Kompenzace válce je analyticky vypočítaná. Jiná je pro povrch, jiná je pro objem. Kompenzace pro kulové plochy je vypočtena na základě měření. Pro oběm na základě inscribed, pro povrch na základě cylinder_surface
Pro nulovou délku tube (pill) je určen korekční faktor.
Pro nenulové délky tube se objevují ještě artefakty na přechodu proto bylo stanoveno měření chyby na těchto přechodech.
Compensation is determined in two steps:
Sphere compensation
For sphere compensation computation run compensation method 1 sphere in run_experiments script.
Use computed x and y in python scripts.
Restart ipython kernel
Join compensation
For sphere-tube join compensation run compensation method measurement 1 tube
Then use computed x and y in python scripts.
restart ipython kernel
In [1]:
%pylab inline
Populating the interactive namespace from numpy and matplotlib
In [2]:
%run evaltools.ipynb
Populating the interactive namespace from numpy and matplotlib
/home/mjirik/miniconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2821: DtypeWarning: Columns (74) have mixed types. Specify dtype option on import or set low_memory=False.
if self.run_code(code, result):
0/3793
In [3]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os.path as op
from pprint import pprint as pp
import scipy.stats
import seaborn as sns
import copy
In [4]:
# datapath = "~/teigen_data/output_rows.csv"
In [5]:
# plotkw ={
# "figsize": [15, 6],
# # "fontsize": 14
# # "linestyle": "-",
# # "marker": "x"
# }
In [6]:
# available_radius_methods = [
# "inscribed", "circumscribed",
# "average",
# "cylinder volume",
# "cylinder surface",
# "cylinder volume + sphere error",
# "cylinder volume + sphere error + man",
# "cylinder surface + sphere error",
# "cylinder surface + sphere error + join error",
# "cylinder surface + sphere error + join error man",
# "best",
# ]
In [7]:
# scripts can be runned
# %run run_experiments.ipynb
In [8]:
# df = pd.read_csv(op.expanduser(datapath))
# # remove duplicates
# ks = copy.copy(list(df.keys()))
# ks.remove("datetime")
# df = df.drop_duplicates(ks)
# df = df.drop_duplicates()
# df["surface error [mm^2]"] = df["numeric surface [mm^2]"] - df["surface [mm^2]"]
# df["surface error [%]"] = df["surface error [mm^2]"] / df["surface [mm^2]"] * 100
# df["volume error [mm^3]"] = df["numeric volume [mm^3]"] - df["volume [mm^3]"]
# df["volume error [%]"] = df["volume error [mm^3]"] / df["volume [mm^3]"] * 100
df = read_data(datapath)
# df = select_df(df, newer_than="2017-10-10")
df["measurement_resolution"] = df["config postprocessing measurement_resolution"]
df["length_distribution_mean"] = df["config generators Unconnected tubes length_distribution_mean"]
df["radius_distribution_mean"] = df["config generators Unconnected tubes radius_distribution_mean"]
df
Out[8]:
config appearance force_rewrite
config appearance noise_preview
config appearance show_aposteriori_surface
config appearance skip_volume_generation
config appearance surface_3d_preview
config areasampling areasize_mm
config areasampling areasize_px
config areasampling voxelsize_mm
config filepattern
config filepattern_abspath
...
measurement_resolution
length_distribution_mean
radius_distribution_mean
element_number
element number
measurement resolution
step 1 time [s]
step 2 time [s]
total time [s]
radius method
0
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...
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inscribed
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[0.1, 0.1, 0.1]
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...
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...
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inscribed
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...
35
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False
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[0.06, 0.06, 0.06]
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...
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13
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...
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14
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...
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15
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...
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...
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...
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18
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...
35
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...
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...
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24
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...
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False
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False
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
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...
9661
False
False
True
True
False
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[50, 50, 50]
[0.2, 0.2, 0.2]
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...
90
0.0
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90
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9663
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
90
0.0
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1.0
1.0
90
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9665
False
False
True
True
False
[10.0, 10.0, 10.0]
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~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
90
0.0
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90
0.059800
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cylinder volume
9667
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
90
0.0
1.0
1.0
1.0
90
0.062651
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cylinder volume + sphere error
9669
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
90
0.0
1.0
1.0
1.0
90
0.061946
0.042676
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cylinder surface + sphere error
9671
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
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1.0
1.0
95
2.516419
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inscribed
9673
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.521368
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cylinder surface
9675
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.481075
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cylinder volume
9677
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.776859
0.059638
2.836497
cylinder volume + sphere error
9679
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.391322
0.063506
2.454828
cylinder surface + sphere error
9681
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.691656
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inscribed
9683
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.611168
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cylinder surface
9685
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
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...
95
0.0
1.0
1.0
1.0
95
2.740709
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cylinder volume
9687
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.777054
0.055298
2.832352
cylinder volume + sphere error
9689
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.522738
0.057277
2.580015
cylinder surface + sphere error
9691
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.058117
0.073984
0.132101
inscribed
9693
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.058959
0.041314
0.100273
cylinder surface
9695
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.058473
0.038415
0.096888
cylinder volume
9697
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.066977
0.037450
0.104427
cylinder volume + sphere error
9699
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.062009
0.037568
0.099577
cylinder surface + sphere error
9701
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.594624
0.038768
2.633392
inscribed
9703
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.695369
0.062075
2.757444
cylinder surface
9705
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.600510
0.053536
2.654046
cylinder volume
9707
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.808582
0.054614
2.863196
cylinder volume + sphere error
9709
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
2.468750
0.058579
2.527329
cylinder surface + sphere error
9711
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.064491
0.053492
0.117983
inscribed
9713
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.067150
0.039774
0.106924
cylinder surface
9715
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.065528
0.039326
0.104854
cylinder volume
9717
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.068041
0.039196
0.107237
cylinder volume + sphere error
9719
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
95
0.0
1.0
1.0
1.0
95
0.069822
0.038845
0.108667
cylinder surface + sphere error
3793 rows × 127 columns
In [9]:
newer_than = pd.to_datetime("2017-10-10 11:57:00.0")
newer_than
Out[9]:
Timestamp('2017-10-10 11:57:00')
In [10]:
print(step2_datetime_key)
# pd.to_datetime(df[step2_datetime_key])
which_keep_time = pd.to_datetime(df[step2_datetime_key]) > pd.to_datetime(newer_than)
which_keep_time
processing_info step2_finish_datetime
Out[10]:
0 False
1 False
2 False
3 False
4 False
5 False
6 False
7 False
8 False
9 False
10 False
11 False
12 False
13 False
14 False
15 False
16 False
17 False
18 False
19 False
20 False
21 False
22 False
23 False
24 False
25 False
26 False
27 False
28 False
29 False
...
9661 True
9663 True
9665 True
9667 True
9669 True
9671 True
9673 True
9675 True
9677 True
9679 True
9681 True
9683 True
9685 True
9687 True
9689 True
9691 True
9693 True
9695 True
9697 True
9699 True
9701 True
9703 True
9705 True
9707 True
9709 True
9711 True
9713 True
9715 True
9717 True
9719 True
Name: processing_info step2_finish_datetime, Length: 3793, dtype: bool
In [11]:
def show_radius(dfs, x_key="measurement resolution"):
dfsp = dfs[["sphere_radius_volume_estimation_numeric", "sphere_radius_surface_estimation_numeric", x_key]].sort_values(
x_key).sort_values(x_key)
if len(dfsp) < 1:
return dfsp
# fig = plt.figure(figsize=[25, 18])
ax = plt.subplot(111)
dfsp.plot(
ax=ax, x=x_key, **plotkw)
# ax = plt.subplot(122)
# dfsp[["volume error [%]"]].plot(
# ax=ax, kind="box", **plotkw)
plt.suptitle(radius_method + " ({})".format(len(dfs)))
return dfsp
def show_error(dfs, x_key="measurement resolution"):
dfsp = dfs[["surface error [%]", "volume error [%]",
x_key]].sort_values(x_key)
if len(dfsp) < 1:
return dfsp
# wilcoxon - čím větší, tím lepší, alespoň 0.05
surf_w = scipy.stats.wilcoxon(x=dfs["surface [mm^2]"], y=dfs["numeric surface [mm^2]"], correction=False, zero_method="pratt")
# spearman čím menší, tím lepší
surf_s = scipy.stats.spearmanr(dfs["surface [mm^2]"], dfs["numeric surface [mm^2]"])
# wilcoxon - čím větší, tím lepší
vol_w = scipy.stats.wilcoxon(x=dfs["volume [mm^3]"], y=dfs["numeric volume [mm^3]"], correction=False, zero_method="pratt")
# spearman čím menší, tím lepší
vol_s = scipy.stats.spearmanr(dfs["volume [mm^3]"], dfs["numeric volume [mm^3]"])
print radius_method, ": \nsurface (w/s): \n" , surf_w,"\n", surf_s, "\nvolume (w/s): \n", vol_w, "\n",vol_s, "\n", len(dfsp)
fig = plt.figure(figsize=[25, 18])
ax = plt.subplot(141)
dfsp.plot(
ax=ax, x=x_key, **plotkw)
ax = plt.subplot(142)
dfsp[["volume error [%]"]].plot(
ax=ax, kind="box", **plotkw)
ax = plt.subplot(143)
dfsp[["surface error [%]"]].plot(
ax=ax, kind="box", **plotkw)
ax = plt.subplot(144)
dfsp[dfsp["measurement resolution"] > dfsp["measurement resolution"].mean()].plot(
ax=ax, x=x_key, **plotkw)
plt.suptitle(radius_method)
return dfsp
Regular polygon equivalent surface area $eqS$ is used for cylinder volume compensation
$$ r_{eqS} = \sqrt{\frac{\theta r^2}{\sin{\theta}}} \qquad \theta = \frac{2 \pi}{n} $$Regular polygon equivalent perimeter $eqP$ is used for cylinder surface compensation
$$ r_{eqP} = \frac{\theta r}{ 2 \sin{\frac{\theta}{2}} } \qquad \theta = \frac{2 \pi}{n} $$
In [12]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 1 cylinder " + radius_method
]
print len(dfs)
if len(dfs) > 1:
dfsp = show_error(dfs) #, x_key="radius_distribution_mean")
0
0
0
0
0
0
0
0
0
0
0
0
In [13]:
# df
In [14]:
# 1 object in scene, 0 variances
# for spheres this would be 0
df["cylinder_volume"] = np.pi * df["radius_distribution_mean"]**2 * df["length_distribution_mean"]
df["cylinder_surface"] = 2 * np.pi * df["radius_distribution_mean"] * df["length_distribution_mean"]
df["sphere_numeric_volume"] = (df["numeric volume [mm^3]"] - df["cylinder_volume"])
df["sphere_volume"] = (df["volume [mm^3]"] - df["cylinder_volume"])
df["sphere_numeric_surface"] = (df["numeric surface [mm^2]"] - df["cylinder_surface"])
df["sphere_surface"] = (df["surface [mm^2]"] - df["cylinder_surface"])
df["sphere_radius_volume_estimation_numeric"] = ((3 * df["sphere_numeric_volume"] / (4* np.pi))**(1.0/3.0))
df["sphere_radius_volume_estimation"] = ((3 * df["sphere_volume"] / (4* np.pi))**(1.0/3.0))
df["sphere_radius_surface_estimation_numeric"] = ((df["sphere_numeric_surface"] / (4 * np.pi))**(.5))
df["sphere_radius_surface_estimation"] = ((df["sphere_surface"] / (4* np.pi))**(.5))
# for sphere and cylinder join compensation
# this works only if sphere error is compensated to 0
# join error is composed from two joins of spheres and cylinder
df["join_surface_error"] = df["sphere_numeric_surface"] - df["sphere_surface"]
# surface of the sphere
df["sphere_join_numeric_surface"] = df["sphere_surface"] + 0.5 * df["join_surface_error"]
df["sphere_radius_join_surface_estimation_numeric"] = ((df["sphere_join_numeric_surface"] / (4 * np.pi))**(.5))
df["sphere_radius_join_surface_estimation"] = ((df["sphere_surface"] / (4* np.pi))**(.5))
df["join_volume_error"] = df["sphere_numeric_volume"] - df["sphere_volume"]
# surface of the sphere
df["sphere_join_numeric_volume"] = df["sphere_volume"] + 0.5 * df["join_volume_error"]
df["sphere_radius_join_surface_estimation_numeric"] = ((df["sphere_join_numeric_volume"] / (4 * np.pi))**(.5))
df["sphere_radius_join_surface_estimation"] = ((df["sphere_volume"] / (4* np.pi))**(.5))
available_radius_methods = [
"inscribed", "circumscribed",
"average",
"cylinder volume",
"cylinder surface",
"cylinder volume + sphere error",
"cylinder surface + sphere error",
