def roitodata(save_fld):
# Local Variables: I_cum, bkg, save_fld, k, j, l, m_bloc, I, background, avg, data
# Function calls: load, save, nanmean, CC, NaN, cd, length, importdata, roitodata, mean, dir, size
#% ROITODATA(save_fld) loads CC from save_fld for data extraction from I.mat
#%Example:
#% [data,bkg] = roitodata(save_fld)
os.chdir(save_fld)
np.load('CC.mat')
m_bloc = dir('*I*.mat')
data = np.array([])
background = np.array([])
avg = np.array([])
bkg = np.array([])
for j in np.arange(1., (length(m_bloc))+1):
I_cum = importdata((m_bloc[int(j)-1].name))
for k in np.arange(1., (length(I_cum))+1):
#%looping through files
data = np.array(np.hstack((data)))
background = np.array(np.hstack((background, bkg)))
plt.save('data.mat', 'data')
plt.save('bkg.mat', 'background')
return [data, bkg]
def average_face(imgpaths,
dest_filename=None,
width=500,
height=500,
background='black',
blur_edges=False,
out_filename='result.png'):
size = (height, width)
images = []
point_set = []
for path in imgpaths:
img, points = load_image_points(path, size)
if img is not None:
images.append(img)
point_set.append(points)
if len(images) == 0:
raise FileNotFoundError(
'Could not find any valid images. Supported formats are .jpg, .png, .jpeg'
)
if dest_filename is not None:
dest_img, dest_points = load_image_points(dest_filename, size)
if dest_img is None or dest_points is None:
raise Exception('No face or detected face points in dest img: ' +
dest_filename)
else:
dest_img = np.zeros(images[0].shape, np.uint8)
dest_points = locator.average_points(point_set)
num_images = len(images)
result_images = np.zeros(images[0].shape, np.float32)
for i in range(num_images):
result_images += warper.warp_image(images[i], point_set[i],
dest_points, size, np.float32)
result_image = np.uint8(result_images / num_images)
face_indexes = np.nonzero(result_image)
dest_img[face_indexes] = result_image[face_indexes]
mask = blender.mask_from_points(size, dest_points)
if blur_edges:
blur_radius = 10
mask = cv2.blur(mask, (blur_radius, blur_radius))
if background in ('transparent', 'average'):
dest_img = np.dstack((dest_img, mask))
if background == 'average':
average_background = np.uint8(locator.average_points(images))
dest_img = blender.overlay_image(dest_img, mask,
average_background)
print('Averaged {} images'.format(num_images))
plt = plotter.Plotter(False, num_images=1, out_filename=out_filename)
plt.save(dest_img)
def ncc(seg, width):
# Local Variables: B, hash, y, i, siz, s, N, seg, width, S, cacheDir, u, x, Nu, xs, ys, uniq, o2, o1
# Function calls: load, hashMat, unique, false, floor, sum, nan, uint8, ceil, uniqfilt, length, save, ncc, find, bwlabel, size
#% N = ncc( seg, w )
#%
#% N(i,j) = number of connected components in the
#% w-by-w window centered on location (i,j) of seg
#%
#% What do I mean by "number of connected components"?
#% Here are some example segmentation patches:
#%
#% 1 1 2 2 1 1
#% 1 1 2 2 1 1 this patch has THREE segments, even
#% 1 1 2 2 1 1 though unique() only returns [1;2].
#% 1 1 2 2 1 1
#%
#% 3 3 7 7 7 7
#% 3 3 7 7 7 7 this patch has FOUR segments, even
#% 7 7 3 3 3 3 though unique() is simply [3;7].
#% 7 7 3 3 3 3
#%
#% THIS CODE IS VERY SLOW, hence all results are cached.
cacheDir = '/home/ashishmenon/labpc/oef/cache/clust/nccCache/'
hash = hashMat(
np.array(np.vstack((np.hstack((seg.flatten(1))), np.hstack((width))))))
try:
np.load(np.array(np.hstack((cacheDir, hash))), 'N')
except:
siz = matcompat.size(seg)
o1 = np.floor(((width - 1.) / 2.))
o2 = np.ceil(((width - 1.) / 2.))
