- merged in the Autolevelling branch and made some PEP8 changes to the bilinearInterpolator.py file
This commit is contained in:
Marius Stanciu
Marius
parent
3ba000a097
commit
5de1701b3d
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@ -15,11 +15,13 @@ CHANGELOG for FlatCAM beta
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- In Excellon Object UI fixed the milling geometry generation
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- updated the translations strings to the changes in the source code
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- some strings changed
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- made the Properties checkbox in the Object UI into a checkable button and added to it an icon
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- fixed crash on using shortcut for creating a new Document Object
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- fixed Cutout Tool to work with the endxy parameter
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- added the exclusion parameters for Drilling Tool to the Preferences area
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- cascaded_union() method will be deprecated in Shapely 1.8 in favor of unary_union; replaced the usage of cascaded_union with unary_union in all the app
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- added some strings to the translatable strings and updated the translation strings
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- merged in the Autolevelling branch and made some PEP8 changes to the bilinearInterpolator.py file
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20.10.2020
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@ -1,8 +1,9 @@
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import csv
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# import csv
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import math
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import numpy as np
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class bilinearInterpolator():
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class bilinearInterpolator:
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"""
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This class takes a collection of 3-dimensional points from a .csv file.
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It contains a bilinear interpolator to find unknown points within the grid.
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@ -15,10 +16,7 @@ class bilinearInterpolator():
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Constructor takes a file with a .csv extension and creates an evenly-spaced 'ideal' grid from the data points.
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This is done to get around any floating point errors that may exist in the data
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"""
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def __init__(
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self,
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pointsFile
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):
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def __init__(self, pointsFile):
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self.pointsFile = pointsFile
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self.points = np.loadtxt(self.pointsFile, delimiter=',')
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@ -28,8 +26,8 @@ class bilinearInterpolator():
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# generate ideal grid to match actually probed points -- this is due to floating-point error issues
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idealGrid = ([
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[(x,y) for x in np.linspace(self.xMin,self.xMax,self.xCount, True)]
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for y in np.linspace(self.yMin,self.yMax,self.yCount, True)
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[(x, y) for x in np.linspace(self.xMin, self.xMax, self.xCount, True)]
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for y in np.linspace(self.yMin, self.yMax, self.yCount, True)
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])
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self._probedGrid = [[0] * self.yCount for i in range(0, self.xCount)]
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@ -41,7 +39,7 @@ class bilinearInterpolator():
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for probed in self.points:
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# find closest point in ideal grid that corresponds to actual tested point
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# put z value in correct index
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sqDist = pow(probed[0] - idealPoint[0], 2) + pow(probed[1] - idealPoint[1],2)
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sqDist = pow(probed[0] - idealPoint[0], 2) + pow(probed[1] - idealPoint[1], 2)
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if sqDist <= minSqDist:
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minSqDist = sqDist
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indexX = rowIndex
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@ -49,13 +47,13 @@ class bilinearInterpolator():
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closestProbed = probed
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self.probedGrid[indexY][indexX] = closestProbed
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"""
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Bilinear interpolation method to determine unknown z-values within grid of known z-values.
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NOTE: If one axis is outside the grid, linear interpolation is used instead.
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If both axes are outside of the grid, the z-value of the closest corner of the grid is returned.
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"""
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def Interpolate(self, point):
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"""
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Bilinear interpolation method to determine unknown z-values within grid of known z-values.
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NOTE: If one axis is outside the grid, linear interpolation is used instead.
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If both axes are outside of the grid, the z-value of the closest corner of the grid is returned.
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"""
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lin = False
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if point[0] < self.xMin:
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@ -68,8 +66,8 @@ class bilinearInterpolator():
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ix1 = math.floor((point[0] - self.xMin)/self.xSpacing)
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ix2 = math.ceil((point[0] - self.xMin)/self.xSpacing)
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def interpolatePoint(p1, p2, p, axis):
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return (p2[2]*(p[axis] - p1[axis]) + p1[2]*(p2[axis] - p[axis]))/(p2[axis] - p1[axis])
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def interpolatePoint(p1, p2, pt, axis):
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return (p2[2]*(pt[axis] - p1[axis]) + p1[2]*(p2[axis] - pt[axis]))/(p2[axis] - p1[axis])
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if point[1] < self.yMin:
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if lin:
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@ -78,11 +76,12 @@ class bilinearInterpolator():
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elif point[1] > self.yMax:
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if lin:
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return self.probedGrid[ix1][self.yCount - 1][2]
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return interpolatePoint(self.probedGrid[ix1][self.yCount - 1], self.probedGrid[ix2][self.yCount - 1], point, 0)
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return interpolatePoint(
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self.probedGrid[ix1][self.yCount - 1], self.probedGrid[ix2][self.yCount - 1], point, 0)
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else:
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iy1 = math.floor((point[1] - self.yMin)/self.ySpacing)
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iy2 = math.ceil((point[1] - self.yMin)/self.ySpacing)
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#if x was at an extrema, but y was not, perform linear interpolation on x axis
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# if x was at an extrema, but y was not, perform linear interpolation on x axis
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if lin:
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return interpolatePoint(self.probedGrid[ix1][iy1], self.probedGrid[ix1][iy2], point, 1)
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@ -104,7 +103,7 @@ class bilinearInterpolator():
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r1 = specialDiv(point[0]-x1, x2-x1)*Q21 + specialDiv(x2-point[0], x2-x1)*Q11
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r2 = specialDiv(point[0]-x1, x2-x1)*Q22 + specialDiv(x2-point[0], x2-x1)*Q12
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p = specialDiv(point[1]-y1, y2-y1)*r2 + specialDiv(y2-point[1], y2-y1)*r1
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p = specialDiv(point[1]-y1, y2-y1)*r2 + specialDiv(y2-point[1], y2-y1)*r1
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return p
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@ -124,4 +123,4 @@ class bilinearInterpolator():
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axisRange = axisMax - axisMin
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axisCount = round((axisRange/axisSpacing) + 1)
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return axisMin, axisMax, axisSpacing, axisCount
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return axisMin, axisMax, axisSpacing, axisCount
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