"cylinder surface + sphere error + join error",
"best",
]
for radius_method in ["inscribed"]:
dfs = df[
df[note_key] == "compensation method 1 sphere " + radius_method
# df["generators Unconnected cylinders radius_distribution_mean"] == 5
]
# show_radius(dfs)
In [39]:
from scipy.interpolate import UnivariateSpline
from scipy.interpolate import InterpolatedUnivariateSpline
radius_method = "inscribed"
dfs = df[
df[note_key] == "compensation method 1 sphere " + radius_method
]
if len(dfs) > 1:
dfsm = dfs.groupby("measurement_resolution").mean().reset_index()
x = list(dfsm["measurement_resolution"])
y = list(dfsm["sphere_radius_volume_estimation_numeric"] / dfsm["sphere_radius_volume_estimation"])
# x.append(100)
# y.append(1.0)
x.append(200)
y.append(1.0)
x = np.asarray(x)
y = np.asarray(y)
spl1 = InterpolatedUnivariateSpline(x, y)
# spl1 = UnivariateSpline(x, y,k=5)
# spl1.set_smoothing_factor(0.1)
xs = np.linspace(6, 100, 100)
plt.plot(
xs[:], spl1(xs[:]), "b" ,
x[:-2], y[:-2], "ro",
)
stx = "x_cvse = ["
for i in x:
stx = stx + str(i) + ", "
stx += "]"
sty = "y_cvse = ["
for i in y:
sty = sty + str(i) + ", "
sty += "]"
print stx
print sty
x_cvse = [5, 6, 7, 8, 10, 12, 14, 17, 19, 21, 23, 29, 30, 31, 34, 36, 39, 42, 44, 46, 48, 50, 53, 58, 60, 63, 67, 70, 73, 78, 80, 84, 87, 90, 95, 200, ]
y_cvse = [0.866160332119, 0.914382318859, 0.933968032022, 0.95218918453, 0.969538675931, 0.978911309831, 0.984540924039, 0.989327370812, 0.99148526871, 0.993049172195, 0.994218638187, 0.996381334592, 0.996658189571, 0.996837044536, 0.997400277914, 0.997681875142, 0.998008753883, 0.998298249903, 0.998449773378, 0.998581941074, 0.998697885167, 0.998800171243, 0.998925745446, 0.999108829618, 0.999167352826, 0.999240932216, 0.999328288662, 0.999388558376, 0.999435320417, 0.999507694313, 0.999532038584, 0.999575592385, 0.999602900653, 0.999630352291, 0.999667137992, 1.0, ]
In [48]:
xs = np.linspace(20, 40, 30)
plt.plot(
xs[:], spl1(xs[:]), "b" ,
x[9:15], y[9:15], "ro",
)
Out[48]:
[<matplotlib.lines.Line2D at 0x7f5478181e10>,
<matplotlib.lines.Line2D at 0x7f5478181fd0>]
In [16]:
# keys = list_filter(df.keys(), contain="areasize_mm")
# df[keys[0]]
# dfs
Out[16]:
config appearance force_rewrite
config appearance noise_preview
config appearance show_aposteriori_surface
config appearance skip_volume_generation
config appearance surface_3d_preview
config areasampling areasize_mm
config areasampling areasize_px
config areasampling voxelsize_mm
config filepattern
config filepattern_abspath
...
sphere_radius_volume_estimation_numeric
sphere_radius_volume_estimation
sphere_radius_surface_estimation_numeric
sphere_radius_surface_estimation
join_surface_error
sphere_join_numeric_surface
sphere_radius_join_surface_estimation_numeric
sphere_radius_join_surface_estimation
join_volume_error
sphere_join_numeric_volume
7921
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.041520
1.044983
1.042494
1.045073
-0.067675
13.690881
0.615212
0.616741
-0.047360
4.756188
7931
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.185586
1.189509
1.186814
1.189728
-0.087030
17.743595
0.747167
0.749016
-0.069521
7.015280
7941
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.034835
1.038379
1.035723
1.038379
-0.069243
13.514837
0.609344
0.610905
-0.047857
4.665897
7951
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.116221
1.119900
1.117508
1.120234
-0.076643
15.731522
0.682557
0.684239
-0.057780
5.854477
7961
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.003407
1.006844
1.004268
1.006844
-0.065102
12.706414
0.581797
0.583287
-0.043628
4.253569
8021
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.037728
1.044983
1.039669
1.045073
-0.141592
13.653923
0.613544
0.616741
-0.098858
4.730439
8031
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.181293
1.189509
1.183624
1.189728
-0.182066
17.696077
0.745152
0.749016
-0.145070
6.977505
8041
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.031088
1.038379
1.032917
1.038379
-0.142189
13.478364
0.607702
0.610905
-0.098100
4.640776
8051
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.112199
1.119900
1.114525
1.120234
-0.160319
15.689684
0.680726
0.684239
-0.120535
5.823100
8061
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
0.999774
1.006844
1.001547
1.006844
-0.133683
12.672123
0.580229
0.583287
-0.089431
4.230667
8071
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.038949
1.044983
1.040577
1.045073
-0.117835
13.665801
0.614080
0.616741
-0.082322
4.738707
8081
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.182675
1.189509
1.184650
1.189728
-0.151520
17.711350
0.745800
0.749016
-0.120807
6.989637
8091
False
False
True
True
False
[10.0, 10.0, 10.0]
[50, 50, 50]
[0.2, 0.2, 0.2]
~/teigen_data/{seriesn:03d}/data{:06d}.jpg
~/teigen_data/005/data{:06d}.jpg
...
