"""
SAS data representations.
Plotting functions for data sets:
:func:`plot_data` plots the data file.
:func:`plot_theory` plots a calculated result from the model.
Wrappers for the sasview data loader and data manipulations:
:func:`load_data` loads a sasview data file.
:func:`set_beam_stop` masks the beam stop from the data.
:func:`set_half` selects the right or left half of the data, which can
be useful for shear measurements which have not been properly corrected
for path length and reflections.
:func:`set_top` cuts the top part off the data.
Empty data sets for evaluating models without data:
:func:`empty_data1D` creates an empty dataset, which is useful for plotting
a theory function before the data is measured.
:func:`empty_data2D` creates an empty 2D dataset.
Note that the empty datasets use a minimal representation of the SasView
objects so that models can be run without SasView on the path. You could
also use these for your own data loader.
"""
import traceback
import numpy as np # type: ignore
from numpy import sqrt, sin, cos, pi
# pylint: disable=unused-import
try:
from typing import Union, Dict, List, Optional
except ImportError:
pass
else:
Data = Union["Data1D", "Data2D", "SesansData"]
# pylint: enable=unused-import
[docs]def load_data(filename, index=0):
# type: (str) -> Data
"""
Load data using a sasview loader.
"""
from sas.sascalc.dataloader.loader import Loader # type: ignore
loader = Loader()
# Allow for one part in multipart file
if '[' in filename:
filename, indexstr = filename[:-1].split('[')
index = int(indexstr)
datasets = loader.load(filename)
if not datasets: # None or []
raise IOError("Data %r could not be loaded" % filename)
if not isinstance(datasets, list):
datasets = [datasets]
for data in datasets:
if hasattr(data, 'x'):
data.qmin, data.qmax = data.x.min(), data.x.max()
data.mask = (np.isnan(data.y) if data.y is not None
else np.zeros_like(data.x, dtype='bool'))
elif hasattr(data, 'qx_data'):
data.mask = ~data.mask
return datasets[index] if index != 'all' else datasets
[docs]def set_beam_stop(data, radius, outer=None):
# type: (Data, float, Optional[float]) -> None
"""
Add a beam stop of the given *radius*. If *outer*, make an annulus.
"""
from sas.sascalc.dataloader.manipulations import Ringcut
if hasattr(data, 'qx_data'):
data.mask = Ringcut(0, radius)(data)
if outer is not None:
data.mask += Ringcut(outer, np.inf)(data)
else:
data.mask = (data.x < radius)
if outer is not None:
data.mask |= (data.x >= outer)
[docs]def set_half(data, half):
# type: (Data, str) -> None
"""
Select half of the data, either "right" or "left".
"""
from sas.sascalc.dataloader.manipulations import Boxcut
if half == 'right':
data.mask += \
Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data)
if half == 'left':
data.mask += \
Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data)
[docs]def set_top(data, cutoff):
# type: (Data, float) -> None
"""
Chop the top off the data, above *cutoff*.
"""
from sas.sascalc.dataloader.manipulations import Boxcut
data.mask += \
Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=cutoff)(data)
[docs]class Data1D(object):
"""
1D data object.
Note that this definition matches the attributes from sasview, with
some generic 1D data vectors and some SAS specific definitions. Some
refactoring to allow consistent naming conventions between 1D, 2D and
SESANS data would be helpful.
**Attributes**
*x*, *dx*: $q$ vector and gaussian resolution
*y*, *dy*: $I(q)$ vector and measurement uncertainty
*mask*: values to include in plotting/analysis
*dxl*: slit widths for slit smeared data, with *dx* ignored
*qmin*, *qmax*: range of $q$ values in *x*
*filename*: label for the data line
*_xaxis*, *_xunit*: label and units for the *x* axis
*_yaxis*, *_yunit*: label and units for the *y* axis
"""
def __init__(self,
x=None, # type: Optional[np.ndarray]
y=None, # type: Optional[np.ndarray]
dx=None, # type: Optional[np.ndarray]
dy=None # type: Optional[np.ndarray]
):
