- Check if you already have Python installed
python --version- Step 1: Update and Refresh Repository Lists (optional)
sudo apt update- Step 2: Install Supporting Software (optional)
sudo apt install software-properties-common- Step 3: Add Deadsnakes PPA (optional)
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update- Step 4: Install Python 3
sudo apt install python3.8- Or FTP installation
wget https://www.python.org/ftp/python/Python-3.9.1.tgz - Step 1: Select Version of Python to Install and Download Python Executable Installer
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Step 2: Run Executable Installer
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Step 3: Verify Python Was Installed On Windows (optional)
- Navigate to the directory in which Python was installed on the system. In our case, it is
C:\Users\Username\AppData\Local\Programs\Python\Python37since we have installed the latest version. - Double-click python.exe.
- Step 4: Verify Pip Was Installed (optional)
- Open the Start menu and type “cmd.”
- Select the Command Prompt application.
- Enter
pip -Vin the console. If Pip was installed successfully, you should see the appropriate output. If not, you might see the message:
’pip’ is not recognized as an internal or external command, Operable program or batch file.
Installation methods include:
- Distributions
- pip
- Package Manager
- Source
- Binaries
Methods differ in ease of use, coverage, maintenance of old versions, system-wide versus local environment use, and control. We recommend the Scientific Python Distribution Anaconda.
# Best practice, use an environment rather than install in the base env
conda create -n my-env
conda activate my-env
# If you want to install from conda-forge
conda config --env --add channels conda-forge
# The actual install command
conda install numpy scipy matplotlib pip install --user numpy scipy matplotlib sudo apt-get install python-numpy python-scipy python-matplotlib###########################################
# Step 1 || Load python libraries #
###########################################
# warning suppression
import warnings
warnings.filterwarnings("ignore")
# load numeric libraries
import numpy as np
# load graph libraries
import matplotlib.pyplot as plt
# load chemometric libraries (Juan Manuel Lombardi)
from alig import FAPV21###########################################
# Step 2 || Load data #
###########################################
# ******** DATA ******** #
# load calibration data
call_file = 'path/to/callfile' # <span style="color:red">Specify your path here</span>
calibration_data = np.array([ np.loadtxt( fname='/'.join(call_file.split('/')[:-1])+'/' +str(n), delimiter=None) for i, n in enumerate([m[:-1] for m in open(call_file, 'r') if m != '' and m != ' ' and len(m) > 2 ]) ])
# load test data
call_file = 'path/to/testfile' # !!!!! Can be modified !!!!!
test_data = np.array([ np.loadtxt( fname='/'.join(call_file.split('/')[:-1])+'/' +str(n), delimiter=None) for i, n in enumerate([m[:-1] for m in open(call_file, 'r') if m != '' and m != ' ' and len(m) > 2 ]) ])
# join test and calibration data
data = np.concatenate( (calibration_data, test_data), )
print(data.shape)
# ******** LOAD spectra ******** #
# load spectra 1 #
spectra01 = np.loadtxt( fname='load your spectra estimation')[:,0] # !!!!! Can be modified !!!!! it is not an strict requirement
# load spectra 2 #
spectra02 = np.loadtxt( fname='load your spectra estimation')[:,1] # !!!!! Can be modified !!!!! it is not an strict requirementNote: In line 24, replace 'path/to/callfile' with your actual path to the calibration samples.
###########################################
# Step 3 || alignment process #
###########################################
# ------ alignment proc ------ #
# creating FAPV21 obj #
FAPV21 = FAPV21()
S = [ np.array([spectra01, spectra02]) ] # !!!!! Can be modified !!!!! you can give FAPV21 spectra information
# - Set system information - #
dictionary = {
'D' : data,
'Ni' : number_of_interferts ,
'Na' : number_of_analites ,
'Nc' : number_of_calibrationsample ,
'N' : number_of_samples ,
'Nt' : number_of_testsamples ,
'a' : number_of_wellalignedchannels ,
'S' : S ,
} #################################################
FAPV21.import_data_dict(dictionary=dictionary) # import data into the models
# Aligned proc
Da, a, b = FAPV21.aling (
area = 'gaussian_coeficient' ,
mu_range = np.arange(150, 300, 3) ,
non_negativity = False ,
sigma_range = np.arange(4, 20, 0.7) ,
SD = {'mode':'constant'} ,
) # solve the model
# after this the well aligned data will be in ‘Da’ you can process with parafac, mcr-als, etc Line 65 call the FAPV21.aling method ( with the parameters given in lines 66 to 71).
area : Mode in which the area is preserved. (default='gaussian_coeficient')
mu_range : default=None (very wide and highly dense space exploration can be time consuming in some cases.). It is highly recommended to conveniently define the region to be explored and increase the density of the grid.
sigma_range : default=None ( very wide and highly dense space exploration can be time consuming in some cases.). It is highly recommended to conveniently define the region to be explored and increase the density of the grid.
Variable space mu and sigma. This parameter contains the values that allow generating a bounded functional subspace to explore. These parameters are used in a Gaussian function space, other types of functional spaces may depend on other parameters.
SD : Defines a formal method to evaluate a good standard deviation reference (All aigen function will considerer ass a well aligned reference). (default : {'mode':'constant'})
All posible inputs to be cosiderer in FAPV21.aling() method:
D=None S=None Nc=None Na=None Ni=None Nt=None a=None shape='gaussian', area='gaussian_coeficient' non_negativity=True SD={'mode':'mean'} interference_elimination=False mu_range=None sigma_range=None save=True v=1,
It is imperative to correctly set all the input parameters. The user must know their data and have enough mathematical knowledge to define all the input variables.