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#94 Histograms for ScatterPlotSysTests #116

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a8d43d3
Add an option to have ScatterPlotSysTests do histograms for dense dat…
msimet Jun 23, 2018
5066e77
Remove spurious diagnostic from test_sys_tests (#94)
msimet Jun 23, 2018
f6be77d
Merge branch 'master' into #94
msimet Jul 12, 2018
7ddaff1
Merge branch 'master' into #94
msimet Jul 14, 2018
97048f8
Update changelog (#94)
msimet Jul 14, 2018
6c70cd5
Remove print statements used in testing (#94)
msimet Jul 14, 2018
84cb85e
Merge branch 'master' into #94
msimet Nov 17, 2018
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
7/13/18: Add 2D histogram option for ScatterPlotSysTest (issue #94)
7/11/18: Update code to python3
3/17/16: Update documentation to Sphinx standard and add documentation build files (issue #17)
2/17/16: Changes to correlation function plots & documentation (issue #77)
Expand Down
107 changes: 78 additions & 29 deletions stile/sys_tests.py
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Expand Up @@ -1856,7 +1856,8 @@ class BaseScatterPlotSysTest(SysTest):
short_name = 'scatterplot'
def __call__(self, array, x_field, y_field, yerr_field, z_field=None, residual=False,
per_ccd_stat=None, xlabel=None, ylabel=None, zlabel=None, color="",
lim=None, equal_axis=False, linear_regression=False, reference_line=None):
lim=None, equal_axis=False, linear_regression=False, reference_line=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
"""
Draw a scatter plot and return a :class:`matplotlib.figure.Figure` object.
This method has a bunch of options for controlling appearance of a plot, which is
Expand Down Expand Up @@ -1902,6 +1903,15 @@ def __call__(self, array, x_field, y_field, yerr_field, z_field=None, residual=F
``x=y`` is drawn. If ``reference_line == 'zero'``, ``y=0`` is drawn.
A user-specific function can be used by passing an object which
has an attribute :func:`__call__` and returns a 1-d Numpy array.
:param histogram: Plot a 2-d histogram (instead of plotting each point individually)
for crowded plots. Setting `histogram=True` will cause any data
given for `z` to be ignored, and any uncertainty on `y` will not be
plotted (though it will still be used to compute a trendline).
[default: False]
:param histogram_n_bins: The number of bins along *each* axis for the histogram; ignored if
`histogram=False`. [default: 40]
:param histogram_cmap: The matplotlib colormap used for the histogram; ignored if
`histogram=False`. [default: 'Blues']
:returns: a :class:`matplotlib.figure.Figure` object
"""
if per_ccd_stat:
Expand Down Expand Up @@ -1946,7 +1956,9 @@ def __call__(self, array, x_field, y_field, yerr_field, z_field=None, residual=F
return self.scatterPlot(x, y, yerr, z,
xlabel=xlabel, ylabel=ylabel,
color=color, lim=lim, equal_axis=False,
linear_regression=True, reference_line=reference_line)
linear_regression=True, reference_line=reference_line,
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)

def getData(self):
"""
Expand All @@ -1958,7 +1970,8 @@ def getData(self):
return self.data

def scatterPlot(self, x, y, yerr=None, z=None, xlabel=None, ylabel=None, zlabel=None, color="",
lim=None, equal_axis=False, linear_regression=False, reference_line=None):
lim=None, equal_axis=False, linear_regression=False, reference_line=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
"""
Draw a scatter plot and return a :class:`matplotlib.figure.Figure` object.
This method has a bunch of options for controlling appearance of a plot, which is
Expand Down Expand Up @@ -1998,11 +2011,19 @@ def scatterPlot(self, x, y, yerr=None, z=None, xlabel=None, ylabel=None, zlabel=
A user-specific function can be used by passing an object which has
an attribute :func:`__call__` and returns a 1-d Numpy array.
[default: False]
:param histogram: Plot a 2-d histogram (instead of plotting each point individually)
for crowded plots. Setting `histogram=True` will cause any data
given for `z` to be ignored, and any uncertainty on `y` will not be
plotted (though it will still be used to compute a trendline).
[default: False]
:param histogram_n_bins: The number of bins along *each* axis for the histogram; ignored if
`histogram=False`. [default: 40]
:param histogram_cmap: The matplotlib colormap used for the histogram; ignored if
`histogram=False`. [default: 'Blues']
:returns: a :class:`matplotlib.figure.Figure` object
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)

# mask data with nan. Emit a warning if an array has nan in it.
x_isnan = numpy.isnan(x)
y_isnan = numpy.isnan(y)
Expand All @@ -2019,6 +2040,8 @@ def scatterPlot(self, x, y, yerr=None, z=None, xlabel=None, ylabel=None, zlabel=
% (numpy.sum(yerr_isnan), len(yerr_isnan)))
sel = numpy.logical_and(sel, numpy.invert(yerr_isnan))
if z is not None:
if histogram:
warnings.warn('Plotting a histogram - z (color) data will be ignored.')
z_isnan = numpy.isnan(z)
if numpy.sum(z_isnan) != 0:
warnings.warn('There are %s nans in z, out of %s.'
Expand Down Expand Up @@ -2057,19 +2080,24 @@ def scatterPlot(self, x, y, yerr=None, z=None, xlabel=None, ylabel=None, zlabel=
ylim = None

# plot
if z is None:
if yerr is None:
p = ax.plot(x, y, ".%s" % color)
if not histogram:
if z is None:
if yerr is None:
p = ax.plot(x, y, ".%s" % color)
else:
p = ax.errorbar(x, y, yerr, fmt=".%s" % color)
# store color for latter use
used_color = p[0].get_color()
else:
p = ax.errorbar(x, y, yerr, fmt=".%s" % color)
# store color for latter use
used_color = p[0].get_color()
if yerr is not None:
plt.errorbar(x, y, yerr=yerr, linestyle="None", color="k", zorder=0)
plt.scatter(x, y, c=z, zorder=1)
cb = plt.colorbar()
used_color = "b"
else:
if yerr is not None:
plt.errorbar(x, y, yerr=yerr, linestyle="None", color="k", zorder=0)
plt.scatter(x, y, c=z, zorder=1)
plt.hist2d(x, y, bins=histogram_n_bins, cmap=histogram_cmap)
cb = plt.colorbar()
used_color = "b"
used_color = color

# make axes ticks equal to each other if specified
if equal_axis:
Expand Down Expand Up @@ -2139,7 +2167,7 @@ def scatterPlot(self, x, y, yerr=None, z=None, xlabel=None, ylabel=None, zlabel=
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if zlabel is not None:
if zlabel is not None and not histogram:
cb.set_label(zlabel)

fig.tight_layout()
Expand Down Expand Up @@ -2238,13 +2266,16 @@ class ScatterPlotStarVsPSFG1SysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('g1', 'g1_err', 'psf_g1')]

def __call__(self, array, per_ccd_stat=None, color='', lim=None):
def __call__(self, array, per_ccd_stat=None, color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
return super(ScatterPlotStarVsPSFG1SysTest,
self).__call__(array, 'psf_g1', 'g1', 'g1_err', residual=False,
per_ccd_stat=per_ccd_stat, xlabel=r'$g^{\rm PSF}_1$',
ylabel=r'$g^{\rm star}_1$', color=color, lim=lim,
equal_axis=False, linear_regression=True,
reference_line='one-to-one')
reference_line='one-to-one',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)


class ScatterPlotStarVsPSFG2SysTest(BaseScatterPlotSysTest):
Expand All @@ -2256,13 +2287,16 @@ class ScatterPlotStarVsPSFG2SysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('g2', 'g2_err', 'psf_g2')]

def __call__(self, array, per_ccd_stat=None, color='', lim=None):
def __call__(self, array, per_ccd_stat=None, color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
return super(ScatterPlotStarVsPSFG2SysTest,
self).__call__(array, 'psf_g2', 'g2', 'g2_err', residual=False,
per_ccd_stat=per_ccd_stat, xlabel=r'$g^{\rm PSF}_2$',
ylabel=r'$g^{\rm star}_2$', color=color, lim=lim,
equal_axis=False, linear_regression=True,
reference_line='one-to-one')
reference_line='one-to-one',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)


class ScatterPlotStarVsPSFSigmaSysTest(BaseScatterPlotSysTest):
Expand All @@ -2274,14 +2308,17 @@ class ScatterPlotStarVsPSFSigmaSysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('sigma', 'sigma_err', 'psf_sigma')]

def __call__(self, array, per_ccd_stat=None, color='', lim=None):
def __call__(self, array, per_ccd_stat=None, color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
return super(ScatterPlotStarVsPSFSigmaSysTest,
self).__call__(array, 'psf_sigma', 'sigma', 'sigma_err', residual=False,
per_ccd_stat=per_ccd_stat,
xlabel=r'$\sigma^{\rm PSF}$ [arcsec]',
ylabel=r'$\sigma^{\rm star}$ [arcsec]',
color=color, lim=lim, equal_axis=False,
linear_regression=True, reference_line='one-to-one')
linear_regression=True, reference_line='one-to-one',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)


class ScatterPlotResidualVsPSFG1SysTest(BaseScatterPlotSysTest):
Expand All @@ -2293,13 +2330,16 @@ class ScatterPlotResidualVsPSFG1SysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('g1', 'g1_err', 'psf_g1')]

def __call__(self, array, per_ccd_stat=None, color='', lim=None):
def __call__(self, array, per_ccd_stat=None, color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
return super(ScatterPlotResidualVsPSFG1SysTest,
self).__call__(array, 'psf_g1', 'g1', 'g1_err', residual=True,
per_ccd_stat=per_ccd_stat, xlabel=r'$g^{\rm PSF}_1$',
ylabel=r'$g^{\rm star}_1 - g^{\rm PSF}_1$',
color=color, lim=lim, equal_axis=False,
linear_regression=True, reference_line='zero')
linear_regression=True, reference_line='zero',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)


class ScatterPlotResidualVsPSFG2SysTest(BaseScatterPlotSysTest):
Expand All @@ -2311,13 +2351,16 @@ class ScatterPlotResidualVsPSFG2SysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('g2', 'g2_err', 'psf_g2')]

def __call__(self, array, per_ccd_stat=None, color='', lim=None):
def __call__(self, array, per_ccd_stat=None, color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
return super(ScatterPlotResidualVsPSFG2SysTest,
self).__call__(array, 'psf_g2', 'g2', 'g2_err', residual=True,
per_ccd_stat=per_ccd_stat, xlabel=r'$g^{\rm PSF}_2$',
ylabel=r'$g^{\rm star}_2 - g^{\rm PSF}_2$',
color=color, lim=lim, equal_axis=False,
linear_regression=True, reference_line='zero')
linear_regression=True, reference_line='zero',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)


class ScatterPlotResidualVsPSFSigmaSysTest(BaseScatterPlotSysTest):
Expand All @@ -2329,14 +2372,17 @@ class ScatterPlotResidualVsPSFSigmaSysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('sigma', 'sigma_err', 'psf_sigma')]

def __call__(self, array, per_ccd_stat=None, color='', lim=None):
def __call__(self, array, per_ccd_stat=None, color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
return super(ScatterPlotResidualVsPSFSigmaSysTest,
self).__call__(array, 'psf_sigma', 'sigma', 'sigma_err', residual=True,
per_ccd_stat=per_ccd_stat,
xlabel=r'$\sigma^{\rm PSF}$ [arcsec]',
ylabel=r'$\sigma^{\rm star} - \sigma^{\rm PSF}$ [arcsec]',
color=color, lim=lim, equal_axis=False,
linear_regression=True, reference_line='zero')
linear_regression=True, reference_line='zero',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)


class ScatterPlotResidualSigmaVsPSFMagSysTest(BaseScatterPlotSysTest):
Expand All @@ -2348,7 +2394,8 @@ class ScatterPlotResidualSigmaVsPSFMagSysTest(BaseScatterPlotSysTest):
objects_list = ['star PSF']
required_quantities = [('sigma', 'sigma_err', 'psf_sigma', 'mag_inst')]

def __call__(self, array, per_ccd_stat='None', color='', lim=None):
def __call__(self, array, per_ccd_stat='None', color='', lim=None,
histogram=False, histogram_n_bins=40, histogram_cmap='Blues'):
self.per_ccd_stat = None if per_ccd_stat == 'None' else per_ccd_stat
import numpy.lib.recfunctions
use_array = numpy.copy(array)
Expand All @@ -2367,5 +2414,7 @@ def __call__(self, array, per_ccd_stat='None', color='', lim=None):
ylabel=
r'$(\sigma^{\rm star} - \sigma^{\rm PSF})/\sigma^{\rm PSF}$',
color=color, lim=lim, equal_axis=False,
linear_regression=True, reference_line='zero')
linear_regression=True, reference_line='zero',
histogram=histogram, histogram_n_bins=histogram_n_bins,
histogram_cmap=histogram_cmap)

28 changes: 28 additions & 0 deletions tests/test_sys_tests.py
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@@ -0,0 +1,28 @@
import numpy
import unittest

try:
import stile
except ImportError:
sys.path.append('..')
import stile

class TestSysTests(unittest.TestCase):
def test_histogram_scatterplot(self):
source_data = stile.ReadASCIITable('../examples/example_source_catalog.dat',
fields={'id': 0, 'ra': 1, 'dec': 2, 'z': 3, 'g1': 4, 'g2': 5})
data = numpy.rec.fromarrays([source_data['ra'], source_data['dec'], source_data['g1'],
source_data['g2'], source_data['g1']+0.1*source_data['ra'], source_data['g2']+0.05*source_data['dec'],
numpy.ones_like(source_data['ra']), numpy.ones_like(source_data['ra'])+0.02,
source_data['g1']*0.05, source_data['g2']*0.05, source_data['g1']*0.05,
source_data['g2']*0.05],
names = ['x', 'y', 'g1', 'g2', 'psf_g1', 'psf_g2', 'sigma', 'psf_sigma',
'g1_err', 'g2_err', 'psf_g1_err', 'psf_g2_err'])
obj = stile.ScatterPlotSysTest('StarVsPSFG1')
obj(data, color='g')
obj(data, histogram=True, color='g')


if __name__=='__main__':
unittest.main()