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visualizer.py
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visualizer.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Jan 7 22:24:59 2023
@author: Rebecca
"""
import sys
import yaml
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredText
#%% Plotsettings
text_fontsize = 32
title_fontsize = 43
tick_fontsize = 28
plt.rcParams.update({
"text.usetex": True, # change if you want Tex labels
"font.family": "sans-serif",
'font.sans-serif': 'Helvetica'
})
#%% manually set parameters
# !!!!!!!!!!!!!!!!!!!!!!! HERE !!!!!!!!!!!!!!!!!!!!!!!!!!! (start)
save = True
# save = False
#ITR = True
ITR = False
#study = 'study1'
#study = 'study2'
#study = 'study3'
#study = 'study3_full'
#study = 'study4'
study = 'study5'
#study = 'study6'
#study = 'study7'
#study = 'study8'
#study = 'paper/1'
# study = 'paper/2'
version = 'SCC'
#version = 'S2V'
# Learn Config
learn_seq= "base_configs/learn_sequences/study1.txt"
#learn_seq= "base_configs/learn_sequences/study7.txt"
# !!!!!!!!!!!!!!!!!!!!!! HERE !!!!!!!!!!!!!!!!!!!!!!!!!!!!! (end)
#%% commandline arguments
sim_config = str("./variants/{study}/sim/".format(study = study)) + str(sys.argv[1]) + ".yaml"
with open(sim_config, 'r') as file:
sim_config_var = yaml.safe_load(file)
#study = 'study5_2'
#%% Parameters
# Simulation Times
# max simulation time in s (f64):
t_sim= sim_config_var['t_sim']
# symbol duration (spray duration + pause duration[min. 0.03]) in s (f64):
t_sym= sim_config_var['t_sym']
# spray duration in s (f64):
t_sp= sim_config_var['t_sp']
# delta time step (either 1/camera fps or something smaller to use sim2vidtool) ins s (f64):
t_dts= sim_config_var['t_dts']
# Layout
# distance between both inj im m (f64):
d= sim_config_var['d']
# distance offset in m from d/2 (f64):
dy= sim_config_var['dy']
# Receiver Props
# cam_fps:
cam_fps= sim_config_var['cam_fps']
# z Position (Sprayer z is 0) in m (f64):
cam_z= sim_config_var['cam_z']
# height - how far can the camera see - commonly tube diameter (camera looks in negative z direction) in m (f64):
cam_height= sim_config_var['cam_height']
# width on tube floor - maximum width seen in landscape picture im m (f64)
cam_w_proj= sim_config_var['cam_w_proj']
# camera ratio length (f64)
cam_ratio_l= sim_config_var['cam_ratio_l'] #maybe name that y
# camera ratio width (f64)
cam_ratio_w= sim_config_var['cam_ratio_w'] #maybe name that x
# Sender Props
# molecules_per_spray (mps) (usize):
mps= sim_config_var['mps']
# initial velocity / velocity mean in m/s (f64):
v_mean= sim_config_var['v_mean']
# velocity sigma (f64):
v_sigma= sim_config_var['v_sigma']
# distribution_sigma for x and z angle (f64):
dist_sigma= sim_config_var['dist_sigma']
# liter_molecule_conversion (None if you want to use mps) in mol/liter (f64):
#lmc= null
# Offset for Inj far in symbol duration (t_sym) if you only got measurements in seconds just divide by t_sym (f64):
off_f= sim_config_var['off_f']
# Message repetitions for Inj far (u32):
rep_f= sim_config_var['rep_f']
# Message path for Inj far (&str):
#msg_f: Msg_f
msg_f= sim_config_var['msg_f']
#msg_f_out: Msg_f_out
# Offset for Inj near in symbol duration (t_sym) (f64):
off_n= sim_config_var['off_n']
# Message repetitions for Inj near (u32):
rep_n= sim_config_var['rep_n']
# Message path for Inj near (&str):
msg_n= sim_config_var['msg_n']
#msg_n_out: Msg_n_out
#%%
# Output file
if version == 'SCC' or (version == 'S2V' and study == 'study7'):
liv_path= sim_config_var['liv_path']
else:
liv_path = "./sim2vid/livs/" + str(sys.argv[1]) + ".txt"
# Apply Config
#frame =
# apply_config = sys.argv[2]
# with open(apply_config, 'r') as file:
# apply_config_var = yaml.safe_load(file)
# frame = apply_config_var['frame']
if version == 'S2V':
t_dts = 1/cam_fps
fps = 1/t_dts
d_travel_near = (d/2) - dy
travel_mean_near = ((d/2.0)-dy) / v_mean
d_travel_far = (d/2) + dy
travel_mean_far = d_travel_far / v_mean
#frames per symbol
frpsym = int(t_sym//t_dts)
frame = int((travel_mean_near + t_sp/3)*fps)
if study == 'study3_full':
if d < 1.4:
frame = 32
elif d < 1.8 and t_sym < 0.06:
frame = 40
elif d < 1.8:
frame = 46
elif d < 2.2:
frame = 52
elif d < 2.6:
frame = 58
elif d < 3.0 and t_sym < 0.08:
frame = 63
elif d < 3.0:
frame = 68
else:
frame = 78
if study == 'study4':
if d < 1.6:
frame = 34
elif d < 1.8 and t_sym < 0.06:
frame = 43
elif d < 1.8:
if off_f < 0.2:
frame = 46
else:
frame = 42
elif d < 2.2:
frame = 52
elif d < 2.6 and off_f > 0:
frame = 58
elif d < 2.6:
frame = 62
elif d < 3.0 and off_f > 0:
frame = 68
elif d < 3.0:
frame = 72
elif d < 3.4 and off_n > 0.46:
frame = 81
else:
frame = 78
if study == 'study5':
if d < 1.4:
fr_n = int((travel_mean_near + 5*t_sp/12)*fps)
fr_f = int((travel_mean_far + 5*t_sp/12)*fps)
elif d < 2.0:
fr_n = int((travel_mean_near + t_sp/3)*fps)
fr_f = int((travel_mean_far + t_sp/3)*fps)
elif d < 3.0:
fr_n = int((travel_mean_near + t_sp/4)*fps)
fr_f = int((travel_mean_far + t_sp/4)*fps)
if study == 'study5' or study == 'study6' or study == 'study7' or study == 'paper/1' or study == 'paper/2':
apply_config = str("./variants/{study}/apply_config/".format(study = study)) + str(sys.argv[1]) + "_0.yaml"
if study == 'study7' or study == 'paper/1' or study == 'paper/2':
apply_config = str("./variants/{study}/apply_config/".format(study = study) + str(sys.argv[2]) + ".yaml")
with open(apply_config, 'r') as file:
apply_config_var = yaml.safe_load(file)
frame = apply_config_var['frame']
#%%
def codes_to_color(arr):
arr_color = []
for code in arr:
if code == 'h':
arr_color.append('b')
elif code == 'f':
arr_color.append('r')
elif code == 'n':
arr_color.append('orange')
elif code == 'l':
arr_color.append('gray')
else:
arr_color.append('white')
return arr_color
def codes_to_label(arr):
#l = ["'11' - both", "'10' - far", "'01' - near", "'00' - none"]
l = ["'11' - H", "'10' - F", "'01' - N", "'00' - L"]
for i in range(4, len(arr)):
l.append('')
return l
def codes_to_alpha(arr, option):
arr_alpha = np.array()
if option == "n":
for code in arr:
if code == 'h':
arr_alpha.append(0)
elif code == 'f':
arr_alpha.append(0.5)
elif code == 'n':
arr_alpha.append(1)
elif code == 'l':
arr_alpha.append(0)
else:
arr_alpha.append(0)
elif option == "f":
for code in arr:
if code == 'h':
arr_alpha.append(0)
elif code == 'f':
arr_alpha.append(1)
elif code == 'n':
arr_alpha.append(0.5)
elif code == 'l':
arr_alpha.append(0)
else:
arr_alpha.append(0)
else:
arr_alpha.append(1)
return arr_alpha
#%%
def labels_fn(i):
if i < 4:
#l = ["'11' - both", "'10' - far", "'01' - near", "'00' - none"]
l = ["'11' - H", "'10' - F", "'01' - N", "'00' - L"]
return l[i]
else:
return ''
#%%
with open(learn_seq, 'r') as f:
codes = f.readline().split(",")
codes = np.array(codes[:])
#%%
with open(liv_path, 'r') as f:
vals = f.readline().split(";")
vals = np.array(vals[:-1], dtype= 'float64')
max_val = max(vals)
n_bit = (len(vals)-frame)//frpsym - 2
if study == 'study3':
n_bit = (len(vals)-frame)//frpsym //2
if d < 1.4:
off = int((d_travel_near - (cam_w_proj/2))/((v_mean + 0.3 * v_sigma) * t_dts))
elif d < 2.2:
off = int((d_travel_near - (cam_w_proj/2))/((v_mean + 0.2 * v_sigma) * t_dts))
else:
off = int((d_travel_near - (cam_w_proj/2))/((v_mean + 0.65 * v_sigma) * t_dts))
if study == 'study5_2':
off = int((d_travel_near - (cam_w_proj/2))/((v_mean + 0 * v_sigma) * t_dts))
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(10)
plt.rc('xtick', labelsize=tick_fontsize)
plt.rc('ytick', labelsize=tick_fontsize)
# learn_seq
colors = codes_to_color(codes)
alphas = [1]
labels = [codes_to_label(codes)]
# by previous code
# colors = ['gray']
# for c in codes_to_color(codes)[:-1]:
# colors.append(c)
# look at near
#alphas = codes_to_alpha(codes, "n")
# look at far
#alphas = codes_to_alpha(codes, "f")
if study == 'study3':
x1 = np.arange(0+off, frpsym+off, 1)
x2 = np.arange(frpsym+off-1, 2*frpsym+off, 1)
for i in range(n_bit):
y1 = np.divide(vals[(2*i*frpsym+off):((2*i+1)*frpsym+off)],max_val)
y2 = np.divide(vals[((2*i+1)*frpsym+off-1):((2*i+2)*frpsym+off)], max_val)
plt.plot(x1, y1, label = labels_fn(i), color= colors[(2*i)%len(colors)], alpha = alphas[i%len(alphas)])
plt.plot(x2, y2, color= colors[(2*i)%len(colors)], alpha = alphas[i%len(alphas)])
# prints also some frames of the next bit for better ISI identification
elif study =='study3_full' or study == 'study4' or study == 'study6' or study == 'study5' or (study == 'study7' or study == 'paper/1' or study == 'paper/2') and ITR == False:
x1 = np.arange(0+off, frpsym+off, 1)
x2 = np.arange(frpsym+off-1, frpsym+off-1+int(frpsym/3), 1)
for i in range(n_bit):
y1 = np.divide(vals[(i*frpsym+off):((i+1)*frpsym+off)],max_val)
y2 = np.divide(vals[((i+1)*frpsym+off-1):((i+1)*frpsym+off-1)+int(frpsym/3)], max_val)
plt.plot(x1, y1, label = labels_fn(i), color= colors[(i)%len(colors)], alpha = alphas[i%len(alphas)])
plt.plot(x2, y2, color= colors[(i)%len(colors)], alpha = 0.35)
else:
x = np.arange(0+off, frpsym+off, 1)
for i in range(n_bit):
y = np.divide(vals[(i*frpsym+off):((i+1)*frpsym+off)],max_val)
plt.plot(x, y, label = labels_fn(i), color= colors[i%len(colors)], alpha = alphas[i%len(alphas)])
text_off = 0.2
plt.xlabel('Image Frames since Symbol Start', fontsize=text_fontsize)
plt.ylabel('LIV / Highest Detected LIV', fontsize=text_fontsize)
#plt.rc('font', family = 'sans-serif')
#plt.axvline(x=25, label="Max Diff 1st half", color= 'black', linestyle = 'dotted')
#plt.axvline(x=38, label="Max Diff 2nd half", color= 'black', linestyle = 'dashed')
# if study == 'study5':
# plt.axvline(x=fr_n, label="Peak Near", color= 'black', linestyle = 'dashed')
# plt.axvline(x=fr_f, label="Peak Far", color= 'black', linestyle = 'dotted')
if study == 'study5_2':
frame = 36
plt.axvline(x=frame, label="Reconstruction Frame", color= 'black', linestyle = 'dashed')
elif study == 'study1' or study == 'study2':
True
else:
plt.axvline(x=frame, label="Sampling Frame", color= 'black', linestyle = 'dashed', linewidth = 4)
#plt.axvline(x=40, label="Reconstruction Frame", color= 'black', linestyle = 'dashed')
if study == 'study3':
r = 2
elif study == 'study3_full' or study == 'study4' or study == 'study5' or study == 'study6' or (study == 'study7' or study == 'paper/1' or study == 'paper/2') and ITR==False:
r = 4/3
else:
r = 1
if study == 'study1':
k = 0.015
else:
k = 0
plt.title('LIV Sorted by Case', fontsize=title_fontsize)
#plt.title('LIV by Prior Code - Near Tx', fontsize=title_fontsize)
#plt.title('LIV by Prior Code - Far Tx', fontsize=title_fontsize)
if (study == 'study7' or study == 'paper/1' or study == 'paper/2') and ITR == False:
plt.text((r*frpsym+off)*1.2, 0.3-text_off, r'$d = {}\, m$'.format(d), fontsize=text_fontsize)
plt.text((r*frpsym+off)*1.2, 0.2-text_off, r'$\Delta y = {}\, m$'.format(dy), fontsize=text_fontsize)
plt.text((r*frpsym+off)*1.04, 0.35-text_off, r'$t_{sym} = {val}\, ms$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
plt.text((r*frpsym+off)*1.04, 0.25-text_off, r'off\_$f = {}$'.format(round(off_f, 2)), fontsize=text_fontsize, style = 'italic')
plt.text((r*frpsym+off)*1.04, 0.15-text_off, r'off\_$n = {}$'.format(round(off_n, 2)), fontsize=text_fontsize, style = 'italic')
else:
if study == 'study2' or study == 'study3_full':
plt.text((r*frpsym+off)*1.04, 0.5-text_off, r'$t_{spray} = {val}\, ms$'.format(spray = "{spray}", val = str(int(t_sp*1000))), fontsize=text_fontsize)
plt.text((r*frpsym+off)*1.04, 0.4-text_off, r'$t_{pause} = {val}\, ms$'.format(pause = "{pause}", val = str(int(np.floor((t_sym - t_sp)*1000)))), fontsize=text_fontsize)
elif study == 'study4' or study == 'study6':
plt.text((r*frpsym+off)*1.04, 0.5-text_off, r'off\_$f = {}$'.format(round(off_f, 2)), fontsize=text_fontsize, style = 'italic')
plt.text((r*frpsym+off)*1.04, 0.4-text_off, r'off\_$n = {}$'.format(round(off_n, 2)), fontsize=text_fontsize, style = 'italic')
if study == 'study5':
# plt.text((r*frpsym+off)*1.04, 0.5-text_off, r'$t_{sym} = {val}\, ms$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
# just for the paper...
plt.text((r*frpsym+off)*1.03, 0.375-text_off, r'$t_{sym} = {val}\, ms$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
plt.text((r*frpsym+off)*1.03, 0.297-text_off, r'$\Delta t = 0.0$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
else:
plt.text((r*frpsym+off)*(1.04 - k), 0.6-text_off, r'$t_{sym} = {val}\, ms$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
# plt.text((r*frpsym+off)*(1.04 - k), 0.3-text_off, r'$d = {}\, m$'.format(d), fontsize=text_fontsize)
# plt.text((r*frpsym+off)*(1.04 - k), 0.2-text_off, r'$\Delta y = {}\, m$'.format(dy), fontsize=text_fontsize)
#just for paper
plt.text((r*frpsym+off)*(1.03 - k), 0.218-text_off, r'$d = {}\, m$'.format(d), fontsize=text_fontsize)
plt.text((r*frpsym+off)*(1.03 - k), 0.14-text_off, r'$\Delta y = {}\, m$'.format(dy), fontsize=text_fontsize)
if d < 2.1:
tickdist = 2
elif d < 3.0:
tickdist = 3
else:
tickdist = 5
if (study == 'study7' or study == 'paper/1' or study == 'paper/2') and ITR == False or study == "study5":
plt.axhline(apply_config_var['th']['large']/max_val, label = "B Threshold", color = 'black', linestyle= (0,(1,1)), linewidth = 3)
plt.axhline(apply_config_var['th']['medium']/max_val, label = "M Threshold", color = 'black', linestyle= (0,(1,3)), linewidth = 3)
plt.axhline(apply_config_var['th']['small']/max_val, label = "S Threshold", color = 'black', linestyle= (0,(1,5)), linewidth = 3)
# MOL-Eye
if study == 'study1' or study == 'study2':
plt.xticks(np.arange(off, frpsym+off, tickdist))
else:
plt.xticks(np.arange(off, frpsym+off+int(frpsym/3), tickdist))
#if study == 'study5' or study == 'study6':
plt.grid(color = 'gray', axis = 'x')
#plt.savefig("./study_results/study3/d_1.6/{dy}.png".format(dy = dy * 100), bbox_inches="tight")
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize=text_fontsize)
if save:
plt.savefig("./study_results/{study}/{version}/{name}.pdf".format(study = study, version = version, name = sys.argv[1]), bbox_inches="tight")
plt.show()
#%%
print(plt.rcParams['font.sans-serif'])
#%%
if study == 'study1' or study == 'study6':
fig = plt.figure()
fig.set_figwidth(2)
fig.set_figheight(3)
plt.rc('xtick', labelsize=13)
plt.rc('ytick', labelsize=13)
x= frame
h = []
f = []
n = []
l = []
for i in range(n_bit):
liv = np.divide(vals[(i*frpsym+frame)], max_val)
group = codes[i%len(codes)]
if group == 'h':
h.append(liv)
elif group == 'f':
f.append(liv)
elif group == 'n':
n.append(liv)
elif group == 'l':
l.append(liv)
else:
raise Exception("code invalid")
plt.plot(x, liv, label = labels_fn(i), color= colors[i%len(colors)], marker = ".")
h_mean = np.mean(h)
f_mean = np.mean(f)
n_mean = np.mean(n)
l_mean = np.mean(l)
plt.plot(x, h_mean, label = "H mean", color = "cyan", marker= "_")
plt.plot(x, f_mean, label = "F mean", color = "firebrick", marker= "_")
plt.plot(x, n_mean, label = "N mean", color = "darkgoldenrod", marker= "_")
plt.plot(x, l_mean, label = "L mean", color = "black", marker= "_")
if study == 'study1':
print(sys.argv[1])
print("Fraction f_mean/h_mean = " + str(f_mean/h_mean) + "\nFraction n_mean/h_mean = " + str(n_mean/h_mean))
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize=13)
#plt.xlabel('Sim2Vid', fontsize=18)
#plt.xlabel('SCC', fontsize=18)
plt.xlabel('Reconstruction Frame')
plt.ylabel('Relative LIV', fontsize=18)
plt.title('LIVs in Reconstructionframe', fontsize=23)
plt.xticks([x])
if save:
plt.savefig("./study_results/{study}/{version}/at_frame_{name}.pdf".format(study = study, version = version, name = sys.argv[1]), bbox_inches="tight")
plt.show()
#%%
now = False
if study == 'study5' and now:
fig = plt.figure()
fig.set_figwidth(2)
fig.set_figheight(3)
plt.rc('xtick', labelsize=13)
plt.rc('ytick', labelsize=13)
N="Near Peak"
F="Far Peak"
n = []
f = []
for i in range(n_bit):
group = codes[i%len(codes)]
if group == 'f':
liv = vals[(i*frpsym+fr_f)]
f.append(liv)
plt.plot(F, liv, color= 'r', marker = ".")
elif group == 'n':
liv = vals[(i*frpsym+fr_n)]
n.append(liv)
plt.plot(N, liv, color= 'orange', marker = ".")
f_mean = np.mean(f)
n_mean = np.mean(n)
plt.plot(F, f_mean, label = "Far Peak Mean", color = "firebrick", marker= "_")
plt.annotate("{}".format(f_mean), (13/12, 1/3-1/5), xycoords = 'axes fraction', color= "firebrick", fontsize = 13)
plt.plot(N, n_mean, label = "Near Peak Mean", color = "darkgoldenrod", marker= "_")
plt.annotate("{}".format(n_mean), (13/12, 2/3-1/5), xycoords = 'axes fraction', color = "darkgoldenrod", fontsize = 13)
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize=13)
plt.xlabel('Frame', fontsize=18)
plt.ylabel('LIV', fontsize=18)
plt.title('Peak LIVs in Frames', fontsize=23)
plt.xticks([N, F])
print(sys.argv[1])
print("N: " + str(n_mean))
print("F: " + str(f_mean))
if save:
plt.savefig("./study_results/{study}/{version}/3/at_frame_{name}.pdf".format(study = study, version = version, name = sys.argv[1]), bbox_inches="tight")
plt.show()
#%%
if study == 'study5_2':
from scipy.stats import norm
datapoints = 5000
v = np.linspace(v_mean - 3*v_sigma, v_mean + 3*v_sigma, datapoints)
nd = norm.pdf(v, loc = v_mean, scale = v_sigma)
# with open('study_results/study5/Calc_diagrams/vel.txt', 'r') as f:
# v = f.readline().split(";")
# v = v[:-1]
# with open('study_results/study5/Calc_diagrams/normal_dist_rust.txt', 'r') as f:
# nd = f.readline().split(";")
# nd = nd[:-1]
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(10)
plt.rc('xtick', labelsize=tick_fontsize)
plt.rc('ytick', labelsize=tick_fontsize)
plt.plot(v, nd, color = 'black', linewidth = 3)
plt.axvline(x = v_mean, color= "c", label= 'Mean')
plt.axvline(x = v_mean+v_sigma, color= 'gray', linestyle = 'dashed', label= r'$1\sigma$')
plt.axvline(x = v_mean-v_sigma, color= 'gray', linestyle = 'dashed')
plt.axvline(x = v_mean+3*v_sigma, color= 'gray', linestyle = 'dotted', label = r'$3\sigma$')
plt.axvline(x = v_mean-3*v_sigma, color= 'gray', linestyle = 'dotted')
plt.xlabel(r'Velocity $[\frac{{m}}{{s}}]$'.format(), fontsize=text_fontsize)
plt.ylabel("Probability", fontsize=text_fontsize)
plt.title("Probability-Distribution of Velocity in a Molecule", fontsize=title_fontsize)
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize = text_fontsize)
#plt.savefig("./study_results/study5/Calc_diagrams/vel.pdf", bbox_inches="tight")
plt.show()
#%%
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(10)
plt.rc('xtick', labelsize=tick_fontsize)
plt.rc('ytick', labelsize=tick_fontsize)
plt.plot(d_travel_far/v, nd, color = 'r', linewidth = 3)
plt.axvline(x = d_travel_far/v_mean, color= "c", label= 'Mean')
plt.axvline(x = d_travel_far/(v_mean+v_sigma), color= 'gray', linestyle = 'dashed', label= r'$1\sigma$')
plt.axvline(x = d_travel_far/(v_mean-v_sigma), color= 'gray', linestyle = 'dashed')
plt.axvline(x = d_travel_far/(v_mean+3*v_sigma), color= 'gray', linestyle = 'dotted', label = r'$3\sigma$')
plt.axvline(x = d_travel_far/(v_mean-3*v_sigma), color= 'gray', linestyle = 'dotted')
plt.xlabel(r'Travel Time $[s]$'.format(), fontsize=text_fontsize)
plt.ylabel("Probability", fontsize=text_fontsize)
plt.title("Distribution of Time of Arrival from Tx far", fontsize=title_fontsize)
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize = text_fontsize)
plt.text(0.18, 0.035, r'$d = {}\, m$'.format(d), fontsize=text_fontsize)
plt.text(0.18, 0.02, r'$\Delta y = {}\, m$'.format(dy), fontsize=text_fontsize)
#plt.savefig("./study_results/study5/Calc_diagrams/time_f.pdf", bbox_inches="tight")
plt.show()
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(10)
plt.rc('xtick', labelsize=tick_fontsize)
plt.rc('ytick', labelsize=tick_fontsize)
plt.plot(d_travel_near/v, nd, color = 'orange', linewidth = 3)
plt.axvline(x = d_travel_near/v_mean, color= "c", label= 'Mean')
plt.axvline(x = d_travel_near/(v_mean+v_sigma), color= 'gray', linestyle = 'dashed', label= r'$1\sigma$')