1.032315
1.038379
1.033835
1.038379
-0.118331
13.490293
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175 rows × 143 columns
In [17]:
import scipy
import copy
if len(dfs) > 1:
# f = scipy.interpolate.interp1d(x,y, kind="quadratic", fill_value="extrapolate")
x = np.asarray(list(dfs["measurement_resolution"]))
y = np.asarray(list(dfs["sphere_radius_volume_estimation"]))
z = np.polyfit(x,y, 2)
f = np.poly1d(z)
plt.plot(x, y, "ro")
plt.plot(xs, f(xs), "b")
plt.show()
In [18]:
from scipy.optimize import curve_fit
def func(x, a, b, c, e, f, g):
return a*np.exp(-b * x) # + # c + np.sin(e*x + f) * np.exp(g*x)
def func1(x, theta, omega0, K):
jmt = (1 - theta**2)**0.5
out = K * (1 - 1/jmt * np.exp(-1 * theta * omega0 * x))
# out = K * (1 - (1 / jmt * np.exp(-1 * theta * omega0 * x)) * np.sin(omega0 * jmt * x))
#+ np.arccos(theta))
return out
if len(dfs) > 1:
x = np.asarray(list(dfs["measurement_resolution"]))
y = np.asarray(list(dfs["sphere_radius_volume_estimation"]))
popt, pcov = curve_fit(func, x, y, bounds=(-np.inf, np.inf))
print popt
plt.plot(xs, func(xs, *popt), "b")
plt.plot(x, y, "ro")
plt.show()
/home/mjirik/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp
/home/mjirik/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in multiply
/home/mjirik/miniconda2/lib/python2.7/site-packages/scipy/optimize/minpack.py:779: OptimizeWarning: Covariance of the parameters could not be estimated
category=OptimizeWarning)
[ 1.07992287e+00 -1.55978007e-10 1.00000000e+00 1.00000000e+00
1.00000000e+00 1.00000000e+00]
In [19]:
radius_method = "cylinder surface"
radius_method = "inscribed"
dfs = df[
df[note_key] == "compensation method 1 sphere " + radius_method
]
dfsm = dfs.groupby("measurement_resolution").mean().reset_index()
show_radius(dfsm)
Out[19]:
sphere_radius_volume_estimation_numeric
sphere_radius_surface_estimation_numeric
measurement resolution
0
0.935386
0.972670
5
1
0.987462
1.011868
6
2
1.008613
1.027113
7
3
1.028291
1.041807
8
4
1.047027
1.055628
10
5
1.057149
1.063120
12
6
1.063228
1.067630
14
7
1.068397
1.071463
17
8
1.070728
1.073198
19
9
1.072417
1.074456
21
10
1.073679
1.075397
23
11
1.076015
1.077138
29
12
1.076314
1.077361
30
13
1.076507
1.077505
31
14
1.077115
1.077959
34
15
1.077419
1.078185
36
16
1.077772
1.078448
39
17
1.078085
1.078681
42
18
1.078249
1.078803
44
19
1.078391
1.078910
46
20
1.078517
1.079003
48
21
1.078627
1.079085
50
22
1.078763
1.079186
53
23
1.078960
1.079334
58
24
1.079024
1.079381
60
25
1.079103
1.079440
63
26
1.079197
1.079512
67
27
1.079263
1.079559
70
28
1.079313
1.079597
73
29
1.079391
1.079655
78
30
1.079418
1.079675
80
31
1.079465
1.079710
84
32
1.079494
1.079732
87
33
1.079524
1.079754
90
34
1.079563
1.079784
95
In [20]:
sns.lmplot(data=dfs, x="measurement resolution", y="sphere_radius_volume_estimation_numeric")
Out[20]:
<seaborn.axisgrid.FacetGrid at 0x7f547a02bb90>
In [21]:
from scipy.interpolate import UnivariateSpline
from scipy.interpolate import InterpolatedUnivariateSpline
if len(dfsm) > 0:
x = list(dfsm["measurement resolution"])
y = list(dfsm["sphere_radius_surface_estimation_numeric"] / dfsm["sphere_radius_surface_estimation"])
# x.append(100)
# y.append(1.0)
x.append(200)
y.append(1.0)
x = np.asarray(x)
y = np.asarray(y)
spl1 = InterpolatedUnivariateSpline(x, y)
# spl1.set_smoothing_factor(0.1)
xs = np.linspace(6, 200, 100)
plt.figure()
plt.plot(x[:], y[:], "ro")
plt.plot(xs[:], spl1(xs[:]), "b")
plt.show()
stx = "x_csse = ["
for i in x:
stx = stx + str(i) + ", "
stx += "]"
sty = "y_csse = ["
for i in y:
sty = sty + str(i) + ", "
sty += "]"
print stx
print sty
x_csse = [5, 6, 7, 8, 10, 12, 14, 17, 19, 21, 23, 29, 30, 31, 34, 36, 39, 42, 44, 46, 48, 50, 53, 58, 60, 63, 67, 70, 73, 78, 80, 84, 87, 90, 95, 200, ]
y_csse = [0.900576771582, 0.936869836439, 0.950985318762, 0.964590043529, 0.977386494167, 0.984323254476, 0.988498485354, 0.992048202584, 0.993654556051, 0.994819174883, 0.995690300838, 0.997301856778, 0.997508981673, 0.997641534309, 0.998061908546, 0.998271760101, 0.998515046246, 0.998731160009, 0.998844108505, 0.998942638136, 0.999029070544, 0.999105325336, 0.99919881978, 0.999335448082, 0.99937908559, 0.99943387055, 0.999500058848, 0.999544024835, 0.999578847301, 0.999632860646, 0.999651016253, 0.99968349269, 0.999703829344, 0.999724328432, 0.999751740122, 1.0, ]
In [22]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 1 sphere " + radius_method
]
dfsp = show_error(dfs)
inscribed :
surface (w/s):
WilcoxonResult(statistic=0.0, pvalue=1.8082562147130171e-30)
SpearmanrResult(correlation=0.92518238203233805, pvalue=1.0563268433716987e-74)
volume (w/s):
WilcoxonResult(statistic=0.0, pvalue=1.8082562147130171e-30)
SpearmanrResult(correlation=0.88814949166482948, pvalue=2.6149998187414588e-60)
175
cylinder volume :
surface (w/s):
WilcoxonResult(statistic=0.0, pvalue=1.8082562147130171e-30)
SpearmanrResult(correlation=0.96557462746558187, pvalue=4.2089304126380024e-103)
volume (w/s):
WilcoxonResult(statistic=1517.0, pvalue=3.1871377334197794e-20)
SpearmanrResult(correlation=0.97909203237294462, pvalue=1.388798878494895e-121)
175
cylinder surface :
surface (w/s):
WilcoxonResult(statistic=0.0, pvalue=1.2393170660537839e-30)
SpearmanrResult(correlation=0.96404427425724315, pvalue=4.4950975714316946e-102)
volume (w/s):
WilcoxonResult(statistic=0.0, pvalue=1.2393170660537839e-30)
SpearmanrResult(correlation=0.94054250189663036, pvalue=1.6316743472940282e-83)
176
cylinder volume + sphere error :
surface (w/s):
