [0.678431, 0.627451, 0.427451], [0.32549, 0.270588, 0.545098], [0.784314, 1.0, 0.85098], [0.666667, 0.27451, 0.0], [1.0, 0.47451, 0.560784], [0.513725, 0.827451, 0.443137], [0.564706, 0.619608, 0.74902], [0.580392, 0.0, 0.960784], [0.921569, 0.815686, 0.607843], [0.678431, 0.545098, 0.694118], [0.0, 0.388235, 0.290196], [1.0, 0.862745, 0.0], [0.533333, 0.466667, 0.317647], [0.494118, 0.670588, 0.639216], [0.0, 0.0, 0.592157], [0.960784, 0.0, 0.776471], [0.396078, 0.2, 0.160784], [0.0, 0.4, 0.470588], [0.015686, 0.890196, 0.784314], [0.654902, 0.215686, 0.682353], [0.772549, 0.858824, 0.882353], [0.301961, 0.431373, 1.0], [0.607843, 0.576471, 0.003922], [0.803922, 0.345098, 0.419608], [0.937255, 0.870588, 0.996078], [0.47451, 0.352941, 0.0], [0.372549, 0.533333, 0.603922], [0.705882, 1.0, 0.572549], [0.368627, 0.447059, 0.419608], [0.321569, 0.0, 0.4], [0.019608, 0.529412, 0.317647], [0.517647, 0.12549, 0.435294], [0.235294, 0.588235, 0.019608], [0.396078, 0.45098, 0.0], [0.945098, 0.627451, 0.423529], [0.372549, 0.313725, 0.270588], [0.741176, 0.0, 0.290196], [0.815686, 0.407843, 0.152941], [0.843137, 0.588235, 0.670588], [0.537255, 0.364706, 1.0], [0.509804, 0.423529, 0.462745], [0.168627, 0.333333, 0.72549], [0.431373, 0.486275, 0.733333], [0.905882, 0.835294, 0.827451], [0.364706, 0.0, 0.094118], [0.486275, 0.231373, 0.003922], [0.501961, 0.694118, 0.490196], [0.784314, 0.85098, 0.490196], [0.0, 0.909804, 0.231373], [0.486275, 0.698039, 1.0], [1.0, 0.333333, 1.0], [0.643137, 0.152941, 0.129412], [0.113725, 0.894118, 1.0], [0.490196, 0.686275, 0.231373], [0.482353, 0.294118, 0.568627], [0.878431, 1.0, 0.282353], [0.419608, 0.0, 0.768627], [0.803922, 0.658824, 0.592157], [0.745098, 0.388235, 0.768627], [0.537255, 0.803922, 0.807843], [0.27451, 0.011765, 0.784314], [0.368627, 0.572549, 0.47451], [0.254902, 0.290196, 0.003922], [0.019608, 0.654902, 0.615686], [0.811765, 0.54902, 0.215686], [1.0, 0.972549, 0.815686], [0.262745, 0.329412, 0.443137], [0.709804, 0.266667, 1.0], [0.811765, 0.286275, 0.576471], [0.811765, 0.643137, 0.87451], [0.580392, 0.831373, 0.0], [0.654902, 0.580392, 0.854902], [0.176471, 0.647059, 0.345098], [0.552941, 0.890196, 0.713725], [0.643137, 0.662745, 0.615686], [0.423529, 0.360784, 0.717647], [1.0, 0.494118, 0.368627], [0.654902, 0.513725, 0.541176], [0.686275, 0.745098, 0.847059], [0.164706, 0.768627, 1.0], [0.65098, 0.407843, 0.239216], [0.964706, 0.568627, 0.996078], [0.529412, 0.294118, 0.392157], [1.0, 0.047059, 0.294118], [0.129412, 0.368627, 0.137255], [0.258824, 0.572549, 1.0], [0.529412, 0.513725, 0.615686], [0.403922, 0.176471, 0.270588], [0.694118, 0.309804, 0.254902], [0.0, 0.305882, 0.32549], [0.372549, 0.105882, 0.0], [0.678431, 0.254902, 0.403922], [0.313725, 0.196078, 0.403922], [0.839216, 1.0, 0.992157], [0.498039, 0.709804, 0.819608], [0.662745, 0.72549, 0.411765], [1.0, 0.588235, 0.796078], [0.784314, 0.454902, 0.584314], [0.211765, 0.313725, 0.223529], [1.0, 0.815686, 0.388235], [0.368627, 0.345098, 0.384314], [0.529412, 0.580392, 0.462745], [0.662745, 0.470588, 1.0], [0.011765, 0.784314, 0.388235], [0.905882, 0.745098, 0.831373], [0.831373, 0.890196, 0.815686], [0.529412, 0.403922, 0.564706], [0.537255, 0.486275, 0.152941], [0.803922, 0.862745, 1.0], [0.666667, 0.403922, 0.419608], [0.196078, 0.203922, 0.454902], [1.0, 0.368627, 0.662745], [0.0, 0.607843, 0.690196], [0.443137, 1.0, 0.866667], [0.470588, 0.360784, 0.219608], [0.313725, 0.396078, 0.607843], [0.8, 0.0, 0.701961], [0.341176, 0.482353, 0.333333], [0.317647, 0.431373, 0.482353], [0.003922, 0.372549, 0.572549], [0.666667, 0.741176, 0.745098], [0.003922, 0.498039, 0.6], [0.015686, 0.866667, 0.592157], [0.529412, 0.227451, 0.172549], [0.941176, 0.588235, 0.556863], [0.458824, 0.776471, 0.666667], [0.439216, 0.411765, 0.364706], [0.8, 0.862745, 0.035294], [0.686275, 0.521569, 0.341176], [0.847059, 0.0, 0.458824], [0.615686, 0.247059, 0.505882], [0.85098, 0.270588, 0.0], [0.866667, 0.403922, 0.329412], [0.372549, 1.0, 0.47451], [0.835294, 0.694118, 0.45098], [0.384314, 0.14902, 0.368627], [0.729412, 0.635294, 0.239216], [0.85098, 0.94902, 0.701961], [0.341176, 0.007843, 0.560784], [0.631373, 0.607843, 0.666667], [0.301961, 0.290196, 0.152941], [0.643137, 0.662745, 1.0], [0.67451, 0.909804, 0.858824], [0.6, 0.34902, 0.003922], [0.67451, 0.0, 0.886275], [0.278431, 0.509804, 0.184314], [0.796078, 0.764706, 0.678431], [0.0, 0.772549, 0.713725], [0.380392, 0.32549, 0.470588], [0.2, 0.427451, 0.407843], [0.647059, 0.572549, 0.501961], [0.517647, 0.6, 0.635294], [0.992157, 0.341176, 0.392157], [0.439216, 0.588235, 0.823529], [0.447059, 0.552941, 0.027451], [0.498039, 0.0, 0.298039], [0.082353, 0.188235, 0.627451], [0.819608, 0.756863, 0.886275], [0.788235, 0.521569, 0.815686], [0.423529, 0.270588, 0.294118], [0.498039, 0.0, 0.141176], [0.0, 0.635294, 0.47451], [0.698039, 0.662745, 0.811765], [0.976471, 0.0, 0.0], [0.690196, 0.913725, 1.0], [0.576471, 0.619608, 0.313725], [0.447059, 0.478431, 0.509804], [0.85098, 0.180392, 0.333333], [0.278431, 0.380392, 0.003922], [0.0, 0.34902, 1.0], [0.466667, 0.25098, 0.709804], [0.67451, 0.894118, 0.376471], [0.403922, 0.270588, 0.145098], [0.321569, 0.364706, 0.317647], [0.584314, 0.45098, 0.407843], [0.662745, 0.894118, 0.603922], [0.639216, 0.0, 0.345098], [0.85098, 0.384314, 0.964706], [0.556863, 0.490196, 0.811765], [1.0, 0.741176, 0.576471], [0.639216, 0.0, 0.572549], [0.603922, 1.0, 0.72549], [0.654902, 0.760784, 1.0], [0.956863, 0.384314, 0.0], [0.898039, 0.941176, 1.0], [0.721569, 0.611765, 0.643137], [0.376471, 0.588235, 0.580392], [1.0, 0.623529, 0.207843], [0.54902, 0.160784, 0.0], [0.447059, 0.419608, 0.196078], [0.87451, 0.509804, 0.305882], [0.686275, 0.482353, 0.835294], [0.737255, 0.176471, 0.0], [0.482353, 0.435294, 0.639216], [0.282353, 0.262745, 0.384314], [0.780392, 0.639216, 1.0], [0.0, 0.301961, 0.156863], [0.768627, 0.776471, 0.556863], [0.878431, 0.282353, 0.843137], [0.905882, 0.913725, 0.396078], [0.898039, 0.756863, 0.043137], [0.0, 0.956863, 0.945098], [0.623529, 0.356863, 0.635294], [0.298039, 0.254902, 0.717647], [0.396078, 0.2, 0.556863], [0.462745, 0.494118, 0.423529], [0.662745, 0.541176, 0.211765]], linestyles=None, markers=['o'], shade_fraction=0.05)