plt.axvline(x = d_travel_near/(v_mean-v_sigma), color= 'gray', linestyle = 'dashed')
plt.axvline(x = d_travel_near/(v_mean+3*v_sigma), color= 'gray', linestyle = 'dotted', label = r'$3\sigma$')
plt.axvline(x = d_travel_near/(v_mean-3*v_sigma), color= 'gray', linestyle = 'dotted')
plt.xlabel(r'Travel Time $[s]$'.format(), fontsize=text_fontsize)
plt.ylabel("Probability", fontsize=text_fontsize)
plt.title("Distribution of Time of Arrival from Tx near", fontsize=title_fontsize)
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize = text_fontsize)
plt.text(0.155, 0.035, r'$d = {}\, m$'.format(d), fontsize=text_fontsize)
plt.text(0.155, 0.02, r'$\Delta y = {}\, m$'.format(dy), fontsize=text_fontsize)
#plt.savefig("./study_results/study5/Calc_diagrams/time_n.pdf", bbox_inches="tight")
plt.show()
#%%
if study == 'study5_2' or study == 'study5':# or study == 'paper/2':# or study == 'study7':# or study == 'study6':
if study == 'study7':
name = sys.argv[3]
else:
name = sys.argv[1]
order = apply_config_var['order']
min_frame = 32
max_frame = 38
mid = off
mad = frpsym+off+int(frpsym/3)
# mad = frpsym+off+int(frpsym/2)
# min_frame = mid
# max_frame = mad
picked_frame = frame
# if dy < 0.1:
# dy = str(int(round(dy, 2)*100))
# dy = "0.0" + dy
# else:
# dy = str(int(round(dy, 2)*100))
# dy = "0." +dy
with open('study_results/{study}/Calc_diagrams/{name}_dist_near.txt'.format(study = study, name = name), 'r') as f:
fr_n_diag = f.readline().split(";")
fr_n_diag = np.array(fr_n_diag[:-1], dtype='float64')
with open('study_results/{study}/Calc_diagrams/{name}_dist_far.txt'.format(study = study, name = name), 'r') as f:
fr_f_diag = f.readline().split(";")
fr_f_diag = np.array(fr_f_diag[:-1], dtype='float64')
fr = np.arange(min_frame, max_frame+1, 1)
with open('study_results/{study}/Calc_diagrams/full_{name}_dist_near.txt'.format(study = study, name = name), 'r') as f:
fr_n_diag_full = f.readline().split(";")
fr_n_diag_full = np.array(fr_n_diag_full[:-1], dtype='float64')
with open('study_results/{study}/Calc_diagrams/full_{name}_dist_far.txt'.format(study = study, name = name), 'r') as f:
fr_f_diag_full = f.readline().split(";")
fr_f_diag_full = np.array(fr_f_diag_full[:-1], dtype='float64')
fr_full = np.arange(0, 150, 1)
with open('study_results/{study}/Calc_diagrams/{name}_diff_fn.txt'.format(study = study, name = name), 'r') as f:
fr_diff_fn = f.readline().split(";")
fr_diff_fn = np.array(fr_diff_fn[:-1], dtype='float64')
with open('study_results/{study}/Calc_diagrams/full_{name}_diff_fn.txt'.format(study = study, name = name), 'r') as f:
fr_diff_fn_full = f.readline().split(";")
fr_diff_fn_full = np.array(fr_diff_fn_full[:-1], dtype='float64')
with open('study_results/{study}/Calc_diagrams/{name}_diff_h.txt'.format(study = study, name = name), 'r') as f:
fr_diff_h = f.readline().split(";")
fr_diff_h = np.array(fr_diff_h[:-1], dtype='float64')
with open('study_results/{study}/Calc_diagrams/full_{name}_diff_h.txt'.format(study = study, name = name), 'r') as f:
fr_diff_h_full = f.readline().split(";")
fr_diff_h_full = np.array(fr_diff_h_full[:-1], dtype='float64')
fig = plt.figure()
fig.set_figwidth(15)
fig.set_figheight(10)
plt.rc('xtick', labelsize=tick_fontsize)
plt.rc('ytick', labelsize=tick_fontsize)
plt.plot(fr_full[mid:mad], (fr_n_diag_full[mid:mad]+fr_f_diag_full[mid:mad]), label = "'11' - H", color = 'b', linewidth = 3)
# plt.plot(fr, (fr_n_diag+fr_f_diag), label = "High [min-frame; max-frame]", color = 'darkblue', linewidth = 4)
plt.plot(fr, (fr_n_diag+fr_f_diag), color = 'darkblue', linewidth = 4)
plt.plot(fr_full[mid:mad], fr_f_diag_full[mid:mad], label = "'10' - F", color = 'r', linewidth = 3)
# plt.plot(fr, fr_f_diag, label = "Far [min-frame; max-frame]", color = 'firebrick', linewidth = 4)
plt.plot(fr, fr_f_diag, color = 'firebrick', linewidth = 4)
