def scene():
color = [ 1.0, 1.0, 1.0, 1.0 ]
#color = [ 1.0, 0.0, 0.0, 1.0 ]
glMaterialfv(GL_FRONT, GL_AMBIENT_AND_DIFFUSE, color)
#glEnable(GL_ALPHA_TEST)
glEnable(GL_TEXTURE_2D)
glEnable(GL_TEXTURE_GEN_S)
glEnable(GL_TEXTURE_GEN_T)
glEnable(GL_TEXTURE_GEN_R)
glEnable(GL_TEXTURE_GEN_Q)
glPushMatrix()
glTranslated(-1.2, 0.0, 0.0)
box(1.0, 1.0, 1.0)
glPopMatrix()
glPushMatrix()
glTranslated(1.2, 0.0, 0.0)
box(1.0, 1.0, 1.0)
glPopMatrix()
glDisable(GL_TEXTURE_GEN_S)
glDisable(GL_TEXTURE_GEN_T)
glDisable(GL_TEXTURE_GEN_R)
glDisable(GL_TEXTURE_GEN_Q)
glDisable(GL_TEXTURE_2D)
def scene():
color = [ 1.0, 1.0, 1.0, 1.0 ]
#color = [ 1.0, 0.0, 0.0, 1.0 ]
glMaterialfv(GL_FRONT, GL_AMBIENT_AND_DIFFUSE, color)
glEnable(GL_ALPHA_TEST)
glEnable(GL_TEXTURE_2D)
box(1.0, 1.0, 1.0)
glDisable(GL_TEXTURE_2D)
glDisable(GL_ALPHA_TEST)
def main():
pygame.init()
screen = display.set_mode((640, 480))
display.set_caption('shard gui')
screen.fill(colours['white'])
pygame.display.update()
t = toolbar(things = [box('label.png'), box('label.png')])
c = cancel('label.png')
cl = clear('label.png')
f = forward('label.png')
b = back('label.png')
t.add([c, cl, b, f])
g = grid(screen, 0, t.height, screen.get_width(), screen.get_height()-t.height)
con = container(t, g, screen)
done = False
while not done:
rs = con.draw(screen)
display.update(rs)
events = pygame.event.get()
for e in events:
if(e.type == QUIT):
done = True
break
elif(e.type == KEYDOWN):
if(e.key == K_ESCAPE):
done = True
break
else:
con.handleEvent(e)
return
def report_error(message, filename, line=None, src='', traceback=None, prefix=u'ERROR'):
"""
Helper for reporting error to logging module.
Inputs:
message[str]: The message to report, ERROR is automatically appended
page[pages.Page]: The page that created the error
info[Information]: The lexer information object
traceback: The traceback (should be a string from traceback.format_exc())
"""
title = '{}: {}'.format(prefix, message)
if src:
src = mooseutils.colorText(box(src, line=line, width=100), 'LIGHT_CYAN')
if line is not None:
filename = mooseutils.colorText('{}:{}\n'.format(filename, line), 'RESET')
else:
filename = mooseutils.colorText('{}\n'.format(filename), 'RESET')
trace = u'\n' + mooseutils.colorText(traceback, 'GREY') if traceback else ''
return u'\n{}\n{}{}{}\n'.format(title, filename, src, trace)
def calc_box_ema_spectra_mse(Neff, Nbox, Nwindow):
"Mean-Squared Error\
1) generates h_ema and h_box given the input parameters;\
2) takes the amplitude of the FFT for each response\
3) computes the cumulative sum of each gain spectrum\
4) computes and retuns the MSE of the two cumulative gain spectra"
h_box = box(Nbox, Nwindow)
h_ema = ema(Neff, Nwindow)
h_box_fft = np.fft.fft(h_box)
h_ema_fft = np.fft.fft(h_ema)
h_box_fft_abs = np.absolute(h_box_fft)
h_ema_fft_abs = np.absolute(h_ema_fft)
h_box_fft_abs_sum = np.cumsum(h_box_fft_abs)
h_ema_fft_abs_sum = np.cumsum(h_ema_fft_abs)
mse = np.sum(np.square(h_box_fft_abs_sum - h_ema_fft_abs_sum)) / Nwindow
return mse
S = cv.getTrackbarPos('S', 'thresh')
V = cv.getTrackbarPos('V', 'thresh')
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
thresh = cv.inRange(hsv, (h, s, v), (H, S, V))
#cv.imshow('thresh', thresh)
show(thresh)
whites = cv.inRange(hsv, (0, 0, v), (H, S, V))
show(whites ^ thresh)
order = sort.shorted(img, thresh)
pt1 = order[0][2]
print('point 1:', pt1)
mid_box = box.box(img)
for i in range(1, len(order)):
pt2 = order[i][2]
mid = ((pt2[0] + pt1[0]) / 2, (pt2[1] + pt1[1]) / 2)
angle = math.atan2(pt2[1] - pt1[1], pt2[0] - pt1[0])
dist = []
for pts in mid_box:
dist.append(math.hypot(mid[0] - pts[0], mid[1] - pts[1]))
min_dist = min(dist)
j = dist.index(min_dist)
#april_ang, april_center = april.april(img)
#theta = angle - april_ang
else:
# show index of accounts
if instanceName == '' and numberOfAccounts == 1:
count = 1
max_count = int(addon.getSetting(PLUGIN_NAME+'_numaccounts'))
loop = True
while loop:
instanceName = PLUGIN_NAME+str(count)
try:
username = addon.getSetting(instanceName+'_username')
if username != '':
#let's log in
oc = box.box(PLUGIN_URL,addon,instanceName, user_agent)
loop = False
except:
break
if count == max_count:
break
count = count + 1
# no accounts defined
elif numberOfAccounts == 0:
#legacy account conversion
try:
username = addon.getSetting('username')
def ImageDetect(pimg, box_list):
img = pimg[:]
(thresh, img_bin) = cv2.threshold(
img, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Thresholding the image
img_bin = 255 - img_bin # Invert the Image_bin
#cv2.imwrite("Image_bin.jpg",img_bin)
# Defining a kernel length
kernel_length = np.array(img).shape[1] // 40
# A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,
(1, kernel_length))
# A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))
# A kernel of (3 X 3) ones.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# Morphological operation to detect verticle lines from an image
img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)
verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)
#cv2.imwrite("verticle_lines.jpg",verticle_lines_img)
# Morphological operation to detect horizontal lines from an image
img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)
horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)
#cv2.imwrite("horizontal_lines.jpg",horizontal_lines_img)
# Weighting parameters, this will decide the quantity of an image to be added to make a new image.
alpha = 0.5
beta = 1.0 - alpha
# This function helps to add two image with specific weight parameter to get a third image as summation of two image.
img_final_bin = cv2.addWeighted(verticle_lines_img, alpha,
horizontal_lines_img, beta, 0.0)
img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)
(thresh,
img_final_bin) = cv2.threshold(img_final_bin, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# For Debugging
# Enable this line to see verticle and horizontal lines in the image which is used to find boxes
#cv2.imwrite("img_final_bin.jpg",img_final_bin)
contours, hierarchy = cv2.findContours(img_final_bin, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# Sort all the contours by top to bottom.
#(contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")
_, _, page_width, _ = cv2.boundingRect(contours[0])
for c in range(1, len(contours), 2):
# Returns the location and width,height for every contour
x, y, w, h = cv2.boundingRect(contours[c])
if (w > 20 and h > 20):
box_Object = box.box(x, y, x + w, y + h, "Image", None, -1)
box_list.append(box_Object)
return (box_list, page_width)
else:
# show index of accounts
if instanceName == '' and numberOfAccounts == 1:
count = 1
max_count = int(addon.getSetting(PLUGIN_NAME + '_numaccounts'))
loop = True
while loop:
instanceName = PLUGIN_NAME + str(count)
try:
username = addon.getSetting(instanceName + '_username')
if username != '':
#let's log in
oc = box.box(PLUGIN_URL, addon, instanceName,
user_agent)
loop = False
except:
break
if count == max_count:
break
count = count + 1
# no accounts defined
elif numberOfAccounts == 0:
#legacy account conversion
try:
username = addon.getSetting('username')
import socket
import re
import logging
from box import Box as box
from colorama import Back, Fore, init, Style
from aiohttp import client_exceptions as clientExcps
init(autoreset=True)
colorSchemes = {
'SUCCESS': f"{Back.GREEN}{Fore.BLACK}{Style.NORMAL}",
'FAILURE': f"{Back.RED}{Fore.WHITE}{Style.BRIGHT}",
'WARNING': f"{Back.YELLOW}{Fore.BLACK}{Style.BRIGHT}",
'RESET': f"{Style.RESET_ALL}"
}
colorSchemes = box(colorSchemes)
logging.basicConfig(format=f'{colorSchemes.FAILURE}[%(levelname) 5s/%(asctime)s] %(name)s: %(message)s', level=logging.ERROR)
bot = discord.Client()
baseUrl = f"https://api.telegram.org/bot{config.TELEGRAM_BOT_TOKEN}"
def replaceMentions(mentions, msg, channel):
if channel:
for ch in mentions:
# msg = msg.replace(str(f"#{ch.id}"), '')
msg = re.sub(f"<#{ch.id}>", '', msg)
msg = re.sub(f"<{ch.id}>", '', msg)
def test_negative_input(self):
self.assertEqual(box(self.data[4][0], self.data[4][1]), None)
try:
z_cord = sum(z_cutoff) / len(z_cutoff)
except ZeroDivisionError:
continue
com.append([x_cord, y_cord, z_cord])
out1 = open(os.path.join(curdir, new + 'out' + '_com ' + '.txt'),
'w')
out1.write(str(com))
out1.close()
one_comp = box.box([x_cord, y_cord, z_cord])
out2 = open(os.path.join(curdir, new + 'out' + '_box ' + '.txt'),
'w')
out2.write(str(one_comp))
out2.close()
x_max = max(one_comp[0])
x_min = min(one_comp[0])
y_max = max(one_comp[1])
y_min = min(one_comp[1])
z_max = max(one_comp[2])
z_min = min(one_comp[2])
ranges = [x_max, x_min, y_max, y_min, z_max, z_min]
out3 = open(os.path.join(curdir, new + 'out' + '_range ' + '.txt'),
'w')
merge = open('%s/plr.txt' % srce).readlines()
final = []
for info in merge[:]:
final_lists = list(info.split()[1:])
final.append(final_lists)
for resi in final[:]:
resi = '\t'.join([str(x) for x in resi])
plr_list.write("%s\n" % resi)
plr_list.close()
ax.scatter([x for x in xcors], [y for y in ycors],
[z for z in zcors],
alpha=0.2,
color="r")
comname = range_paths[i].split('/')[-1].split('_')[0] + "_com .txt"
compath = srce + '/' + comname
comfiles = open(compath).read().strip('[').strip(']').split(',')
points = box.box(
[float(comfiles[0]),
float(comfiles[1]),
float(comfiles[2])])
plt.savefig('filter.png')
plt.close("all")
from box import box
obj = box(12, 33, "RED")
print("Box1")
print("The length of the box is :", obj.length)
print("The width of the box is :", obj.width)
print("The colour of the box is :", obj.colour)
print("The Area of the box is:", obj.area)
print("The Parameter of the box is:", obj.parameter)
obj2 = box(10, 2, "RED")
print("\n Box2")
print("The length of the box is :", obj2.length)
print("The width of the box is :", obj2.width)
