"""
Fast Plotting Tricks
====================

Sometimes you want to quickly plot a diffraction pattern but things seem slow,
this mostly happens with "large" data that is loaded Lazily.

There are a couple of different ways that plotting in hyperspy/pyxem can be slow:

1. The data is too large and the navigator is being recalculated every time you plot. (i.e. calling s.plot()
 takes a long time to render)
2. Dragging the navigator is slow and laggy.

"""

from pyxem.data import fe_multi_phase_grains
import numpy as np
import hyperspy.api as hs

s = fe_multi_phase_grains().as_lazy()

# %%
# Pre Computing a Navaigator
# --------------------------
# To solve the first problem, you can:
#
# 1. Precompute the navigator using the :meth:`hyperspy.api.signals.plot` method or
# the :meth:`hyperspy._signals.LazySignal.compute_navigator` method
# which will compute the navigator and store it in the signal. This will make plotting faster.

s.compute_navigator()
print(s.navigator)

# %%
# Setting a Navigator
# -------------------
# 2. You can also set the navigator directly using `s.navigator = ...` if you have a navigator
# that you want to use. This is useful if a virtual image is created along with the signal when
# the data is acquired.  This will also save the navigator in the metadata. This is similar to the
# :meth:`hyperspy._signals.LazySignal.compute_navigator`method of the signal and
# will be saved when the signal is saved.

dummy_navigator = hs.signals.Signal2D(np.ones((20, 20)))  # just a dummy navigator
s.navigator = dummy_navigator
# or
s.plot(navigator=dummy_navigator)

# %%
# Using a Slider to Navigate
# --------------------------
# 3. You also don't need to plot the navigator every time you plot the signal. You can set
# `navigator = "slider"` to avoid plotting the navigator altogether and just use the sliders.

s.plot(navigator="slider")


# %%
# Using the QT Backend and Blitting
# ---------------------------------
# To solve the second problem, you can:
#
# 1. Use the Qt backend by running `%matplotlib qt` in a Jupyter notebook cell. This will make the
# navigator much more responsive using "blitting" which only updates the parts of the plot that
# have changed. Note that the QT backend is not available in Google Colab or when running in a
# Jupyter notebook on a remote server.
#
# Using Shift + Click to Jump
# ---------------------------
# 2. You can use the Shift + Click feature to "Jump" to a specific location in the navigator.
# This is useful if you want to quickly move to a specific location in the navigator without
# dragging the navigator and loading all the data in between.
#
# 3. You can also set the navigator point using the `axes_manager.indices` attribute.

s.axes_manager.indices = (5, 5)  # jump to the center of the navigator
s.plot()

# %%
# Saving the Data
# ---------------
# 4. Finally, you can always consider saving the data in a more performant format like `.zspy`
# This will make loading the data faster which will in turn make plotting faster!

s.save("fast_and_compressed.zspy")

hs.load("fast_and_compressed.zspy").plot()  # reload the data and plot it