Plot peaks in 3D space. 2 axies for spacial dimensions and one for Q space.
"""
#3D plot import
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
import matplotlib.pyplot as plt
import numpy as np
from data_loading.data_grid_TiNiSn import DataGrid, DataGrid_TiNiSn_500C, DataGrid_TiNiSn_600C
data_dir = "/home/sasha/Desktop/iterative_curve_fitting_save_test/"
regex = """params_(?P<num>.*?).csv"""
peakGrid = DataGrid(data_dir, regex)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for loc in peakGrid.data.keys():
x, y = peakGrid.coord(loc)
for i, peak in enumerate(peakGrid.data_at_loc(loc)[:, 1]):
if peakGrid.data_at_loc(loc)[i, 0] > 10:
ax.scatter([x], [y], [peak], marker='o', color='red')
#xs = [x for p in peaks]
#ys = [y for p in peaks]
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Q')
import numpy as np
import math
import statistics
"""
######################
Plotting found peaks on the diffraction patterns
######################
"""
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakBBAGrid = DataGrid(data_dir, regex)
data_dir = "/home/sasha/Desktop/peakTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkg_curveParams.csv"""
curveBBAGrid = DataGrid(data_dir, regex)
draw_peaks = False
draw_curves = True
draw_min_peaks = False
#load Min block peak data
peakMinGrid = DataGrid(data_dir, regex)
data_dir = "/home/sasha/Desktop/MinBlockCurveParams/"
for loc in range(1, dataGrid.size + 1):
file = data_dir + str(loc) + ".txt"
#peaks = eval(open(file).read())
from data_loading.data_grid_TiNiSn import DataGrid, DataGrid_TiNiSn_500C, DataGrid_TiNiSn_600C
import matplotlib.pyplot as plt
import numpy as np
from utils.utils import dict_to_csv
from scipy.signal import find_peaks
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
#data_dir = "/home/sasha/Desktop/peakTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakGrid = DataGrid(data_dir, regex)
save_path = "/home/sasha/Desktop/python/peak_error/peak_errors.csv"
def selectPeak(event):
"""
print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %
('double' if event.dblclick else 'single', event.button,
event.x, event.y, event.xdata, event.ydata))
"""
global cur
cur = nearest_index(event.xdata)
def key_press(event):
################################
Perform clustering using peak based dimension reduction (DBSCAN), then
PCA reduction, and then L2 based agglomerative clustering.
################################
"""
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
#######################################################
data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakGrid = DataGrid(data_dir, regex)
#TODO: BETTER WAY OF DOING THIS
# WITHOUT EXCESSIVE LOADING
peakMode = 3 #2,3
# 1 - minblock peak fitting
# 2 - BBA peak fitting
# 3 - BBA curve fitting
#move values to right column when curve params used
#easier that duplicating code below
if peakMode == 3:
#Reload peak grid with curve data
#NOTE: NOT EFFICIENT
data_dir = "/home/sasha/Desktop/peakTest/"
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
import matplotlib.pyplot as plt
import colorsys
import numpy as np
from data_loading.data_grid_TiNiSn import DataGrid, DataGrid_TiNiSn_500C, DataGrid_TiNiSn_600C
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/iterative_curve_fitting_save_test/"
regex = """params_(?P<num>.*?).csv"""
peakGrid = DataGrid(data_dir, regex)
used_points = set() #dictionary of used points
total_peaks = 0
for loc in peakGrid.data.keys():
total_peaks += len(peakGrid.data_at_loc(loc))
"""
Get the adjacent peaks that "connect" to a given peak
"""
def get_adjacent_points(x, y, Q_i, is_vert):
def dir(dx, dy):
if not peakGrid.in_grid(x + dx, y + dy):
return None
Q = peakGrid.data_at(x, y)[Q_i, 1]
parser = argparse.ArgumentParser(description='Run Peak Clustering')
parser.add_argument('-d',
'--delta',
type=float,
default=.1,
help='peak shift allowance')
args = parser.parse_args()
#folder with data files
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/saveTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakGrid = DataGrid(data_dir, regex)
# dataGrid.data[0][1:,1] is peak locations
# dataGrid.data[0][1:,3] is peak intensity
##################################
# LOAD PEAK DATA
'''
k1 = 97
k2 = 134
X = dataGrid.data[k1][:,0]
Y = dataGrid.data[k1][:,1]
plt.plot(X,Y)
################################
Perform clustering using peak based dimension reduction and then L1 similarity
in the agglomerative clustering algorithm.