plt.plot(fr_full[mid:mad], fr_n_diag_full[mid:mad], label = "'01' - N", color = 'orange', linewidth = 3)
# plt.plot(fr, fr_n_diag, label = "Near [min-frame; max-frame]", color = 'darkgoldenrod', linewidth = 4)
plt.plot(fr, fr_n_diag, color = 'darkgoldenrod', linewidth = 4)
plt.plot(fr_full[mid:mad], fr_diff_fn_full[mid:mad], label = "abs(F--N)", color = 'c', linewidth = 2)
# plt.plot(fr, fr_diff, label = "abs(diff) [min-frame; max-frame]", color = 'teal', linewidth = 3)
plt.plot(fr, fr_diff_fn, color = 'teal', linewidth = 3)
if order == "HFNL":
plt.plot(fr_full[mid:mad], fr_diff_h_full[mid:mad], label = "abs(H--\_\_$_1$)", color = 'c', linewidth = 2)
elif order =="HNFL":
plt.plot(fr_full[mid:mad], fr_diff_h_full[mid:mad], label = "abs(H--\_\_$_1$)", color = 'c', linewidth = 2)
# plt.plot(fr, fr_diff, label = "abs(diff) [min-frame; max-frame]", color = 'teal', linewidth = 3)
plt.plot(fr, fr_diff_h, color = 'teal', linewidth = 3)
plt.axvline(min_frame, color = 'black', alpha = 0.8, label = "Min Frame", linestyle = 'dashed', linewidth = 3)
plt.axvline(max_frame, color = 'black', alpha = 0.8, label = "Max Frame", linestyle = 'dotted', linewidth = 3)
plt.axvline(picked_frame, color = 'black', label = "Sampling Frame {}".format(picked_frame), linewidth = 3, linestyle = '-')
plt.grid(color = 'gray', axis = 'x')
plt.xlabel("Image Frames since Symbol Start", fontsize=text_fontsize)
plt.ylabel("Probability of Molecules", fontsize=text_fontsize)
plt.title("Expected CIR Shapes", fontsize=title_fontsize)
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize = text_fontsize)
plt.text(mad + 3, 0.035, r'$t_{sym} = {val}\, ms$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
plt.text(mad + 3, 0.02, r'$\Delta t = 0.0$'.format(sym = "{sym}", val = str(int(t_sym * 1000))), fontsize=text_fontsize)
plt.text(mad + 3, 0.005, r'$d = {}\, m$'.format(d), fontsize=text_fontsize)
plt.text(mad + 3, -0.01, r'$\Delta y = {}\, m$'.format(dy), fontsize=text_fontsize)
plt.xticks(np.arange(mid, mad, 2))
#save = True
if save:
plt.savefig("./study_results/{study}/Calc_diagrams/complete_{sim}.pdf".format(study = study, sim = sys.argv[1]), bbox_inches="tight")
plt.show()
#%% ITR
#Plot HFN with h,l,f,l,n,l
#put cutoff at symbol duration
#and put cutoff at symbol duration + t_ftr, v3sigma
#add values before and behind the cutoffs up.
ITR = False
if ITR:
p = 2.9
fig = plt.figure()
fig.set_figwidth(23)
fig.set_figheight(15)
plt.rc('xtick', labelsize=tick_fontsize+2)
plt.rc('ytick', labelsize=tick_fontsize+2)
i = 0
while vals[i] == 0:
i = i+1
off = i-1
h = np.zeros((int(np.ceil(frpsym*p))+1, int(n_bit/9)+1))
f = np.zeros((int(np.ceil(frpsym*p))+1, int(n_bit/9)+1))
n = np.zeros((int(np.ceil(frpsym*p))+1, int(n_bit/9)+1))
l = np.zeros((int(np.ceil(frpsym*p))+1, int(n_bit/9)+1))
h_count = 0
f_count = 0
n_count = 0
l_count = 0
for j in range(0, n_bit-3, 3):
# print(codes[j%len(codes)])
if codes[j%len(codes)] == 'h':
for k in range(0, int(np.ceil(frpsym*p))+1):
h[k][h_count] = vals[j*frpsym + off + k]
h_count = h_count + 1
x = np.arange(off, off + int(np.ceil(p*frpsym)), 1)
y = vals[j*frpsym + off: j*frpsym + off + int(np.ceil(frpsym*p))]
plt.plot(x, y, color = "blue")
elif codes[j%len(codes)] == 'f':
for k in range(0, int(np.ceil(frpsym*p))+1):
f[k][f_count] = vals[j*frpsym + off + k]
f_count = f_count + 1
x = np.arange(off, off + int(np.ceil(p*frpsym)), 1)
y = vals[j*frpsym + off: j*frpsym + off + int(np.ceil(frpsym*p))]
plt.plot(x, y, color = "red")
elif codes[j%len(codes)] == 'n':
for k in range(0, int(np.ceil(frpsym*p))+1):
n[k][n_count] = vals[j*frpsym + off + k]
n_count = n_count + 1
x = np.arange(off, off + int(np.ceil(p*frpsym)), 1)
y = vals[j*frpsym + off: j*frpsym + off + int(np.ceil(frpsym*p))]
plt.plot(x, y, color = "orange")
h_mean = []