print("The colour of the box is :", obj2.colour)
print("The Area of the box is:", obj2.area)
print("The Parameter of the box is:", obj2.parameter)
def generate_boxes(self, row, column):
boxes = []
for row in range(row):
for col in range(column):
boxes.append(box(row, col))
return boxes
if __name__ == "__main__":
N = 10000
L = 10 | units.parsec
rho = 1.14 * 1 | units.amu / units.cm**3
u = (5.e11 | units.erg / units.g).in_(units.cm**2 / units.s**2) # =5000K
tend = 1. | units.Myr
dt = 10000 | units.yr
print(u**0.5).in_(units.kms)
print((L / u**0.5) / dt)
particles = box(N, L, rho, u)
UnitLength = L
UnitMass = particles.mass.sum()
UnitVelocity = units.kms
convert = generic_unit_converter.ConvertBetweenGenericAndSiUnits(
UnitLength, UnitMass, UnitVelocity)
sph = Gadget2(convert, mode='periodic_nogravity') #,redirection='none')
sph.parameters.periodic_box_size = L
sph.gas_particles.add_particles(particles)
i = 0
t = 0. | units.Myr
def test_raises_exception_with_bad_arguments(self):
with self.assertRaises(Exception):
box(length=0, width=0, height=0)
def test_creates_box_with_valid_arguments(self):
result = box(1, 1, 1)
self.assertIsNotNone(result['model'])
self.assertIsNotNone(result['computed'])
self.assertEqual(result['computed']['volume'], 1)
axarr[0, 0].set_ylim(0, 1.1)
axarr[0, 0].set_title("Ideal delay")
axarr[0, 0].plot(impulse_response_fde)
axarr[0, 0].plot(impulse_response_direct, 'o', markerfacecolor='none')
# b) Box
Nbox = 32
lag = 1
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_box_filter(impulse, Nbox)
impulse_response_fde = candidate[lag:]
impulse_response_direct = box(Nbox, Nwindow)
axarr[0, 1].set_title("Box")
axarr[0, 1].plot(impulse_response_fde)
axarr[0, 1].plot(impulse_response_direct, 'o', markerfacecolor='none')
# c) Ema
Neff = 32
lag = 1
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_ema_filter(impulse, Neff)
impulse_response_fde = candidate[lag:]
if item.startswith('ATOM'):
item = item.replace('',"")
item = item.split()
if item[4] == chain_name:
#print item
x.append(float(item[6]))
y.append(float(item[7]))
z.append(float(item[8]))
for item in z[:]:
if -15.0<= item and item <=15.0:
z_select.append(item)
x_cord = sum(x)/len(x)
y_cord = sum(y)/len(y)
z_cord = sum(z_select)/len(z_select)
one_comp = box.box((x_cord,y_cord,z_cord))
out1 = open(names + '_box ' + '.txt','w')
out1.write(str(one_comp))
out1.close()
##########################3d-plot###################################
centre = ax.scatter([x_cord],[y_cord],[z_cord],color="g",s=100)
points = box.box((x_cord,y_cord,z_cord))
scatterfile = ax.scatter(points[0], points[1], points[2],color = "r")
plt.show()
invokepores.invoke()
import numpy as np import matplotlib.pyplot as plt from ema import ema from box import box from comb import comb Nwindow = 1024 # (a) Ema and box Neff = 32 h_ema = ema(Neff, Nwindow) Nbox = Neff / (1-np.exp(-1)) h_box = box(Nbox, Nwindow) # (b) Comb with period = 256 Nperiod = 256 h_comb = comb(Nperiod, Nwindow) # (c-f) Convolution candidate1 = np.convolve(h_comb, h_ema) candidate2 = np.convolve(h_comb, h_box) h_ema_replicated = candidate1[:Nwindow] h_box_replicated = candidate2[:Nwindow] H_ema = np.absolute(np.fft.fft(h_ema)) H_box = np.absolute(np.fft.fft(h_box)) H_ema_replicated = np.absolute(np.fft.fft(h_ema_replicated)) / Nperiod
# Image
aspect_ratio = 16 / 9
width = 400
height = int(width / aspect_ratio)
samples_per_pixel = None
# World
background = glm.vec3(1, 1, 1) # background color
diffuse_material = lambertian(glm.vec3(.5, .2, .1))
world = cornell_box()
world.add(sphere(glm.vec3(-1.0, -.5, -1.5), .5, diffuse_material))
