def plotRecognitionErrors():
N = 5
theta = radar_factory(N, frame='polygon')
data = getRecognitionErrorData()
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
fig.text(0.15, 0.425, 'RE1: Inadequate Surveillance')
fig.text(0.15, 0.40, 'RE2: Internal Distraction')
fig.text(0.15, 0.375, 'RE3: External Distraction')
fig.text(0.15, 0.35, 'RE4: Inattention')
fig.text(0.15, 0.325, 'RE5: Unknown Recognition Error')
colors = ['b', 'r']
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(2, 2, n + 1, projection='radar')
plt.rgrids([10, 20, 30, 40, 50])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
plt.show()
def plot_list(points, primes=False, labels=False, file_name=""):
max_len = 0
for pt in points:
length, angle = pt.polar_form()
if (length > max_len):
max_len = length
r = pt.real
i = pt.imaginary
if pt.is_prime():
plt.polar(angle, length, 'ro')
else:
plt.polar(angle, length, 'bo')
if (labels):
plt.text(angle,
length,
str(multiple),
horizontalalignment='center',
verticalalignment='bottom')
plt.thetagrids(range(0, 360, 60),
('1', '1+ω', 'ω', '-1', '-ω-1', '-ω'))
plt.rgrids(np.arange(0, max_len, max_len / 10), labels=[])
if file_name != "":
plt.savefig(file_name)
plt.clf()
else:
plt.show()
return plt
def plotPerformanceErrors():
N = 7
theta = radar_factory(N, frame='polygon')
data = getPerformanceErrorData()
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
fig.text(0.075, 0.425, 'PE1: Overcompensation')
fig.text(0.075, 0.40, 'PE2: Poor Directional Control')
fig.text(0.075, 0.375, 'PE3: Sleep')
fig.text(0.075, 0.35, 'PE4: Heart Attack or Other Physical Impairment')
fig.text(0.075, 0.325, 'PE5: Panic/Freezing')
fig.text(0.075, 0.30, 'PE6: Unknown Performance Error')
fig.text(0.075, 0.275, 'PE7: Unknown Critical Nonperformance Error')
colors = ['b', 'r']
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(2, 2, n + 1, projection='radar')
plt.rgrids([5, 10, 15, 20, 25, 30])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
plt.show()
def radar_plot():
"""
radar plot
"""
# 生成测试数据
labels = np.array(["A组", "B组", "C组", "D组", "E组", "F组"])
data = np.array([68, 83, 90, 77, 89, 73])
theta = np.linspace(0, 2*np.pi, len(data), endpoint=False)
# 数据预处理
data = np.concatenate((data, [data[0]]))
theta = np.concatenate((theta, [theta[0]]))
# 画图方式
plt.subplot(111, polar=True)
plt.title("雷达图", fontproperties=myfont)
# 设置"theta grid"/"radar grid"
plt.thetagrids(theta*(180/np.pi), labels=labels, fontproperties=myfont)
plt.rgrids(np.arange(20, 100, 20), labels=np.arange(20, 100, 20), angle=0)
plt.ylim(0, 100)
# 画雷达图,并填充雷达图内部区域
plt.plot(theta, data, "bo-", linewidth=2)
plt.fill(theta, data, color="red", alpha=0.25)
# 图形显示
plt.show()
return
def plot_radar(example_data,nVar):
N = nVar
theta = radar_factory(N, frame='polygon')
data = example_data
people_Num=len(data)
spoke_labels = data.pop('column names')
fig = plt.figure(figsize=(9, 2*people_Num))
fig.subplots_adjust(wspace=0.55, hspace=0.10, top=0.95, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
for n, title in enumerate(data.keys()):
ax = fig.add_subplot(int(people_Num/3)+1, 3, n+1, projection='radar')
plt.rgrids([0.2, 0.4, 0.6, 0.8])
plt.setp(ax.get_yticklabels(), visible=False)
plt.ylim([0,1])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),color='b',
horizontalalignment='center', verticalalignment='center',fontproperties=zhfont)
for d, color in zip(data[title], colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
plt.subplot(int(people_Num/3)+1, 3, 1)
plt.figtext(0.5, 0.965, '战力统计',fontproperties=zhfont,
ha='center', color='black', weight='bold', size='large')
plt.show()
def plot(self, legend=True, fig_title=None):
theta = radar_factory(self.get_data_size(), frame="polygon")
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ["b", "r", "g", "m", "y"]
# Plot the four cases from the example data on separate axes
for n, title in enumerate(self._data.keys()):
ax = fig.add_subplot(2, 2, n + 1, projection="radar")
plt.rgrids([0.02, 0.04, 0.06, 0.08])
ax.set_title(
title,
weight="bold",
size="medium",
position=(0.5, 1.1),
horizontalalignment="center",
verticalalignment="center",
)
for d, color in zip(self._data[title], colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(self._labels)
if legend:
self.plot_legend()
if fig_title is not None:
self.plot_title(fig_title)
plt.show()
def plot_radar(data, titles, title='', legends=None, normalize=False, colors=None, fill=True):
r = data.copy()
if normalize:
r.loc[:, titles] = MaxAbsScaler().fit_transform(r.loc[:, titles])
case_data = r
case_data.reset_index(inplace=True)
theta = radar_factory(len(titles), frame='polygon')
fig = plt.figure(figsize=(7, 7))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
if colors is None:
colors = "bgrcmykw"
ax = fig.add_subplot(1, 1, 1, projection='radar')
plt.rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data[titles].values, colors):
ax.plot(theta, d, color=color)
if fill:
ax.fill(theta, d, facecolor=color, alpha=0.25)
# ax.set_rmax(1.0)
ax.set_varlabels(titles)
# add legend relative to top-left plot
if legends:
plt.subplot(1, 1, 1)
labels = legends
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.2)
plt.setp(legend.get_texts(), fontsize=15)
plt.setp(legend.get_lines(), linewidth=13, alpha=0.50)
plt.show()
def plot_model(data):
N = 5
#初始化
theta = radar_factory(N, frame='polygon')
fig = plt.figure(figsize=(8, 6))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes
#['L', 'R', 'F', 'M', 'C']('ZL', 'ZC', 'ZM', 'ZF', 'ZR')
spoke_labels = ('ZL', 'ZC', 'ZM', 'ZF', 'ZR')
ax = fig.add_subplot(111, projection='radar')
plt.rgrids([0.1, 0.5, 1, 1.5, 2.0, 2.5])
ax.set_title(u'客户群分析',
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
labels = ('1', '2', '3', '4', '5')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
plt.show()
def single(multiple, total_num):
fig = plt.figure(0, figsize=(10, 10))
fig.show()
"""theta's, r's"""
plt.polar([i * np.pi * 2 / total_num for i in range(total_num + 1)],
[1] * (total_num + 1),
c=(1, 0, 0))
plt.thetagrids([None])
plt.rgrids([30])
plt.autoscale(False)
for i in range(1, total_num + 1):
plt.polar((i * np.pi * 2 / total_num,
((i * multiple) % total_num) * np.pi * 2 / total_num),
[1] * 2,
c=(0, 0, 0))
with plt.xkcd(randomness=0):
plt.annotate(i,
xy=(i * np.pi * 2 / total_num, 1.05),
horizontalalignment='center',
verticalalignment='center')
plt.show()
def plot_graph(var_stats, var_clean, var_evil):
try:
import matplotlib.pyplot as plt
except:
sys.sderr.write("matploitlib not found")
