def import_labview_data(path, base_filename):
try:
# data
path_and_filename = path + base_filename + ".csv"
data = numpy.loadtxt(path_and_filename, dtype = "float", delimiter = ",")
data = data.T
# time axis in fringes
path_and_filename = path + base_filename + "_t1.csv"
t1_axis = numpy.loadtxt(path_and_filename, dtype = "float", delimiter = ",")
# frequency axis
path_and_filename = path + base_filename + "_w3.csv"
w3_axis = numpy.loadtxt(path_and_filename, dtype = "float", delimiter = ",")
# phase
path_and_filename = path + base_filename + "_phase.txt"
f = open(path_and_filename)
for line in f:
temp = line
f.close()
if temp == "NaN":
D.printWarning("Phase is Not-a-Number, will be set to 0 ", inspect.stack())
phase = 0
else:
phase = float(temp)
# last pump
path_and_filename = path + base_filename + "_lastpump.txt"
f = open(path_and_filename)
for line in f:
lastpump = line
f.close()
# determine number of fringes
n_fringes = int((len(t1_axis)+1)/2)
n_pixels = len(w3_axis)
# convert NaN to zeros
data = numpy.nan_to_num(data)
# labview measures 4000-N to 4000+N, we want the data split into 4000-N to 4000 (non-rephasing) and 4000 to 4000+N (rephasing)
R = data[n_fringes-1:, :]
NR = numpy.flipud(data[:n_fringes, :])
# fringe 4000 is included twice.
# for the FFT, we don't want 4000 to be zero. The axes run from 0 to N
# also: calculate the axis in fs
t1fr_axis = numpy.arange(n_fringes)
t1fs_axis = numpy.arange(n_fringes) * C.hene_fringe_fs
# return everything
return R, NR, t1fs_axis, t1fr_axis, w3_axis, phase, lastpump, n_fringes, n_pixels
except IOError:
D.printError("Unable to LabView data from file " + path + "/" + base_filename, inspect.stack())
raise
return 0, 0, 0
def import_binned_data(path_and_filename, n_pixels, diagram):
try:
data = numpy.fromfile(path_and_filename)
fringes = [data[-2], data[-1]]
b_axis = numpy.arange(fringes[0], fringes[1] + 1)
n_fringes = len(b_axis)
# without the two fringes at the end, the number of elements should be equal to (2*n_pixels + 1), for each chopper state the pixels and the count
if ((len(data)-2)/n_fringes) == (2 * n_pixels + 2):
b_count = [0]*2
b_count[0] = data[-2 - 2*n_fringes:-2 - n_fringes]
b_count[1] = data[-2 - n_fringes:-2]
data = data[:2*n_pixels*n_fringes]
# rearrange the data
b = numpy.zeros((4, n_fringes, n_pixels), dtype = "cfloat")
for i in range(2): # the two chopper states
for j in range(n_fringes):
for p in range(n_pixels):
b[i+diagram*2,j,p] = data[i * n_fringes * n_pixels + j * n_pixels + p]
return b, b_count, b_axis
else:
D.printWarning("The file does not contain binned data: " + path_and_filename, inspect.stack())
return 1, 1, 1
except IOError:
D.printError("Unable to import binned data from file " + path_and_filename, inspect.stack())
raise
return 0, 0, 0
def contourplot(data, x_axis, y_axis, x_range = [0, 0], y_range = [0, -1], zlimit = -1, contours = 12, filled = True, black_contour = True, x_label = "", y_label = "", title = "", diagonal_line = True, new_figure = True, invert_colors = False, flag_aspect_ratio = True, linewidth = 1):
"""
croc.Plotting.contourplot
INPUT:
- data (2d ndarray): the data, in the form of (y, x)
- x_axis, y_axis (ndarray): the axes. Should have the same length as the corresponding axes in data
- x_range, y_range (array with 2 elements): the range to be plotted.
Possible cases:
- [min, max]: plot range min to max
- [0, 0]: plot the whole range
- [0, -1]: use the range from the other axis. If both have this, it will plot both axes complete. (ie. it is identical to both having [0,0])
- zlimit (number): the z-range that will be used
Possible cases:
zlimit = 0, show all, not don't care about centering around zero
zlimit = -1, show all, centered around zero
zlimit = all else, use that, centered around zero
zlimit = [a,b], plot from a to b
- contours (number): number of contours to be used
- filled (BOOL): use colors
- black_contour (BOOL): use the black contour lines
- x_label, y_label (string): the labels for the x and y axes
- title (string): the title of the plot
- diagonal_line (BOOL): plot a diagonal line
- new_figure (BOOL): will make a new figure. To make subplots etc, set it to False
- invert_colors (BOOL, False): will make the red blue and the other way around
- flag_aspect_ratio (BOOL, True): the aspect ratio will be corresponding to the range plotted
"""
# CHECKS
if invert_colors:
data = -data
# check if the lengths of the axes correspond to the shape of the data
y, x = numpy.shape(data)
try:
if len(x_axis) != x or len(y_axis) != y:
# oops, the shape of the data does not correspond with the axes.
# see if they are switched
if len(x_axis) == y and len(y_axis) == x:
D.printWarning("The shape of the data seems to be flipped. The data will be transposed.", inspect.stack())
data = data.T
else:
D.printError("The shape of the data does not correspond with the axes.", inspect.stack())
return 0
except TypeError:
D.printError("The x or y axis seems to have no length. It should be an array, not an integer.", inspect.stack())
print("x-axis:", x_axis)
print("y-axis:", y_axis)
return 0
# determine the range to be plotted
x_min, x_max, y_min, y_max = find_axes(x_axis, y_axis, x_range, y_range)
# make the contours
# first find the area to be plotted
# not the most elegant way I guess
try:
y_min_i = numpy.where(y_axis < y_min)[0][-1]
except:
y_min_i = 0
try:
y_max_i = numpy.where(y_axis > y_max)[0][0] + 1
except:
y_max_i = len(y_axis)
try:
x_min_i = numpy.where(x_axis < x_min)[0][-1]
except:
x_min_i = 0
try:
x_max_i = numpy.where(x_axis > x_max)[0][0] + 1
except:
x_max_i = len(x_axis)
# print(x_axis[x_max_i], x_max)
# truncate the data, this speeds up the plotting
data = data[y_min_i:y_max_i,x_min_i:x_max_i]
x_axis = x_axis[x_min_i:x_max_i]
y_axis = y_axis[y_min_i:y_max_i]
# now make the actual contours
#V = make_contours_2d(data[y_min_i:y_max_i, x_min_i:x_max_i], zlimit, contours)
V = make_contours_2d(data, zlimit, contours)
# print some extra stuff if the debug flag is set
if debug_flag:
print("Range to be plotted (x_min, x_max, y_min, y_max):")
print(x_min, x_max, y_min, y_max)
print("Indices of plotted range (x_min_i, x_max_i, y_min_i, y_max_i):")
print(x_min_i, x_max_i, y_min_i, y_max_i)
if zlimit == -1:
print("zlimit: " + str(V[-1]))
try:
# make the actual figure
if new_figure:
plt.figure()
if filled:
plt.contourf(x_axis, y_axis, data, V, cmap = my_cmap)
if debug_flag:
plt.colorbar()
if black_contour:
if filled:
plt.contour(x_axis, y_axis, data, V, linewidths = linewidth, linestyles = "solid", colors = "k")
else:
# this will make dashed lines for negative values
plt.contour(x_axis, y_axis, data, V, colors = "k", linewidths = linewidth)
if flag_aspect_ratio and new_figure:
# setting the aspect ration will break the subplots
plt.axes().set_aspect("equal")
# the diagonal line
if diagonal_line:
# plt.plot([0, 10000], [0, 10000], "k", linewidth = linewidth)
plt.plot([x_axis[0]-100,x_axis[0]+100], [x_axis[0]-100,x_axis[0]+100], "k", linewidth = linewidth)
# we only want to see a certain part of the spectrum
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
# add some text
if x_label != "" and x_label != "no_label":
plt.xlabel(x_label)
if y_label != "" and y_label != "no_label":
plt.ylabel(y_label)
if title != "":
plt.title(title)
# show it!
if new_figure:
plt.show()
except:
if D.FlagRunningOn == "server":
D.printError("Is the server correctly configured to show plots?", inspect.stack())
else:
raise
