dfed7a844a
* implement nonzero for Boolean arrays * remove axtls from build script * extend nonzero to ndarrays of arbitrary dtype, and iterable, fix float tests * temporarily disable circuitpython tests * add nonzero documentation * Added test script for np.nonzero() Co-authored-by: Tejal Ashwini Barnwal <64950661+tejalbarnwal@users.noreply.github.com>
158 lines
3.9 KiB
Text
158 lines
3.9 KiB
Text
Testing np.min:
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1
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1.0
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1
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array([1, 2, 3], dtype=uint8)
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array([1, 4, 7], dtype=uint8)
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235
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array([235, 236, 237, 238, 239], dtype=uint8)
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array([250, 235, 245], dtype=uint8)
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-21
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array([-21, -20, -19, -18, -17], dtype=int8)
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array([-6, -21, -11], dtype=int8)
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235
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array([235, 236, 237, 238, 239], dtype=uint16)
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array([250, 235, 245], dtype=uint16)
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235
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array([235, 236, 237, 238, 239], dtype=int16)
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array([250, 235, 245], dtype=int16)
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253.0
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array([253.0, 254.0, 255.0], dtype=float64)
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array([7.205759403792794e+16, 65533.0, 253.0], dtype=float64)
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Testing np.max:
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1
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1.0
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9
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array([7, 8, 9], dtype=uint8)
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array([3, 6, 9], dtype=uint8)
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254
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array([250, 251, 252, 253, 254], dtype=uint8)
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array([254, 239, 249], dtype=uint8)
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-2
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array([-6, -5, -4, -3, -2], dtype=int8)
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array([-2, -17, -7], dtype=int8)
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254
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array([250, 251, 252, 253, 254], dtype=uint16)
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array([254, 239, 249], dtype=uint16)
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254
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array([250, 251, 252, 253, 254], dtype=int16)
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array([254, 239, 249], dtype=int16)
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7.205759403792794e+16
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array([7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16], dtype=float64)
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array([7.205759403792794e+16, 65535.0, 255.0], dtype=float64)
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Testing np.argmin:
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0
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0
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0
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array([0, 0, 0], dtype=int16)
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array([0, 0, 0], dtype=int16)
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5
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array([1, 1, 1, 1, 1], dtype=int16)
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array([0, 0, 0], dtype=int16)
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5
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array([1, 1, 1, 1, 1], dtype=int16)
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array([0, 0, 0], dtype=int16)
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5
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array([1, 1, 1, 1, 1], dtype=int16)
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array([0, 0, 0], dtype=int16)
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5
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array([1, 1, 1, 1, 1], dtype=int16)
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array([0, 0, 0], dtype=int16)
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6
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array([2, 2, 2], dtype=int16)
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array([0, 0, 0], dtype=int16)
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Testing np.argmax:
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0
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0
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8
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array([2, 2, 2], dtype=int16)
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array([2, 2, 2], dtype=int16)
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4
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array([0, 0, 0, 0, 0], dtype=int16)
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array([4, 4, 4], dtype=int16)
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4
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array([0, 0, 0, 0, 0], dtype=int16)
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array([4, 4, 4], dtype=int16)
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4
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array([0, 0, 0, 0, 0], dtype=int16)
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array([4, 4, 4], dtype=int16)
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4
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array([0, 0, 0, 0, 0], dtype=int16)
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array([4, 4, 4], dtype=int16)
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0
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array([0, 0, 0], dtype=int16)
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array([0, 2, 2], dtype=int16)
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Testing np.minimum:
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9
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9.0
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array([[252.0, 253.0, 254.0],
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[237.0, 238.0, 239.0],
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[247.0, 248.0, 249.0]], dtype=float64)
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Testing np.maximum:
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array([[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16],
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[65533.0, 65534.0, 65535.0],
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[253.0, 254.0, 255.0]], dtype=float64)
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10
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10.0
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array([[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16],
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[65533.0, 65534.0, 65535.0],
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[253.0, 254.0, 255.0]], dtype=float64)
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Testing np.sort:
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array([237, 238, 239, 247, 248, 249, 252, 253, 254], dtype=uint8)
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array([253.0, 254.0, 255.0, 65533.0, 65534.0, 65535.0, 7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16], dtype=float64)
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array([[237, 238, 239],
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[247, 248, 249],
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[252, 253, 254]], dtype=uint8)
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array([[253.0, 254.0, 255.0],
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[65533.0, 65534.0, 65535.0],
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[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16]], dtype=float64)
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array([[252, 253, 254],
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[237, 238, 239],
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[247, 248, 249]], dtype=uint8)
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array([[7.205759403792794e+16, 7.205759403792794e+16, 7.205759403792794e+16],
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[65533.0, 65534.0, 65535.0],
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[253.0, 254.0, 255.0]], dtype=float64)
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Testing np.sum:
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762
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250
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2217.0
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array([736.0, 739.0, 742.0], dtype=float64)
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array([759.0, 714.0, 744.0], dtype=float64)
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Testing np.mean:
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254.0
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254.0
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True
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True
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True
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True
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True
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True
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True
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Testing np.std:
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True
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True
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True
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True
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True
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True
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True
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True
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True
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Testing np.median:
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254.0
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254.0
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248.0
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array([247.0, 248.0, 249.0], dtype=float64)
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array([253.0, 238.0, 248.0], dtype=float64)
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Testing np.roll:
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int16)
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array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7], dtype=int16)
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array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1], dtype=int16)
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array([7.0, 8.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float64)
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array([3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 1.0, 2.0], dtype=float64)
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Testing np.clip:
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5
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6
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3
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array([3, 3, 3, 4, 5, 5, 5], dtype=int16)
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array([3.0, 3.0, 3.0, 4.0, 5.0, 5.0, 5.0], dtype=float64)
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