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Commit 40fab0a2 authored by Nuno Pimpão Santos Martins's avatar Nuno Pimpão Santos Martins
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add notebooks for ssim data, regarind double degradation experiment

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jupyternotebooks/data_violinplots_ssim_dcx1_vs_degx2.ipynb 0 → 100644
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%% Cell type:code id:78cf33ef-5d5f-4ef4-8b9a-05d0c62f7ec3 tags:
``` python
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import numpy as np
```
%% Cell type:code id:71683726-4bc2-45e7-b71c-244546fb7330 tags:
``` python
data_path = 'W:/NPC_adult_new/quantification_data/ssim_quantification/'
# data_path = '/mnt/e/Data/contrast_enhancement_paper/quantification_data/'
```
%% Cell type:code id:3d243ded-8625-43cf-8797-92a32846129c tags:
``` python
filename = 'dcx1_vs_degx2_all_ssim_20230725.csv'
```
%% Cell type:code id:652cbe9c-3ce1-4bb0-9849-2088accd2c46 tags:
``` python
data = pd.read_csv(os.path.join(data_path, filename))
```
%% Cell type:code id:e96ecefd-4808-4521-950b-c57c1b81778f tags:
``` python
data_filtered = data.loc[data['Real Z'] < 300]
data_filtered = data_filtered.loc[data_filtered['Real Z'] > 10]
```
%% Cell type:code id:13322995-5ee7-4101-8c9b-eac3263d66ec tags:
``` python
#ref line properties
ref_alpha = 0.2
ref_color = 'k'
ref_style = 'dashed'
#Plot 1
# fig = plt.figure(figsize=(12, 6))
# fig.add_subplot(1, 2, 1)
fig, ax1 = plt.subplots(1, 1, figsize=(6,6))
#Z depth reference line, qualitative results
# plt.vlines(x=[11.1, 50.4, 90.9], ymin=0, ymax=10, colors=ref_color, alpha=ref_alpha, linestyles=ref_style)
sns.lineplot(
data=data_filtered, x='Real Z scaled', #x=[x for x in range(len(perc_ci_mean))],
y="SSIM",
# hue="Condition",
errorbar='se',
linewidth=3,
# palette=["tab:grey","tab:blue", "tab:green", "tab:orange"],
# palette=["grey","blue", "darkblue", "lightgreen", "mediumseagreen", "darkgreen", "sandybrown", "orange", "darkorange"],
# palette=["tab:grey", "b", "g", "orange"],
ax=ax1,
)
sns.despine()
# plt.ylabel('Percentile Contrast Index')
plt.ylabel(' ')
plt.xlabel(' ')
handles, labels = ax1.get_legend_handles_labels()
ax1.legend(
handles=handles,
labels=labels,
# ["Raw", "CLAHE", "Deconvolution", "FCE-Net", "DeepContrast"],
# labelcolor = ["grey","blue", "darkblue", "seagreen", "red", "darkorange"],
loc='upper right',
bbox_to_anchor=(1.15, 1.10),
# fontsize=font_size,
)
# plt.ylim(0.5, 3.0)
# plt.xlim(5.7, 93)
# fig.savefig(data_path+'figures/contrast_quantification_alldata_avg_se_pci_withZref.png', dpi=300, bbox_inches='tight')
```
%% Output
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
<matplotlib.legend.Legend at 0x1ef52ada280>
%% Cell type:markdown id:d93fd489-2789-420a-83a2-850e6cc47ae1 tags:
## Getting some simple statistics per condition
%% Cell type:markdown id:fa83a48d-c091-4e74-bb35-c242ddc78a5a tags:
### Percentile Contrast Index stats per condition
mean
%% Cell type:code id:d5e7f3c1-a71e-410d-a8f4-8ff20ae0c971 tags:
``` python
ssim_mean = data_filtered.groupby(['Position'])['SSIM'].mean()
ssim_mean
```
%% Output
Position
Intermediate 0.753602