################################
"""
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakGrid = DataGrid(data_dir,regex)
"""
Create a list of peaks in the form [x,y,p]
"""
SCALE = 100
def to_point(x,y,p):
return [(x-1)/15.,(y-1)/15.,SCALE*float(p)/5]
peaks = []
for k in peakGrid.data.keys():
x,y = peakGrid.coord(k)
[peaks.append(to_point(x,y,p)) for p in peakGrid.data_at_loc(k)[:,1]]
"""
import imageio
import os
"""
######################
Plotting found peaks on the diffraction patterns
######################
"""
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
#data_dir = "/home/sasha/Desktop/peakTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakGrid = DataGrid(data_dir, regex)
data_dir = "/home/sasha/Desktop/peakData_temp/"
for loc in range(1, dataGrid.size + 1):
file = data_dir + str(loc) + ".txt"
try:
peaks = eval(open(file).read())
except:
print("Generating " + str(loc))
X = dataGrid.data_at_loc(loc)[:, 0]
Y = dataGrid.data_at_loc(loc)[:, 1]
#curve_params = fit_curves_to_data(X,Y)
peaks = get_peak_indices(X, Y)
open(file, 'w+').write(str(peaks))
peakGrid.data[loc] = peaks
################################
"""
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
#data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
#data_dir = "/home/sasha/Desktop/peakTest/"
data_dir = "/home/sasha/Desktop/peakParamsOnMinBlocks/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
#regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkg_curveParams.csv"""
peakGrid = DataGrid(data_dir,regex)
ShowBBA = True
isCurveParams = False
#move values to right column when curve params used
#easier that duplicating code below
if isCurveParams:
for loc in range(1,dataGrid.size+1):
peakGrid.data[loc][:,1] = peakGrid.data[loc][:,2]
if not ShowBBA:
data_dir = "/home/sasha/Desktop/peakData_temp/"
for loc in range(1,dataGrid.size+1):
Script for plotting spectra at several data values to
visually compare differences.
'''
from data_loading.data_grid_TiNiSn import DataGrid, DataGrid_TiNiSn_500C, DataGrid_TiNiSn_600C
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import math
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/iterative_curve_fitting_save_test/"
regex = """params_(?P<num>.*?).csv"""
peakGrid = DataGrid(data_dir, regex)
# grid locations to plot
locations = [152, 151, 150, 149, 148, 147, 137, 136, 123]
#how much to shift each grid location vertically
#(makes it easier to see peaks)
#shifts = [0,100,200,300,400]
shifts = [100 * i for i in range(len(locations))]
colors = cm.get_cmap("viridis")
for i, k in enumerate(locations):
y = dataGrid.data[k][:, 1]
if len(shifts) == len(locations):
y = y + shifts[i]
import colorsys
from sklearn.cluster import AgglomerativeClustering
from sklearn.decomposition import PCA
import numpy as np
from data_loading.data_grid_TiNiSn import DataGrid
"""
Load Data and Peak Data
"""
data_dir = "/home/sasha/Desktop/iterative_curve_fitting_save_test/"
data_dir = "/home/sasha/Desktop/TiNiSn_500C-20190604T152446Z-001/TiNiSn_500C/params/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSub_1D_params.csv"""
peakGrid = DataGrid(data_dir, regex)
used_points = set() #dictionary of used points
total_peaks = 0
for loc in peakGrid.data.keys():
total_peaks += len(peakGrid.data_at_loc(loc))
"""
Get the adjacent peaks that "connect" to a given peak
"""
def get_adjacent_points(x, y, Q_i):
def dir(dx, dy):
if not peakGrid.in_grid(x + dx, y + dy):
return None
from data_loading.data_grid_TiNiSn import DataGrid, DataGrid_TiNiSn_500C, DataGrid_TiNiSn_600C
import matplotlib.pyplot as plt
import numpy as np
from utils.utils import csv_to_dict
from scipy.signal import find_peaks
"""
Load Data and Peak Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/TiNiSn_500C_PeakData_0.5/"
#data_dir = "/home/sasha/Desktop/peakTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkgdSu_peakParams.csv"""
peakGrid = DataGrid(data_dir, regex)
data_dir = "/home/sasha/Desktop/peakTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkg_curveParams.csv"""
curveGrid = DataGrid(data_dir, regex)
"""
Smoothing function (not used)
"""
def smooth(list, k):
smooth = []
for i in range(len(list)):
a = max(i - int(k / 2), 0)
b = min(i + int(k / 2), len(list) - 1)
smooth.append(sum(list[a:b + 1]) / (b - a))
from peak_fitting.fit_data_sample import fit_curves_to_data, get_peak_indices
from scipy.optimize import curve_fit
from scipy.signal import find_peaks
from scipy.special import wofz
import matplotlib.pyplot as plt
import numpy as np
"""
Load Data
"""
dataGrid = DataGrid_TiNiSn_500C()
data_dir = "/home/sasha/Desktop/peakTest/"
regex = """TiNiSn_500C_Y20190218_14x14_t60_(?P<num>.*?)_bkg_curveParams.csv"""
peakGrid = DataGrid(data_dir, regex)
def line(x, slope, shift):
return slope * x + shift
def gaussian(x, amp, cen, sig):
return amp * np.exp(-(x - cen)**2 /
(2 * sig**2)) / (sig * np.sqrt(2 * np.pi))
def gaussian_shift(x, amp, cen, sig, shift, slope):
return amp * np.exp(
-(x - cen)**2 /
(2 * sig**2)) / (sig * np.sqrt(2 * np.pi)) + slope * x + shift