def plot_fit_function(self, num_points=100):
'''
try:
x_coords = np.linspace(self.x_vec[0], self.x_vec[-1], num_points)
except Exception as message:
print 'no x axis information specified', message
return
'''
if self.landscape:
for trace in self.landscape:
try:
#plt.clear()
plt.plot(self.x_vec, trace)
plt.fill_between(self.x_vec,
trace + float(self.span) / 2,
trace - float(self.span) / 2,
alpha=0.5)
except Exception:
print 'invalid trace...skip'
plt.axhspan(self.y_vec[0],
self.y_vec[-1],
facecolor='0.5',
alpha=0.5)
plt.show()
else:
print 'No trace generated.'
def plot_z_vs_obsangle_for_given_age(self, t1, t2, t3, t4, t5, t6, t7,
obsanglemin, obsanglemax, step):
obsangles = np.arange(obsanglemin, obsanglemax, step)
plt.figure(figsize=(6, 6))
plt.rc('axes', linewidth=2)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t1, obsanglemin,
obsanglemax, step)[5],
color='r',
label='%s yrs' % t1)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t2, obsanglemin,
obsanglemax, step)[5],
color='orange',
label='%s yrs' % t2)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t3, obsanglemin,
obsanglemax, step)[5],
color='y',
label='%s yrs' % t3)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t4, obsanglemin,
obsanglemax, step)[5],
color='g',
label='%s yrs' % t4)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t5, obsanglemin,
obsanglemax, step)[5],
color='b',
label='%s yrs' % t5)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t6, obsanglemin,
obsanglemax, step)[5],
color='purple',
label='%s yrs' % t6)
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(t7, obsanglemin,
obsanglemax, step)[5],
color='m',
label='%s yrs' % t7)
#plt.axhline(y=1630,linestyle='--',color='k',label='Edge of LMC')
#plt.axhline(y=-1630,linestyle='--',color='k')
plt.axhspan(-1630,
1630,
color='skyblue',
alpha=0.5,
label='Thickness of LMC')
#plt.axhline(y=2000,linestyle='-.',color='k',label='Edge of 30 Dor')
plt.xlabel('Observed Angular Separation', fontsize=18)
plt.ylabel('z (lyrs)', fontsize=18)
plt.tight_layout()
plt.legend(loc='upper left', prop={'size': 15})
def plot_episode(args):
"""Plot an episode plucked from the large h5 database"""
print "plot_episode"
# load the data file
tblfilename = "bf_optimize_mavlink.h5"
h5file = tb.open_file(tblfilename, mode = "a")
# get the table handle
table = h5file.root.v2.evaluations
# selected episode
episode_row = table.read_coordinates([int(args.epinum)])
# compare episodes
episode_row_1 = table.read_coordinates([2, 3, 22, 46]) # bad episodes
print "row_1", episode_row_1.shape
# episode_row = table.read_coordinates([3, 87])
episode_target = episode_row["alt_target"]
episode_target_1 = [row["alt_target"] for row in episode_row_1]
print "episode_target_1.shape", episode_target_1
episode_timeseries = episode_row["timeseries"][0]
episode_timeseries_1 = [row["timeseries"] for row in episode_row_1]
print "row", episode_timeseries.shape
print "row_1", episode_timeseries_1
sl_start = 0
sl_end = 2500
sl_len = sl_end - sl_start
sl = slice(sl_start, sl_end)
pl.plot(episode_timeseries[sl,1], "k-", label="alt", lw=2.)
print np.array(episode_timeseries_1)[:,:,1]
pl.plot(np.array(episode_timeseries_1)[:,:,1].T, "k-", alpha=0.2)
# alt_hold = episode_timeseries[:,0] > 4
alt_hold_act = np.where(episode_timeseries[sl,0] == 11)
print "alt_hold_act", alt_hold_act[0].shape, sl_len
alt_hold_act_min = np.min(alt_hold_act)
alt_hold_act_max = np.max(alt_hold_act)
print "min, max", alt_hold_act_min, alt_hold_act_max, alt_hold_act_min/float(sl_len), alt_hold_act_max/float(sl_len),
# pl.plot(episode_timeseries[sl,0] * 10, label="mode")
pl.axhspan(-100., 1000,
alt_hold_act_min/float(sl_len),
alt_hold_act_max/float(sl_len),
facecolor='0.5', alpha=0.25)
pl.axhline(episode_target, label="target")
pl.xlim((0, sl_len))
pl.xlabel("Time steps [1/50 s]")
pl.ylabel("Alt [cm]")
pl.legend()
if args.plotsave:
pl.gcf().set_size_inches((10, 3))
pl.gcf().savefig("%s.pdf" % (sys.argv[0][:-3]), dpi=300, bbox_inches="tight")
pl.show()
def plot_fit_function(self, num_points = 100):
'''
try:
x_coords = np.linspace(self.x_vec[0], self.x_vec[-1], num_points)
except Exception as message:
print 'no x axis information specified', message
return
'''
if self.landscape:
for trace in self.landscape:
try:
#plt.clear()
plt.plot(self.x_vec, trace)
plt.fill_between(self.x_vec, trace+float(self.span)/2, trace-float(self.span)/2, alpha=0.5)
except Exception:
print 'invalid trace...skip'
plt.axhspan(self.y_vec[0], self.y_vec[-1], facecolor='0.5', alpha=0.5)
plt.show()
else:
print 'No trace generated.'
def plot_z_vs_Ang_Sep_for_ages(self, obsanglemin, obsanglemax, step):
obsangles = np.arange(obsanglemin, obsanglemax, step)
ages = np.arange(200, 5000, 100)
alphas = np.linspace(0, 1, len(ages))
plt.figure(figsize=(6, 6))
plt.rc('axes', linewidth=2)
for i in range(0, len(ages)):
plt.plot(obsangles,
self.get_SB_vs_obsangle_for_given_age(
ages[i], obsanglemin, obsanglemax, step)[5],
color='b',
alpha=alphas[i])
plt.axhspan(-1630,
1630,
color='r',
alpha=0.5,
label='Thickness of LMC')
plt.ylabel('z (ly)', fontsize=20, fontweight='bold')
plt.xlabel('Angular Separation', fontsize=20, fontweight='bold')
plt.legend(fontsize=18)
plt.show()
def make_area_prob_plot(self):
#plt.axvspan(0, 0.49475, alpha=0.2, color='gray',label='400')
plt.axvspan(0, 0.49475, alpha=0.2, color='gray', label='600')
plt.axvspan(0, 0.4435, alpha=0.2, color='gray', label='800')
#plt.axvspan(0, 0.4267, alpha=0.2, color='gray',label='200')
plt.axvspan(0, 0.3665, alpha=0.2, color='gray', label='1000')
plt.axvspan(0, 0.245, alpha=0.2, color='gray', label='1200')
plt.axhspan(500, 1200, alpha=0.2, color='gray', label='Ages')
plt.axhspan(500, 1000, alpha=0.2, color='gray', label='Ages')
plt.axhspan(500, 800, alpha=0.2, color='gray', label='Ages')
plt.axhspan(500, 600, alpha=0.2, color='gray', label='Ages')
plt.xlim(-0.1, 1.1)
plt.ylim(0, 2000)
plt.xlabel('Observed Angular Separation')
plt.ylabel('Age')
print
t0 = time.time()
subprocess.call(ffmpegcommand, stdout=DEVNULL, stderr=subprocess.STDOUT,
shell=True)
t1 = time.time()
print "in", str(round(t1 - t0, 3)), "seconds (" +\
str(round(options.images / (t1-t0), 3)) + " images per second)"
filename = os.path.join(FileSavePath,
"snapshot_%03d" % (int(round(options.images / 2.0))) + ".jpg")
image = plt.imread(filename)
plt.imshow(image, origin="lower")
figuretitle = "Snapshot", str(int(round(options.images / 2.0))), "of",\
str(options.images), "from", FileSavePath, "\nwith an exposure time of",\
str(options.exposuretime / 10), "ms",
if options.preview:
plt.axhspan(ymin=CMOSheight-previewheight, ymax=CMOSheight,
xmin=0, xmax=float(previewwidth)/CMOSwidth,
facecolor='r', alpha=0.5)
plt.xlim([0, CMOSwidth])
plt.ylim([0, CMOSheight])
figuretitle += "\nred=preview area",
plt.title(' '.join(figuretitle))
plt.show()
print 'Images saved to',
print os.path.abspath(os.path.join(FileSavePath, 'snapshot*.jpg'))
print 80 * "-"
print "done"
def plot(self,
title,
xlim=None,
ylim=None,
plot_type='amplitude',
show_limits=False,
show_ssa_limits=False,
beam_on=False,
annotate=False,
beam_start=20,
beam_end=22,
hide_fwd=False):
""" Plot signals of interest in RF Station time-series simulation.
Supports amplitude, phase and cartesian plots with a number of options.
