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 all the env would be listed in users/current_user/.conda/environments.txt the content is : C:\ProgramData\miniconda3 C:\ProgramData\miniconda3\envs\GMS_VENV_PYTHON C:\ProgramData\Miniconda3\envs\GMS_VENV_PYTHON C:\ProgramData\Miniconda3\envs\em C:\ProgramData\Miniconda3\envs\tf

https://github.com/Renfong/dm-script-tool/blob/master/UI_Template_2.s // Interactive UI template // // 2021/05/06 // Renfong interface call_functions{ number GetBoxNumber(object self); }; class UI_Functions : object { number true, false number UIObjectID object MainUI void SetUIObjectID(object self, number id) { UIObjectID = id MainUI = GetScriptObjectFromID(UIObjectID) }; UI_Functions(object self) { true = 1; false = 0 result("Obect \"UI_Functions\" ["+self.ScriptObjectGetID()+"] constructed. \n") }; ~UI_Functions(object self) { true = 1; false = 0 result("Obect\"UI_Functions\" ["+self.ScriptObjectGetID()+"] deconstructed. \n") }; void btn1response(object self) { number num = MainUI.GetBoxNumber() OKdialog("Number = "+num) }; }; class MainUI : UIFrame { TagGroup btn1, numbox object UI_Functions number true, false, ver MainUI(object self){ true = 1...

PCA is the popular statistic tool to denoise the EELS spectrum image. Here is the simple example to apply PCA by numpy package ================================== """ ref: https://stackoverflow.com/questions/13224362/principal-component-analysis-pca-in-python/49629816#49629816 @author: renfong """ import numpy as np import matplotlib.pyplot as plt import hyperspy.api as hs from numpy import argsort from numpy.linalg import eigh #%% load data data = hs.load('t1.dm3').data #%% define pca def pca(data, pc_count = None):     """     Principal component analysis using eigenvalues     note: this mean-centers and auto-scales the data (in-place)     """     C = np.dot(data.T, data)       # covariance matrix     E, V = eigh(C)     key = argsort(E)[::-1][:pc_count]     E, V = E[key], V[:, key]     U = data @ V  # equvalent to np.dot(data, V)     return U, E, V #%% reconstruc...