WilcoxonResult(statistic=0.0, pvalue=1.8082562147130171e-30)
SpearmanrResult(correlation=0.96125545666677958, pvalue=9.6308424836064136e-99)
volume (w/s):
WilcoxonResult(statistic=2901.0, pvalue=8.6555322207123146e-13)
SpearmanrResult(correlation=0.97981189417274495, pvalue=6.9148116137372243e-123)
175
cylinder surface + sphere error :
surface (w/s):
WilcoxonResult(statistic=1075.0, pvalue=5.5589359990006488e-23)
SpearmanrResult(correlation=0.97981189417274495, pvalue=6.9148116137372243e-123)
volume (w/s):
WilcoxonResult(statistic=139.0, pvalue=1.9393351469752702e-29)
SpearmanrResult(correlation=0.96365499599944737, pvalue=4.2343265895066386e-101)
175
In [23]:
# surface
from scipy.interpolate import UnivariateSpline
from scipy.interpolate import InterpolatedUnivariateSpline
radius_method = "cylinder surface + sphere error"
dfs = df[
# df["output note"] == "compensation method 1 tube" + radius_method
df[note_key] == "compensation method measurement 1 tube " + radius_method
]
if len(dfs) > 0:
dfsm = dfs.groupby("measurement_resolution").mean().reset_index()
# x = list(dfsm["measurement_resolution"])
# y = list(dfsm["radius_volume_estimation_numeric"] / dfsm["radius_volume_estimation"])
x = list(dfsm["measurement_resolution"])
# y = list(dfsm["radius_surface_estimation_numeric"] / dfsm["radius_surface_estimation"])
y = list(dfsm["sphere_radius_join_surface_estimation_numeric"] / dfsm["sphere_radius_join_surface_estimation"])
# y = list(dfsm["sphere_radius_join_surface_estimation_numeric"] / dfsm["radius_distribution_mean"])
x.append(100)
y.append(1.0)
x.append(200)
y.append(1.0)
x = np.asarray(x)
y = np.asarray(y)
spl1 = InterpolatedUnivariateSpline(x, y)
# spl1 = UnivariateSpline(x, y,k=6)
# spl1.set_smoothing_factor(10.01)
xs = np.linspace(5, 100, 100)
plt.plot(
xs[:], spl1(xs[:]), "b" ,
x[:-2], y[:-2], "ro",
list(dfs["measurement_resolution"])[:],
list(dfs["sphere_radius_join_surface_estimation_numeric"]/dfs["sphere_radius_join_surface_estimation"])[:],
"g."
)
stx = "x_csseje = ["
for i in x:
stx = stx + str(i) + ", "
stx += "]"
sty = "y_csseje = ["
for i in y:
sty = sty + str(i) + ", "
sty += "]"
print stx
print sty
In [24]:
dfsp = show_error(dfs)
In [25]:
with pd.option_context('display.max_columns', None):
display(dfsm)
display(dfsm[["measurement_resolution", "radius_distribution_mean",
"surface [mm^2]", "numeric surface [mm^2]",
"cylinder_surface", "sphere_surface",
"surface error [mm^2]", "surface error [%]",
"volume [mm^3]", "numeric volume [mm^3]",
"cylinder_volume", "sphere_volume",
"volume error [mm^3]", "volume error [%]",
]])
measurement_resolution
config appearance force_rewrite
config appearance noise_preview
config appearance show_aposteriori_surface
config appearance skip_volume_generation
config appearance surface_3d_preview
config filepattern_series_number
config generators Continuous tubes element_number
config generators Continuous tubes radius_distribution_fixed
config generators Continuous tubes radius_distribution_maximum
config generators Continuous tubes radius_distribution_mean
config generators Continuous tubes radius_distribution_minimum
config generators Continuous tubes radius_distribution_normal
config generators Continuous tubes radius_distribution_standard_deviation
config generators Continuous tubes radius_distribution_uniform
config generators Continuous tubes random_generator_seed
config generators Gensei n_objects
config generators Unconnected tubes allow_overlap
config generators Unconnected tubes element_number
config generators Unconnected tubes last_element_can_be_smaller
config generators Unconnected tubes length_distribution_mean
config generators Unconnected tubes length_distribution_standard_deviation
config generators Unconnected tubes maximum_1000_iteration_number
config generators Unconnected tubes orientation_alpha_rad
config generators Unconnected tubes orientation_anisotropic
config generators Unconnected tubes orientation_beta_rad
config generators Unconnected tubes orientation_variance_rad
config generators Unconnected tubes radius_distribution_fixed
config generators Unconnected tubes radius_distribution_maximum
config generators Unconnected tubes radius_distribution_mean
config generators Unconnected tubes radius_distribution_minimum
config generators Unconnected tubes radius_distribution_normal
config generators Unconnected tubes radius_distribution_standard_deviation
config generators Unconnected tubes radius_distribution_uniform
config generators Unconnected tubes random_generator_seed
config generators Unconnected tubes tube_shape
config generators Unconnected tubes volume_fraction
config generators Voronoi tubes element_number
config generators Voronoi tubes radius_distribution_fixed
config generators Voronoi tubes radius_distribution_maximum
config generators Voronoi tubes radius_distribution_mean
config generators Voronoi tubes radius_distribution_minimum
config generators Voronoi tubes radius_distribution_normal
config generators Voronoi tubes radius_distribution_standard_deviation
config generators Voronoi tubes radius_distribution_uniform
config generators Voronoi tubes random_generator_seed
config measurement tube_shape
config output aposteriori_measurement
config output aposteriori_measurement_multiplier
config postprocessing add_noise
config postprocessing background_intensity
config postprocessing gaussian_blur
config postprocessing gaussian_filter_sigma_mm
config postprocessing limit_negative_intensities