sys.exit(1)
N = 4 # az AZ 09 other
theta = radar_factory(N, frame='circle')
spoke_labels = ["[a-z]", "[A-Z]", "[0-9]", "[other]"]
fig = plt.figure(figsize=(15, 8))
fig.subplots_adjust(wspace=0.20, hspace=0.20, top=1.00, bottom=0.00, left=0.05, right=0.93)
"""
# Overall
ax = fig.add_subplot(2, 2, 3, projection='radar')
plt.rgrids([20, 40, 60, 80])
ax.set_title("Overall", weight='bold', size='medium', position=(0.5, 1.1), horizontalalignment='center', verticalalignment='center')
for varname, varfeatures in var_stats.iteritems():
d = [ varfeatures['az'], varfeatures['AZ'], varfeatures['09'], varfeatures['other'] ]
color = 'green' if varname in var_clean else 'red'
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
"""
# Clean
ax = fig.add_subplot(1, 2, 1, projection='radar')
plt.rgrids([20, 40, 60, 80])
ax.set_title("Clean", weight='bold', size='medium', position=(0.5, 1.1), horizontalalignment='center', verticalalignment='center')
for varname, varfeatures in var_clean.iteritems():
d = [ varfeatures['az'], varfeatures['AZ'], varfeatures['09'], varfeatures['other'] ]
ax.plot(theta, d, color='green')
ax.fill(theta, d, facecolor='green', alpha=0.25)
ax.set_varlabels(spoke_labels)
# Evil
ax = fig.add_subplot(1, 2, 2, projection='radar')
plt.rgrids([20, 40, 60, 80])
ax.set_title("Evil", weight='bold', size='medium', position=(0.5, 1.1), horizontalalignment='center', verticalalignment='center')
for varname, varfeatures in var_evil.iteritems():
d = [ varfeatures['az'], varfeatures['AZ'], varfeatures['09'], varfeatures['other'] ]
ax.plot(theta, d, color='red')
ax.fill(theta, d, facecolor='red', alpha=0.25)
ax.set_varlabels(spoke_labels)
plt.figtext(0.5, 0.965, 'Variable/Function name features distribution',
ha='center', color='black', weight='bold', size='large')
plt.figtext(0.5, 0.935, 'PRICK v0.1',
ha='center', color='black', weight='normal', size='medium')
plt.show()
def sonar_graph(ping_readings):
#print "ping reading:", ping_readings
#print type(ping_readings[1])
# force square figure and square axes looks better for polar, IMO
fig = figure(figsize=(3.8,3.8))
ax = P.subplot(1, 1, 1, projection='polar')
P.rgrids([28, 61, 91])
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)
try:
theta = 346
angle = theta * np.pi / 180.0
radii = [ping_readings[0]]
width = .15
bars1 = ax.bar(0, 100, width=0.001, bottom=0.0)
#print "theta, radii, width: ", theta, radii, width
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 6
angle = theta * np.pi / 180.0
radii = [ping_readings[1]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 86
angle = theta * np.pi / 180.0
radii = [ping_readings[2]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 266
angle = theta * np.pi / 180.0
radii = [ping_readings[3]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
img_to_return = fig2img(fig)
P.close(fig)
return img_to_return
except:
print "Sonar data error... can't graph"
pass
#print "finshed graph"
#pil_img = fig2img(fig)
#sonar_image = pil_img
#print type(pil_img), pil_img
#sonar_image = PILtoCV_4Channel(pil_img)
#cv.ShowImage("Sonar", sonar_image )
#cv.MoveWindow ('Sonar',50 ,50 )
#time.sleep(.01)
#cv.WaitKey(10)
#enable line below to make basestation work 12/13/2012
#fig.savefig('sonar_image.png')
#Image.open('sonar_image.png').save('sonar_image.jpg','JPEG')
#print "finished saving"
#stop
#garbage cleanup
#fig.clf()
#gc.collect()
#del fig
P.close(fig)
def stereoProjPlot(phi, psi, int=None, lineSpec='', titleText=None, maxPsi=90, fig=None, partPlot=False, thetaStep=None, rStep=None, rMax=None, cbarOri='vertical'): # only allow defined psi values phi = phi[(psi >= -maxPsi) & (psi <= maxPsi)] if int is not None: int = int[(psi >= 0) & (psi <= maxPsi)] psi = psi[(psi >= 0) & (psi <= maxPsi)] #h = polarplot(np.deg2rad(phi),tand(0.5 * psi),lineSpec) if fig is None: fig = plt.figure() ax = fig.add_subplot(111, projection='polar') if int is None: #c = ax.scatter(np.deg2rad(phi), bc.tand(0.5 * psi)) if len(lineSpec) > 0: c = plt.polar(np.deg2rad(phi), bc.tand(0.5 * psi), lineSpec) else: c = plt.polar(np.deg2rad(phi), bc.tand(0.5 * psi)) else: c = ax.scatter(np.deg2rad(phi), bc.tand(0.5 * psi), c = int, cmap='jet') fig.colorbar(c, orientation=cbarOri) if thetaStep is not None: plt.thetagrids(np.array(range(0, 360, thetaStep))) if rMax is not None and rStep is not None: plt.rgrids(np.array(range(0, rMax, rStep))) if not partPlot: c[0].axes.set_ylim(0, 1) if titleText is not None: ax.set_title(titleText) plt.tight_layout() # layout without overlapping plt.show() return fig, c, ax
def _generate_complexity_stats(fig, stats):
"""
Synopsis:
Generates a complexity statistic png in the root folder from the given stats.
:param fig: current matplotlib figure
:param stats: stats file containing data from which to generate statistics
"""
if stats['nodes_over_time']:
theta = _radar_factory(dimensions=3)
colors = ['orange', 'white', 'white']
title = [
'Model complexity', '\nData complexity', '\nMethod complexity'
]
case_data = [[
stats['model_complexity_over_time'][-1],
stats['class_data_complexity'][-1],
stats['class_function_complexity'][-1]
], [0, 0, 0], [1, 1, 1]]
ax = fig.add_subplot(111, projection='radar')
plt.rgrids([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [''] * 9)
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(title)
else:
plt.text(0.5,
0.5,
'Not enough data',
horizontalalignment='center',
verticalalignment='center',
bbox=dict(facecolor='red', alpha=0.5))
plt.savefig('complexity.png', dpi=90, facecolor='#411f48')
plt.clf()
def polar_coo_A():
co_A = np.zeros(t.size, dtype=complex)
counter_A = np.zeros(t.size, dtype=complex)
co_A.real = -co_A_P(t)
co_A.imag = -co_A_S(t)
counter_A.real = -counter_A_P(t)
counter_A.imag = -counter_A_S(t)
co_A_theta = np.angle(co_A)
co_A_abs = abs(co_A)
counter_A_theta = np.angle(counter_A)
counter_A_abs = abs(counter_A)
plt.figure(figsize=(10, 5))
plt.subplot(121, polar=True)
plt.plot(co_A_theta,
co_A_abs,
linewidth=1.5,
color="y",
label="Co-rotating")
plt.rgrids(np.arange(0.1, 1.1 * max(co_A_abs), 0.3), angle=180)
plt.legend(loc=(0.7, 1), prop={'size': 12})
plt.subplot(122, polar=True)
plt.plot(counter_A_theta,
counter_A_abs,
linewidth=1.5,
color="c",
label="Counter-rotating")
plt.rgrids(np.arange(0.1, 1.1 * max(counter_A_abs), 0.3), angle=180)
plt.legend(loc=(0.7, 1), prop={'size': 12})
plt.tight_layout(pad=1.0, w_pad=4)
plt.show()
return None
def subplot(data, spoke_labels, sensor_labels,saveto=None,frame_type='polygon'):
