def showmat(name, mat, alpha, beta):
sample_dir = 'samples/'
alpha = [a.decode('utf-8') for a in alpha]
beta = [b.decode('utf-8') for b in beta]
fig = plt.figure(figsize=(20, 20), dpi=80)
plt.clf()
matplotlib.rcParams.update({'font.size': 12})
ax = fig.add_subplot(111)
ax.set_aspect(1)
ax.xaxis.tick_top()
res = ax.imshow(mat, cmap=plt.cm.Blues, interpolation='nearest')
font_prop = FontProperties()
font_prop.set_file(
'D:\users\chxing\\aaai2017Exp\\filterDouban\data.r50.q1min30max.tokenized.2-20.1-20\model_for_test\\attention\\simfang.ttf'
)
font_prop.set_size('large')
plt.xticks(range(len(alpha)), alpha, rotation=60, fontproperties=font_prop)
plt.yticks(range(len(beta)), beta, fontproperties=font_prop)
cax = plt.axes([0.0, 0.0, 0.0, 0.0])
plt.colorbar(mappable=res, cax=cax)
plt.savefig(name + '.png', format='png')
plt.close()
def draw_annot_by_text(file_name, image_dir,GT_dir,output_dir, isGT=True, inch=40, font_path=None, draw_bbox=True):
for file in file_name:
print('Draw predicted result to file', file)
GT_file = os.path.join(GT_dir, file + '.txt')
img_path = os.path.join(image_dir, file + '.png')
if os.path.exists(os.path.join(image_dir, file + '.jpg')):
img_path = os.path.join(image_dir, file + '.jpg')
content = []
with codecs.open(GT_file, 'r', encoding='utf8') as f:
for line in f:
content.append(line.replace('\n', ''))
fig, ax = plt.subplots(1)
fig.set_size_inches(inch, inch)
ori_img = cv2.imread(img_path, cv2.IMREAD_IGNORE_ORIENTATION | cv2.IMREAD_COLOR)
img = cv2.cvtColor(ori_img, cv2.COLOR_RGB2GRAY)
plt.imshow(img, cmap='Greys_r')
for c in content:
#print(str(c))
obj = c.split(" ")
class_nm = obj[0]
color = 'b'
conf = 1.0
if not isGT:
conf = float(obj[1])
xmin = int(obj[2])
ymin = int(obj[3])
width = int(obj[4])
height = int(obj[5])
else:
xmin = int(obj[1])
ymin = int(obj[2])
width = int(obj[3])
height = int(obj[4])
if (conf >= 0.8 and conf < 0.9):
color = 'cyan'
if(conf>=0.7 and conf<0.8):
color = 'green'
if(conf>=0.6 and conf<0.7):
color = 'orange'
if(conf>=0.5 and conf<0.6):
color = 'red'
if(conf<0.5):
color = 'black'
if (height > 0):
if font_path is None:
plt.text(xmin - 2, ymin - 4, class_nm, fontsize=max(int(height / 2), 12), fontdict={"color": 'r'})
else:
prop = FontProperties()
prop.set_file(font_path)
plt.text(xmin-2, ymin-4, class_nm, fontsize= max(int(height/2), 1), fontdict={"color": 'r'}, fontproperties=prop)
if not draw_bbox and color=='b':
continue
ax.add_patch(patches.Rectangle((xmin, ymin), width, height,
linewidth=2, edgecolor=color, facecolor='none'))
# plt.show()
save_img_path=os.path.join(output_dir,file+'_visualize_result.jpg')
fig.savefig(save_img_path,bbox_inches='tight')
def main():
# Font properties to show unicode symbols on plot
global prop
prop = FontProperties()
prop.set_file(FONT_PROPERTIES_TTF_FILE)
rad, dens, pres = read_model()
plot_density(rad, dens)
plot_pressure(rad, pres)
def ramachandran(self):
"""
Plots the torsion anlge as a ramachandran plot.
"""
prop = FontProperties()
prop.set_file('resources/STIXGeneral.ttf')
ax = plt.scatter(self.phi, self.psi)
plt.xlabel(u"\u03C6", fontproperties=prop)
plt.ylabel(u"\u03C8", fontproperties=prop)
plt.show()
def plot_gammaB_energy(path, file_name, gamma_dict, total_energy):
"""Create the plot of gamma*b to energy in log scale."""
plot_gamma = list(gamma_dict.keys())
gamma_array = list(gamma_dict.values())
plot_ener = []
for i in range(len(gamma_array)):
plot_ener.append(sum(gamma_array[i:]))
plot_ener_integral = [i / total_energy for i in plot_ener
] # ratio of energy to the total energy
plot_ener_single = [i / total_energy for i in gamma_array]
prop = FontProperties() # Font properties to show unicode symbols on plot
prop.set_file(FONT_PROPERTIES_TTF_FILE)
name = f'result_ener_vel_{file_name}'
if not args.overlap:
plt.clf()
else:
name = f'result_ener_vel_overlap_{file_name}'
# default style for plot
plt.rcdefaults()
plt.rcParams['figure.figsize'] = [10, 10]
plt.subplots_adjust(hspace=0.5)
plt.style.use('ggplot')
# Plot cumulative
plt.subplot(2, 1, 1)
plt.plot(plot_gamma, plot_ener_integral)
plt.legend()
plt.xlabel(G + B, fontproperties=prop)
plt.ylabel(f'E(>{G}{B})/E0', fontproperties=prop)
plt.ylim(BOTTOM, TOP)
plt.xlim(LEFT, RIGHT)
plt.xscale('log')
plt.yscale('log')
plt.title('Lorentz factor to energy distribution (cumulative)')
# Plot non-cumulative
plt.subplot(2, 1, 2)
plt.plot(plot_gamma, plot_ener_single)
plt.legend()
plt.xlabel(G + B, fontproperties=prop)
plt.ylabel(f'E({G}{B})/E0', fontproperties=prop)
plt.ylim(BOTTOM, TOP)
plt.xlim(LEFT, 2E2)
plt.xscale('log')
plt.yscale('log')
plt.title('Lorentz factor to energy distribution (non-cumulative)')
# Save to file
plt.savefig(f'{path}\\{name}.pdf', bbox_inches='tight')
def get_ds_font(fontName="OpenSans-Regular.ttf"):
"""
载入字体
"OpenSans-Regular.ttf"
"simhei.ttf"
"微软雅黑.ttf"
"""
selfpath = os.path.split(os.path.realpath(__file__))[0]
font0 = FontProperties()
font_path = os.path.join(selfpath, "FNT", fontName)
if os.path.isfile(font_path):
font0.set_file(font_path)
return font0
return None
def get_DS_Font(fontName='OpenSans-Regular.ttf'):
'''
载入字体
'OpenSans-Regular.ttf'
'simhei.ttf'
'微软雅黑.ttf'
'''
selfpath = os.path.split(os.path.realpath(__file__))[0]
font0 = FontProperties()
font_path = os.path.join(selfpath, 'FNT', fontName)
if os.path.isfile(font_path):
font0.set_file(font_path)
return font0
return None
def graph_maker_bar(active, deaths, recovered, filename):
x = ["Active cases", "Deaths", "Recovered"]
y = [active, deaths, recovered]
colours = ["#F79F1F", "#EA2027", "#4cd137"]
plt.style.use("dark_background")
fig, ax = plt.subplots(dpi=150)
for i in range(0, len(x)):
ax.bar(x[i], y[i], 0.75,
align="center",
color=colours[i]
)
ax.yaxis.set_major_formatter(ticker.FuncFormatter(format_values))
plt.minorticks_off()
plt.xticks(fontsize=font_size)
plt.yticks(fontsize=font_size)
fig.autofmt_xdate(rotation=0, ha="center")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.grid(which="both",
axis="y",
color="grey",
linewidth=grid_line_width,
alpha=grid_opacity
)
title_font = FontProperties()
title_font.set_family("sans-serif")
title_font.set_file("./Whitney-Font/whitney-semibold.otf")
title_font.set_size(14)
title_pad = 18
ax.set_title(f"Bar chart", pad=title_pad, fontproperties=title_font)
plt.savefig(f"{filename}.png", transparent=True)
# plt.show()
fig.clear()
plt.close(fig)
def graph_maker_list_bar(numbers, country_codes, filename):
x = country_codes
y = numbers
colour_list = ["#F44336", "#4CAF50", "#3F51B5", "#9C27B0", "#03A9F4", "#CDDC39"]
plt.style.use("dark_background")
fig, ax = plt.subplots(dpi=150)
for i in range(0, len(x)):
ax.bar(x[i], y[i], 0.75,
align="center",
color=colour_list[i]
)
ax.yaxis.set_major_formatter(ticker.FuncFormatter(format_values))
plt.minorticks_off()
plt.xticks(fontsize=font_size)
plt.yticks(fontsize=font_size)
fig.autofmt_xdate(rotation=0, ha="center")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.grid(which="both",
axis="y",
color="grey",
linewidth=grid_line_width,
alpha=grid_opacity
)
title_font = FontProperties()
title_font.set_family("sans-serif")
title_font.set_file("./Whitney-Font/whitney-semibold.otf")
title_font.set_size(14)
title_pad = 18
ax.set_title(f"Bar chart", pad=title_pad, fontproperties=title_font)
plt.savefig(f"{filename}.png", transparent=True)
# plt.show()
fig.clear()
plt.close(fig)
def get_DV_Font(fontName=None):
'''
载入字体
'OpenSans-Regular.ttf'
'simhei.ttf'
'winhei.ttf' 微软雅黑
'''
font0 = FontProperties()
if fontName:
font_path = os.path.join(selfPath, 'FNT', fontName)
if os.path.isfile(font_path):
font0.set_file(font_path)
return font0
# 默认字体
font_path = os.path.join(selfPath, 'FNT', "SourceHanSansCN-Normal.otf")
font0.set_file(font_path)
return font0
def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
# To show UTF-8 Chinese glyphs, these 2 lines are needed
from matplotlib.font_manager import FontProperties
prop = FontProperties()
prop.set_file('wt064.ttf') # [TODO] Specify a font (or at least solve this problem more elegantly)
plt.figure(figsize=(20, 20)) #in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i,:]
plt.scatter(x, y)
plt.annotate(label,
xy=(x, y),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom',
fontproperties=prop)
plt.savefig(filename)
def get_DV_Font(fontName=None):
"""
载入字体
'OpenSans-Regular.ttf'
'simhei.ttf'
'winhei.ttf' 微软雅黑
"""
font0 = FontProperties()
if fontName:
font_path = selfPath / 'FNT' / fontName
if font_path.is_file(): # os.path.isfile(font_path):
font0.set_file(str(font_path))
return font0
# 默认字体
# font_path = str(selfPath / 'FNT' / "SourceHanSansCN-Normal.otf")
font_path = str(selfPath / 'FNT' / "sarasa-ui-sc-regular.ttf")
font0.set_file(font_path)
return font0
def showmat(name, mat, alpha, beta):
sample_dir = 'samples/'
alpha = [a.decode('utf-8') for a in alpha]
beta = [b.decode('utf-8') for b in beta]
fig = plt.figure(figsize=(20, 20), dpi=80)
plt.clf()
matplotlib.rcParams.update({'font.size': 18})
ax = fig.add_subplot(111)
ax.set_aspect(1)
ax.xaxis.tick_top()
res = ax.imshow(mat, cmap=plt.cm.Blues, interpolation='nearest')
font_prop = FontProperties()
font_prop.set_file('./wqy-zenhei.ttf')
font_prop.set_size('large')
plt.xticks(range(len(alpha)), alpha, rotation=60, fontproperties=font_prop)
plt.yticks(range(len(beta)), beta, fontproperties=font_prop)
cax = plt.axes([0.0, 0.0, 0.0, 0.0])
plt.colorbar(mappable=res, cax=cax)
plt.savefig(name + '.png', format='png')
plt.close()
from keras.layers import Input, Dense, Activation, BatchNormalization
from keras.layers import Reshape, Lambda, GlobalAveragePooling2D
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.layers.recurrent import GRU
# from keras.optimizers import SGD
from keras.utils.data_utils import get_file
from keras.preprocessing import image
import keras.callbacks
from matplotlib.font_manager import FontProperties
from keras.utils import to_categorical
# matplotlib.rc('font', family="Lohit Devanagari")
prop = FontProperties(size=16)
prop.set_file('fonts/Sarun_ThangLuang.ttf')
OUTPUT_DIR = 'image_ocr_LP'
# character classes and matching regex filter
regex = r'^[a-z ]+$'
# alphabet = u'abcdefghijklmnopqrstuvwxyz '
alphabet = u'กขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮ๑๒๓๔๕๖๗๘๙๐0123456789 '
np.random.seed(55)
# this creates larger "blotches" of noise which look
# more realistic than just adding gaussian noise
# assumes greyscale with pixels ranging from 0 to 1
def speckle(img):
def main(argv):
note_labels = ["C", 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
if len(argv) < 3:
return
notes = np.load(argv[1])
if notes.shape[-1] == 5:
notes = notes[:,:,:,:,[2,4]]
notes = notes[:20]
notes = notes.reshape((20,4,48,84,2)) # 48 for lakh, 8 for ours
notes[notes < 0] = 0
notes[notes > 0] = 1
# Pitch metrics
pitches = np.amax(notes.transpose(0,1,2,4,3).flatten().reshape(-1,84), axis=0)
pc = np.sum(pitches)
print("pitch count: {}".format(pc))
ranges = np.nonzero(pitches)[0]
pr = ranges[-1] - ranges[0]
print("pitch range: {}".format(pr))
# Pitch and note metrics
histogram = np.zeros(12)
transitions = np.zeros((12,12))
note_histogram = np.zeros(6)
note_tr = np.zeros((6,6))
music = notes.reshape(-1,84,2).transpose(2,0,1)
lengths = {}
lengths2 = set()
lastlastlength = -1
lastlength = -1
for track in music:
for i in range(0, music.shape[1]-5, 4):
indices = np.nonzero(track[i])[0] % 12
ls = []
#print(indices)
for l in lengths.keys():
if l not in indices:
lastlastlength = lastlength
if l == 1:
note_histogram[0] += 1 #8th
lastlength = 0
elif l == 2:
note_histogram[1] += 1 #quarter
lastlength = 1
elif l == 3:
note_histogram[2] += 1 #quarter dot
lastlength = 2
elif l == 4:
note_histogram[3] += 1 #half
lastlength = 3
elif l <= 6:
note_histogram[4] += 1 #half dot
lastlength = 4
elif l <= 8:
note_histogram[5] += 1
lastlength = 5
ls.append(l)
lengths2.add(l)
else:
lengths[l] += 1
if lastlastlength > 0 and lastlength > 0:
note_tr[lastlastlength][lastlength] += 1
for l in ls:
del lengths[l]
for l in indices:
if l not in lengths:
lengths[l] = 1
histogram[indices] += 1
next_indices = np.nonzero(track[i+1])[0] % 12
i1 = indices#np.asarray(list(set(indices).difference(next_indices)), dtype='int64')
#print(indices, next_indices)
i2 = next_indices#np.asarray(list(set(next_indices).difference(indices)), dtype='int64')
#print(i1, i2)
transitions[i1.repeat(len(i2)),np.tile(i2, len(i1))] += 1
indices = np.nonzero(track[-1])[0] % 12
histogram[indices] += 1
plt.bar(range(12), histogram)
plt.yticks([])
plt.xticks(range(12), note_labels)
plt.savefig(argv[2] + '_pch.png')
print('pitch histogram saved')
plt.clf()
#fig, ax = plt.subplots()
transitions_norm = transitions / max(1, transitions.max())
plt.xticks(range(12), note_labels)
plt.yticks(range(12), note_labels)
plt.imshow(transitions_norm, cmap='jet')
plt.colorbar()
plt.savefig(argv[2] + '_pctm.png')
print('pitch transitions saved')
plt.clf()
nc = len(lengths2)
prop = FontProperties()
prop.set_file('FreeSerif.ttf')
print('note count {}'.format(nc))
fig, ax = plt.subplots()
symbolsx = [u"\U0001D160", u"\U0001D15F", u"\U0001D15F\U0001D16D", u"\U0001D15E", u"\U0001D15E\U0001D16D", u"\U0001D15D"]
plt.bar(range(6), note_histogram)
ax.set_xticks(range(len(symbolsx)))
ax.set_xticklabels(symbolsx, fontsize=500, fontproperties=prop)
ax.set_yticks([])
plt.savefig(argv[2] + '_nlh.png')
plt.clf()
print('note histogram saved')
transitions_norm = note_tr / max(1, note_tr.max())
plt.xticks(range(6), symbolsx, size=500, fontproperties=prop)
plt.yticks(range(6), symbolsx, size=500, fontproperties=prop)
plt.imshow(transitions_norm, cmap='jet')
plt.colorbar()
plt.savefig(argv[2] + '_nctm.png')
plt.clf()
print('note transitions saved')
from matplotlib.font_manager import FontProperties
from numpy import *
from pylab import *
# Define vectors and scalars
x = array([1, 2])
y = array([2, 1])
z = x + y
half_x = 0.5 * x
two_x = 2 * x
# Plot vectors
fig = figure(figsize=(20, 8))
suptitle('Simple Vector Operations')
prop = FontProperties()
prop.set_file('STIXMath-Regular.otf')
subplot(1, 2, 1)
quiver((0, 0, 0), (0, 0, 0), (two_x[0], x[0], half_x[0]), (two_x[1], x[1], half_x[1]), angles='xy', scale_units='xy', scale=1, color=('purple', 'r', 'b'))
labels = [u'2x = \u27E82, 4\u27E9', u'x = \u27E81, 2\u27E9', u'0.5x = \u27E80.5, 1\u27E9']
xs = [two_x[0], x[0], half_x[0]]
ys = [two_x[1], x[1], half_x[1]]
for label, u, v in zip(labels, xs, ys):
annotate(label, xy = (u + 0.05, v), fontproperties=prop)
title('Scalar Product')
xlim([0, 5])
ylim([0, 5])
draw()
subplot(1, 2, 2)
plot((x[0], z[0]), (x[1], z[1]), '--r')
def graph_maker_list(country_code_list, filename, cases_type="confirmed", graph_type="linear"):
country_code_list_upper = []
x = []
colour_list = ["#F44336", "#4CAF50", "#3F51B5", "#9C27B0", "#03A9F4", "#CDDC39"]
if len(country_code_list) <= country_limit:
plt.style.use("dark_background")
fig, ax = plt.subplots(dpi=150)
y = []
for u in range(0, len(country_code_list)):
url_graph = f"https://api.covid19api.com/total/country/{country_code_list[u]}/status/{cases_type}"
response = requests.get(url_graph)
data_graph = response.json()
country_code_list_upper.append(str.upper(country_code_list[u]))
if u == 0:
for i in range(0, len(data_graph)):
day = str(data_graph[i]["Date"][8:10])
month = date_formatter(str(data_graph[i]["Date"][5:7]))
dates = month + " " + day
x.append(dates)
y.append([])
for i in range(0, len(data_graph)):
value = per_mil_calculator(data_graph[i]["Country"], data_graph[i]["Cases"])
y[u].append(value)
ax.plot(x, y[u],
color=colour_list[u],
linewidth=line_width,
marker=".",
markerfacecolor=colour_list[u],
markersize=marker_size
)
ax.fill_between(x, 0, y[u], facecolor=colour_list[u], alpha=0.25)
title_font = FontProperties()
title_font.set_family("sans-serif")
title_font.set_file("./Whitney-Font/whitney-semibold.otf")
title_font.set_size(14)
title_pad = 18
if graph_type == "linear":
ax.set_title(f"Linear graph", pad=title_pad, fontproperties=title_font)
plt.yscale("linear")
elif graph_type == "log":
ax.set_title(f"Logarithmic graph", pad=title_pad, fontproperties=title_font)
plt.yscale("log")
plt.ylim(bottom=y_limit)
plt.minorticks_off()
ax.yaxis.set_major_formatter(ticker.FuncFormatter(format_values))
plt.xticks(fontsize=font_size)
plt.yticks(fontsize=font_size)
ax.xaxis.set_major_locator(ticker.IndexLocator(base=20, offset=2))
fig.autofmt_xdate(rotation=0, ha="center")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.grid(which="both",
axis="y",
color="grey",
linewidth=grid_line_width,
alpha=grid_opacity
)
ax.legend(country_code_list_upper, prop={"size": legend_size}, fancybox=True, facecolor="0.25")
plt.savefig(f"{filename}.png", transparent=True)
# plt.show()
fig.clear()
plt.close(fig)
def graph_maker(graph_type, country_code, filename):
url_cases = f"https://api.covid19api.com/total/country/{country_code}/status/confirmed"
response_cases = requests.get(url_cases)
data_cases = response_cases.json()
url_deaths = f"https://api.covid19api.com/total/country/{country_code}/status/deaths"
response_deaths = requests.get(url_deaths)
data_deaths = response_deaths.json()
# url_recovered = f"https://api.covid19api.com/total/country/{country_code}/status/recovered"
# response_recovered = requests.get(url_recovered)
# data_recovered = response_recovered.json()
x = []
y = []
y1 = []
# y2 = []
for i in range(0, len(data_cases)):
day = str(data_cases[i]["Date"][8:10])
month = date_formatter(str(data_cases[i]["Date"][5:7]))
dates = month + " " + day
x.append(dates)
y.append(data_cases[i]["Cases"])
y1.append(data_deaths[i]["Cases"])
# y2.append(data_recovered[i]["Cases"])
# https://www.geeksforgeeks.org/graph-plotting-in-python-set-1/
# https://towardsdatascience.com/customizing-plots-with-python-matplotlib-bcf02691931f
plt.style.use("dark_background")
fig, ax = plt.subplots(dpi=150)
# Plot confirmed cases
ax.plot(x, y,
color="#F79F1F",
linewidth=line_width,
marker=".",
markerfacecolor="#F79F1F",
markersize=marker_size
)
# Plot deaths
ax.plot(x, y1,
color="#EA2027",
linewidth=line_width,
marker=".",
markerfacecolor="#EA2027",
markersize=marker_size
)
# Plot recovered
# ax.plot(x, y2,
# color="#4cd137",
# linewidth=line_width,
# marker=".",
# markerfacecolor="#4cd137",
# markersize=marker_size
# )
# https://matplotlib.org/3.1.1/gallery/ticks_and_spines/tick-locators.html
ax.xaxis.set_major_locator(ticker.IndexLocator(base=20, offset=2))
plt.xticks(fontsize=font_size)
plt.yticks(fontsize=font_size)
fig.autofmt_xdate(rotation=0, ha="center")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.grid(which="both",
axis="y",
color="grey",
linewidth=grid_line_width,
alpha=grid_opacity
)
ax.fill_between(x, y1, y, facecolor="#ff8400", alpha=0.25)
ax.fill_between(x, 0, y1, facecolor="#EA2027", alpha=0.25)
title_font = FontProperties()
title_font.set_family("sans-serif")
title_font.set_file("./Whitney-Font/whitney-semibold.otf")
title_font.set_size(14)
title_pad = 18
ax.legend(["Total", "Deaths"], prop={"size": legend_size}, fancybox=True, facecolor="0.25")
if graph_type == "linear":
ax.set_title(f"Linear graph", pad=title_pad, fontproperties=title_font)
plt.yscale("linear")
elif graph_type == "log":
ax.set_title(f"Logarithmic graph", pad=title_pad, fontproperties=title_font)
plt.yscale("log")
plt.ylim(bottom=y_limit)
plt.minorticks_off()
ax.yaxis.set_major_formatter(ticker.FuncFormatter(format_values))
plt.savefig(f"{filename}.png", transparent=True)
# plt.show()
fig.clear()
plt.close(fig)