config postprocessing measurement_resolution
config postprocessing negative
config postprocessing noise_exponent
config postprocessing noise_lambda0
config postprocessing noise_lambda1
config postprocessing noise_mean
config postprocessing noise_rng_seed
config postprocessing noise_std
measurement area volume [mm^3]
measurement count []
measurement length [mm]
measurement length d. [mm^-2]
measurement negative numeric volume [mm^3]
measurement negative numeric volume fraction []
measurement numeric surface [mm^2]
measurement numeric volume [mm^3]
measurement numeric volume fraction []
measurement surface [mm^2]
measurement surface d. [mm^-1]
measurement volume [mm^3]
measurement volume d. []
processing_info step1_finished
processing_info step1_generate_time_s
processing_info step1_generate_vtk_time_s
processing_info step1_total_time_s
processing_info step2_finished
processing_info step2_generate_volume_time_s
processing_info step2_numeric_measurement_time_s
processing_info step2_save_volume_time_s
processing_info step2_total_time_s
volume [mm^3]
numeric volume [mm^3]
surface [mm^2]
numeric surface [mm^2]
surface difference [mm^2]
surface difference [-]
surface difference [%]
volume difference [mm^3]
volume difference [-]
volume difference [%]
surface error [mm^2]
surface error [-]
surface error [%]
volume error [mm^3]
volume error [-]
volume error [%]
length_distribution_mean
radius_distribution_mean
element_number
element number
measurement resolution
step 1 time [s]
step 2 time [s]
total time [s]
cylinder_volume
cylinder_surface
sphere_numeric_volume
sphere_volume
sphere_numeric_surface
sphere_surface
sphere_radius_volume_estimation_numeric
sphere_radius_volume_estimation
sphere_radius_surface_estimation_numeric
sphere_radius_surface_estimation
join_surface_error
sphere_join_numeric_surface
sphere_radius_join_surface_estimation_numeric
sphere_radius_join_surface_estimation
join_volume_error
sphere_join_numeric_volume
0
5
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
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True
0
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True
20.0
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1.0
True
5
False
0.0
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1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
996.531034
0.996531
11.935486
3.468966
0.003469
14.714019
0.014714
5.335697
0.005336
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0.034973
0.031290
0.066264
False
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0.002728
0.015191
5.335697
3.468966
14.714019
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35.032878
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1.0
5
0.066264
0.015191
0.073631
0.0
0.0
3.468966
5.335697
11.935486
14.714019
0.935386
1.079923
0.972670
1.080052
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13.324752
0.589319
0.648838
-1.866731
4.402332
1
6
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
6
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
995.914198
0.995914
12.920627
4.085802
0.004086
14.714019
0.014714
5.335697
0.005336
True
0.030641
0.038510
0.069150
False
0.0
0.011961
0.002782
0.014743
5.335697
4.085802
14.714019
12.920627
-1.793392
-0.122626
-12.262606
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1.793392
0.122626
12.262606
1.249895
0.235995
23.599483
0.0
1.0
1.0
1.0
6
0.069150
0.014743
0.083893
0.0
0.0
4.085802
5.335697
12.920627
14.714019
0.987462
1.079923
1.011868
1.080052
-1.793392
13.817323
0.609502
0.648838
-1.249895
4.710749
2
7
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
7
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
995.652133
0.995652
13.307882
4.347867
0.004348
14.714019
0.014714
5.335697
0.005336
True
0.032787
0.046343
0.079130
False
0.0
0.012393
0.003014
0.015407
5.335697
4.347867
14.714019
13.307882
-1.406137
-0.095688
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1.406137
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9.568819
0.987830
0.185385
18.538450
0.0
1.0
1.0
1.0
7
0.079130
0.015407
0.094537
0.0
0.0
4.347867
5.335697
13.307882
14.714019
1.008613
1.079923
1.027113
1.080052
-1.406137
14.010950
0.618056
0.648838
-0.987830
4.841782
3
8
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
8
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
995.390664
0.995391
13.692871
4.609336
0.004609
14.714019
0.014714
5.335697
0.005336
True
0.029544
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False
0.0
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0.002709
0.015006
5.335697
4.609336
14.714019
13.692871
-1.021148
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13.693786
0.0
1.0
1.0
1.0
8
0.077556
0.015006
0.092562
0.0
0.0
4.609336
5.335697
13.692871
14.714019
1.028291
1.079923
1.041807
1.080052
-1.021148
14.203445
0.626298
0.648838
-0.726361
4.972516
4
10
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
10
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
995.135619
0.995136
14.057373
4.864381
0.004864
14.714019
0.014714
5.335697
0.005336
True
0.030829
0.060181
0.091011
False
0.0
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0.003102
0.016362
5.335697
4.864381
14.714019
14.057373
-0.656646
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0.044802
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1.0
1.0
10
0.091011
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0.107372
0.0
0.0
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5.335697
14.057373