"""
fund_k_probe = self.fund_mode_dict['k_probe']
fund_k_drive = self.fund_mode_dict['k_drive']
fund_k_em = self.fund_mode_dict['k_em']
fund_k_beam = self.fund_mode_dict['k_beam']
# Amplitude
if plot_type == 'amplitude':
if hide_fwd == False:
plt.plot(self.trang * 1e3,
np.abs(self.E_fwd) * fund_k_drive,
'-',
label=r'Forward $\left(\vec E_{\rm fwd}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.abs(self.E_reverse / fund_k_em),
'-',
label=r'Reverse $\left(\vec E_{\rm reverse}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.abs(self.cav_v),
'-',
label=r'Cavity Field',
linewidth=2,
color='r')
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe),
'-',
label=r'Set-point $\left(\vec E_{\rm sp}\right)$',
linewidth=2,
color='c')
# Y label
plt.ylabel('Amplitude [V]', fontsize=30)
if show_limits == True:
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) * (1 + 1e-4),
label='Upper limit',
linewidth=2,
color='m')
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) * (1 - 1e-4),
label='Lower limit',
linewidth=2,
color='y')
plt.axhspan(16e6 / 1.00005,
16e6 * 1.00005,
color='blue',
alpha=0.2)
if show_ssa_limits == True:
# If SSA noise were a sine wave, this would be the amplitude limit
ssa_limit = 4e-2 * fund_k_drive * np.sqrt(3.8e3)
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) +
ssa_limit / np.sqrt(2) / 2,
label='SSA Upper (RMS) limit',
linewidth=2,
color='m')
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) -
ssa_limit / np.sqrt(2) / 2,
label='SSA Lower (RMS) limit',
linewidth=2,
color='y')
low_index = np.where(self.trang == 20e-3)[0][0]
high_index = np.where(self.trang == 49.9e-3)[0][0]
ssa_std = np.std(self.E_fwd[low_index:high_index] *
fund_k_drive)
ssa_std_percent = 100 * ssa_std / (fund_k_drive *
np.sqrt(3.8e3))
ssa_std_text = r'$\sigma_{\rm SSA}\,=\,$' + '%.2f %% RMS' % (
np.abs(ssa_std_percent))
plt.text(35,
13e6,
ssa_std_text,
verticalalignment='top',
fontsize=30)
# Phase
if plot_type == 'phase':
plt.plot(self.trang * 1e3,
np.angle(self.cav_v, deg=True),
'-',
label=r'Cavity Field',
linewidth=2,
color='r')
plt.plot(self.trang * 1e3,
np.angle(self.set_point / fund_k_probe, deg=True),
label=r'Set-point $\left(\vec E_{\rm sp}\right)$',
linewidth=2,
color='c')
if show_limits:
plt.plot(self.trang * 1e3,
1e-2 * np.ones(len(self.trang)),
label='Upper limit',
linewidth=2,
color='m')
plt.plot(self.trang * 1e3,
-1e-2 * np.ones(len(self.trang)),
label='Lower limit',
linewidth=2,
color='y')
plt.axhspan(-4e-3, 4e-3, color='blue', alpha=0.2)
# Y label
plt.ylabel('Phase [degrees]', fontsize=30)
# Cartesian coordinates
if plot_type == 'cartesian':
plt.plot(self.trang * 1e3,
np.real(self.E_fwd) * fund_k_drive,
'-',
label=r'Forward $\Re \left(\vec E_{\rm fwd}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.imag(self.E_fwd) * fund_k_drive,
'-',
label=r'Forward $\Im \left(\vec E_{\rm fwd}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.real(self.cav_v),
'-',
label=r'$\Re$ Cavity Field',
linewidth=2)
plt.plot(self.trang * 1e3,
np.imag(self.cav_v),
'-',
label=r'$\Im$ Cavity Field',
linewidth=2)
# Add annotation for a very specific plot
if annotate:
delta_E_fwd_text = r'$\Delta \left| \vec E_{\rm fwd} \right| \approx \,$' + '%.d MV' % (
round(np.abs(fund_k_beam) * 100e-6 * 1e-6))
plt.annotate(delta_E_fwd_text, xy=(21.05, 18e6), fontsize=25)
index_of_21 = np.where(self.trang == 21e-3)
plt.annotate(s='',
xy=(21,
np.abs(self.E_fwd[index_of_21]) * fund_k_drive),
xytext=(21, np.abs(self.cav_v[index_of_21])),
arrowprops=dict(arrowstyle='<->'))
# Add clear red shade if beam current is on to highlight the location of the beam train
if beam_on:
plt.axvspan(beam_start, beam_end, color='red', alpha=0.2)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.title(title, fontsize=40, y=1.02)
plt.xlabel('Time [ms]', fontsize=30)
if xlim:
plt.xlim(xlim)
if ylim:
plt.ylim(ylim)
plt.rc('font', **{'size': 20})
plt.legend(loc='upper right')
plt.show()
def run(appfolder, stationName, data):
try:
Data={}
for k, v in data.iteritems():
Data[int(k)] = {}
for a, b in v.iteritems():
Data[int(k)][int(a)] = map(lambda x: x if x!=None else 0,b)
print "DATA TO RUN...", Data
Years = Data.keys() # My Index with Years
#-------------------------------------------------------------------------------
# Calculation for Thronwaite Potential Evapotranspiration Formula
# FinalData: Data = {Year:Month:[Rain,Temp,i_index,PET}
#-------------------------------------------------------------------------------
#Calculation i_index
for Year in Years:
for i in [1,2,3,4,5,6,7,8,9,10,11,12]:
if Data[Year][i][1]>0:
i_index = 0.09*(Data[Year][i][1])**1.514
else: #if Data[Year][i][1]<=0:
i_index = 0
Data[Year][i].append(i_index)
#Daylight Hour (Just for Now) Constant =12 hour <----- THIS WILL CHANGE!!!
N=12
for Year in Years:
sum_I=0
for i in [1,2,3,4,5,6,7,8,9,10,11,12]:
sum_I = sum_I + Data[Year][i][2]
for i in [1,2,3,4,5,6,7,8,9,10,11,12]:
DaysPerMonth = [31,28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
if calendar.isleap(Year) == True:
DaysPerMonth[1]=29
N=12
if Data[Year][i][1] == 0:
PET = 0
else:
if sum_I != 0: #prevent division by zero
PET = 16 * (10 * (Data[Year][i][1])/sum_I)* ( DaysPerMonth[i-1] * N/360)
else:
print stationName, Data
PET = 0
if PET <= 0 :
Data[Year][i].append(0)
else:
Data[Year][i].append(PET)
#-------------------------------------------------------------------------------
# FINAL DATASET IS READY FOR CALC SPI AND RDI
#-------------------------------------------------------------------------------
# Get Special Rain and Rain/PET DataSet :
rain1_data = []
rain3_data = []
rain6_data = []
rain12_data = []
rainpet1 = []
rainpet3 = []
rainpet6 = []
rainpet12 = []
for Year in Years:
#Rain1
for i in [1,2,3,4,5,6,7,8,9,10,11,12]:
rain1_data.append(Data[Year][i][0])
if Data[Year][i][3]==0:
rainpet1.append(0)
else:
rainpet1.append(float(Data[Year][i][0])/Data[Year][i][3])
#Rain3 Selected Months
Months= [[1,2,3],[4,5,6],[7,8,9],[10,11,12]] # Every 3 Months Selections
for month in Months:
sum_rain3 = 0 # Loop for Summing
sum_RP3 = 0
for i in [0,1,2]:
sum_rain3 = sum_rain3+Data[Year][month[i]][0] # Selected Values
if Data[Year][month[i]][3] != 0:
sum_RP3 = sum_RP3 +float(Data[Year][month[i]][0])/(Data[Year][month[i]][3])
else:
sum_RP3 = sum_RP3 + 0
rain3_data.append(sum_rain3)
rainpet3.append(sum_RP3)
#Rain 6 Selected
Months= [[1,2,3,4,5,6],[7,8,9,10,11,12]]
for month in Months:
sum_rain6 = 0
sum_RP6 = 0
for i in [0,1,2,3,4,5]:
sum_rain6 = sum_rain6+Data[Year][month[i]][0]
if Data[Year][month[i]][3] !=0:
sum_RP6 = sum_RP6 +float(Data[Year][month[i]][0])/(Data[Year][month[i]][3])
else:
sum_RP6 = sum_RP6 + 0
rain6_data.append(sum_rain6)
rainpet6.append(sum_RP6)
#Rain12 Selected not so pythonic way
Months= [[1,2,3,4,5,6,7,8,9,10,11,12]]
for month in Months:
sum_rain12 = 0
sum_RP12 = 0
for i in [0,1,2,3,4,5,6,7,8,9,10,11]:
sum_rain12 = sum_rain12+Data[Year][month[i]][0]
if Data[Year][month[i]][3] !=0:
sum_RP12 = sum_RP12 +float(Data[Year][month[i]][0])/(Data[Year][month[i]][3])
else:
sum_RP12 = sum_RP12 + 0
rain12_data.append(sum_rain12)
rainpet12.append(sum_RP12)
#-------------------------------------------------------------------------------------------
# Datasets For Spi Analusis - YOU NEED TO TEST FOR RANDOM NULL VALUES!!!
dataspi = {"Spi1":rain1_data,"Spi3":rain3_data,"Spi6":rain6_data,"Spi12":rain12_data}
datardi = {"Rdi1":rainpet1,"Rdi3":rainpet3,"Rdi6":rainpet6,"Rdi12":rainpet12}
#-------------------------------------------------------------------------------------------
# RESULTS
# Note : Problem with RDI (Cant figure out yet)
#-------------------------------------------------------------------------------------------
#THIS THE OUTPUT YOU NEED FOR THE WEB APP -- Spi diffferent time Span Resulsv
spiresults = {"Spi1": spi_calc(dataspi["Spi1"]),"Spi3": spi_calc(dataspi["Spi3"]),"Spi6": spi_calc(dataspi["Spi6"]),"Spi12": spi_calc(dataspi["Spi12"])}
rdiresults = {"Rdi1": rdi_calc(datardi["Rdi1"]),"Rdi3": rdi_calc(datardi["Rdi3"]),"Rdi6": rdi_calc(datardi["Rdi6"]),"Rdi12": rdi_calc(datardi["Rdi12"])}
#-------------------------------------------------------------------------------------------
# Charts
#-------------------------------------------------------------------------------------------
stationname = stationName
minyear=min(Years)
maxyear=max(Years)
keysspi = spiresults.keys()
keysrdi = rdiresults.keys()
# Spi1 and Rdi1 Chart-------------------------------------------------------------------------------------------
plt.figure(figsize=(6*3.13,4*3.13))
plt.subplot(211)
plt.axhspan(0, -0.8, facecolor='0.5', alpha=0.5,color ='yellow',linewidth = True)
plt.axhspan(-0.8, -1.3, facecolor='0.5', alpha=0.5,color ='orange',linewidth = True)
plt.axhspan(-1.3, -1.6, facecolor='0.5', alpha=0.5,color ='orangered',linewidth = True)
plt.axhspan(-1.6, -2, facecolor='0.5', alpha=0.5,color ='r',linewidth = True)
plt.axhspan(-2, -3, facecolor='0.5', alpha=0.5,color ='maroon',linewidth = True)
Spi1= spiresults["Spi1"]['Spi_result']
Rdi1= rdiresults["Rdi1"]['Rdi_result']
n=len(Spi1)
ind = np.arange(n)
width = 0.40
rects1 = plt.bar(ind, Spi1, width, color='b', label='SPI ')
rects2 = plt.bar(ind+width, Rdi1, width, color='lightblue', label='RDI ')
plt.ylabel('Drought Index - std units')
plt.title('{} {} Drought Propagation Chart for The Period {} - {}'.format('[1 month]', stationname,minyear,maxyear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0,n,12)
plt.xticks(myTicks,xYears,rotation ="vertical")
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax = n)
plt.ylim([-3,3])
plt.text( len(Spi1), 2.5, 'Wet Conditions', rotation=90)
plt.text( len(Spi1), -0.5, 'Dry Conditions', rotation=90)
plt.subplot(212)
plt.scatter(Spi1,rain1_data,label='Spi ')
plt.scatter(Rdi1,rain1_data,c="lightblue",label='Rdi ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
#plt.show()
#return [os.path.join(os.getcwd(), f) for f in os.listdir(os.getcwd())]
plt.savefig(appfolder+'/static/charts'
'/'+stationName+'_1month.png')
# <--------------------------------------------PIC ANTONIS
plt.close()
# Note some info inside the chart will be added later
# Spi3 and Rdi3 Chart-------------------------------------------------------------------------------------------
Spi3= spiresults["Spi3"]['Spi_result']
Rdi3= rdiresults["Rdi3"]['Rdi_result']
plt.figure(figsize=(6*3.13,4*3.13))
plt.subplot(211)
plt.axhspan(0, -0.8, facecolor='0.5', alpha=0.5,color ='yellow',linewidth = True)
plt.axhspan(-0.8, -1.3, facecolor='0.5', alpha=0.5,color ='orange',linewidth = True)
plt.axhspan(-1.3, -1.6, facecolor='0.5', alpha=0.5,color ='orangered',linewidth = True)
plt.axhspan(-1.6, -2, facecolor='0.5', alpha=0.5,color ='r',linewidth = True)
plt.axhspan(-2, -3, facecolor='0.5', alpha=0.5,color ='maroon',linewidth = True)
n=len(Spi3)
ind = np.arange(n)
width = 0.40
rects1 = plt.bar(ind, Spi3, width, color='b', label='SPI ')
rects2 = plt.bar(ind + width, Rdi3, width, color='lightblue', label='RDI ')
plt.ylabel('Drought Index - std units')
plt.title('{} {} Drought Propagation Chart for The Period {} - {}'.format('[3 Month]', stationname,minyear,maxyear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0,n,4)
plt.xticks(myTicks,xYears,rotation='vertical')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax = n)
plt.ylim([-3,3])
plt.text( len(Spi3), 2.5, 'Wet Conditions', rotation=90)
plt.text( len(Spi3), -0.5, 'Dry Conditions', rotation=90)
plt.grid()
#plt.show()
plt.subplot(212)
plt.scatter(Spi3,rain3_data,label='SPI ')
plt.scatter(Rdi3,rain3_data,c="lightblue",label='RDI ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
plt.savefig(appfolder+'static/charts'
'/'+stationName+'_3month.png')
plt.close()
# Spi6 and Rdi6 Chart-------------------------------------------------------------------------------------------
plt.figure(figsize=(6*3.13,4*3.13))
plt.subplot(211)
Spi6= spiresults["Spi6"]['Spi_result']
Rdi6= rdiresults["Rdi6"]['Rdi_result']
plt.axhspan(0, -0.8, facecolor='0.5', alpha=0.5,color ='yellow',linewidth = True)
plt.axhspan(-0.8, -1.3, facecolor='0.5', alpha=0.5,color ='orange',linewidth = True)
plt.axhspan(-1.3, -1.6, facecolor='0.5', alpha=0.5,color ='orangered',linewidth = True)
plt.axhspan(-1.6, -2, facecolor='0.5', alpha=0.5,color ='r',linewidth = True)