# type: (...) -> None
self.x, self.y, self.dx, self.dy = x, y, dx, dy
self.dxl = None
self.filename = None
self.qmin = x.min() if x is not None else np.NaN
self.qmax = x.max() if x is not None else np.NaN
# TODO: why is 1D mask False and 2D mask True?
self.mask = (np.isnan(y) if y is not None
else np.zeros_like(x, 'b') if x is not None
else None)
self._xaxis, self._xunit = "x", ""
self._yaxis, self._yunit = "y", ""
[docs] def xaxis(self, label, unit):
# type: (str, str) -> None
"""
set the x axis label and unit
"""
self._xaxis = label
self._xunit = unit
[docs] def yaxis(self, label, unit):
# type: (str, str) -> None
"""
set the y axis label and unit
"""
self._yaxis = label
self._yunit = unit
[docs]class SesansData(Data1D):
"""
SESANS data object.
This is just :class:`Data1D` with a wavelength parameter.
*x* is spin echo length and *y* is polarization (P/P0).
"""
isSesans = True
def __init__(self, **kw):
Data1D.__init__(self, **kw)
self.lam = None # type: Optional[np.ndarray]
[docs]class Data2D(object):
"""
2D data object.
Note that this definition matches the attributes from sasview. Some
refactoring to allow consistent naming conventions between 1D, 2D and
SESANS data would be helpful.
**Attributes**
*qx_data*, *dqx_data*: $q_x$ matrix and gaussian resolution
*qy_data*, *dqy_data*: $q_y$ matrix and gaussian resolution
*data*, *err_data*: $I(q)$ matrix and measurement uncertainty
*mask*: values to exclude from plotting/analysis
*qmin*, *qmax*: range of $q$ values in *x*
*filename*: label for the data line
*_xaxis*, *_xunit*: label and units for the *x* axis
*_yaxis*, *_yunit*: label and units for the *y* axis
*_zaxis*, *_zunit*: label and units for the *y* axis
*Q_unit*, *I_unit*: units for Q and intensity
*x_bins*, *y_bins*: grid steps in *x* and *y* directions
"""
def __init__(self,
x=None, # type: Optional[np.ndarray]
y=None, # type: Optional[np.ndarray]
z=None, # type: Optional[np.ndarray]
dx=None, # type: Optional[np.ndarray]
dy=None, # type: Optional[np.ndarray]
dz=None # type: Optional[np.ndarray]
):
# type: (...) -> None
self.qx_data, self.dqx_data = x, dx
self.qy_data, self.dqy_data = y, dy
self.data, self.err_data = z, dz
self.mask = (np.isnan(z) if z is not None
else np.zeros_like(x, dtype='bool') if x is not None
else None)
self.q_data = np.sqrt(x**2 + y**2)
self.qmin = 1e-16
self.qmax = np.inf
self.detector = []
self.source = Source()
self.Q_unit = "1/A"
self.I_unit = "1/cm"
self.xaxis("Q_x", "1/A")
self.yaxis("Q_y", "1/A")
self.zaxis("Intensity", "1/cm")
self._xaxis, self._xunit = "x", ""
self._yaxis, self._yunit = "y", ""
self._zaxis, self._zunit = "z", ""
self.x_bins, self.y_bins = None, None
self.filename = None
[docs] def xaxis(self, label, unit):
# type: (str, str) -> None
"""
set the x axis label and unit
"""
self._xaxis = label
self._xunit = unit
[docs] def yaxis(self, label, unit):
# type: (str, str) -> None
"""
set the y axis label and unit
"""
self._yaxis = label
self._yunit = unit
[docs] def zaxis(self, label, unit):
# type: (str, str) -> None
"""
set the y axis label and unit
"""
self._zaxis = label
self._zunit = unit
[docs]class Vector(object):
"""
3-space vector of *x*, *y*, *z*
"""
def __init__(self, x=None, y=None, z=None):
# type: (float, float, Optional[float]) -> None
self.x, self.y, self.z = x, y, z
[docs]class Detector(object):
"""
Detector attributes.
"""
def __init__(self, pixel_size=(None, None), distance=None):
# type: (Tuple[float, float], float) -> None
self.pixel_size = Vector(*pixel_size)
self.distance = distance
[docs]class Source(object):
"""
Beam attributes.
"""
def __init__(self):
# type: () -> None
self.wavelength = np.NaN
self.wavelength_unit = "A"
[docs]class Sample(object):
"""
Sample attributes.
