def histplot1d_part(ax, x, prob=None, N_bins='knuth', histrange=None,
x_lim=None, y_lim=None):
'''
This take the additional value of an array axes. for use with subplots
similar to histplot1d but for subplot purposes I believe
'''
# compare bin width to knuth bin width
# if type(N_bins) is int:
# print "specified bin width is {0}, Knuth bin size is {1}".format(
# N_bins, knuth_N_bins)
if N_bins == 'knuth':
binwidth, bins = de.knuth_bin_width(x, return_bins=True)
knuth_N_bins = bins.size - 1
N_bins = knuth_N_bins
hist, binedges, tmp = ax.hist(
x, bins=N_bins, histtype='step', weights=prob, range=histrange,
color='k', linewidth=1)
# Calculate the location and %confidence intervals
# Since my location and confidence calculations can't take weighted data I
# need to use the weighted histogram data in the calculations
for i in np.arange(N_bins):
if i == 0:
x_binned = \
np.ones(hist[i]) * (binedges[i] + binedges[i + 1]) / 2
elif np.size(x_binned) == 0:
x_binned = \
np.ones(hist[i]) * (binedges[i] + binedges[i + 1]) / 2
else:
x_temp = \
np.ones(hist[i]) * (binedges[i] + binedges[i + 1]) / 2
x_binned = np.concatenate((x_binned, x_temp))
loc = biweightLoc(x_binned)
ll_68, ul_68 = bcpcl(loc, x_binned, 1)
ll_95, ul_95 = bcpcl(loc, x_binned, 2)
# Create location and confidence interval line plots
# find the binedge that the location falls into
# so that the line indicating the location only extends to top of
# histogram
loc_ix = find_bin_ix(binedges, loc)
ll_68_ix = find_bin_ix(binedges, ll_68)
ul_68_ix = find_bin_ix(binedges, ul_68)
ll_95_ix = find_bin_ix(binedges, ll_95)
ul_95_ix = find_bin_ix(binedges, ul_95)
ax.plot((loc, loc), (0, hist[loc_ix - 1]), ls='--', lw=1, color="k")
width = binedges[ll_68_ix + 1] - binedges[ll_68_ix]
for i in range(ll_68_ix, ul_68_ix):
ax.bar(binedges[i], hist[i], width, lw=0, color="b", alpha=.6)
for i in range(ll_95_ix, ul_95_ix):
ax.bar(binedges[i], hist[i], width, lw=0, color="b", alpha=.3)
if x_lim is not None:
ax.set_xlim(x_lim)
if y_lim is not None:
ax.set_ylim(y_lim)
return loc, ll_68, ul_68, ll_95, ul_95
def hist(x, bins=10, range=None, *args, **kwargs):
"""Enhanced histogram
This is a histogram function that enables the use of more sophisticated
algorithms for determining bins. Aside from the `bins` argument allowing
a string specified how bins are computed, the parameters are the same
as pylab.hist().
Parameters
----------
x : array_like
array of data to be histogrammed
bins : int or list or str (optional)
If bins is a string, then it must be one of:
'blocks' : use bayesian blocks for dynamic bin widths
'knuth' : use Knuth's rule to determine bins
'scott' : use Scott's rule to determine bins
'freedman' : use the Freedman-diaconis rule to determine bins
range : tuple or None (optional)
the minimum and maximum range for the histogram. If not specified,
it will be (x.min(), x.max())
ax : Axes instance (optional)
specify the Axes on which to draw the histogram. If not specified,
then the current active axes will be used.
**kwargs :
other keyword arguments are described in pylab.hist().
"""
x = np.asarray(x)
if 'ax' in kwargs:
ax = kwargs['ax']
del kwargs['ax']
else:
ax = plt.gca()
# if range is specified, we need to truncate the data for
# the bin-finding routines
if (range is not None and (bins in ['blocks',
'knuth', 'knuths',
'scott', 'scotts',
'freedman', 'freedmans'])):
x = x[(x >= range[0]) & (x <= range[1])]
if bins in ['blocks']:
bins = bayesian_blocks(x)
elif bins in ['knuth', 'knuths']:
dx, bins = knuth_bin_width(x, True)
elif bins in ['scott', 'scotts']:
dx, bins = scotts_bin_width(x, True)
elif bins in ['freedman', 'freedmans']:
dx, bins = freedman_bin_width(x, True)
elif isinstance(bins, str):
raise ValueError("unrecognized bin code: '%s'" % bins)
return ax.hist(x, bins, range, **kwargs)
def hist(x, bins=10, range=None, *args, **kwargs):
"""Enhanced histogram
This is a histogram function that enables the use of more sophisticated
algorithms for determining bins. Aside from the `bins` argument allowing
a string specified how bins are computed, the parameters are the same
as pylab.hist().