plt.axhspan(-2, -3, facecolor='0.5', alpha=0.5,color ='maroon',linewidth = True)
n=len(Spi6)
ind = np.arange(n)
width = 0.40
rects1 = plt.bar(ind, Spi6, width, color='b', label='SPI')
rects2 = plt.bar(ind + width, Rdi6, width, color='lightblue', label='RDI')
plt.ylabel('Drought Index - std units')
plt.title('{} {} Drought Propagation Chart for The Period {} - {}'.format('[6 Month]', stationname,minyear,maxyear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0,n,2)
plt.xticks(myTicks,xYears,rotation='vertical')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax = n)
plt.ylim([-3,3])
plt.grid()
plt.text( len(Spi6) , 2.5, 'Wet Conditions', rotation=90)
plt.text( len(Spi6) , -0.5, 'Dry Conditions', rotation=90)
#plt.show()
plt.subplot(212)
plt.scatter(Spi6,rain6_data,label='SPI')
plt.scatter(Rdi6,rain6_data,c="r",label='RDI ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
plt.savefig(appfolder+'static/charts'
'/'+stationName+'_6month.png')
plt.close()
# Spi12 and Rdi12 Chart-------------------------------------------------------------------------------------------
Spi12= spiresults["Spi12"]['Spi_result']
Rdi12= rdiresults["Rdi12"]['Rdi_result']
plt.figure(figsize=(6*3.13,4*3.13))
plt.subplot(211)
n=len(Spi12)
ind = np.arange(n)
width = 0.40
plt.axhspan(0, -0.8, facecolor='0.5', alpha=0.5,color ='yellow',linewidth = True)
plt.axhspan(-0.8, -1.3, facecolor='0.5', alpha=0.5,color ='orange',linewidth = True)
plt.axhspan(-1.3, -1.6, facecolor='0.5', alpha=0.5,color ='orangered',linewidth = True)
plt.axhspan(-1.6, -2, facecolor='0.5', alpha=0.5,color ='r',linewidth = True)
plt.axhspan(-2, -3, facecolor='0.5', alpha=0.5,color ='maroon',linewidth = True)
rects1 = plt.bar(ind, Spi12, width, color='b', label='Spi ')
rects2 = plt.bar(ind + width, Rdi12, width, color='lightblue', label='Rdi ')
plt.ylabel('Drought Index - std units')
plt.title('{} {} Drought Propagation Chart for The Period {} - {}'.format('[12 Month]', stationname,minyear,maxyear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks=range(0,n)
plt.xticks(myTicks,xYears,rotation='vertical')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax = n)
plt.ylim([-3,3])
plt.grid()
plt.text( len(Spi12), 2.5, 'Wet Conditions', rotation=90)
plt.text( len(Spi12), -0.5, 'Dry Conditions', rotation=90)
#plt.show()
plt.subplot(212)
plt.scatter(Spi12,rain12_data,label='SPI ')
plt.scatter(Rdi12,rain12_data,c="lightblue",label='RDI')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
plt.savefig(appfolder+'static/charts'
'/'+stationName+'_12month.png')
plt.close()
print stationName, "Charts Update run completed... OK"
except Exception, e:
print "Drought Error for", stationName,":", e.message, e.args
def pid_pca(args):
# import modular data processing toolkit
import mdp
# load data file
tblfilename = "bf_optimize_mavlink.h5"
h5file = tb.open_file(tblfilename, mode = "a")
# table = h5file.root.v1.evaluations
# get tabke handle
table = h5file.root.v2.evaluations
# sort rows
if not table.cols.mse.is_indexed:
table.cols.mse.createCSIndex()
if not args.sorted:
pids = [ [x["alt_p"], x["alt_i"], x["alt_d"], x["vel_p"], x["vel_i"], x["vel_d"]]
for x in table.iterrows() ]
mses = [ [x["mse"]] for x in table.iterrows() ]
else:
pids = [ [x["alt_p"], x["alt_i"], x["alt_d"], x["vel_p"], x["vel_i"], x["vel_d"]] for x in table.itersorted("mse")]
mses = [ [x["mse"]] for x in table.itersorted("mse")]
print "best two", pids
mses_a = np.log(np.clip(np.array(mses), 0, 200000.))
mses_a /= np.max(mses_a)
# FIXME: try kernel pca on this
from sklearn.decomposition import PCA, KernelPCA, SparsePCA
kpca = KernelPCA(n_components = None,
kernel="rbf", degree=6, fit_inverse_transform=True,
gamma=1/6., alpha=1.)
# kpca = SparsePCA(alpha=2., ridge_alpha=0.1)
X_kpca = kpca.fit_transform(np.asarray(pids).astype(float))
# X_back = kpca.inverse_transform(X_kpca)
Z_kpca = kpca.transform(np.asarray(pids).astype(float))
print Z_kpca.shape, X_kpca.shape
print "|Z_kpca|", np.linalg.norm(Z_kpca, 2, axis=1)
# for i in range(8):
# pl.subplot(8,1,i+1)
# pl.plot(Z_kpca[:,i])
# pl.legend()
# pl.show()
# fast PCA
# pid_p = mdp.pca(np.array(pids).astype(float))
pid_array = np.array(pids).astype(float)
print "pid_array.shape", pid_array.shape
pcanode = mdp.nodes.PCANode(output_dim = 6)
# pcanode.desired_variance = 0.75
pcanode.train(np.array(pids).astype(float))
pcanode.stop_training()
print "out dim", pcanode.output_dim
pid_p = pcanode.execute(np.array(pids).astype(float))
# pid_array_mse = np.hstack((np.array(pids).astype(float), mses_a))
pid_ica = mdp.fastica(np.array(pids).astype(float))
print "ica.shape", pid_ica.shape
# pid_p = np.asarray(pids)[:,[0, 3]]
# pid_p = pids[:,0:2]
# [:,0:2]
sl_start = 0
sl_end = 100
sl = slice(sl_start, sl_end)
print "expl var", pcanode.explained_variance
pl.subplot(111)
colors = np.zeros((100, 3))
# colors = np.hstack((colors, 1-(0.5*mses_a)))
colors = np.hstack((colors, 1-(0.8*mses_a)))
# print colors.shape
# pl.scatter(pid_p[sl,0], pid_p[sl,1], color=colors)
# ica spektrum
pid_ica_sum = np.sum(np.square(pid_ica), axis=0)
# pid_ica_sum_sort = np.sort(pid_ica_sum)
pid_ica_sum_0 = np.argmax(pid_ica_sum)
pid_ica_sum[pid_ica_sum_0] = 0
pid_ica_sum_1 = np.argmax(pid_ica_sum)
# pl.scatter(pid_p[sl,0], pid_p[sl,1], color=colors)
pl.scatter(pid_ica[sl,pid_ica_sum_0], pid_ica[sl,pid_ica_sum_1], color=colors)
# pl.scatter(X_kpca[:,0], X_kpca[:,1], color=colors)
pl.gca().set_aspect(1)
# pl.scatter(pid_p[:,0], pid_p[:,1], alpha=1.)
# pl.show()
# plot raw pid values
pl.subplot(411)
pl.plot(pid_array[sl,[0,3]], "o")
pl.xlim((sl_start - 0.2, sl_end + 0.2))
pl.subplot(412)
pl.plot(pid_array[sl,[1,4]], "o")
pl.xlim((sl_start - 0.2, sl_end + 0.2))
pl.subplot(413)
pl.plot(pid_array[sl,[2,5]], "o")
# plot compressed pid values: pca, ica, ...
# pl.subplot(211)
# pl.plot(pid_p, ".")
# pl.plot(pid_p[sl], "o")
# pl.plot(pid_ica[sl] + np.random.uniform(-0.01, 0.01, size=pid_ica[sl].shape), "o")
pl.xlim((sl_start - 0.2, sl_end + 0.2))
# pl.plot(Z_kpca[:,:], "-o", label="kpca")
# pl.plot(Z_kpca[:,:], ".", label="kpca")
# pl.legend()
# pl.subplot(212)
pl.subplot(414)
pl.plot(mses_a[sl], "ko")
# pl.gca().set_yscale("log")
pl.xlim((sl_start - 0.2, sl_end + 0.2))
pl.show()
# gp fit
x = mses_a[sl]
x_sup = np.atleast_2d(np.arange(0, x.shape[0])).T
x_ones = x != 1.
x_ones[0:20] = False
print x, x_sup, x_ones, x_ones.shape
print "x[x_ones]", x[x_ones].shape
print "x_sup[x_ones]", x_sup[x_ones].shape
from sklearn.gaussian_process import GaussianProcess
# gp = GaussianProcess(regr='constant', corr='absolute_exponential',
# theta0=[1e-4] * 1, thetaL=[1e-12] * 1,
# thetaU=[1e-2] * 1, nugget=1e-2, optimizer='Welch')
gp = GaussianProcess(corr="squared_exponential",
theta0=1e-2, thetaL=1e-4, thetaU=1e-1,
nugget=1e-1/x[x_ones])
gp.fit(x_sup[x_ones,np.newaxis], x[x_ones,np.newaxis])
x_pred, sigma2_pred = gp.predict(x_sup, eval_MSE=True)
print x_pred, sigma2_pred
from sklearn import linear_model
clf = linear_model.Ridge (alpha = .5)
clf.fit(x_sup[x_ones,np.newaxis], x[x_ones,np.newaxis])
x_pred = clf.predict(x_sup[20:100])
pl.subplot(111)
pl.plot(mses_a[sl], "ko")
x_mean = np.mean(x[0:20])
pl.plot(np.arange(0, 20), np.ones((20, )) * x_mean, "k-", alpha=0.5)
pl.plot(np.arange(20, 100), x_pred, "k-", alpha=0.5)
pl.axhspan(0.5, 1.1, 0, 0.19, facecolor="0.5", alpha=0.25)
# pl.plot(x_pred + sigma2_pred, "k-", alpha=0.5)
# pl.plot(x_pred - sigma2_pred, "k-", alpha=0.5)
# pl.gca().set_yscale("log")
pl.xlim((sl_start - 0.2, sl_end + 0.2))
pl.ylim((0.5, 1.1))
pl.text(5, 0.6, "Random\ninitialization")
pl.text(40, 0.6, "Optimizer\nsuggestions")
pl.xlabel("Episode #")
pl.ylabel("MSE")
if args.plotsave:
pl.gcf().set_size_inches((10, 3))
