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Merge pull request#11047 from jklymak/fix-image-respect-norm-limits

FIX: image respect norm limits w/ NoneConflicts:lib/matplotlib/image.py - keep changes backported from master. Looks like conflict was due to some white-space clean up done on master.

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lib/matplotlib
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`‎lib/matplotlib/image.py`

Lines changed: 18 additions & 17 deletions

Original file line number	Diff line number	Diff line change
`@@ -401,26 +401,27 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,`
`401`	`401`	`# float64's ability to represent changes. Applying`
`402`	`402`	`# a norm first would be good, but ruins the interpolation`
`403`	`403`	`# of over numbers.`
`404`		`-ifself.norm.vminisnotNoneandself.norm.vmaxisnotNone:`
`405`		`-dv= (np.float64(self.norm.vmax)-`
`406`		`-np.float64(self.norm.vmin))`
`407`		`-vmid=self.norm.vmin+dv/2`
`408`		`-newmin=vmid-dv*1.e7`
`409`		`-ifnewmin<a_min:`
`410`		`-newmin=None`
`411`		`-else:`
`412`		`-a_min=np.float64(newmin)`
`413`		`-newmax=vmid+dv*1.e7`
`414`		`-ifnewmax>a_max:`
`415`		`-newmax=None`
`416`		`-else:`
`417`		`-a_max=np.float64(newmax)`
`418`		`-ifnewmaxisnotNoneornewminisnotNone:`
`419`		`-A_scaled=np.clip(A_scaled,newmin,newmax)`
	`404`	`+self.norm.autoscale_None(A)`
	`405`	`+dv= (np.float64(self.norm.vmax)-`
	`406`	`+np.float64(self.norm.vmin))`
	`407`	`+vmid=self.norm.vmin+dv/2`
	`408`	`+fact=1e7ifscaled_dtype==np.float64else1e4`
	`409`	`+newmin=vmid-dv*fact`
	`410`	`+ifnewmin<a_min:`
	`411`	`+newmin=None`
	`412`	`+else:`
	`413`	`+a_min=np.float64(newmin)`
	`414`	`+newmax=vmid+dv*fact`
	`415`	`+ifnewmax>a_max:`
	`416`	`+newmax=None`
	`417`	`+else:`
	`418`	`+a_max=np.float64(newmax)`
	`419`	`+ifnewmaxisnotNoneornewminisnotNone:`
	`420`	`+A_scaled=np.clip(A_scaled,newmin,newmax)`
`420`	`421`
`421`	`422`	`A_scaled-=a_min`
`422`	`423`	`# a_min and a_max might be ndarray subclasses so use`
`423`		`-# asscalar toensure they are scalars toavoid errors`
	`424`	`+# asscalar to avoid errors`
`424`	`425`	`a_min=np.asscalar(a_min.astype(scaled_dtype))`
`425`	`426`	`a_max=np.asscalar(a_max.astype(scaled_dtype))`
`426`	`427`

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`‎lib/matplotlib/image.py`

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