def plotring(image, position_matrix, angles, r, max, step, m):
im = image[position_matrix == r]
imang = angles[position_matrix == r]
imang, im = zip(*sorted(zip(imang, im)))
mean = Calculate.statistics1d(image, position_matrix=position_matrix,
max=max, step=step, statistic='mean')
med = Calculate.statistics1d(image, position_matrix=position_matrix,
max=max, step=step, statistic='median')
std = Calculate.statistics1d(image, position_matrix=position_matrix,
max=max, step=step, statistic=np.std)
# mean = np.mean(im)
# med = np.median(im)
# std = np.std(im)
print 'std: ', std
print 'std/mean%: ', std / mean * 100
print 'abs(mean-median)/mean%: ', np.abs(mean - med) / mean * 100
thresh = m * std
size = np.shape(im)[0]
print 'size: ', size
meana = np.ones(size) * mean
meda = np.ones(size) * med
upperT = np.ones(size) * (mean + thresh)
lowerT = np.ones(size) * (mean - thresh)
t = np.arange(0, 2 * np.pi, 2 * np.pi / size)
plt.ioff()
plt.plot(t, im, 'r', t, meda, 'b', t, meana, 'g', t, upperT, 'o', t,
lowerT, 'o')
plt.show()
def coma():
name = 'coma'
if name not in Materials.aber:
Materials.aber[name] = {}
gauss = Calculate.first_para()[-1]['L']
for k in Materials.K[name]:
if str(k[0]) + '_' + str(k[1]) not in Materials.aber[name]:
Materials.K2 = k[1]
Materials.K1 = 0
light = Calculate.meri_off()
y = (light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
Materials.K1 = -k[0]
light = Calculate.meri_off()
y_b = (light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
Materials.K1 = k[0]
light = Calculate.meri_off()
y_a = (light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
Materials.aber[name][str(k[0]) + '_' +
str(k[1])] = y - (y_a + y_b) / 2
def Draw(para, filename, draw_type):
data = Calculate.Alogrithim(para[0], para[1], para[2], para[3], para[4], para[5], para[6], para[7])
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot(111, projection = "3d")
#Determine the edges of plots
edges = []
for i in range(3):
ax_min = np.min(data[i])
ax_max = np.max(data[i])
gap = (ax_max - ax_min) / 20
edges.append([ax_min - gap, ax_max + gap])
#Lable some important points
ax.scatter(data[0,0], data[1,0], data[2,0], lw = 0.8, alpha = 0.5, color = 'b', label = "Start Point")
ax.scatter(data[0,-1], data[1,-1], data[2,-1], lw = 0.8, alpha = 0.5, color = 'r', label = "Last Point")
ax.scatter(0, 0, 0, lw = 0.8, color = 'black', alpha = 0.5, label = "Origin")
#for R < 1, the only fixed point is the origin
if para[4] > 1:
p = Calculate.fix_point(para[4], para[5], para[6])
ax.scatter(p[:,0],p[:,1],p[:,2], color = "brown", alpha = 0.5, lw = 0.8, label = "Fixed Points")
ax.legend(loc = "best")
# Setting the axes properties
ax.set_xlim3d(edges[0])
ax.set_xlabel('X')
ax.set_ylim3d(edges[1])
ax.set_ylabel('Y')
ax.set_zlim3d(edges[2])
ax.set_zlabel('Z')
ax.legend(loc = "upper left")
if draw_type == "anima":
line, = ax.plot(data[0,0:2], data[1,0:2], data[2,0:2], lw = 0.6, color = 'g')
time = ax.text(edges[0][0], edges[1][1], edges[2][1], "T = 0")
# Creating the Animation object
line_ani = animation.FuncAnimation(fig, update_lines_time, frames = 1000, fargs=(data, line, time, para[3]), blit=False)
#Store the video
ffmpegpath = os.path.abspath("../../ffmpeg/ffmpeg-20200422-2e38c63-win64-static/ffmpeg-20200422-2e38c63-win64-static/bin/ffmpeg.exe")
matplotlib.rcParams["animation.ffmpeg_path"] = ffmpegpath
mywriter = animation.FFMpegWriter(fps = 100)
line_ani.save(filename, writer=mywriter)
if draw_type == "static":
#Draw the static final plot
ax.plot(data[0], data[1], data[2], lw = 0.6, color = 'g')
ax.text(edges[0][0], edges[1][1], edges[2][1], "[R,P,B] = [{0},{1},{2:.2f}]\n[X,Y,Z] = [{3},{4},{5}]".format(para[4],para[5],para[6],para[0],para[1],para[2]))
plt.savefig(filename)
else:
return "Wrong Draw Type!"
def spherical():
name = 'spherical'
if name + Materials.extend not in Materials.aber:
Materials.aber[name + Materials.extend] = {}
gauss = Calculate.first_para()[-1]['L']
for k in Materials.K[name]:
if str(k[0]) + '_' + str(k[1]) not in Materials.aber[name +
Materials.extend]:
Materials.K1 = k[0]
Materials.K2 = k[1]
Materials.aber[name + Materials.extend][str(k[0]) + '_' + str(k[1])] = \
Calculate.meri_on()[-1]['L'] - gauss
def off_axis():
lights = Calculate.meri_off()
s = Materials.lens[0]['d'] / math.cos(math.radians(lights[0]['U']))
t = s
h = []
for i in range(0, len(Materials.lens)):
h.append(Materials.lens[i]['r'] *
math.sin(math.radians(lights[i]['U'] + lights[i]['I'])))
for i in range(0, len(Materials.lens)):
if i == 0:
n = 1
else:
n = Materials.lens[i - 1]['n']
spie = func_spie(s, n, Materials.lens[i]['n'], Materials.lens[i]['r'],
lights[i]['I'], lights[i]['Ipie'])
tpie = func_tpie(t, n, Materials.lens[i]['n'], Materials.lens[i]['r'],
lights[i]['I'], lights[i]['Ipie'])
if i == len(Materials.lens) - 1:
s = spie
t = tpie
else:
# 过渡公式
light = lights[i]
D = func_D(h[i], h[i + 1], light['L'], light['U'],
Materials.lens[i]['r'], n, Materials.lens[i]['n'])
t = tpie - D
s = spie - D
return [s, t]
def main():
max_num = 10
problem = int(input('请输入需要的题目数量：'))
i = 1
correct = 0
wrong = 0
print("本次测试共{}题，满分100分".format(problem))
while i < problem + 1:
ari = Ari_Expression(max_num)
inf = infix_to_suffix()
real_res = Calculate.getResult(
inf.str_to_fraction(inf.to_suffix_expression(ari.str)))
if real_res >= 0:
print("------------------------------")
real_res = ari.str_num(real_res)
print(str(i) + '. ' + ari.str, end='')
res = input()
if res == real_res:
correct += 1
print("回答正确！")
else:
wrong += 1
print("回答错误！正确答案为：{}".format(real_res))
i += 1
print('\n得分为：' + str(correct / problem * 100))
def Main():
if (len(sys.argv) < 2):
sys.exit("No input file path provided.")
Path = sys.argv[1]
ExportPath = None
if (len(sys.argv) > 2):
ExportPath = sys.argv[2]
os.makedirs(ExportPath, exist_ok=True)
if (not os.path.exists(ExportPath)):
ExportPath = None
JSONPath = os.path.splitext(Path)[0] + ".txt"
if (not os.path.exists(Path)):
sys.exit("File does not exist.")
if (not os.path.exists(JSONPath)):
sys.exit("JSON file does not exist.")
JSON = ReadJSON(JSONPath, Path)
JSON = IdentifyRunway.IdentifyRunway(JSON, ExportPath)
Display.Display(JSON, ExportPath)
Correct.Correct(JSON, ExportPath)
JSON = Calculate.find_position(JSON)
#Export JSON:
if (ExportPath is not None):
File = open(ExportPath + "/11 JSON.txt", "w")
json.dump(JSON, File, indent=4)
File.close()
print(json.dumps(JSON, indent=4))
def _Calculate_depth_fired(self):
# take the values from the calculated spectral density
nu = np.array(self.nu)
spectrum_y = np.array(self.spectrum_y)
spectrum_yy = np.array(self.spectrum_yy)
nu_calc = nu[np.logical_and(nu >= self.spec_min, nu <= self.spec_max)]
spectrum_y_calc = []
spectrum_yy_calc = []
i = 0
for i in range(0, len(nu)):
if nu[i] in nu_calc:
spectrum_y_calc.append(spectrum_y[i])
if spectrum_yy != []:
spectrum_yy_calc.append(spectrum_yy[i])
# performing lorentzian fits to the spectral data
spectrum_y_calc = np.array(spectrum_y_calc)
self.lorentz_fit_parameters_y = Fit.Fit(nu_calc, spectrum_y_calc,
Fit.Lorentzian,
Fit.LorentzianEstimator)
self.density_plot_fit_y = Fit.Lorentzian(
*self.lorentz_fit_parameters_y)(nu)
if spectrum_yy != []:
spectrum_yy_calc = np.array(spectrum_yy_calc)
self.lorentz_fit_parameters_yy = Fit.Fit(nu_calc, spectrum_yy_calc,
Fit.Lorentzian,
Fit.LorentzianEstimator)
self.density_plot_fit_yy = Fit.Lorentzian(
*self.lorentz_fit_parameters_yy)(nu)
# update the spectral density plots with the fits in it
self._plot_density_changed(nu, spectrum_y, spectrum_yy)
# calculate the area from the lorentz fit
gamma_e = self.gamma_e
rho = self.proton_density * 1e27 #to get 1/m^3
Area1 = self.lorentz_fit_parameters_y[2] #in MHz^2
print Area1
#Area1 = 0.0721
B1 = abs(2 * Area1 / (gamma_e * gamma_e))**(0.5) #in Tesla
self.depth1 = Calculate.calculate_depth_simple(B1, rho)
print B1
if spectrum_yy != []:
Area2 = self.lorentz_fit_parameters_yy[2] #in MHz^2
print Area2
B2 = abs(2 * Area2 / (gamma_e * gamma_e))**(0.5) #in Tesla
self.depth2 = Calculate.calculate_depth_simple(B2, rho)
print B2
def distortion():
name = 'distortion'
if name not in Materials.aber:
Materials.aber[name] = {}
for k in Materials.K[name]:
if str(k[0]) + '_' + str(k[1]) not in Materials.aber[name]:
Materials.K1 = k[0]
Materials.K2 = k[1]
gauss = Calculate.first_para()[-1]['L']
light = Calculate.meri_off()
yp = -(light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
y = Materials.obj['r'] * k[1]
w = math.radians(Materials.obj['w']) * k[1]
y0 = -Calculate.height(y, w)
Materials.aber[name][str(k[0]) + '_' + str(k[1])] = yp - y0
def format_cvm(Region_Id):
response = Calculate.get_cvm(Region_Id)
if response['TotalCount'] != 0:
result = []
for line in response['InstanceSet']:
data = (line.get('InstanceId'), line.get('Placement')['Zone'],
line.get('InstanceName'), line.get('InstanceType'),
line.get('OsName'), line.get('PrivateIpAddresses')[0])
result.append(data)
return result
def Covid19_Data_Update():
Calculate.Forming_Covid19_SCV(Calculate.Download_Covid19_SCV())
frame_L2_frame2[
'text'] = text = "本日" + Calculate._covid19_data_calculate[0]['公表_年月日']
f3_Label_02['text'] = text = Calculate._covid19_data_calculate[0][
'陽性者数（累計）']
f3_Label_04['text'] = text = Calculate._covid19_data_calculate[0]['入院中']
f3_Label_06['text'] = text = Calculate._covid19_data_calculate[0]['軽症・中等症']
f3_Label_08['text'] = text = Calculate._covid19_data_calculate[0]['重症']
f3_Label_10['text'] = text = Calculate._covid19_data_calculate[0]['死亡']
f3_Label_12['text'] = text = Calculate._covid19_data_calculate[0]['退院']
frame_L2_frame1[
'text'] = text = "昨日" + Calculate._covid19_data_calculate[1]['公表_年月日']
f2_Label_02['text'] = text = Calculate._covid19_data_calculate[1][
'陽性者数（累計）']
f2_Label_04['text'] = text = Calculate._covid19_data_calculate[1]['入院中']
f2_Label_06['text'] = text = Calculate._covid19_data_calculate[1]['軽症・中等症']
f2_Label_08['text'] = text = Calculate._covid19_data_calculate[1]['重症']
f2_Label_10['text'] = text = Calculate._covid19_data_calculate[1]['死亡']
f2_Label_12['text'] = text = Calculate._covid19_data_calculate[1]['退院']
def main():
parser = OptionParser()
parser.add_option("-n",
action="store",
type="int",
dest="problem",
default='10',
help='题目个数')
parser.add_option(
"-r",
action="store",
type='int',
dest="max_num",
default="10",
help="题目操作数取值范围",
)
i = 1
(options, args) = parser.parse_args()
correct = []
wrong = []
p = open('Exercises.txt', 'w')
r = open('Answer.txt', 'w')
while i < options.problem + 1:
ari = Ari_Expression(options.max_num)
inf = infix_to_suffix()
try:
real_res = Calculate.getResult(
inf.str_to_fraction(inf.to_suffix_expression(ari.str)))
except ValueError:
continue
if real_res >= 0:
real_res = ari.str_num(real_res)
print(str(i) + '. ' + ari.str, end='')
p.write(str(i) + '. ' + ari.str + '\n')
r.write(str(i) + '. ' + real_res + '\n')
res = input()
if res == real_res:
correct.append(i)
else:
wrong.append(i)
i += 1
p.close()
r.close()
print('题目正确率：' + str(100 * len(correct) / options.problem) + '%')
g = open('Grade.txt', 'w')
g.write('Correct:' + str(len(correct)) + '(')
for x in correct:
g.write(str(x) + ', ')
g.write(')\n')
g.write('Wrong:' + str(len(wrong)) + '(')
for x in wrong:
g.write(str(x) + ', ')
g.write(')\n')
def plotmask(maskwoutput=None, maxr=None, xray_image=None,
position_matrix=None):
"""
Plots the mask metrics allowing for evaluation of mask quality
"""
# highper,lowper,totalper, mask, imask,StatsA=maskwoutput
highper, lowper, totalper, mask, imask = maskwoutput
# May need matplotlib help from Tom on this
plt.ioff()
plt.clf()
# plot the graph of how the mask depends on radial distance
# TODO: use Q instead of r in the final product?
plt.plot(np.arange(0, maxr + 1, 1), highper[:], '-',
np.arange(0, maxr + 1, 1), lowper[:], 'o',
np.arange(0, maxr + 1, 1), totalper[:], '^')
plt.show()
# Plot the mask itself
# plot the overlay of the mask and the image
mask = plt.imshow(mask)
plt.show()
imagemask = plt.imshow(xray_image * imask)
plt.show()
t = np.arange(0, maxr, 1)
plt.plot(t, Calculate.mean1d(xray_image,
position_matrix=position_matrix * imask,
step=1), 'b',
# t, Median1D(xray_image, max=maxr, step=1, position_matrix=position_matrix*imask), 'r',\
# t,Median1D(xray_image, max=maxr, step=1, position_matrix=position_matrix*imask)-Mean1D(xray_image, max=maxr, step=1, position_matrix=position_matrix*imask)
)
plt.show()
oldstd = np.sqrt(
Calculate.variance1d(xray_image, position_matrix=position_matrix,
max=maxr, step=1))
newstd = np.sqrt(Calculate.variance1d(xray_image,
position_matrix=position_matrix * imask,
max=maxr, step=1))
plt.plot(t, oldstd, 'b', t, newstd, 'r', t, oldstd - newstd, 'g')
plt.show()
def InvokeMultiFunction(f, values):
l = []
nf = f.copy()
num = nf.pop(0)
variables = []
for i in range(num):
variables.append(nf.pop(0))
for i in nf:
if i in variables:
l.append(str(values[variables.index(i)]))
else:
l.append(i)
return Calculate.RPNCalc(l)
def mag_chromatism():
name = 'mag_chromatism'
if name not in Materials.aber:
Materials.aber[name] = {}
for k in Materials.K[name]:
if str(k[0]) + '_' + str(k[1]) not in Materials.aber[name]:
Materials.K1 = k[0]
Materials.K2 = k[1]
gauss = Calculate.first_para()[-1]['L']
light = Calculate.meri_off()
y_d = -(light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
for item in range(0, len(Materials.lens)):
Materials.lens[item]['n'] = Materials.nf[item]
Materials.extend = '_f'
light = Calculate.meri_off()
y_f = -(light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
for item in range(0, len(Materials.lens)):
Materials.lens[item]['n'] = Materials.nc[item]
Materials.extend = '_c'
light = Calculate.meri_off()
y_c = -(light[-1]['L'] - gauss) * math.tan(
math.radians(light[-1]['U']))
Materials.aber[name][str(k[0]) + '_' + str(k[1])] = y_f - y_c
Materials.basic['d height'] = y_d
Materials.basic['F height'] = y_f
Materials.basic['C height'] = y_c
for item in range(0, len(Materials.lens)):
Materials.lens[item]['n'] = Materials.nd[item]
Materials.extend = ''
def curvature():
name = 'curvature'
if name not in Materials.aber:
Materials.aber[name + '_s'] = {}
Materials.aber[name + '_t'] = {}
Materials.aber['astigmatism'] = {}
for k in Materials.K[name]:
if str(k[0]) + '_' + str(k[1]) not in Materials.aber[name + '_s']:
Materials.K1 = k[0]
Materials.K2 = k[1]
gauss = Calculate.first_para()[-1]['L']
out = OffAxis.off_axis()
light = Calculate.meri_off()[-1]
cos_u = math.cos(math.radians(light['U']))
Materials.aber[name + '_s'][str(k[0]) + '_' +
str(k[1])] = out[0] * cos_u - gauss
Materials.aber[name + '_t'][str(k[0]) + '_' +
str(k[1])] = out[1] * cos_u - gauss
Materials.aber['astigmatism'][str(k[0]) + '_' + str(k[1])] = \
Materials.aber[name + '_t'][str(k[0]) + '_' + str(k[1])] - \
Materials.aber[name + '_s'][str(k[0]) + '_' + str(k[1])]
def main():
log()
global strings
strings = get_yml("./strings.yml")
Calculate.set_strings(strings)
updater = Updater(cfg['agecalculator']['bottoken'])
dp = updater.dispatcher
dp.add_handler(CommandHandler(
"start",
start,
pass_chat_data=True,
))
dp.add_handler(MessageHandler(Filters.text, send, pass_chat_data=True))
dp.add_handler(
CallbackQueryHandler(try_button,
pass_chat_data=True,
pass_job_queue=True))
updater.start_polling()
updater.idle()
def update_total(bot, job):
update, chat_data, job_queue = job.context
if "cur" in chat_data and chat_data[
"cur"] == 'total' and chat_data['counter'] > 0:
text = strings[chat_data["lang"]][
"total"] + ":\n" + Calculate.total_time(chat_data)
try:
update.callback_query.message.edit_text(
get_text(chat_data) + "\n\n" + text,
parse_mode=ParseMode.MARKDOWN,
reply_markup=get_result_keyboard(1, chat_data))
chat_data['counter'] = chat_data['counter'] - 1
except error.BadRequest:
pass
job_queue.run_once(update_total,
1,
context=[update, chat_data, job_queue])
def Setting(ip, port):
serverSock = socket(AF_INET, SOCK_STREAM)
print("소켓시작")
serverSock.bind((ip, port))
print("bind")
serverSock.listen(1)
print("listen")
while (1):
connectionSock, addr = serverSock.accept()
print(str(addr), '에서 접속이 확인되었습니다.')
