def clip(input_array, clip_val, top_or_bottom, output=False):
"""
Clip an input numpy array and set the clipped pixels to the
clipping value. Bottom clip scales pixels below the clip_val.
Top does the same for pixels above.
"""
# Input validation
error = "input_array for clip must be a numpy array instance."
assert isinstance(input_array, N.ndarray), error
error = "clip_val must be float"
assert isinstance(clip_val, float), error
error = 'top_or_bottom for clip must be "top" or "bottom".'
assert top_or_bottom in ["top", "bottom"], error
if top_or_bottom == "bottom":
index = N.where(input_array < clip_val)
elif top_or_bottom == "top":
index = N.where(input_array > clip_val)
output_array = copy.deepcopy(input_array)
output_array[index] = clip_val
if output != False:
before_after(
before_array=input_array,
after_array=output_array,
before_array_name="Input Data",
after_array_name="Clip " + top_or_bottom + " at " + str(clip_val),
output=output,
)
return output_array
def log(self, output=False):
"""
Take the log of self.data.
"""
array_log = N.log(self.data)
if output != False:
before_after(
before_array=self.data,
after_array=array_log,
before_array_name="Input Data",
after_array_name="Log",
output=output,
)
self.data = array_log
def threshold_clip(self, minimum, maximum, output=False):
"""
Set all values below minum and above maximum to the
minimum and maximum, respectively
"""
output_array = copy.deepcopy(self.data)
output_array[output_array > maximum] = maximum
output_array[output_array < minimum] = minimum
if output != False:
before_after(
before_array=self.data,
after_array=output_array,
before_array_name="Input Data",
after_array_name="Threshold Clipped",
output=output,
pause=False,
)
self.data = output_array
def positive(self, output=False):
"""
Shift all the values in self.data so there are no negative or 0 pixels.
Needed to prevent taking the log of negative values.
This function is not currently used.
"""
min_val = N.min(self.data)
if min_val <= 0:
output_array = self.data + ((min_val * -1.0) + 0.0001)
else:
output_array = self.data
if output != False:
before_after(
before_array=self.data,
after_array=output_array,
before_array_name="Input Data",
after_array_name="Positive Corrected",
output=output,
pause=False,
)
self.data = output_array