"""
def __init__(self):
# type: () -> None
pass
[docs]def empty_data1D(q, resolution=0.0, L=0., dL=0.):
# type: (np.ndarray, float) -> Data1D
r"""
Create empty 1D data using the given *q* as the x value.
rms *resolution* $\Delta q/q$ defaults to 0%. If wavelength *L* and rms
wavelength divergence *dL* are defined, then *resolution* defines
rms $\Delta \theta/\theta$ for the lowest *q*, with $\theta$ derived from
$q = 4\pi/\lambda \sin(\theta)$.
"""
#Iq = 100 * np.ones_like(q)
#dIq = np.sqrt(Iq)
Iq, dIq = None, None
q = np.asarray(q)
if L != 0 and resolution != 0:
theta = np.arcsin(q*L/(4*pi))
dtheta = theta[0]*resolution
## Solving Gaussian error propagation from
## Dq^2 = (dq/dL)^2 DL^2 + (dq/dtheta)^2 Dtheta^2
## gives
## (Dq/q)^2 = (DL/L)**2 + (Dtheta/tan(theta))**2
## Take the square root and multiply by q, giving
## Dq = (4*pi/L) * sqrt((sin(theta)*dL/L)**2 + (cos(theta)*dtheta)**2)
dq = (4*pi/L) * sqrt((sin(theta)*dL/L)**2 + (cos(theta)*dtheta)**2)
else:
dq = resolution * q
data = Data1D(q, Iq, dx=dq, dy=dIq)
data.filename = "fake data"
return data
[docs]def empty_data2D(qx, qy=None, resolution=0.0):
# type: (np.ndarray, Optional[np.ndarray], float) -> Data2D
"""
Create empty 2D data using the given mesh.
If *qy* is missing, create a square mesh with *qy=qx*.
*resolution* dq/q defaults to 5%.
"""
if qy is None:
qy = qx
qx, qy = np.asarray(qx), np.asarray(qy)
# 5% dQ/Q resolution
Qx, Qy = np.meshgrid(qx, qy)
Qx, Qy = Qx.flatten(), Qy.flatten()
Iq = 100 * np.ones_like(Qx) # type: np.ndarray
dIq = np.sqrt(Iq)
if resolution != 0:
# https://www.ncnr.nist.gov/staff/hammouda/distance_learning/chapter_15.pdf
# Should have an additional constant which depends on distances and
# radii of the aperture, pixel dimensions and wavelength spread
# Instead, assume radial dQ/Q is constant, and perpendicular matches
# radial (which instead it should be inverse).
Q = np.sqrt(Qx**2 + Qy**2)
dqx = resolution * Q
dqy = resolution * Q
else:
dqx = dqy = None
data = Data2D(x=Qx, y=Qy, z=Iq, dx=dqx, dy=dqy, dz=dIq)
data.x_bins = qx
data.y_bins = qy
data.filename = "fake data"
# pixel_size in mm, distance in m
detector = Detector(pixel_size=(5, 5), distance=4)
data.detector.append(detector)
data.source.wavelength = 5 # angstroms
data.source.wavelength_unit = "A"
return data
[docs]def plot_data(data, view='log', limits=None):
# type: (Data, str, Optional[Tuple[float, float]]) -> None
"""
Plot data loaded by the sasview loader.
*data* is a sasview data object, either 1D, 2D or SESANS.
*view* is log or linear.
*limits* sets the intensity limits on the plot; if None then the limits
are inferred from the data.
"""
# Note: kind of weird using the plot result functions to plot just the
# data, but they already handle the masking and graph markup already, so
# do not repeat.
if hasattr(data, 'isSesans') and data.isSesans:
_plot_result_sesans(data, None, None, use_data=True, limits=limits)
elif hasattr(data, 'qx_data') and not getattr(data, 'radial', False):
_plot_result2D(data, None, None, view, use_data=True, limits=limits)
else:
_plot_result1D(data, None, None, view, use_data=True, limits=limits)
[docs]def plot_theory(data, # type: Data
theory, # type: Optional[np.ndarray]
resid=None, # type: Optional[np.ndarray]
view='log', # type: str
use_data=True, # type: bool
limits=None, # type: Optional[np.ndarray]
Iq_calc=None # type: Optional[np.ndarray]
):
# type: (...) -> None
"""
Plot theory calculation.
*data* is needed to define the graph properties such as labels and
units, and to define the data mask.
*theory* is a matrix of the same shape as the data.
*view* is log or linear
*use_data* is True if the data should be plotted as well as the theory.
*limits* sets the intensity limits on the plot; if None then the limits
are inferred from the data.
*Iq_calc* is the raw theory values without resolution smearing
"""
if hasattr(data, 'isSesans') and data.isSesans:
_plot_result_sesans(data, theory, resid, use_data=True, limits=limits)
elif hasattr(data, 'qx_data') and not getattr(data, 'radial', False):
_plot_result2D(data, theory, resid, view, use_data, limits=limits)
else:
_plot_result1D(data, theory, resid, view, use_data,
limits=limits, Iq_calc=Iq_calc)
[docs]def protect(func):
# type: (Callable) -> Callable
"""
Decorator to wrap calls in an exception trapper which prints the
exception and continues. Keyboard interrupts are ignored.
"""
def wrapper(*args, **kw):
"""
Trap and print errors from function.
"""
try:
return func(*args, **kw)
except Exception:
traceback.print_exc()
return wrapper
@protect
def _plot_result1D(data, # type: Data1D
theory, # type: Optional[np.ndarray]
resid, # type: Optional[np.ndarray]
view, # type: str
use_data, # type: bool
limits=None, # type: Optional[Tuple[float, float]]
Iq_calc=None # type: Optional[np.ndarray]
):
# type: (...) -> None
"""
Plot the data and residuals for 1D data.
"""
import matplotlib.pyplot as plt # type: ignore
from numpy.ma import masked_array, masked # type: ignore
if getattr(data, 'radial', False):
data.x = data.q_data
data.y = data.data
use_data = use_data and data.y is not None
use_theory = theory is not None
use_resid = resid is not None
use_calc = use_theory and Iq_calc is not None
num_plots = (use_data or use_theory) + use_calc + use_resid
non_positive_x = (data.x <= 0.0).any()
scale = data.x**4 if view == 'q4' else 1.0
xscale = yscale = 'linear' if view == 'linear' else 'log'
if use_data or use_theory:
if num_plots > 1:
plt.subplot(1, num_plots, 1)
#print(vmin, vmax)
all_positive = True
some_present = False
if use_data:
mdata = masked_array(data.y, data.mask.copy())
mdata[~np.isfinite(mdata)] = masked
if view is 'log':
mdata[mdata <= 0] = masked
plt.errorbar(data.x, scale*mdata, yerr=data.dy, fmt='.')
all_positive = all_positive and (mdata > 0).all()
some_present = some_present or (mdata.count() > 0)
if use_theory:
# Note: masks merge, so any masked theory points will stay masked,
# and the data mask will be added to it.
#mtheory = masked_array(theory, data.mask.copy())
theory_x = data.x[data.mask == 0]
mtheory = masked_array(theory)
mtheory[~np.isfinite(mtheory)] = masked
if view is 'log':
mtheory[mtheory <= 0] = masked
plt.plot(theory_x, scale*mtheory, '-')
all_positive = all_positive and (mtheory > 0).all()
some_present = some_present or (mtheory.count() > 0)
if limits is not None:
plt.ylim(*limits)
xscale = ('linear' if not some_present or non_positive_x
else view if view is not None
else 'log')
yscale = ('linear'
if view == 'q4' or not some_present or not all_positive
else view if view is not None
else 'log')
plt.xscale(xscale)
plt.xlabel("$q$/A$^{-1}$")
plt.yscale(yscale)
plt.ylabel('$I(q)$')
title = ("data and model" if use_theory and use_data
else "data" if use_data
else "model")
plt.title(title)
if use_calc:
# Only have use_calc if have use_theory
plt.subplot(1, num_plots, 2)
qx, qy, Iqxy = Iq_calc
plt.pcolormesh(qx, qy[qy > 0], np.log10(Iqxy[qy > 0, :]))
plt.xlabel("$q_x$/A$^{-1}$")
plt.xlabel("$q_y$/A$^{-1}$")
plt.xscale('log')
plt.yscale('log')
#plt.axis('equal')
if use_resid:
theory_x = data.x[data.mask == 0]
mresid = masked_array(resid)
mresid[~np.isfinite(mresid)] = masked
some_present = (mresid.count() > 0)
if num_plots > 1:
plt.subplot(1, num_plots, use_calc + 2)
plt.plot(theory_x, mresid, '.')
