notebook.community

Edit and run 





In [189]:



    


%run ./msgc_experiments_CMBBEIV_init.ipynb



    


















    






Populating the interactive namespace from numpy and matplotlib










    






C:\Users\Jirik\Miniconda3\envs\lisa\lib\site-packages\IPython\core\magics\pylab.py:160: UserWarning: pylab import has clobbered these variables: ['axes', 'table']
`%matplotlib` prevents importing * from pylab and numpy
  "\n`%matplotlib` prevents importing * from pylab and numpy"

















In [190]:



    


import pandas as pd
import seaborn as sns

Data processing graphs, statistics

Seaborn graphs





In [247]:



    


df = pd.read_csv(fnamenew)
df["image voxel number"] = df["data size"].pow(3)
df["relative object size"] = df["data object size px"] / df["image voxel number"]
df["log(relative object size)"] = log(df["data object size px"] / df["image voxel number"])
# df.rename(columns={"msgc time": "MSGC time"})
# dfs = df[(df["data seedsz"]==3) & (df["data offset"] == 3) & (df["data radius"] == 10) & (df["experiment"] == "image size")]
dfs = df[df["experiment"] == "image size"]
dfs_plus = dfs[dfs['data size'] > 160]

sns.set_context("paper")
sns.set_style("white")



    











In [192]:



    


df.keys()



    


















    
Out[192]:








Index(['_create_nlinks time', 'block size', 'data image size px',
       'data object size px', 'data offset', 'data radius', 'data seedsz',
       'data size', 'edge number', 'error', 'experiment',
       'experiment iteration start time', 'gc time', 'low level image voxels',
       'low level object voxels', 'machine hostname', 'method',
       'nlinks number', 't graph 01', 't graph 10', 't graph 11', 't graph 13',
       't graph 14', 't graph high', 't graph low', 't split 01', 't split 02',
       't split 03', 't split 04', 't split 05', 't split 06', 't split 07',
       't split 08', 't split 081', 't split 082', 't split 0821',
       't split 09', 't split 10', 't1', 't10', 't2', 't3', 't3.1', 't3.2',
       't3.3', 't4', 't5', 't6', 't7', 't8', 't9', 'time', 'tlinks number',
       'image voxel number', 'relative object size'],
      dtype='object')

















In [193]:



    


sns.boxplot(data=dfs_plus, y="time", x="method", showfliers=False)



    


















    
Out[193]:








<matplotlib.axes._subplots.AxesSubplot at 0x2008cbcda58>










    
























In [194]:



    


# df



    











In [195]:



    


uu = pd.melt(dfs_plus.rename(columns={"gc time": "gc", "time": "total"}), value_vars=["gc", "total"], id_vars=["method"], var_name="time type", value_name="time")
# uu = pd.melt(dfs, value_vars=["gc time", "time"], id_vars=["method"], var_name="type", value_name="time")
# uu



    











In [196]:



    


bp = sns.boxplot(data=uu, hue="time type",y="time", x="method", showfliers=False)
# bp.
bp.set(yscale="log")
# ax.set(xscale="log", yscale="log")
plt.savefig(op.join(latex_dir, "msgc_time_boxplot.pdf"), dpi=1000)



    


















    
























In [197]:



    


bp.set(yscale="log")



    


















    
Out[197]:








[None]

















In [198]:



    


# sns.boxplot(data=dfs, y="error", x="method")



    











In [199]:



    


lm = sns.lmplot(data=df, x="data size", y="time", hue="method", order=3, scatter_kws={"s": 3, "marker": "x", "alpha": 0.5})
axes = lm.axes
axes[0,0].set_xlim(30,200)
axes[0,0].set_ylim(0,50)

lines = lm.ax.get_lines()
line = lines[0]
line.set_linestyle("--")
# line.set_marker("s")

plt.savefig(op.join(latex_dir, "msgc_time_datasize_plot.pdf"), dpi=1000)
# axes[0,1].set_ylim(0,)

# lm.ax.get_lines()

Vykreslení dvou lmplotů do jednoho není tak snadné





In [200]:



    


# test better melt

dfm = pd.DataFrame({'City': ['Houston', 'Austin', 'Hoover'],
                   'State': ['Texas', 'Texas', 'Alabama'],
                   'Name':['Aria', 'Penelope', 'Niko'],
                   'Mango':[4, 10, 90],
                   'Orange': [10, 8, 14], 
                   'Watermelon':[40, 99, 43],
                   'Gin':[16, 200, 34],
                   'Vodka':[20, 33, 18]},
                 columns=['City', 'State', 'Name', 'Mango', 'Orange', 'Watermelon', 'Gin', 'Vodka'])
melt(dfm, id_vars=['City', 'State'], value_vars=[['Mango', 'Orange', 'Watermelon'], ['Gin', 'Vodka']], 
     var_name=['Fruit', 'Drink'], value_name=['Pounds', 'Ounces'])



    


















    
Out[200]:











  
    

      
      City
      State
      Fruit
      Pounds
      Drink
      Ounces
    

  
  
    

      0
      Houston
      Texas
      Mango
      4
      Gin
      16.0
    

    

      1
      Austin
      Texas
      Mango
      10
      Gin
      200.0
    

    

      2
      Hoover
      Alabama
      Mango
      90
      Gin
      34.0
    

    

      3
      Houston
      Texas
      Orange
      10
      Vodka
      20.0
    

    

      4
      Austin
      Texas
      Orange
      8
      Vodka
      33.0
    

    

      5
      Hoover
      Alabama
      Orange
      14
      Vodka
      18.0
    

    

      6
      Houston
      Texas
      Watermelon
      40
      nan
      NaN
    

    

      7
      Austin
      Texas
      Watermelon
      99
      nan
      NaN
    

    

      8
      Hoover
      Alabama
      Watermelon
      43
      nan
      NaN
    

  




















In [201]:



    


# df.keys()



    











In [202]:



    


# df.rename(columns={"gc time": "gc", "time": "total"})[["gc", "total", "data size"]]



    











In [203]:



    


# df.rename(columns={"gc time": "gc", "time": "total"})



    











In [221]:



    


dfs = df[df["experiment"] == "image size"]
uu = melt(dfs.rename(columns={"gc time": "gc", "time": "total"}), value_vars=["gc", "total"], id_vars=["method", "data size", "image voxel number", "relative object size"], var_name=["time type"], value_name=["Time [s]"])
uu["mth"] = uu["method"] + " " + uu["time type"]
# uu



    











In [205]:



    


current_palette = sns.color_palette()
sns.palplot(current_palette)

Artifical data - constant object size





In [222]:



    


white_palette = [(1., 1., 1., 0.5)]
lm = sns.lmplot(data=uu, x="data size", y="Time [s]", hue="mth", order=3, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3]
                palette=white_palette
               )
axes = lm.axes
axes[0,0].set_xlim(30,200)
axes[0,0].set_ylim(0,50)

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())



    


















    
























In [224]:



    


white_palette = [(1., 1., 1., 0.5)]
lm = sns.lmplot(
#     data=uu[uu["time type"] == "total"], 
    data=uu, 
    x="data size", y="Time [s]", hue="mth", order=2, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3],
                palette=white_palette,
                legend=None,
#     legend="off"
               )
axes = lm.axes
axes[0,0].set_xlim(30,200)
axes[0,0].set_ylim(0,50)

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
line1.set_color(line2.get_color())


# body
sc = sns.scatterplot(data=uu[uu["time type"] == "total"], x="data size", y="Time [s]", hue="mth", ax=lm.ax, alpha=0.8, s=3)
texts = lm.ax.legend_.get_texts()
leglines = lm.ax.legend_.get_lines()

# texts[0].set_text("short")
# texts.pop(0)
# texts.pop(0)

# plt.legend(lm.ax.legend_[3:6])

from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color=current_palette[0], lw=4),
                Line2D([0], [0], color=current_palette[1], lw=4),
                Line2D([0], [0], color=current_palette[2], lw=4)]
lm.ax.legend(
    custom_lines, 
    [tx.get_text()[:-3] for tx in texts[:3]],
#     ['Cold', 'Medium', 'Hot'],
)
# lg = lm.ax.legend()