Plot normalized metric values as colored points on a cartesian plane.

Parameters:

summarypandas.DataFrame

Values to be plotted. Should only include runs and metrics that should actually appear on the plot.

baseline_runstr

Name of the run to use as the baseline for normalization (see (archive.normalize_metric_summaries).

vertical_quantity{‘run’, ‘metric’, ‘value’}

Should the run, metric name, or metric value be mapped onto the y axis?

horizontal_quantity{‘run’, ‘metric’, ‘value’}

Should the run, metric name, or metric value be mapped onto the x axis?

vwidthfloat

The width of the plot, in normalized metrics summary units. (The limits of the x axis will be 1-vwidth/2 and 1+width/2).

run_label_mapmapping

A python mapping between canonical run names and run labels as they should appear on plot labels. Use of this option is discouraged, because it makes it harder to match plots to data. run_label_map could be created by archive.get_runs().loc[these_runs][‘brief’]

metric_label_mapmapping

A python mapping between canonical metric names and metric labels as they should appear on plot labels. Use this option carefully, because it makes it harder to match plots to metric calculation code.. metric_label_map could be equivalent to metric_set[‘short_name’]

metric_setpandas.DataFrame

Metric metadata as returned by archive.get_metric_sets

axmatplotlib.axes.Axes

The axes on which to plot the data.

cmapmatplotlib.colors.ColorMap

The color map to use for point colors.

linestyleslist

A list of matplotlib linestyles to use to connect the lines

markerslist, opt

A list of matplotlib markers to use to represent the points

shade_fractionfloat, opt

Add a red/green shading to the plot, starting at 1 +/- shade_fraction. Set to 0 or None for no shading.

Returns:

figmatplotlib.figure.Figure

The plot figure.

axmatplotilb.axes.Axes

The plot axes.

The run order and metric order (imposed into the summary dataframe passed here as summary)

are important and preserved in the plot. These should be set in the (subset) summary dataframe

passed here; the metric_set is available, but used for normalization and plot styling.