data = connectionSock.recv(1024).decode()
data_2 = Calculate.in_data(data)
data_2 = data_2.split(",")
data_3 = Trilateration.Trilateration(data_2) # 삼변측량알고리즘 적용
return data_3 #위치좌표 X,Y반
def format_clb(Region_Id):
response = Calculate.get_clb(Region_Id)
if response['TotalCount'] != 0:
result = []
for line in response['LoadBalancerSet']:
if line.get('Forward') == 1:
Type = '应用型'
else:
Type = '传统型'
if line.get('Status') == 1:
Status = '正常运行'
else:
Status = '创建中'
data = (line.get('LoadBalancerId'), Region_Id,
line.get('LoadBalancerName'), Type, Status,
line.get('LoadBalancerVips')[0])
result.append(data)
return result
def BTNEquals(self):
#Passes the information to the calculator module
#Try method to prevent crashes
try:
self.ids.input.text = ""
a = self.ids.result.text
a = a.split()
calc = []
for x in a:
if x != " ":
#add it
calc.append(x)
else:
pass
a = Calculate.calculate(calc)
#a = Calcula.tor.SingularMethod(self.ids.result.text)
self.ids.input.text = str(a)
except:
return
def equalpress():
# Try and except statement is used
# for handling the errors like zero
# division error etc.
# Put that code inside the try block
# which may generate the error
try:
global expression
# calculate
c = Calculate.Calculate(expression)
total = str(c.process())
equation.set(total)
# reinitialze the expression variable
# by empty string
expression = ""
except:
equation.set(" error ")
expression = ""
def main():
global turn, stand, player, dealer
run = True
n = Network.Network()
player = eval(n.getPlayer())
print(type(player))
p1 = Player.Player(player)
dealer = eval(n.send('dealer'))
print(type(dealer))
p2 = Player.Player(dealer)
hit_button = Button.Button(light_grey, 50, 625, 200, 70, 'Hit')
stand_button = Button.Button(light_grey, 300, 625, 200, 70, 'Stand')
result = Calculate.Calculate(dealer, player)
while run:
pos = pygame.mouse.get_pos()
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
pygame.quit()
sys.exit()
if event.type == pygame.MOUSEBUTTONDOWN:
if hit_button.isOver(pos):
if turn % 2 == 0:
player.append(n.send('draw'))
turn += 1
if turn % 2 == 1 and not result.handcheck():
dealer.append(n.send('draw'))
turn += 1
if stand_button.isOver(pos):
stand = True
turn += 1
if stand:
n.send(result.handcal())
redrawWindow(win, p1, p2, hit_button, stand_button, result)
clock.tick(60)
def tif_to_iq(
# Calibration info
poni_file=None, dspace_file=None, detector=None,
calibration_imagefile=None, wavelength=None,
# image info
image_path=None, out_ending='',
# Correction info
solid=False, pol=True, polarization=0.95,
# old_files info
generate_mask=False, mask_file=None, initial_m=None,
# integration info
space='Q', resolution=None, average_method='median',
plot_verbose=False):
"""
Takes TIFF images to CHI files, including masking, integrating, and CHI
writing
Parameters
----------
poni_file: str
Location of poni file, a detector geometry file produced by pyFAI.
If none is given a GUI prompt will ask for the file.
dspace_file : str
Location of the calibrant d-spacing file. This is used to generate
the poni file if no poni file is loaded.
detector: str
Name of the detector. This is used to generate
the poni file if no poni file is loaded.
calibration_imagefile : str
Location of the calibration image. This is used to generate
the poni file if no poni file is loaded
wavelength: float
The wavelength of light in angstrom. This is used to generate
the poni file if no poni file is loaded.
image_path: str or list of str or tuple of str
The image(s) to be masked and integrated. If more than one image is
passed via a list or tuple, sum the images together before proceeding.
out_ending: str
The ending to append to the output file name before the extension
solid: bool
If true, apply solid angle correction as calculated by pyFAI, else do not
pol: bool
If true, apply polarization angle correction
polarization: float
The polarization of the beam used in the polarization correction
generate_mask: bool
If true, generate mask via mask writing algorithum
mask_file: str
Location of mask file
initial_m: float
Initial guess at ring threshold mask scale factor
space: {'Q'}
Eventually will support TTH as well
resolution:
The resolution in Q or TTH of the detector
average_method: str or function
The function to use in binned statistics to calculate the average
plot_verbose: bool
If true, generate a bunch of plots related to statistics of the rings
Returns
-------
str:
The name of the chi file
str:
The name of the mask, if generated
"""
# load the calibration file/calibrate
resolution = resolution if resolution is not None else .02
a = IO.calibrate(poni_file, calibration_imagefile, dspace_file, wavelength,
detector)
# load image
xray_image, bulk_image_path_no_ext, image_path = IO.loadimage(image_path)
# mask image, expect out an array of 1s and 0s which is the mask, if the
# mask is 0 then mask that pixel
mask_array = None
if generate_mask is True:
"""This should handle all mask writing functions including binning,
applying criteria, plotting, and writing the mask out as a numpy
array where masked pixels are zero. This should also give the
option to combine the calculated mask with a previous user
generated mask."""
mask_array = Calculate.write_mask(xray_image, a, mask_file, initial_m,
resolution)
# write mask to FIT2D file for later use
mask_file_name = IO.fit2d_save(mask_array, bulk_image_path_no_ext)
elif mask_file is not None:
# LOAD THE MASK
mask_array = IO.loadmask(mask_file)
# mask_array = np.abs(mask_array-1)
if mask_array is None:
mask_array = np.ones(np.shape(xray_image))
if plot_verbose:
plt.imshow(mask_array, cmap='cubehelix'), plt.colorbar()
plt.savefig('/home/christopher/mask.png', bbox_inches='tight',
transparent=True)
plt.show()
plt.imshow(mask_array*xray_image, cmap='cubehelix'), plt.colorbar()
plt.show()
# correct for corrections
corrected_image = Calculate.corrections(xray_image, geometry_object=a,
solid=solid, pol=pol,
polarization_factor=polarization)
position_matrix = None
# TODO: Add options for tth
if space == 'Q':
position_matrix = a.qArray(np.shape(xray_image)) / 10
max_position = np.max(position_matrix)
masked_position = position_matrix * mask_array
# Integrate using the global average
independant_out_array = np.arange(0, max_position + resolution, resolution)
integrated = Calculate.statistics1d(corrected_image,
position_matrix=masked_position,
max=max_position, step=resolution,
statistic=average_method)
integrated[np.isnan(integrated)] = 0
uncertainty_array = np.sqrt(
Calculate.variance1d(corrected_image, position_matrix=masked_position,
max=max_position, step=resolution))
uncertainty_array[np.isnan(uncertainty_array)] = 0
if plot_verbose is True:
no_mask_integrated = Calculate.statistics1d(corrected_image,
position_matrix=position_matrix,
max=max_position,
step=resolution,
statistic=average_method)
integrated_mean = Calculate.statistics1d(corrected_image,
position_matrix=masked_position,
max=max_position,
step=resolution,
statistic='mean')
fig, ax = plt.subplots()
ax.plot(independant_out_array, integrated, 'g', label='Median I[Q]')
ax.plot(independant_out_array, integrated_mean, 'b', label='Mean I[Q]')
ax.plot(independant_out_array, ((integrated - integrated_mean)/((
integrated+integrated_mean)/2))*10e6,
'k', label='((Median-Mean)/('
'Median+Mean)/2)x10^6')
ax.legend(loc='upper right')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/integrator.png', bbox_inches='tight',
transparent=True)
plt.show()
no_mask_uncertainty_percent_array = np.sqrt(
Calculate.variance1d(corrected_image,
position_matrix=position_matrix,
max=max_position, step=resolution)) / no_mask_integrated
fig, ax = plt.subplots()
ax.plot(independant_out_array, integrated, 'g', label='I[Q]')
ax.plot(independant_out_array, no_mask_integrated, 'k', label='No '
'old_files I[Q]')
ax.plot(independant_out_array, integrated - no_mask_integrated, 'r',
label='Difference')
legend = ax.legend(loc='upper right')
plt.show()
old_settings = np.seterr(divide='ignore')
uncertainty_percent_array = uncertainty_array / integrated
uncertainty_percent_array[np.isnan(uncertainty_percent_array)] = 0
np.seterr(**old_settings)
fig, ax = plt.subplots()
ax.plot(independant_out_array, uncertainty_percent_array * 100, 'g',
label='Sigma I[Q]')
plt.xlabel('Q (1/A)')
plt.ylabel('% uncertainty')
legend = ax.legend(loc='upper right')
plt.show()
ring_std = Calculate.statistics1d(corrected_image,
position_matrix=masked_position,
max=max_position, step=resolution,
statistic=np.std) / integrated
ring_std_no_mask = Calculate.statistics1d(corrected_image,
position_matrix=position_matrix,
max=max_position,
step=resolution,
statistic=np.std) / no_mask_integrated
ring_s = Calculate.statistics1d(corrected_image,
position_matrix=masked_position,
max=max_position, step=resolution,
statistic=stats.skew)
ring_s_no_mask = Calculate.statistics1d(corrected_image,
position_matrix=position_matrix,
max=max_position,
step=resolution,
statistic=stats.skew)
fig, ax = plt.subplots()
ax.plot(independant_out_array, ring_std, 'g',
label='Ring Standard Deviation')
ax.plot(independant_out_array, ring_std_no_mask, 'b',
label='Ring Standard Deviation No old_files')
legend = ax.legend(loc='upper right')
plt.show()
fig, ax = plt.subplots()
ax.plot(independant_out_array, ring_s, 'g',
label='Ring Skew')
ax.plot(independant_out_array, ring_s_no_mask, 'b',
label='Ring Skew No old_files')
legend = ax.legend(loc='upper right')
plt.show()
# plt.plot(independant_out_array, test_uncertainty / integrated * 100)
# plt.show()
plt.plot(
independant_out_array, integrated, 'g',
independant_out_array, integrated - uncertainty_array, 'b',
independant_out_array, integrated + uncertainty_array, 'r'
)
plt.show()
# plt.plot(
# independant_out_array, integrated, 'g',
# independant_out_array, integrated - test_uncertainty, 'b',
# independant_out_array, integrated + test_uncertainty, 'r'
# )
# plt.show()
# Save file as chi file with uncertainties
out_array = np.c_[independant_out_array, integrated, uncertainty_array]
# print out_array.shape
chi_filename = os.path.splitext(bulk_image_path_no_ext)[0] + out_ending
out_file_name = IO.save_chi(out_array, a, chi_filename, mask_file, image_path)
if generate_mask is True:
return out_file_name, mask_file_name
return out_file_name
def ripple_minima(chi_file, background_file, background_level, composition,
qmin, qmaxinst):
pwd = os.getcwd()
if os.path.exists(os.path.join(pwd, 'ripple_minima_test')) is False:
os.mkdir(os.path.join(pwd, 'ripple_minima_test'))
ripple_list = []
gr_list = []
qmax_min = 18.5
qmax_max = qmaxinst
qmax_step = .01
print 'Start qmax refinement'
for qmax in np.arange(qmax_min, qmax_max, qmax_step):
print qmax
gr_file, bg, q=Calculate.pdfgetx3(
chi_file,
background_file,
background_level,
composition,
qmax,
qmin,
qmax,
'QA',
os.path.join(pwd, 'ripple_minima_test', '@b_ripple_test_'+str(
qmax).ljust(5,'0')+'.@o'))
gr_file = os.path.join(pwd, 'ripple_minima_test', os.path.split(
os.path.splitext(chi_file)[0])[1]+'_ripple_test_'+str(qmax).ljust(5,'0')+'.gr')
x, y = IO.load_gr_file(gr_file)
w = y - np.convolve(y, np.ones(3)/3, 'same')
ripple_sum = np.sum(abs(w))
ripple_list.append(ripple_sum)
gr_list.append(gr_file)
t = np.arange(qmax_min, qmax_max, qmax_step)
ripple_array=np.array(ripple_list)
minima_index = signal.argrelextrema(ripple_array, np.greater_equal,
order=2*len(ripple_array)/100)[0]
minima = ripple_array[minima_index]
minima_q = t[minima_index]
maxima_index = signal.argrelextrema(ripple_array, np.less_equal,
order=2*len(ripple_array)/100)[0]
maxima = ripple_array[maxima_index]
maxima_q = t[maxima_index]
plt.plot(t, ripple_array, 'g')
plt.plot(minima_q, minima, 'o', markerfacecolor='none', mec='r')
plt.plot(maxima_q, maxima, 'o', markerfacecolor='none', mec='b')
plt.title('Ripples in PDF')
plt.xlabel('Qmax (1/A)')
plt.ylabel('Ripple Cumulative Sum')
plt.show()
plt.cla()
# data_list = []
# key_list = []
# for minima_q_point in minima_q:
# data = IO.load_gr_file([x for x in gr_list if str(qmax) in x] [0])
# data_list_element = data
# key_list_element = minima_q_point
# data_list.append(data_list_element)
# key_list.append(key_list_element)
# plot_stack(data_list, key_list)
ziped_minima = zip(minima_q, minima)
while True:
for q, value in ziped_minima:
print 'q= ', q,'rippleness= ', value
qmax= float(raw_input('Please pick a qmax. qmax= '))
gr_file = [x for x in gr_list if str(qmax) in x] [0]
print gr_file
x, y = IO.load_gr_file(gr_file)
plt.plot(x, y)
plt.show()
if raw_input('Is this the file you wanted? y/[n] ') == 'y':
break
return qmax
Created on 2015��12��9��
@author: Rhapsody
'''
import numpy as np
import pylab as pl
import GeneData
import Calculate
def func(x):
return np.sin(x * 2.0 * np.pi)
n = 10
m = 20
sqare = 0.1
x, y0 = GeneData.average(n, 0.0, 1.0, func)
y1 = np.random.normal(0, sqare, n) + y0
_w = Calculate.descent(x, y1, m, 100000, 0.01, Calculate.func_df)
w = list(_w[0])
print 'w =', w
pl.plot(x, y0, label='Pure')
pl.plot(x, y1, 'o', label='Impurity')
_x, _y = Calculate.showLine(0.0, 1.0, w)
pl.plot(np.array(_x), np.array(_y), label='Around')
pl.legend()
pl.show()
def write_pdf(chi_file=None, background_file=None, background_level=None,
pdf_format='QA', output='@r.@o', qmax='statistics',
composition=None, qmaxinst=0.0,
qmin=0.0, relative_max_uncertainty=.90,
plot_verbose=False, bg_refine_qmin_index=None,
bg_refine_qmax_index=None):
"""
Generates the G(r) and F(q) files, potentially using optimized values
for the background subtraction and qmax determination
Parameters
----------
chi_file: str
Location of the chi file to use as the foreground to generate the PDF
background_file: str
Location of the chi file to use as the background
background_level: float
The background level, if not set automatically generate the background
scale
pdf_format: str
The format of the chi file
out_put: str
The naming convention for the output PDF
qmax: float or {'statistics', 'ripple'}
If float, the qmax value to be used in the FFT. If statistics,
generate qmax by examining the statistical uncertainty of I(Q). If
ripple generate the PDFs at various qmax and compare the rippleness
between the various qmax values
composition: str
The compostion of the material to be studied
qmaxinst: float
The maximum reliable qmax generated by the detector, if 0.0 the max
of the chi file
qmin: float
The minimum q to be used in the FFT
relative_max_uncertainty: float [0,1]
If qmax is statistics the percentile of the uncertainty to except
plot_verbose: bool
If true, generate plots
bg_refine_qmin_index: int
The lower bound on the range of values to fit between the background
and foreground for determining the background level.
bg_refine_qmax_index: int
The upper bound on the range of values to fit between the background
and foreground for determining the background level.
Returns
-------
str:
The file name of the output
float:
The background level
float:
The qmax
int:
The background refine lower bound
int:
The background refine upper bound
"""
#Get composition
if composition is None:
composition = pdf_parameters.generate_composition()
# load up sample I[Q]
chi, chi_file = IO.load_chi_file(chi_file)
if os.path.split(chi_file)[0] != '':
os.chdir(os.path.split(chi_file)[0])
sample_q, sample, uncertainty = chi[:, 0], chi[:, 1], chi[:, 2]
if qmaxinst > np.amax(sample_q):
qmaxinst = np.amax(sample_q)
elif qmaxinst == 0:
qmaxinst = np.amax(sample_q)
# perform background subtraction
if background_file is not '' and background_level is None:
# implies that you want to load a background file
background_level, background_q, background, background_file, \
bg_refine_qmin_index, bg_refine_qmax_index = \
pdf_parameters.background_subtract(sample, background_file,
background_level,
bg_refine_qmin_index=bg_refine_qmin_index,
bg_refine_qmax_index=bg_refine_qmax_index)
if plot_verbose is True:
fig, ax = plt.subplots()
ax.plot(sample_q, sample, 'g', label='Sample')
ax.legend(loc='upper right')
plt.title('Sample I(Q)')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/sample.png',
bbox_inches='tight',
transparent=True)
plt.show()
fig, ax = plt.subplots()
ax.plot(sample_q, background, 'b',
label='Background')
ax.legend(loc='upper right')
plt.title('Background (IQ)')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/background.png',
bbox_inches='tight',
transparent=True)
plt.show()
fig, ax = plt.subplots()
ax.plot(sample_q, sample, 'g', label='Sample')
ax.plot(sample_q, background * background_level, 'b',
label='Scaled Background')
ax.plot(sample_q, sample - background * background_level, 'k',
label='Sample-Background')
ax.legend(loc='upper right')
plt.title('Background Subtraction')
plt.xlabel('Q (1/A)')
plt.ylabel('Raw Counts')
plt.savefig('/home/christopher/integrated_minus_background.png',
bbox_inches='tight',
transparent=True)
plt.show()
# Determine Qmax
if qmax is 'statistics':
qmax = pdf_parameters.qmax_statistics(sample_q, sample, uncertainty,
relative_max_uncertainty)
elif qmax is 'ripple':
qmax = pdf_parameters.ripple_minima(chi_file, background_file,
background_level, composition,
qmin, qmaxinst)
if plot_verbose is True:
plt.plot(sample_q, uncertainty/sample * 100)
plt.show()
# PDFGETX3 call/ Generate PDF
gr_file, background_level, qmax = Calculate.pdfgetx3(chi_file,
background_file, background_level,
composition, qmax, qmin, qmaxinst, pdf_format, output)
return gr_file, background_level, qmax, bg_refine_qmin_index, bg_refine_qmax_index
def background_subtract(sample, background_file=None, background_level=None,
bg_refine_qmin_index=None, bg_refine_qmax_index=None):
"""
Wrap the background subtraction optimization with file IO
Parameters
----------
:param bg_refine_qmin_index:
sample: ndarray
Sample I(Q) array
background_file: str, optional
The file name of the background file, if not specified give an IO
gui to select the file, f '' no file is loaded
background_level: float, optional
The background level if none given, calculate via minimization
Returns
-------
float:
The background level
array:
The background array
array:
The background q array
str:
The name of the background file
"""
#File IO
background_data, background_file = IO.load_chi_file(
background_file)
#Unpack file IO
if background_data is not None:
background = background_data[:, 1]
background_q = background_data[:, 0]
percentile_peak_pick = np.where(
background >= np.percentile(background, 98))[0]
print len(percentile_peak_pick)
print percentile_peak_pick[0]
print percentile_peak_pick[-1]
if len(percentile_peak_pick) >= 2:
bg_refine_qmin_index = percentile_peak_pick[0]
bg_refine_qmax_index = percentile_peak_pick[-1]
else:
if bg_refine_qmax_index is None:
plt.plot(background)
plt.plot(sample)
plt.show()
bg_refine_qmax_index = int(raw_input('High end of the '
'background to fit: '))
if bg_refine_qmin_index is None:
plt.plot(background)
plt.plot(sample)
plt.show()
bg_refine_qmin_index = int(raw_input('Low end of the '
'background to fit: '))
# Fit the background
if background_level is None:
background_level = Calculate.optimize_background_level(background,
sample,
bg_refine_qmin_index,
bg_refine_qmax_index)
print 'background level= ', background_level
else:
background = None
background_q = None
return background_level, background_q, background, background_file, \
bg_refine_qmin_index, bg_refine_qmax_index
# Syntax for import is:
# import modulename
# OR
# import module1, module2,... module n.
# Here is the program which is used to import module "Calculate.py" using import statement.
# importing "Calculate.py" using import statement.
import Calculate
# Getting user input for variables a and b.
a = int( input ( "\nEnter a value for \"a\" : " ) )
b = int( input ( "\nEnter a value for \"b\" : " ) )
# Accessing the functionality present in the module "Calculate" using dot( . ) operator.
Calculate.Add( a,b )
Calculate.Sub( a,b )
Calculate.Multi( a,b )
Calculate.Divide( a,b )
print( ) # Used to print new line for readability.
itr = 50
alpha= 20.0
#------------------------------------------
U = Start.start(l,N).cold_start()
ll = 1
while (ll < itr+1):
for s in range(l):
for t in range(l):
for r in range(2):
U = Update.link(r,U,s,t,l,alpha,N)
avp = Calculate.avplqt(U,l,N)
#avp = Wilson11(U0)
print avp
plt.figure(1)
plt.scatter(ll,avp)
plt.show()
ll = ll+1
#-------- DAY 53 "exercise" --------
import Calculate as cal
import datetime
x = datetime.datetime.now()
print(x.strftime('%c'), '\n---------------------')
print('We have the basic operations wich are: +, -, * and /')
print('Examples for each one:')
print('1 + 8 =', cal.add(1, 8))
print('4 - 2 =', cal.subtract(4, 2))
print('6 * 6 =', cal.multiply(6, 6))
print('8 / 2 =', cal.divide(8, 2))
import request_and_store
import Calculate
import graph
import translation
if __name__ == "__main__":
language = input("Language choice: ")
abr = translation.lang_abr(language)
product = input(
translation.translateText(
abr,
"Type of food you are looking for (Ex. pork, beef, fish, etc.): "))
mode = input(
translation.translateText(
abr,
"What do you care about the most? 1 for calories, 2 for cost, 3 for nutritients: "
))
product = translation.translateText("en", product)
while True:
if mode != "1" and mode != "2" and mode != "3":
mode = input("Invalid mode! Try again:")
else:
break
request_and_store.main(product)
Calculate.calculate_cost_and_write(abr, product)
graph.main(mode, product)
def ProcessSrcmod(fileName):
print '\nStarting calculations for ' + fileName + '...\n'
FSBFilesFolder = './' #path to folder with fsp file
# Parameters for CFS calculation and visualization
lambdaLame = 3e10 # First Lame parameter (Pascals)
muLame = 3e10 # Second Lame parameter (shear modulus, Pascals)
coefficientOfFriction = 0.4 # Coefficient of friction
obsDepth = 0; # depth of observation coordinates just for disc visualization
useUtm = True
catalogType = 'REVIEWED'
# Options are:
# 'COMPREHENSIVE': most earthquakes. Not human reviewed
# 'REVIEWED': slightly fewer earthquakes. Some human quality control
# 'EHB': many fewer earthquakes. Human quality control and precise relocations
captureDays = 365 # Consider ISC earthquakes for this many days after day of main shock
nearFieldDistance = 100e3 # Keep only those ISC earthquakes withing this distance of the SRCMOD epicenter
spacingGrid = 5e3
maxDepth = 50000
zBuffer = -1.*np.arange(0, maxDepth+spacingGrid, spacingGrid)
zBufferFillGridVec = -1.*(np.arange(spacingGrid, maxDepth+spacingGrid, spacingGrid)-spacingGrid/2.)
# get exact time of earthquake
with open('MainShockTimes.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if (row['earthquake'][2:-10] == fileName[1:-4]):
ExactStart = row['time']
datetime_Exact = datetime.datetime.strptime(ExactStart[:-4], '%Y-%m-%d %H:%M:%S')
print 'Reading in SRCMOD fault geometry and slip distribution for this representation of the event...\n'
EventSrcmod = Readsrcmod.ReadSrcmodFile(fileName, FSBFilesFolder)
print 'Calculating regular grid over region inside fault buffer...\n'
xBuffer, yBuffer, polygonBuffer = Calculate.calcFaultBuffer(EventSrcmod, nearFieldDistance)
print 'Calculating fault buffer grid points...\n'
xBufferFillGridVec, yBufferFillGridVec, gridcellsInBuffer = Calculate.calcBufferGridPoints(xBuffer, yBuffer, polygonBuffer, spacingGrid)
print 'Generating unique grid identifiers and flattening observation arrays...\n'
gridIdFlat, xBufferFillGridVecFlat, yBufferFillGridVecFlat, zBufferFillGridVecFlat = Calculate.getGridIdNames(xBufferFillGridVec, yBufferFillGridVec, zBufferFillGridVec)
ti = time.time()
print 'Calculating displacement vectors and stress tensors at observation coordinates...'