#def subplot(data, spoke_labels, sensor_labels,saveto=None,frame_type='polygon'):
num_of_picks=9
theta = radar_factory(len(spoke_labels), frame='circle')
fig = plt.figure(figsize=(num_of_picks, num_of_picks))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
num_col=np.floor(np.sqrt(len(data)))
num_row=np.ceil(num_of_picks/num_col)
for k,(data_col,sensor_label_) in enumerate(zip(data,sensor_labels)):
#subplot(num_col,num_row,i+1)
ax = fig.add_subplot(num_col,num_row,k+1, projection="radar")
ax.plot(theta, data_col)
ax.fill(theta, data_col, alpha=0.2)
ax.set_varlabels(spoke_labels)
#plt.title(sensor_label_,fontsize='small')
legend = plt.legend([sensor_label_], loc=(-0.2, 1.1), labelspacing=0.01)
plt.setp(legend.get_texts(), fontsize='small')
radar_bnd=max(max(data))
#import pdb;pdb.set_trace()
rgrid_spacing=np.round(list(np.arange(0.1,radar_bnd,float(radar_bnd)/5)),2)
plt.rgrids(rgrid_spacing)
#plt.rgrids([0.1 + 2*i / 10.0 for i in range(radar_bnd)])
##radar_chart.plot(data_col, spoke_labels, sensor_label_, saveto="time_radar.png",frame_type='circle')
if saveto != None:
plt.savefig(saveto)
def radar_plot():
"""
radar plot
"""
# 生成测试数据
labels = np.array(["A", "B", "C", "D", "E", "F"])
data = np.array([38, 43, 90, 67, 89, 73])
theta = np.linspace(0, 2 * np.pi, len(data), endpoint=False)
# 数据预处理
data = np.concatenate((data, [data[0]]))
theta = np.concatenate((theta, [theta[0]]))
# 画图方式
plt.subplot(111, polar=True)
# 设置"theta grid"/"radar grid"
plt.thetagrids(theta * (180 / np.pi), labels=labels)
plt.rgrids(np.arange(20, 101, 20), labels=np.arange(20, 101, 20), angle=0)
plt.ylim(0, 100)
# 画雷达图,并填充雷达图内部区域
plt.plot(theta, data, "bo-", linewidth=2)
plt.fill(theta, data, color="red", alpha=0.25)
# 图形显示
plt.show()
return
def radar_plot():
"""
radar plot
"""
# 生成测试数据
labels = np.array(["A组", "B组", "C组", "D组", "E组", "F组"])
data = np.array([68, 83, 90, 77, 89, 73])
theta = np.linspace(0, 2 * np.pi, len(data), endpoint=False)
# 数据预处理
data = np.concatenate((data, [data[0]]))
theta = np.concatenate((theta, [theta[0]]))
# 画图方式
plt.subplot(111, polar=True)
plt.title("雷达图", fontproperties=myfont)
# 设置"theta grid"/"radar grid"
plt.thetagrids(theta * (180 / np.pi), labels=labels, fontproperties=myfont)
plt.rgrids(np.arange(20, 100, 20), labels=np.arange(20, 100, 20), angle=0)
plt.ylim(0, 100)
# 画雷达图,并填充雷达图内部区域
plt.plot(theta, data, "bo-", linewidth=2)
plt.fill(theta, data, color="red", alpha=0.25)
# 图形显示
plt.show()
return
def create_plot():
N = 9
theta = radar_factory(N, frame='circle')
data = example_data()
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(2, 2, n + 1, projection='radar')
plt.rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
plt.subplot(2, 2, 1)
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
ha='center', color='black', weight='bold', size='large')
plt.show()
def request_radar_plot(request, pname):
N = 10
theta = radar_factory(N, frame='polygon')
data = fetch_data(pname)
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(14, 14))
colors = ['b', 'r']
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(1, 1, 1, projection='radar')
plt.rgrids([10, 20, 30, 40])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(case_data, colors):
d = np.array(d)
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
plt.subplot(1, 1, 1)
labels = ('Statistics', 'MaxKDA')
legend = plt.legend(labels, loc=(0.90, .95), labelspacing=0.2)
plt.setp(legend.get_texts(), fontsize='small')
fig.set_facecolor('white')
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
def plot(self, legend=True, fig_title=None):
theta = radar_factory(self.get_data_size(), frame='polygon')
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes
for n, title in enumerate(self._data.keys()):
ax = fig.add_subplot(2, 2, n + 1, projection='radar')
plt.rgrids([0.02, 0.04, 0.06, 0.08])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(self._data[title], colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(self._labels)
if legend:
self.plot_legend()
if fig_title is not None:
self.plot_title(fig_title)
plt.show()
def buildRadar(data, name):
N = 7
theta = radar_factory(N, frame="polygon")
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(7, 7))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ["b", "r", "g", "m", "y"]
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(2, 2, n + 1, projection="radar")
plt.rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(
title,
weight="bold",
size="medium",
position=(0.5, 1.1),
horizontalalignment="center",
verticalalignment="center",
)
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
plt.subplot(2, 2, 1)
labels = ("Factor 1", "Factor 2", "Factor 3", "Factor 4", "Factor 5")
plt.figtext(0.5, 0.965, "TITLE", ha="center", color="black", weight="bold", size="large")
savefig(name)
plt.clf()
def create_polar_plot(sim_group,
sim_id,
outfile_idx,
r_min=0.2,
r_max=5.0,
tick_step_size=0.2):
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(polar=True)
plotting.gas_density.polar_2D(ax,
sim_group,
sim_id,
outfile_idx,
r_max_crop=1.4)
save_loc = os.path.join(FIGURE_DIR, sim_group, sim_id,
f'polar_{outfile_idx}.png')
plt.grid(True, color='black')
# ticks = np.arange(r_min, r_max, tick_step_size)
ticks = list(np.arange(0.5, 5.5, 1))
plt.yticks(ticks, size=20)
plt.rgrids(ticks)
plt.plot(np.linspace(0, 2 * np.pi, 100), [0.2] * 100,
color='black',
linewidth=1)
ax.set_rlabel_position(90)
if sim_id.startswith('10') or outfile_idx == 50:
cb = plt.colorbar(fraction=0.046, pad=0.04)
cb.ax.tick_params(labelsize=20)
if sim_id not in os.listdir(os.path.join(FIGURE_DIR, sim_group)):
os.mkdir(os.path.join(FIGURE_DIR, sim_group, sim_id))
plt.savefig(save_loc)
def initialisation_graphe(ondes,enonce=True):
plt.clf() # Nettoyage, on commence un nouveau graphe
plt.axes(polar=True) # On initie un graphe en polaires
# Puis on définit le titre
if enonce: titre = 'Enonce: Sommer '
else : titre = 'Corrige: Sommer '
titre += ", ".join(['${}$'.format(o) for o in ondes[:-1]])
titre += " et ${}$.".format(ondes[-1])
plt.title(titre)
# et finalement les grilles en distances
plt.rgrids([i+1 for i in range(max_size)])
plt.thetagrids([i*15 for i in range(360//15)]) # et en angles
def overlaid_plot(terms, ICA_component_number, savepath=None, filtered_num=False):
"""
Args:
- ICA_component_number: integer that corresponds to ICA analysis and directory
- savepath: just shows if no path is provided
- filtered_num: Integer that corresponds to the number of terms desired in
the radar plot.
"""
ICA_path = '/Volumes/Huettel/KBE.01/Analysis/Neurosynth/ICA/ICA%s' %ICA_component_number
big_list = get_all_term_weights(terms, ICA_path)
if filtered_num:
big_list = term_weight_filter(big_list, filtered_num)
N = filtered_num
else:
N = ICA_component_number
data = convert_final_data(big_list)
theta = radar_factory(N, frame='polygon')
spoke_labels = data.pop('column names')
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'y', 'm']
# Plot the four cases from the example data on separate axes
for n, title in enumerate(data.keys()):
ax = fig.add_subplot(1, 1, n+1, projection='radar')
plt.rgrids([2, 4, 6, 8])
#plt.rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(data[title], colors):
ax.set_ylim(0,10)
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
plt.subplot(1, 1, 1)
labels = tuple(terms)
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='large')
plt.figtext(0.5, 0.965, '',
ha='center', color='black', weight='bold', size='large')
if savepath == None:
plt.show()
else:
plt.savefig(savepath)
def add_axis(self, fig, subplot):
ax = fig.add_subplot(subplot, polar=True)
# Set up ticks and labels
self.r_ticks = range(self.r0, self.r0 + (self.nrange+1) * self.dr, self.dr)
self.theta_ticks = [self.theta0 + self.dtheta * b for b in range(self.nbeam+1)]
rlabels = [""] * len(self.r_ticks)
for i in range(0, len(rlabels), 5):
rlabels[i] = i
plt.rgrids(self.r_ticks, rlabels)
plt.thetagrids(self.theta_ticks, range(self.nbeam))
return ax
def draw_radar(weapons, color_cluster, fitness_cluster, num_samples):
N = 10
theta = radar_factory(N, frame='polygon')
data = get_data(weapons)
spoke_labels = data.pop('column names')
fig = plt.figure(figsize=(16, 9))
fig.subplots_adjust(wspace=0.50, hspace=0.25)
colors = ['b', 'r', 'g', 'm', 'y']
weapons = data['Weapon1'], data['Weapon2']
for i in range(len(weapons)) :
ax = fig.add_subplot(2, 3, i+1, projection='radar')
plt.rgrids([0.5], (''))
ax.set_title("Weapon" + str(i+1), weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
ax.plot(theta, weapons[i], color=color_cluster)
ax.fill(theta, weapons[i], facecolor=color_cluster, alpha=0.25)
ax.set_varlabels(spoke_labels)
ax = fig.add_subplot(2, 3, 4)
plt.ylim(0, 3)
ax.set_title("Balance")
ax.boxplot( [fitness_cluster[i][0] for i in range(len(fitness_cluster))] )
ax = fig.add_subplot(2, 3, 5)
plt.ylim(0, 2500)
ax.set_title("Distance")
ax.boxplot( [fitness_cluster[i][1] for i in range(len(fitness_cluster))] )
ax = fig.add_subplot(2, 3, 6)
plt.ylim(0, 20)
ax.set_title("Kill Streak")
ax.boxplot( [fitness_cluster[i][2] for i in range(len(fitness_cluster))] )
# add legend relative to top-left plot
'''
plt.subplot(2, 2, 1)
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
'''
plt.figtext(0.5, 0.965, 'Mean of clustered weapon, samples = ' + str(num_samples),
ha='center', color='black', weight='bold', size='large')
def create_figure(all_data): # takes in data and title and creates and saves plot
width = 0.45 # width of the bars (in radians)
# create the figure, dont change
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
# angle positions, 0 to 360 with increments of 360/5
xo = list(range(0, 360, 360 / 5))
# Convert to radians and subtract half the width
# of a bar to center it.
x = [i * pi / 180 for i in xo]
# set the labels for each bar, do not change
ax.set_xticks(x)
ax.set_xticklabels(['Military\nProwess', 'Productivity', 'Resource', 'Self-\nSufficiency', 'Morale'])
ax.set_thetagrids(xo, frac=1.15) # frac changes distance of label from circumference of circle
plt.ylim(0, 100) # sets range for radial grid
fig.suptitle("India \n1993-2012", fontsize=20, y=0.5, x=0.1) # title of plot
plt.rgrids([20, 40, 60, 80, 100], angle=33, fontsize=10) # the numbers you see along radius, angle changes position
colorList = [];
count = -1
for key in all_data:
count = count + 1
data = all_data[key]
mylist = [item+0.5*(count-len(all_data)/2)/len(all_data) for item in x]
bars = ax.bar(mylist, data, width=width, align='center') # do the plotting
i = 0
for r, bar in zip(data, bars):
bar.set_facecolor( cm.jet(0.8*count/len(all_data))) # set color for each bar, intensity proportional to height of bar
colorList.append(cm.jet(0.8*count/len(all_data)))
#bar.set_alpha(0.2) # make color partly transparent
height = bar.get_height() # this is basically the radial height, or radius of bar
# write value of each bar inside it
# first param is angle, second is radius -10 makes it go inside the bar
if i == 3 and count == 0:
ax.text(mylist[i]-width/4*3, height+5, key, ha='center', va='center', fontsize=11)
if i == 3 and count == len(all_data)-1:
ax.text(mylist[i]+width/4*3, height-5, key, ha='center', va='center', fontsize=11)
i = i + 1
plt.savefig('examples/multiple.png')
def radar_sglv(data, labels, title, rand=False):
# Plot the four cases from the example data on separate axes
N, M = labels.shape
j = 0
theta = radar_factory(M, frame='polygon')
color = ['red', 'blue', 'green', 'grey']
for w in range(ceil(N / 4)):
c = 0
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
for row in data[4 * w:min(4 * w + 4, N), ::]:
index = np.arange(M)
if rand:
random.shuffle(index)
ax = fig.add_subplot(2, 2, c + 1, projection='radar')
ax.set_title('Singular Value n°' + str(j + 1),
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
ax.plot(theta, row[index], color=color[c])
ax.fill(theta, row[index], facecolor=color[c], alpha=0.25)
ax.set_varlabels(labels[j, index])
plt.rgrids([0.2, 0.4, 0.6, 0.8])
c = c + 1
j = j + 1
# add legend relative to top-left plot
plt.subplot(2, 2, 1)
#label = ('Singular Value n°'+ str(4*w), 'Singular Value n°'+str(4*(w+1)),
# 'Singular Value n°'+ str(4*(w+2)), 'Singular Value n°'+str(4*(w+3)))
#legend = plt.legend(label, loc=(0.9, .95), labelspacing=0.1)
#@plt.setp(legend.get_texts(), fontsize='small')
plt.figtext(0.5,
0.965,
title,
ha='center',
color='black',
weight='bold',
size='large')
plt.show()
def plot_hsi_image(self, show_plot=True):
plt.subplot(221)
plt.gray()
plt.imshow(self.hsi_image[:, :, 0] / (2 * 3.14) * 255.,
interpolation='nearest')
plt.axis('off')
plt.subplot(222, projection='polar')
plt.gray()
azi = self.hsi_image[:, :, 0] / (2 * 3.14) * 360.
azi = azi.flatten()
azi = list(azi)
azi.append(359.)
azi = np.array(azi)
z = np.cos(np.radians(azi / 2.))
coll = rose_graph.rose(azi, z=z, bidirectional=False, bins=50)
plt.xticks(np.radians(range(0, 360, 10)), [
'Red', '', '', 'Red-Magenta', '', '', 'Magenta', '', '',
'Magenta-Blue', '', '', 'Blue', '', '', 'Blue-Cyan', '', '',
'Cyan', '', '', 'Cyan-Green', '', '', 'Green', '', '',
'Green-Yellow', '', '', 'Yellow', '', '', 'Yellow-Red', '', ''
])
plt.colorbar(coll, orientation='horizontal')
plt.xlabel('A rose diagram colored by a second variable')
plt.rgrids(range(5, 20, 5), angle=360)
plt.subplot(223)
plt.imshow(self.hsi_image[:, :, 0] / (2 * 3.14) * 255.,
cmap=matplotlib.cm.hsv)
plt.axis('off')
plt.subplot(224)
data = self.hsi_image[:, :, 0] / (2 * 3.14) * 255.
data = data.flatten()
data = list(data)
data.append(359.)
n, bins, patches = plt.hist(data, 36)
bin_centers = 0.5 * (bins[:-1] + bins[1:])
col = bin_centers
col /= 360
col = col
print bins
for c, p in zip(col, patches):
plt.setp(p, 'facecolor', matplotlib.cm.hsv(c))
plt.xticks([np.ceil(x) for i, x in enumerate(bins) if i % 3 == 0])
if show_plot:
plt.show()
def __init__(self, rrre_class, elective_class, filename):
self.filename = filename
plt.cla()
self.rrre_class = rrre_class
self.elective_class = elective_class
N = len(rrre_class.grade_dict)
theta = self.radar_factory(N, frame='polygon')
data = self.example_data()
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(9, 9))
#fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = [
'#90EE90', '#4F4F4F', '#00FF00', '#00FFFF', '#FF7256', '#FFA500',
'#8B7E66', '#FF8247'
]
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(1, 1, n + 1, projection='radar')
if rrre_class.biggest <= 4:
plt.rgrids([1, 2, 3, 4])
elif rrre_class.biggest <= 8:
plt.rgrids([2, 4, 6, 8])
elif rrre_class.biggest <= 12:
plt.rgrids([3, 6, 9, 12])
else:
plt.rgrids([4, 8, 13, 17])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center',
fontproperties=zhfont)
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
#plt.subplot(2, 2, 1)
labels = [
semester_list[3]
for semester_list in self.rrre_class.SEMESTERS_LIST
]
legend = plt.legend(labels,
loc=(0.52, .88),
labelspacing=0.1,
prop=zhfont)
#plt.setp(legend.get_texts(), fontsize='small')
#plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',ha='center', color='black', weight='bold', size='large')
self.loc = str(self.elective_class.message_list[0]
) + '/' + self.filename + '_2.png'
plt.savefig('/var/www/html/szx/' + self.loc, bbox_inches='tight')
def test_polar_wrap():
D2R = np.pi / 180.0
fig = plt.figure()
plt.subplot( 111, polar=True, resolution=1 )
plt.polar( [179*D2R, -179*D2R], [0.2, 0.1], "b.-" )
plt.polar( [179*D2R, 181*D2R], [0.2, 0.1], "g.-" )
plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
fig.savefig( 'polar_wrap_180' )
fig = plt.figure()
plt.subplot( 111, polar=True, resolution=1 )
plt.polar( [2*D2R, -2*D2R], [0.2, 0.1], "b.-" )
plt.polar( [2*D2R, 358*D2R], [0.2, 0.1], "g.-" )
plt.polar( [358*D2R, 2*D2R], [0.2, 0.1], "r.-" )
plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
fig.savefig( 'polar_wrap_360' )
def main():
azi = wd_11_12
z = ws_11_12
plt.figure(figsize=(5,6))
plt.subplot(111, projection='polar')
coll = rose(azi, z=z, bidirectional=True)
plt.xticks(np.radians(range(0, 360, 45)),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
plt.colorbar(coll, orientation='horizontal')
plt.xlabel('2011 - 2012 3m Wind rose colored by mean wind speed')
plt.rgrids(range(5, 20, 5), angle=290)
plt.savefig('/home/cparr/Snow_Patterns/figures/wind/winter_11_12.png',dpi = 300)
plt.show()
def flowers(n):
a=[4,5,6,7,0.75,2.5,3.5,5.0/3.0,7.0/3.0,7.0/4.0,1.0/6.0,1.0/8.0,1.0/7.0,2.0/9.0]
if a[n-1]>=4:
b='yellow'
elif a[n-1]<=1:
b='red'
else:
b='magenta'
pyplot.axes(polar=True)
pyplot.thetagrids([])
pyplot.rgrids([2])
theta = arange(-9, 9, 1./180)*pi # angular coordinates
figure(1)
pyplot.plot(theta, cos(a[n-1]*theta), color=b, linewidth=5) # drawing the polar rose
show()
return
def plotSpeedBear(self, sAllData, bAllData):
"""
Plot speed and bearing against each other
"""
plt.figure(self.figurenum(), figsize=(7, 7))
plt.subplot(111)
ii = np.where((sAllData < sys.maxsize) & (bAllData < sys.maxsize))
plt.polar((np.pi / 2. - np.radians(bAllData[ii])),
sAllData[ii],
'k.',
markersize=2)
thetalabels = (90 - np.arange(0, 360, 45))
ii = np.where(thetalabels < 0)
thetalabels[ii] += 360
lines, labels = plt.rgrids(np.arange(20., 101., 20.),
labels=None,
angle=67.5)
lines, labels = plt.thetagrids(np.arange(0., 360., 45.), thetalabels)
plt.ylim(0, 100.)
plt.grid(True)
r = np.corrcoef(bAllData[ii], sAllData[ii])[1, 0]
plt.text(45,
125,
"r = %5.3f" % r,
ha='center',
va='center',
color='r',
size=14)
plt.title("Speed vs bearing")
self.savefig('spd_bear_corr')
def _open_figure(self):
# Create axis
self.fig = plt.figure(figsize=(12, 9))
self.ax = self.fig.add_subplot(111, polar=True)
# Set up ticks and labels
self.r_ticks = range(self.r0, self.r0 + (self.nrange + 1) * self.dr,
self.dr)
self.theta_ticks = [
self.theta0 + self.dtheta * b for b in range(self.nbeam + 1)
]
rlabels = [""] * len(self.r_ticks)
for i in range(0, len(rlabels), 5):
rlabels[i] = i
plt.rgrids(self.r_ticks, rlabels)
plt.thetagrids(self.theta_ticks, range(self.nbeam))
def plot_phaseplot_l(dictdata, keys, autok, title, withn='yes'):
"""Plots a phase plot where the radius is not fixed to 1."""
colors = ['r', 'b', 'g', 'y', 'k']
plt.suptitle(title, fontsize='large' )
if autok == 'yes':
k = dictdata.keys()
for i, condition in enumerate(keys):
datac = colors[i]
data = dictdata[condition]
try:
n = len(data)
theta, r = zip(*data)
except TypeError:
theta, r = data
n = 1
if withn == 'yes':
plt.polar(theta, r, 'o', color=datac, label=condition + '\n n=' + str(n))
if withn == 'no':
plt.polar(theta, r, 'o', color=datac, label=condition)
lines, labels = plt.rgrids( (1.0, 1.4), ('', ''), angle=0 )
tlines, tlabels = plt.thetagrids( (0, 90, 180, 270), ('0', 'pi/2', 'pi', '3pi/2') )
leg = plt.legend(loc=(0.93,0.8))
for t in leg.get_texts():
t.set_fontsize('small')
plt.subplots_adjust(top=0.85)
plt.draw()
def polar_axis():
theta = np.arange(0, 2 * np.pi, 0.02)
plt.subplot(121, polar=True)
plt.plot(theta, 1.6 * np.ones_like(theta), lw=2)
plt.plot(3 * theta, theta / 3, '--', lw=2)
plt.plot(theta, 1.4 * np.cos(5 * theta), '--', color='green', lw=2)
plt.plot(theta, 1.8 * np.cos(4 * theta), lw=2)
# 设置同心圆栅格的大小和文字标准的角度
plt.rgrids(np.arange(0.5, 2, 0.5), angle=45)
# 设置放射线栅格的角度
plt.thetagrids([0, 45])
plt.show()
def makeSpiderChart(jobID, savedLengths, outputDir):
#setting up the directory to save the chart in
cwd = os.getcwd()
prefix = "".join([cwd,'/',jobID,'_SignificantWords.png'])
#local variables
data = []
labels = []
largestWords = 0
#assiging values to lists for the spider chart
for key, value in savedLengths.items():
data.append(value)
labels.append(key)
if value > largestWords:
largestWords = value
N = len(labels)
theta = radar_factory(N, frame='circle')
spoke_labels = labels
#size of the file
fig = plt.figure(figsize=(13, 13))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
color = 'b'
# Plot the four cases from the example data on separate axes
ax = fig.add_subplot(2, 2, 1, projection='radar')
plt.rgrids([1, (largestWords*0.25), (largestWords*0.5), (largestWords*0.75), largestWords])
ax.set_title(jobID, weight='bold', size='medium', position=(0.5, 1.1), horizontalalignment='center', verticalalignment='center')
ax.plot(theta, data, color)
ax.fill(theta, data, color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
plt.subplot(2, 2, 1)
labels = ('Significant Words', 'Fill')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
plt.figtext(0.5, 0.965, 'Significant Words for Interesting Word Lengths',
ha='center', color='black', weight='bold', size='large')
savefig(prefix)
shutil.move(prefix, outputDir)
def refresh(self, t):
# print "refresh radarmap " + str(t)
N = 4
theta = radar_factory(N, frame='circle')
data = self.pick_data(t)
spoke_labels = data.pop(0)
self.fig = plt.figure()
self.fig.subplots_adjust(wspace=0.25,
hspace=0.20,
top=0.95,
bottom=0.05)
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self.main_frame)
colors = ['r', 'g', 'm', 'y', 'b']
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = self.fig.add_subplot(1, 2, n + 1, projection='radar')
plt.rgrids([0.2, 0.4, 0.6, 0.8])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
plt.subplot(1, 2, 1)
labels = ('Cell 1', 'Cell 2', 'Cell 3', 'Cell 4')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
plt.figtext(0.5,
0.965,
'Feature Snapshots Panel',
ha='center',
color='black',
weight='bold',
size='large')
def plot(data, spoke_labels, sensor_labels,saveto=None,frame_type='polygon'):
theta = radar_factory(len(spoke_labels), frame=frame_type)
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
ax = fig.add_subplot(111, projection="radar")
plt.rgrids([np.round(0.1 + i / 10.0,2) for i in range(10)])
for d in data:
ax.plot(theta, d)
ax.fill(theta, d, alpha=0.2)
ax.set_varlabels(spoke_labels)
legend = plt.legend(sensor_labels, loc=(0.0, 0.9), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
if saveto != None:
plt.savefig(saveto)
def main():
azi = np.random.uniform(0, 180, 100000)
azi = azi.tolist()
azi.append(359.)
azi = np.array(azi)
z = np.cos(np.radians(azi / 2.))
plt.figure(figsize=(5, 6))
plt.subplot(111, projection='polar')
coll = rose(azi, z=z, bidirectional=False)
plt.xticks(np.radians(range(0, 360, 45)),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
plt.colorbar(coll, orientation='horizontal')
plt.xlabel('A rose diagram colored by a second variable')
plt.rgrids(range(5, 20, 5), angle=360)
plt.show()
def plot_hsi_image(self, show_plot=True):
plt.subplot(221)
plt.gray()
plt.imshow(self.hsi_image[:, :, 0] / (2 * 3.14) * 255., interpolation='nearest')
plt.axis('off')
plt.subplot(222, projection='polar')
plt.gray()
azi = self.hsi_image[:, :, 0] / (2 * 3.14) * 360.
azi = azi.flatten()
azi = list(azi)
azi.append(359.)
azi = np.array(azi)
z = np.cos(np.radians(azi / 2.))
coll = rose_graph.rose(azi, z=z, bidirectional=False, bins=50)
plt.xticks(np.radians(range(0, 360, 10)),
['Red', '', '', 'Red-Magenta', '', '', 'Magenta', '', '', 'Magenta-Blue', '', '', 'Blue', '', '',
'Blue-Cyan', '', '', 'Cyan', '', '', 'Cyan-Green', '', '', 'Green', '', '', 'Green-Yellow', '', '',
'Yellow', '', '', 'Yellow-Red', '', ''])
plt.colorbar(coll, orientation='horizontal')
plt.xlabel('A rose diagram colored by a second variable')
plt.rgrids(range(5, 20, 5), angle=360)
plt.subplot(223)
plt.imshow(self.hsi_image[:, :, 0] / (2 * 3.14) * 255., cmap=matplotlib.cm.hsv)
plt.axis('off')
plt.subplot(224)
data = self.hsi_image[:, :, 0] / (2 * 3.14) * 255.
data = data.flatten()
data = list(data)
data.append(359.)
n, bins, patches = plt.hist(data, 36)
bin_centers = 0.5 * (bins[:-1] + bins[1:])
col = bin_centers
col /= 360
col = col
print bins
for c, p in zip(col, patches):
plt.setp(p, 'facecolor', matplotlib.cm.hsv(c))
plt.xticks([np.ceil(x) for i, x in enumerate(bins) if i % 3 == 0])
if show_plot:
plt.show()
def plotDecisionErrors():
N = 8
theta = radar_factory(N, frame='polygon')
data = getDecisionErrorData()
spoke_labels = data.pop(0)
fig = plt.figure(figsize=(9, 9))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
fig.text(0.1, 0.425, 'DE1: Too Fast for Conditions')
fig.text(0.1, 0.40, 'DE2: Too Fast for Curve')
fig.text(0.1, 0.375, 'DE3: False Assumption of Other\'s Action')
fig.text(0.1, 0.35, 'DE4: Illegal Maneuver')
fig.text(0.1, 0.325, 'DE5: Misjudgment of Gap or Other\'s Speed')
fig.text(0.1, 0.30, 'DE6: Following Too Closely')
fig.text(0.1, 0.275, 'DE7: Aggressive Driving Behavior')
fig.text(0.1, 0.25, 'DE8: Unknown Decision Error')
# colors = ['b', 'r', 'g', 'm', 'y']
colors = ['b', 'r']
# Plot the four cases from the example data on separate axes
for n, (title, case_data) in enumerate(data):
ax = fig.add_subplot(2, 2, n + 1, projection='radar')
plt.rgrids([5, 10, 15, 20, 25, 30])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
horizontalalignment='center',
verticalalignment='center')
for d, color in zip(case_data, colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
## plt.subplot(2, 2, 1)
## labels = ('Conventional Vehicles', 'Vehicles with Cognition')
## legend = plt.legend(labels, loc=(-0.1, -0.3), labelspacing=0.1)
## plt.setp(legend.get_texts(), fontsize='small')
# plt.figtext(0.5, 0.965, 'Comparison of Critical Reasons for Pre-Crash Events',
# ha='center', color='black', weight='bold', size='large')
plt.show()
def main():
azi = np.random.uniform(0, 180, 100000)
azi = azi.tolist()
azi.append(359.)
azi = np.array(azi)
z = np.cos(np.radians(azi / 2.))
plt.figure(figsize=(5, 6))
plt.subplot(111, projection='polar')
coll = rose(azi, z=z, bidirectional=False)
plt.xticks(np.radians(range(0, 360, 45)),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
plt.colorbar(coll, orientation='horizontal')
plt.xlabel('A rose diagram colored by a second variable')
plt.rgrids(range(5, 20, 5), angle=360)
plt.show()
def test_polar_wrap():
D2R = np.pi / 180.0
fig = plt.figure()
plt.subplot(111, polar=True)
plt.polar( [179*D2R, -179*D2R], [0.2, 0.1], "b.-" )
plt.polar( [179*D2R, 181*D2R], [0.2, 0.1], "g.-" )
plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
assert len(fig.axes) == 1, 'More than one polar axes created.'
fig = plt.figure()
plt.subplot( 111, polar=True)
plt.polar( [2*D2R, -2*D2R], [0.2, 0.1], "b.-" )
plt.polar( [2*D2R, 358*D2R], [0.2, 0.1], "g.-" )
plt.polar( [358*D2R, 2*D2R], [0.2, 0.1], "r.-" )
plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
def test_polar_wrap():
D2R = np.pi / 180.0
fig = plt.figure()
plt.subplot(111, polar=True)
plt.polar([179 * D2R, -179 * D2R], [0.2, 0.1], "b.-")
plt.polar([179 * D2R, 181 * D2R], [0.2, 0.1], "g.-")
plt.rgrids([0.05, 0.1, 0.15, 0.2, 0.25, 0.3])
assert len(fig.axes) == 1, 'More than one polar axes created.'
fig = plt.figure()
plt.subplot(111, polar=True)
plt.polar([2 * D2R, -2 * D2R], [0.2, 0.1], "b.-")
plt.polar([2 * D2R, 358 * D2R], [0.2, 0.1], "g.-")
plt.polar([358 * D2R, 2 * D2R], [0.2, 0.1], "r.-")
plt.rgrids([0.05, 0.1, 0.15, 0.2, 0.25, 0.3])
def display_climate(geography, year, tempdata, raindata):
#Create figure and polar axis
fig = plt.figure(facecolor='white', figsize=(8,8))
ax = fig.add_subplot(111, polar = True, frameon=False)
mintemp=-30
maxtemp=40
ax.text(0,mintemp, geography.upper(), color='#555555', horizontalalignment='center', verticalalignment='center', size=30)
ax.text(0,maxtemp+1, str(year), color='#555555', horizontalalignment='center', size=14)
#Min/Max temps as bars
for i,(tmin,tmax,tmean) in enumerate(tempdata):
if np.abs(tmax-tmin)<1:
tmin=tmin-0.5
tmax=tmax+0.5
ax.plot([2*np.pi*i/365.0]*2, [tmin,tmax], color=cm.spectral((tmean+5)/45.0), linewidth=1.5, alpha=0.6);
# plot rainfall as scatters
ax.scatter([2*np.pi*r/365. for r in raindata['rainydays']], raindata['tcenters'], s=[100*r for r in raindata['rainfalls']], alpha=0.5, facecolor='#99aacc', linewidth=0)
# tweak ranges and orientation of polar plot
ax.set_rmax(maxtemp)
ax.set_rmin(mintemp)
ax.set_theta_direction(-1)
ax.set_theta_zero_location("N")
#Tweak polar axes, gridding, labels
ax.tick_params(axis='both', colors='#b0b0b0')
ax.set_xticks([m*2*np.pi/12 for m in range(12)])
months = ['JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC']
ax.set_xticklabels( months, fontsize=10 )
ax.get_xaxis().grid(False)
plt.rgrids( (0.01, 10, 20, 30, 40), labels=('0 C', '', '20 C', '', '40 C' ), angle=180) # radii only positive here, but override later
ax.get_yaxis().grid(which='minor',linestyle='-',color='#bbbbbb', alpha=0.3)
ax.get_yaxis().grid(which='major',linestyle='-',color='#bbbbbb', alpha=0.4, linewidth=1.4)
ax.set_yticks([10, 30], minor=True)
ax.set_yticks([0, 20, 40])
#ax.set_yticklabels( ['0 $^\circ$C', '20 $^\circ$C', '40 $^\circ$C' ], fontsize=10)
ax.set_yticklabels( ['0 C', '20 C', '40 C' ], fontsize=10)
plt.show()
def plot_radar(example_data, nVar):
N = nVar
theta = radar_factory(N, frame='polygon')
data = example_data
people_Num = len(data)
spoke_labels = data.pop('column names')
fig = plt.figure(figsize=(9, 2 * people_Num))
fig.subplots_adjust(wspace=0.55, hspace=0.10, top=0.95, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y']
for n, title in enumerate(data.keys()):
ax = fig.add_subplot(int(people_Num / 3) + 1,
3,
n + 1,
projection='radar')
plt.rgrids([0.2, 0.4, 0.6, 0.8])
plt.setp(ax.get_yticklabels(), visible=False)
plt.ylim([0, 1])
ax.set_title(title,
weight='bold',
size='medium',
position=(0.5, 1.1),
color='b',
horizontalalignment='center',
verticalalignment='center',
fontproperties=zhfont)
for d, color in zip(data[title], colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
plt.subplot(int(people_Num / 3) + 1, 3, 1)
plt.figtext(0.5,
0.965,
'战力统计',
fontproperties=zhfont,
ha='center',
color='black',
weight='bold',
size='large')
plt.show()
def sonar_graph(ping_readings):
# force square figure and square axes looks better for polar, IMO
fig = figure(figsize=(6,6))
ax = P.subplot(1, 1, 1, projection='polar')
P.rgrids([28, 61, 91])
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)
theta = 356
angle = theta * np.pi / 180.0
radii = [ping_readings[0]]
width = .15
bars1 = ax.bar(0, 100, width=0.001, bottom=0.0)
#print "theta, radii, width: ", theta, radii, width
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 86
angle = theta * np.pi / 180.0
radii = [ping_readings[1]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 176
angle = theta * np.pi / 180.0
radii = [ping_readings[2]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 266
angle = theta * np.pi / 180.0
radii = [ping_readings[3]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
pil_img = fig2img(fig)
#print type(pil_img), pil_img
sonar_image = PILtoCV_4Channel(pil_img)
cv.ShowImage("Sonar", sonar_image )
#cv.MoveWindow ('Sonar',50 ,50 )
#time.sleep(.01)
cv.WaitKey(10)
#garbage cleanup
fig.clf()
P.close()
return sonar_image
def sonar_graph(ping_readings):
# force square figure and square axes looks better for polar, IMO
fig = figure(figsize=(6, 6))
ax = P.subplot(1, 1, 1, projection='polar')
P.rgrids([28, 61, 91])
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)
theta = 356
angle = theta * np.pi / 180.0
radii = [ping_readings[0]]
width = .15
bars1 = ax.bar(0, 100, width=0.001, bottom=0.0)
#print "theta, radii, width: ", theta, radii, width
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 86
angle = theta * np.pi / 180.0
radii = [ping_readings[1]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 176
angle = theta * np.pi / 180.0
radii = [ping_readings[2]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
theta = 266
angle = theta * np.pi / 180.0
radii = [ping_readings[3]]
width = .15
bars = ax.bar(angle, radii, width=width, bottom=0.0, color='blue')
pil_img = fig2img(fig)
#print type(pil_img), pil_img
sonar_image = PILtoCV_4Channel(pil_img)
cv.ShowImage("Sonar", sonar_image)
#cv.MoveWindow ('Sonar',50 ,50 )
#time.sleep(.01)
cv.WaitKey(10)
#garbage cleanup
fig.clf()
P.close()
return sonar_image
def display_climate_radial(geography, year, tempdata, raindata):
#Create figure and polar axis
fig = plt.figure('%s_radial' % geography, facecolor='white', figsize=(8,8))
ax = fig.add_subplot(111, polar = True, frameon=False)
mintemp=-30
maxtemp=40
ax.text(0,mintemp, geography.upper(), color='#555555', horizontalalignment='center', size=30)
ax.text(0,maxtemp+1, str(year), color='#555555', horizontalalignment='center', size=10)
#Min/Max temps as bars
for i,(tmin,tmax,tmean) in enumerate(tempdata):
if np.abs(tmax-tmin)<1:
tmin=tmin-0.5
tmax=tmax+0.5
ax.plot([2*np.pi*i/365.0]*2, [tmin,tmax], color=cm.spectral((tmean+5)/45.0), linewidth=1.5, alpha=0.6);
# plot rainfall as scatters
ax.scatter([2*np.pi*r/365. for r in raindata['rainydays']], raindata['tcenters'], s=[100*r for r in raindata['rainfalls']], alpha=0.5, facecolor='#99aacc', linewidth=0)
# tweak ranges and orientation of polar plot
ax.set_rmax(maxtemp)
ax.set_rmin(mintemp)
ax.set_theta_direction(-1)
ax.set_theta_zero_location("N")
#Tweak polar axes, gridding, labels
ax.tick_params(axis='both', colors='#bbbbbb')
ax.set_xticks([m*2*np.pi/12 for m in range(12)])
months = ['JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC']
ax.set_xticklabels( months, fontsize=10 )
ax.get_xaxis().grid(False)
plt.rgrids( (0.01, 10, 20, 30, 40), labels=('0 C', '', '20 C', '', '40 C' ), angle=180) # radii only positive here, but override later
ax.get_yaxis().grid(which='minor',linestyle='-',color='#bbbbbb', alpha=0.3)
ax.get_yaxis().grid(which='major',linestyle='-',color='#bbbbbb', alpha=0.4, linewidth=1.4)
ax.set_yticks([10, 30], minor=True)
ax.set_yticks([0, 20, 40])
ax.set_yticklabels( ['0 C', '20 C', '40 C' ], fontsize=10)
plt.show()
def test_polar_wrap():
D2R = np.pi / 180.0
fig = plt.figure()
#NOTE: resolution=1 really should be the default
plt.subplot( 111, polar=True, resolution=1 )
plt.polar( [179*D2R, -179*D2R], [0.2, 0.1], "b.-" )
plt.polar( [179*D2R, 181*D2R], [0.2, 0.1], "g.-" )
plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
fig = plt.figure()
#NOTE: resolution=1 really should be the default
plt.subplot( 111, polar=True, resolution=1 )
plt.polar( [2*D2R, -2*D2R], [0.2, 0.1], "b.-" )
plt.polar( [2*D2R, 358*D2R], [0.2, 0.1], "g.-" )
plt.polar( [358*D2R, 2*D2R], [0.2, 0.1], "r.-" )
plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
def subtract(n0,n1):
plt.figure()
needles2d = np.array([n0,n1,0.])
f = [1.,0.,0.]
sn0=signal2d(S0,f,b,d,needles2d,angles2d)
f = [0.,1.,0.]
sn1=signal2d(S0,f,b,d,needles2d,angles2d)
#plt.polar(angles2d,s00,label='fibre at 0')
#plt.polar(angles2d,s90,label='fibre at 90')
plt.subplot(2,4,1,polar=True)
plt.polar(angles2d,(sn0+sn1)/2.,label='mix')
lines, labels = plt.rgrids( (20, 40, 60) )
#plt.polar(angles2d,(s00-s90)/2.,label='diff fibres 0 and 90')
for (i,theta) in enumerate(np.linspace(-np.pi/2,np.pi/2,7)):
plt.subplot(2,4,i+2,polar=True)
needles2d = np.array([0.,theta,0.])
f = [0.,1.,0.]
stheta=signal2d(S0,f,b,d,needles2d,angles2d)
plt.polar(angles2d,(sn0+sn1)/2.-stheta,label=str(-180*theta/np.pi))
lines, labels = plt.rgrids( (20, 40, 60) )
plt.legend(loc='best')
plt.suptitle('Subtraction from '+str(180*n0/np.pi)+'+'+str(180*n1/np.pi))
def draw_radar():
N = 10
theta = radar_factory(N, frame='polygon')
data = get_data()
spoke_labels = data.pop('column names')
fig = plt.figure(figsize=(16, 9))
fig.subplots_adjust(wspace=0.50, hspace=0.20)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes
for n, title in enumerate(data.keys()):
ax = fig.add_subplot(1, 2, n+1, projection='radar')
plt.rgrids([0.5], (''))
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
horizontalalignment='center', verticalalignment='center')
for d, color in zip(data[title], colors):
ax.plot(theta, d, color=color)
ax.fill(theta, d, facecolor=color, alpha=0.25)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
'''
plt.subplot(2, 2, 1)
labels = ('Factor 1')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
'''
plt.figtext(0.5, 0.965, 'Weapons selected',
ha='center', color='black', weight='bold', size='large')
#plt.show()
plt.savefig("flak_vs_rocket.png", bbox_inches='tight', dpi = 200)
plt.close()
def draw_radar(weapons, color_cluster, fitness_cluster):
N = 10
theta = radar_factory(N, frame='polygon')
data = get_data(weapons)
spoke_labels = data.pop('column names')
fig = plt.figure(figsize=(16, 9))
fig.subplots_adjust(wspace=0.50, hspace=0.25)
colors = ['b', 'r', 'g', 'm', 'y']
# Plot the four cases from the example data on separate axes
for n, title in enumerate(data.keys()):
ax = fig.add_subplot(1, 3, n+1, projection='radar')
plt.rgrids([0.5])
for d, color in zip(data[title], colors):
ax.plot(theta, d, color=color_cluster)
ax.fill(theta, d, facecolor=color_cluster, alpha=0.25)
ax.set_varlabels(spoke_labels)
ax = fig.add_subplot(1, 3, 3)
plt.ylim(0, 3)
ax.boxplot(fitness_cluster)
# add legend relative to top-left plot
'''
plt.subplot(2, 2, 1)
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
plt.setp(legend.get_texts(), fontsize='small')
'''
plt.figtext(0.5, 0.965, 'Mean of clustered weapons',
ha='center', color='black', weight='bold', size='large')
def schmidt(name="strike.xlsx", Width=1, Color='k'):
if("csv"in name):schmidtraw = pd.read_csv(name)
elif("xlsx"in name):schmidtraw = pd.read_excel(name)
Data = []
plt.axes(polar=True)
plt.polar([0], [90])
plt.xlim((0, 360))
plt.ylim((0, 90))
list1 = [eqar(x) for x in range(0, 90, 15)]
list2 = [str(x) for x in range(0, 90, 15)]
plt.rgrids(list1, list2)
for i in range(len(schmidtraw)):
Data.append(
[schmidtraw.at[i, "Name"], schmidtraw.at[i, "Strike"], schmidtraw.at[i, "Dip"], schmidtraw.at[i, "Color"],
schmidtraw.at[i, "Width"], schmidtraw.at[i, "Alpha"], schmidtraw.at[i, "Marker"]])
S = schmidtraw.at[i, "Strike"]
D = schmidtraw.at[i, "Dip"]
Width = schmidtraw.at[i, "Width"]
Color = schmidtraw.at[i, "Color"]
Alpha = schmidtraw.at[i, "Alpha"]
Marker = schmidtraw.at[i, "Marker"]
plt.plot(np.radians(90 - S), eqar(D), color=Color, linewidth=Width, alpha=Alpha, marker=Marker)
plt.plot(120, 30, color='K', linewidth=4, alpha=Alpha, marker='o')
plt.thetagrids(range(360 + 90, 0 + 90, -20), [str(x) for x in range(0, 360, 20)])
plt.savefig("Schmidt.png", dpi=600)
plt.savefig("Schmidt.svg", dpi=600)
plt.show()
def wulf(name="strike.xlsx", Width=1, Color='k'):
if("csv"in name):wulfraw = pd.read_csv(name)
elif("xlsx"in name):wulfraw = pd.read_excel(name)
Data = []
plt.axes(polar=True)
plt.polar([0], [90])
plt.xlim((0, 360))
plt.ylim((0, 90))
list1 = [eqan(x) for x in range(0, 90, 15)]
list2 = [str(x) for x in range(0, 90, 15)]
plt.rgrids(list1, list2)
for i in range(len(wulfraw)):
Data.append([wulfraw.at[i, "Name"], wulfraw.at[i, "Strike"], wulfraw.at[i, "Dip"], wulfraw.at[i, "Color"],
wulfraw.at[i, "Width"], wulfraw.at[i, "Alpha"]])
S = wulfraw.at[i, "Strike"]
D = wulfraw.at[i, "Dip"]
Width = wulfraw.at[i, "Width"]
Color = wulfraw.at[i, "Color"]
Alpha = wulfraw.at[i, "Alpha"]
r = np.arange(S - 90, S + 91, 1)
BearR = [np.radians(-A + 90) for A in r]
Line = (eqan(getangular(D, S, r)))
plt.plot(BearR, Line, color=Color, linewidth=Width, alpha=Alpha)
plt.thetagrids(range(360 + 90, 0 + 90, -20), [str(x) for x in range(0, 360, 20)])
plt.savefig("Wulff.png", dpi=600)
plt.savefig("Wulff.svg", dpi=600)
plt.show()