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Henry Lloyd-Laney
committedSep 7, 2020
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1 parent eec1286 commit a8dbed3

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‎CMakeCache.txt

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‎CMakeFiles/isolver_martin_brent.dir/build.make

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@@ -82,6 +82,7 @@ isolver_martin_brent.cpython-37m-x86_64-linux-gnu.so: CMakeFiles/isolver_martin_
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isolver_martin_brent.cpython-37m-x86_64-linux-gnu.so: CMakeFiles/isolver_martin_brent.dir/link.txt
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# Rule to build all files generated by this target.
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CMakeFiles/isolver_martin_brent.dir/build: isolver_martin_brent.cpython-37m-x86_64-linux-gnu.so

‎CMakeFiles/isolver_martin_brent.dir/flags.make

+1-1
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# compile CXX with /usr/lib64/ccache/c++
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‎CMakeFiles/isolver_martin_brent.dir/link.txt

+1-1
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‎Inferred_results/CMAES/Noramp_10_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_1_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_2_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_3_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_4_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_5_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_6_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_7_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_8_cv_high_ru_alpha_disp

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‎Inferred_results/CMAES/Noramp_9_cv_high_ru_alpha_disp

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‎SV_paper.egg-info/PKG-INFO

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Metadata-Version: 1.0
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Name: SV-paper
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Version: 0.0.1
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Summary: The companion code for the sinusoidal voltammetry paper
5+
Home-page: UNKNOWN
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Author: UNKNOWN
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Author-email: UNKNOWN
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License: UNKNOWN
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Description: UNKNOWN
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Platform: UNKNOWN

‎SV_paper.egg-info/SOURCES.txt

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README.md
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setup.py
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SV_paper.egg-info/PKG-INFO
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SV_paper.egg-info/SOURCES.txt
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SV_paper.egg-info/dependency_links.txt
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SV_paper.egg-info/not-zip-safe
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SV_paper.egg-info/requires.txt
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SV_paper.egg-info/top_level.txt

‎SV_paper.egg-info/dependency_links.txt

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‎SV_paper.egg-info/not-zip-safe

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‎SV_paper.egg-info/requires.txt

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pints

‎SV_paper.egg-info/top_level.txt

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SV_paper

‎cmake_install.cmake

+1-1
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string(REGEX REPLACE "^[^A-Za-z0-9_]+" ""
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else()
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set(CMAKE_INSTALL_CONFIG_NAME "")
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set(CMAKE_INSTALL_CONFIG_NAME "Release")
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endif()
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message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"")
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endif()

‎figures/Alice_10_sims.png

594 KB
Loading

‎figures/Ramped_comparison.png

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Loading

‎figures/experiment_comparison.png

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‎figures/figure1.py

+5-4
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@@ -68,21 +68,21 @@
6868
fourier_ylabels=["Real", "Imaginary"]
6969
fourier_times=[ramp_time_results, time_results]
7070
fourier_currents=[ramped_cmaes_time, cmaes_time]
71-
harm_xlabels=["Time(s)", "Voltage(V)"]
71+
harm_xlabels=["Time(s)", "Potential(V)"]
7272
fig=multiplot(2, 4, **{"harmonic_position":3, "num_harmonics":num_harms, "fourier_position":2,"orientation":"portrait", "plot_width":6, "col_spacing":2, "font_size":15})
7373
keys=sorted(fig.axes_dict.keys())
7474

7575
fig.axes_dict["col1"][0].plot(ramp_time_results, ramp_voltage_results)
7676
fig.axes_dict["col1"][0].set_xlabel("Time(s)")
77-
fig.axes_dict["col1"][0].set_ylabel("Voltage(V)")
77+
fig.axes_dict["col1"][0].set_ylabel("Potential(V)")
7878
fig.axes_dict["col1"][1].plot(voltage_times, voltage_plots)
7979
fig.axes_dict["col1"][1].set_xlabel("Time(s)")
80-
fig.axes_dict["col1"][1].set_ylabel("Voltage(V)")
80+
fig.axes_dict["col1"][1].set_ylabel("Potential(V)")
8181
fig.axes_dict["col2"][0].plot(ramp_time_results, ramped_cmaes_time)
8282
fig.axes_dict["col2"][0].set_xlabel("Time(s)")
8383
fig.axes_dict["col2"][0].set_ylabel("Current(mA)")
8484
fig.axes_dict["col2"][1].plot(voltage_results, cmaes_time)
85-
fig.axes_dict["col2"][1].set_xlabel("Voltage(V)")
85+
fig.axes_dict["col2"][1].set_xlabel("Potential(V)")
8686
fig.axes_dict["col2"][1].set_ylabel("Current(mA)")
8787
for i in range(0, 2):
8888
for j in range(0, 2):
@@ -133,6 +133,7 @@
133133
plt.show()
134134
save_path="experiment_comparison.png"
135135
fig.savefig(save_path, dpi=500)
136+
if resize==True:
136137
img = Image.open(save_path)
137138
basewidth = float(img.size[0])//2
138139
wpercent = (basewidth/float(img.size[0]))

‎figures/figure3.py

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44
import pints
55
import os
66
import sys
7+
try:
8+
from PIL import Image
9+
resize=True
10+
except:
11+
resize=False
712
current_dir=os.getcwd()
813
dir_list=current_dir.split("/")
914
upper_level_list=dir_list[:dir_list.index("SV_paper")+1]
@@ -97,11 +102,12 @@
97102
file="Noramp_2_cv_high_ru_alpha_disp"
98103
CMAES_path=("/").join([upper_level, "Inferred_results", "CMAES"])
99104
noramp_results=single_electron(CMAES_path+"/"+file)
100-
print(noramp_results.simulation_options["GH_quadrature"])
105+
print(noramp_results.dim_dict["num_peaks"])
101106
noramp_results.simulation_options["dispersion_bins"]=[15,15]
107+
noramp_results.dim_dict["sampling_freq"]=1/2000.0
102108
noramp_results.dim_dict["phase"]=3*math.pi/2
103109
plot_params=["E0_mean", "E0_std","k_0","Ru","Cdl","CdlE1", "cap_phase", "alpha_mean", "alpha_std"]
104-
param_vals=[0.25, 0.05, 100, 100,1e-5, 1e-5,3*math.pi/2, 0.5, 0.05]
110+
param_vals=[0.25, 0.05, 0.1, 100,1e-5, 1e-5,3*math.pi/2, 0.5, 0.05]
105111
noramp_results.dim_dict["Cdl"]=1e-5
106112
noramp_results.dim_dict["CdlE1"]=0
107113
noramp_results.dim_dict["CdlE2"]=0
@@ -141,7 +147,7 @@
141147
xlims=ax.get_xlim()
142148
ax.set_xlim(noramp_results.dim_dict["E_start"], 0.8)
143149
if row_idx==row_len-1:
144-
ax.set_xlabel("Voltage(V)")
150+
ax.set_xlabel("Potential(V)")
145151
else:
146152
ax.set_xticks([])
147153
if col_idx==0:
@@ -158,7 +164,14 @@
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fig=plt.gcf()
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fig.set_size_inches((14, 9))
161-
plt.subplots_adjust(left=0.08, bottom=0.1, right=0.98, top=0.94, wspace=0.23, hspace=0.10)
167+
plt.subplots_adjust(left=0.08, bottom=0.1, right=0.98, top=0.94, wspace=0.25, hspace=0.10)
162168
plt.show()
163-
#save_path="parameter_scans.png"
164-
#fig.savefig(save_path, dpi=500)
169+
save_path="parameter_scans.png"
170+
fig.savefig(save_path, dpi=500)
171+
if resize==True:
172+
img = Image.open(save_path)
173+
basewidth = float(img.size[0])//2
174+
wpercent = (basewidth/float(img.size[0]))
175+
hsize = int((float(img.size[1])*float(wpercent)))
176+
img = img.resize((int(basewidth),hsize), Image.ANTIALIAS)
177+
img.save(save_path, "PNG", quality=95, dpi=(500, 500))

‎figures/figure5.py

+18-4
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dir_path = os.path.dirname(os.path.realpath(__file__))
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results_dict="Inferred_params"
2020
Electrode="Yellow"
21-
run="Run_6"
21+
run="Run_7"
2222
concs=["1e-1M", "1e0M"]
2323
file_numbers=[str(x) for x in range(1, 4)]
2424
figure=multiplot(3, 1, **{"harmonic_position":2, "num_harmonics":7, "orientation":"landscape", "fourier_position":1, "plot_width":5, "row_spacing":2, "plot_height":1})
@@ -30,14 +30,28 @@
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plt.rcParams.update({'font.size': 9})
3131
def RMSE(series1, series2):
3232
return np.sqrt((np.sum(1/(len(series1))*np.power(np.subtract(series1, series2),2))))
33+
param_mat=[[0.24696678941258246, 0.02150713711591799, 99.45832461312207, 774.704213209354, 7.701956070978554e-05, 0.003017010623506844, -0.00039055262925760496, 7.758858523468079e-11, 8.940640920538685, 4.410033377066397,5.1069657955459, 0.5825105457091572, 0.14328732063771737] ,
34+
[0.2438679560342186, 0.04933431717529572, 191.31132541897506, 497.5055066560729, 7.69338292657605e-05, 0.0031326600706418906, -0.0004312564011993529, 7.271953365051911e-11, 8.94052385943315, 4.3351579045955795,4.925339460865147, 0.6496639586748201, 0.17177231234727677] ,
35+
[0.24430949230426569, 0.04731795471469772, 176.34896534285173, 521.4259532758771, 7.647752507363084e-05, 0.002994039070869009, -0.0004190299720139099, 7.275919967176139e-11, 8.940534898414818, 4.342816983799178, 4.943663135736721, 0.6362752123549957, 0.1720460446880876] ,
36+
[0.24390113149368509, 0.0457048751819453, 161.20949406256256, 536.934970450984, 7.621126298448553e-05, 0.0028379095150146416, -0.000413526916786134, 7.281036191448345e-11, 8.940539372196708, 4.34670259354896, 4.957545393990694, 0.6227137060882207, 0.1713086829766499] ,
37+
[0.24264240746946078, 0.04862334843813994, 174.5319444179432, 501.7381191076415, 7.621609295572e-05, 0.0025967801809421154, -0.00041475793632247265, 7.236585251042315e-11, 8.94052009496047, 4.336548651366052, 4.934251569752124, 0.6291348730960813, 0.17453584870953875] ,
38+
[0.24160781666577544, 0.05036058151555205, 183.91624120363147, 478.0527573517239, 7.62482655530355e-05, 0.002429757046640779, -0.00041668758601426555, 7.209545158956022e-11, 8.940507420558243, 4.330200828730097, 4.9203855923331705, 0.633836591099598, 0.17606178914368914] ,
39+
[0.24122209427368013, 0.049981782635136945, 179.81828332106306, 480.1074385658014, 7.615225208615455e-05, 0.002305928382454525, -0.00041429576393527123, 7.19385317237139e-11, 8.940492881836837, 4.331293568942899, 4.923830104418481, 0.6292049920997411, 0.17637075996545973] ,
40+
[0.24100346908927325, 0.05022099349663932, 178.28549652948317, 477.9310528442049, 7.59455358542439e-05, 0.002273221478669929, -0.00041353404857902287, 7.160330874703819e-11, 8.940504038013483, 4.329624942864246, 4.92163560153165, 0.6277651371395521, 0.17665475180369178] ,
41+
[0.2412440781552388, 0.050226126358631266, 180.12768848217763, 485.192042495465, 7.548546629894573e-05, 0.0021505398969171585, -0.00040389051055751864, 7.11413102722647e-11, 8.940519733026564, 4.3302796214687795,4.9224469580118715, 0.6290049217531675, 0.17737634083075238],
42+
[0.24055747088306997, 0.05047523191357855, 176.56116945811587, 474.43867201525205, 7.584083348640005e-05, 0.0021028609527935505, -0.0004094927674505519, 7.146018738751102e-11, 8.940505868813535, 4.327745982059323, 4.920060562270928,0.6246797783009037, 0.1779493870477656],
43+
]
44+
3345
for i in range(10,11):
3446
file="Noramp_"+str(i)+"_cv_high_ru_alpha_disp"
3547
method="timeseries"
3648
master_optim_list=["E0_mean", "E0_std","k_0","Ru","Cdl","CdlE1", "CdlE2","gamma","omega","cap_phase","phase", "alpha"]
3749
noramp_results=single_electron(CMAES_path+"/"+file)
3850
master_optim_list=["E0_mean", "E0_std", "k_0","Ru","Cdl","CdlE1", "CdlE2","gamma","omega","cap_phase","phase", "alpha_mean", "alpha_std"]
3951
param_vals=([noramp_results.save_dict["params"][0][noramp_results.save_dict["optim_list"].index(key)] if (key in noramp_results.save_dict["optim_list"]) else noramp_results.dim_dict[key] for key in master_optim_list])
40-
noramp_results.simulation_options["dispersion_bins"]=[5,5]
52+
param_vals=param_mat[i-1]
53+
noramp_results.simulation_options["dispersion_bins"]=[5,8]
54+
noramp_results.dim_dict["simulation_freq"]=1/2000.0
4155
noramp_results.def_optim_list(master_optim_list)
4256
cmaes_time=noramp_results.i_nondim(noramp_results.test_vals(param_vals, method))
4357
current_results=noramp_results.i_nondim(noramp_results.other_values["experiment_current"])#[0::dec_amount]
@@ -48,7 +62,7 @@ def RMSE(series1, series2):
4862
time_results=noramp_results.t_nondim(noramp_results.other_values["experiment_time"])#[0::dec_amount]
4963
figure.axes_dict[keys[0]][plot_counter].plot(voltage_results*1e3, (cmaes_time)*1e3, label="Sim")
5064
figure.axes_dict[keys[0]][plot_counter].plot(voltage_results*1e3,(current_results)*1e3, alpha=0.5, label="Exp")
51-
figure.axes_dict[keys[0]][plot_counter].set_xlabel("Voltage(mV vs. Ref.)")
65+
figure.axes_dict[keys[0]][plot_counter].set_xlabel("Potential(mV vs. Ref.)")
5266
figure.axes_dict[keys[0]][plot_counter].set_ylabel("Current(mA)")
5367
figure.axes_dict[keys[0]][plot_counter].plot(voltage_results*1e3, np.subtract(cmaes_time*1e3, current_results*1e3), label="Residual")
5468
figure.axes_dict[keys[0]][plot_counter].legend()
@@ -80,7 +94,7 @@ def RMSE(series1, series2):
8094
if q==len(data_harms)//2:
8195
figure.axes_dict[keys[2]][h_counter].set_ylabel("Current($\mu$A)")
8296
if q==len(data_harms)-1:
83-
figure.axes_dict[keys[2]][h_counter].set_xlabel("Voltage(mV vs. Ref.)")
97+
figure.axes_dict[keys[2]][h_counter].set_xlabel("Potential(mV vs. Ref.)")
8498
figure.axes_dict[keys[2]][h_counter].legend(framealpha=1.0,bbox_to_anchor=(-0.3, 0.0), loc="upper left")
8599
else:
86100
figure.axes_dict[keys[2]][h_counter].set_xticks([])

