def pvalue_plot(x, y, fill_y=None, col=None, color=None, label=None,
plotargs={}, fillargs={}):
"""Plot of a 1D confidence level distribution, where the y axis is 1-CL.
Parameters:
- `x`: x values
- `y`: y values
- `fill_y`: for x-values where y is larger than this number, the area
between the x-axis and the curve will be filled
- `col`: (optional) integer to select one of the colours from the default
palette
- `plotargs`: keyword arguments passed to the `plot` function
- `fillargs`: keyword arguments passed to the `fill_between` function
"""
ax = plt.gca()
_plotargs = {}
_fillargs = {}
# default values
_plotargs['linewidth'] = 0.6
if label is not None:
_plotargs['label'] = label
color = get_color(col=col, color=color)
_plotargs['color'] = color
_fillargs['facecolor'] = lighten_color(color, 0.5)
_fillargs.update(fillargs)
_plotargs.update(plotargs)
ax.plot(x, y, **_plotargs)
if fill_y is not None:
x_zoom = scipy.ndimage.zoom(x, zoom=10, order=1)
y_zoom = scipy.ndimage.zoom(y, zoom=10, order=1)
ax.fill_between(x_zoom, 0, y_zoom,
where=y_zoom > fill_y,
**_fillargs)
ax.set_ylim([0, 1])
def contour(x, y, z, levels,
interpolation_factor=1,
interpolation_order=2,
col=None, color=None, label=None,
filled=True,
contour_args={}, contourf_args={}):
r"""Plot coloured confidence contours (or bands) given numerical input
arrays.
Parameters:
- `x`, `y`: 2D arrays containg x and y values as returned by numpy.meshgrid
- `z` value of the function to plot. 2D array in the same shape as `x` and
`y`. The lowest value of the function should be 0 (i.e. the best fit
point).
- levels: list of function values where to draw the contours. They should
be positive and in ascending order.
- `interpolation factor` (optional): in between the points on the grid,
the functioncan be interpolated to get smoother contours.
This parameter sets the number of subdivisions (default: 1, i.e. no
interpolation). It should be larger than 1.
- `col` (optional): number between 0 and 9 to choose the color of the plot
from a predefined palette
- `label` (optional): label that will be added to a legend created with
maplotlib.pyplot.legend()
- `filled` (optional): if False, contours will be drawn without shading
- `contour_args`: dictionary of additional options that will be passed
to matplotlib.pyplot.contour() (that draws the contour lines)
- `contourf_args`: dictionary of additional options that will be passed
to matplotlib.pyplot.contourf() (that paints the contour filling).
Ignored if `filled` is false.
"""
if interpolation_factor > 1:
x = scipy.ndimage.zoom(x, zoom=interpolation_factor, order=1)
y = scipy.ndimage.zoom(y, zoom=interpolation_factor, order=1)
z = scipy.ndimage.zoom(z, zoom=interpolation_factor, order=interpolation_order)
_contour_args = {}
_contourf_args = {}
color = get_color(col=col, color=color)
_contour_args['colors'] = color
if filled:
_contour_args['linewidths'] = 0.6
else:
_contour_args['linewidths'] = 0.8
N = len(levels)
_contourf_args['colors'] = [lighten_color(color, 0.5)# RGB
+ (max(1-n/N, 0),) # alpha, decreasing for contours
for n in range(N)]
_contour_args['linestyles'] = 'solid'
_contour_args.update(contour_args)
_contourf_args.update(contourf_args)
# for the filling, need to add zero contour
levelsf = [np.min(z)] + list(levels)
ax = plt.gca()
if filled:
ax.contourf(x, y, z, levels=levelsf, **_contourf_args)
CS = ax.contour(x, y, z, levels=levels, **_contour_args)
if label is not None:
CS.collections[0].set_label(label)
return CS
def likelihood_plot(x,
y,
fill_x=None,
col=None,
color=None,
label=None,
plotargs={},
fillargs={},
flipped=False):
"""Plot of a 1D probability density function.
Parameters:
- `x`: x values
- `y`: y values
- `fill_x`: 2-tuple of x-values in between which the curve will be filled
- `col`: (optional) integer to select one of the colours from the default
palette
- `plotargs`: keyword arguments passed to the `plot` function
- `fillargs`: keyword arguments passed to the `fill_between` function
- `flipped`: exchange x and y axes (needed for `density_contour_joint`)
"""
ax = plt.gca()
_plotargs = {}
_fillargs = {}
# default values
_plotargs['linewidth'] = 0.6
if label is not None:
_plotargs['label'] = label
color = get_color(col=col, color=color)
_plotargs['color'] = color
_fillargs['facecolor'] = lighten_color(color, 0.5)
_fillargs.update(fillargs)
_plotargs.update(plotargs)
if not flipped:
ax.plot(x, y, **_plotargs)
if fill_x is not None:
ax.fill_between(x,
0,
y,
where=np.logical_and(fill_x[0] < x, x < fill_x[1]),
**_fillargs)
else:
ax.plot(y, x, **_plotargs)
if fill_x is not None:
ax.fill_betweenx(x,
0,
y,
where=np.logical_and(fill_x[0] < x,
x < fill_x[1]),
**_fillargs)
def contour(x,
y,
z,
levels,
*,
z_min=None,
interpolation_factor=1,
interpolation_order=2,
col=None,
color=None,
label=None,
filled=True,
contour_args={},
contourf_args={},
**kwargs):
r"""Plot coloured confidence contours (or bands) given numerical input
arrays.
Parameters:
- `x`, `y`: 2D arrays containg x and y values as returned by numpy.meshgrid
- `z` value of the function to plot. 2D array in the same shape as `x` and
`y`.
- levels: list of function values where to draw the contours. They should
be positive and in ascending order.
- `z_min` (optional): lowest value of the function to plot (i.e. value at
the best fit point). If not provided, the smallest value on the grid is
used.
- `interpolation factor` (optional): in between the points on the grid,
the functioncan be interpolated to get smoother contours.
This parameter sets the number of subdivisions (default: 1, i.e. no
interpolation). It should be larger than 1.
- `col` (optional): number between 0 and 9 to choose the color of the plot
from a predefined palette
- `label` (optional): label that will be added to a legend created with
maplotlib.pyplot.legend()
- `filled` (optional): if False, contours will be drawn without shading
- `contour_args`: dictionary of additional options that will be passed
to matplotlib.pyplot.contour() (that draws the contour lines)
- `contourf_args`: dictionary of additional options that will be passed
to matplotlib.pyplot.contourf() (that paints the contour filling).
Ignored if `filled` is false.
"""
if z_min is None:
warnings.warn(
"The smallest `z` value on the grid will be used as the "
"minimum of the function to plot. This can lead to "
"undesired results if the actual minimum is considerably "
"different from the minimum on the grid. For better "
"precision, the actual minimum should be provided in the "
"`z_min` argument.")
z_min = np.min(z) # use minmum on the grid
elif np.min(z) < z_min:
raise ValueError("The provided minimum `z_min` has to be smaller than "
"the smallest `z` value on the grid.")
z = z - z_min # subtract z minimum to make value of new z minimum 0
if interpolation_factor > 1:
x = scipy.ndimage.zoom(x, zoom=interpolation_factor, order=1)
y = scipy.ndimage.zoom(y, zoom=interpolation_factor, order=1)
z = scipy.ndimage.zoom(z,
zoom=interpolation_factor,
order=interpolation_order)
_contour_args = {}
_contourf_args = {}
color = get_color(col=col, color=color)
_contour_args['colors'] = color
if filled:
_contour_args['linewidths'] = 0.6
else:
_contour_args['linewidths'] = 0.8
N = len(levels)
_contourf_args['colors'] = [
lighten_color(color, 0.5) # RGB
+ (max(1 - n / N, 0), ) # alpha, decreasing for contours
for n in range(N)
]
_contour_args['linestyles'] = 'solid'
_contour_args.update(contour_args)
_contourf_args.update(contourf_args)
# for the filling, need to add zero contour
zero_contour = min(np.min(z), np.min(levels) * (1 - 1e-16))
levelsf = [zero_contour] + list(levels)
ax = plt.gca()
if filled:
ax.contourf(x, y, z, levels=levelsf, **_contourf_args)
CS = ax.contour(x, y, z, levels=levels, **_contour_args)
if label is not None:
CS.collections[0].set_label(label)
return CS