DisplacementVectorBuffer, StrainTensorBuffer, StressTensorBuffer, Distances = Calculate.calcOkadaDisplacementStress(xBufferFillGridVecFlat, yBufferFillGridVecFlat, zBufferFillGridVecFlat, EventSrcmod, lambdaLame, muLame)
print 'Calculations took ' + str(time.time()-ti) + ' seconds.\n'
print 'Resolving Coulomb failure stresses on receiver planes...\n'
Cfs, BigBig = Calculate.StressMasterCalc(EventSrcmod, StressTensorBuffer, StrainTensorBuffer, DisplacementVectorBuffer, coefficientOfFriction)
print 'Getting start/end dates...'
startDateTime = EventSrcmod['datetime']
datetime_ExactStart = datetime_Exact + datetime.timedelta(seconds=1)
assert datetime_ExactStart - datetime.timedelta(hours=36) < startDateTime < datetime_ExactStart + datetime.timedelta(hours=36)
endDateTime = datetime_ExactStart + datetime.timedelta(days=captureDays) - datetime.timedelta(seconds=1)
ti = time.time()
print 'Reading in ISC data for capture days after the date of the SRCMOD event...'
Catalog = ISCFunctions.getIscEventCatalog(datetime_ExactStart, endDateTime, EventSrcmod, catalogType)
print 'ISC retrieval and time/quality filtering took ' + str(time.time()-ti) + ' seconds in total.\n'
print 'Events within the fault buffer...\n'
Catalog = ISCFunctions.getNearFieldIscEventsBuffer(Catalog, EventSrcmod, polygonBuffer)
ti = time.time()
print 'Binning the events by grid cell and assembing magnitudes, times, counts, etc...\n'
Counts = ISCFunctions.binNearFieldIscEventsBuffer(Catalog, EventSrcmod, polygonBuffer, gridcellsInBuffer, zBuffer)
print 'Binning ISC events took ' + str(time.time()-ti) + ' seconds.\n'
BigBig['x'] = xBufferFillGridVecFlat
BigBig['y'] = yBufferFillGridVecFlat
BigBig['z'] = zBufferFillGridVecFlat
BigBig['grid_id'] = gridIdFlat
BigBig['grid_aftershock_count'] = Counts
print 'Writing output CSV file...\n'
ti = time.time()
fileNameReturn = WriteOutput.WriteCSV(fileName, BigBig)
print 'Writing output took ' + str(time.time()-ti) + ' seconds.\n'
return
turtle.goto(100 - screen.window_width() / 2, 10 - screen.window_height() / 2)
turtle.pendown()
turtle.hideturtle()
turtle.speed(0)
# Расчет углов
alpha = sigma
epsilon = ro / 2
beta = epsilon + 180 / n
gamma = 180 - (alpha + beta)
count = 1
row = []
a_side, c_side = Calculate.solve_triangle(alpha, beta, gamma, b_side)
print('Треугольник', count, [alpha, beta, gamma, a_side, b_side, c_side])
# считаем угол противоположного треугольника
angle = Calculate.get_angle(a_side, c_side, 180 - gamma - alpha - ro)
for i in range(n): # Добавить 1, если делаем lampshade
# row.append(turtle.clone())
turtle.forward(b_side)
row.append(turtle.clone())
turtle.left(ro)
# Создать копию массива row,
# чтобы отрисовать нижние треугольники, если делаем lampshade
# lets see about "Renaming a Modules" in python.
# It is possible to import the module by using specific user defined name called as "alias name".
# It is posssibile to access the functionality of the module by using its "alias name" which was declared while importing the module.
# Syntax for renaming a module is:
# import moduleName as aliasName
# Here is the program which is used to import module "Calculate.py" by using an alias name.
import Calculate as Cal
# Getting user input for variables a and b.
a = int( input ( "\nEnter a value for \"a\" : " ) )
b = int( input ( "\nEnter a value for \"b\" : " ) )
# Accessing the functionality present in the module "Calculate" using alias name by using dot( . ) operator.
Cal.Add( a, b)
Cal.Multi( a,b )
print( ) # Used to print new line for readability.
Session_state.index = Session_state.index + 1
#Inspect order
if st.button("Analyze"):
st.write(df.iloc[Session_state.index - 1])
data = MarketstackApi.requestData(Session_state.index - 1, df)
x = Graph.axis_generate(data)[0]
y = Graph.axis_generate(data)[1]
xlabel = Graph.axis_generate(data)[2]
myline = np.linspace(0, 7, 200) #Curve line
mymodel = np.poly1d(np.polyfit(x, y, 3)) #3rd degree model
#Draw Matplotlib chart
fig = plt.figure()
plt.xticks(x, np.flip(xlabel))
plt.title('Weighted-price of ' +
df.iloc[Session_state.index - 1].StockCode)
plt.xlabel('Date')
plt.ylabel('Price')
plt.scatter(x, y, label='Weighted prices')
plt.plot(myline, mymodel(myline), label='Best fit line')
# plt.scatter(3,df.iloc[Session_state.index-1].Price, marker = 'o', color='r', label = "Order Price")
plt.legend(loc=4)
st.pyplot(fig)
#Find Critical values:
min_max = Graph.find_min_max(mymodel, y)
Score = Calculate.calculateScore(Session_state.index - 1, df, min_max)
st.subheader("This order rating is:")
st.subheader(Score)
import Calculate
print("Please two number for calculate operator basic....")
print()
num = int(input("Enter num one please: "))
num2 = int(input("Enter num two please: "))
print()
print("Calculate the operator.................")
print()
calculate = Calculate.Calculate(num, num2)
print(f"Teh sun is : {calculate.sum()}")
print(f"The subtraction is: {calculate.subtraction()}")
print(f"The Divide is: {calculate.divide()}")
print(f"The Multiply is: {calculate.multiply()}")
print()
print("Calculate the operator finish.................")
print("Thanks....")
import Calculate
poni_file = '/media/Seagate_Backup_Plus_Drive/Mar2014/Ni/17_21_24_NiSTD_300K-00002.poni'
geometry_object = IO.loadgeometry(poni_file)
# load image
xray_image = IO.loadimage(
'/media/Seagate_Backup_Plus_Drive/Mar2014/Ni/17_21_24_NiSTD_300K-00002.tif')
# generate radial array based on image shape
# TODO: use a more Q/angle based system
Radi = geometry_object.rArray(xray_image.shape)
angles = geometry_object.chiArray(xray_image.shape)
# create integer array of radi by division by the pixel resolution
roundR = np.around(Radi / geometry_object.pixel1).astype(int)
# define the maximum radius for creation of numpy arrays
# make maxr into an integer so it can be used as a counter
maxr = int(np.ceil(np.amax(roundR)))
pols = []
print 'start opts'
for r in range(0, maxr, 100):
pols.append(
Calculate.optomize_polarization(r, xray_image, geometry_object, roundR, angles))
pols = np.array(pols)
plt.plot(pols)
plt.show()