‎figures/figure6.py

+13-3
Original file line numberDiff line numberDiff line change
@@ -18,6 +18,8 @@
1818
from multiplotter import multiplot
1919
from harmonics_plotter import harmonics
2020
import time
21+
def RMSE(series1, series2):
22+
return np.sqrt((np.sum(1/(len(series1))*np.power(np.subtract(series1, series2),2))))
2123
method="timeseries"
2224
file="Noramp_2_cv_high_ru_alpha_disp"
2325
CMAES_path=("/").join([upper_level, "Inferred_results", "CMAES"])
@@ -51,7 +53,7 @@
5153
ramped_time_results=ramped_current_results[:,0]
5254
ramped_current_results=ramped_current_results[:,1]
5355
ramped_data_harmonics=ramp_data_harm_class.generate_harmonics(ramped_time_results, ramped_current_results)
54-
values=[[0.24116928285797873, 0.044189985533550226, 135.53440184454033, 510.6019261842285, 7.57155256924869e-05, 0.001459719577206274, -0.00037125091413634306, 7.200978526571967e-11, 8.884799587792013, 0, 0.5973834903322666, 0.1788138464200429],
56+
values=[[0.24055747088306997, 0.05047523191357855, 176.56116945811587, 474.43867201525205, 7.584083348640005e-05, 0.0021028609527935505, -0.0004094927674505519, 7.146018738751102e-11, 8.88475177102702,0,0.6246797783009037, 0.1779493870477656],
5557
[0.22925918516708654, 0.04595696579195954, 123.33007397100599, 873.5412252656006, 3.3412012933121965e-05, 0.057928207116134806, -0.0021217096115628917, 7.178042062464878e-11, 8.884751771027027, 0,0.43751189633466997, 0.15509617967563671]]
5658
ramp_data_class.dim_dict["alpha_mean"]=None
5759
ramp_data_class.dim_dict["alpha_std"]=None
@@ -62,16 +64,22 @@
6264
axes=multiplot(1, 2, **{"harmonic_position":0, "num_harmonics":5, "orientation":"landscape", "plot_width":5})
6365
ramp_data_class.harmonic_range=harmonic_range
6466
j=0
67+
6568
labels=["Sinusoidal parameters", "Ramped parameters", "Interpolated+shifted"]
6669
for i in range(0, 2):
70+
errors=[]
6771
ramped_time_series=ramp_data_class.i_nondim(ramp_data_class.test_vals(values[i], "timeseries"))
6872
alpha_val=round(ramp_data_class.dim_dict["alpha"],3)
6973
ramped_times=ramp_data_class.t_nondim(ramp_data_class.time_vec[ramp_data_class.time_idx])
7074
ramped_harmonics=ramp_data_harm_class.generate_harmonics(ramped_times, ramped_time_series)
7175
for harm_counter in range(0, len(ramped_harmonics),1):
76+
syn_harms=ramped_harmonics[harm_counter,:]*1e6
77+
data_harms=ramped_data_harmonics[harm_counter,:]*1e6
78+
interped_data=np.interp(ramped_times, ramped_time_results, data_harms)
7279
ax=axes.axes_dict["row1"][j]
73-
ax.plot(ramped_times, (ramped_harmonics[harm_counter,:]*1e6), label="Sim")
74-
ax.plot(ramped_time_results, (ramped_data_harmonics[harm_counter,:]*1e6), label="Exp", alpha=0.6)
80+
errors.append(RMSE(abs(syn_harms), abs(interped_data)))
81+
ax.plot(ramped_times, (syn_harms), label="Sim")
82+
ax.plot(ramped_time_results, (data_harms), label="Exp", alpha=0.6)
7583
ax2=ax.twinx()
7684
ax2.set_ylabel(harmonic_range[harm_counter], rotation=0)
7785
ax2.set_yticks([])
@@ -84,6 +92,8 @@
8492
else:
8593
ax.set_xticks([])
8694
j+=1
95+
print(errors)
96+
print("Error", np.mean(errors))
8797
fig=plt.gcf()
8898
fig.set_size_inches((7, 4.5))
8999
plt.show()

‎figures/figureSX.py

+2
Original file line numberDiff line numberDiff line change
@@ -58,6 +58,8 @@
5858
current_results_noramp=noramp_results.i_nondim(noramp_results.other_values["experiment_current"])
5959
voltage_results_noramp=noramp_results.e_nondim(noramp_results.other_values["experiment_voltage"])
6060
harms=harmonics(list(range(start_harm, end_harm)),noramp_results.dim_dict["omega"] , 0.05)
61+
with_high_sf=[0.2401406696585979, 0.5298857718190104, 39.81306601771508, 597.6679747134392, 7.512874040749519e-05, 0.0035853012585091437, -0.00048155562888435414, 7.379570283254313e-11, 8.940517097944886,4.3588467282676415, 5.164737129584613, 0.5405149242350893, 0.1046787847226505]
62+
6163
values=[[0.24076655487877807, 0.9160545932435216, 54.57673630269225, 544.0405150098435, 7.588258963750423e-05, 0.0024096777244230483, -0.00042186418363351214, 7.061673890583758e-11, 8.940521614985501, 5.087607311638155, 4.359094730658887, 0.54708141682858, 0.07163914467232897],
6264
[0.24076655487877807, 0.9160545932435216, 54.57673630269225, 544.0405150098435, 7.588258963750423e-05, 0.0024096777244230483, -0.00042186418363351214, 7.061673890583758e-11, 8.884751771027027, 0, 0.54708141682858, 0.07163914467232897]]
6365