Parameters
----------
x : array_like
array of data to be histogrammed
bins : int or list or str (optional)
If bins is a string, then it must be one of:
'blocks' : use bayesian blocks for dynamic bin widths
'knuth' : use Knuth's rule to determine bins
'scott' : use Scott's rule to determine bins
'freedman' : use the Freedman-diaconis rule to determine bins
range : tuple or None (optional)
the minimum and maximum range for the histogram. If not specified,
it will be (x.min(), x.max())
ax : Axes instance (optional)
specify the Axes on which to draw the histogram. If not specified,
then the current active axes will be used.
**kwargs :
other keyword arguments are described in pylab.hist().
"""
x = np.asarray(x)
if 'ax' in kwargs:
ax = kwargs['ax']
del kwargs['ax']
else:
ax = plt.gca()
# if range is specified, we need to truncate the data for
# the bin-finding routines
if (range is not None and (bins in [
'blocks', 'knuth', 'knuths', 'scott', 'scotts', 'freedman',
'freedmans'
])):
x = x[(x >= range[0]) & (x <= range[1])]
if bins in ['blocks']:
bins = bayesian_blocks(x)
elif bins in ['knuth', 'knuths']:
dx, bins = knuth_bin_width(x, True)
elif bins in ['scott', 'scotts']:
dx, bins = scotts_bin_width(x, True)
elif bins in ['freedman', 'freedmans']:
dx, bins = freedman_bin_width(x, True)
elif isinstance(bins, str):
raise ValueError("unrecognized bin code: '%s'" % bins)
return ax.hist(x, bins, range, **kwargs)
def _size_bins(self,hist,bin_tool,**kwargs):
"""Wrapper for astroML routines to choose optimal bin widths."""
if bin_tool == 'freedman':
_,bins = density_estimation.freedman_bin_width(hist,return_bins=True)
elif bin_tool == 'scotts':
_,bins = density_estimation.scotts_bin_width(hist,return_bins=True)
elif bin_tool == 'knuth':
_,bins = density_estimation.knuth_bin_width(hist,return_bins=True, disp=False)
elif bin_tool == 'blocks':
bins = density_estimation.bayesian_blocks(hist,**kwargs)
elif type(bin_tool) == type(int()) or type(bin_tool) == type(np.int64()) or type(bin_tool) == type(np.int32()):
bins=bin_tool
else:
self.logger.warning("Unrecognized bin_tool option. Using Freedman-Diaconis rule.")
_,bins = density_estimation.freedman_bin_width(hist,return_bins=True)
return bins
def get_knuth_bins(x,bounds=None):
'''
Code for finding the 'ideal' bin width for plotting histograms. The 'best'
width is then converted to the the nearest 'OK' value (ie. a multiple of
0.1, 0.25, 0.5 or 1)
Inputs:
-------
x: data that we wish to bin.
bounds: upper and lower bounds to the data. If None, then the limits of the
x data are used. Default is None.
Returns:
--------
dx: optimal bin width
bins: optimal bins for x.
'''
ok_widths = (0.1,0.25,0.5,1)
if bounds is not None:
x_mask = (x >= bounds[0]) & (x <= bounds[1])
x_plot = x[x_mask]
else:
x_plot = x.copy()
bounds = (np.min(x_plot),np.max(x_plot))
dx = knuth_bin_width(x_plot)
n_zeros = math.floor(math.log10(dx))
ok_widths_modified = [w*10**(n_zeros+1) for w in ok_widths]
dx_modified = find_nearest_value(ok_widths_modified,dx)
bin_range = (dx_modified*(math.floor(bounds[0]/dx_modified)),
dx_modified*(math.ceil(bounds[1]/dx_modified)))
bins = np.arange(bin_range[0],bin_range[1]+dx_modified*0.01,
dx_modified)
return dx_modified, bins
def get_knuth_bins(x, bounds=None):
'''
Code for finding the 'ideal' bin width for plotting histograms. The 'best'
width is then converted to the the nearest 'OK' value (ie. a multiple of
0.1, 0.25, 0.5 or 1)
Inputs:
-------
x: data that we wish to bin.
bounds: upper and lower bounds to the data. If None, then the limits of the
x data are used. Default is None.
