pyamares-xmris 0.4.0

Requirements

Installation

pip install pyamares-xmris

(The import name remains pyAMARES. To install the original upstream distribution instead, use pip install pyAMARES — note that it does not install on arm64.)

See the Installation Guide for detailed information.

Run pyAMARES in any web browser

New: PyAMARES now offers a user-friendly web interface for fitting AMARES models without writing any code. The web app provides a graphical interface to:

• Upload your FID data file

• Upload the prior knowledge spreadsheet (Excel or CSV)

• Edit the prior knowledge spreadsheet as needed

• Set MR parameters (MHz, spectrum width, deadtime, etc)

• Visualize results with interactive plots

• Download fitted results and figures

No installation required - just visit the link and start fittting your MRS data right away!

Run pyAMARES as standard-alone script

amaresFit -f ./pyAMARES/examples/fid.txt -p  ./pyAMARES/examples/example_human_brain_31P_7T.csv --MHz 120.0 --sw 10000 --deadtime 300e-6 --ifplot --xlim 10 -20 -o simple_example

Run pyAMARES in a Jupyter Notebook

Try Jupyter Notebook on Google Colab here

import pyAMARES
# Load FID from a 2-column ASCII file, and set the MR parameters
MHz = 120.0 # 31P nuclei at 7T
sw = 10000 # spectrum width in Hz
deadtime = 300e-6 # 300 us begin time for the FID signal acquisition

fid = pyAMARES.readmrs('./pyAMARES/examples/fid.txt')
# Load Prior Knowledge
FIDobj = pyAMARES.initialize_FID(fid=fid,
                                 priorknowledgefile='./pyAMARES/examples/example_human_brain_31P_7T.csv',
                                 MHz=MHz,
                                 sw=sw,
                                 deadtime=deadtime,
                                 preview=False,
                                 normalize_fid=False,
                                 xlim=(10, -20))# Region of Interest for visualization, -20 to 10 ppm

# Initialize the parameter using Levenberg-Marquard method
out1 = pyAMARES.fitAMARES(fid_parameters=FIDobj,
                           fitting_parameters=FIDobj.initialParams,
                           method='leastsq',
                           ifplot=False)

# Fitting the MRS data using the optimized parameter

out2 = pyAMARES.fitAMARES(fid_parameters=out1,
                          fitting_parameters=out1.fittedParams, # optimized parameter for last step
                          method='least_squares',
                          ifplot=False)

# Save the data
out2.styled_df.to_html('simple_example.html') # Save highlighted table to an HTML page
                                              # Python 3.6 does not support to_html.
out2.result_sum.to_csv('simple_example.csv') # Save table to CSV spreadsheet
out2.plotParameters.lb = 2.0 # Line Broadening factor for visualization
out2.plotParameters.ifphase = True # Phase the spectrum for visualization
pyAMARES.plotAMARES(fid_parameters=out1, filename='simple_example.svg') # Save plot to SVG

Fitting Result for Example 31P MRS data