#% This takes ~80 seconds per image
N = np.nan(siz)
B = false(siz)
B[int(o1 + 1.) - 1:0 - o2, int(o1 + 1.) - 1:0 - o2] = 1.
Nu = uniqfilt(seg, o2)
N[int(np.logical_and(B, Nu == 1.)) - 1] = 1.
[ys, xs] = nonzero(np.logical_and(B, Nu > 1.))
for i in np.arange(1., (length(ys)) + 1):
y = ys[int(i) - 1]
x = xs[int(i) - 1]
S = seg[int(y - o1) - 1:y + o2, int(x - o1) - 1:x + o2]
uniq = np.unique(S)
N[int(y) - 1, int(x) - 1] = 0.
for s in uniq.flatten(0).conj():
u = np.unique(bwlabel((S == s), 4.))
N[int(y) - 1,
int(x) - 1] = N[int(y) - 1, int(x) - 1] + np.sum((u != 0.))
#% convert to uint8 to save disk space
#% (but this converts NaNs to 0s..)
N = np.uint8(N)
plt.save(np.array(np.hstack((cacheDir, hash))), 'N')
return [N]
def stackstoroi(save_fld, sd):
# Local Variables: I_bw2, I_cum, I_bw1, I, I_overlay, locs, CC, i,
# j, img_content, I_edge, m_bloc, I_mean, marks, save_fld, sd
# Function calls: save, stackstoroi, mad, findpeaks, cat, bwareaopen,
# length, edge, bwconncomp, importdata, max, threshold, mode, cd, dir, mean
#% STACKSTOROI(image_fld,sd) loads *I*.mat from save_fld for image
#% segmentation. sd is how many standard deviations above the mean the
#% threshold is set for segmentation. Outputs:
#% CC: connected components found in I_bw2
#% I_mean: averaged image of *I*.mat
#% I_bw2: binary image containing image segementation of *I*.mat
#% I_overlay: overlay of identified ROI over I_mean
#%Example:
#% [CC,I_mean,I_bw2,I_overlay] = stackstoroi(save_fld,sd);
chdir(save_fld)
m_bloc = listdir('*I*.mat*')
i_cum = np.array([])
img_content = np.array([])
if length(m_bloc) == 11:
for i in np.arange(1, 12.0):
i_cum = cat(3, i_cum, importdata((m_bloc[int(i)-1].name)))
else:
I_cum = importdata((m_bloc[0].name))
for j in np.arange(1., (length(I_cum))+1):
I = I_cum[:,:,int(j)-1]
img_content[int(j)-1] = np.mean(I.flatten(1))
[marks, locs] = findpeaks(img_content, 'minpeakdistance', 100.)
I_mean = np.mean(I_cum[:,:,int(locs)-1], 3.)
#%I_bw1 = bwspecial(otsu(I_mean,3));
I_bw1 = threshold(I_mean, (mode(I_mean.flatten(1))+np.dot(sd, mad(I_mean.flatten(1), 1.))))
I_bw2 = bwareaopen(I_bw1, 20., 4.)
CC = bwconncomp(I_bw2)
I_edge = edge(I_bw2)
I_overlay = I_mean
I_overlay[int(I_edge)-1] = 10.*matcompat.max(I_mean.flatten(1))/9.
plt.save('CC.mat', 'CC')
return [CC, I_mean, I_bw2, I_overlay]
def vis_eigen_cutoff(refvec,
evc_avg,
cutoff_list,
G,
figdir="",
savestr="StyleGAN2_proj",
RND=None):
if RND is None: RND = np.random.randint(10000)
samp_n = refvec.shape[0]
codes_all = [refvec]
for cutoff in cutoff_list:
refvec_proj = refvec @ evc_avg[:, -cutoff:] @ evc_avg[:, -cutoff:].T
codes_all.append(refvec_proj)
codes_all = np.concatenate(tuple(codes_all), axis=0)
img_all = G.visualize_batch_np(codes_all)
mtg = make_grid(img_all, nrow=samp_n)
ToPILImage()(mtg).show()
ctf_str = "_".join([str(ct) for ct in cutoff_list])
plt.save(
join(figdir, "%s_%s_%04d.png" % (savestr, ctf_str, RND)),
mtg.numpy(),
)
return mtg
def picture(df, plt, bizhong):
x_data = list(map(str, df['时间']))
print(x_data)
y_maijia_data = df['买价']
y_maioutjia_data = df['卖价']
min_number = y_maijia_data.min()
max_number = y_maioutjia_data.max()
print(min_number, max_number)
xiaoshu_number = str(10000 * (max_number - min_number)).split('.')[0]
print(xiaoshu_number)
mid_num = 5 - len(str(xiaoshu_number))
if mid_num:
step = 0.1**mid_num
else:
step = 1 / int(xiaoshu_number[0])
y_kedu = [min_number - step * 2 + i * step for i in range(0, 10)]
print(y_kedu)
plt.clf() # 清除刷新前的图表，防止数据量过大消耗内存
fig, ax = plt.subplots(1, 1)
plt.plot(x_data, y_maijia_data, 'r')
plt.plot(x_data, y_maioutjia_data, 'g')
plt.xticks(x_data, rotation=90)
plt.yticks(y_kedu)
for label in ax.get_xticklabels():
label.set_visible(False)
for label in ax.get_xticklabels()[::5]:
label.set_visible(True)
plt.xlabel('时间', fontproperties=font_set) # 设置x轴名称
plt.ylabel('价格', fontproperties=font_set) # 设置y轴名称
plt.title(df['币种'].values[0], fontproperties=font_set) # 设置图片名称
plt.legend(['买价', '卖价'], loc="upper right", prop=font_set)
if len(x_data) == 60:
plt.save('%s.jpg' % bizhong)
plt.show()
def findbruteforcefit(kit=None, *args, **kwargs):
if kit != 1:
pylab.save('bftfile', 'kit')
else:
pylab.load('bftfile', 'kit')
kit['tightnesssettings'] = copy(
settingsfromtightness(kit['tightnesssettings']['scalartightness']))
# findbruteforcefit.m:6
random(1)
ABCexact = array([8572.0553, 3640.1063, 2790.9666])
# findbruteforcefit.m:9
ABCguess = array([8572.0, 3640.0, 2790.0])
# findbruteforcefit.m:10
ABCguess = multiply(ABCguess, array([0.99, 1.01, 0.99]))
# findbruteforcefit.m:11
kit = trimkit(kit,
kit['tightnesssettings']['bruteforce']['numexperimentlines'])
# findbruteforcefit.m:12
flimits = array([min(kit['onedpeakfs']), max(kit['onedpeakfs'])])
# findbruteforcefit.m:14
theoryset = linesforbruteforce2(
ABCguess, flimits,
kit['tightnesssettings']['bruteforce']['numtheorylines'],
max(kit['onedpeakhsunassigned']))
# findbruteforcefit.m:15
linestouse['lines'] = copy(theoryset)
# findbruteforcefit.m:17
linestouse['heighttouse'] = copy('sixKweakpulsestrength')
# findbruteforcefit.m:18
linestouse['fitdescriptor'] = copy('made up brute force fit')
# findbruteforcefit.m:19
linestouse['ABCxxxxx'] = copy(array([ABCguess, 0, 0, 0, 0, 0]))
# findbruteforcefit.m:20
#addC13swithlinelist
ABClist = [ABCguess]
# findbruteforcefit.m:23
dAdBdC = array([0.01, 0.01, 0.01])
# findbruteforcefit.m:24
linestouse['ABClist'] = copy(ABClist)
# findbruteforcefit.m:26
linestouse['dAdBdC'] = copy(dAdBdC)
# findbruteforcefit.m:27
kit['findfitsettings'] = copy(kit['tightnesssettings']['bruteforce'])
# findbruteforcefit.m:28
allfits = findfits(linestouse, kit)
# findbruteforcefit.m:30
fit = pullbest(allfits, kit)
# findbruteforcefit.m:32
fit['patternType'] = copy('bruteforce')
# findbruteforcefit.m:33
kit = addfittokit(kit, fit)
# findbruteforcefit.m:34
displaykitwithfits(kit)
1
return fit
verbose=100)
y_val_pred = np.argmax(network.predict(X_val.T), axis=0)
cm = confusion_matrix(np.argmax(y_val.T, axis=0),
y_val_pred,
labels=list(range(10)))
print(cm)
plt.imshow(cm)
plt.xlabel("predicted")
plt.ylabel("true")
plt.xticks(list(range(10)))
plt.yticks(list(range(10)))
plt.ylim([-0.5, 9.5])
plt.save('mnist-cm.png')
plt.show()
# print(network.predict(X_test.T))
plt.plot(network.cost, label='loss')
plt.plot(network.val_cost, label='val_loss')
plt.plot(network.acc, label='acc')
plt.plot(network.val_acc, label='val_acc')
plt.legend()
plt.show()
pred = np.argmax(network.predict(X_test.T), axis=0)
cm = confusion_matrix(np.argmax(y_test.T, axis=0),
pred,
labels=list(range(10)))
a = float(i * m) / float(M)
b = float(j * n) / float(N)
expo = 1j * 2.0 * 3.1416 * (a + b)
tran_act += mtz[j][i] * np.exp(expo)
mtrans[m, n] = (tran_act.real)
return (mtrans)
#Sube imagen que ingreso el usuario
byne = sube_imagen(nombre)
#Aplicacion de la transformada a la imagen
transim = transformada(byne)
#Aplicacion de la gaussiana a la matriz de la imagen
ga = gaussiana(byne, sig)
#Aplicacion de la transformada a la gaussiana
transgau = transformada(ga)
#Convolucion entre las transformadas (gaussiana e imagen)
con = transgau * transim
#Aplicacion de la transformada inversa al resultado de lo anterior entra como parametro la matriz de la convoluncion anteriormente realizada
rta = invtra(con)
plt.figure()
plt.imshow(rta, cmap="gray")
plt.save("suave.png")
def pltwxt520(x, y):
plt.grid()
plt.plot(x, y)
plt.save("wxt520.png", 'PNG') #??? save the file to disk
tws = get_all_tweets(user["ceo"])
user_tweets[user["ceo"]] = tws
tws = get_all_tweets(user["company"])
user_tweets[user["company"]] = tws
tweets.append(user_tweets)
return tweets
tweets_structure = get_tweets(users.users)
for dictionary in tweets_structure:
for key in dictionary:
print(f"{key}, {len(dictionary[key])}")
plt.save("women-ceo-tweets.npy", tweets_structure)
'''
json_obj = tweet._json
print(json.dumps(json_obj))
dtime = json_obj['created_at']
print(json_obj["retweeted"])
if json_obj["retweeted"]:
print("This is retweeted!")
# new_datetime = datetime.strftime(datetime.strptime(dtime,'%a %b %d %H:%M:%S +0000 %Y'), '%Y-%m-%d %H:%M:%S')
new_datetime = datetime.strptime(dtime, '%a %b %d %H:%M:%S +0000 %Y')
print(new_datetime, new_datetime.year, new_datetime.month, new_datetime.day)
user_tweets[user["ceo"]].append(json_obj)
'''
# -*- coding: utf-8 -*-
from scipy.signal.ltisys import lti, lsim
from matplotlib.pylab import save, randn
from Numeric import sqrt, array, arange
n = 128
Q = 1.
R = 1.
w = 0.3 * sqrt(Q) * randn(n)
v = 0.2 * sqrt(R) * randn(n)
ureq = array([[-1.743] * n])
t = arange(0, 0.9999, 1. / 128)
#Generator().generateSin(n, 3, 33) #-0.37727
u = ureq + w
#A, B, C, D = [[-6.,-25.], [1.,0.]], [[1.],[0.]], [[0., 1.]], [[0.]]