Returns:
--------
dx: optimal bin width
bins: optimal bins for x.
'''
ok_widths = (0.1, 0.25, 0.5, 1)
if bounds is not None:
x_mask = (x >= bounds[0]) & (x <= bounds[1])
x_plot = x[x_mask]
else:
x_plot = x.copy()
bounds = (np.min(x_plot), np.max(x_plot))
dx = knuth_bin_width(x_plot)
n_zeros = math.floor(math.log10(dx))
ok_widths_modified = [w * 10**(n_zeros + 1) for w in ok_widths]
dx_modified = find_nearest_value(ok_widths_modified, dx)
bin_range = (dx_modified * (math.floor(bounds[0] / dx_modified)),
dx_modified * (math.ceil(bounds[1] / dx_modified)))
bins = np.arange(bin_range[0], bin_range[1] + dx_modified * 0.01,
dx_modified)
return dx_modified, bins
def test_knuth_bin_width(N=10000, rseed=0):
np.random.seed(0)
X = np.random.normal(size=N)
dx, bins = knuth_bin_width(X, return_bins=True)
assert_allclose(len(bins), 59)
def test_knuth_bin_width(N=10000, rseed=0):
np.random.seed(0)
X = np.random.normal(size=N)
dx, bins = knuth_bin_width(X, return_bins=True)
assert_allclose(len(bins), 59)
def test_knuth_bin_width(N=10000, rseed=0):
np.random.seed(0)
X = np.random.normal(size=N)
with catch_warnings(AstroMLDeprecationWarning):
dx, bins = knuth_bin_width(X, return_bins=True)
assert_allclose(len(bins), 59)
def test_knuth_bin_width(N=10000, rseed=0):
np.random.seed(0)
X = np.random.normal(size=N)
with catch_warnings(AstroMLDeprecationWarning):
dx, bins = knuth_bin_width(X, return_bins=True)
assert_allclose(len(bins), 59)
def histogram(self,
data,
bin_width='knuth',
weights=None,
density=None,
norm=None,
ax=None,
**kwargs):
"""
Plots a histogram.
Parameters
----------
data : list or array
Data to plot.
bin_width : {'knuth', 'bayesian'} or float, optional
Automatically determine the bin width using Knuth's rule
(2006physics...5197K), with Bayesian blocks (2013ApJ...764..167S),
or manually, choosing a floating point value.
weights : array, optional
An array of weights, of the same shape as `a`. Each value in `a`
only contributes its associated weight towards the bin count
(instead of 1). If `density` is True, the weights are normalized,
so that the integral of the density over the range remains 1.
density : bool, optional
If False, the result will contain the number of samples
in each bin. If True, the result is the value of the
probability *density* function at the bin, normalised such that
the *integral* over the range is 1. Note that the sum of the
histogram values will not be equal to 1 unless bins of unity
width are chosen; it is not a probability *mass* function.
norm : int or float
Custom normalisation.
ax : `matplotlib.axes.Axes`, optional
Axes instance.
"""
# Axes instance:
if ax is None:
ax = self.axes[0]
elif not isinstance(ax, Axes):
raise TypeError('ax must be of type `matplotlib.axes.Axes`')
# Convert list to array:
if isinstance(data, list):
data = np.array(data)
if bin_width == 'knuth':
_, bins = knuth_bin_width(data, return_bins=True)
elif bin_width == 'bayesian':
bins = bayesian_blocks(data)
elif isinstance(bin_width, (int, float)):
bins = np.arange(data.min(), data.max(), bin_width)
else:
raise ValueError('bin_width must be a number, or one of'
'(`knuth`, `bayesian`)')
# Ensure padding with empty bins:
dx = np.diff(bins).min()
bins = np.pad(bins, (1, 2),
mode='linear_ramp',
end_values=(bins[0] - dx, bins[-1] + 2 * dx))
# Calculate histogram:
histogram, bins = np.histogram(data,
bins,
weights=weights,
density=density)
if norm:
histogram /= norm
# Plot data:
ax.plot(bins[:-1] + np.diff(bins) / 2,
histogram,
drawstyle='steps-mid',
**kwargs)
def knuth_n_bins(data):
bandwidth = knuth_bin_width(data)
return np.ceil((np.max(data) - np.min(data)) / bandwidth)
def histogram(self, data, bin_width='knuth', weights=None, density=None,
norm=None, ax=None, **kwargs):
"""
Plots a histogram.
Parameters
----------
data : list or array
Data to plot.
bin_width : {'knuth', 'bayesian'} or float, optional
Automatically determine the bin width using Knuth's rule
(2006physics...5197K), with Bayesian blocks (2013ApJ...764..167S),
or manually, choosing a floating point value.
weights : array, optional
An array of weights, of the same shape as `a`. Each value in `a`
only contributes its associated weight towards the bin count
(instead of 1). If `density` is True, the weights are normalized,
so that the integral of the density over the range remains 1.
density : bool, optional
If False, the result will contain the number of samples
in each bin. If True, the result is the value of the
probability *density* function at the bin, normalised such that
the *integral* over the range is 1. Note that the sum of the
histogram values will not be equal to 1 unless bins of unity
width are chosen; it is not a probability *mass* function.
norm : int or float
Custom normalisation.
ax : `matplotlib.axes.Axes`, optional
Axes instance.
"""
# Axes instance:
if ax is None:
ax = self.axes[0]
elif not isinstance(ax, Axes):
raise TypeError('ax must be of type `matplotlib.axes.Axes`')
# Convert list to array:
if isinstance(data, list):
data = np.array(data)
if bin_width == 'knuth':
_, bins = knuth_bin_width(data, return_bins=True)
elif bin_width == 'bayesian':
bins = bayesian_blocks(data)
elif isinstance(bin_width, (int, float)):
bins = np.arange(data.min(), data.max(), bin_width)
else:
raise ValueError('bin_width must be a number, or one of'
'(`knuth`, `bayesian`)')
# Ensure padding with empty bins:
dx = np.diff(bins).min()
bins = np.pad(bins, (1, 2), mode='linear_ramp',
end_values=(bins[0] - dx, bins[-1] + 2 * dx))
# Calculate histogram:
histogram, bins = np.histogram(
data, bins, weights=weights, density=density)
if norm:
histogram /= norm
# Plot data:
ax.plot(bins[:-1] + np.diff(bins) / 2, histogram,
drawstyle='steps-mid', **kwargs)
def hist(x,
bins=10,
fitness='events',
gamma=None,
p0=0.05,
errorbars=None,
suppress_zero=False,
*args,
**kwargs):
"""Enhanced histogram, based on `hist` function from astroML.
This is a histogram function that enables the use of more sophisticated
algorithms for determining bins. Aside from the `bins` argument allowing
a string specified how bins are computed, additional scaling, errorbar, and
plotting methods are introduced. All other kwargs can be used as in `pylab.hist()`.
Parameters
----------
x : array_like
Array of data to be histogrammed
bins : int or list or str (optional)
If bins is a string, then it must be one of:
'blocks' : use bayesian blocks for dynamic bin widths
'knuth' : use Knuth's rule to determine bins
'scott' : use Scott's rule to determine bins
'freedman' : use the Freedman-diaconis rule to determine bins
fitness : str
Param used for Bayesian Blocks binning.
gamma: Number
Param used for Bayesian Blocks binning, ignored if `p0` is present
p0 : Number
Fake rate value for Bayesian Blocks binning, supersedes `gamma`
ax : Axes instance (optional)
Specify the Axes on which to draw the histogram. If not specified,
then the current active axes will be used.
scale : Number or str (optional)
If Number, all bin contents are multiplied by the given value.
If str:
'binwidth' : every bin content is divided by the bin width.
**kwargs :
Overloaded kwargs variants:
histtype:
'markers' : plot the bin contents as markers, centered on the bin centers.
If this method is chosen, all additional kwargs for `pylab.plot()` can be used.
Other keyword arguments are described in `pylab.hist()`.
"""
# do initial checks and set-up for overloaded arguments
x = np.asarray(x)
if isinstance(bins, str) and "weights" in kwargs:
warnings.warn(
"weights argument is not supported for this binning method: it will be ignored."
)
kwargs.pop('weights')
if 'ax' in kwargs:
ax = kwargs.pop('ax')
else:
ax = plt.gca()
# if range is specified, we need to truncate the data for
# the bin-finding routines
if ('range' in kwargs and kwargs['range'] is not None and (bins in [
'blocks', 'knuth', 'knuths', 'scott', 'scotts', 'freedman',
'freedmans'
])):
x = x[(x >= kwargs['range'][0]) & (x <= kwargs['range'][1])]
if bins in ['block', 'blocks']:
bins = bayesian_blocks(t=x, fitness=fitness, p0=p0, gamma=gamma)
elif bins in ['knuth', 'knuths']:
dx, bins = knuth_bin_width(x, True, disp=False)
elif bins in ['scott', 'scotts']:
dx, bins = scotts_bin_width(x, True)
elif bins in ['freedman', 'freedmans']:
dx, bins = freedman_bin_width(x, True)
elif isinstance(bins, str):
raise ValueError("unrecognized bin code: '%s'" % bins)
if 'scale' in kwargs:
scale = kwargs.pop('scale')
else:
scale = None
if scale and "stacked" in kwargs:
warnings.warn(
"scaling is not currently supported for stacked histograms: scaling will be ignored."
)
scale = None
if 'histtype' in kwargs and kwargs['histtype'] == 'marker':
marker = kwargs.pop('histtype')
else:
marker = None
# generate histogram-like object
vis_objects = None
vis_objects_err = None
if marker:
if 'normed' in kwargs:
normed = kwargs.pop('normed')
else:
normed = False
if 'marker' in kwargs:
markerstyle = kwargs.pop('marker')
else:
markerstyle = '.'
if 'linestyle' in kwargs:
linestyle = kwargs.pop('linestyle')
else:
linestyle = ''
hrange = None
if 'range' in kwargs:
hrange = kwargs.pop('range')
bin_content, bins = np.histogram(x, bins, density=normed, range=hrange)
bin_content = np.asarray(bin_content, dtype=float)
if normed:
bin_content_raw, _ = np.histogram(x,
bins,
density=False,
range=hrange)
bin_content_raw = np.asarray(bin_content_raw)
else:
bin_content_raw = bin_content
width = (bins[1:] - bins[:-1])
bin_centers = bins[:-1] + width * 0.5
# bin_error = np.sqrt(bin_content)
err_low = np.asarray(
[poisson_error(bc, suppress_zero)[0] for bc in bin_content_raw])
err_hi = np.asarray(
[poisson_error(bc, suppress_zero)[1] for bc in bin_content_raw])
err_scale = bin_content / bin_content_raw
err_low *= err_scale
err_hi *= err_scale
bin_error = [err_low, err_hi]
if errorbars:
vis_objects_err = ax.errorbar(bin_centers,
bin_content,
linestyle=linestyle,
marker=markerstyle,
yerr=bin_error,
**kwargs)
else:
vis_objects = ax.plot(bin_centers,
bin_content,
linestyle=linestyle,
marker=markerstyle,
**kwargs)
if 'color' in kwargs:
kwargs.pop('color')
else:
if 'normed' in kwargs:
normed = kwargs.pop('normed')
else:
normed = False
hrange = None
if 'range' in kwargs:
hrange = kwargs.pop('range')
bin_content, bins, vis_objects = ax.hist(x,
bins,
range=hrange,
normed=normed,
**kwargs)
if 'color' in kwargs:
kwargs.pop('color')
bin_content = np.asarray(bin_content, dtype=float)
if normed:
bin_content_raw, _ = np.histogram(x,
bins,
density=False,
range=hrange)
bin_content_raw = np.asarray(bin_content_raw)
else:
bin_content_raw = bin_content
err_low = np.asarray(
[poisson_error(bc, suppress_zero)[0] for bc in bin_content_raw])
err_hi = np.asarray(
[poisson_error(bc, suppress_zero)[1] for bc in bin_content_raw])
err_scale = bin_content / bin_content_raw
err_low *= err_scale
