def print_save_all_mean_faces(x_class_mean, global_mean, show, save):
rows = 4
cols = 14
index = 1
font_size = 10
plt.figure(figsize=(20, 10))
plt.subplot(rows, cols,
index), plt.imshow(np.reshape(global_mean, (46, 56)).T,
cmap='gist_gray')
title = str("Global Mean")
plt.title(title, fontsize=font_size).set_position([0.5, 0.95]), plt.xticks(
[]), plt.yticks([])
index = index + 1
for i in range(0, x_class_mean.shape[1]):
title = str("Class Mean " + str(i + 1))
plt.subplot(rows, cols,
index), plt.imshow(np.reshape(x_class_mean[:, i],
(46, 56)).T,
cmap='gist_gray')
plt.title(title, fontsize=font_size).set_position(
[0.5, 0.95]), plt.xticks([]), plt.yticks([])
index = index + 1
if show == 'yes':
plt.show()
if save == 'yes':
plt.savefig('Global and Class Mean')
plt.close()
def plot_sleep(label, pred, file_time, kfold, name):
x = range(0, len(label))
y = np.arange(5)
y_stage = ["W", "REM", "N1", "N2", "N3"]
plt.figure(figsize=(24, 8))
plt.ylabel("Sleep Stage")
plt.xlabel("Sleep Time")
time_choice = np.array([
s.decode('UTF-8')
for s in file_time[0:len(file_time):int(len(file_time) / 10)]
])
plt.xticks(range(0, len(file_time), int(len(file_time) / 10)),
time_choice) ##为了将坐标刻度设为字
plt.yticks(y, y_stage) ##为了将坐标刻度设为字
# plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签
plt.plot(x,
pred,
'r-',
color='blue',
alpha=1,
linewidth=1,
label='predict')
plt.plot(x, label, 'r-', color='red', alpha=1, linewidth=1, label='label')
plt.legend(loc='best')
plt.savefig(sleepPicture_path + str(kfold) + "sdt" + str(name) +
".svg") ##保存睡眠模型图文件
plt.close()
def chartData(data, keyword):
#Takes input of the results of the fts search and the phrase that was searched for
#and creates bar chart of occurences by book for the phrase
from matplotlib import pyplot as plt
import numpy as np
import math
data = sorted(data, key=lambda x: x[1], reverse=True)
info = []#number of occurences
books = []#list of books
for d in data:
info.append(d[1])
b = d[0]
books.append(b[b.find('LIBER'):])#Use only book number for label since name is the same for all books
#create chart
plt.close()
fig = plt.figure()
width = 0.4
ind = np.arange(len(books))
plt.bar(ind, info, width=width)
plt.xticks(ind + width/2., books)
plt.yticks(np.arange(0,max(info)*2,math.ceil(max(info)/5)))
plt.ylabel('Number of Occurences')
plt.xlabel('Books')
plt.title('Occurences of "' + keyword + '" by book in Curtius Rufus (Latin)')
fig.autofmt_xdate()
plt.show(block=False)#display plot, and continue with program
def shist(self,data,sflag,spath,sname,pflag):
g.tprinter('Running sist',pflag)
xdata=data[:,0]
ydata=data[:,1]
plt.plot(xdata,ydata,'ro')
plt.savefig(os.path.join(spath,str(sname))+'.pdf')
plt.close()
def plotConfusionMatrix(lbllist, predlist, classes, type):
confusionMatrix = confusion_matrix(lbllist, predlist)
# print(confusionMatrix)
plt.imshow(confusionMatrix, interpolation="nearest", cmap=plt.cm.Blues)
if type == 'train':
plt.title("Confusion matrix training")
elif type == 'test':
plt.title("Confusion matrix testing")
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = "d"
thresh = confusionMatrix.max() / 2.
for i, j in itertools.product(range(confusionMatrix.shape[0]),
range(confusionMatrix.shape[1])):
plt.text(j,
i,
format(confusionMatrix[i, j], fmt),
horizontalalignment="center",
color="white" if confusionMatrix[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel("True label")
plt.xlabel("Predicted label")
# plt.show()
if type == 'train':
plt.savefig(LOG_PATH + 'Confusion matrix training.png')
elif type == 'test':
plt.savefig(LOG_PATH + 'Confusion matrix testing.png')
plt.close()
def plot_stacked_barchart(self, dataframe, sort, title, xlable, ylable,
kurs):
x = []
tutor = []
y = []
for i in dataframe['tutor']:
if i not in tutor:
tutor.append(i)
y.append([])
for i, elem in enumerate(dataframe[sort]):
print(y, elem)
if elem in x:
y[tutor.index(dataframe['tutor'][i])][x.index(elem)] += 1
else:
x.append(elem)
for j, elem2 in enumerate(tutor):
y[j].append(0)
y[tutor.index(dataframe['tutor'][i])][x.index(elem)] += 1
for i, elem in enumerate(y):
plt.bar(range(len(elem)), elem, label=tutor[i])
plt.xlabel(xlable)
plt.ylabel(ylable)
plt.legend(loc="best")
plt.savefig('./PDFcreater/Plots/{}/{}.png'.format(kurs, title))
#plt.show()
# loescht den Plot fuer den Naechsten Plot
plt.clf()
plt.cla()
plt.close()
def print_path_prob_figure(filename,
bins,
histo,
dx,
path_prob,
smooth_path_prob,
cutoff=200):
assert isinstance(filename, str), 'filename must be a string'
filename = os.path.splitext(filename)[0] + '.png'
matplotlib = try_import_matplotlib()
if matplotlib is None:
return
else:
from matplotlib import pyplot as plt
figure = plt.figure(figsize=(7, 7))
s = np.sum(histo, axis=0)
v1 = np.where(s >= cutoff, path_prob, 0)
v2 = np.where(s < cutoff, path_prob, 0)
v3 = np.where(s >= cutoff, smooth_path_prob, 0.)
plt.bar(bins[:-1], v1, width=dx, align='edge', color='red', alpha=1)
plt.bar(bins[:-1], v2, width=dx, align='edge', color='red', alpha=0.7)
plt.plot(bins[:-1] + dx / 2, v3, color='orange')
plt.ylabel('pathogenicity prob.')
plt.xlabel('predicted score')
plt.ylim((0, 1))
figure.savefig(filename, format='png', bbox_inches='tight')
plt.close()
plt.rcParams.update(plt.rcParamsDefault)
LOGGER.info(f'Pathogenicity plot saved to {filename}')
def plot_projected_3faces_onto_fisherspace(Wopt,X_train,Y_train,Mpca,mode):
if mode == 'none':
return None
projected = np.dot(Wopt.T,X_train.T)
projected = projected.T
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
label = 0
for i in range(0,24):
Label = Y_train[(label)*8+i]
x_train = projected[(label)*8+i][0]
y_train = projected[(label)*8+i][1]
z_train = projected[(label)*8+i][2]
ax.text(x_train,y_train,z_train,str(Label), color="black", fontsize=12)
if i == 0:
ax.scatter(x_train,y_train,z_train, c='black', marker='o', s=5, label='Training Data')
else:
ax.scatter(x_train,y_train,z_train, c='black', marker='o', s=5)
ax.legend(loc = 'lower right')
title = str("Projection of first 3 Classes with Mpca="+str(Mpca)+" into best 3 Principal Components")
plt.title(title)
if mode == 'show':
plt.show()
plt.close()
if mode == 'save':
plt.savefig(title)
plt.close()
def plot_df(
df1, df2, plot_title, x_axis, y_axis, plot, save
): # function for plotting high-dimension and low-dimension eigenvalues
y1 = df1[df1.columns[1]]
x1 = df1['M']
y2 = df2[df2.columns[1]]
x2 = df2['M']
plt.figure(figsize=(8, 8))
plt.plot(x1, y1, color='red')
plt.plot(x2, y2, color='blue')
plt.grid(color='black', linestyle='-',
linewidth=0.1) # parameters for plot grid
plt.xticks(np.arange(0,
max(x1) * 1.1,
int(max(x1) / 10))) # adjusting the intervals to 250
plt.yticks(np.arange(0, max(y1) * 1.1, int(max(y1) / 10)))
plt.title(plot_title).set_position([0.5, 1.05])
plt.xlabel(x_axis)
plt.ylabel(y_axis)
