#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pyopencl as cl
from alive_progress import alive_bar
from matplotlib.backend_bases import MouseButton

img_res_x = 1000
img_res_y = 1000
total_pixels = img_res_x * img_res_y # so we don't gotta compute it every time

periods = 1
square_x = 0
square_y = 0

x_min = (-periods * np.pi) + (square_x * np.pi)
x_max = (periods  * np.pi) + (square_x * np.pi)
y_min = (-periods * np.pi) + (square_y * np.pi)
y_max = (periods * np.pi) + (square_y * np.pi)

escape = 10000
iterations = 255*3
c_x = 2 * np.pi
c_y = 2 * np.pi

animation_progres_save = "ani1_less.mp4"
single_frame_save = "asdf.png"

opencl_context = cl.create_some_context(interactive=False)
opencl_queue = cl.CommandQueue(opencl_context)

kernel_src_path = "./kernel.c"

frames = 1
rendered_frames = []

image = np.empty([img_res_x, img_res_y], np.uint32)
image_buffer = cl.Buffer(opencl_context, cl.mem_flags.WRITE_ONLY, image.nbytes)

plt.style.use('dark_background')
# fuck this shit
fig = plt.figure(frameon=False)
fig.set_size_inches(img_res_x/fig.dpi, img_res_y/fig.dpi)
#fig.set_size_inches(width/height, 1, forward=False)


ax = plt.Axes(fig, [0., 0., 1., 1.])
#ax.set_axis_off()
fig.add_axes(ax)

cmap = plt.cm.viridis
cmap.set_bad((0,0,0))
cmap.set_over((0,0,0))
cmap.set_under((0,0,0))

bruh = None

def on_click(event):
    global bruh
    split_ratio = 1
    if (event.button is MouseButton.MIDDLE) and (event.inaxes):
        # there's probably a way to set global coordinates;
        # I don't expect this to go anywhere so I don't really care
        on_x = ((event.xdata / img_res_x) * abs(x_max - x_min)) + x_min
        on_y = ((event.ydata / img_res_y) * abs(y_max - y_min)) + y_min

        #I, uh, also don't know the best way to replicate the openCL code automaticly in python
        #so ajust if nessesary
        x_hops = []
        y_hops = []
        for i in range(iterations):
            x_hops.append(((on_x - x_min) / abs(x_max - x_min)) * img_res_x)
            y_hops.append(((on_y - y_min) / abs(y_max - y_min)) * img_res_y)
            next_x = on_x/np.tan(on_y)
            on_y = on_y/np.tan(on_x)
            on_x = next_x
            if on_x**2 + on_y**2 > escape:
                break
        print(y_hops[0])
        print(on_y)
        print("{} hops".format(len(x_hops)))
        if bruh:
            bruh.pop(0).remove()
        bruh = ax.plot(x_hops, y_hops)
        plt.draw()

         

        print(on_x, on_y)


print("compiling openCL kernel...")
with open(kernel_src_path, 'r') as kernel_src:
  compiled_kernel = cl.Program(opencl_context, kernel_src.read()).build()

encoding_progress = alive_bar(frames, bar = 'filling', spinner = 'waves')

def display_encoder_progress(current_frame: int, total_frames: int):
    print("Encoding: frame {}/{}".format(current_frame, frames))



print("Rendering {} frames...".format(frames))
if frames > 1:
    with alive_bar(frames, bar = 'filling', spinner = 'waves') as bar_total:
        for frame_i in range(0, frames):
            compiled_kernel.render_frame(opencl_queue, image.shape, None, image_buffer,
                                         np.double(abs(x_max - x_min) / img_res_x),
                                         np.double(abs(y_max - y_min) / img_res_y),
                                         np.double(x_min), np.double(y_min), 
                                         np.uint32(iterations), np.uint32(escape),
                                         np.double(frame_i / frames))


            cl.enqueue_copy(opencl_queue, image, image_buffer).wait()
            rendered_frame = ax.imshow(image, norm="log", aspect="auto", cmap=cmap, animated="True")
            rendered_frames.append([rendered_frame])
            bar_total()
    print("Encoding/Saving...")
    ani = animation.ArtistAnimation(fig, rendered_frames, interval=30, blit=True)
    ani.save(animation_progres_save, extra_args=['-preset', 'lossless'], progress_callback=display_encoder_progress, codec="h264_nvenc")
else:
        compiled_kernel.render_frame(opencl_queue, image.shape, None, image_buffer,
                                     np.double(abs(x_max - x_min) / img_res_x),
                                     np.double(abs(y_max - y_min) / img_res_y),
                                     np.double(x_min), np.double(y_min), 
                                     np.uint32(iterations), np.uint32(escape),
                                     np.double(1))
        cl.enqueue_copy(opencl_queue, image, image_buffer).wait()
        ax.imshow(image, norm="log", aspect="auto", cmap=cmap)
        fig.savefig(single_frame_save)
        plt.autoscale(False)
        plt.connect('motion_notify_event', on_click)
        plt.show()