I'm looking for a solution that would help me do the following.

Suppose I have to generate HTML code using hand-drawn design. I got element type, x, y coordination width, and height as NumPy array. I need to sort these arrays according to the y coordination if the two elements are in the same value then sort according to the y value. Then I need to group the elements in the same type.

I created array like this :

import numpy
s = numpy.array([
          #element type, x,y,width,height
          ["hyperlink", 5, 150, 25, 10],
          ["paragraph", 20, 60, 10, 10],
          ["image", 85, 150, 25, 10],
          ["radio", 20, 60, 10, 10],
          ["radio", 85, 150, 25, 10],
          ["button", 20, 60, 10, 10],
          ["text_field", 20, 40, 25, 10],  
          ["label", 10, 10, 20, 10]])

print(s)
lists = s.tolist()
print([{"element_type ": x[0] ,  "x": x[1], "y": x[2], "width":x[3], "height" :x[4]} for i, x in enumerate(lists)])

I'd like to sort it such that my points are ordered by y-coordinate, and then by x in cases where their coordinate is the same.

Then I need to group the elements such as two radio buttons as in the above example. I expected output as follows.

{
'element_type ': 'hyperlink',
 'group_id' :"1",
 'x': '5', 
 'y': '150', 
 'width': '25', 
 'height': '10'
},
    
{
'element_type ': 'image',
'group_id' :"3", 
'x': '85', 
'y': '150', 
'width': '25', 
'height': '10'
}, 

{'element_type ': 'radio', 
'group_id' :"4",
'x': '20', 
'y': '60', 
'width': '10', 
'height': '10'
}, 

{
'element_type ': 'radio',
'group_id' :"4", 
'x': '85', 
'y': '150', 
'width': '25', 
'height': '10'
}, 

 {
'element_type ': 'text_field', 
'group_id' :"5",
'x': '20', 
'y': '40', 
'width': '25', 
'height': '10'
}, 

{'element_type ': 'label', 
**group_id :"5",** 
'x': '10', 
'y': '10', 
'width': '20', 
'height': '10'
}]

Can I get an idea for this? I used python.

CodePudding user response：

numpy is not designed for groupby operations. Except the case you force it. pandas is a better option here.

import pandas as pd
df = pd.DataFrame(s, columns = ['element_type', 'x', 'y', 'width', 'height'])
df['group_id'] = df.groupby(['x', 'y', 'width', 'height']).ngroup()
>>> df.to_dict('records')

[{'element type': 'hyperlink',
  'x': '5',
  'y': '150',
  'width': '25',
  'height': '10',
  'group_id': 3},
 ...,
 {'element type': 'label',
  'x': '10',
  'y': '10',
  'width': '20',
  'height': '10',
  'group_id': 0}]

CodePudding user response：

I thought grouping was tied to the sorting, so here goes.

• You can use numpy.lexsort method to sort by y first and then x.

• Then use pandas.groupby to group by element_type and assign group numbers.

• Finally convert back to a dictionary.

  import numpy as np
  import pandas as pd
  s = s[np.lexsort((s[:,2], s[:,1]))]
  df = pd.DataFrame(s, columns = ['element_type', 'x', 'y', 'width', 'height'])
  df['group_id'] = (df.groupby(['element_type']).ngroup()   1).astype(str)
  output = df.to_dict("records")