#sys=lti(A, B, C, D)
#y = lsim(sys, u, t)
yv = u + v
##save('Q.txt', Q)
##save('R.txt', R)
save('w.txt', w)
save('v.txt', v)
save('yv.txt', yv)
save('u.txt', u)
save('ureq.txt', ureq)
# if RND is None:
RND = np.random.randint(10000)
classid = np.random.randint(0, 1000, samp_n)
refvec = np.vstack((0.7 * np.random.randn(128, samp_n), EmbedMat[:,
classid])).T
codes_all = [refvec]
for cutoff in cutoff_list:
refvec_proj = refvec @ evc_BG[:, -cutoff:] @ evc_BG[:, -cutoff:].T
codes_all.append(refvec_proj)
codes_all = np.concatenate(tuple(codes_all), axis=0)
img_all = BG.visualize_batch_np(codes_all)
mtg = make_grid(img_all, nrow=samp_n)
ToPILImage()(mtg).show()
ctf_str = "_".join([str(ct) for ct in cutoff_list])
plt.save(
join(figdir, "BigGAN_proj_%s_%04d.png" % (ctf_str, RND)),
mtg.numpy(),
)
#%% StyleGAN2
Hessdir = join(rootdir, 'StyleGAN2')
modellist = [
"ffhq-512-avg-tpurun1", "stylegan2-cat-config-f",
"2020-01-11-skylion-stylegan2-animeportraits"
]
modelsnms = ["Face512", "Cat256", "Anime"]
#%% for modelnm, modelsnm in zip(modellist, modelsnms):
modelnm, modelsnm = modellist[1], modelsnms[1]
SGAN = loadStyleGAN2(modelnm + ".pt", size=256)
SG = StyleGAN2_wrapper(SGAN)
figdir = join(compressdir, "StyleGAN2", modelsnm)
os.makedirs(figdir, exist_ok=True)
for user in users:
if user is plt.np.nan:
print("THIS IS THE NAN WE WANT TO AVOID")
continue
tws = get_all_tweets(user)
tweets[user] = tws
return tweets
tweets_structure = get_tweets(users.users)
for key in tweets_structure:
print(f"{key}, {len(tweets_structure[key])}")
# plt.save("company-tweets.npy", tweets_structure)
plt.save("competitor-tweets.npy", tweets_structure)
'''
json_obj = tweet._json
print(json.dumps(json_obj))
dtime = json_obj['created_at']
print(json_obj["retweeted"])
if json_obj["retweeted"]:
print("This is retweeted!")
# new_datetime = datetime.strftime(datetime.strptime(dtime,'%a %b %d %H:%M:%S +0000 %Y'), '%Y-%m-%d %H:%M:%S')
new_datetime = datetime.strptime(dtime, '%a %b %d %H:%M:%S +0000 %Y')
print(new_datetime, new_datetime.year, new_datetime.month, new_datetime.day)
user_tweets[user["ceo"]].append(json_obj)
'''
chi0 = np.arange(chi0min, (chi0max)+(dchi0), dchi0)
#% make full fixed chi array (chi0)
#% make all-phi qchi arrays
Qchi0_allphi = np.zeros(lchi0int, lq0int)
#% empty array for combined phi image
Qchi0log_allphi = np.zeros(lchi0int, lq0int)
#% empty array for combined log phi image
#%% Stitch together all the files!
#% for phi
for p in np.arange(1., (numphi)+1):
#% make qchi arrays
#% save q and chi data files
chi0name = np.array(np.hstack((dumpname, '\\', imgname, 'chi0.mat')))
q0name = np.array(np.hstack((dumpname, '\\', imgname, 'Q0.mat')))
plt.save(q0name, 'Q0')
plt.save(chi0name, 'chi0')
#%% Normalize and save qchi with all phis
Qchi0_allphi = matdiv(Qchi0_allphi, numphi)
#% normalize the combined-phi image
for n in np.arange(1., (lchi0int)+1):
#% make a log plot
#% Plot qchi
H = imagesc(Q0, chi0, Qchi0log_allphi)
plt.xlabel('Q (A^-1)')
plt.ylabel('chi (deg)')
plt.colorbar
colormap(plt.jet(256.))
#%xlim([2 8]) % these values specific to your data; CHECK!!!
#%ylim([-80 30]) % these values specific to your data; CHECK!!!