    


















    
Out[224]:








<matplotlib.legend.Legend at 0x2008e050518>










    
























In [159]:



    


texts[0].get_text()



    


















    
Out[159]:








'ssgc  gc'

















In [160]:



    


# dir(lm.ax.legend_)



    











In [161]:



    


# L=plt.legend()
lm.ax.legend_.get_texts()
# L.get_texts()[0].set_text('make it short')



    


















    
Out[161]:








<a list of 3 Text objects>

With voxel number





In [227]:



    


rename_time = {
    "time": "Time [s]"
}
# uu = 
current_palette = sns.color_palette()
white_palette = [
    (1., 1., 1., 0.5),
    (1., 1., 1., 0.5),
    (1., 1., 1., 0.5),
    current_palette[0],
    current_palette[1],
    current_palette[2],
]
lm = sns.lmplot(
#     data=uu[uu["time type"] == "total"], 
    data=uu, 
    x="image voxel number", y="Time [s]", hue="mth", order=3, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3],
                palette=white_palette,
                legend=None,
#     legend="off"
               )
axes = lm.axes
axes[0,0].set_xlim(30,uu["image voxel number"].max())
axes[0,0].set_ylim(0,50)

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
line1.set_color(line2.get_color())


# body
# sc = sns.scatterplot(
#     data=uu[uu["time type"] == "total"], x="image voxel number", y="time", hue="mth", 
#     ax=lm.ax, alpha=1.0, s=5
# )
# texts = lm.ax.legend_.get_texts()
# leglines = lm.ax.legend_.get_lines()


# plt.legend(lm.ax.legend_[3:6])

from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color=current_palette[0], lw=4),
                Line2D([0], [0], color=current_palette[1], lw=4),
                Line2D([0], [0], color=current_palette[2], lw=4)]
# lm.ax.legend(
#     custom_lines, 
#     [tx.get_text()[:-3] for tx in texts[:3]],
# )
# lg = lm.ax.legend()
lm.ax.legend(
    custom_lines, 
    labels
#     [tx.get_text()[:-3] for tx in texts[:3]],
)



    


















    
Out[227]:








<matplotlib.legend.Legend at 0x20090c1d2e8>










    
























In [228]:



    


uu



    


















    
Out[228]:











  
    

      
      method
      data size
      image voxel number
      relative object size
      time type
      Time [s]
      mth
    

  
  
    

      0
      ssgc
      44
      85184
      0.093727
      gc
      0.194480
      ssgc  gc
    

    

      1
      msgc_hi2lo
      44
      85184
      0.093727
      gc
      0.213427
      msgc_hi2lo  gc
    

    

      2
      msgc_lo2hi
      44
      85184
      0.093727
      gc
      0.269278
      msgc_lo2hi  gc
    

    

      3
      ssgc
      44
      85184
      0.093727
      gc
      0.194480
      ssgc  gc
    

    

      4
      msgc_hi2lo
      44
      85184
      0.093727
      gc
      0.221408
      msgc_hi2lo  gc
    

    

      5
      msgc_lo2hi
      44
      85184
      0.093727
      gc
      0.257281
      msgc_lo2hi  gc
    

    

      6
      ssgc
      49
      117649
      0.069376
      gc
      0.256315
      ssgc  gc
    

    

      7
      msgc_hi2lo
      49
      117649
      0.069376
      gc
      0.301195
      msgc_hi2lo  gc
    

    

      8
      msgc_lo2hi
      49
      117649
      0.069376
      gc
      0.303154
      msgc_lo2hi  gc
    

    

      9
      ssgc
      49
      117649
      0.069376
      gc
      0.269243
      ssgc  gc
    

    

      10
      msgc_hi2lo
      49
      117649
      0.069376
      gc
      0.283278
      msgc_hi2lo  gc
    

    

      11
      msgc_lo2hi
      49
      117649
      0.069376
      gc
      0.315156
      msgc_lo2hi  gc
    

    

      12
      ssgc
      54
      157464
      0.057023
      gc
      0.338121
      ssgc  gc
    

    

      13
      msgc_hi2lo
      54
      157464
      0.057023
      gc
      0.410927
      msgc_hi2lo  gc
    

    

      14
      msgc_lo2hi
      54
      157464
      0.057023
      gc
      0.435834
      msgc_lo2hi  gc
    

    

      15
      ssgc
      54
      157464
      0.057023
      gc
      0.331151
      ssgc  gc
    

    

      16
      msgc_hi2lo
      54
      157464
      0.057023
      gc
      0.389957
      msgc_hi2lo  gc
    

    

      17
      msgc_lo2hi
      54
      157464
      0.057023
      gc
      0.425854
      msgc_lo2hi  gc
    

    

      18
      ssgc
      59
      205379
      0.045852
      gc
      0.456780
      ssgc  gc
    

    

      19
      msgc_hi2lo
      59
      205379
      0.045852
      gc
      0.588427
      msgc_hi2lo  gc
    

    

      20
      msgc_lo2hi
      59
      205379
      0.045852
      gc
      0.532542
      msgc_lo2hi  gc
    

    

      21
      ssgc
      59
      205379
      0.046061
      gc
      0.478720
      ssgc  gc
    

    

      22
      msgc_hi2lo
      59
      205379
      0.046061
      gc
      0.570475
      msgc_hi2lo  gc
    

    

      23
      msgc_lo2hi
      59
      205379
      0.046061
      gc
      0.532610
      msgc_lo2hi  gc
    

    

      24
      ssgc
      64
      262144
      0.036087
      gc
      0.592416
      ssgc  gc
    

    

      25
      msgc_hi2lo
      64
      262144
      0.036087
      gc
      0.712127
      msgc_hi2lo  gc
    

    

      26
      msgc_lo2hi
      64
      262144
      0.036087
      gc
      0.610395
      msgc_lo2hi  gc
    

    

      27
      ssgc
      64
      262144
      0.036697
      gc
      0.590387
      ssgc  gc
    

    

      28
      msgc_hi2lo
      64
      262144
      0.036697
      gc
      0.718113
      msgc_hi2lo  gc
    

    

      29
      msgc_lo2hi
      64
      262144
      0.036697
      gc
      0.628353
      msgc_lo2hi  gc
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      378
      ssgc
      189
      6751269
      0.003132
      total
      27.856519
      ssgc  total
    

    

      379
      msgc_hi2lo
      189
      6751269
      0.003132
      total
      24.145477
      msgc_hi2lo  total
    

    

      380
      msgc_lo2hi
      189
      6751269
      0.003132
      total
      12.741931
      msgc_lo2hi  total
    

    

      381
      ssgc
      189
      6751269
      0.003132
      total
      26.482217
      ssgc  total
    

    

      382
      msgc_hi2lo
      189
      6751269
      0.003132
      total
      24.646075
      msgc_hi2lo  total
    

    

      383
      msgc_lo2hi
      189
      6751269
      0.003132
      total
      12.590336
      msgc_lo2hi  total
    

    

      384
      ssgc
      194
      7301384
      0.002984
      total
      29.613786
      ssgc  total
    

    

      385
      msgc_hi2lo
      194
      7301384
      0.002984
      total
      25.845931
      msgc_hi2lo  total
    

    

      386
      msgc_lo2hi
      194
      7301384
      0.002984
      total
      13.029152
      msgc_lo2hi  total
    

    

      387
      ssgc
      194
      7301384
      0.003073
      total
      29.845204
      ssgc  total
    

    

      388
      msgc_hi2lo
      194
      7301384
      0.003073
      total
      26.343529
      msgc_hi2lo  total
    

    

      389
      msgc_lo2hi
      194
      7301384
      0.003073
      total
      15.206337
      msgc_lo2hi  total
    

    

      390
      ssgc
      199
      7880599
      0.002935
      total
      32.886094
      ssgc  total
    

    

      391
      msgc_hi2lo
      199
      7880599
      0.002935
      total
      29.192944
      msgc_hi2lo  total
    

    

      392
      msgc_lo2hi
      199
      7880599
      0.002935
      total
      14.790452
      msgc_lo2hi  total
    

    

      393
      ssgc
      199
      7880599
      0.002935
      total
      32.973836
      ssgc  total
    

