
import pandas as pd
import numpy as np
import argparse
import os





import pandas as pd
import numpy as np
import argparse
import os

def _analysis(img_mat, voi_mat, voi_no, eps=1e-12):
    """ Extract VOI statistices for each VOI.
    """
    vec = img_mat[voi_mat == voi_no]
    vec2 = vec[~np.isnan(vec)]

    # Statistics
    v_mean = float(vec2.mean())
    v_sd = float(vec2.std(ddof=1))
    v_cov = v_sd / (v_mean + eps) * 100.
    v_max = float(vec2.max())
    v_min = float(vec2.min())
    n_vox = vec.size

    # Output
    out_tab = pd.DataFrame({"VOI No." :  [voi_no],
                            "No. of voxels" : [n_vox],
                            "Mean" : [v_mean],
                            "SD" : [v_sd],
                            "CoV" : [v_cov],
                            "Max" : [v_max],
                            "Min" : [v_min]})

    return out_tab

def voi_analysis(img_file, voi_file, lut_file=None):
    """ Extract VOI values.
    It outputs Pandas DataFrame for VOI statistics.

    Inputs:
        img_file   :    Path for image to extract VOI values

        voi_file    :   Path for VOI map

        lut_file    :   Path for look-up table for VOI map.
                        If not None, look-up table is applied to output table.

    Output:
        out_tab     :   Pandas DataFrame for VOI statistics.
    """
    # Load image & VOI
    img_mat, img_aff = utils.loadImage(img_file)[:2]
    voi_mat = utils.loadImage(voi_file)[0].astype(np.int16)
    
    # Extract
    maxNo = voi_mat.max()
    out_tab = pd.concat([_analysis(img_mat, voi_mat, v_no)
                            for v_no in range(1, maxNo + 1, 1)])

    # Calculate volumes (unit: cm3)
    vol_per_vox = np.abs(np.prod(np.diag(img_aff[:3, :3])))
    out_tab.loc[:, "Volume"] = out_tab.loc[:, "No. of voxels"] / 1000.

    # Apply look-up table
    if lut_file is not None:
        lut = utils.loadLUT(lut_file)
        out_tab.loc[:, "VOI"] = out_tab.loc[:, "VOI No."].map(lut)

    # Image file name
    out_tab.loc[:, "Path"] = img_file

    return out_tab


Implement basis for VOI analyzer.
Implement basis for VOI analyzer.

  
  import pandas as pd
  import numpy as np
  import argparse
  import os
  
+ def _analysis(img_mat, voi_mat, voi_no, eps=1e-12):
+     """ Extract VOI statistices for each VOI.
+     """
+     vec = img_mat[voi_mat == voi_no]
+     vec2 = vec[~np.isnan(vec)]
+ 
+     # Statistics
+     v_mean = float(vec2.mean())
+     v_sd = float(vec2.std(ddof=1))
+     v_cov = v_sd / (v_mean + eps) * 100.
+     v_max = float(vec2.max())
+     v_min = float(vec2.min())
+     n_vox = vec.size
+ 
+     # Output
+     out_tab = pd.DataFrame({"VOI No." :  [voi_no],
+                             "No. of voxels" : [n_vox],
+                             "Mean" : [v_mean],
+                             "SD" : [v_sd],
+                             "CoV" : [v_cov],
+                             "Max" : [v_max],
+                             "Min" : [v_min]})
+ 
+     return out_tab
+ 
+ def voi_analysis(img_file, voi_file, lut_file=None):
+     """ Extract VOI values.
+     It outputs Pandas DataFrame for VOI statistics.
+ 
+     Inputs:
+         img_file   :    Path for image to extract VOI values
+ 
+         voi_file    :   Path for VOI map
+ 
+         lut_file    :   Path for look-up table for VOI map.
+                         If not None, look-up table is applied to output table.
+ 
+     Output:
+         out_tab     :   Pandas DataFrame for VOI statistics.
+     """
+     # Load image & VOI
+     img_mat, img_aff = utils.loadImage(img_file)[:2]
+     voi_mat = utils.loadImage(voi_file)[0].astype(np.int16)
+     
+     # Extract
+     maxNo = voi_mat.max()
+     out_tab = pd.concat([_analysis(img_mat, voi_mat, v_no)
+                             for v_no in range(1, maxNo + 1, 1)])
+ 
+     # Calculate volumes (unit: cm3)
+     vol_per_vox = np.abs(np.prod(np.diag(img_aff[:3, :3])))
+     out_tab.loc[:, "Volume"] = out_tab.loc[:, "No. of voxels"] / 1000.
+ 
+     # Apply look-up table
+     if lut_file is not None:
+         lut = utils.loadLUT(lut_file)
+         out_tab.loc[:, "VOI"] = out_tab.loc[:, "VOI No."].map(lut)
+ 
+     # Image file name
+     out_tab.loc[:, "Path"] = img_file
+ 
+     return out_tab
  
  
- 
Implement basis for VOI analyzer.
## Code Before:

import pandas as pd
import numpy as np
import argparse
import os




## Instruction:
Implement basis for VOI analyzer.
## Code After:

import pandas as pd
import numpy as np
import argparse
import os

def _analysis(img_mat, voi_mat, voi_no, eps=1e-12):
    """ Extract VOI statistices for each VOI.
    """
    vec = img_mat[voi_mat == voi_no]
    vec2 = vec[~np.isnan(vec)]

    # Statistics
    v_mean = float(vec2.mean())
    v_sd = float(vec2.std(ddof=1))
    v_cov = v_sd / (v_mean + eps) * 100.
    v_max = float(vec2.max())
    v_min = float(vec2.min())
    n_vox = vec.size

    # Output
    out_tab = pd.DataFrame({"VOI No." :  [voi_no],
                            "No. of voxels" : [n_vox],
                            "Mean" : [v_mean],
                            "SD" : [v_sd],
                            "CoV" : [v_cov],
                            "Max" : [v_max],
                            "Min" : [v_min]})

    return out_tab

def voi_analysis(img_file, voi_file, lut_file=None):
    """ Extract VOI values.
    It outputs Pandas DataFrame for VOI statistics.

    Inputs:
        img_file   :    Path for image to extract VOI values

        voi_file    :   Path for VOI map

        lut_file    :   Path for look-up table for VOI map.
                        If not None, look-up table is applied to output table.

    Output:
        out_tab     :   Pandas DataFrame for VOI statistics.
    """
    # Load image & VOI
    img_mat, img_aff = utils.loadImage(img_file)[:2]
    voi_mat = utils.loadImage(voi_file)[0].astype(np.int16)
    
    # Extract
    maxNo = voi_mat.max()
    out_tab = pd.concat([_analysis(img_mat, voi_mat, v_no)
                            for v_no in range(1, maxNo + 1, 1)])

    # Calculate volumes (unit: cm3)
    vol_per_vox = np.abs(np.prod(np.diag(img_aff[:3, :3])))
    out_tab.loc[:, "Volume"] = out_tab.loc[:, "No. of voxels"] / 1000.

    # Apply look-up table
    if lut_file is not None:
        lut = utils.loadLUT(lut_file)
        out_tab.loc[:, "VOI"] = out_tab.loc[:, "VOI No."].map(lut)

    # Image file name
    out_tab.loc[:, "Path"] = img_file

    return out_tab