plt.legend(loc='best') # creating legend and placing in at the top right
if save == 'yes':
plt.savefig(plot_title)
if plot == 'yes':
plt.show()
plt.close()
def fnctn(a,c):
df2 = pd.DataFrame(data.groupby(a)[c].count())
df2=df2.astype(float)
plot1 = plt.bar(x=df2.index,y=df2.iloc[:,0])
plot1.set_xticklabels(plot1.get_xticklabels(), rotation=30, fontsize=6)
plot1.figure.savefig("./static/q18.jpg")
plt.close()
def forward(self, x):
# polar plot
dic = creatRealDictionary(self.T, self.rr, self.theta, self.gid)
sparsecode = fista(dic, x, self.lam, 100, self.gid)
if random.randint(1, 20) == 1:
plt.figure()
plt.pcolormesh(sparsecode[0, :, :].data.cpu().detach().numpy(),
cmap='RdBu')
plt.colorbar()
plt.savefig('C_evolution_subsIni.png') #, dpi=200
# plt.show()
plt.close()
rr = self.rr.data.cpu().detach().numpy()
theta = self.theta.data.cpu().detach().numpy()
ax = plt.subplot(1, 1, 1, projection='polar')
ax.scatter(0, 1, c='black')
# unactive poles
ax.scatter(theta, rr)
ax.scatter(-theta, rr)
ax.scatter(np.pi - theta, rr)
ax.scatter(theta - np.pi, rr)
#
ax.set_rmax(1.2)
ax.set_title("Dictionary", va='bottom')
plt.savefig('usedPolesDCT.png')
# plt.show()
plt.close()
return Variable(sparsecode)
def print_pred_distrib_figure(filename, bins, histo, dx, J_opt):
assert isinstance(filename, str), 'filename must be a string'
filename = os.path.splitext(filename)[0] + '.png'
matplotlib = _try_import_matplotlib()
if matplotlib is None:
return
else:
from matplotlib import pyplot as plt
figure = plt.figure(figsize=(7, 7))
plt.bar(bins[:-1],
histo[0],
width=dx,
align='edge',
color='blue',
alpha=0.7,
label='neutral')
plt.bar(bins[:-1],
histo[1],
width=dx,
align='edge',
color='red',
alpha=0.7,
label='deleterious')
plt.axvline(x=J_opt, color='k', ls='--', lw=1)
plt.ylabel('distribution')
plt.xlabel('predicted score')
plt.legend()
figure.savefig(filename, format='png', bbox_inches='tight')
plt.close()
plt.rcParams.update(plt.rcParamsDefault)
LOGGER.info(f'Predictions distribution saved to {filename}')
def plotFeatImportance(pathOut,
imp,
oob,
oos,
method,
tag=0,
simNum=0,
**kargs):
# plot mean imp bars with std
mpl.figure(figsize=(10, imp.shape[0] / 5.))
imp = imp.sort_values('mean', ascending=True)
ax = imp['mean'].plot(kind='barh',
color='b',
alpha=0.25,
xerr=imp['std'],
error_kw={'ecolor': 'r'})
if method == 'MDI':
mpl.xlim([0, imp.sum(axis=1).max()])
mpl.axvline(1. / imp.shape[0], lw=1., color='r', ls='dotted')
ax.get_yaxis().set_visible(False)
for i, j in zip(ax.patches, imp.index):
ax.text(i.get_width() / 2,
i.get_y() + i.get_height() / 2,
j,
ha='center',
va='center',
color='k')
mpl.title('tag=' + tag + ' | simNUm=' + str(simNum) + ' | oob=' +
str(round(oob, 4)) + ' | oos=' + str(round(oos, 4)))
mpl.savefig(pathOut + 'featImportance_' + str(simNum) + '.png', dpi=100)
mpl.clf()
mpl.close()
return
def print_ROC_figure(filename, fpr, tpr, auc_stat):
assert isinstance(filename, str), 'filename must be a string'
filename = os.path.splitext(filename)[0] + '.png'
matplotlib = _try_import_matplotlib()
if matplotlib is None:
return
else:
from matplotlib import pyplot as plt
fig = plt.figure(figsize=(7, 7))
plt.plot([0, 1], [0, 1], linestyle='--', lw=1, color='k')
plt.plot(fpr,
tpr,
linestyle='-',
lw=2,
color='r',
label='AUROC = {:.3f} +/- {:.3f}'.format(*auc_stat))
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('mean ROC curve from cross-validation')
plt.legend(loc="lower right")
fig.savefig(filename, format='png', bbox_inches='tight')
plt.close()
plt.rcParams.update(plt.rcParamsDefault)
LOGGER.info(f'ROC plot saved to {filename}')
def plot_3df(
df, plot_title, x_axis, y_axis, plot, save
): # function for plotting high-dimension and low-dimension eigenvalues
x1 = df[df.columns[0]]
y1 = df[df.columns[1]]
y2 = df[df.columns[2]]
y3 = df[df.columns[3]]
min1 = df[df.columns[1]].min()
min2 = df[df.columns[2]].min()
min3 = df[df.columns[3]].min()
df_min = min(min1, min2, min3)
plt.figure(figsize=(8, 8))
plt.plot(x1, y1, color='red')
plt.plot(x1, y2, color='blue')
plt.plot(x1, y3, color='green')
plt.grid(color='black', linestyle='-',
linewidth=0.1) # parameters for plot grid
plt.xticks(np.arange(0,
max(x1) * 1.1,
int(max(x1) / 10))) # adjusting the intervals to 250
plt.yticks(np.arange(df_min * 0.9, 100, int(max(y1) / 10)))
plt.title(plot_title).set_position([0.5, 1.05])
plt.xlabel(x_axis)
plt.ylabel(y_axis)
plt.legend(loc='best') # creating legend and placing in at the top right
if save == 'yes':
plt.savefig(plot_title)
if plot == 'yes':
plt.show()
plt.close()
def plotTrainingAccuracy(trainAccuracyPerIteration):
dataFrame, fullAccuracyList, finalAccuracyList = getOptimizerLists(
trainAccuracyPerIteration)
styles = []
colors = []
for i in range(0, len(trainAccuracyPerIteration)):
styles.append('.--')
colors.append('grey')
styles[finalAccuracyList.index(max(finalAccuracyList))] = '.-'
colors[finalAccuracyList.index(max(finalAccuracyList))] = 'green'
styles[finalAccuracyList.index(min(finalAccuracyList))] = '.-'
colors[finalAccuracyList.index(min(finalAccuracyList))] = 'red'
dataFrame.plot(x='Epoch',
y=fullAccuracyList,
style=styles,
color=colors,
legend=False)
# plt.xticks(rotation=45)
plt.title("Accuracy performance training")
plt.xlabel("Epoch")
plt.ylabel("Accuracy (batch)")
# plt.show()
plt.savefig(LOG_PATH + 'Training accuracy.png')
plt.close()
def plotPC(PC1, PC2, labelList):
"""Plots a scatter plot of the any 2 specified dimensions after running PCA."""
pc1 = [[], [], [], [], [], [], [], [], [], []]
pc2 = [[], [], [], [], [], [], [], [], [], []]
for l in range(len(labelList)):
# l returns a number within a numpy array
actualNum = labelList[l][0]
pc1[actualNum].append(PC1[l])
pc2[actualNum].append(PC2[l])
fig = plt.figure()
ax = fig.add_subplot(111)
colorList = [
"red", "green", "blue", "black", "gray", "yellow", "cyan", "magenta",
"burlywood", "purple"
]
for count in range(10):
plt.scatter(pc1[count],
pc2[count],
c=colorList[count],
lw=0,
label=str(count))
plt.legend(scatterpoints=1)
ax.set_xlabel("PC1")
ax.set_ylabel("PC2")
fig.savefig("2D_10MNistGraph.png")
plt.close()
def write_pc_hist(df, p, args):
# Pivot, and then drop the "cycles" level of the multi-index.
df = pd.pivot_table(df, index=["pc"], columns="operation")
df = df.fillna(0)
df = df.droplevel(level=0, axis="columns")
# Use the colors key order above to stack the bars,
# but first we have to pick stalls that are actually IN the CSV (not all are printed)
cols = [k for k in colors.keys() if k in df.columns]
df = df[cols]
# Remove all PCs that were specified using the without flag
filts = {int(pc, 16) for pc in args.without}
fi = [pc for pc in df.index if int(pc, 16) not in filts]
removed = [pc for pc in df.index if int(pc, 16) in filts]
df = df.loc[fi]
print(f"Removed PCs: {removed}")
height = df.shape[0] * (labelsize + 4) / 72
ax = df.plot.barh(stacked=True, figsize=(11, height), color=colors)
ax.set_ylabel("Program Counter")
ax.set_xlabel(f"Cycles * 10^{math.floor(math.log10(ax.get_xlim()[1]))}")
ax.set_title(f"HammerBlade Program Counter Cycles Histogram")
ax.tick_params(labelsize=labelsize)
fig = ax.get_figure()
plt.gca().invert_yaxis()
plt.legend(loc="upper left")
plt.tight_layout()
fig.savefig(p / "pc_hist.pdf")
plt.close(fig)
def generate_hashtag_top_five_piechart(self):
#This function generates pie chart for different attributs of tweets that we collected.
tweet_attribute = tweets_by_hashtag(self.tweet_frame)
fig = tweet_attribute[0:5].plot.pie(figsize=(10, 10))
fig.set_title('Pie chart of Top 5 ' + str(self.input_string))
plt.savefig('Pie chart of Top 5 ' + str(self.input_string) + '.pdf')
plt.show()
plt.close()
def fnctn(a,c):
df2 = pd.DataFrame(data.groupby(a)[c].count())
df2=df2.astype(float)
plot = df2.plot.pie(y=c, figsize=(20, 20), autopct='%1.0f%%', pctdistance=1.1, labeldistance=1.2, textprops={'fontsize': 18})
print "xyz"
plt.show()
plot.figure.savefig("./static/q18.jpg")
plt.close()
def write_bb_hist(df, p, args):
# Pivot, and then drop the "cycles" level of the multi-index.
df = pd.pivot_table(df, index=["pc"], columns="operation")
df = df.fillna(0)
df = df.droplevel(level=0, axis="columns")
# Group together floating point and regular instructionos
tot_instrs = df.Instruction + df["FPU Instruction"]
# Group together PCs that have the same number of executions
bb_ranges = (tot_instrs != tot_instrs.shift()).cumsum()
df = df.groupby(bb_ranges).sum()
# Get the new grouped index
bb_tups = [(bb_ranges[bb_ranges == x].index.min(),
bb_ranges[bb_ranges == x].index.max()) for x in df.index]
bb_ranges = [range(int(x, 16), int(y, 16) + 1) for (x, y) in bb_tups]
# Filter BBs that include PCs that were specified with --without
filts = {int(pc, 16) for pc in args.without}
df.index = [x + "-" + y for (x, y) in bb_tups]
fi = [
x + "-" + y for (x, y) in bb_tups
if (all(e not in range(int(x, 16),
int(y, 16) + 1) for e in filts))
]
removed = [
x + "-" + y for (x, y) in bb_tups
if (any(e in range(int(x, 16),
int(y, 16) + 1) for e in filts))
]
# TODO: Print filtered BBs on graph?
print(f"Removed Basic Blocks: {removed}")
df = df.loc[fi]
ipc = (df.Instruction + df["FPU Instruction"]) / df.sum(axis=1)
pct = 100.0 * df.sum(axis=1) / df.sum(axis=1).sum()
idx = df.index.to_series()
idx = idx.combine(pct, lambda i, pct: f"{i} ({pct:.0f}%".rjust(10))
idx = idx.combine(ipc, (lambda i, ipc: f"{i} @ {ipc:1.3f})"))
df.index = idx
# Use the colors key order above to stack the bars,
# but first we have to pick stalls that are actually IN the CSV (not all are printed)
cols = [k for k in colors.keys() if k in df.columns]
height = df.shape[0] * (labelsize + 4) / 72
ax = df[cols].plot.barh(stacked=True, figsize=(11, height), color=colors)
ax.set_ylabel("Basic Block Range (% Cycles @ IPC)")
ax.set_xlabel(f"Cycles * 10^{math.floor(math.log10(ax.get_xlim()[1]))}")
ax.set_title(f"HammerBlade Basic Block Cycles Histogram")
ax.tick_params(labelsize=labelsize)
fig = ax.get_figure()
plt.gca().invert_yaxis()
plt.legend(loc="upper right")
plt.tight_layout()
fig.savefig(p / "bb_hist.pdf")
plt.close(fig)
def Histogram(filename):
volume = ReadInAttributeFromCSV(filename, 'Personal Injury')
plt.title('Annual Personal Injury')
plt.hist(volume, color='black')
plt.ylabel('Number of')
plt.xlabel('Seasons')
plt.savefig('visual')
plt.close()
def fnctn(a,c):
#os.remove(filename)
df2 = pd.DataFrame(data.groupby(a)[c].sum())
df2=df2.astype(float)
plot1 = plt.plot(df2.index,df2[c])
plt.xticks(fontsize=10, rotation=30)
#plt.set_size_inches(8, 6)
plt.savefig("./static/q18.jpg")
plt.close()
plt.show()
def plot_pie(self, nx_dataframe, topic, se_title, kurs):
gender = self.sort_column(nx_dataframe[topic])
labels = [gender[0][i] for i,elem in enumerate(gender[0])]
fracs = [gender[1][i] for i,elem in enumerate(gender[1])]
explode = [0.05 for i,elem in enumerate(gender[1])]
plt.pie(fracs, explode=explode, labels=labels, autopct='%.0f%%', shadow=True)
plt.savefig('./PDFcreater/Plots/{}/1{}.png'.format(kurs,se_title))
plt.clf()
plt.cla()
plt.close()
def plot_boring_barchart(self,dataframe,x,y,title,xlable,ylable, kurs):
plt.bar(x, y, color='blue')
#plt.title(title)
plt.xlabel(xlable)
plt.ylabel(ylable)
plt.savefig('./PDFcreater/Plots/{}/{}.png'.format(kurs,title))
#loescht den Plot fuer den Naechsten Plot
plt.clf()
plt.cla()
plt.close()
def feature_summary(x_col, y_col, show_r2=False):
"""Gives a summary of a feature
:return:
"""
# Preparation
x_name = x_col.name
y_name = y_col.name
df = pd.concat([x_col, y_col], axis=1).sort_index()
plt.rcParams["figure.figsize"] = (10, 7)
breaks(1)
print("%s" % x_name)
print('Quantile:\n', x_col.quantile([0.0, 0.1, 0.25, 0.5, 0.75, 1.0]))
# Histogram
plt.subplot(221)
try:
plt.hist(x_col, bins=30)
plt.xlabel(x_name)
plt.title('Histogram (CF GHP): %s' % x_name)
except ValueError:
print("No histogram for %s available" % x_name)
# Correlation
if y_name != x_name:
df = df.sort_values(x_name)
# df[x_name + "_2"] = df[x_name] * df[x_name]
# df[x_name + "_3"] = df[x_name] * df[x_name] * df[x_name]
x = df.drop(y_name, 1)
reg = linear_model.LinearRegression(normalize=True)
reg.fit(x, df[y_name])
# Plot
plt.subplot(222)
plt.scatter(df[x_name], df[y_name])
plt.plot(df[x_name], reg.predict(x), color='g')
plt.xlabel(x_name)
plt.xlim([df[x_name].min(), df[x_name].max()])
plt.title('x:%s / y:%s ' % (x_name, y_name))
plt.ylabel("Target function: %s" % y_name)
if show_r2:
print("R²:", r2_score(df[y_name], reg.predict(x)))
print(feature_importance(x, reg.coef_))
# Show plots
plt.show()
# Timeline
x_col.rolling(window=10,
center=False).mean().plot(title='%s: Timeline' % x_name,
figsize=(10, 2),
xlim=(170000, 175000))
plt.show()
plt.close('all')
return " "
def draw_boxes_on_image(image_path: str,
boxes: np.ndarray,
scores: np.ndarray,
labels: np.ndarray,
label_names: List[str],
score_thresh: float = 0.5,
save_path: str = 'result'):
"""Draw boxes on images."""