14.714019
1.047027
1.079923
1.055628
1.080052
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14.385696
0.634311
0.648838
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5.100039
5
12
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
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0.007479
0.001404
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0.0
1.0
1.0
1.0
80
1.193100
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1.234615
0.0
0.0
5.328218
5.335697
14.703759
14.714019
1.079418
1.079923
1.079675
1.080052
-0.010260
14.708889
0.648610
0.648838
-0.007479
5.331957
31
84
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
84
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
994.671087
0.994671
14.704713
5.328913
0.005329
14.714019
0.014714
5.335697
0.005336
True
0.034922
1.320315
1.355237
False
0.0
0.012016
0.031474
0.043490
5.335697
5.328913
14.714019
14.704713
-0.009306
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-0.001273
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0.009306
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0.063340
0.006783
0.001273
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0.0
1.0
1.0
1.0
84
1.355237
0.043490
1.398727
0.0
0.0
5.328913
5.335697
14.704713
14.714019
1.079465
1.079923
1.079710
1.080052
-0.009306
14.709366
0.648631
0.648838
-0.006783
5.332305
32
87
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
87
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
994.670652
0.994671
14.705310
5.329348
0.005329
14.714019
0.014714
5.335697
0.005336
True
0.031577
1.300579
1.332156
False
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0.044885
5.335697
5.329348
14.714019
14.705310
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1.0
1.0
87
1.332156
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5.329348
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14.714019
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1.079732
1.080052
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14.709665
0.648644
0.648838
-0.006349
5.332522
33
90
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
90
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
994.670212
0.994670
14.705914
5.329788
0.005330
14.714019
0.014714
5.335697
0.005336
True
0.032259
1.568027
1.600286
False
0.0
0.014693
0.037621
0.052314
5.335697
5.329788
14.714019
14.705914
-0.008105
-0.000552
-0.055168
-0.005909
-0.001109
-0.110911
0.008105
0.000552
0.055168
0.005909
0.001109
0.110911
0.0
1.0
1.0
1.0
90
1.600286
0.052314
1.652600
0.0
0.0
5.329788
5.335697
14.705914
14.714019
1.079524
1.079923
1.079754
1.080052
-0.008105
14.709966
0.648658
0.648838
-0.005909
5.332743
34
95
False
False
True
True
False
5.0
30
False
10.0
5.0
2.0
False
5.0
True
0.0
10
False
1.0
False
0.0
0.1
10.0
0.0
False
0.0
0.1
True
3.0
1.0
1.0
True
0.1
False
103
True
0.1
30
False
10.0
5.0
2.0
False
5.0
True
0
True
False
1.0
True
20.0
True
1.0
True
95
False
0.0
0.02
1.0
30.0
0.0
40.0
1000.0
1.0
0.051888
0.000052
994.669625
0.994670
14.706719
5.330375
0.005330
14.714019
0.014714
5.335697
0.005336
True
0.032246
1.552816
1.585061
False
0.0
0.013666
0.038865
0.052531
5.335697
5.330375
14.714019
14.706719
-0.007300
-0.000497
-0.049677
-0.005322
-0.000999
-0.099865
0.007300
0.000497
0.049677
0.005322
0.000999
0.099865
0.0
1.0
1.0
1.0
95
1.585061
0.052531
1.637592
0.0
0.0
5.330375
5.335697
14.706719
14.714019
1.079563
1.079923
1.079784
1.080052
-0.007300
14.710369
0.648676
0.648838
-0.005322
5.333036
measurement_resolution
radius_distribution_mean
surface [mm^2]
numeric surface [mm^2]
cylinder_surface
sphere_surface
surface error [mm^2]
surface error [%]
volume [mm^3]
numeric volume [mm^3]
cylinder_volume
sphere_volume
volume error [mm^3]
volume error [%]
0
5
1.0
14.714019
11.935486
0.0
14.714019
2.778533
18.908261
5.335697
3.468966
0.0
5.335697
1.866731
35.032878
1
6
1.0
14.714019
12.920627
0.0
14.714019
1.793392
12.262606
5.335697
4.085802
0.0
5.335697
1.249895
23.599483
2
7
1.0
14.714019
13.307882
0.0
14.714019
1.406137
9.568819
5.335697
4.347867
0.0
5.335697
0.987830
18.538450
3
8
1.0
14.714019
13.692871
0.0
14.714019
1.021148
6.972469
5.335697
4.609336
0.0
5.335697
0.726361
13.693786
4
10
1.0
14.714019
14.057373
0.0
14.714019
0.656646
4.480166
5.335697
4.864381
0.0
5.335697
0.471316
8.877000
5
12
1.0
14.714019
14.257088
0.0
14.714019
0.456931
3.116032
5.335697
5.006151
0.0
5.335697
0.329546
6.202813
6
14
1.0
14.714019
14.378020
0.0
14.714019
0.335999
2.290568
5.335697
5.092674
0.0
5.335697
0.243023
4.572145
7
17
1.0
14.714019
14.481092
0.0
14.714019
0.232927
1.585040
5.335697
5.166818
0.0
5.335697
0.168878
3.169064
8
19
1.0
14.714019
14.527996
0.0
14.714019
0.186023
1.265863
5.335697
5.200671
0.0
5.335697
0.135026
2.533784
9
21
1.0
14.714019
14.562049
0.0
14.714019
0.151970
1.034134
5.335697
5.225298
0.0
5.335697
0.110399
2.071644
10
23
1.0
14.714019
14.587547
0.0
14.714019
0.126472
0.860626
5.335697
5.243764
0.0
5.335697
0.091933
1.725111
11
29
1.0
14.714019
14.634775
0.0
14.714019
0.079244
0.539240
5.335697
5.278029
0.0
5.335697
0.057668
1.082117
12
30
1.0
14.714019
14.640880
0.0
14.714019
0.073139
0.498095
5.335697
5.282468
0.0
5.335697
0.053229
0.999985
13
31
1.0
14.714019
14.644740
0.0
14.714019
0.069279
0.471433
5.335697
5.285268
0.0
5.335697
0.050428
0.946273
14
34
1.0
14.714019
14.657096
0.0
14.714019
0.056923
0.387622
5.335697
5.294253
0.0
5.335697
0.041444
0.778467
15
36
1.0
14.714019
14.663253
0.0
14.714019
0.050766
0.345680
5.335697
