Growth Mechanism with Details#
%load_ext autoreload
%autoreload 2
%matplotlib inline
import sys
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib import colors as mcolors
from mayavi import mlab
from m3util.viz.style import set_style
from m3util.viz.printing import printer
from m3util.viz.layout import imagemap, layout_fig
from m3util.viz.text import labelfigs
from sto_rheed.Dataset import RHEED_parameter_dataset, RHEED_spot_Dataset
from sto_rheed.Viz import Viz, rgba_to_rgb
from sto_rheed.drawing_3d import draw_box, sphere_to_surface
from sto_rheed.Analysis import detect_peaks, analyze_curves, process_rheed_data
from sto_rheed.Packed_functions import decay_curve_examples
from sto_rheed.Fit import NormalizeData
printing = printer(basepath = '../Figures/3.Growth_mechanism/')
printing_plot = printer(basepath = '../Figures/3.Growth_mechanism/', fileformats=['png', 'svg', 'tif'], dpi=600)
if not os.path.isdir('../Data/Plume_results'): os.mkdir('../Data/Plume_results')
set_style("printing")
plot_size = (6, 2)
color_blue = (44/255,123/255,182/255)
color_gray = (128/255, 128/255, 128/255, 0.5)
seq_colors = ['#00429d','#2e59a8','#4771b2','#5d8abd','#73a2c6','#8abccf','#a5d5d8','#c5eddf','#ffffe0']
bgc1, bgc2 = (*mcolors.hex2color(seq_colors[0]), 0.3), (*mcolors.hex2color(seq_colors[5]), 0.3)
bgc1_, bgc2_ = rgba_to_rgb(bgc1), rgba_to_rgb(bgc2)
********************************************************************************
WARNING: Imported VTK version (9.4) does not match the one used
to build the TVTK classes (9.3). This may cause problems.
Please rebuild TVTK.
********************************************************************************
printing set for seaborn
1. Visualize the spots from collected data#
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/STO_STO_test6_06292022-standard.h5'
D1_spot = RHEED_spot_Dataset(path, sample_name='treated_213nm')
D1_spot.data_info
Growth: background_with_heater, Size of data: f(2251, 300, 300)
Growth: background_without_heater, Size of data: f(2942, 300, 300)
Growth: growth_1, Size of data: f(13189, 300, 300)
Growth: growth_10, Size of data: f(12336, 300, 300)
Growth: growth_11, Size of data: f(12799, 300, 300)
Growth: growth_12, Size of data: f(12563, 300, 300)
Growth: growth_2, Size of data: f(12858, 300, 300)
Growth: growth_3, Size of data: f(11392, 300, 300)
Growth: growth_4, Size of data: f(13067, 300, 300)
Growth: growth_5, Size of data: f(12619, 300, 300)
Growth: growth_6, Size of data: f(12610, 300, 300)
Growth: growth_7, Size of data: f(12431, 300, 300)
Growth: growth_8, Size of data: f(12553, 300, 300)
Growth: growth_9, Size of data: f(12568, 300, 300)
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/STO_STO_test6_06292022-standard.h5'
D1_spot = RHEED_spot_Dataset(path, sample_name='treated_213nm')
D1_spot.data_info
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/test6_gaussian_fit_parameters_all-04232023.h5'
D1_para = RHEED_parameter_dataset(path, camera_freq=500, sample_name='treated_213nm')
D1_para.data_info
Growth: background_with_heater, Size of data: f(2251, 300, 300)
Growth: background_without_heater, Size of data: f(2942, 300, 300)
Growth: growth_1, Size of data: f(13189, 300, 300)
Growth: growth_10, Size of data: f(12336, 300, 300)
Growth: growth_11, Size of data: f(12799, 300, 300)
Growth: growth_12, Size of data: f(12563, 300, 300)
Growth: growth_2, Size of data: f(12858, 300, 300)
Growth: growth_3, Size of data: f(11392, 300, 300)
Growth: growth_4, Size of data: f(13067, 300, 300)
Growth: growth_5, Size of data: f(12619, 300, 300)
Growth: growth_6, Size of data: f(12610, 300, 300)
Growth: growth_7, Size of data: f(12431, 300, 300)
Growth: growth_8, Size of data: f(12553, 300, 300)
Growth: growth_9, Size of data: f(12568, 300, 300)
Growth: background_with_heater:
--spot: spot_1:
----height:, Size of data: (2251,)
----img_max:, Size of data: (2251,)
----img_mean:, Size of data: (2251,)
----img_rec_max:, Size of data: (2251,)
----img_rec_mean:, Size of data: (2251,)
----img_rec_sum:, Size of data: (2251,)
----img_sum:, Size of data: (2251,)
----raw_image:, Size of data: (2251, 40, 60)
----reconstructed_image:, Size of data: (2251, 40, 60)
----width_x:, Size of data: (2251,)
----width_y:, Size of data: (2251,)
----x:, Size of data: (2251,)
----y:, Size of data: (2251,)
--spot: spot_2:
----height:, Size of data: (2251,)
----img_max:, Size of data: (2251,)
----img_mean:, Size of data: (2251,)
----img_rec_max:, Size of data: (2251,)
----img_rec_mean:, Size of data: (2251,)
----img_rec_sum:, Size of data: (2251,)
----img_sum:, Size of data: (2251,)
----raw_image:, Size of data: (2251, 29, 48)
----reconstructed_image:, Size of data: (2251, 29, 48)
----width_x:, Size of data: (2251,)
----width_y:, Size of data: (2251,)
----x:, Size of data: (2251,)
----y:, Size of data: (2251,)
--spot: spot_3:
----height:, Size of data: (2251,)
----img_max:, Size of data: (2251,)
----img_mean:, Size of data: (2251,)
----img_rec_max:, Size of data: (2251,)
----img_rec_mean:, Size of data: (2251,)
----img_rec_sum:, Size of data: (2251,)
----img_sum:, Size of data: (2251,)
----raw_image:, Size of data: (2251, 32, 65)
----reconstructed_image:, Size of data: (2251, 32, 65)
----width_x:, Size of data: (2251,)
----width_y:, Size of data: (2251,)
----x:, Size of data: (2251,)
----y:, Size of data: (2251,)
Growth: background_without_heater:
--spot: spot_1:
----height:, Size of data: (2942,)
----img_max:, Size of data: (2942,)
----img_mean:, Size of data: (2942,)
----img_rec_max:, Size of data: (2942,)
----img_rec_mean:, Size of data: (2942,)
----img_rec_sum:, Size of data: (2942,)
----img_sum:, Size of data: (2942,)
----raw_image:, Size of data: (2942, 40, 60)
----reconstructed_image:, Size of data: (2942, 40, 60)
----width_x:, Size of data: (2942,)
----width_y:, Size of data: (2942,)
----x:, Size of data: (2942,)
----y:, Size of data: (2942,)
--spot: spot_2:
----height:, Size of data: (2942,)
----img_max:, Size of data: (2942,)
----img_mean:, Size of data: (2942,)
----img_rec_max:, Size of data: (2942,)
----img_rec_mean:, Size of data: (2942,)
----img_rec_sum:, Size of data: (2942,)
----img_sum:, Size of data: (2942,)
----raw_image:, Size of data: (2942, 29, 48)
----reconstructed_image:, Size of data: (2942, 29, 48)
----width_x:, Size of data: (2942,)
----width_y:, Size of data: (2942,)
----x:, Size of data: (2942,)
----y:, Size of data: (2942,)
--spot: spot_3:
----height:, Size of data: (2942,)
----img_max:, Size of data: (2942,)
----img_mean:, Size of data: (2942,)
----img_rec_max:, Size of data: (2942,)
----img_rec_mean:, Size of data: (2942,)
----img_rec_sum:, Size of data: (2942,)
----img_sum:, Size of data: (2942,)
----raw_image:, Size of data: (2942, 32, 65)
----reconstructed_image:, Size of data: (2942, 32, 65)
----width_x:, Size of data: (2942,)
----width_y:, Size of data: (2942,)
----x:, Size of data: (2942,)
----y:, Size of data: (2942,)
Growth: growth_1:
--spot: spot_1:
----height:, Size of data: (13189,)
----img_max:, Size of data: (13189,)
----img_mean:, Size of data: (13189,)
----img_rec_max:, Size of data: (13189,)
----img_rec_mean:, Size of data: (13189,)
----img_rec_sum:, Size of data: (13189,)
----img_sum:, Size of data: (13189,)
----raw_image:, Size of data: (13189, 40, 60)
----reconstructed_image:, Size of data: (13189, 40, 60)
----width_x:, Size of data: (13189,)
----width_y:, Size of data: (13189,)
----x:, Size of data: (13189,)
----y:, Size of data: (13189,)
--spot: spot_2:
----height:, Size of data: (13189,)
----img_max:, Size of data: (13189,)
----img_mean:, Size of data: (13189,)
----img_rec_max:, Size of data: (13189,)
----img_rec_mean:, Size of data: (13189,)
----img_rec_sum:, Size of data: (13189,)
----img_sum:, Size of data: (13189,)
----raw_image:, Size of data: (13189, 29, 48)
----reconstructed_image:, Size of data: (13189, 29, 48)
----width_x:, Size of data: (13189,)
----width_y:, Size of data: (13189,)
----x:, Size of data: (13189,)
----y:, Size of data: (13189,)
--spot: spot_3:
----height:, Size of data: (13189,)
----img_max:, Size of data: (13189,)
----img_mean:, Size of data: (13189,)
----img_rec_max:, Size of data: (13189,)
----img_rec_mean:, Size of data: (13189,)
----img_rec_sum:, Size of data: (13189,)
----img_sum:, Size of data: (13189,)
----raw_image:, Size of data: (13189, 32, 65)
----reconstructed_image:, Size of data: (13189, 32, 65)
----width_x:, Size of data: (13189,)
----width_y:, Size of data: (13189,)
----x:, Size of data: (13189,)
----y:, Size of data: (13189,)
Growth: growth_10:
--spot: spot_1:
----height:, Size of data: (12336,)
----img_max:, Size of data: (12336,)
----img_mean:, Size of data: (12336,)
----img_rec_max:, Size of data: (12336,)
----img_rec_mean:, Size of data: (12336,)
----img_rec_sum:, Size of data: (12336,)
----img_sum:, Size of data: (12336,)
----raw_image:, Size of data: (12336, 40, 60)
----reconstructed_image:, Size of data: (12336, 40, 60)
----width_x:, Size of data: (12336,)
----width_y:, Size of data: (12336,)
----x:, Size of data: (12336,)
----y:, Size of data: (12336,)
--spot: spot_2:
----height:, Size of data: (12336,)
----img_max:, Size of data: (12336,)
----img_mean:, Size of data: (12336,)
----img_rec_max:, Size of data: (12336,)
----img_rec_mean:, Size of data: (12336,)
----img_rec_sum:, Size of data: (12336,)
----img_sum:, Size of data: (12336,)
----raw_image:, Size of data: (12336, 29, 48)
----reconstructed_image:, Size of data: (12336, 29, 48)
----width_x:, Size of data: (12336,)
----width_y:, Size of data: (12336,)
----x:, Size of data: (12336,)
----y:, Size of data: (12336,)
--spot: spot_3:
----height:, Size of data: (12336,)
----img_max:, Size of data: (12336,)
----img_mean:, Size of data: (12336,)
----img_rec_max:, Size of data: (12336,)
----img_rec_mean:, Size of data: (12336,)
----img_rec_sum:, Size of data: (12336,)
----img_sum:, Size of data: (12336,)
----raw_image:, Size of data: (12336, 32, 65)
----reconstructed_image:, Size of data: (12336, 32, 65)
----width_x:, Size of data: (12336,)
----width_y:, Size of data: (12336,)
----x:, Size of data: (12336,)
----y:, Size of data: (12336,)
Growth: growth_11:
--spot: spot_1:
----height:, Size of data: (12799,)
----img_max:, Size of data: (12799,)
----img_mean:, Size of data: (12799,)
----img_rec_max:, Size of data: (12799,)
----img_rec_mean:, Size of data: (12799,)
----img_rec_sum:, Size of data: (12799,)
----img_sum:, Size of data: (12799,)
----raw_image:, Size of data: (12799, 40, 60)
----reconstructed_image:, Size of data: (12799, 40, 60)
----width_x:, Size of data: (12799,)
----width_y:, Size of data: (12799,)
----x:, Size of data: (12799,)
----y:, Size of data: (12799,)
--spot: spot_2:
----height:, Size of data: (12799,)
----img_max:, Size of data: (12799,)
----img_mean:, Size of data: (12799,)
----img_rec_max:, Size of data: (12799,)
----img_rec_mean:, Size of data: (12799,)
----img_rec_sum:, Size of data: (12799,)
----img_sum:, Size of data: (12799,)
----raw_image:, Size of data: (12799, 29, 48)
----reconstructed_image:, Size of data: (12799, 29, 48)
----width_x:, Size of data: (12799,)
----width_y:, Size of data: (12799,)
----x:, Size of data: (12799,)
----y:, Size of data: (12799,)
--spot: spot_3:
----height:, Size of data: (12799,)
----img_max:, Size of data: (12799,)
----img_mean:, Size of data: (12799,)
----img_rec_max:, Size of data: (12799,)
----img_rec_mean:, Size of data: (12799,)
----img_rec_sum:, Size of data: (12799,)
----img_sum:, Size of data: (12799,)
----raw_image:, Size of data: (12799, 32, 65)
----reconstructed_image:, Size of data: (12799, 32, 65)
----width_x:, Size of data: (12799,)
----width_y:, Size of data: (12799,)
----x:, Size of data: (12799,)
----y:, Size of data: (12799,)
Growth: growth_12:
--spot: spot_1:
----height:, Size of data: (12563,)
----img_max:, Size of data: (12563,)
----img_mean:, Size of data: (12563,)
----img_rec_max:, Size of data: (12563,)
----img_rec_mean:, Size of data: (12563,)
----img_rec_sum:, Size of data: (12563,)
----img_sum:, Size of data: (12563,)
----raw_image:, Size of data: (12563, 40, 60)
----reconstructed_image:, Size of data: (12563, 40, 60)
----width_x:, Size of data: (12563,)
----width_y:, Size of data: (12563,)
----x:, Size of data: (12563,)
----y:, Size of data: (12563,)
--spot: spot_2:
----height:, Size of data: (12563,)
----img_max:, Size of data: (12563,)
----img_mean:, Size of data: (12563,)
----img_rec_max:, Size of data: (12563,)
----img_rec_mean:, Size of data: (12563,)
----img_rec_sum:, Size of data: (12563,)
----img_sum:, Size of data: (12563,)
----raw_image:, Size of data: (12563, 29, 48)
----reconstructed_image:, Size of data: (12563, 29, 48)
----width_x:, Size of data: (12563,)
----width_y:, Size of data: (12563,)
----x:, Size of data: (12563,)
----y:, Size of data: (12563,)
--spot: spot_3:
----height:, Size of data: (12563,)
----img_max:, Size of data: (12563,)
----img_mean:, Size of data: (12563,)
----img_rec_max:, Size of data: (12563,)
----img_rec_mean:, Size of data: (12563,)
----img_rec_sum:, Size of data: (12563,)
----img_sum:, Size of data: (12563,)
----raw_image:, Size of data: (12563, 32, 65)
----reconstructed_image:, Size of data: (12563, 32, 65)
----width_x:, Size of data: (12563,)
----width_y:, Size of data: (12563,)
----x:, Size of data: (12563,)
----y:, Size of data: (12563,)
Growth: growth_2:
--spot: spot_1:
----height:, Size of data: (12858,)
----img_max:, Size of data: (12858,)
----img_mean:, Size of data: (12858,)
----img_rec_max:, Size of data: (12858,)
----img_rec_mean:, Size of data: (12858,)
----img_rec_sum:, Size of data: (12858,)
----img_sum:, Size of data: (12858,)
----raw_image:, Size of data: (12858, 40, 60)
----reconstructed_image:, Size of data: (12858, 40, 60)
----width_x:, Size of data: (12858,)
----width_y:, Size of data: (12858,)
----x:, Size of data: (12858,)
----y:, Size of data: (12858,)
--spot: spot_2:
----height:, Size of data: (12858,)
----img_max:, Size of data: (12858,)
----img_mean:, Size of data: (12858,)
----img_rec_max:, Size of data: (12858,)
----img_rec_mean:, Size of data: (12858,)
----img_rec_sum:, Size of data: (12858,)
----img_sum:, Size of data: (12858,)
----raw_image:, Size of data: (12858, 29, 48)
----reconstructed_image:, Size of data: (12858, 29, 48)
----width_x:, Size of data: (12858,)
----width_y:, Size of data: (12858,)
----x:, Size of data: (12858,)
----y:, Size of data: (12858,)
--spot: spot_3:
----height:, Size of data: (12858,)
----img_max:, Size of data: (12858,)
----img_mean:, Size of data: (12858,)
----img_rec_max:, Size of data: (12858,)
----img_rec_mean:, Size of data: (12858,)
----img_rec_sum:, Size of data: (12858,)
----img_sum:, Size of data: (12858,)
----raw_image:, Size of data: (12858, 32, 65)
----reconstructed_image:, Size of data: (12858, 32, 65)
----width_x:, Size of data: (12858,)
----width_y:, Size of data: (12858,)
----x:, Size of data: (12858,)
----y:, Size of data: (12858,)
Growth: growth_3:
--spot: spot_1:
----height:, Size of data: (11392,)
----img_max:, Size of data: (11392,)
----img_mean:, Size of data: (11392,)
----img_rec_max:, Size of data: (11392,)
----img_rec_mean:, Size of data: (11392,)
----img_rec_sum:, Size of data: (11392,)
----img_sum:, Size of data: (11392,)
----raw_image:, Size of data: (11392, 40, 60)
----reconstructed_image:, Size of data: (11392, 40, 60)
----width_x:, Size of data: (11392,)
----width_y:, Size of data: (11392,)
----x:, Size of data: (11392,)
----y:, Size of data: (11392,)
--spot: spot_2:
----height:, Size of data: (11392,)
----img_max:, Size of data: (11392,)
----img_mean:, Size of data: (11392,)
----img_rec_max:, Size of data: (11392,)
----img_rec_mean:, Size of data: (11392,)
----img_rec_sum:, Size of data: (11392,)
----img_sum:, Size of data: (11392,)
----raw_image:, Size of data: (11392, 29, 48)
----reconstructed_image:, Size of data: (11392, 29, 48)
----width_x:, Size of data: (11392,)
----width_y:, Size of data: (11392,)
----x:, Size of data: (11392,)
----y:, Size of data: (11392,)
--spot: spot_3:
----height:, Size of data: (11392,)
----img_max:, Size of data: (11392,)
----img_mean:, Size of data: (11392,)
----img_rec_max:, Size of data: (11392,)
----img_rec_mean:, Size of data: (11392,)
----img_rec_sum:, Size of data: (11392,)
----img_sum:, Size of data: (11392,)
----raw_image:, Size of data: (11392, 32, 65)
----reconstructed_image:, Size of data: (11392, 32, 65)
----width_x:, Size of data: (11392,)
----width_y:, Size of data: (11392,)
----x:, Size of data: (11392,)
----y:, Size of data: (11392,)
Growth: growth_4:
--spot: spot_1:
----height:, Size of data: (13067,)
----img_max:, Size of data: (13067,)
----img_mean:, Size of data: (13067,)
----img_rec_max:, Size of data: (13067,)
----img_rec_mean:, Size of data: (13067,)
----img_rec_sum:, Size of data: (13067,)
----img_sum:, Size of data: (13067,)
----raw_image:, Size of data: (13067, 40, 60)
----reconstructed_image:, Size of data: (13067, 40, 60)
----width_x:, Size of data: (13067,)
----width_y:, Size of data: (13067,)
----x:, Size of data: (13067,)
----y:, Size of data: (13067,)
--spot: spot_2:
----height:, Size of data: (13067,)
----img_max:, Size of data: (13067,)
----img_mean:, Size of data: (13067,)
----img_rec_max:, Size of data: (13067,)
----img_rec_mean:, Size of data: (13067,)
----img_rec_sum:, Size of data: (13067,)
----img_sum:, Size of data: (13067,)
----raw_image:, Size of data: (13067, 29, 48)
----reconstructed_image:, Size of data: (13067, 29, 48)
----width_x:, Size of data: (13067,)
----width_y:, Size of data: (13067,)
----x:, Size of data: (13067,)
----y:, Size of data: (13067,)
--spot: spot_3:
----height:, Size of data: (13067,)
----img_max:, Size of data: (13067,)
----img_mean:, Size of data: (13067,)
----img_rec_max:, Size of data: (13067,)
----img_rec_mean:, Size of data: (13067,)
----img_rec_sum:, Size of data: (13067,)
----img_sum:, Size of data: (13067,)
----raw_image:, Size of data: (13067, 32, 65)
----reconstructed_image:, Size of data: (13067, 32, 65)
----width_x:, Size of data: (13067,)
----width_y:, Size of data: (13067,)
----x:, Size of data: (13067,)
----y:, Size of data: (13067,)
Growth: growth_5:
--spot: spot_1:
----height:, Size of data: (12619,)
----img_max:, Size of data: (12619,)
----img_mean:, Size of data: (12619,)
----img_rec_max:, Size of data: (12619,)
----img_rec_mean:, Size of data: (12619,)
----img_rec_sum:, Size of data: (12619,)
----img_sum:, Size of data: (12619,)
----raw_image:, Size of data: (12619, 40, 60)
----reconstructed_image:, Size of data: (12619, 40, 60)
----width_x:, Size of data: (12619,)
----width_y:, Size of data: (12619,)
----x:, Size of data: (12619,)
----y:, Size of data: (12619,)
--spot: spot_2:
----height:, Size of data: (12619,)
----img_max:, Size of data: (12619,)
----img_mean:, Size of data: (12619,)
----img_rec_max:, Size of data: (12619,)
----img_rec_mean:, Size of data: (12619,)
----img_rec_sum:, Size of data: (12619,)
----img_sum:, Size of data: (12619,)
----raw_image:, Size of data: (12619, 29, 48)
----reconstructed_image:, Size of data: (12619, 29, 48)
----width_x:, Size of data: (12619,)
----width_y:, Size of data: (12619,)
----x:, Size of data: (12619,)
----y:, Size of data: (12619,)
--spot: spot_3:
----height:, Size of data: (12619,)
----img_max:, Size of data: (12619,)
----img_mean:, Size of data: (12619,)
----img_rec_max:, Size of data: (12619,)
----img_rec_mean:, Size of data: (12619,)
----img_rec_sum:, Size of data: (12619,)
----img_sum:, Size of data: (12619,)
----raw_image:, Size of data: (12619, 32, 65)
----reconstructed_image:, Size of data: (12619, 32, 65)
----width_x:, Size of data: (12619,)
----width_y:, Size of data: (12619,)
----x:, Size of data: (12619,)
----y:, Size of data: (12619,)
Growth: growth_6:
--spot: spot_1:
----height:, Size of data: (12610,)
----img_max:, Size of data: (12610,)
----img_mean:, Size of data: (12610,)
----img_rec_max:, Size of data: (12610,)
----img_rec_mean:, Size of data: (12610,)
----img_rec_sum:, Size of data: (12610,)
----img_sum:, Size of data: (12610,)
----raw_image:, Size of data: (12610, 40, 60)
----reconstructed_image:, Size of data: (12610, 40, 60)
----width_x:, Size of data: (12610,)
----width_y:, Size of data: (12610,)
----x:, Size of data: (12610,)
----y:, Size of data: (12610,)
--spot: spot_2:
----height:, Size of data: (12610,)
----img_max:, Size of data: (12610,)
----img_mean:, Size of data: (12610,)
----img_rec_max:, Size of data: (12610,)
----img_rec_mean:, Size of data: (12610,)
----img_rec_sum:, Size of data: (12610,)
----img_sum:, Size of data: (12610,)
----raw_image:, Size of data: (12610, 29, 48)
----reconstructed_image:, Size of data: (12610, 29, 48)
----width_x:, Size of data: (12610,)
----width_y:, Size of data: (12610,)
----x:, Size of data: (12610,)
----y:, Size of data: (12610,)
--spot: spot_3:
----height:, Size of data: (12610,)
----img_max:, Size of data: (12610,)
----img_mean:, Size of data: (12610,)
----img_rec_max:, Size of data: (12610,)
----img_rec_mean:, Size of data: (12610,)
----img_rec_sum:, Size of data: (12610,)
----img_sum:, Size of data: (12610,)
----raw_image:, Size of data: (12610, 32, 65)
----reconstructed_image:, Size of data: (12610, 32, 65)
----width_x:, Size of data: (12610,)
----width_y:, Size of data: (12610,)
----x:, Size of data: (12610,)
----y:, Size of data: (12610,)
Growth: growth_7:
--spot: spot_1:
----height:, Size of data: (12431,)
----img_max:, Size of data: (12431,)
----img_mean:, Size of data: (12431,)
----img_rec_max:, Size of data: (12431,)
----img_rec_mean:, Size of data: (12431,)
----img_rec_sum:, Size of data: (12431,)
----img_sum:, Size of data: (12431,)
----raw_image:, Size of data: (12431, 40, 60)
----reconstructed_image:, Size of data: (12431, 40, 60)
----width_x:, Size of data: (12431,)
----width_y:, Size of data: (12431,)
----x:, Size of data: (12431,)
----y:, Size of data: (12431,)
--spot: spot_2:
----height:, Size of data: (12431,)
----img_max:, Size of data: (12431,)
----img_mean:, Size of data: (12431,)
----img_rec_max:, Size of data: (12431,)
----img_rec_mean:, Size of data: (12431,)
----img_rec_sum:, Size of data: (12431,)
----img_sum:, Size of data: (12431,)
----raw_image:, Size of data: (12431, 29, 48)
----reconstructed_image:, Size of data: (12431, 29, 48)
----width_x:, Size of data: (12431,)
----width_y:, Size of data: (12431,)
----x:, Size of data: (12431,)
----y:, Size of data: (12431,)
--spot: spot_3:
----height:, Size of data: (12431,)
----img_max:, Size of data: (12431,)
----img_mean:, Size of data: (12431,)
----img_rec_max:, Size of data: (12431,)
----img_rec_mean:, Size of data: (12431,)
----img_rec_sum:, Size of data: (12431,)
----img_sum:, Size of data: (12431,)
----raw_image:, Size of data: (12431, 32, 65)
----reconstructed_image:, Size of data: (12431, 32, 65)
----width_x:, Size of data: (12431,)
----width_y:, Size of data: (12431,)
----x:, Size of data: (12431,)
----y:, Size of data: (12431,)
Growth: growth_8:
--spot: spot_1:
----height:, Size of data: (12553,)
----img_max:, Size of data: (12553,)
----img_mean:, Size of data: (12553,)
----img_rec_max:, Size of data: (12553,)
----img_rec_mean:, Size of data: (12553,)
----img_rec_sum:, Size of data: (12553,)
----img_sum:, Size of data: (12553,)
----raw_image:, Size of data: (12553, 40, 60)
----reconstructed_image:, Size of data: (12553, 40, 60)
----width_x:, Size of data: (12553,)
----width_y:, Size of data: (12553,)
----x:, Size of data: (12553,)
----y:, Size of data: (12553,)
--spot: spot_2:
----height:, Size of data: (12553,)
----img_max:, Size of data: (12553,)
----img_mean:, Size of data: (12553,)
----img_rec_max:, Size of data: (12553,)
----img_rec_mean:, Size of data: (12553,)
----img_rec_sum:, Size of data: (12553,)
----img_sum:, Size of data: (12553,)
----raw_image:, Size of data: (12553, 29, 48)
----reconstructed_image:, Size of data: (12553, 29, 48)
----width_x:, Size of data: (12553,)
----width_y:, Size of data: (12553,)
----x:, Size of data: (12553,)
----y:, Size of data: (12553,)
--spot: spot_3:
----height:, Size of data: (12553,)
----img_max:, Size of data: (12553,)
----img_mean:, Size of data: (12553,)
----img_rec_max:, Size of data: (12553,)
----img_rec_mean:, Size of data: (12553,)
----img_rec_sum:, Size of data: (12553,)
----img_sum:, Size of data: (12553,)
----raw_image:, Size of data: (12553, 32, 65)
----reconstructed_image:, Size of data: (12553, 32, 65)
----width_x:, Size of data: (12553,)
----width_y:, Size of data: (12553,)
----x:, Size of data: (12553,)
----y:, Size of data: (12553,)
Growth: growth_9:
--spot: spot_1:
----height:, Size of data: (12568,)
----img_max:, Size of data: (12568,)
----img_mean:, Size of data: (12568,)
----img_rec_max:, Size of data: (12568,)
----img_rec_mean:, Size of data: (12568,)
----img_rec_sum:, Size of data: (12568,)
----img_sum:, Size of data: (12568,)
----raw_image:, Size of data: (12568, 40, 60)
----reconstructed_image:, Size of data: (12568, 40, 60)
----width_x:, Size of data: (12568,)
----width_y:, Size of data: (12568,)
----x:, Size of data: (12568,)
----y:, Size of data: (12568,)
--spot: spot_2:
----height:, Size of data: (12568,)
----img_max:, Size of data: (12568,)
----img_mean:, Size of data: (12568,)
----img_rec_max:, Size of data: (12568,)
----img_rec_mean:, Size of data: (12568,)
----img_rec_sum:, Size of data: (12568,)
----img_sum:, Size of data: (12568,)
----raw_image:, Size of data: (12568, 29, 48)
----reconstructed_image:, Size of data: (12568, 29, 48)
----width_x:, Size of data: (12568,)
----width_y:, Size of data: (12568,)
----x:, Size of data: (12568,)
----y:, Size of data: (12568,)
--spot: spot_3:
----height:, Size of data: (12568,)
----img_max:, Size of data: (12568,)
----img_mean:, Size of data: (12568,)
----img_rec_max:, Size of data: (12568,)
----img_rec_mean:, Size of data: (12568,)
----img_rec_sum:, Size of data: (12568,)
----img_sum:, Size of data: (12568,)
----raw_image:, Size of data: (12568, 32, 65)
----reconstructed_image:, Size of data: (12568, 32, 65)
----width_x:, Size of data: (12568,)
----width_y:, Size of data: (12568,)
----x:, Size of data: (12568,)
----y:, Size of data: (12568,)
img_all = D1_spot.growth_dataset(growth='growth_2', index=0)
img_spot1 = D1_para.growth_dataset(growth='growth_2', metric='raw_image', spot='spot_1', index=100)
img_spot2 = D1_para.growth_dataset(growth='growth_2', metric='raw_image', spot='spot_2', index=100)
img_spot3 = D1_para.growth_dataset(growth='growth_2', metric='raw_image', spot='spot_3', index=100)
imgs = [img_all, img_spot1, img_spot2, img_spot3]
width_ratios = [img.shape[1]/img.shape[0] for img in imgs]
titles = ['All spots', 'Spot 1', 'Spot 2', 'Spot 3']
fig = plt.figure(figsize=(6.5, 1))
gs = gridspec.GridSpec(nrows=1, ncols=4, width_ratios=width_ratios, wspace=0.1)
axes = [fig.add_subplot(gs[i]) for i in range(4)]
for i, ax in enumerate(axes):
if i == 3:
imagemap(ax, imgs[i], colorbars=True, clim=(0,120), divider_=False)
else:
imagemap(ax, imgs[i], colorbars=False, clim=(0,120), divider_=False)
labelfigs(ax, None, string_add=titles[i], loc='ct', size=10)
plt.show()
D1_para.viz_RHEED_parameter(growth='growth_2', spot='spot_1', index=10)
D1_para.viz_RHEED_parameter(growth='growth_2', spot='spot_2', index=10)
D1_para.viz_RHEED_parameter(growth='growth_2', spot='spot_3', index=10)
Fig. a: RHEED spot image, b: reconstructed RHEED spot image, c: difference between original and reconstructed image for growth_2 at index 10.
img_sum=47210.00, img_max=27.00, img_mean=19.67
img_rec_sum=47069.62, img_rec_max=21.57, img_rec_mean=19.61
height=21.57, x=22.41, y=30.54, width_x=34.65, width_y_max=65.43
Fig. a: RHEED spot image, b: reconstructed RHEED spot image, c: difference between original and reconstructed image for growth_2 at index 10.
img_sum=120347.00, img_max=255.00, img_mean=86.46
img_rec_sum=107956.04, img_rec_max=247.07, img_rec_mean=77.55
height=247.70, x=16.68, y=21.86, width_x=7.42, width_y_max=11.00
Fig. a: RHEED spot image, b: reconstructed RHEED spot image, c: difference between original and reconstructed image for growth_2 at index 10.
img_sum=48979.00, img_max=38.00, img_mean=23.55
img_rec_sum=48411.60, img_rec_max=28.72, img_rec_mean=23.27
height=28.73, x=14.61, y=26.34, width_x=20.42, width_y_max=42.69
2. Examples of Fitted RHEED Intenstity Curve#
spot = 'spot_2'
metric = 'img_sum'
camera_freq = 500
fit_settings = {'savgol_window_order': (15, 3), 'pca_component': 10, 'I_diff': 15000, 'unify':False,
'bounds':[0.001, 1], 'p_init':[0.1, 0.4, 0.1]}
x_all, y_all = D1_para.load_multiple_curves(['growth_1'], spot, metric, x_start=0, interval=0)
parameters_all, x_coor_all, info = analyze_curves(D1_para, {'growth_1': 1}, spot, metric, interval=0, fit_settings=fit_settings)
[xs_all, ys_all, ys_fit_all, ys_nor_all, ys_nor_fit_all, ys_nor_fit_failed_all, labels_all, losses_all] = info
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/test6_gaussian_fit_parameters_all-04232023.h5'
D1_para = RHEED_parameter_dataset(path, camera_freq=500, sample_name='treated_213nm')
decay_curve_examples(D1_para, 'spot_2', 'img_sum', fit_settings)
printing.savefig(fig, 'treated_81nm-curve_examples')
../Figures/3.Growth_mechanism/treated_81nm-curve_examples.png
../Figures/3.Growth_mechanism/treated_81nm-curve_examples.svg
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/test6_gaussian_fit_parameters_all-04232023.h5'
D1_para = RHEED_parameter_dataset(path, camera_freq=500, sample_name='treated_213nm')
decay_curve_examples(D1_para, 'spot_2', 'img_sum', fit_settings)
printing.savefig(fig, 'treated_81nm-curve_examples')
../Figures/3.Growth_mechanism/treated_81nm-curve_examples.png
../Figures/3.Growth_mechanism/treated_81nm-curve_examples.svg
3. Predict the growth mechanism#
3.1 Sample 1 - treated_213nm#
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/test6_gaussian_fit_parameters_all-04232023.h5'
D1_para = RHEED_parameter_dataset(path, camera_freq=500, sample_name='treated_213nm')
growth_list = ['growth_1', 'growth_2', 'growth_3', 'growth_4', 'growth_5', 'growth_6',
'growth_7', 'growth_8', 'growth_9', 'growth_10', 'growth_11', 'growth_12']
D1_para.viz_RHEED_parameter_trend(growth_list, spot='spot_2', metric_list=['img_sum'], head_tail=(100,300), interval=200)
Gaussian fitted parameters in time: Fig. a: maximum intensity of original cropped RHEED spot, b: maximum intensity of resonstructed cropped RHEED spot, c: spot center in spot x coordinate, d: spot center in y coordinate, e: spot width in x coordinate, f: spot width in y coordinate.
