diff options
Diffstat (limited to 'field_tests')
-rw-r--r-- | field_tests/backup | 62 | ||||
-rw-r--r-- | field_tests/basic_field_test.py | 168 | ||||
-rwxr-xr-x | field_tests/field.py | 191 | ||||
-rwxr-xr-x | field_tests/gpu_migration.py | 137 | ||||
-rw-r--r-- | field_tests/kernel.c | 26 | ||||
-rw-r--r-- | field_tests/makefile | 4 |
6 files changed, 588 insertions, 0 deletions
diff --git a/field_tests/backup b/field_tests/backup new file mode 100644 index 0000000..2e60664 --- /dev/null +++ b/field_tests/backup @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +import numpy as np +import matplotlib.pyplot as plt +from alive_progress import alive_bar + +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 = .25 +square_x = 1 +square_y = 1 + +xmin = (-periods * np.pi) + (square_x * np.pi) +xmax = (periods * np.pi) + (square_x * np.pi) +ymin = (-periods * np.pi) + (square_y * np.pi) +ymax = (periods * np.pi) + (square_y * np.pi) + +escape = 10000 +iterations = 255*3 +c_x = 2 * np.pi +c_y = 2 * np.pi + + +image = np.empty([img_res_y, img_res_x]) + +with alive_bar(img_res_y, bar = 'filling', spinner = 'waves') as bar: + for pix_y, y in enumerate(np.linspace(ymin, ymax, img_res_y)): + for pix_x, x in enumerate(np.linspace(xmin, xmax, img_res_x)): + on_x = x + on_y = y + for i in range(iterations): + completed_ratio = (((pix_x * pix_y * 1)) / total_pixels) + next_x = (completed_ratio * (on_x/np.sin(on_y))) + ((1 - completed_ratio) * on_x/np.tan(on_y)) + on_y = (completed_ratio * (on_y/np.sin(on_x))) + ((1 - completed_ratio) * on_y/np.tan(on_x)) + on_x = next_x + if on_x**2 + on_y**2 > escape: + break + image[pix_y][pix_x] = i + bar() + + + + +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)) + +ax.imshow(image, norm="log", aspect="auto", cmap=cmap) +fig.savefig("linear_transform_sin_tan_arnolds_tongue_hotspot.png") +plt.show() diff --git a/field_tests/basic_field_test.py b/field_tests/basic_field_test.py new file mode 100644 index 0000000..faf3c2c --- /dev/null +++ b/field_tests/basic_field_test.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +import numpy as np +import matplotlib.pyplot as plt +from alive_progress import alive_bar + +img_res_x = 100 +img_res_y = 100 +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 + +#xmin = (-periods * np.pi) + (square_x * np.pi) +#xmax = (periods * np.pi) + (square_x * np.pi) +#ymin = (-periods * np.pi) + (square_y * np.pi) +#ymax = (periods * np.pi) + (square_y * np.pi) + +xmin = -10 +xmax = 10 +ymin = -10 +ymax = 10 + +escape = 10000 +iterations = 255*3 +c_x = 2 * np.pi +c_y = 2 * np.pi + + +image = np.empty([img_res_y, img_res_x]) +grid = np.meshgrid(np.linspace(ymin, ymax, img_res_y), np.linspace(xmin, xmax, img_res_x)) +print(grid[0].dtype) + + +class point_charge(): + def __init__(self, x, y, c, mod): + self.x = x + self.y = y + self.c = c + self.mod = mod + def get_field(self, to_x, to_y): + if(self.mod): + to_x = (to_x % self.mod) + to_y = (to_y % self.mod) + return ( + ((self.c * (self.x - to_x)) / ((self.x - to_x)**2 + (self.y - to_y)**2)**1.5), + ((self.c * (self.y - to_y)) / ((self.x - to_x)**2 + (self.y - to_y)**2)**1.5)) + +#will remove all the point charge code if it turns out to be good enough to be impliemnted into openCL +#point_charges = [point_charge(-5, -5, 100), point_charge(-5, 5, -100), point_charge(5, 0, 100)] +point_charges = [point_charge(5,5, 100, 10), point_charge(0,0,-100, 0)] + + +plt.ion() +ax = plt.gca() +fig = plt.gcf() +ax.set_autoscale_on(False) +ax.set_xlim([xmin, xmax]) +ax.set_ylim([ymin, ymax]) + +vector_arrows = None + +def show_field(): + global vector_arrows + grid_f = np.zeros_like(grid) + for p in point_charges: + grid_f += p.get_field(grid[0], grid[1]) + #plt.streamplot(grid[0], grid[1], grid_f[0], grid_f[1], density=5) + vector_arrows = plt.quiver(grid[0], grid[1], grid_f[0], grid_f[1]) + plt.show(block=False) + plt.pause(.1) + + +show_field() + +timestep = .1 +def test_sim(): + particle_grid = np.meshgrid(np.linspace(ymin, ymax, 100), np.linspace(xmin, xmax, 100)) + pos = particle_grid + acceleration = np.zeros_like(particle_grid) + velocity = np.zeros_like(particle_grid) + velocity = [np.ones_like(particle_grid[0]) * 1, np.ones_like(particle_grid[0]) * .5] + mass = 10 + charge = 1 + particle_plot = ax.plot(velocity[0], velocity[1], 'bo', animated=True) + #velocity += .1 + + background = fig.canvas.copy_from_bbox(ax.bbox) + ax.draw_artist(vector_arrows) + fig.canvas.blit(fig.bbox) + + while True: + fig.canvas.restore_region(background) + field = np.zeros_like(particle_grid) + # TODO can make this quicker by skipping initilization + for p in point_charges: + field += p.get_field(pos[0], pos[1]) + acceleration = ((charge * field) / mass) * timestep + #print(acceleration) + velocity += acceleration * timestep + pos += velocity * timestep + + fig.canvas.restore_region(background) + particle_plot[0].set_data(pos[0],pos[1]) + ax.draw_artist(particle_plot[0]) + fig.canvas.blit(fig.bbox) + fig.canvas.flush_events() + plt.pause(1/60) + + #fig.canvas.draw_idle() +test_sim() + +exit(1) + + + + + +#with alive_bar(iterations, bar = 'filling', spinner = 'waves') as bar: +# for i in range(iterations): +# next_x = xx / np.sin(yy) +# yy = yy / np.sin(xx) +# xx = next_x +# bar() +#image = np.vstack([xx.ravel(), yy.ravel()]) + + +#meshgrid makes things slower as we can't test individual points for breaking to infinity +fractal_test = False +if fractal_test: + with alive_bar(img_res_y, bar = 'filling', spinner = 'waves') as bar: + for pix_y, y in enumerate(np.linspace(ymin, ymax, img_res_y)): + for pix_x, x in enumerate(np.linspace(xmin, xmax, img_res_x)): + on_x = x + on_y = y + for i in range(iterations): + completed_ratio = (((pix_x * pix_y * 1)) / total_pixels) + next_x = (completed_ratio * (on_x/np.sin(on_y))) + ((1 - completed_ratio) * on_x/np.tan(on_y)) + on_y = (completed_ratio * (on_y/np.sin(on_x))) + ((1 - completed_ratio) * on_y/np.tan(on_x)) + on_x = next_x + if on_x**2 + on_y**2 > escape: + break + image[pix_y][pix_x] = i + bar() +else: + exit() + + +exit(1) + +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)) + +ax.imshow(image, norm="log", aspect="auto", cmap=cmap) +fig.savefig("linear_transform_sin_tan_arnolds_tongue_hotspot.png") +plt.show() diff --git a/field_tests/field.py b/field_tests/field.py new file mode 100755 index 0000000..af2abbf --- /dev/null +++ b/field_tests/field.