#!/usr/bin/env python
# coding: utf-8

# This notebook is part of the PyImageJ [Tutorial Series](./notebooks.rst), and assumes familiarity with the ImageJ API. Dedicated tutorials for ImageJ can be found [here](https://imagej.net/tutorials/).S

# # 12 Troubleshooting
# 
# This notebook covers some basic troubleshooting with PyImageJ. Let's initalize ImageJ before exploring some troublehsooting options.
# 

# In[1]:


import imagej

# initialize ImageJ
ij = imagej.init()
print(f"ImageJ2 version: {ij.getVersion()}")


# ## 12.1 I can't pass my numpy image to an `Op`
# 
# `ij.py` is really good at converting numpy images into `RandomAccessibleInterval`s. However many `Op`s, like `addPoissonNoise`, take other forms of ImageJ images, like `IterableInterval`.

# In[2]:


print(ij.op().help('filter.addPoissonNoise'))


# We can't call this `Op` on a numpy array since it is a specialized type of `RandomAccessibleInterval`, which does not extend `IterableInterval`.

# In[3]:


from skimage import io

# Create a numpy image using scikit
img = io.imread('https://imagej.net/images/clown.jpg')

ij.py.show(img)
print(type(ij.py.to_java(img)))


# We can fix this by using `transform.flatIterableView` on both the input and output, which will convert the `ReferenceGuardedRandomAccessibleInterval`s into `IterableInterval`s, allowing us to pass our numpy image into `addPoissonNoise`:

# In[4]:


import numpy as np

result = np.zeros(img.shape) # HINT: Uses float dtype, for more accurate noising.

imgIterable = ij.op().transform().flatIterableView(ij.py.to_java(img))
resIterable = ij.op().transform().flatIterableView(ij.py.to_java(result))

ij.op().filter().addPoissonNoise(resIterable, imgIterable)

ij.py.show(result)


# Uh oh. Something's off with the data values—see next section!

# If you need to troubleshoot op workings, look for implementations that use only `IterableInterval`s or `RandomAccessibleInterval`s. To find the implementations use the `print(ij.op().help())` function.
# 
# For the multiply function the implementation we used is second to last (`net.imagej.ops.math.IIToRAIOutputII$Multiply`)

# If you need to troubleshoot op workings, look for implementations that use only `IterableInterval`s or `RandomAccessibleInterval`s. To find the implementations use the `print(ij.op().help())` function.
# 
# For the multiply function the implementation we used is second to last (`net.imagej.ops.math.IIToRAIOutputII$Multiply`)

# ## 12.2 Clipping input data to the valid range for imshow with RGB data ([0..1] for floats of [0..255] for integers
# 
# This message is given either because (as described in the warning)
# 
# * The data consists of floats that are outside the range of [0..1]. This is pretty common and definitely is the case as to why the above data looks so bad.
# * The data consists of ints that are outside the range [0..255].
# 
# Let's be certain which is the culprit by checking some values of `result`:

# In[5]:


# grab the RGB values in a line from [0][5] to [0][10] in our image
print(result[0][5:10])


# Thus we not only have floats outside of [0..1] but also values outside of [0..255]; we are faulty of both points in the warning. We can fix this by first [clipping](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.clip.html) the entire array within the integer range, then cast to `uint8` so that the float range no longer applies:

# In[6]:


ij.py.show(img.astype(int))
       
result = np.clip(result, 0, 255)
ij.py.show(result.astype(np.uint8))


# Now our noisy image displays nicely alongside the original. Note that depending on your data this might not be the right path for you, but using `clip` and `astype` are great tools for rearranging your data within these bounds.

# If you need to troubleshoot op workings, look for implementations that use only `IterableInterval`s or `RandomAccessibleInterval`s. To find the implementations use the `print(ij.op().help())` function.
# 
# For the multiply function the implementation we used is second to last (`net.imagej.ops.math.IIToRAIOutputII$Multiply`)

# In[7]:


# print is required to render new lines
print(ij.op().help('multiply'))