Surface 0.797983
undefined 0.765423
Name: SSIM, dtype: float64
%% Cell type:markdown id:29788037-a481-4396-906d-e42e679cb9b2 tags:
standard Deviation
%% Cell type:markdown id:66afa544-97e1-457b-b826-9a32baf89e0c tags:
___
%% Cell type:code id:4eb0ce2e-9a1a-4e81-a04b-ad9fd948bd0d tags:
``` python
data_ssim_all = data_filtered['SSIM'].mean()
data_ssim_all
```
%% Output
0.7654951771536332
%% Cell type:code id:8feb4341-0263-4a82-b5cf-7058b3d53a7a tags:
``` python
data_ssim_verydeep = data_filtered.loc[data_filtered['Real Z scaled'] > 85]
# data_psnr = data_psnr.loc[data_psnr['Real Z scaled'] > 93]
```
%% Cell type:code id:6340b6b7-f5a7-4bb7-be53-36e172716724 tags:
``` python
ssim_mean = data_ssim_verydeep['SSIM'].mean()
print(ssim_mean)
ssim_sd = data_ssim_verydeep['SSIM'].std()
print(ssim_sd)
```
%% Output
0.7045112525625001
0.07506882273592072
%% Cell type:code id:7f34999b-66f6-4641-90b1-79de449e19de tags:
``` python
data_ssim_deep = data_filtered.loc[data_filtered['Real Z scaled'] < 85]
data_ssim_deep = data_ssim_deep.loc[data_ssim_deep['Real Z scaled'] > 70]
```
%% Cell type:code id:61a1e022-1e9f-4f80-8658-c671f75c7543 tags:
``` python
ssim_mean = data_ssim_deep['SSIM'].mean()
print(ssim_mean)
ssim_sd = data_ssim_deep['SSIM'].std()
print(ssim_sd)
```
%% Output
0.7179216334719999
0.08817865781126294
%% Cell type:code id:bcbfbd70-0b54-4d57-90a6-13f83d59f93c tags:
``` python
data_ssim_intermediate = data_filtered.loc[data_filtered['Real Z scaled'] < 70]
data_ssim_intermediate = data_ssim_intermediate.loc[data_ssim_intermediate['Real Z scaled'] > 30]
```
%% Cell type:code id:aa0eea4b-745e-4863-b6fc-4c9110331596 tags:
``` python
ssim_mean = data_ssim_intermediate['SSIM'].mean()
print(ssim_mean)
ssim_sd = data_ssim_intermediate['SSIM'].std()
print(ssim_sd)
```
%% Output
0.7694167237889725
0.05672699485707282
%% Cell type:code id:18ee702b-bdcf-4a9a-84a1-b50d12d71d75 tags:
``` python
data_ssim_shallow = data_filtered.loc[data_filtered['Real Z scaled'] > 15]
data_ssim_shallow = data_ssim_shallow.loc[data_ssim_shallow['Real Z scaled'] < 30]
```
%% Cell type:code id:63cb526e-946c-4840-8a66-a0a4783da50f tags:
``` python
ssim_mean = data_ssim_shallow['SSIM'].mean()
print(ssim_mean)
ssim_sd = data_ssim_shallow['SSIM'].std()
print(ssim_sd)
```
%% Output
0.8010575571891156
0.0520739904052131
%% Cell type:code id:acfceaeb-6a19-4f5c-83c0-2267a59bce2c tags:
``` python
data_ssim_veryshallow = data_filtered.loc[data_filtered['Real Z scaled'] < 15]
```
%% Cell type:code id:5c96e298-c496-4eeb-b808-ca1b5b547fb1 tags:
``` python
ssim_mean = data_ssim_veryshallow['SSIM'].mean()
print(ssim_mean)
ssim_sd = data_ssim_veryshallow['SSIM'].std()
print(ssim_sd)
```
%% Output
0.7918207946974359
0.05674874985648138
%% Cell type:code id:376d9867-56ca-4ae8-9a2b-1212162d01cd tags:
``` python
```
jupyternotebooks/data_violinplots_ssim_dcx2_vs_degx3.ipynb 0 → 100644
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jupyternotebooks/data_violinplots_ssim_dcx3_vs_degx4.ipynb 0 → 100644
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jupyternotebooks/data_violinplots_ssim_raw_vs_degx1.ipynb 0 → 100644
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