D1_para.viz_RHEED_parameter_trend(['growth_1'], spot='spot_2', metric_list=['img_sum'])
Gaussian fitted parameters in time: Fig. a: maximum intensity of original cropped RHEED spot, b: maximum intensity of resonstructed cropped RHEED spot, c: spot center in spot x coordinate, d: spot center in y coordinate, e: spot width in x coordinate, f: spot width in y coordinate.
growth_dict = {'growth_1':1, 'growth_2':1, 'growth_3':1, 'growth_4':3, 'growth_5':3}
x_all, y_all = D1_para.load_multiple_curves(growth_dict.keys(), spot, metric, x_start=0, interval=0)
parameters_all, x_coor_all, info = analyze_curves(D1_para, growth_dict, spot, metric, interval=0, fit_settings=fit_settings)
[xs_all, ys_all, ys_fit_all, ys_nor_all, ys_nor_fit_all, ys_nor_fit_failed_all, labels_all, losses_all] = info
x_y1 = x_coor_all[losses_all[:,0]>losses_all[:,1]]
x_y2 = x_coor_all[losses_all[:,0]<losses_all[:,1]]
color_array = Viz.two_color_array(x_coor_all, x_y1, x_y2, bgc1, bgc2)
color_array = np.concatenate([np.expand_dims(x_coor_all, 1), color_array], axis=1)
loss_diff = np.abs(losses_all[:,0] - losses_all[:,1])
fig, ax = plt.subplots(1, 1, figsize=(6, 2))
Viz.plot_loss_difference(ax, x_all, y_all, x_coor_all, loss_diff, color_array, color_2=seq_colors[0])
# Viz.plot_loss_difference(ax, x_all, y_all, x_coor_all, loss_diff, color_array, color_2=seq_colors[0])
We used two exponential decay functions ( y=1-exp(t/tau) and y=exp(-t/tau) ) to fit with RHEED intensity curves. By calculate the Mean Squared Error (MSE) loss of fitted data and raw data, we can determine which function fits better by use the fitting function with a lower MSE loss. Here we plotted two diagrams: 1. RHEED intensity curve with two colors as the background to indicate the selected fitted functions (left y axis); 2. MSE loss difference between raw data and fitted data (right y axis).
3.1.1 Check Prediction Outliers#
If the difference between MSE losses of two fitting functions with raw data is small, it’s hard to determine fitting function based on the loss. Therefore, we need to review these examples to exclude the incorrect prediction. Here are curves with a small loss difference.
# get the most confused 50 curve
confused_index = np.argsort(loss_diff)[:50]
# confused_index = np.sort(confused_index)
print("Indices:", confused_index)
xs_confused = xs_all[confused_index]
ys_nor_confused = ys_nor_all[confused_index]
ys_nor_fit_confused = ys_nor_fit_all[confused_index]
ys_nor_fit_failed_confused = ys_nor_fit_failed_all[confused_index]
# labels_confused = list(str(i)+'-'+labels_all[i] for i in confused_index)
Viz.plot_fit_details(xs_confused, ys_nor_confused, ys_nor_fit_confused, ys_nor_fit_failed_confused, confused_index)
Indices: [ 38 105 73 141 142 143 106 138 9 140 8 104 139 102 72 69 137 67
36 30 32 136 31 35 135 144 103 37 33 7 71 34 10 70 66 39
182 6 107 101 217 68 5 74 11 179 65 149 145 147]
Based on the observation, there are three situations for these curves with small loss difference:
Correct prediction: acceptable. Indexes: 5, 6, 7, 8, 9, 11, 33, 34, 35, 36, 37.
Incorrect predictions: exclude these predictions in final results. There is no curve in this situation.
Flat curves when the sample transits from one terminated surface to another kind, so we should exclude these predictions as well. We will remove the predictions that are not in situation 1.
3.1.2 Remove Incorrect Predictions#
# replace the wrong prediction with result from previous ablation
wrong_prediction_list = confused_index
wrong_prediction_list = np.delete(wrong_prediction_list, [5, 6, 7, 8, 9, 11, 33, 34, 35, 36, 37])
# wrong_prediction_list = [8, 37, 36, 32, 68, 38, 34, 7, 35, 72, 6, 5]
for i in wrong_prediction_list:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(6, 2), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
3.1.3 Label the Condition Change#
boxes = [(25.5, 28), (51, 53), (73.5, 75.5), (99, 100)]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
Viz.draw_boxes(ax, boxes, color_gray)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
3.1.4 Clean plot#
for (box_start, box_end) in boxes:
for i, x in enumerate(color_array[:,0]):
if x>box_start and x<box_end:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
np.save('../Data/Plume_results/treated_213nm-x_all.npy', np.copy(x_all))
np.save('../Data/Plume_results/treated_213nm-y_all.npy', np.copy(y_all))
np.save('../Data/Plume_results/treated_213nm-bg_growth.npy', np.copy(color_array))
np.save('../Data/Plume_results/treated_213nm-boxes.npy', np.copy(boxes))
np.save('../Data/Plume_results/treated_213nm-losses_all.npy', np.copy(losses_all))
3.2 Sample 2 - treated_81nm#
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/test7_gaussian_fit_parameters_all-04232023.h5'
D2_para = RHEED_parameter_dataset(path, camera_freq=500, sample_name='treated_81nm')
growth_list = ['growth_1', 'growth_2', 'growth_3', 'growth_4', 'growth_5', 'growth_6',
'growth_7', 'growth_8', 'growth_9', 'growth_10', 'growth_11', 'growth_12']
D2_para.viz_RHEED_parameter_trend(growth_list, spot='spot_2', metric_list=['img_sum'], head_tail=(100,300), interval=200)
Gaussian fitted parameters in time: Fig. a: maximum intensity of original cropped RHEED spot, b: maximum intensity of resonstructed cropped RHEED spot, c: spot center in spot x coordinate, d: spot center in y coordinate, e: spot width in x coordinate, f: spot width in y coordinate.
D2_para.viz_RHEED_parameter_trend(['growth_1'], spot='spot_2', metric_list=['img_sum'], figsize=(6,3))
Gaussian fitted parameters in time: Fig. a: maximum intensity of original cropped RHEED spot, b: maximum intensity of resonstructed cropped RHEED spot, c: spot center in spot x coordinate, d: spot center in y coordinate, e: spot width in x coordinate, f: spot width in y coordinate.
growth_dict = {'growth_1':1, 'growth_2':1, 'growth_3':1, 'growth_4':3, 'growth_5':3}
x_all, y_all = D2_para.load_multiple_curves(growth_dict.keys(), spot, metric, x_start=0, interval=0)
parameters_all, x_coor_all, info = analyze_curves(D2_para, growth_dict, spot, metric, interval=0, fit_settings=fit_settings)
[xs_all, ys_all, ys_fit_all, ys_nor_all, ys_nor_fit_all, ys_nor_fit_failed_all, labels_all, losses_all] = info
x_y1 = x_coor_all[losses_all[:,0]>losses_all[:,1]]
x_y2 = x_coor_all[losses_all[:,0]<losses_all[:,1]]
color_array = Viz.two_color_array(x_coor_all, x_y1, x_y2, bgc1, bgc2, transparency=0.5)
color_array = np.concatenate([np.expand_dims(x_coor_all, 1), color_array], axis=1)
loss_diff = np.abs(losses_all[:,0] - losses_all[:,1])
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.plot_loss_difference(ax, x_all, y_all, x_coor_all, loss_diff, color_array, color_2=seq_colors[0])
3.2.1 Check Prediction Outliers#
# get the most confused 50 curve
confused_index = np.argsort(loss_diff)[:50]
confused_index.sort()
print("Indices:", confused_index)
xs_confused = xs_all[confused_index]
ys_nor_confused = ys_nor_all[confused_index]
ys_nor_fit_confused = ys_nor_fit_all[confused_index]
ys_nor_fit_failed_confused = ys_nor_fit_failed_all[confused_index]
labels_confused = list(str(i)+'-'+labels_all[i] for i in confused_index)
Viz.plot_fit_details(xs_confused, ys_nor_confused, ys_nor_fit_confused, ys_nor_fit_failed_confused, confused_index)
Indices: [ 0 1 2 3 4 5 6 7 8 9 10 20 22 24 26 27 29 30
31 32 33 34 35 37 52 71 72 73 75 76 108 110 111 112 113 123
124 135 138 139 140 141 142 143 145 146 148 149 150 163]
Correct prediction: acceptable. Indexes: 1, 2, 3, 4, 7, 8, 9, 26, 27, 30, 31, 33, 35.
Incorrect predictions: exclude these predictions in final results. There is no curve in this situation.
Flat curves when the sample transits from one terminated surface to another kind, so we should exclude these predictions as well. We will remove the predictions that are not in situation 1.
3.2.2 Remove Incorrect Predictions#
# replace the wrong prediction with results from previous ablation
wrong_prediction_list = confused_index
wrong_prediction_list = np.delete(wrong_prediction_list, [1, 2, 3, 4, 7, 8, 9, 26, 27, 30, 31, 33, 35])
for i in wrong_prediction_list:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
3.2.3 Label the Condition Change#
boxes = [(16, 18), (37, 39), (60, 62), (84, 86)]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
Viz.draw_boxes(ax, boxes, color_gray)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
3.2.4 Clean plot#
for (box_start, box_end) in boxes:
for i, x in enumerate(color_array[:,0]):
if x>box_start and x<box_end:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
Conclusion:#
The model can predict most of the “decay flipping phenomenon”. However, it missed the one at around t=75s. We can see the decrease decay function feature is harder to obeserve and predict after a few unit cell have been grown.
By observing the fitted examples, we can derive similar conclusion as the previous growth. Because in later growth, the reason of decay function y=exp(-t/tau) have lower MSE loss is mainly resulted from the general intensity change and noise instead of inherent decay drop phenomenon, we can assume the curve after growth_3 fit better with y=1-exp(-t/tau) decay function.
np.save('../Data/Plume_results/treated_81nm-x_all.npy', np.copy(x_all))
np.save('../Data/Plume_results/treated_81nm-y_all.npy', np.copy(y_all))
np.save('../Data/Plume_results/treated_81nm-bg_growth.npy', np.copy(color_array))
np.save('../Data/Plume_results/treated_81nm-boxes.npy', np.copy(boxes))
np.save('../Data/Plume_results/treated_81nm-losses_all.npy', np.copy(losses_all))
3.3 Sample 3 - untreated_162nm#
path = 'D:/STO_STO-Data/RHEED/STO_STO_Berkeley/test9_gaussian_fit_parameters_all-04232023.h5'
D3_para = RHEED_parameter_dataset(path, camera_freq=500, sample_name='untreated_162nm')
growth_list = ['growth_1', 'growth_2', 'growth_3', 'growth_4', 'growth_5', 'growth_6',
'growth_7', 'growth_8', 'growth_9', 'growth_10', 'growth_11', 'growth_12',
'growth_13', 'growth_14', 'growth_15', 'growth_16', 'growth_17', 'growth_18']
D3_para.viz_RHEED_parameter_trend(growth_list, spot='spot_2', metric_list=['img_sum'], head_tail=(100,300), interval=1000)
Gaussian fitted parameters in time: Fig. a: maximum intensity of original cropped RHEED spot, b: maximum intensity of resonstructed cropped RHEED spot, c: spot center in spot x coordinate, d: spot center in y coordinate, e: spot width in x coordinate, f: spot width in y coordinate.
D3_para.viz_RHEED_parameter_trend(['growth_1'], spot='spot_2', metric_list=['img_sum'], figsize=(6,3))
Gaussian fitted parameters in time: Fig. a: maximum intensity of original cropped RHEED spot, b: maximum intensity of resonstructed cropped RHEED spot, c: spot center in spot x coordinate, d: spot center in y coordinate, e: spot width in x coordinate, f: spot width in y coordinate.
growth_dict = {'growth_1':1, 'growth_2':1, 'growth_3':1, 'growth_4':1, 'growth_5':1, 'growth_6':1, 'growth_7':1, 'growth_8':1}
x_all, y_all = D3_para.load_multiple_curves(growth_dict.keys(), spot, metric, x_start=0, interval=0)
parameters_all, x_coor_all, info = analyze_curves(D3_para, growth_dict, spot, metric, interval=0, fit_settings=fit_settings)
[xs_all, ys_all, ys_fit_all, ys_nor_all, ys_nor_fit_all, ys_nor_fit_failed_all, labels_all, losses_all] = info
x_y1 = x_coor_all[losses_all[:,0]>losses_all[:,1]]
x_y2 = x_coor_all[losses_all[:,0]<losses_all[:,1]]
color_array = Viz.two_color_array(x_coor_all, x_y1, x_y2, bgc1, bgc2, transparency=0.5)
color_array = np.concatenate([np.expand_dims(x_coor_all, 1), color_array], axis=1)
loss_diff = np.abs(losses_all[:,0] - losses_all[:,1])
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.plot_loss_difference(ax, x_all, y_all, x_coor_all, loss_diff, color_array, color_2=seq_colors[0])
3.3.1 Check Prediction Outliers#
# get the most confused 50 curve
confused_index = np.argsort(loss_diff)[:50]
confused_index = np.sort(confused_index)
print("Indices:", confused_index)
xs_confused = xs_all[confused_index]
ys_nor_confused = ys_nor_all[confused_index]
ys_nor_fit_confused = ys_nor_fit_all[confused_index]
ys_nor_fit_failed_confused = ys_nor_fit_failed_all[confused_index]
Viz.plot_fit_details(xs_confused, ys_nor_confused, ys_nor_fit_confused, ys_nor_fit_failed_confused, confused_index)
Indices: [ 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 29 30 34 38 43 44 46 47 69 71 72 74 75 78 101 104 106
107 110 123 127 134 135 136 137 138 139 140 141 167 170]
Correct prediction: acceptable. Indexes: 1, 2, 3, 4, 7, 8, 9, 26, 27, 30, 31, 33, 35.
Incorrect predictions: exclude these predictions in final results. There is no curve in this situation.
Flat curves when the sample transits from one terminated surface to another kind, so we should exclude these predictions as well. We will remove the predictions that are not in situation 1.
3.3.2 Remove Incorrect Predictions#
# replace the wrong prediction with result from previous ablation
wrong_prediction_list = confused_index
wrong_prediction_list = np.delete(wrong_prediction_list, [6, 7, 8, 9, 12, 17, 23, 24])
for i in wrong_prediction_list:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
We notice the predictions around index=45 are not consistent with our assumption, so we review some curves here:
wrong_prediction_list = [43, 44, 45, 46, 47, 48]
xs_confused = xs_all[wrong_prediction_list]
ys_nor_confused = ys_nor_all[wrong_prediction_list]
ys_nor_fit_confused = ys_nor_fit_all[wrong_prediction_list]
ys_nor_fit_failed_confused = ys_nor_fit_failed_all[wrong_prediction_list]
Viz.plot_fit_details(xs_confused, ys_nor_confused, ys_nor_fit_confused, ys_nor_fit_failed_confused, wrong_prediction_list)
for i in wrong_prediction_list:
print(i, labels_all[i])
43 growth_2-index 19:
y1=(0.0t+0.12)*(1-exp(-t/0.03))
44 growth_2-index 20:
y2=(1.0t+0.15)*(exp(-t/0.3))
45 growth_2-index 21:
y2=(1.0t+0.18)*(exp(-t/0.18))
46 growth_2-index 22:
y2=(1.0t+0.13)*(exp(-t/0.06))
47 growth_3-index 1:
y1=(0.04t+0.07)*(1-exp(-t/0.06))
48 growth_3-index 2:
y1=(0.06t+0.11)*(1-exp(-t/0.04))
Remove index 44, 45, 46 because they are not a complete decay curve:
# replace the wrong prediction with result from previous ablation
wrong_prediction_list = [44, 45, 46]
for i in wrong_prediction_list:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
3.3.3 Label the Condition Change#
boxes = [(26, 28), (46, 48), (67, 70), (93, 96), (119, 121), (144, 147), (169, 172)]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
Viz.draw_boxes(ax, boxes, color_gray)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
3.3.4 Clean plot#
for (box_start, box_end) in boxes:
for i, x in enumerate(color_array[:,0]):
if x>box_start and x<box_end:
color_array[i][1:] = color_array[i-1][1:]
fig, ax = plt.subplots(1, 1, figsize=(8, 2.5), layout='compressed')
Viz.draw_background_colors(ax, color_array)
ax.scatter(x_all, y_all, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)')
plt.show()
np.save('../Data/Plume_results/untreated_162nm-x_all.npy', np.copy(x_all))
np.save('../Data/Plume_results/untreated_162nm-y_all.npy', np.copy(y_all))
np.save('../Data/Plume_results/untreated_162nm-bg_growth.npy', np.copy(color_array))
np.save('../Data/Plume_results/untreated_162nm-boxes.npy', np.copy(boxes))
np.save('../Data/Plume_results/untreated_162nm-losses_all.npy', np.copy(losses_all))
All of the examples classified to y=exp(-t/tau) decay function are resulted from noise instead of from actual decay behavior. Therefore, we can conclude that the untreated does not shows “decay flipping phenomenon”.
4. Summary - Comparison between Three Samples#
# load data if needed
x_all_sample1, y_all_sample1 = np.load('../Data/Plume_results/treated_213nm-x_all.npy'), np.load('../Data/Plume_results/treated_213nm-y_all.npy')
color_array_sample1 = np.load('../Data/Plume_results/treated_213nm-bg_growth.npy')
boxes_sample1 = np.load('../Data/Plume_results/treated_213nm-boxes.npy')
losses_all_sample1 = np.load('../Data/Plume_results/treated_213nm-losses_all.npy')
loss_diff_sample1 = np.abs(losses_all_sample1[:,0] - losses_all_sample1[:,1])
x_all_sample2, y_all_sample2 = np.load('../Data/Plume_results/treated_81nm-x_all.npy'), np.load('../Data/Plume_results/treated_81nm-y_all.npy')
color_array_sample2 = np.load('../Data/Plume_results/treated_81nm-bg_growth.npy')
boxes_sample2 = np.load('../Data/Plume_results/treated_81nm-boxes.npy')
losses_all_sample2 = np.load('../Data/Plume_results/treated_81nm-losses_all.npy')
loss_diff_sample2 = np.abs(losses_all_sample2[:,0] - losses_all_sample2[:,1])
x_all_sample3, y_all_sample3 = np.load('../Data/Plume_results/untreated_162nm-x_all.npy'), np.load('../Data/Plume_results/untreated_162nm-y_all.npy')
color_array_sample3 = np.load('../Data/Plume_results/untreated_162nm-bg_growth.npy')
boxes_sample3 = np.load('../Data/Plume_results/untreated_162nm-boxes.npy')
losses_all_sample3 = np.load('../Data/Plume_results/untreated_162nm-losses_all.npy')
loss_diff_sample3 = np.abs(losses_all_sample3[:,0] - losses_all_sample3[:,1])
4.1 RHEED Intensity with Loss Difference#
fig, axes = layout_fig(3, 1, figsize=(8, 8))
Viz.plot_loss_difference(axes[0], x_all_sample1, y_all_sample1, color_array_sample1[:,0], loss_diff_sample1,
color_array_sample1, color_2=seq_colors[0], title='treated_213nm')
labelfigs(axes[0], number=0, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
Viz.plot_loss_difference(axes[1], x_all_sample2, y_all_sample2, color_array_sample2[:,0], loss_diff_sample2,
color_array_sample2, color_2=seq_colors[0], title='treated_81nm')
labelfigs(axes[1], number=1, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
Viz.plot_loss_difference(axes[2], x_all_sample3, y_all_sample3, color_array_sample3[:,0], loss_diff_sample3,
color_array_sample3, color_2=seq_colors[0], title='untreated_162nm')
labelfigs(axes[2], number=2, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
printing_plot.savefig(fig, 'loss_difference', dpi=300)
plt.show()
C:\Users\yig319\Anaconda3\envs\test_rheed\Lib\site-packages\m3util\viz\layout.py:255: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.
fig.subplots_adjust(wspace=wspace, hspace=hspace)
../Figures/3.Growth_mechanism/loss_difference.png
../Figures/3.Growth_mechanism/loss_difference.svg
../Figures/3.Growth_mechanism/loss_difference.tif
Temporal evolution of RHEED intensity extracted from spot 2 region. RHEED intensity of deposition of a) treated_213nm, b) treated_81nm, and c) untreated_162nm, respectively. The medium and light background represents different fitting functions used to fit with the decay curves. Typical decay curves are provided on the corresponding background color. And the blue scatter plot represents the loss difference between predictions using two exponential curve functions and raw RHEED intensity data. Higher loss difference means the model predict with less confidence.
4.2 Plot with gray box covering the growth condition changing region#
y_all_sample1 = y_all_sample1[x_all_sample1<110]
x_all_sample1 = x_all_sample1[x_all_sample1<110]
color_array_sample1 = color_array_sample1[color_array_sample1[:, 0]<110]
boxes_sample1 = boxes_sample1[boxes_sample1[:,0]<110]
y_all_sample2 = y_all_sample2[x_all_sample2<110]
x_all_sample2 = x_all_sample2[x_all_sample2<110]
color_array_sample2 = color_array_sample2[color_array_sample2[:, 0]<110]
boxes_sample2 = boxes_sample2[boxes_sample2[:,0]<110]
y_all_sample3 = y_all_sample3[x_all_sample3<110]
x_all_sample3 = x_all_sample3[x_all_sample3<110]
color_array_sample3 = color_array_sample3[color_array_sample3[:, 0]<110]
boxes_sample3 = boxes_sample3[boxes_sample3[:,0]<110]
fig, ax = layout_fig(1, 1, figsize=(8, 2.6))
Viz.draw_background_colors(ax, color_array_sample1)
Viz.draw_boxes(ax, boxes_sample1, color_gray)
# Normalize the data
y_all_sample1 = NormalizeData(y_all_sample1, lb=np.min(y_all_sample1), ub=np.max(y_all_sample1))
ax.scatter(x_all_sample1, y_all_sample1, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)', ylim=(-0.1, 1.1), yaxis_style='sci')
labelfigs(ax, number=0, loc='tr', style='b', size=18, inset_fraction=(0.08, 0.03))
printing_plot.savefig(fig, 'fitting_results-gray_boxes-a', dpi=600, transparent=True)
plt.show()
fig, ax = layout_fig(1, 1, figsize=(8, 2.6))
Viz.draw_background_colors(ax, color_array_sample2)
Viz.draw_boxes(ax, boxes_sample2, color_gray)
# Normalize the data
y_all_sample2 = NormalizeData(y_all_sample2, lb=np.min(y_all_sample2), ub=np.max(y_all_sample2))
ax.scatter(x_all_sample2, y_all_sample2, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)', ylim=(-0.1, 1.1), yaxis_style='sci')
labelfigs(ax, number=1, loc='tr', style='b', size=18, inset_fraction=(0.08, 0.03))
printing_plot.savefig(fig, 'fitting_results-gray_boxes-b', dpi=600, transparent=True)
plt.show()
fig, ax = layout_fig(1, 1, figsize=(8, 2.6))
Viz.draw_background_colors(ax, color_array_sample3)
Viz.draw_boxes(ax, boxes_sample3, color_gray)
# Normalize the data
y_all_sample3 = NormalizeData(y_all_sample3, lb=np.min(y_all_sample3), ub=np.max(y_all_sample3))
ax.scatter(x_all_sample3, y_all_sample3, color='k', s=1)
Viz.set_labels(ax, xlabel='Time (s)', ylabel='Intensity (a.u.)', ylim=(-0.1, 1.1), yaxis_style='sci')
labelfigs(ax, number=2, loc='tr', style='b', size=18, inset_fraction=(0.08, 0.03))
printing_plot.savefig(fig, 'fitting_results-gray_boxes-c', dpi=600, transparent=True)
plt.show()
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-a.png
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-a.svg
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-a.tif
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-b.png
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-b.svg
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-b.tif
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-c.png
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-c.svg
../Figures/3.Growth_mechanism/fitting_results-gray_boxes-c.tif
Temporal evolution of RHEED intensity extracted from spot 2 region. RHEED intensity of deposition of a) treated_213nm, b) treated_81nm, and c) untreated_162nm, respectively. The gray background indicates the changing growth condition, while the medium and light background represents different fitting functions used to fit with the decay curves. Typical decay curves are provided on the corresponding background color.
4.2 Clean version plot#
fig, axes = layout_fig(3, 1, figsize=(8, 8))
Viz.draw_background_colors(axes[0], color_array_sample1)
axes[0].scatter(x_all_sample1, y_all_sample1, color='k', s=1)
Viz.set_labels(axes[0], xlabel='Time (s)', ylabel='Intensity (a.u.)', title='treated_213nm')
labelfigs(axes[0], number=0, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
Viz.draw_background_colors(axes[1], color_array_sample2)
axes[1].scatter(x_all_sample2, y_all_sample2, color='k', s=1)
Viz.set_labels(axes[1], xlabel='Time (s)', ylabel='Intensity (a.u.)', title='treated_81nm')
labelfigs(axes[1], number=1, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
Viz.draw_background_colors(axes[2], color_array_sample3)
axes[2].scatter(x_all_sample3, y_all_sample3, color='k', s=1)
Viz.set_labels(axes[2], xlabel='Time (s)', ylabel='Intensity (a.u.)', title='untreated_162nm')
labelfigs(axes[2], number=2, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
# printing_plot.savefig(fig, 'fitting_results_clear', dpi=300)
plt.show()
Temporal evolution of RHEED intensity extracted from spot 2 region. RHEED intensity of deposition of a) treated_213nm, b) treated_81nm, and c) untreated_162nm, respectively. The medium and light background represents different fitting functions used to fit with the decay curves. Typical decay curves are provided on the corresponding background color.
4.3 Align the time for better visualization of growth rate#
y_all_sample1 = y_all_sample1[x_all_sample1<110]
x_all_sample1 = x_all_sample1[x_all_sample1<110]
x_coor = color_array_sample1[:, 0]
color_array_sample1 = color_array_sample1[x_coor<110]
y_all_sample2 = y_all_sample2[x_all_sample2<110]
x_all_sample2 = x_all_sample2[x_all_sample2<110]
x_coor = color_array_sample2[:, 0]
color_array_sample2 = color_array_sample2[x_coor<110]
y_all_sample3 = y_all_sample3[x_all_sample3<110]
x_all_sample3 = x_all_sample3[x_all_sample3<110]
x_coor = color_array_sample3[:, 0]
color_array_sample3 = color_array_sample3[x_coor<110]
fig, axes = layout_fig(3, 1, figsize=(8, 8))
Viz.draw_background_colors(axes[0], color_array_sample1)
axes[0].scatter(x_all_sample1, y_all_sample1, color='k', s=1)
Viz.set_labels(axes[0], xlabel='Time (s)', ylabel='Intensity (a.u.)', title='treated_213nm')
labelfigs(axes[0], number=0, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
# axes[0].set_xlim(0, 115)
Viz.draw_background_colors(axes[1], color_array_sample2)
axes[1].scatter(x_all_sample2, y_all_sample2, color='k', s=1)
Viz.set_labels(axes[1], xlabel='Time (s)', ylabel='Intensity (a.u.)', title='treated_81nm')
labelfigs(axes[1], number=1, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
Viz.draw_background_colors(axes[2], color_array_sample3)
axes[2].scatter(x_all_sample3, y_all_sample3, color='k', s=1)
Viz.set_labels(axes[2], xlabel='Time (s)', ylabel='Intensity (a.u.)', title='untreated_162nm')
labelfigs(axes[2], number=2, loc='tr', style='b', size=12, inset_fraction=(0.08, 0.03))
# printing_plot.savefig(fig, 'fitting_results_clear', dpi=300)
plt.show()
4.4 3D structure drawing#
ti_color, sr_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[5]), 0.6)), rgba_to_rgb((*mcolors.hex2color(seq_colors[0]), 0.6))
box1_ti_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[5]), 0.5))
box2_ti_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[5]), 0.6))
box3_ti_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[5]), 0.7))
box1_sr_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[0]), 0.5))
box2_sr_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[0]), 0.6))
box3_sr_color = rgba_to_rgb((*mcolors.hex2color(seq_colors[0]), 0.7))
size = 1.5
n1 = 100
mlab.figure(size = (1024,768), bgcolor = (1,1,1), fgcolor = (146/255,197/255,222/255))
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(4, 6), color=box1_ti_color)
sphere_to_surface(xrange=(0, 30), yrange=(0, 50), z=6, n=n1, size=size, color=sr_color, offset=(0,0))
draw_box(xrange=(0, 50), yrange=(0, 50), zrange=(2, 4), color=box2_ti_color)
sphere_to_surface(xrange=(30, 50), yrange=(0, 50), z=4, n=n1, size=size, color=sr_color, offset=(0,0))
draw_box(xrange=(0, 70), yrange=(0, 50), zrange=(0, 2), color=box3_ti_color)
sphere_to_surface(xrange=(50, 70), yrange=(0, 50), z=2, n=n1, size=size, color=sr_color, offset=(0,0))
mlab.savefig('../Figures/3.Growth_mechanism/stage_1.png')
mlab.show()
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[44], line 2
1 n1 = 100
----> 2 mlab.figure(size = (1024,768), bgcolor = (1,1,1), fgcolor = (146/255,197/255,222/255))
3 draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(4, 6), color=box1_ti_color)
4 sphere_to_surface(xrange=(0, 30), yrange=(0, 50), z=6, n=n1, size=size, color=sr_color, offset=(0,0))
File ~\Anaconda3\envs\test_rheed\Lib\site-packages\mayavi\tools\figure.py:64, in figure(figure, bgcolor, fgcolor, engine, size)
62 else:
63 if engine is None:
---> 64 engine = get_engine()
65 if figure is None:
66 name = max(__scene_number_list) + 1
File ~\Anaconda3\envs\test_rheed\Lib\site-packages\mayavi\tools\engine_manager.py:94, in EngineManager.get_engine(self)
91 suitable = [e for e in engines
92 if e.__class__.__name__ == 'Engine']
93 if len(suitable) == 0:
---> 94 return self.new_engine()
95 else:
96 # Return the most engine add to the list most recently.
97 self.current_engine = suitable[-1]
File ~\Anaconda3\envs\test_rheed\Lib\site-packages\mayavi\tools\engine_manager.py:139, in EngineManager.new_engine(self)
135 def new_engine(self):
136 """ Creates a new engine, envisage or not depending on the
137 options.
138 """
--> 139 check_backend()
140 if options.backend == 'envisage':
141 from mayavi.plugins.app import Mayavi
File ~\Anaconda3\envs\test_rheed\Lib\site-packages\mayavi\tools\engine_manager.py:42, in check_backend()
37 if (options.backend != 'test' and not options.offscreen) and \
38 (ETSConfig.toolkit in ('null', '') and env_toolkit != 'null'):
39 msg = '''Could not import backend for traitsui. Make sure you
40 have a suitable UI toolkit like PyQt/PySide or wxPython
41 installed.'''
---> 42 raise ImportError(msg)
ImportError: Could not import backend for traitsui. Make sure you
have a suitable UI toolkit like PyQt/PySide or wxPython
installed.
n1 = 800
mlab.figure(size = (1024,768), bgcolor = (1,1,1), fgcolor = (146/255,197/255,222/255))
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(4, 6), color=box1_ti_color)
sphere_to_surface(xrange=(0, 30), yrange=(0, 50), z=6, n=n1, size=size, color=sr_color, offset=(1,1))
draw_box(xrange=(0, 50), yrange=(0, 50), zrange=(2, 4), color=box2_ti_color)
sphere_to_surface(xrange=(30, 50), yrange=(0, 50), z=4, n=n1, size=size, color=sr_color, offset=(1,1))
draw_box(xrange=(0, 70), yrange=(0, 50), zrange=(0, 2), color=box3_ti_color)
sphere_to_surface(xrange=(50, 70), yrange=(0, 50), z=2, n=n1, size=size, color=sr_color, offset=(1,1))
mlab.savefig('../Figures/3.Growth_mechanism/stage_2.png')
mlab.show()
n1 = 8000
mlab.figure(size = (1024,768), bgcolor = (1,1,1), fgcolor = (146/255,197/255,222/255))
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(4, 6), color=box1_ti_color)
sphere_to_surface(xrange=(0, 30), yrange=(0, 50), z=6, n=n1, size=size, color=sr_color, offset=(1,1))
draw_box(xrange=(0, 50), yrange=(0, 50), zrange=(2, 4), color=box2_ti_color)
sphere_to_surface(xrange=(30, 50), yrange=(0, 50), z=4, n=n1, size=size, color=sr_color, offset=(1,1))
draw_box(xrange=(0, 70), yrange=(0, 50), zrange=(0, 2), color=box3_ti_color)
sphere_to_surface(xrange=(50, 70), yrange=(0, 50), z=2, n=n1, size=size, color=sr_color, offset=(1,1))
mlab.savefig('../Figures/3.Growth_mechanism/stage_3.png')
mlab.show()
n2 = 500
mlab.figure(size = (1024,768), bgcolor = (1,1,1), fgcolor = (146/255,197/255,222/255))
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(4, 6), color=box1_ti_color)
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(6, 7), color=box1_sr_color)
sphere_to_surface(xrange=(0, 30), yrange=(0, 50), z=7, n=n2, size=size, color=ti_color, offset=(1,1))
draw_box(xrange=(0, 50), yrange=(0, 50), zrange=(2, 4), color=box2_ti_color)
draw_box(xrange=(30, 50), yrange=(0, 50), zrange=(4, 5), color=box2_sr_color)
sphere_to_surface(xrange=(30, 50), yrange=(0, 50), z=5, n=n2, size=size, color=ti_color, offset=(1,1))
draw_box(xrange=(0, 70), yrange=(0, 50), zrange=(0, 2), color=box3_ti_color)
draw_box(xrange=(50, 70), yrange=(0, 50), zrange=(2, 3), color=box3_sr_color)
sphere_to_surface(xrange=(50, 70), yrange=(0, 50), z=3, n=n2, size=size, color=ti_color, offset=(1,1))
mlab.savefig('../Figures/3.Growth_mechanism/stage_4.png')
mlab.show()
n2 = 8000
mlab.figure(size = (1024,768), bgcolor = (1,1,1), fgcolor = (146/255,197/255,222/255))
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(4, 6), color=box1_ti_color)
draw_box(xrange=(0, 30), yrange=(0, 50), zrange=(6, 7), color=box1_sr_color)
sphere_to_surface(xrange=(0, 30), yrange=(0, 50), z=7, n=n2, size=size, color=ti_color, offset=(1,1))
draw_box(xrange=(0, 50), yrange=(0, 50), zrange=(2, 4), color=box2_ti_color)
draw_box(xrange=(30, 50), yrange=(0, 50), zrange=(4, 5), color=box2_sr_color)
sphere_to_surface(xrange=(30, 50), yrange=(0, 50), z=5, n=n2, size=size, color=ti_color, offset=(1,1))
draw_box(xrange=(0, 70), yrange=(0, 50), zrange=(0, 2), color=box3_ti_color)
draw_box(xrange=(50, 70), yrange=(0, 50), zrange=(2, 3), color=box3_sr_color)
sphere_to_surface(xrange=(50, 70), yrange=(0, 50), z=3, n=n2, size=size, color=ti_color, offset=(1,1))
mlab.savefig('../Figures/3.Growth_mechanism/stage_5.png')
# mlab.savefig('../Figures/3.Growth_mechanism/stage_5.svg')
mlab.show()