image = np.array(PIL.Image.open(image_path))
plt.figure()
_, ax = plt.subplots(1)
ax.imshow(image)
image_name = image_path.split('/')[-1]
print("Image {} detect: ".format(image_name))
colors = {}
for box, score, label in zip(boxes, scores, labels):
if score < score_thresh:
continue
if box[2] <= box[0] or box[3] <= box[1]:
continue
label = int(label)
if label not in colors:
colors[label] = plt.get_cmap('hsv')(label / len(label_names))
x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
rect = plt.Rectangle((x1, y1),
x2 - x1,
y2 - y1,
fill=False,
linewidth=2.0,
edgecolor=colors[label])
ax.add_patch(rect)
ax.text(x1,
y1,
'{} {:.4f}'.format(label_names[label], score),
verticalalignment='bottom',
horizontalalignment='left',
bbox={
'facecolor': colors[label],
'alpha': 0.5,
'pad': 0
},
fontsize=8,
color='white')
print("\t {:15s} at {:25} score: {:.5f}".format(
label_names[int(label)], str(list(map(int, list(box)))), score))
image_name = image_name.replace('jpg', 'png')
plt.axis('off')
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.savefig("{}/{}".format(save_path, image_name),
bbox_inches='tight',
pad_inches=0.0)
plt.cla()
plt.close('all')
def imgOut(epoch, generator):
rand_nums = np.random.randint(0, x_test_hr.shape[0], size=1)
image_batch_hr = denormalize(x_test_hr[rand_nums])
image_batch_lr = x_test_lr[rand_nums]
gen_img = generator.predict(image_batch_lr)
generated_image = denormalize(gen_img)
image_batch_lr = denormalize(image_batch_lr)
image_batch_bc = upsample(image_batch_lr, 4)
plt.figure(figsize=(15, 5))
dim = (1, 4) # 4 wide
# lowres
plt.subplot(dim[0], dim[1], 1)
plt.title('Low Res')
plt.imshow(image_batch_lr[0], interpolation='nearest')
plt.axis('off')
# naive resize
plt.subplot(dim[0], dim[1], 2)
plt.title('Naive Resize')
plt.imshow(image_batch_bc[0], interpolation='nearest')
plt.axis('off')
# generated
plt.subplot(dim[0], dim[1], 3)
plt.title('Generated SR')
plt.imshow(generated_image[0], interpolation='nearest')
plt.axis('off')
# truth
plt.subplot(dim[0], dim[1], 4)
plt.title('Truth')
plt.imshow(image_batch_hr[0], interpolation='nearest')
plt.axis('off')
# save file
plt.tight_layout()
plt.savefig(imgSav % epoch)
plt.close('all')
# learning gif images
me_dir = os.path.join('C:\\', 'Users', 'cruze', 'Documents', 'CS767',
'finalProj--')
me_path = os.path.join(me_dir, 'lowresme.jpg')
me = Image.open(me_path)
me = np.expand_dims(me, axis=0)
me_lr = np.array(me)
me_lr_norm = normalize(me_lr)
gen_img = generator.predict(me_lr_norm)
generated_image = denormalize(gen_img)
plt.imshow(generated_image[0])
plt.savefig(imgMeSav % epoch)
plt.close('all')
def cm_plot(original_label, predict_label, kunm, pic=None):
prec_score = precision_score(original_label, predict_label, average=None)
recall = recall_score(original_label, predict_label, average=None)
f1 = f1_score(original_label, predict_label, average=None)
cm = confusion_matrix(original_label, predict_label)
cm_new = np.empty(shape=[5, 5])
for x in range(5):
t = cm.sum(axis=1)[x]
for y in range(5):
cm_new[x][y] = round(cm[x][y] / t * 100, 2)
plt.figure()
plt.matshow(cm_new, cmap=plt.cm.Blues)
plt.colorbar()
x_numbers = []
y_numbers = []
cm_percent = []
for x in range(5):
y_numbers.append(cm.sum(axis=1)[x])
x_numbers.append(cm.sum(axis=0)[x])
for y in range(5):
percent = format(cm_new[x, y] * 100 / cm_new.sum(axis=1)[x], ".2f")
cm_percent.append(str(percent))
plt.annotate(format(cm_new[x, y] * 100 / cm_new.sum(axis=1)[x],
".2f"),
xy=(y, x),
horizontalalignment='center',
verticalalignment='center',
fontsize=10)
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.title('confusion matrix')
y_stage = [
"W\n(" + str(y_numbers[0]) + ")", "N1\n(" + str(y_numbers[1]) + ")",
"N2\n(" + str(y_numbers[2]) + ")", "N3\n(" + str(y_numbers[3]) + ")",
"REM\n(" + str(y_numbers[4]) + ")"
]
x_stage = [
"W\n(" + str(x_numbers[0]) + ")", "N1\n(" + str(x_numbers[1]) + ")",
"N2\n(" + str(x_numbers[2]) + ")", "N3\n(" + str(x_numbers[3]) + ")",
"REM\n(" + str(x_numbers[4]) + ")"
]
y = [0, 1, 2, 3, 4]
plt.xticks(y, x_stage)
plt.yticks(y, y_stage)
#sns.heatmap(cm_percent, fmt='g', cmap="Blues", annot=True, cbar=False, xticklabels=x_stage, yticklabels=y_stage) # 画热力图,annot=True 代表 在图上显示 对应的值， fmt 属性 代表输出值的格式，cbar=False, 不显示 热力棒
plt.savefig(savepath + "matrix" + str(kunm) + ".svg")
#plt.show()
plt.show()
plt.close()
# plt.savefig("/home/data_new/zhangyongqing/flx/pythoncode/"+str(knum)+"matrix.jpg")
return kappa(cm), classification_report(original_label, predict_label)
def plot_summary_to_pdf(outfile, spath='./*.sum'):
files = glob.glob(spath)
with PdfPages(outfile) as pdf:
for f in files:
print(f)
sum_f = Summary()
sum_f.read(f)
sum_f.data.plot(subplots=True, figsize=(10, 10), title=str(f))
pdf.savefig() # saves the current figure into a pdf page
plt.close()
def _render_to_rgb(self):
""" convert the output of render_to_figure to a rgb_array """
self._render_to_figure()
self._figure.canvas.draw()
buf = self._figure.canvas.tostring_rgb()
num_cols, num_rows = self._figure.canvas.get_width_height()
plt.close(self._figure)
self._figure = None
result = np.fromstring(buf,
dtype=np.uint8).reshape(num_rows, num_cols, 3)
return result
def print_scatter_data():
import matplotlib.pylab as plt
filename = Par.dirname + ('/Scatter.dat')
fitnesses = []
self_reliences = []
life_times = []
self_reliences_dead = []
for Agent in Par.Agents:
if Agent.dead != True:
fitnesses.append(Agent.fitness)
Needs = Agent.needs
Production = Agent.production
selfReli = [0.0]*Par.num_resources
for i in range(Par.num_resources):
selfReli[i] = Production[i]*Needs[i]
self_reliences.append(abs(sum(selfReli)))
else:
life_time = Agent.t_death - Agent.t_discovery
life_times.append(life_time)
Needs = Agent.needs
Production = Agent.production
selfReli = [0.0]*Par.num_resources
for i in range(Par.num_resources):
selfReli[i] = Production[i]*Needs[i]
self_reliences_dead.append(abs(sum(selfReli)))
file =open(filename, 'w')
for i in range(len(fitnesses)):
s= str(fitnesses[i]) +' '+ str(self_reliences[i])
file.write(s)
file.write('\n')
file.close()
plt.scatter(self_reliences, fitnesses)
plt.ylabel('Fitness')
plt.xlabel('self_reliences')
plt.savefig('FitnessVSR.png')
plt.close()
plt.scatter(self_reliences_dead, life_times)
plt.ylabel('LifeTimes')
plt.xlabel('self_reliences')
plt.savefig('LifeTimeVSR.png')
plt.close()
def plot_data():
import matplotlib.pylab as plt
t, suffering = np.loadtxt('suffering.dat', unpack= True, usecols = (0,1))
t, fitness = np.loadtxt('fitness.dat', unpack = True, usecols = (0,1))
plt.plot(t, suffering)
plt.xlabel('t')
plt.ylabel('suffering')
plt.savefig('suffering.png')
plt.close()
plt.plot(t, fitness)
plt.xlabel('t')
plt.ylabel('fitness')
plt.savefig('fitness.png')
plt.close()
def __save(self,n,plot,sfile):
p.figure(figsize=sfile)
p.xlabel(plot.xlabel)
p.ylabel(plot.ylabel)
p.xscale(plot.xscale)
p.yscale(plot.yscale)
p.grid()
for curve in plot.curves:
if curve[1] == None: p.plot(curve[0],curve[2], label=curve[3])
else: p.plot(curve[0], curve[1], curve[2], label=curve[3])
p.rc('legend', fontsize='small')
p.legend(shadow=0, loc='best')
p.axes().set_aspect(plot.aspect)
if not plot.dir: plot.dir = './plots/'
if not plot.name: plot.name = self.__global_name+'_%0*i'%(2,n)
if not os.path.isdir(plot.dir): os.mkdir(plot.dir)
if plot.pgf: p.savefig(plot.dir+plot.name+'.pgf')
else: p.savefig(plot.dir+plot.name+'.pdf', bbox_inches='tight')
p.close()