    

      394
      msgc_hi2lo
      199
      7880599
      0.002935
      total
      28.576625
      msgc_hi2lo  total
    

    

      395
      msgc_lo2hi
      199
      7880599
      0.002935
      total
      17.005531
      msgc_lo2hi  total
    

    

      396
      ssgc
      204
      8489664
      0.002805
      total
      36.402665
      ssgc  total
    

    

      397
      msgc_hi2lo
      204
      8489664
      0.002805
      total
      30.925347
      msgc_hi2lo  total
    

    

      398
      msgc_lo2hi
      204
      8489664
      0.002805
      total
      17.042434
      msgc_lo2hi  total
    

    

      399
      ssgc
      204
      8489664
      0.002743
      total
      36.800602
      ssgc  total
    

    

      400
      msgc_hi2lo
      204
      8489664
      0.002743
      total
      29.621830
      msgc_hi2lo  total
    

    

      401
      msgc_lo2hi
      204
      8489664
      0.002743
      total
      13.773175
      msgc_lo2hi  total
    

    

      402
      ssgc
      209
      9129329
      0.002699
      total
      40.120692
      ssgc  total
    

    

      403
      msgc_hi2lo
      209
      9129329
      0.002699
      total
      31.731159
      msgc_hi2lo  total
    

    

      404
      msgc_lo2hi
      209
      9129329
      0.002699
      total
      17.852267
      msgc_lo2hi  total
    

    

      405
      ssgc
      209
      9129329
      0.002604
      total
      40.782995
      ssgc  total
    

    

      406
      msgc_hi2lo
      209
      9129329
      0.002604
      total
      31.860845
      msgc_hi2lo  total
    

    

      407
      msgc_lo2hi
      209
      9129329
      0.002604
      total
      13.493921
      msgc_lo2hi  total
    

  



408 rows × 7 columns



















In [163]:



    


xx = "relative object size"
white_palette = [(1., 1., 1., 0.5)]
lm = sns.lmplot(
#     data=uu[uu["time type"] == "total"], 
    data=uu, 
    x=xx, y="time", hue="mth", 
    order=5, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3],
                palette=white_palette,
                legend=None,
#                 logx=True
#     legend="off"
               )
axes = lm.axes
# axes[0,0].set_xlim(30,uu[xx].max())
# axes[0,0].set_ylim(0,50)
# lm.set(
#     xscale="log",
# #     yscale="log"
# )

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
line1.set_color(line2.get_color())


# body
sc = sns.scatterplot(
    data=uu[uu["time type"] == "total"], x=xx, y="time", hue="mth", 
    ax=lm.ax, alpha=1.0, s=5
)
texts = lm.ax.legend_.get_texts()
leglines = lm.ax.legend_.get_lines()

# texts[0].set_text("short")
# texts.pop(0)
# texts.pop(0)

# plt.legend(lm.ax.legend_[3:6])

from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color=current_palette[0], lw=4),
                Line2D([0], [0], color=current_palette[1], lw=4),
                Line2D([0], [0], color=current_palette[2], lw=4)]
lm.ax.legend(
    custom_lines, 
    [tx.get_text()[:-3] for tx in texts[:3]],
#     ['Cold', 'Medium', 'Hot'],
)
# lg = lm.ax.legend()



    


















    
Out[163]:








<matplotlib.legend.Legend at 0x2008e15e208>










    
























In [164]:



    


sc = sns.scatterplot(
    data=uu[uu["time type"] == "total"], x=xx, y="time", hue="mth", 
#     ax=lm.ax,
    alpha=1.0, s=5
)
sc.set(xscale="log",
#       yscale="log"
      )
# grid.set(xscale="log", )



    


















    
Out[164]:








[None]










    
























In [165]:



    


lm.ax



    


















    
Out[165]:








<matplotlib.axes._subplots.AxesSubplot at 0x2008f551f28>

















In [166]:



    


dir(lm.ax)



    


















    
Out[166]:








['__class__',
 '__delattr__',
 '__dict__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__getstate__',
 '__gt__',
 '__hash__',
 '__init__',
 '__init_subclass__',
 '__le__',
 '__lt__',
 '__module__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__setattr__',
 '__setstate__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 '__weakref__',
 '_add_text',
 '_adjustable',
 '_agg_filter',
 '_alpha',
 '_anchor',
 '_animated',
 '_aspect',
 '_autoscaleXon',
 '_autoscaleYon',
 '_autotitlepos',
 '_axes',
 '_axes_class',
 '_axes_locator',
 '_axisbelow',
 '_cachedRenderer',
 '_clipon',
 '_clippath',
 '_connected',
 '_contains',
 '_current_image',
 '_facecolor',
 '_frameon',
 '_gci',
 '_gen_axes_patch',
 '_gen_axes_spines',
 '_get_axis_list',
 '_get_lines',
 '_get_patches_for_fill',
 '_get_view',
 '_gid',
 '_gridOn',
 '_hold',
 '_in_layout',
 '_init_axis',
 '_label',
 '_layoutbox',
 '_left_title',
 '_make_twin_axes',
 '_mouseover',
 '_mouseover_set',
 '_navigate',
 '_navigate_mode',
 '_oid',
 '_on_units_changed',
 '_originalPosition',
 '_path_effects',
 '_pcolorargs',
 '_picker',
 '_position',
 '_poslayoutbox',
 '_process_unit_info',
 '_prop_order',
 '_propobservers',
 '_quiver_units',
 '_rasterization_zorder',
 '_rasterized',
 '_remove_legend',
 '_remove_method',
 '_right_title',
 '_sci',
 '_set_artist_props',
 '_set_gc_clip',
 '_set_lim_and_transforms',
 '_set_position',
 '_set_title_offset_trans',
 '_set_view',
 '_set_view_from_bbox',
 '_shared_x_axes',
 '_shared_y_axes',
 '_sharex',
 '_sharey',
 '_sketch',
 '_snap',
 '_stale',
 '_sticky_edges',
 '_subplotspec',
 '_tight',
 '_transform',
 '_transformSet',
 '_twinned_axes',
 '_update_image_limits',
 '_update_line_limits',
 '_update_patch_limits',
 '_update_title_position',
 '_update_transScale',
 '_url',
 '_use_sticky_edges',
 '_validate_converted_limits',
 '_visible',
 '_xaxis_transform',
 '_xcid',
 '_xmargin',
 '_yaxis_transform',
 '_ycid',
 '_ymargin',
 'acorr',
 'add_artist',
 'add_callback',
 'add_child_axes',
 'add_collection',
 'add_container',
 'add_image',
 'add_line',
 'add_patch',
 'add_table',
 'aname',
 'angle_spectrum',
 'annotate',
 'apply_aspect',
 'arrow',
 'artists',
 'autoscale',
 'autoscale_view',
 'axes',
 'axhline',
 'axhspan',
 'axis',
 'axison',
 'axvline',
 'axvspan',
 'bar',
 'barbs',
 'barh',
 'bbox',
 'boxplot',
 'broken_barh',
 'bxp',
 'callbacks',
 'can_pan',
 'can_zoom',
 'change_geometry',
 'child_axes',
 'cla',
 'clabel',
 'clear',
 'clipbox',
 'cohere',
 'colNum',
 'collections',
 'containers',
 'contains',
 'contains_point',
 'contour',
 'contourf',
 'convert_xunits',
 'convert_yunits',
 'csd',
 'dataLim',
 'drag_pan',
 'draw',
 'draw_artist',
 'end_pan',
 'errorbar',
 'eventplot',
 'eventson',
 'figbox',
 'figure',
 'fill',
 'fill_between',
 'fill_betweenx',
 'findobj',
 'fmt_xdata',
 'fmt_ydata',
 'format_coord',
 'format_cursor_data',
 'format_xdata',
 'format_ydata',
 'get_adjustable',
 'get_agg_filter',
 'get_alpha',
 'get_anchor',
 'get_animated',
 'get_aspect',
 'get_autoscale_on',
 'get_autoscalex_on',
 'get_autoscaley_on',
 'get_axes_locator',
 'get_axisbelow',
 'get_children',
 'get_clip_box',
 'get_clip_on',
 'get_clip_path',
 'get_contains',
 'get_cursor_data',
 'get_data_ratio',
 'get_data_ratio_log',
 'get_default_bbox_extra_artists',
 'get_facecolor',
 'get_fc',
 'get_figure',
 'get_frame_on',
 'get_geometry',
 'get_gid',
 'get_gridspec',
 'get_images',
 'get_in_layout',
 'get_label',
 'get_legend',
 'get_legend_handles_labels',
 'get_lines',
 'get_navigate',
 'get_navigate_mode',
 'get_path_effects',
 'get_picker',
 'get_position',
 'get_rasterization_zorder',
 'get_rasterized',
 'get_renderer_cache',
 'get_shared_x_axes',
 'get_shared_y_axes',
 'get_sketch_params',
 'get_snap',
 'get_subplotspec',
 'get_tightbbox',
 'get_title',
 'get_transform',
 'get_transformed_clip_path_and_affine',
 'get_url',
 'get_visible',
 'get_window_extent',
 'get_xaxis',
 'get_xaxis_text1_transform',
 'get_xaxis_text2_transform',
 'get_xaxis_transform',
 'get_xbound',
 'get_xgridlines',
 'get_xlabel',
 'get_xlim',
 'get_xmajorticklabels',
 'get_xminorticklabels',
 'get_xscale',
 'get_xticklabels',
 'get_xticklines',
 'get_xticks',
 'get_yaxis',
 'get_yaxis_text1_transform',
 'get_yaxis_text2_transform',
 'get_yaxis_transform',
 'get_ybound',
 'get_ygridlines',
 'get_ylabel',
 'get_ylim',
 'get_ymajorticklabels',
 'get_yminorticklabels',
 'get_yscale',
 'get_yticklabels',
 'get_yticklines',
 'get_yticks',
 'get_zorder',
 'grid',
 'has_data',
 'have_units',
 'hexbin',
 'hist',
 'hist2d',
 'hitlist',
 'hlines',
 'ignore_existing_data_limits',
 'images',
 'imshow',
 'in_axes',
 'indicate_inset',
 'indicate_inset_zoom',
 'inset_axes',
 'invert_xaxis',
 'invert_yaxis',
 'is_figure_set',
 'is_first_col',
 'is_first_row',
 'is_last_col',
 'is_last_row',
 'is_transform_set',
 'label_outer',
 'legend',
 'legend_',
 'lines',
 'locator_params',
 'loglog',
 'magnitude_spectrum',
 'margins',
 'matshow',
 'minorticks_off',
 'minorticks_on',
 'mouseover',
 'mouseover_set',
 'name',
 'numCols',
 'numRows',
 'patch',
 'patches',
 'pchanged',
 'pcolor',
 'pcolorfast',
 'pcolormesh',
 'phase_spectrum',
 'pick',
 'pickable',
 'pie',
 'plot',
 'plot_date',
 'properties',
 'psd',
 'quiver',
 'quiverkey',
 'redraw_in_frame',
 'relim',
 'remove',
 'remove_callback',
 'reset_position',
 'rowNum',
 'scatter',
 'semilogx',
 'semilogy',
 'set',
 'set_adjustable',
 'set_agg_filter',
 'set_alpha',
 'set_anchor',
 'set_animated',
 'set_aspect',
 'set_autoscale_on',
 'set_autoscalex_on',
 'set_autoscaley_on',
 'set_axes_locator',
 'set_axis_off',
 'set_axis_on',
 'set_axisbelow',
 'set_clip_box',
 'set_clip_on',
 'set_clip_path',
 'set_contains',
 'set_facecolor',
 'set_fc',
 'set_figure',
 'set_frame_on',
 'set_gid',
 'set_in_layout',
 'set_label',
 'set_navigate',
 'set_navigate_mode',
 'set_path_effects',
 'set_picker',
 'set_position',
 'set_prop_cycle',
 'set_rasterization_zorder',
 'set_rasterized',
 'set_sketch_params',
 'set_snap',
 'set_subplotspec',
 'set_title',
 'set_transform',
 'set_url',
 'set_visible',
 'set_xbound',
 'set_xlabel',
 'set_xlim',
 'set_xmargin',
 'set_xscale',
 'set_xticklabels',
 'set_xticks',
 'set_ybound',
 'set_ylabel',
 'set_ylim',
 'set_ymargin',
 'set_yscale',
 'set_yticklabels',
 'set_yticks',
 'set_zorder',
 'specgram',
 'spines',
 'spy',
 'stackplot',
 'stale',
 'stale_callback',
 'start_pan',
 'stem',
 'step',
 'sticky_edges',
 'streamplot',
 'table',
 'tables',
 'text',
 'texts',
 'tick_params',
 'ticklabel_format',
 'title',
 'titleOffsetTrans',
 'transAxes',
 'transData',
 'transLimits',
 'transScale',
 'tricontour',
 'tricontourf',
 'tripcolor',
 'triplot',
 'twinx',
 'twiny',
 'update',
 'update_datalim',
 'update_datalim_bounds',
 'update_from',
 'update_params',
 'use_sticky_edges',
 'viewLim',
 'violin',
 'violinplot',
 'vlines',
 'xaxis',
 'xaxis_date',
 'xaxis_inverted',
 'xcorr',
 'yaxis',
 'yaxis_date',
 'yaxis_inverted',
 'zorder']

















In [167]:



    


lm = sns.lmplot(data=uu, x="data size", y="time", hue="time type", order=3, scatter_kws={"s": 3, "marker": "x", "alpha": 0.5})
axes = lm.axes
axes[0,0].set_xlim(30,200)
axes[0,0].set_ylim(0,50)

lines = lm.ax.get_lines()
line = lines[0]
line.set_linestyle("--")
# line.set_marker("s")

plt.savefig(op.join(latex_dir, "msgc_time_datasize_plot.pdf"), dpi=1000)
# axes[0,1].set_ylim(0,)

# lm.ax.get_lines()



    


















    
























In [ ]:



    






    











In [168]:



    


# this work jus for not duplicit values of data siz
# sns.tsplot(data=df, time="data size", value="time", unit="method", condition="method")
# plt.savefig(op.join(latex_dir, "msgc_size_time.pdf"), dpi=1000)



    











In [169]:



    


line.set_marker("s")



    











In [170]:



    


# df



    











In [171]:



    


# df["method"]

Experiment 2: fixed image size





In [276]:



    


# uu = df[df["experiment"]=="object size"]



    











In [277]:



    


dfs = df[df["experiment"] == "object size"]
uu = melt(dfs.rename(columns={"gc time": "gc", "time": "total"}), value_vars=["gc", "total"], id_vars=["method", "data size", "image voxel number", "data object size px", "relative object size", "log(relative object size)"], var_name=["time type"], value_name=["Time [s]"])
uu["mth"] = uu["method"] + " " + uu["time type"]
# uu



    











In [278]:



    


dfs.keys()



    


















    
Out[278]:








Index(['_create_nlinks time', 'block size', 'data image size px',
       'data object size px', 'data offset', 'data radius', 'data seedsz',
       'data size', 'edge number', 'error', 'experiment',
       'experiment iteration start time', 'gc time', 'low level image voxels',
       'low level object voxels', 'machine hostname', 'method',
       'nlinks number', 't graph 01', 't graph 10', 't graph 11', 't graph 13',
       't graph 14', 't graph high', 't graph low', 't split 01', 't split 02',
       't split 03', 't split 04', 't split 05', 't split 06', 't split 07',
       't split 08', 't split 081', 't split 082', 't split 0821',
       't split 09', 't split 10', 't1', 't10', 't2', 't3', 't3.1', 't3.2',
       't3.3', 't4', 't5', 't6', 't7', 't8', 't9', 'time', 'tlinks number',
       'image voxel number', 'relative object size',
       'log(relative object size)'],
      dtype='object')

















In [279]:



    


white_palette = [(1., 1., 1., 0.5)]
lm = sns.lmplot(
#     data=uu[uu["time type"] == "total"], 
    data=uu, 
#     x="image voxel number", 
    x="data object size px",
    y="Time [s]", hue="mth", order=2, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3],
                palette=white_palette,
                legend=None,
#     legend="off"
               )
axes = lm.axes
axes[0,0].set_xlim(30,uu["data object size px"].max())
axes[0,0].set_ylim(0,50)

