OOMMF Conversion of Micrographs into MP4 File (Aug 24, 2023)

command-ffmpeg.sh

#!/bin/sh
# Converts an array of images into an MP4 video
# SOURCE: https://askubuntu.com/a/610945

base="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/magick-export"
fps=60
output_path="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/dist/"

ffmpeg -framerate $fps -i $base/overlaid-micrograph-%07d.png -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p $output_path/ffmpeg-output-$fps"fps".mp4

command-imagemagick.sh

#!/bin/sh
# Bulk-overlay converted OMF micrograph files

# Predetermined paths
file_background="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/assets/background.png"
folder_export="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/magick-export"
folder_source_micrograph="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-8/png-files"
folder_source_plot="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/matplotlib-output"
list_energy_demag="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/data/energy-demag.txt"
list_energy_total="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/data/energy-total.txt"
list_energy_ue="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/data/energy-uniform-exchange.txt"
list_micrograph="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/data/micrographs-list.txt"
list_time="/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/data/simulation-time.txt"

# Predetermined variables
export_filename_extension=".png"
export_filename_prefix="overlaid-micrograph-"
source_filename_extension=".png"
source_filename_prefix="GSnanowire-Oxs_TimeDriver-Magnetization-00-"
plot_filename_extension=".png"
plot_filename_prefix="matplotlib-"

# Dealing with and processing 10000 micrograph files
start=1
end=10000
for i in $(eval echo {$start..$end}); do
    j=$(echo $(( $i-1 )))
    k=$(cat $list_time | sed "$i"'!d')
    l=$(cat $list_micrograph | sed "$i"'!d')
    m=$(cat $list_energy_demag | sed "$i"'!d')
    n=$(cat $list_energy_total | sed "$i"'!d')
    o=$(cat $list_energy_ue | sed "$i"'!d')
    
    magick $file_background \
    -font Montserrat-Bold -weight 100 -pointsize 22 -fill Black -annotate +465+175 $j \
    -font Montserrat-Bold -weight 100 -pointsize 22 -fill Black -annotate +465+203 $k \
    -font Montserrat-Bold -weight 100 -pointsize 22 -fill Black -annotate +465+230 $n \
    -font Montserrat-Bold -weight 100 -pointsize 22 -fill Black -annotate +465+258 $o \
    -font Montserrat-Bold -weight 100 -pointsize 22 -fill Black -annotate +465+285 $m \
    \( \
        $folder_source_micrograph/$source_filename_prefix$l$source_filename_extension \
         -geometry +667+62 -resize 60% \
    \) -composite \
    \( \
        $folder_source_plot/$plot_filename_prefix$l$plot_filename_extension \
         -geometry +70+325 -resize 60% \
    \) -composite \
    $folder_export/$export_filename_prefix$l$export_filename_extension
    echo step: $j, time: $k, energy demag: $m, energy total: $n, energy ue: $o
done

command-matplotlib.py

#!/bin/python
# Bulk-export energy graph of the 100nm model into image files from 0 nanosecond to 5.76 nanosecond

import matplotlib.pyplot as plt
import pandas as pd

# The working directory
wd = '/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10'

# The micrograph list file input
mgl_fi = '/ssynthesia/ghostcity/git-local/02306.00013-neptunal-ix-oommf-nanoparticle/ix-meh-10/data/micrographs-list.txt'

# Reading the source file containing energy data
src = f'{wd}/data-matplotlib/source.csv'
df = pd.read_csv(src)

# Preparing the column data and obtaining the values
# 'c1' --> energy-total
# 'c2' --> energy-uniform-exchange
# 'c3' --> energy-demag
# 't'  --> time
c1 = df['energy-total'].values
c2 = df['energy-uniform-exchange'].values
c3 = df['energy-demag'].values
t = df['time'].values

# Reading the list of microgaphs, for file output naming
# The range should not exceed 1800 numbers, otherwise matplotlib cache
# will consume Monked's precious 4 GiB of RAM from the inside :"(
with open(mgl_fi, 'r') as mgl:
    rl = mgl.readlines()  # --- 'rl' stands for 'read lines'
    
    # Iterating through every micrograph's energy data
    # for i in range(len(rl)):
    for i in range(8999, len(rl)):
        
        # Preamble
        print(f'Plotting the micrograph number {i}. Please wait ...')
        
        # Preparing and configuring the graph figure
        plt.figure(dpi=150.0)
        plt.xlabel('time (nanosecond)')
        plt.ylabel('energy (attojoule)')
        
        # Plotting the data
        plt.plot(t[:(i)], c1[:(i)], label='energy-total')
        plt.plot(t[:(i)], c2[:(i)], label='energy-uniform-exchange')
        plt.plot(t[:(i)], c3[:(i)], label='energy-demag')
        
        # Drawing the legend
        plt.legend(fontsize='medium')
        
        # Setting the plot image name and saving the figure
        ext = '.png'
        pin = 'matplotlib-' + rl[i].strip() + ext  # --- 'pin' stands for 'plot image name
        plt.savefig(f'{wd}/matplotlib-output/{pin}', bbox_inches='tight')
        
        # Clearing the figure for the next iteration's plotting
        plt.close()
        
        # Continuing the iteration
        continue

# Good bye
exit()