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 plottrace(self, point):
# 使用matplotlib之pyplot绘制船舶轨迹
# point = 38
def initial(ax):
ax.axis("equal") #设置图像显示的时候XY轴比例
ax.set_xlabel('Horizontal Position')
ax.set_ylabel('Vertical Position')
ax.set_title('Vessel trajectory')
plt.grid(True) #添加网格
return ax
es_time = np.zeros([point])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax = initial(ax)
# test
ax2 = fig.add_subplot(1, 1, 1)
ax2 = initial(ax2)
plt.ion() #interactive mode on 动态绘制
# IniObsX=0000
# IniObsY=4000
# IniObsAngle=135
# IniObsSpeed=10*math.sqrt(2) #米/秒
# print('开始仿真')
obsX = []
obsX2 = []
# obsY = [4000,]
obsY = []
obsY2 = []
for t in range(point):
# t0 = time.time()
#障碍物船只轨迹
# obsX.append(IniObsX+IniObsSpeed*math.sin(IniObsAngle/180*math.pi)*t)
obsX.append(sim_res.SHIP1POS[t][0])
obsX2.append(sim_res.SHIP2POS[t][0])
# obsY.append(IniObsY+IniObsSpeed*math.cos(IniObsAngle/180*math.pi)*t)
obsY.append(sim_res.SHIP1POS[t][1])
obsY2.append(sim_res.SHIP2POS[t][1])
plt.cla()
ax = initial(ax)
ax.plot(obsX, obsY, '-g', marker='*') #散点图
# test
ax2 = initial(ax2)
ax2.plot(obsX2, obsY2, '-r', marker='o')
risk_value_text = 'Risk value: ' + str(
sim_res.RISKVALUE[t])
plt.text(0, 7, risk_value_text)
plt.pause(0.5)
# es_time[t] = 1000*(time.time() - t0)
plt.pause(0)
# return es_time
pass
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 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')
z = np.array([pose_3d[i[0]][2], pose_3d[i[1]][2]])
ax.plot3D(x, y, z, 'red')
def add_point(pose_3d, ax):
x, y, z = pose_3d[:, 0], pose_3d[:, 1], pose_3d[:, 2]
ax.scatter3D(x, y, z, cmap='Greens')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection='3d')
plt.ion()
i = 0
for pose_3d in pose_3d_all:
add_line(pose_3d, ax)
add_point(pose_3d, ax)
square_x = np.array([-2000, 1000])
square_y = np.array([-1000, 2000])
square_z = np.array([500, 3500])
ax.scatter3D(square_x, square_y, square_z, color='whitesmoke')
plt.draw()
plt.pause(0.01)
if i == 0:
plt.pause(2)
i += 1
plt.cla()
##############################################################################################################
##### using lstm #####
##############################################################################################################
# generates lstm model for s (takes about 1-2 minutes) :
src = lol.weather_station(df_t, dhist, dhoriz, n_years_2train, 'lstm')
start = src.data.index[src.year_train*365+src.dd_historic+src.dd_horizon] #first day after validation data
end = df_t.index[-src.dd_horizon-1]
Harvest, Harvest_hat = start, start
for today in pd.date_range(start,end):
ind_curr = pd.date_range(today-timedelta(dhist-1),today)
x_input = df_t.loc[ind_curr]
yhat = yhat = src.lstm_predict(x_input)
plt.figure(1), plt.cla()
x_input.plot(style='b-')
df_t.loc[yhat.index].plot(style='g-')
yhat.plot(style='r--')
plt.pause(0.0001)
ind_pred = yhat.index
yhat = lol.gdd(df_t.loc[pd.date_range(today-timedelta(365),today)].append(yhat))
ind_curr_year = df_aGDD_s[df_aGDD_s.index. year == today.year].index
Harvest = df_aGDD_s[ind_curr_year][df_aGDD_s[ind_curr_year] <= 500].index[-1].date()
ind_curr_year = yhat[yhat.index. year == today.year].index
Harvest_hat = yhat[ind_curr_year][yhat[ind_curr_year] <= 500].index[-1].date()
plt.figure(2), plt.cla()
seismic_activity = np.array(
[at, seismic_activity[7], seismic_activity[8], seismic_activity[-1]])
injection_wells = np.loadtxt(injWell_OK.txt).T
at_bin = np.arange(dPar['tmin'], 2018, dPar['dt_map'])
for i in range(at_bin.shape[0] - 1):
t1, t2 = at_bin[i], at_bin[i + 1]
sel_eq = np.logical_and(seismic_activity[0] >= t1,
seismic_activity[0] < t2)
sel_we = np.logical_and(injection_wells[1] <= t1, injection_wells[1] < t1)
print(t1, t2, 'No. earthquakes', sel_eq.sum(), 'No. of wells',
sel_we.sum())
#create basemap object
matplotlib.figure(2)
matplotlib.cla()
ax2 = matplotlib.subplot(111)
lon_0, lat_0 = .5 * (dPar['xmin'] + dPar['xmax']), .5 * (dPar['ymin'] +
dPar['ymax'])
m = Basemap(
llcrnrlon=dPar['xmin'],
urcrnrlon=dPar['xmax'],
llcrnrlat=dPar['ymin'],
urcrnrlat=dPar['ymax'],
lon_0=lon_0,
lat_0=lat_0,
resolution='l',
projection=dPar['projection'],
)
m.drawcoastlines()