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python3 +import numpy as np +import matplotlib.pyplot as plt +from alive_progress import alive_bar + +img_res_x = 250 +img_res_y = 250 +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 + +xmin = (-periods * np.pi) + (square_x * np.pi) +xmax = (periods * np.pi) + (square_x * np.pi) +ymin = (-periods * np.pi) + (square_y * np.pi) +ymax = (periods * np.pi) + (square_y * np.pi) + +#xmin = -10 +#xmax = 10 +#ymin = -10 +#ymax = 10 + +escape = 10000 +iterations = 255*3 +c_x = 2 * np.pi +c_y = 2 * np.pi + + +grid = np.meshgrid(np.linspace(ymin, ymax, 200), np.linspace(xmin, xmax, 200)) +#image = np.meshgrid(np.linspace(ymin, ymax, img_res_y), np.linspace(xmin, xmax, img_res_x)) +image = np.zeros([img_res_y, img_res_x], dtype=np.double) + + +class point_charge(): + def __init__(self, x, y, c, mod): + self.x = x + self.y = y + self.c = c + self.mod = mod + def get_field(self, to_x, to_y): + if(self.mod): + to_x = (to_x % self.mod) + to_y = (to_y % self.mod) + return np.array([ + ((self.c * (self.x - to_x)) / ((self.x - to_x)**2 + (self.y - to_y)**2)**1.5), + ((self.c * (self.y - to_y)) / ((self.x - to_x)**2 + (self.y - to_y)**2)**1.5)]) + +#will remove all the point charge code if it turns out to be good enough to be impliemnted into openCL +#point_charges = [point_charge(-5, -5, 100), point_charge(-5, 5, -100), point_charge(5, 0, 100)] +#point_charges = [point_charge(-1,-1, 100, 10), point_charge(1,1,-100, 0)] +point_charges = [] + + +#plt.ion() +ax = plt.gca() +fig = plt.gcf() +#ax.set_autoscale_on(False) +#ax.set_xlim([xmin, xmax]) +#ax.set_ylim([ymin, ymax]) + +vector_arrows = None + +def show_field(): + global vector_arrows + grid_f = np.zeros_like(grid) + for p in point_charges: + grid_f += p.get_field(grid[0], grid[1]) + #plt.streamplot(grid[0], grid[1], grid_f[0], grid_f[1], density=5) + vector_arrows = plt.quiver(grid[0], grid[1], grid_f[0], grid_f[1]) + plt.show(block=False) + plt.pause(.1) + + +#show_field() + +timestep = .1 +def test_sim(): + particle_grid = np.meshgrid(np.linspace(ymin, ymax, 100), np.linspace(xmin, xmax, 100)) + pos = particle_grid + acceleration = np.zeros_like(particle_grid) + velocity = np.zeros_like(particle_grid) + velocity = [np.ones_like(particle_grid[0]) * 1, np.ones_like(particle_grid[0]) * .5] + mass = 10 + charge = 1 + particle_plot = ax.plot(velocity[0], velocity[1], 'bo', animated=True) + #velocity += .1 + + background = fig.canvas.copy_from_bbox(ax.bbox) + ax.draw_artist(vector_arrows) + fig.canvas.blit(fig.bbox) + + while True: + fig.canvas.restore_region(background) + field = np.zeros_like(particle_grid) + # TODO can make this quicker by skipping initilization + for p in point_charges: + field += p.get_field(pos[0], pos[1]) + acceleration = ((charge * field) / mass) * timestep + #print(acceleration) + velocity += acceleration * timestep + pos += velocity * timestep + + fig.canvas.restore_region(background) + particle_plot[0].set_data(pos[0],pos[1]) + ax.draw_artist(particle_plot[0]) + fig.canvas.blit(fig.bbox) + fig.canvas.flush_events() + plt.pause(1/60) + + #fig.canvas.draw_idle() +#test_sim() + +#exit(1) + + +max_timesteps = 10 + +def get_fractal_iter(img): + next_x = img[1][y][x] / np.sin(img[0][y][x]) + img[0][y][x] = img[0][y][x] / np.sin(img[1][y][x]) + img[1][y][x] = next_x + if (np.square(img[0][y][x]) + np.square(img[1][y][x])) >= escape: + z[y][x] = i + + +#meshgrid makes things slower as we can't test individual points for breaking to infinity; +#however, I will fix that later. +cmap = plt.cm.viridis +cmap.set_bad((0,0,0)) +cmap.set_over((0,0,0)) +cmap.set_under((0,0,0)) + +with alive_bar(img_res_y, bar = 'filling', spinner = 'waves') as bar: + for pix_y, y in enumerate(np.linspace(ymin, ymax, img_res_y)): + for pix_x, x in enumerate(np.linspace(xmin, xmax, img_res_x)): + on_x = x + on_y = y + for i in range(iterations): + completed_ratio = (((pix_x * pix_y * 1)) / total_pixels) + next_x = on_x/np.sin(on_y) + on_y = on_y/np.sin(on_x) + on_x = next_x + + + # do physics here - we could just use vectors but i keep rewriting things + timesteps = max_ + acceleration = np.array([0, 0], dtype=np.double) + velocity = np.array([on_x, on_y], dtype=np.double) # maybe multiply by stuff + pos = np.array([0,0], dtype=np.double) + for t in range(timesteps): + for p in point_charges: + acceleration += p.get_field(on_x, on_y) + velocity += acceleration + pos += velocity * (timesteps) + on_x += pos[0] + on_y += pos[1] + + if on_x**2 + on_y**2 > escape: + image[pix_x][pix_y] = i + break + bar() + +ax.imshow(image, norm="log", aspect="auto", cmap=cmap) +plt.show() + + +# yeah, I shouldn't have switched to a meshgrid, oh well +#z = np.empty_like(image[0]) +exit(0) +with alive_bar(img_res_x, bar = 'filling', spinner = 'waves') as bar: + for y in range(img_res_y): + for x in range(img_res_x): + for i in range(iterations): + if image[0][y][x] == np.NAN: + continue + next_x = image[1][y][x] / np.sin(image[0][y][x]) + image[0][y][x] = image[0][y][x] / np.sin(image[1][y][x]) + image[1][y][x] = next_x + if (np.square(image[0][y][x]) + np.square(image[1][y][x])) >= escape: + z[y][x] = i + image[0][y][x] = np.NAN + image[1][y][x] = np.NAN + break +# #do physics here + + + + bar() +#image = np.clip(image, -escape, escape) + diff --git a/field_tests/gpu_migration.py b/field_tests/gpu_migration.py new file mode 100755 index 0000000..718fe7d --- /dev/null +++ b/field_tests/gpu_migration.py @@ -0,0 +1,137 @@ +#!/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() + + diff --git a/field_tests/kernel.c b/field_tests/kernel.c new file mode 100644 index 0000000..08b9137 --- /dev/null +++ b/field_tests/kernel.c @@ -0,0 +1,26 @@ +__kernel void basic_test() { + printf("this cores ID: (%lu, %lu)\n", get_global_id(0), get_global_id(1)); +} + +__kernel void render_frame(__global unsigned int *frame_output, + double x_step, double y_step, + double x_start, double y_start, + unsigned int iterations, unsigned int escape, double ratio) { + unsigned int result; + double on_x = (get_global_id(0) * x_step) + x_start; + double on_y = (get_global_id(1) * y_step) + y_start; + double next_x; + unsigned int iter; + + orig_x = on_x; + orig_y = on_y; + for(iter = 0; iter < iterations; iter++) { + if(orig_x == 123 && orig_y == 5) printf("%d, %d\n", orig_x,orig_y); + next_x = on_x / sin(on_y); + on_y = on_y / sin(on_x); + on_x = next_x; + if((pow(on_x, 2) + pow(on_y, 2)) >= escape) break; + } + + frame_output[(get_global_id(1) * get_global_size(1)) + get_global_id(0)] = iter; +} diff --git a/field_tests/makefile b/field_tests/makefile new file mode 100644 index 0000000..539deb1 --- /dev/null +++ b/field_tests/makefile @@ -0,0 +1,4 @@ +make: + gcc -Wall -fpic -c field.c + gcc -Wall -shared -o field.so field.o + python3 field.py |