Background:
I am attempting to determine the difference between technical drawing dimensions and actual measured dimensions. These dimensions are stored within two dictionaries: actual_data and drawing_data. Due to the fact that the actual measurements can be taken in many ways, they are stored as lists to accommodate the following cases:
To avoid too many conditionals, I break up all matrices into lists and assign each new list (either 1x1 or 1x2) a new key.
My Script:
import numpy as np
drawing_data = {
'A': [394.60],
'B': [629.85, 629.92],
'C': [759.95, 760.00],
'D': [839.95, 840.00],
'E': [1779.50, 1780.50]
}
actual_data = {
'A': [390.00],
'B': [629.88, 629.90],
'C': [760.17, 760.25],
'D': [[840.12, 840.18], [840.04, 840.06], [840.07, 840.07]],
'E': [1780.00, 1780.00]
}
As you can see, Dimension D has an expected measurement ranging between 839.95mm and 840.00mm. However, due to the size of the component, it is measured in three places (hence, the list of lists).
def dimensional_deviation(drawing, actual):
# If the actual data is of the type Nx1 or Nx2, it is split and converted into new dimensions of the
# type 1x1 or 1x2 - new dictionary keys are created to accommodate the new dimensions, and the
# original dictionary entries are deleted.
#---------------------------------------------------------------------------------------------------
new_dict_drawing, new_dict_actual, keys_to_remove = {}, {}, []
for dimension in drawing:
if type(actual.get(dimension)[0]) is list:
keys_to_remove.append(dimension) # Create a list of unnecessery dimensions
for i, sublist in enumerate(actual.get(dimension)):
new_dict_drawing[f'{dimension}{i + 1}'] = drawing.get(dimension) # Create new dimension
new_dict_actual[f'{dimension}{i + 1}'] = sublist # Create new dimension
for dimension in keys_to_remove: # Remove all unnecessary dimensions from the original dicts
drawing.pop(dimension, None)
actual.pop(dimension, None)
# Merge dictionaries:
drawing = {**drawing, **new_dict_drawing}
actual = {**actual, **new_dict_actual}
#---------------------------------------------------------------------------------------------------
# Determine the deviation between the drawing and actual dimensions. The average of the upper and
# lower bounds is used to simplify each case.
#---------------------------------------------------------------------------------------------------
inspection_results = {}
for dimension in drawing:
drawing_value, actual_value = drawing.get(dimension), actual.get(dimension)
drawing_ave, actual_ave = np.mean(drawing_value), np.mean(actual_value)
deviation = drawing_ave - actual_ave
# Create new dictionary of the repair requirements:
#---------------------------------------------------------------------------------------------------
inspection_results[f'{dimension}'] = round(deviation, 3)
return inspection_results
My Problem:
When I call the above for the first time, I get the desired output. Dimension D is broken up as expected and the deviation is calculated. However, when I call the same function a second time, everything regarding Dimension D is completely neglected as though the key did not exist:
print('First function call:')
print(dimensional_deviation(drawing=drawing_data, actual=actual_data))
print('---------------------------------------------------------------------------------------')
print('Second function call:')
print(dimensional_deviation(drawing=drawing_data, actual=actual_data))
print('---------------------------------------------------------------------------------------')
Resulting in:
First function call:
{'A': 4.6, 'B': -0.005, 'C': -0.235, 'E': 0.0, 'D1': -0.175, 'D2': -0.075, 'D3': -0.095}
---------------------------------------------------------------------------------------
Second function call:
{'A': 4.6, 'B': -0.005, 'C': -0.235, 'E': 0.0}
---------------------------------------------------------------------------------------
I believe I am overwriting my drawing_data and actual_data somewhere, but I cannot find the issue. Additionally, this is one of my first times using dictionaries and I suspect that my key creation and deletion may not be best practice.
In my comments you will see Create a list of unnecessary dimensions - an example of this would be Dimension D, as it is newly accounted for in D1, D2 and D3.
Could somebody please explain to me why I get this result on each subsequent function call after the first?
The issue was that the original dictionaries were being modified (see above comments):
import numpy as np
drawing_data = {
'A': [394.60],
'B': [629.85, 629.92],
'C': [759.95, 760.00],
'D': [839.95, 840.00],
'E': [1779.50, 1780.50]
}
actual_data = {
'A': [390.00],
'B': [629.88, 629.90],
'C': [760.17, 760.25],
'D': [[840.12, 840.18], [840.04, 840.06], [840.07, 840.07]],
'E': [1780.00, 1780.00]
}
#-------------------------------------------------------------------------------------------------------
# The 'dimensional deviation' function takes the drawing data and actual data as arguments, returning a
# dictionary of dimensions and whether they require rectification or not (based one the drawing data).
#-------------------------------------------------------------------------------------------------------
def dimensional_deviation(drawing, actual):
temp_dict_drawing = {}
for key in drawing:
temp_dict_drawing[key] = drawing.get(key)
temp_dict_actual = {}
for key in drawing:
temp_dict_actual[key] = actual.get(key)
# If the actual data is of the type Nx1 or Nx2, it is split and converted into new dimensions of the
# type 1x1 or 1x2 - new dictionary keys are created to accommodate the new dimensions, and the
# original dictionary entries are deleted.
#---------------------------------------------------------------------------------------------------
new_dict_drawing, new_dict_actual, keys_to_remove = {}, {}, []
for dimension in temp_dict_drawing:
if type(temp_dict_actual.get(dimension)[0]) is list:
keys_to_remove.append(dimension) # Create a list of unnecessery dimensions
for i, sublist in enumerate(temp_dict_actual.get(dimension)):
new_dict_drawing[f'{dimension}{i + 1}'] = temp_dict_drawing.get(dimension) # Create new dimension
new_dict_actual[f'{dimension}{i + 1}'] = sublist # Create new dimension
for dimension in keys_to_remove: # Remove all unnecessary dimensions from the original dicts
temp_dict_drawing.pop(dimension)
temp_dict_actual.pop(dimension)
# Merge dictionaries:
drawing_new = {**temp_dict_drawing, **new_dict_drawing}
actual_new = {**temp_dict_actual, **new_dict_actual}
#---------------------------------------------------------------------------------------------------
# Determine the deviation between the drawing and actual dimensions. The average of the upper and
# lower bounds is used to simplify each case.
#---------------------------------------------------------------------------------------------------
inspection_results = {}
for dimension in drawing_new:
drawing_value, actual_value = drawing_new.get(dimension), actual_new.get(dimension) # Fetch the data
drawing_ave, actual_ave = np.mean(drawing_value), np.mean(actual_value) # Calculate averages
deviation = drawing_ave - actual_ave
# Create new dictionary of the repair requirements:
#---------------------------------------------------------------------------------------------------
inspection_results[f'{dimension}'] = round(deviation, 3)
return inspection_results
Alternatively, copy.deepcopy() also yielded the correct results: