e2ecdcfe33
Former-commit-id:a02aeb236c[formerly9f19e3f712] [formerlya02aeb236c[formerly9f19e3f712] [formerly06a8b51d6d[formerly 64fa9254b946eae7e61bbc3f513b7c3696c4f54f]]] Former-commit-id:06a8b51d6dFormer-commit-id:8e80217e59[formerly3360eb6c5f] Former-commit-id:377dcd10b9
88 lines
No EOL
2.8 KiB
Python
88 lines
No EOL
2.8 KiB
Python
##
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# This software was developed and / or modified by Raytheon Company,
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# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
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#
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# U.S. EXPORT CONTROLLED TECHNICAL DATA
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# This software product contains export-restricted data whose
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# export/transfer/disclosure is restricted by U.S. law. Dissemination
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# to non-U.S. persons whether in the United States or abroad requires
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# an export license or other authorization.
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#
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# Contractor Name: Raytheon Company
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# Contractor Address: 6825 Pine Street, Suite 340
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# Mail Stop B8
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# Omaha, NE 68106
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# 402.291.0100
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#
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# See the AWIPS II Master Rights File ("Master Rights File.pdf") for
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# further licensing information.
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###
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## @file PartialDerivative.py
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from numpy import isscalar
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from numpy import NaN
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##
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# Calculate d/dx and d/dy of Qty.
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# This method assumes masked arrays as input and returns masked
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# arrays as output. That saves work when the return value would just
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# be masked again for use in other calculations. When in doubt, call
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# execute() instead.
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#
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# The returned arrays are the same size as Qty, with all the outermost
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# edges masked. d/dx could extend to the top and bottom, and
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# d/dy to the left and right edge, but this has not been done, in order
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# to maintain the same output as the old g2gkinematics.f function #7.
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#
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# @param Qty: Quantity to differentiate.
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# @type Qty: 2D numpy array
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# @param dx: Spacing between data points in X direction.
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# @type dx: scalar or 2D numpy array
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# @param dy: Spacing between data points in Y direction.
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# @type dy: scalar or 2D numpy array
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# @return: d/dx and d/dy of Qty.
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# @rtype: tuple(dq/dx,dq/dy) of masked 2D numpy arrays.
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def calculate(Qty, dx, dy):
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"Calculate d/dx and d/dy of Qty."
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if not isscalar(dx):
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dx = dx[1:-1,1:-1]
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if not isscalar(dy):
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dy = dy[1:-1,1:-1]
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cropped_dqdx = Qty[1:-1,2:] - Qty[1:-1,0:-2]
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cropped_dqdx /= dx * 2
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cropped_dqdy = Qty[2:,1:-1] - Qty[0:-2,1:-1]
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cropped_dqdy /= dy * 2
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dqdx = Qty + NaN
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dqdy = Qty + NaN
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dqdx[1:-1,1:-1] = cropped_dqdx
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dqdy[1:-1,1:-1] = cropped_dqdy
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return (dqdx, dqdy)
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##
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# Calculate d/dx and d/dy of Qty.
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# This method takes unmasked arrays and returns unmasked arrays.
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#
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# @param Qty: Quantity to differentiate.
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# @type Qty: 2D numpy array
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# @param dx: Spacing between data points in X direction.
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# @type dx: scalar or 2D numpy array
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# @param dy: Spacing between data points in Y direction.
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# @type dy: scalar or 2D numpy array
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# @return: d/dx and d/dy of Qty.
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# @rtype: tuple(dq/dx,dq/dy) of 2D numpy arrays.
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def execute(Qty, dx, dy):
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""
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# assume dx and dy are never zero or near-inifinite
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dqdx, dqdy = calculate(Qty, dx, dy)
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return (dqdx, dqdy) |