world.add(box(glm.vec3(.5, -.9, -2.5), glm.vec3(1, -.4, -2), diffuse_material))
metallic_material = metal(.01, glm.vec3(0.5, 0.5, 0.2))
world.add(sphere(glm.vec3(.0, -.5, -1.5), .5, metallic_material))
# Camera
cam = camera()
output_file = sys.argv[1] if len(sys.argv) > 1 else 'problem1' + '.ppm'
samples_per_pixel = int(sys.argv[2].strip()) if len(sys.argv) > 2 else 1
max_depth = int(sys.argv[3].strip()) if len(sys.argv) > 3 else 100
with open(output_file, 'w') as f:
f.write('P3\n%d %d\n255\n' % (width, height))
for j in tqdm(range(height - 1, -1, -1), desc='loading:'):
for i in range(width):
color = glm.vec3(0, 0, 0)
# init sender for pitch and volume
sender = com.ThereminCommunication()
sender.connect()
# get the camera
cap = cv2.VideoCapture(0)
fps = fpsCounter.FpsCounter()
# determine the image resolution
width = int(cap.get(3))
height = int(cap.get(4))
print("This Video Resulation is " + str(width) + " by " + str(height))
# init box with image resolution
boxer = box.box(width, height)
while (True):
# Capture new frame
fps.newFrame()
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# get individual sections for pitch and volume
volSection, pitchSection = boxer.getVolPitchSection(frame)
# find colored points in each sections
pointsVol = cf.findColor(volSection, 'red', False)
pointsPitch = cf.findColor(pitchSection, 'red', False)
# transform coordinates back to the full frame
global_vars.batch = pyglet.graphics.Batch() global_vars.background = pyglet.graphics.OrderedGroup(0) global_vars.foreground = pyglet.graphics.OrderedGroup(1) Objects = [] Player = player(400, 250, 'playerr1.png', Objects) Platform1 = platform(80, 280, 'platform.png', 30, 260, Objects) Platform2 = platform(110, 260, 'platform.png', 60, 260, Objects) Platform3 = platform(140, 240, 'platform.png', 90, 260, Objects) Platform4 = platform(170, 220, 'platform.png', 120, 260, Objects) Platform5 = platform(200, 200, 'platform.png', 150, 360, Objects) Box = box(450, 400, 'box.png', Objects) Box = box(470, 400, 'box.png', Objects) Box = box(490, 400, 'box.png', Objects) Box = box(510, 400, 'box.png', Objects) Box = box(530, 400, 'box.png', Objects) Box = box(550, 400, 'box.png', Objects) Box = box(570, 400, 'box.png', Objects) Box = box(590, 400, 'box.png', Objects) Box = box(450, 440, 'box.png', Objects) Box = box(470, 440, 'box.png', Objects) Box = box(490, 440, 'box.png', Objects) Box = box(510, 440, 'box.png', Objects) Box = box(530, 440, 'box.png', Objects) Box = box(550, 440, 'box.png', Objects) Box = box(570, 440, 'box.png', Objects) Box = box(590, 440, 'box.png', Objects)
axarr[0, 0].set_ylim(0, 1.1)
axarr[0, 0].set_title("Ideal delay")
axarr[0, 0].plot(impulse_response_fde)
axarr[0, 0].plot(impulse_response_direct, 'o', markerfacecolor='none')
# b) Box
Nbox = 32
lag = 1
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_box_filter(impulse, Nbox)
impulse_response_fde = candidate[lag:]
impulse_response_direct = box(Nbox, Nwindow)
axarr[0, 1].set_title("Box")
axarr[0, 1].plot(impulse_response_fde)
axarr[0, 1].plot(impulse_response_direct, 'o',markerfacecolor='none')
# c) Ema
Neff = 32
lag = 1
impulse = np.zeros(Nwindow)
impulse[lag] = 1
candidate = apply_ema_filter(impulse, Neff)
impulse_response_fde = candidate[lag:]
def test_zero_box(self):
self.assertEqual(box(self.data[2][0], self.data[2][1]), None)
def test_one_by_one(self):
self.assertEqual(box(self.data[3][0], self.data[3][1]), None)
def __init__(self, message, *args, **kwargs):
err = kwargs.pop('error', None)
msg = message.format(*args)
if err is not None:
msg += u'\n\n{}'.format(box(err))
Exception.__init__(self, msg.encode('utf-8'))