plt.xlabel("$q$/A$^{-1}$")
plt.ylabel('residuals')
plt.title('(model - Iq)/dIq')
plt.xscale(xscale)
plt.yscale('linear')
@protect
def _plot_result_sesans(data, # type: SesansData
theory, # type: Optional[np.ndarray]
resid, # type: Optional[np.ndarray]
use_data, # type: bool
limits=None # type: Optional[Tuple[float, float]]
):
# type: (...) -> None
"""
Plot SESANS results.
"""
import matplotlib.pyplot as plt # type: ignore
use_data = use_data and data.y is not None
use_theory = theory is not None
use_resid = resid is not None
num_plots = (use_data or use_theory) + use_resid
if use_data or use_theory:
is_tof = data.lam is not None and (data.lam != data.lam[0]).any()
if num_plots > 1:
plt.subplot(1, num_plots, 1)
if use_data:
if is_tof:
plt.errorbar(data.x, np.log(data.y)/(data.lam*data.lam),
yerr=data.dy/data.y/(data.lam*data.lam))
else:
plt.errorbar(data.x, data.y, yerr=data.dy)
if theory is not None:
if is_tof:
plt.plot(data.x, np.log(theory)/(data.lam*data.lam), '-')
else:
plt.plot(data.x, theory, '-')
if limits is not None:
plt.ylim(*limits)
plt.xlabel('spin echo length ({})'.format(data._xunit))
if is_tof:
plt.ylabel(r'(Log (P/P$_0$))/$\lambda^2$')
else:
plt.ylabel('polarization (P/P0)')
if resid is not None:
if num_plots > 1:
plt.subplot(1, num_plots, (use_data or use_theory) + 1)
plt.plot(data.x, resid, 'x')
plt.xlabel('spin echo length ({})'.format(data._xunit))
plt.ylabel('residuals (P/P0)')
@protect
def _plot_result2D(data, # type: Data2D
theory, # type: Optional[np.ndarray]
resid, # type: Optional[np.ndarray]
view, # type: str
use_data, # type: bool
limits=None # type: Optional[Tuple[float, float]]
):
# type: (...) -> None
"""
Plot the data and residuals for 2D data.
"""
import matplotlib.pyplot as plt # type: ignore
use_data = use_data and data.data is not None
use_theory = theory is not None
use_resid = resid is not None
num_plots = use_data + use_theory + use_resid
# Put theory and data on a common colormap scale
vmin, vmax = np.inf, -np.inf
target = None # type: Optional[np.ndarray]
if use_data:
target = data.data[~data.mask]
datamin = target[target > 0].min() if view == 'log' else target.min()
datamax = target.max()
vmin = min(vmin, datamin)
vmax = max(vmax, datamax)
if use_theory:
theorymin = theory[theory > 0].min() if view == 'log' else theory.min()
theorymax = theory.max()
vmin = min(vmin, theorymin)
vmax = max(vmax, theorymax)
# Override data limits from the caller
if limits is not None:
vmin, vmax = limits
# Plot data
if use_data:
if num_plots > 1:
plt.subplot(1, num_plots, 1)
_plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax)
plt.title('data')
h = plt.colorbar()
h.set_label('$I(q)$')
# plot theory
if use_theory:
if num_plots > 1:
plt.subplot(1, num_plots, use_data+1)
_plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax)
plt.title('theory')
h = plt.colorbar()
h.set_label(r'$\log_{10}I(q)$' if view == 'log'
else r'$q^4 I(q)$' if view == 'q4'
else '$I(q)$')
# plot resid
if use_resid:
if num_plots > 1:
plt.subplot(1, num_plots, use_data+use_theory+1)
_plot_2d_signal(data, resid, view='linear')
plt.title('residuals')
h = plt.colorbar()
h.set_label(r'$\Delta I(q)$')
@protect
def _plot_2d_signal(data, # type: Data2D
signal, # type: np.ndarray
vmin=None, # type: Optional[float]
vmax=None, # type: Optional[float]
view='log' # type: str
):
# type: (...) -> Tuple[float, float]
"""
Plot the target value for the data. This could be the data itself,
the theory calculation, or the residuals.
*scale* can be 'log' for log scale data, or 'linear'.
"""
import matplotlib.pyplot as plt # type: ignore
from numpy.ma import masked_array # type: ignore
image = np.zeros_like(data.qx_data)
image[~data.mask] = signal
valid = np.isfinite(image)
if view == 'log':
valid[valid] = (image[valid] > 0)
if vmin is None:
vmin = image[valid & ~data.mask].min()
if vmax is None:
vmax = image[valid & ~data.mask].max()
image[valid] = np.log10(image[valid])
elif view == 'q4':
image[valid] *= (data.qx_data[valid]**2+data.qy_data[valid]**2)**2
if vmin is None:
vmin = image[valid & ~data.mask].min()
if vmax is None:
vmax = image[valid & ~data.mask].max()
else:
if vmin is None:
vmin = image[valid & ~data.mask].min()
if vmax is None:
vmax = image[valid & ~data.mask].max()
image[~valid | data.mask] = 0
#plottable = Iq
plottable = masked_array(image, ~valid | data.mask)
# Divide range by 10 to convert from angstroms to nanometers
xmin, xmax = min(data.qx_data), max(data.qx_data)
ymin, ymax = min(data.qy_data), max(data.qy_data)
if view == 'log':
vmin_scaled, vmax_scaled = np.log10(vmin), np.log10(vmax)
else:
vmin_scaled, vmax_scaled = vmin, vmax
#nx, ny = len(data.x_bins), len(data.y_bins)
x_bins, y_bins, image = _build_matrix(data, plottable)
plt.imshow(image,
interpolation='nearest', aspect=1, origin='lower',
extent=[xmin, xmax, ymin, ymax],
vmin=vmin_scaled, vmax=vmax_scaled)
plt.xlabel("$q_x$/A$^{-1}$")
plt.ylabel("$q_y$/A$^{-1}$")
return vmin, vmax
# === The following is modified from sas.sasgui.plottools.PlotPanel
def _build_matrix(self, plottable):
"""
Build a matrix for 2d plot from a vector
Returns a matrix (image) with ~ square binning
Requirement: need 1d array formats of
self.data, self.qx_data, and self.qy_data
where each one corresponds to z, x, or y axis values
"""
# No qx or qy given in a vector format
if self.qx_data is None or self.qy_data is None \
or self.qx_data.ndim != 1 or self.qy_data.ndim != 1:
return self.x_bins, self.y_bins, plottable
# maximum # of loops to fillup_pixels
# otherwise, loop could never stop depending on data
max_loop = 1
# get the x and y_bin arrays.
x_bins, y_bins = _get_bins(self)
# set zero to None
#Note: Can not use scipy.interpolate.Rbf:
# 'cause too many data points (>10000)<=JHC.
# 1d array to use for weighting the data point averaging
#when they fall into a same bin.
weights_data = np.ones([self.data.size])
# get histogram of ones w/len(data); this will provide
#the weights of data on each bins
weights, xedges, yedges = np.histogram2d(x=self.qy_data,
y=self.qx_data,
bins=[y_bins, x_bins],
weights=weights_data)
# get histogram of data, all points into a bin in a way of summing
image, xedges, yedges = np.histogram2d(x=self.qy_data,
y=self.qx_data,
bins=[y_bins, x_bins],
weights=plottable)
# Now, normalize the image by weights only for weights>1:
# If weight == 1, there is only one data point in the bin so
# that no normalization is required.
image[weights > 1] = image[weights > 1] / weights[weights > 1]
# Set image bins w/o a data point (weight==0) as None (was set to zero
# by histogram2d.)
image[weights == 0] = None
# Fill empty bins with 8 nearest neighbors only when at least
#one None point exists
loop = 0
# do while loop until all vacant bins are filled up up
#to loop = max_loop
while (weights == 0).any():
if loop >= max_loop: # this protects never-ending loop
break
image = _fillup_pixels(image=image, weights=weights)
loop += 1
return x_bins, y_bins, image
def _get_bins(self):
"""
get bins
set x_bins and y_bins into self, 1d arrays of the index with
~ square binning
Requirement: need 1d array formats of
self.qx_data, and self.qy_data
where each one corresponds to x, or y axis values
"""
# find max and min values of qx and qy
xmax = self.qx_data.max()
xmin = self.qx_data.min()
ymax = self.qy_data.max()
ymin = self.qy_data.min()
# calculate the range of qx and qy: this way, it is a little
# more independent
x_size = xmax - xmin
y_size = ymax - ymin
# estimate the # of pixels on each axes
npix_y = int(np.floor(np.sqrt(len(self.qy_data))))
npix_x = int(np.floor(len(self.qy_data) / npix_y))
# bin size: x- & y-directions
xstep = x_size / (npix_x - 1)
ystep = y_size / (npix_y - 1)
# max and min taking account of the bin sizes
xmax = xmax + xstep / 2.0
xmin = xmin - xstep / 2.0
ymax = ymax + ystep / 2.0
ymin = ymin - ystep / 2.0
# store x and y bin centers in q space
x_bins = np.linspace(xmin, xmax, npix_x)
y_bins = np.linspace(ymin, ymax, npix_y)
return x_bins, y_bins
def _fillup_pixels(image=None, weights=None):
"""
Fill z values of the empty cells of 2d image matrix
with the average over up-to next nearest neighbor points
:param image: (2d matrix with some zi = None)
:return: image (2d array )
:TODO: Find better way to do for-loop below
"""
# No image matrix given
if image is None or np.ndim(image) != 2 \
or np.isfinite(image).all() \
or weights is None:
return image
# Get bin size in y and x directions
len_y = len(image)
len_x = len(image[1])
temp_image = np.zeros([len_y, len_x])
weit = np.zeros([len_y, len_x])
# do for-loop for all pixels
for n_y in range(len(image)):
for n_x in range(len(image[1])):
# find only null pixels
if weights[n_y][n_x] > 0 or np.isfinite(image[n_y][n_x]):
continue
else:
# find 4 nearest neighbors
# check where or not it is at the corner
if n_y != 0 and np.isfinite(image[n_y - 1][n_x]):
temp_image[n_y][n_x] += image[n_y - 1][n_x]
weit[n_y][n_x] += 1
if n_x != 0 and np.isfinite(image[n_y][n_x - 1]):
temp_image[n_y][n_x] += image[n_y][n_x - 1]
weit[n_y][n_x] += 1
if n_y != len_y - 1 and np.isfinite(image[n_y + 1][n_x]):
temp_image[n_y][n_x] += image[n_y + 1][n_x]
weit[n_y][n_x] += 1
if n_x != len_x - 1 and np.isfinite(image[n_y][n_x + 1]):
temp_image[n_y][n_x] += image[n_y][n_x + 1]
weit[n_y][n_x] += 1
# go 4 next nearest neighbors when no non-zero
# neighbor exists
if n_y != 0 and n_x != 0 and \
np.isfinite(image[n_y - 1][n_x - 1]):
temp_image[n_y][n_x] += image[n_y - 1][n_x - 1]
weit[n_y][n_x] += 1
if n_y != len_y - 1 and n_x != 0 and \
np.isfinite(image[n_y + 1][n_x - 1]):
temp_image[n_y][n_x] += image[n_y + 1][n_x - 1]
weit[n_y][n_x] += 1
if n_y != len_y and n_x != len_x - 1 and \
np.isfinite(image[n_y - 1][n_x + 1]):
temp_image[n_y][n_x] += image[n_y - 1][n_x + 1]
weit[n_y][n_x] += 1
if n_y != len_y - 1 and n_x != len_x - 1 and \
np.isfinite(image[n_y + 1][n_x + 1]):
temp_image[n_y][n_x] += image[n_y + 1][n_x + 1]
weit[n_y][n_x] += 1
# get it normalized
ind = (weit > 0)
image[ind] = temp_image[ind] / weit[ind]
return image
[docs]def demo():
# type: () -> None
"""
Load and plot a SAS dataset.
"""
data = load_data('DEC07086.DAT')
set_beam_stop(data, 0.004)
plot_data(data)
import matplotlib.pyplot as plt # type: ignore
plt.show()
if __name__ == "__main__":
demo()