5.298730
0.0
5.335697
0.036967
0.694327
16
39
1.0
14.714019
14.670380
0.0
14.714019
0.043639
0.296957
5.335697
5.303912
0.0
5.335697
0.031785
0.596425
17
42
1.0
14.714019
14.676737
0.0
14.714019
0.037282
0.253838
5.335697
5.308540
0.0
5.335697
0.027157
0.509999
18
44
1.0
14.714019
14.680054
0.0
14.714019
0.033965
0.231252
5.335697
5.310954
0.0
5.335697
0.024743
0.464654
19
46
1.0
14.714019
14.682947
0.0
14.714019
0.031072
0.211547
5.335697
5.313060
0.0
5.335697
0.022637
0.425090
20
48
1.0
14.714019
14.685485
0.0
14.714019
0.028534
0.194261
5.335697
5.314908
0.0
5.335697
0.020789
0.390374
21
50
1.0
14.714019
14.687725
0.0
14.714019
0.026294
0.179009
5.335697
5.316538
0.0
5.335697
0.019159
0.359742
22
53
1.0
14.714019
14.690466
0.0
14.714019
0.023553
0.160272
5.335697
5.318533
0.0
5.335697
0.017163
0.322059
23
58
1.0
14.714019
14.694485
0.0
14.714019
0.019534
0.132976
5.335697
5.321462
0.0
5.335697
0.014235
0.267271
24
60
1.0
14.714019
14.695767
0.0
14.714019
0.018252
0.124246
5.335697
5.322395
0.0
5.335697
0.013301
0.249733
25
63
1.0
14.714019
14.697374
0.0
14.714019
0.016645
0.113265
5.335697
5.323565
0.0
5.335697
0.012132
0.227638
26
67
1.0
14.714019
14.699320
0.0
14.714019
0.014699
0.100029
5.335697
5.324960
0.0
5.335697
0.010737
0.201448
27
70
1.0
14.714019
14.700614
0.0
14.714019
0.013405
0.091247
5.335697
5.325926
0.0
5.335697
0.009770
0.183423
28
73
1.0
14.714019
14.701636
0.0
14.714019
0.012383
0.084266
5.335697
5.326670
0.0
5.335697
0.009027
0.169375
29
78
1.0
14.714019
14.703225
0.0
14.714019
0.010794
0.073471
5.335697
5.327829
0.0
5.335697
0.007868
0.147699
30
80
1.0
14.714019
14.703759
0.0
14.714019
0.010260
0.069838
5.335697
5.328218
0.0
5.335697
0.007479
0.140398
31
84
1.0
14.714019
14.704713
0.0
14.714019
0.009306
0.063340
5.335697
5.328913
0.0
5.335697
0.006783
0.127336
32
87
1.0
14.714019
14.705310
0.0
14.714019
0.008709
0.059262
5.335697
5.329348
0.0
5.335697
0.006349
0.119130
33
90
1.0
14.714019
14.705914
0.0
14.714019
0.008105
0.055168
5.335697
5.329788
0.0
5.335697
0.005909
0.110911
34
95
1.0
14.714019
14.706719
0.0
14.714019
0.007300
0.049677
5.335697
5.330375
0.0
5.335697
0.005322
0.099865
In [26]:
# volume
from scipy.interpolate import UnivariateSpline
from scipy.interpolate import InterpolatedUnivariateSpline
radius_method = "cylinder volume + sphere error"
# radius_method = "cylinder surface + sphere error"
dfs = df[
df[note_key] == "compensation method measurement 1 tube " + radius_method
]
if len(dfs) > 0:
dfsm = dfs.groupby("measurement_resolution").mean().reset_index()
x = list(dfsm["measurement_resolution"])
y = list(dfsm["sphere_radius_join_volume_estimation_numeric"] / dfsm["sphere_radius_join_volume_estimation"])
# x.append(100)
# y.append(1.0)
x.append(200)
y.append(1.0)
x = np.asarray(x)
y = np.asarray(y)
spl1 = InterpolatedUnivariateSpline(x, y)
spl1 = UnivariateSpline(x, y,k=2)
spl1.set_smoothing_factor(50.5)
xs = np.linspace(5, 100, 100)
plt.plot(
xs[:], spl1(xs[:]), "b" ,
x[:-2], y[:-2], "ro",
)
stx = "x_cvseje = ["
for i in x:
stx = stx + str(i) + ", "
stx += "]"
sty = "y_cvseje = ["
for i in y:
sty = sty + str(i) + ", "
sty += "]"
print stx
print sty
In [27]:
dfsp = show_error(dfs)
In [28]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method measurement 1 tube " + radius_method
]
dfsp = show_error(dfs)
In [29]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 1 tube " + radius_method
]
dfsp = show_error(dfs)
In [30]:
run_label = "compensation method 3 spheres various radius "
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == run_label + radius_method
]
dfsp = show_error(dfs, x_key="radius_distribution_mean")
In [31]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 1 tube " + radius_method
]
dfsp = show_error(dfs) #, x_key="radius_distribution_mean")
In [32]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 1 tube various radius " + radius_method
]
dfsp = show_error(dfs, x_key="radius_distribution_mean")
print len(dfs)
0
0
0
0
0
0
0
0
0
In [33]:
run_label = "compensation method 1 tube various length "
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == run_label + radius_method
]
dfsp = show_error(dfs, x_key="length_distribution_mean")
# print len(dfs)
In [34]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 1 tube mix " + radius_method
]
dfsp = show_error(dfs)
In [35]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "compensation method 5 tubes " + radius_method
]
dfsp = show_error(dfs)
In [36]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "fixed resolution " + radius_method
]
dfsp = show_error(dfs)
In [37]:
for radius_method in available_radius_methods:
dfs = df[
df[note_key] == "best test " + radius_method
]
dfsp = show_error(dfs)
In [38]:
# just for control - this should be exactly same as selected radius
radius_method = "inscribed"
dfs = df[
df[note_key] == "compensation method 1 sphere " + radius_method
# df["generators Unconnected cylinders radius_distribution_mean"] == 5
]
if len(dfs) > 1:
dfs["radius_volume_compensation"] = (3 * dfs["volume [mm^3]"] / (4* np.pi))**(1.0/3.0)
dfsp = dfs[["radius_volume_compensation", "measurement_resolution"]].sort_values(
"measurement_resolution")
fig = plt.figure(figsize=[25, 18])
ax = plt.subplot(111)
dfsp.plot(
ax=ax, x="measurement_resolution", **plotkw)
plt.suptitle(radius_method)
/home/mjirik/miniconda2/lib/python2.7/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Content source: mjirik/teigen
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