‎figures/parameter_scans.png

1.54 MB
Loading

‎figures/toc_graphic.png

98.7 KB
Loading

‎figures/toc_plot.py

+64
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,64 @@
1+
import numpy as np
2+
import matplotlib.pyplot as plt
3+
import matplotlib as mpl
4+
import pints
5+
import os
6+
import sys
7+
current_dir=os.getcwd()
8+
dir_list=current_dir.split("/")
9+
upper_level_list=dir_list[:dir_list.index("SV_paper")+1]
10+
upper_level=("/").join(upper_level_list)
11+
class_loc=upper_level+"/src"
12+
sys.path.insert(0, class_loc)
13+
from single_e_class_unified import single_electron
14+
import pints.plot
15+
import math
16+
from multiplotter import multiplot
17+
from harmonics_plotter import harmonics
18+
dir_path = os.path.dirname(os.path.realpath(__file__))
19+
results_dict="Inferred_params"
20+
Electrode="Yellow"
21+
run="Run_7"
22+
concs=["1e-1M", "1e0M"]
23+
file_numbers=[str(x) for x in range(1, 4)]
24+
25+
plot_counter=0
26+
h_counter=0
27+
f_counter=0
28+
CMAES_path=("/").join([upper_level, "Inferred_results", "CMAES"])
29+
plt.rcParams.update({'font.size': 9})
30+
def RMSE(series1, series2):
31+
return np.sqrt((np.sum(1/(len(series1))*np.power(np.subtract(series1, series2),2))))
32+
33+
34+
for i in range(1,11):
35+
file="Noramp_"+str(i)+"_cv_high_ru_alpha_disp"
36+
method="timeseries"
37+
master_optim_list=["E0_mean", "E0_std","k_0","Ru","Cdl","CdlE1", "CdlE2","gamma","omega","cap_phase","phase", "alpha"]
38+
noramp_results=single_electron(CMAES_path+"/"+file)
39+
master_optim_list=["E0_mean", "E0_std", "k_0","Ru","Cdl","CdlE1", "CdlE2","gamma","omega","cap_phase","phase", "alpha_mean", "alpha_std"]
40+
param_vals=([noramp_results.save_dict["params"][2][noramp_results.save_dict["optim_list"].index(key)] if (key in noramp_results.save_dict["optim_list"]) else noramp_results.dim_dict[key] for key in master_optim_list])
41+
noramp_results.simulation_options["dispersion_bins"]=[8,8]
42+
noramp_results.dim_dict["simulation_freq"]=1/2000.0
43+
noramp_results.def_optim_list(master_optim_list)
44+
cmaes_time=noramp_results.i_nondim(noramp_results.test_vals(param_vals, method))
45+
current_results=noramp_results.i_nondim(noramp_results.other_values["experiment_current"])#[0::dec_amount]
46+
voltage_results=noramp_results.e_nondim(noramp_results.other_values["experiment_voltage"])#[0::dec_amount]
47+
print_vals=np.append(param_vals, RMSE(current_results, cmaes_time)*1e6)
48+
49+
print(list(print_vals), ",")
50+
"""
51+
harms=harmonics(range(1, 8),noramp_results.dim_dict["omega"] , 0.05)
52+
time_results=noramp_results.t_nondim(noramp_results.other_values["experiment_time"])#[0::dec_amount]
53+
plt.plot(voltage_results*1e3, (cmaes_time)*1e3, label="Simulation")
54+
plt.plot(voltage_results*1e3,(current_results)*1e3, alpha=0.5, label="Experiment")
55+
plt.xlabel("Potential(mV vs. Ref.)")
56+
plt.ylabel("Current(mA)")
57+
plt.legend()
58+
fig=plt.gcf()
59+
60+
fig.set_size_inches((3.25, 1.75))
61+
plt.show()
62+
save_path="toc_graphic.png"
63+
fig.savefig(save_path, dpi=500)
64+
"""

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