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critical_point_interpolate.py
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critical_point_interpolate.py
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"""
This module interpolates the n and k data of an alloy based on the known values at certain specific compositions.
The way it works is by interpolating in a smart way certain critical points (usually the Adachi critical points) and
then filling the gaps in between.
"""
import numpy as np
import os
from configparser import ConfigParser
def interpolate_critical_points(critical_points, target):
fractions_available = np.array(sorted(critical_points.keys()))
target_index = fractions_available.searchsorted(target)
lower = fractions_available[target_index - 1]
upper = fractions_available[target_index]
fraction_along = (target - lower) / (upper - lower)
lower_points = critical_points[lower]
upper_points = critical_points[upper]
result_critical_points = {k: lower_points[k] + fraction_along * (upper_points[k] - lower_points[k]) for k in
lower_points.keys()}
return result_critical_points, (lower, upper), fraction_along
def load_data_from_directory(directory):
result_config = ConfigParser()
result_config.read(os.path.join(directory, "critical_points.txt"))
sections = result_config.sections()
fractions_config_file = [float(s) for s in sections]
critical_points = {}
fraction_dict = {}
for f, s in zip(fractions_config_file, sections):
options = sorted(result_config.options(s))
critical_points[f] = {o: np.array([float(f) for f in result_config.get(s, o).split()]) for o in (options) if
o != "file"}
datapath = os.path.join(directory, result_config.get(s, "file"))
fraction_dict[f] = np.loadtxt(datapath, unpack=True)
return fraction_dict, critical_points
def split_data_at_points(data, points):
split_at = data[0].searchsorted(points[:, 0])
split_data = (np.split(data, split_at, axis=1))
return split_data
def transform(data, from_critical_points, to_critical_points):
# sort the critical points according to "from" side order
key_order, ordered_from_critical_points = zip(*sorted(from_critical_points.items(), key=lambda item: item[1][0]))
moo, ordered_to_critical_points = zip(
*sorted(to_critical_points.items(), key=lambda item: key_order.index(item[0])))
ordered_to_critical_points = np.array(ordered_to_critical_points)
ordered_from_critical_points = np.array(ordered_from_critical_points)
split_data = split_data_at_points(data, ordered_from_critical_points)
full_interval_bounds = np.concatenate(([0], ordered_from_critical_points[:, 0], [1e10]))
full_interval_bounds_target = np.concatenate(([0], ordered_to_critical_points[:, 0], [1e10]))
full_interval_bounds_y = np.concatenate(([0], ordered_from_critical_points[:, 1], [0]))
full_interval_bounds_target_y = np.concatenate(([0], ordered_to_critical_points[:, 1], [0]))
result_x, result_y = [], []
for interval, data_subset in enumerate(split_data):
interval_bounds = full_interval_bounds[interval], full_interval_bounds[interval + 1]
interval_bounds_y = full_interval_bounds_y[interval], full_interval_bounds_y[interval + 1]
interval_bounds_target = full_interval_bounds_target[interval], full_interval_bounds_target[interval + 1]
interval_bounds_target_y = full_interval_bounds_target_y[interval], full_interval_bounds_target_y[interval + 1]
if len(data_subset) == 0:
continue
x, y, *_ = data_subset # heh heh heh, *_
fraction_along = (x - interval_bounds[0]) / (interval_bounds[1] - interval_bounds[0])
fraction_up = (y - interval_bounds_y[0]) / (interval_bounds_y[1] - interval_bounds_y[0])
transformed_x = interval_bounds_target[0] + fraction_along * (
interval_bounds_target[1] - interval_bounds_target[0])
transformed_y = interval_bounds_target_y[0] + fraction_up * (
interval_bounds_target_y[1] - interval_bounds_target_y[0])
result_x.append(transformed_x)
result_y.append(transformed_y)
return np.array((np.concatenate(result_x), np.concatenate(result_y)))
def critical_point_interpolate(data, critical_points, target_fraction, grid):
result_critical_points, (lower_fract, higher_fract), fraction_along = \
interpolate_critical_points(critical_points, target_fraction)
interpolate_from_lower = transform(
data[lower_fract],
from_critical_points=critical_points[lower_fract],
to_critical_points=result_critical_points)
interpolate_from_higher = transform(
data[higher_fract],
from_critical_points=critical_points[higher_fract],
to_critical_points=result_critical_points)
newgrid_lower = np.interp(grid, interpolate_from_lower[0], interpolate_from_lower[1])
newgrid_higher = np.interp(grid, interpolate_from_higher[0], interpolate_from_higher[1])
return grid, (newgrid_higher * fraction_along + newgrid_lower * (1 - fraction_along)), result_critical_points
if __name__ == "__main__":
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import animation
# Animate only works with Tk, so we tell matpotlib to use it
matplotlib.use("TkAgg")
data, critical_points = load_data_from_directory(os.path.join(os.path.split(__file__)[0], 'Data', 'AlGaAs nk'))
fig = plt.figure()
plt.yscale("log")
nm = np.linspace(200, 1000, 1000)
for k in data.keys():
# print (data[k][0]*1e9, data[k][1])
plt.plot(data[k][0] * 1e9, data[k][1], label=str(k), color="grey")
resultx, resulty, crit = critical_point_interpolate(data, critical_points, 0, nm * 1e-9)
lines, = plt.plot(resultx * 1e9, resulty, label="result", color="black", linewidth=2)
plt.xlim(0, 1000)
crit = np.array(list(crit.values())).transpose()
print(crit, "lr")
markers, = plt.plot(crit[0] * 1e9, crit[1], "+", label="result", color="red", linewidth=2, markersize=10)
def animate(i):
f = i / 100
resultx, resulty, crit = critical_point_interpolate(data, critical_points, f, nm * 1e-9)
# crit = crit.transpose()
lines.set_ydata(resulty)
# markers.set_ydata(crit[1])
# markers.set_xdata(crit[0]*1e9)
fig.canvas.get_tk_widget().update() # process events
return lines # , markers
anim = animation.FuncAnimation(fig, animate, init_func=None,
frames=100, interval=2, blit=False)
# anim.save('mpp.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
w = animation.FFMpegFileWriter(fps=10)
w.fps = 10
# anim.save('ellipse.mp4', writer=w, fps=10, bitrate=100, extra_args=['-vcodec', 'libx264'])
plt.show()
#
# print (lines)
# for f in np.linspace(0.01,1,100):
# resultx, resulty, crit=critical_point_interpolate(data, critical_points, f,nm)
# crit = crit.transpose()
# lines.set_ydata(resulty)
# markers.set_ydata(crit[1])
# markers.set_xdata(crit[0]*1e9)
# fig.canvas.get_tk_widget().update() # process events
#
# draw()
# for f in np.linspace(1,0.01,100):
# resultx, resulty, crit=critical_point_interpolate(data, critical_points, f,nm)
# crit = crit.transpose()
#
# lines.set_ydata(resulty)
# markers.set_ydata(crit[1])
# markers.set_xdata(crit[0]*1e9)
#
# fig.canvas.get_tk_widget().update() # process events
#
# draw()
#
# ioff()
# show()
# # legend()
#