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
line1.set_color(line2.get_color())


# body
sc = sns.scatterplot(
    data=uu[uu["time type"] == "total"], x="data object size px", y="Time [s]", hue="mth", 
    ax=lm.ax, alpha=1.0, s=5
)
texts = lm.ax.legend_.get_texts()
leglines = lm.ax.legend_.get_lines()

# texts[0].set_text("short")
# texts.pop(0)
# texts.pop(0)

# plt.legend(lm.ax.legend_[3:6])

from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color=current_palette[0], lw=4),
                Line2D([0], [0], color=current_palette[1], lw=4),
                Line2D([0], [0], color=current_palette[2], lw=4)]
lm.ax.legend(
    custom_lines, 
    [tx.get_text()[:-3] for tx in texts[:3]],
#     ['Cold', 'Medium', 'Hot'],
)
# lg = lm.ax.legend()
plt.savefig(op.join(latex_dir, "msgc_time_datasize_plot.pdf"), dpi=1000)



    


















    
























In [280]:



    


uu



    


















    
Out[280]:











  
    

      
      method
      data size
      image voxel number
      data object size px
      relative object size
      log(relative object size)
      time type
      Time [s]
      mth
    

  
  
    

      0
      ssgc
      200
      8000000
      5654
      0.000707
      -7.254834
      gc
      28.538692
      ssgc  gc
    

    

      1
      msgc_hi2lo
      200
      8000000
      5654
      0.000707
      -7.254834
      gc
      1.506011
      msgc_hi2lo  gc
    

    

      2
      msgc_lo2hi
      200
      8000000
      5654
      0.000707
      -7.254834
      gc
      1.063156
      msgc_lo2hi  gc
    

    

      3
      ssgc
      200
      8000000
      18962
      0.002370
      -6.044760
      gc
      27.174341
      ssgc  gc
    

    

      4
      msgc_hi2lo
      200
      8000000
      18962
      0.002370
      -6.044760
      gc
      2.105370
      msgc_hi2lo  gc
    

    

      5
      msgc_lo2hi
      200
      8000000
      18962
      0.002370
      -6.044760
      gc
      1.318475
      msgc_lo2hi  gc
    

    

      6
      ssgc
      200
      8000000
      39248
      0.004906
      -5.317296
      gc
      26.972847
      ssgc  gc
    

    

      7
      msgc_hi2lo
      200
      8000000
      39248
      0.004906
      -5.317296
      gc
      2.251008
      msgc_hi2lo  gc
    

    

      8
      msgc_lo2hi
      200
      8000000
      39248
      0.004906
      -5.317296
      gc
      1.538916
      msgc_lo2hi  gc
    

    

      9
      ssgc
      200
      8000000
      72397
      0.009050
      -4.705032
      gc
      27.713899
      ssgc  gc
    

    

      10
      msgc_hi2lo
      200
      8000000
      72397
      0.009050
      -4.705032
      gc
      3.398947
      msgc_hi2lo  gc
    

    

      11
      msgc_lo2hi
      200
      8000000
      72397
      0.009050
      -4.705032
      gc
      2.036527
      msgc_lo2hi  gc
    

    

      12
      ssgc
      200
      8000000
      121075
      0.015134
      -4.190787
      gc
      27.492516
      ssgc  gc
    

    

      13
      msgc_hi2lo
      200
      8000000
      121075
      0.015134
      -4.190787
      gc
      4.116960
      msgc_hi2lo  gc
    

    

      14
      msgc_lo2hi
      200
      8000000
      121075
      0.015134
      -4.190787
      gc
      2.636949
      msgc_lo2hi  gc
    

    

      15
      ssgc
      200
      8000000
      182047
      0.022756
      -3.782932
      gc
      27.422712
      ssgc  gc
    

    

      16
      msgc_hi2lo
      200
      8000000
      182047
      0.022756
      -3.782932
      gc
      5.219046
      msgc_hi2lo  gc
    

    

      17
      msgc_lo2hi
      200
      8000000
      182047
      0.022756
      -3.782932
      gc
      3.480690
      msgc_lo2hi  gc
    

    

      18
      ssgc
      200
      8000000
      266438
      0.033305
      -3.402055
      gc
      27.809667
      ssgc  gc
    

    

      19
      msgc_hi2lo
      200
      8000000
      266438
      0.033305
      -3.402055
      gc
      6.631270
      msgc_hi2lo  gc
    

    

      20
      msgc_lo2hi
      200
      8000000
      266438
      0.033305
      -3.402055
      gc
      3.995283
      msgc_lo2hi  gc
    

    

      21
      ssgc
      200
      8000000
      363493
      0.045437
      -3.091437
      gc
      27.528430
      ssgc  gc
    

    

      22
      msgc_hi2lo
      200
      8000000
      363493
      0.045437
      -3.091437
      gc
      7.715371
      msgc_hi2lo  gc
    

    

      23
      msgc_lo2hi
      200
      8000000
      363493
      0.045437
      -3.091437
      gc
      5.066454
      msgc_lo2hi  gc
    

    

      24
      ssgc
      200
      8000000
      490796
      0.061350
      -2.791168
      gc
      27.811675
      ssgc  gc
    

    

      25
      msgc_hi2lo
      200
      8000000
      490796
      0.061350
      -2.791168
      gc
      9.949500
      msgc_hi2lo  gc
    

    

      26
      msgc_lo2hi
      200
      8000000
      490796
      0.061350
      -2.791168
      gc
      6.279212
      msgc_lo2hi  gc
    

    

      27
      ssgc
      200
      8000000
      647042
      0.080880
      -2.514786
      gc
      27.918317
      ssgc  gc
    

    

      28
      msgc_hi2lo
      200
      8000000
      647042
      0.080880
      -2.514786
      gc
      11.195065
      msgc_hi2lo  gc
    

    

      29
      msgc_lo2hi
      200
      8000000
      647042
      0.080880
      -2.514786
      gc
      7.143895
      msgc_lo2hi  gc
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      174
      ssgc
      200
      8000000
      5654
      0.000707
      -7.254834
      total
      36.343821
      ssgc  total
    

    

      175
      msgc_hi2lo
      200
      8000000
      5654
      0.000707
      -7.254834
      total
      28.164693
      msgc_hi2lo  total
    

    

      176
      msgc_lo2hi
      200
      8000000
      5654
      0.000707
      -7.254834
      total
      11.432434
      msgc_lo2hi  total
    

    

      177
      ssgc
      200
      8000000
      56107
      0.007013
      -4.959936
      total
      35.705530
      ssgc  total
    

    

      178
      msgc_hi2lo
      200
      8000000
      56107
      0.007013
      -4.959936
      total
      30.246128
      msgc_hi2lo  total
    

    

      179
      msgc_lo2hi
      200
      8000000
      56107
      0.007013
      -4.959936
      total
      25.266443
      msgc_lo2hi  total
    

    

      180
      ssgc
      200
      8000000
      182047
      0.022756
      -3.782932
      total
      37.333176
      ssgc  total
    

    

      181
      msgc_hi2lo
      200
      8000000
      182047
      0.022756
      -3.782932
      total
      34.461890
      msgc_hi2lo  total
    

    

      182
      msgc_lo2hi
      200
      8000000
      182047
      0.022756
      -3.782932
      total
      67.706967
      msgc_lo2hi  total
    

    

      183
      ssgc
      200
      8000000
      428533
      0.053567
      -2.926829
      total
      35.117106
      ssgc  total
    

    

      184
      msgc_hi2lo
      200
      8000000
      428533
      0.053567
      -2.926829
      total
      39.880368
      msgc_hi2lo  total
    

    

      185
      msgc_lo2hi
      200
      8000000
      428533
      0.053567
      -2.926829
      total
      160.711376
      msgc_lo2hi  total
    

    

      186
      ssgc
      200
      8000000
      816188
      0.102024
      -2.282552
      total
      44.290605
      ssgc  total
    

    

      187
      msgc_hi2lo
      200
      8000000
      816188
      0.102024
      -2.282552
      total
      53.318438
      msgc_hi2lo  total
    

    

      188
      msgc_lo2hi
      200
      8000000
      816188
      0.102024
      -2.282552
      total
      337.085669
      msgc_lo2hi  total
    

    

      189
      ssgc
      200
      8000000
      5654
      0.000707
      -7.254834
      total
      47.073882
      ssgc  total
    

    

      190
      msgc_hi2lo
      200
      8000000
      5654
      0.000707
      -7.254834
      total
      29.044147
      msgc_hi2lo  total
    

    

      191
      msgc_lo2hi
      200
      8000000
      5654
      0.000707
      -7.254834
      total
      8.543126
      msgc_lo2hi  total
    

    

      192
      ssgc
      200
      8000000
      55085
      0.006886
      -4.978319
      total
      37.615456
      ssgc  total
    

    

      193
      msgc_hi2lo
      200
      8000000
      55085
      0.006886
      -4.978319
      total
      31.542660
      msgc_hi2lo  total
    

    

      194
      msgc_lo2hi
      200
      8000000
      55085
      0.006886
      -4.978319
      total
      25.549687
      msgc_lo2hi  total
    

    

      195
      ssgc
      200
      8000000
      179201
      0.022400
      -3.798689
      total
      37.183579
      ssgc  total
    

    

      196
      msgc_hi2lo
      200
      8000000
      179201
      0.022400
      -3.798689
      total
      47.817525
      msgc_hi2lo  total
    

    

      197
      msgc_lo2hi
      200
      8000000
      179201
      0.022400
      -3.798689
      total
      63.689707
      msgc_lo2hi  total
    

    

      198
      ssgc
      200
      8000000
      423029
      0.052879
      -2.939756
      total
      49.508061
      ssgc  total
    

    

      199
      msgc_hi2lo
      200
      8000000
      423029
      0.052879
      -2.939756
      total
      43.702245
      msgc_hi2lo  total
    

    

      200
      msgc_lo2hi
      200
      8000000
      423029
      0.052879
      -2.939756
      total
      153.727997
      msgc_lo2hi  total
    

    

      201
      ssgc
      200
      8000000
      806947
      0.100868
      -2.293939
      total
      43.760886
      ssgc  total
    

    

      202
      msgc_hi2lo
      200
      8000000
      806947
      0.100868
      -2.293939
      total
      50.445796
      msgc_hi2lo  total
    

    

      203
      msgc_lo2hi
      200
      8000000
      806947
      0.100868
      -2.293939
      total
      326.368365
      msgc_lo2hi  total
    

  



204 rows × 9 columns



















In [281]:



    


xx = "relative object size"
white_palette = [(1., 1., 1., 0.5)]
lm = sns.lmplot(
#     data=uu[uu["time type"] == "total"], 
    data=uu, 
#     x="image voxel number", 
    x=xx,
    y="Time [s]", hue="mth", 
#     order=1, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3],
                palette=white_palette,
                legend=None,
    logx=True,
#     logy=True
#     legend="off"
               )
axes = lm.axes
# axes[0,0].set_xlim(30,uu[xx].max())
# axes[0,0].set_ylim(0,50)
# yscale="log"

lm.set(
    xscale="log",
# #     yscale="log"
)

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
line1.set_color(line2.get_color())


# body
sc = sns.scatterplot(
    data=uu[uu["time type"] == "total"], x=xx, y="Time [s]", hue="mth", 
    ax=lm.ax, alpha=1.0, s=5
)
texts = lm.ax.legend_.get_texts()
leglines = lm.ax.legend_.get_lines()

# texts[0].set_text("short")
# texts.pop(0)
# texts.pop(0)

# plt.legend(lm.ax.legend_[3:6])

from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color=current_palette[0], lw=4),
                Line2D([0], [0], color=current_palette[1], lw=4),
                Line2D([0], [0], color=current_palette[2], lw=4)]
lm.ax.legend(
    custom_lines, 
    [tx.get_text()[:-3] for tx in texts[:3]],
#     ['Cold', 'Medium', 'Hot'],
)
# lg = lm.ax.legend()
plt.savefig(op.join(latex_dir, "msgc_time_datasize_plot.pdf"), dpi=1000)



    


















    
























In [282]:



    


xx = "log(relative object size)"
white_palette = [(1., 1., 1., 0.5)]
lm = sns.lmplot(
#     data=uu[uu["time type"] == "total"], 
    data=uu, 
#     x="image voxel number", 
    x=xx,
    y="Time [s]", hue="mth", order=2, 
                scatter_kws={"s": 3, "marker": "x", "alpha": 0.5}, 
#                 line_kws={"alpha": 1},
#                 palette=current_palette[:3],
                palette=white_palette,
                legend=None,
#     legend="off"
               )
axes = lm.axes
axes[0,0].set_xlim(uu[xx].min(),uu[xx].max())
axes[0,0].set_ylim(0,50)

current_palette = sns.color_palette()
nlines = 3
n = 0
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 1
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
# line1.set_color(line2.get_color())

n = 2
lines = lm.ax.get_lines()
line1 = lines[n]
line2 = lines[n + nlines]
line1.set_linestyle("--")
line1.set_color(current_palette[n])
line2.set_color(current_palette[n])
line1.set_color(line2.get_color())


# body
sc = sns.scatterplot(
    data=uu[uu["time type"] == "total"], x=xx, y="Time [s]", hue="mth", 
    ax=lm.ax, alpha=1.0, s=5
)
texts = lm.ax.legend_.get_texts()
leglines = lm.ax.legend_.get_lines()

# texts[0].set_text("short")
# texts.pop(0)
# texts.pop(0)

# plt.legend(lm.ax.legend_[3:6])

from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color=current_palette[0], lw=4),
                Line2D([0], [0], color=current_palette[1], lw=4),
                Line2D([0], [0], color=current_palette[2], lw=4)]
lm.ax.legend(
    custom_lines, 
    [tx.get_text()[:-3] for tx in texts[:3]],
#     ['Cold', 'Medium', 'Hot'],
)
# lg = lm.ax.legend()
plt.savefig(op.join(latex_dir, "msgc_time_datasize_plot.pdf"), dpi=1000)



    


















    
























In [283]:



    


## Volume ration

Statistics





In [284]:



    


from scipy import stats



dfs_plus_describe = dfs_plus.describe()
display(dfs_plus_describe)

print("pokud je pvalue mensi nez zvolena hladina vyznamnosti (0.01=1%), je vsechno ok")
tt = stats.ttest_rel(dfs_plus.loc[dfs_plus["method"] == "ssgc"]['time'], dfs_plus.loc[dfs_plus["method"] == "msgc_lo2hi"]['time'])
# tt



    


















    











  
    

      
      _create_nlinks time
      block size
      data image size px
      data object size px
      data offset
      data radius
      data seedsz
      data size
      edge number
      error
      ...
      t5
      t6
      t7
      t8
      t9
      time
      tlinks number
      image voxel number
      relative object size
      log(relative object size)
    

  
  
    

      count
      60.000000
      60.0
      6.000000e+01
      60.000000
      60.000000
      60.0
      60.0
      60.000000
      6.000000e+01
      60.0
      ...
      40.000000
      40.000000
      40.000000
      20.000000
      40.000000
      60.000000
      6.000000e+01
      6.000000e+01
      60.000000
      60.000000
    

    

      mean
      1.418090
      10.0
      6.707625e+06
      21376.600000
      0.040000
      10.0
      3.0
      186.500000
      1.062951e+07
      0.0
      ...
      7.222456
      9.830084
      10.603417
      22.164569
      17.210374
      21.909572
      2.662752e+06
      6.602286e+06
      0.003353
      -5.709740
    

    

      std
      1.267243
      0.0
      1.536213e+06
      1788.619202
      0.010084
      0.0
      0.0
      14.482602
      1.201118e+07
      0.0
      ...
      4.891553
      2.857558
      2.244970
      4.066646
      5.905478
      7.596595
      3.017893e+06
      1.519978e+06
      0.000523
      0.154264
    

    

      min
      0.001992
      10.0
      4.491960e+06
      18669.000000
      0.030000
      10.0
      3.0
      164.000000
      2.228287e+06
      0.0
      ...
      1.639582
      5.053457
      6.775847
      17.306725
      8.876233
      10.416205
      5.522830e+05
      4.410944e+06
      0.002604
      -5.950865
    

    

      25%
      0.003027
      10.0
      5.359200e+06
      20050.000000
      0.030000
      10.0
      3.0
      174.000000
      2.474089e+06
      0.0
      ...
      2.452450
      7.299722
      8.769535
      18.591283
      11.800209
      15.172936
      6.132982e+05
      5.268024e+06
      0.002935
      -5.831158
    

    

      50%
      1.293525
      10.0
      6.595125e+06
      21143.000000
      0.040000
      10.0
      3.0
      186.500000
      2.752303e+06
      0.0
      ...
      5.965551
      9.765922
      10.854974
      21.316504
      16.482911
      20.654752
      6.827750e+05
      6.490386e+06
      0.003227
      -5.736572
    

    

      75%
      2.340748
      10.0
      7.999800e+06
      23127.000000
      0.050000
      10.0
      3.0
      199.000000
      2.134493e+07
      0.0
      ...
      11.413743
      11.693983
      11.693983
      25.735430
      21.136736
      26.825792
      5.359200e+06
      7.880599e+06
      0.003738
      -5.589479
    

    

      max
      3.991326
      10.0
      9.260790e+06
      24638.000000
      0.050000
      10.0
      3.0
      209.000000
      3.691086e+07
      0.0
      ...
      15.211293
      15.606235
      15.606235
      29.063290
      29.098197
      40.782995
      9.260790e+06
      9.129329e+06
      0.004278
      -5.454165
    

  



8 rows × 53 columns












    






pokud je pvalue mensi nez zvolena hladina vyznamnosti (0.01=1%), je vsechno ok

















In [285]:



    


ssgc_rows = dfs_plus[dfs_plus["method"].str.contains(labels[0])]
ssgc_hi2lo_rows = dfs_plus[dfs_plus["method"].str.contains(labels[1])]
ssgc_lo2hi_rows = dfs_plus[dfs_plus["method"].str.contains(labels[2])]
pp0 = stats.ttest_rel(ssgc_rows["time"], ssgc_hi2lo_rows["time"])
pp1 = stats.ttest_rel(ssgc_rows["time"], ssgc_lo2hi_rows["time"])
pp2 = stats.ttest_rel(ssgc_hi2lo_rows["time"], ssgc_lo2hi_rows["time"])

print("pokud je pvalue mensi nez zvolena hladina vyznamnosti (0.01=1%), je vsechno ok") 
#mozna staci i dvojnasobek hladiny vyzamnosi
print("statistic musi byt vetsi nez 0")
display(pp0)
display(pp1)
display(pp2)

float_to_latex_file(pp0.pvalue, op.join(latex_dir, "ttest_pvalue_ssgc_msgc_hi2lo.tex"))
float_to_latex_file(pp1.pvalue, op.join(latex_dir, "ttest_pvalue_ssgc_msgc_lo2hi.tex"))
float_to_latex_file(pp2.pvalue, op.join(latex_dir, "ttest_pvalue_msgc_hi2lo_msgc_lo2hi.tex"))



    


















    






pokud je pvalue mensi nez zvolena hladina vyznamnosti (0.01=1%), je vsechno ok
statistic musi byt vetsi nez 0










    








Ttest_relResult(statistic=2.4217253268042547, pvalue=0.025619028630150264)










    








Ttest_relResult(statistic=8.0579125382709762, pvalue=1.5054366679739918e-07)










    








Ttest_relResult(statistic=12.749891441724124, pvalue=9.2457196426892928e-11)

















In [286]:



    


# dfs_plus["method"] == "ssgc "

Time evaluation





In [287]:



    


table = pd.pivot_table(
    dfs, values=['gc time', 'time', "t2", "t3", "t3.1", "t3.2", "t3.3", "t4", "t5", "t6", "t7", "t8", "low level object voxels", "low level image voxels"], index=['method'], 
    aggfunc=np.mean
)
table



    


















    
Out[287]:











  
    

      
      gc time
      low level image voxels
      low level object voxels
      t2
      t3
      t3.1
      t3.2
      t3.3
      t4
      t5
      t6
      t7
      t8
      time
    

    

      method
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      msgc_hi2lo
      7.170511
      8820.0
      304.882353
      0.191924
      0.195210
      NaN
      NaN
      NaN
      1.458510
      3.103871
      9.377705
      13.777411
      29.214732
      37.097172
    

    

      msgc_lo2hi
      4.785642
      8820.0
      284.617647
      0.256634
      0.260099
      0.260099
      0.336361
      113.970566
      113.970566
      116.222868
      116.610739
      116.610739
      NaN
      122.183454
    

    

      ssgc
      30.698810
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      37.137266
    

  




















In [288]:



    


table = pd.pivot_table(
    dfs, values=['gc time', 'time', "t graph 01", "t graph 10", "t graph 11", "t graph 13", "t graph 14", "low level image voxels", "low level object voxels"], index=['method'], 
    aggfunc=np.mean
)
table



    


















    
Out[288]:











  
    

      
      gc time
      low level image voxels
      low level object voxels
      t graph 01
      t graph 10
      t graph 11
      t graph 13
      t graph 14
      time
    

    

      method
      
      
      
      
      
      
      
      
      
    

  
  
    

      msgc_hi2lo
      7.170511
      8820.0
      304.882353
      NaN
      NaN
      NaN
      NaN
      NaN
      37.097172
    

    

      msgc_lo2hi
      4.785642
      8820.0
      284.617647
      0.074943
      0.076263
      0.07638
      113.461263
      113.710409
      122.183454
    

    

      ssgc
      30.698810
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      37.137266

Datasets





In [289]:



    


dfs_describe = dfs.describe()
display(dfs_describe)
dfs_plus_describe = dfs_plus.describe()
display(dfs_plus_describe)



    


















    











  
    

      
      _create_nlinks time
      block size
      data image size px
      data object size px
      data offset
      data radius
      data seedsz
      data size
      edge number
      error
      ...
      t5
      t6
      t7
      t8
      t9
      time
      tlinks number
      image voxel number
      relative object size
      log(relative object size)
    

  
  
    

      count
      102.000000
      102.0
      102.0
      1.020000e+02
      102.000000
      102.000000
      102.0
      102.0
      1.020000e+02
      102.000000
      ...
      68.000000
      68.000000
      68.000000
      34.000000
      68.000000
      102.000000
      1.020000e+02
      102.0
      102.000000
      102.000000
    

    

      mean
      1.755140
      10.0
      8120400.0
      3.244142e+05
      0.040000
      26.411765
      3.0
      200.0
      1.565608e+07
      0.294118
      ...
      59.663370
      62.994222
      65.194075
      29.214732
      72.968003
      65.472631
      3.917584e+06
      8000000.0
      0.040552
      -4.043294
    

    

      std
      1.520692
      0.0
      0.0
      3.193062e+05
      0.010049
      14.005906
      0.0
      0.0
      1.237803e+07
      0.668799
      ...
      100.808296
      99.172868
      98.007777
      3.608151
      94.175092
      80.719946
      3.111935e+06
      0.0
      0.039913
      1.622428
    

    

      min
      0.002991
      10.0
      8120400.0
      5.654000e+03
      0.030000
      5.000000
      3.0
      200.0
      1.570746e+06
      0.000000
      ...
      2.882295
      6.856632
      6.856632
      24.842575
      6.873623
      8.543126
      3.892680e+05
      8000000.0
      0.000707
      -7.254834
    

    

      25%
      0.003990
      10.0
      8120400.0
      5.508500e+04
      0.030000
      15.000000
      3.0
      200.0
      4.946490e+06
      0.000000
      ...
      3.042614
      9.113906
      12.061509
      26.641017
      26.228900
      33.248121
      1.228142e+06
      8000000.0
      0.006886
      -4.978319
    

    

      50%
      1.542857
      10.0
      8120400.0
      1.820470e+05
      0.040000
      25.000000
      3.0
      200.0
      1.067330e+07
      0.000000
      ...
      5.146197
      10.149927
      16.613553
      28.478241
      30.189305
      36.464999
      2.654496e+06
      8000000.0
      0.022756
      -3.782932
    

    

      75%
      3.458033
      10.0
      8120400.0
      4.907960e+05
      0.050000
      37.000000
      3.0
      200.0
      3.236040e+07
      0.000000
      ...
      61.774351
      62.143865
      62.143865
      30.874946
      62.208420
      47.843421
      8.120400e+06
      8000000.0
      0.061350
      -2.791168
    

    

      max
      4.921808
      10.0
      8120400.0
      1.037628e+06
      0.050000
      49.000000
      3.0
      200.0
      3.236040e+07
      2.000000
      ...
      400.979863
      401.431653
      401.431653
      41.034660
      401.601202
      415.931847
      8.120400e+06
      8000000.0
      0.129703
      -2.042504
    

  



8 rows × 53 columns












    











  
    

      
      _create_nlinks time
      block size
      data image size px
      data object size px
      data offset
      data radius
      data seedsz
      data size
      edge number
      error
      ...
      t5
      t6
      t7
      t8
      t9
      time
      tlinks number
      image voxel number
      relative object size
      log(relative object size)
    

  
  
    

      count
      60.000000
      60.0
      6.000000e+01
      60.000000
      60.000000
      60.0
      60.0
      60.000000
      6.000000e+01
      60.0
      ...
      40.000000
      40.000000
      40.000000
      20.000000
      40.000000
      60.000000
      6.000000e+01
      6.000000e+01
      60.000000
      60.000000
    

    

      mean
      1.418090
      10.0
      6.707625e+06
      21376.600000
      0.040000
      10.0
      3.0
      186.500000
      1.062951e+07
      0.0
      ...
      7.222456
      9.830084
      10.603417
      22.164569
      17.210374
      21.909572
      2.662752e+06
      6.602286e+06
      0.003353
      -5.709740
    

    

      std
      1.267243
      0.0
      1.536213e+06
      1788.619202
      0.010084
      0.0
      0.0
      14.482602
      1.201118e+07
      0.0
      ...
      4.891553
      2.857558
      2.244970
      4.066646
      5.905478
      7.596595
      3.017893e+06
      1.519978e+06
      0.000523
      0.154264
    

    

      min
      0.001992
      10.0
      4.491960e+06
      18669.000000
      0.030000
      10.0
      3.0
      164.000000
      2.228287e+06
      0.0
      ...
      1.639582
      5.053457
      6.775847
      17.306725
      8.876233
      10.416205
      5.522830e+05
      4.410944e+06
      0.002604
      -5.950865
    

    

      25%
      0.003027
      10.0
      5.359200e+06
      20050.000000
      0.030000
      10.0
      3.0
      174.000000
      2.474089e+06
      0.0
      ...
      2.452450
      7.299722
      8.769535
      18.591283
      11.800209
      15.172936
      6.132982e+05
      5.268024e+06
      0.002935
      -5.831158
    

    

      50%
      1.293525
      10.0
      6.595125e+06
      21143.000000
      0.040000
      10.0
      3.0
      186.500000
      2.752303e+06
      0.0
      ...
      5.965551
      9.765922
      10.854974
      21.316504
      16.482911
      20.654752
      6.827750e+05
      6.490386e+06
      0.003227
      -5.736572
    

    

      75%
      2.340748
      10.0
      7.999800e+06
      23127.000000
      0.050000
      10.0
      3.0
      199.000000
      2.134493e+07
      0.0
      ...
      11.413743
      11.693983
      11.693983
      25.735430
      21.136736
      26.825792
      5.359200e+06
      7.880599e+06
      0.003738
      -5.589479
    

    

      max
      3.991326
      10.0
      9.260790e+06
      24638.000000
      0.050000
      10.0
      3.0
      209.000000
      3.691086e+07
      0.0
      ...
      15.211293
      15.606235
      15.606235
      29.063290
      29.098197
      40.782995
      9.260790e+06
      9.129329e+06
      0.004278
      -5.454165
    

  



8 rows × 53 columns



















In [290]:



    


dfs_plus_size = int(len(dfs_plus) / len(labels))
to_file(str(dfs_plus_size), op.join(latex_dir, "msgc_dataset_subset_size.tex"))



    











In [291]:



    


# df[["GC total time", "MSGC total time", "GC time", "MSGC time"]]
df.keys()



    


















    
Out[291]:








Index(['_create_nlinks time', 'block size', 'data image size px',
       'data object size px', 'data offset', 'data radius', 'data seedsz',
       'data size', 'edge number', 'error', 'experiment',
       'experiment iteration start time', 'gc time', 'low level image voxels',
       'low level object voxels', 'machine hostname', 'method',
       'nlinks number', 't graph 01', 't graph 10', 't graph 11', 't graph 13',
       't graph 14', 't graph high', 't graph low', 't split 01', 't split 02',
       't split 03', 't split 04', 't split 05', 't split 06', 't split 07',
       't split 08', 't split 081', 't split 082', 't split 0821',
       't split 09', 't split 10', 't1', 't10', 't2', 't3', 't3.1', 't3.2',
       't3.3', 't4', 't5', 't6', 't7', 't8', 't9', 'time', 'tlinks number',
       'image voxel number', 'relative object size',
       'log(relative object size)'],
      dtype='object')

















In [292]:



    


table = pd.pivot_table(
    df, values=['gc time', 'time'], index=['method'], 
    aggfunc={'gc time': np.mean, 'time': [min, max, np.mean]}
)
table



    


















    
Out[292]:











  
    

      
      gc time
      time
    

    

      
      mean
      max
      mean
      min
    

    

      method
      
      
      
      
    

  
  
    

      msgc_hi2lo
      3.394327
      53.318438
      20.256188
      0.579411
    

    

      msgc_lo2hi
      2.281926
      415.931847
      46.519378
      0.632335
    

    

      ssgc
      16.091058
      49.508061
      19.774274
      0.233341
    

  




















In [293]:



    


df_mn = df[["GC total time", "MSGC total time", "GC time", "MSGC time"]].describe()
display(df_mn)
to_latex_file(df_mn, "../includes/exp062-all2data_size.tex")

dfs_mn = dfs[["GC total time", "MSGC total time", "GC time", "MSGC time"]].describe()
display(dfs_mn)
to_latex_file(dfs_mn, "../includes/exp062-selection2data_size.tex")



    


















    






---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-293-e7e71b79ce86> in <module>
----> 1 df_mn = df[["GC total time", "MSGC total time", "GC time", "MSGC time"]].describe()
      2 display(df_mn)
      3 to_latex_file(df_mn, "../includes/exp062-all2data_size.tex")
      4 
      5 dfs_mn = dfs[["GC total time", "MSGC total time", "GC time", "MSGC time"]].describe()

~\Miniconda3\envs\lisa\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2932                 key = list(key)
   2933             indexer = self.loc._convert_to_indexer(key, axis=1,
-> 2934                                                    raise_missing=True)
   2935 
   2936         # take() does not accept boolean indexers

~\Miniconda3\envs\lisa\lib\site-packages\pandas\core\indexing.py in _convert_to_indexer(self, obj, axis, is_setter, raise_missing)
   1352                 kwargs = {'raise_missing': True if is_setter else
   1353                           raise_missing}
-> 1354                 return self._get_listlike_indexer(obj, axis, **kwargs)[1]
   1355         else:
   1356             try:

~\Miniconda3\envs\lisa\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
   1159         self._validate_read_indexer(keyarr, indexer,
   1160                                     o._get_axis_number(axis),
-> 1161                                     raise_missing=raise_missing)
   1162         return keyarr, indexer
   1163 

~\Miniconda3\envs\lisa\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
   1244                 raise KeyError(
   1245                     u"None of [{key}] are in the [{axis}]".format(
-> 1246                         key=key, axis=self.obj._get_axis_name(axis)))
   1247 
   1248             # We (temporarily) allow for some missing keys with .loc, except in

KeyError: "None of [Index(['GC total time', 'MSGC total time', 'GC time', 'MSGC time'], dtype='object')] are in the [columns]"
















In [ ]:



    


dfs_plus[["method"]]

Content source: mjirik/pysegbase

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