import numpy as np
from spmFunctions import import_matrix_file, get_path_gui
import matplotlib.pyplot as plt
file_path = get_path_gui()
root_path = file_path.split("/default")
# TODO include input somehow in the function calling. Need to think about the use cases.
print("Type 0: up forward, 1: up retrace, 2: down forward, 3: down retrace")
file_index = int(input())
image, image_raw = import_matrix_file(file_index, file_path)
# Averaging image per row
mean_array = np.mean(image, axis=1, dtype=np.float64)
# Getting image size (number of points) and image height
image_height = image_raw.height
image_size = image.shape[0]
# Creating an axis to plot the average value of Df
x_axis = np.arange(start=0.0,
stop=image_height,
step=image_height / image_size)
# in case you need to slice x,y to show in graph
#x_axis = x_axis[:300]
#mean_array = mean_array[:300]
temp_title = file_path.split("--")
plot_title = temp_title[1].replace("_mtrx", "")
import access2thematrix
import matplotlib.pyplot as plt
import numpy as np
from spmFunctions import get_path_gui
# Load data
mtrx_data = access2thematrix.MtrxData()
data_file = get_path_gui()
traces, message = mtrx_data.open(data_file)
im, _ = mtrx_data.select_image(traces[1])
image = im.data
# Plot images
plt.imshow(image)
# Take max, min of each line
im_max = np.max(image, axis=1)
im_min = np.min(image, axis=1)
im_diff = im_max - im_min
# Split im_diff to get data we actually give a toss about
im_diff_filtered = im_diff[im_diff <= 2e-9]
# Make histrogram
plt.hist(im_diff_filtered, bins=40)
plt.xlabel("Height Difference.")
plt.ylabel("Num.")
plt.show()
# Starts with a .txt called in a folder.
# Atributes order - #file_number,spec type (Df(Z),I(Z)),spec_files_number(234_1, 235_1, 233_1, 123 etc)
# Creates a canvas based on the height of image number. Asks in terminal
# if you want to get another image (like a .png).
# root_paths must be edited for each folder you are working on
# may need to change the forward slash in windows or linux!
# Sybolic Links don't work here. Need to be a real path!
root = f"/mnt/bigdrive/LTData/2020-08-03/"
root_matrix = f"20200803-130406_GaAs(110)--AFM_NonContact_QPlus--"
root_image_path = f"{root}{root_matrix}"
root_specs_path = f"{root}specs/{root_matrix}"
print("Select the input file")1
f = open(get_path_gui(), "r")
txt_input = (f.read()).split(" ")
# If looking at other images then Z you must change image_path string variable
image_path = f"{root_image_path}{txt_input[0]}.Z_mtrx"
specs_list = txt_input[2:]
specs_list[-1] = specs_list[-1].rstrip("\n")
specs_path = []
for item in specs_list:
specs_path.append(f"{root_specs_path}{item}-{txt_input[1]}_1.txt")
# Retreive size of image from image_path - and check if must keep it
# TODO check if I really need this. For now use file_index=0
print("Select which image you want")
# Script for opening .txt and saving spec files exported from vernisage.
import numpy as np
import matplotlib.pyplot as plt
from spmFunctions import load_spec, get_path_gui
print("Select the ON curve")
data_on_path = get_path_gui()
print("Select the OFF curve")
data_off_path = get_path_gui()
data_on_tuple = load_spec(data_on_path)
data_off_tuple = load_spec(data_off_path)
# TODO rewrite this so it only uses the object now. Makes it simpler to read.
data_forward_diff = np.asarray(
[x - y for x, y in zip(data_on_tuple[0][:, 1], data_off_tuple[0][:, 1])]
)
data_retrace_diff = np.asarray(
[x - y for x, y in zip(data_on_tuple[1][:, 1], data_off_tuple[1][:, 1])]
)
#TODO add the tick positions in this graph as I did in loadspecsript.py.
plot_title_on = data_on_path.split("--")[1].replace(".txt", "")
plot_title_off = data_off_path.split("--")[1].replace(".txt", "")
plot_title = f"Df difference = {plot_title_on} (ON) - {plot_title_off} (OFF)"
plt.plot(data_on_tuple[0][:, 0], data_forward_diff, "#2A9D8F")
plt.plot(data_on_tuple[1][:, 0], data_retrace_diff, "#E9C46A")
plt.title(plot_title)
plt.xlabel("Z[nm]")
plt.ylabel("Df[Hz]")