err_hi *= err_scale
bin_error = [err_low, err_hi]
width = (bins[1:] - bins[:-1])
bin_centers = bins[:-1] + width * 0.5
vis_color = vis_objects[0].get_facecolor()
if 'histtype' in kwargs:
if kwargs['histtype'] == 'step':
vis_color = vis_objects[0].get_edgecolor()
kwargs.pop('histtype')
if 'weights' in kwargs:
kwargs.pop('weights')
if 'label' in kwargs:
kwargs.pop('label')
if 'linewidth' in kwargs:
kwargs.pop('linewidth')
if errorbars:
vis_objects_err = ax.errorbar(bin_centers,
bin_content,
linestyle='',
marker='.',
yerr=bin_error,
linewidth=2,
color=vis_color,
**kwargs)
# perform any scaling if necessary, including redrawing of the scaled objects
if scale:
bin_content_scaled = []
if vis_objects is not None:
if isinstance(vis_objects[0], matplotlib.patches.Rectangle):
if scale == 'binwidth':
for i, bc in enumerate(bin_content):
width = (bins[i + 1] - bins[i])
bin_content_scaled.append(bin_content[i] / width)
plt.setp(vis_objects[i], 'height',
vis_objects[i].get_height() / width)
elif isinstance(scale, Number):
for i, bc in enumerate(bin_content):
bin_content_scaled.append(bin_content[i] * scale)
plt.setp(vis_objects[i], 'height',
vis_objects[i].get_height() * scale)
else:
warnings.warn("scale argument value `", scale,
"` not supported: it will be ignored.")
elif isinstance(vis_objects[0], matplotlib.patches.Polygon):
xy = vis_objects[0].get_xy()
j = 0
if scale == 'binwidth':
for i, bc in enumerate(bin_content):
width = (bins[i + 1] - bins[i])
bin_content_scaled.append(bin_content[i] / width)
xy[j + 1, 1] = bin_content_scaled[i]
xy[j + 2, 1] = bin_content_scaled[i]
j += 2
elif isinstance(scale, Number):
for i, bc in enumerate(bin_content):
bin_content_scaled.append(bin_content[i] * scale)
xy[j + 1, 1] = bin_content_scaled[i]
xy[j + 2, 1] = bin_content_scaled[i]
j += 2
else:
warnings.warn("scale argument value `", scale,
"` not supported: it will be ignored.")
plt.setp(vis_objects[0], 'xy', xy)
if vis_objects_err is not None:
if scale == 'binwidth':
for i, bc in enumerate(bin_content):
width = (bins[i + 1] - bins[i])
if len(bin_content_scaled) != len(bin_content):
bin_content_scaled.append(bin_content[i] / width)
bin_error[0][i] /= width
bin_error[1][i] /= width
elif isinstance(scale, Number):
for i, bc in enumerate(bin_content):
if len(bin_content_scaled) != len(bin_content):
bin_content_scaled.append(bin_content[i] * scale)
bin_error[0][i] *= scale
bin_error[1][i] *= scale
else:
warnings.warn("scale argument value `", scale,
"` not supported: it will be ignored.")
bin_content_scaled = np.asarray(bin_content_scaled)
vis_objects_err[0].set_ydata(bin_content_scaled)
vis_objects_err[1][0].set_ydata(bin_content_scaled - bin_error[0])
vis_objects_err[1][1].set_ydata(bin_content_scaled + bin_error[1])
#vis_objects_err[1][0].set_ydata(bin_error[0])
#vis_objects_err[1][1].set_ydata(bin_error[1])
tmplines = vis_objects_err[2][0].get_segments()
for i, bc in enumerate(bin_content_scaled):
tmplines[i][0][1] = bin_content_scaled[i] - bin_error[0][i]
tmplines[i][1][1] = bin_content_scaled[i] + bin_error[1][i]
#tmplines[i][0][1] = bin_error[0][i]
#tmplines[i][1][1] = bin_error[1][i]
vis_objects_err[2][0].set_segments(tmplines)
ax.relim()
ax.autoscale_view(False, False, True)
try:
bc = bin_content_scaled
except:
bc = bin_content
return bc, bins, vis_objects
def knuth_n_bins(data):
bandwidth = knuth_bin_width(data)
return np.ceil((np.max(data) - np.min(data)) / bandwidth)