f_mean = []
n_mean = []
for fr in h:
h_mean.append(np.mean(fr))
for fr in f:
f_mean.append(np.mean(fr))
for fr in n:
n_mean.append(np.mean(fr))
x2 = np.arange(off, off + int(np.ceil(p*frpsym))+1, 1)
plt.plot(x2, h_mean, color = 'cyan', label = "H mean", linewidth = 3)
plt.plot(x2, f_mean, color = 'firebrick', label = "F mean", linewidth = 3)
plt.plot(x2, n_mean, color = 'darkgoldenrod', label = "N mean", linewidth = 3)
h_A_SYM = np.sum(h_mean[:frpsym])
h_A_ISI = np.sum(h_mean[frpsym:])
f_A_SYM = np.sum(f_mean[:frpsym])
f_A_ISI = np.sum(f_mean[frpsym:])
n_A_SYM = np.sum(n_mean[:frpsym])
n_A_ISI = np.sum(n_mean[frpsym:])
plt.annotate("$A_{SYM}(H) = $\n${val}$".format(SYM = "{SYM}", val = str(round(h_A_SYM, 2))), (off + frpsym*0.05, h_mean[int(frpsym*0.8)]*0.75), fontsize = tick_fontsize, color = "black")
plt.annotate("$A_{ISI}(H) = $\n${val}$".format(ISI = "{ISI}", val = str(round(h_A_ISI, 2))), (off + frpsym*2.3, h_mean[int(frpsym*0.8)]*0.75), fontsize = tick_fontsize, color = "black")
plt.annotate("$A_{SYM}(F) = $\n${val}$".format(SYM = "{SYM}", val = str(round(f_A_SYM, 2))), (off + frpsym*0.05, h_mean[int(frpsym*0.8)]*0.6), fontsize = tick_fontsize, color = "black")
plt.annotate("$A_{ISI}(F) = $\n${val}$".format(ISI = "{ISI}", val = str(round(f_A_ISI, 2))), (off + frpsym*2.3, h_mean[int(frpsym*0.8)]*0.6), fontsize = tick_fontsize, color = "black")
plt.annotate("$A_{SYM}(N) = $\n${val}$".format(SYM = "{SYM}", val = str(round(n_A_SYM, 2))), (off + frpsym*0.05, h_mean[int(frpsym*0.8)]*0.45), fontsize = tick_fontsize, color = "black")
plt.annotate("$A_{ISI}(N) = $\n${val}$".format(ISI = "{ISI}", val = str(round(n_A_ISI, 2))), (off + frpsym*2.3, h_mean[int(frpsym*0.8)]*0.45), fontsize = tick_fontsize, color = "black")
plt.text(off + frpsym*p * 1.02, h_mean[int(frpsym*0.8)]*0.75, "$ITR(H) = $\n${val}$".format(val = str(round(h_A_ISI/(h_A_SYM+h_A_ISI), 5))), fontsize = tick_fontsize, color = "black")
plt.text(off + frpsym*p * 1.02, h_mean[int(frpsym*0.8)]*0.6, "$ITR(F) = $\n${val}$".format(val = str(round(f_A_ISI/(f_A_SYM+f_A_ISI), 5))), fontsize = tick_fontsize, color = "black")
plt.text(off + frpsym*p * 1.02, h_mean[int(frpsym*0.8)]*0.45, "$ITR(N) = $\n${val}$".format(val = str(round(n_A_ISI/(n_A_SYM+n_A_ISI), 5))), fontsize = tick_fontsize, color = "black")
plt.text((p*frpsym+off)*1.02, h_mean[int(frpsym*0.8)]*0.3, r"$t_{sym} = {val} \, ms$\\[1ex]off$_f = {offf}$\\[1ex]off$_n = {offn}$\\[1ex]$d = {d}$\\[1ex]$\Delta y = {dy}\, m$".format(sym = "{sym}", val = str(int(t_sym * 1000)), offf = round(off_f, 2), offn = round(off_n, 2), d = d, dy = dy), fontsize=text_fontsize-3)
plt.axvline(off + frpsym, color = 'black', linewidth = 3)
plt.annotate(r"$t_s$", xy = (off + frpsym, 0), fontsize = text_fontsize, xytext = (-5, -30), textcoords = 'offset points')
plt.axvline(off + 2*frpsym, color = 'black', linewidth = 3, linestyle = "dashed")
plt.annotate(r"$2t_s$", xy = (off + 2*frpsym, 0), fontsize = text_fontsize, xytext = (-10, -30), textcoords = 'offset points')
plt.axvline(frpsym+frame, color = 'black', linewidth = 3, linestyle = "dotted")
plt.annotate("Reconstruction\nFrame", xy = (frpsym+frame, h_mean[int(frpsym*0.8)]), fontsize = tick_fontsize, xytext = (-200, 0), textcoords = 'offset points')
plt.xlabel("Frame", fontsize = text_fontsize)
plt.ylabel("LIV", fontsize = text_fontsize)
plt.title("ITR", fontsize = title_fontsize)
plt.ylim(ymin = 0)
plt.xlim(xmin = off, xmax = off+frpsym*p)
plt.legend(bbox_to_anchor=(1,1), loc = "upper left", fontsize=text_fontsize)
#save = True
if save:
plt.savefig("study_results/study7/ITR/{name}.pdf".format(name = sys.argv[1]), bbox_inches="tight")
plt.show()
#plot
#means
#add means from off to off+frpsym
#add means from off+frpsym to liv[j] == 0
#%% MOL Eye
import statistics as stats
MOL_Eye = False
if MOL_Eye: