original_docstring: Takes all user input, uses default values if none are provided
      Returns images, plots, and data
docstring: Takes all user input, uses default values if none are provided
  Returns images, plots, and data
docstring_tokens: Takes all user input uses default values if none are provided Returns images plots and data
code: def output(name, mag=0, Alpha=1.7, Beta=0, dp=3, minDist=20, para1=150, para2=50, minradius=0, maxradius=30, mean_contour_Area=1200):
      """Takes all user input, uses default values if none are provided
      Returns images, plots, and data"""
      
      # Read file input
      imageInput = cv2.imread(name, 0)
      x = cv2.imread(name, 0)
      
      # Run through nanoballsfuncs
      convFactor = nan.getConv(name, mag)
      canny2, img2 = nan.adjustment(name, Alpha, Beta)
      copy_canny = canny2
      circle_radii, circ_img = nan.get_circles(
          imageInput, dp, minDist, para1, para2, minradius, maxradius)
      major_axis, minor_axis = nan.get_ellipse(canny2, mean_contour_Area)
      segments, segment_locations, opening, canny = nan.imageseg(canny2)
      comp = nan.compare(major_axis, minor_axis)
      circular_particle, ellipsoidal_particle = nan.predict_shape(
          major_axis, minor_axis)
      
      # Calculate statistics from function outputs
      meanRadius = round(np.mean(circle_radii) * convFactor)
      stdRadius = round(np.std(circle_radii) * convFactor)
      maxRadius = round(np.max(circle_radii) * convFactor)
      minRadius = round(np.min(circle_radii) * convFactor)
      circleCount = np.size(major_axis)
      ellipseCount = ellipsoidal_particle
      
      # Create statistic dataframe and add statistics
      data = [['Mean', meanRadius], ['Standard Deviation', stdRadius], ['Maximum', maxRadius], [
          'Minimum', minRadius], ['Circle Count', circleCount], ['Ellipse Count', ellipseCount]]
      outputDF = pd.DataFrame(data, columns=['Statistic', 'Value'])
      display(outputDF)
      
      # Create plot, add images and ellipse plot
      fig, ax = plt.subplots(2, 2, figsize=(10, 10))
      ax[0, 0].imshow(x, cmap='gray')  # Image input
      ax[0, 0].set_title("Input Image")
      ax[1, 0].imshow(segments)  # Segmentation type 2
      ax[1, 0].set_title("Image Segmentation")
      ax[0, 1].imshow(circ_img, cmap='gray')  # Circle Fit image
      ax[0, 1].set_title("Circle Detection")
      ax[1, 1].imshow(canny2)  # Ellipse Fit image
      ax[1, 1].set_title("Ellipse fit")
      return
code_tokens: def output ( name , mag = 0 , Alpha = 1.7 , Beta = 0 , dp = 3 , minDist = 20 , para1 = 150 , para2 = 50 , minradius = 0 , maxradius = 30 , mean_contour_Area = 1200 ) : imageInput = cv2 . imread ( name , 0 ) x = cv2 . imread ( name , 0 ) convFactor = nan . getConv ( name , mag ) canny2 , img2 = nan . adjustment ( name , Alpha , Beta ) copy_canny = canny2 circle_radii , circ_img = nan . get_circles ( imageInput , dp , minDist , para1 , para2 , minradius , maxradius ) major_axis , minor_axis = nan . get_ellipse ( canny2 , mean_contour_Area ) segments , segment_locations , opening , canny = nan . imageseg ( canny2 ) comp = nan . compare ( major_axis , minor_axis ) circular_particle , ellipsoidal_particle = nan . predict_shape ( major_axis , minor_axis ) meanRadius = round ( np . mean ( circle_radii ) * convFactor ) stdRadius = round ( np . std ( circle_radii ) * convFactor ) maxRadius = round ( np . max ( circle_radii ) * convFactor ) minRadius = round ( np . min ( circle_radii ) * convFactor ) circleCount = np . size ( major_axis ) ellipseCount = ellipsoidal_particle data = [ [ 'Mean' , meanRadius ] , [ 'Standard Deviation' , stdRadius ] , [ 'Maximum' , maxRadius ] , [ 'Minimum' , minRadius ] , [ 'Circle Count' , circleCount ] , [ 'Ellipse Count' , ellipseCount ] ] outputDF = pd . DataFrame ( data , columns = [ 'Statistic' , 'Value' ] ) display ( outputDF ) fig , ax = plt . subplots ( 2 , 2 , figsize = ( 10 , 10 ) ) ax [ 0 , 0 ] . imshow ( x , cmap = 'gray' ) ax [ 0 , 0 ] . set_title ( "Input Image" ) ax [ 1 , 0 ] . imshow ( segments ) ax [ 1 , 0 ] . set_title ( "Image Segmentation" ) ax [ 0 , 1 ] . imshow ( circ_img , cmap = 'gray' ) ax [ 0 , 1 ] . set_title ( "Circle Detection" ) ax [ 1 , 1 ] . imshow ( canny2 ) ax [ 1 , 1 ] . set_title ( "Ellipse fit" ) return
short_docstring: Takes all user input, uses default values if none are provided
  Returns images, plots, and data
short_docstring_tokens: Takes all user input uses default values if none are provided Returns images plots and data
comment: """Takes all user input, uses default values if none are provided
      Returns images, plots, and data"""$SEP$# Read file input$SEP$# Run through nanoballsfuncs$SEP$# Calculate statistics from function outputs$SEP$# Create statistic dataframe and add statistics$SEP$# Create plot, add images and ellipse plot$SEP$# Image input$SEP$# Segmentation type 2$SEP$# Circle Fit image$SEP$# Ellipse Fit image