pl.gcf().savefig("%s-mse.pdf" % (sys.argv[0][:-3]), dpi=300,
bbox_inches="tight")
pl.show()
def run(appfolder, stationName, data, userid):
try:
userid = str(userid)
print appfolder + 'static/usercharts/' + userid + '.png'
Data = {}
for k, v in data.iteritems():
Data[int(k)] = {}
for a, b in v.iteritems():
Data[int(k)][int(a)] = map(lambda x: x if x != None else 0, b)
Years = Data.keys() # My Index with Years
im = Image.open(appfolder + 'static/images/logo.jpg')
height = im.size[1]
# We need a float array between 0-1, rather than
# a uint8 array between 0-255
im = np.array(im).astype(np.float) / 255
#Exaple how to get Rain or Temp for my Data
#print "Example: 01/1956 Rain And Temp"
#print "Data[2008][1][0] : {}".format( Data[1956][1][0])
#print "Data[2009][1][1] : {}" .format( Data[1956][1][1])
#-------------------------------------------------------------------------------
# Calculation for Thronwaite Potential Evapotranspiration Formula
# FinalData: Data = {Year:Month:[Rain,Temp,i_index,PET}
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# FINAL DATASET IS READY FOR CALC SPI AND RDI
#-------------------------------------------------------------------------------
# Get Special Rain and Rain/PET DataSet :
Rain1_data = []
Rain3_data = []
Rain6_data = []
Rain12_data = []
for Year in Years:
#Rain1
for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]:
Rain1_data.append(Data[Year][i][0])
#Rain3 Selected Months
Months = [[1, 2, 3], [4, 5, 6], [7, 8, 9],
[10, 11, 12]] # Every 3 Months Selections
for month in Months:
sum_rain3 = 0 # Loop for Summing
for i in [0, 1, 2]:
sum_rain3 = sum_rain3 + Data[Year][month[i]][
0] # Selected Values
#print Year,month[i],sum_rain3 #Debug
Rain3_data.append(sum_rain3)
#Rain 6 Selected
Months = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
for month in Months:
sum_rain6 = 0
for i in [0, 1, 2, 3, 4, 5]:
sum_rain6 = sum_rain6 + Data[Year][month[i]][0]
Rain6_data.append(sum_rain6)
#Rain12 Selected not so pythonic way
Months = [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]
for month in Months:
sum_rain12 = 0
for i in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]:
sum_rain12 = sum_rain12 + Data[Year][month[i]][0]
Rain12_data.append(sum_rain12)
#-------------------------------------------------------------------------------------------
# Datasets For Spi Analusis - YOU NEED TO TEST FOR RANDOM NULL VALUES!!!
DataSPi = {
"Spi1": Rain1_data,
"Spi3": Rain3_data,
"Spi6": Rain6_data,
"Spi12": Rain12_data
}
#-------------------------------------------------------------------------------------------
# RESULTS
# Note : Problem with RDI (Cant figure out yet)
#-------------------------------------------------------------------------------------------
#THIS THE OUTPUT YOU NEED FOR THE WEB APP -- Spi diffferent time Span Resulsv
SPIresults = {
"Spi1": spi_calc(DataSPi["Spi1"]),
"Spi3": spi_calc(DataSPi["Spi3"]),
"Spi6": spi_calc(DataSPi["Spi6"]),
"Spi12": spi_calc(DataSPi["Spi12"])
}
#-------------------------------------------------------------------------------------------
# Charts
#-------------------------------------------------------------------------------------------
StationName = stationName
MinYear = min(Years)
MaxYear = max(Years)
KeysSpi = SPIresults.keys()
# Spi1 and Rdi1 Chart-------------------------------------------------------------------------------------------
fig = plt.figure(figsize=(6 * 3.13, 4 * 3.13))
plt.subplot(211)
plt.axhspan(0,
-0.8,
facecolor='0.5',
alpha=0.5,
color='yellowgreen',
linewidth=True)
plt.axhspan(-0.8,
-1.3,
facecolor='0.5',
alpha=0.5,
color='yellow',
linewidth=True)
plt.axhspan(-1.3,
-1.6,
facecolor='0.5',
alpha=0.5,
color='goldenrod',
linewidth=True)
plt.axhspan(-1.6,
-2,
facecolor='0.5',
alpha=0.5,
color='r',
linewidth=True)
plt.axhspan(-2,
-3,
facecolor='0.5',
alpha=0.5,
color='darkred',
linewidth=True)
Spi1 = SPIresults["Spi1"]['Spi_result']
n = len(Spi1)
ind = np.arange(n)
width = 0.40
rects1 = plt.bar(ind, Spi1, width, color='b', label='Spi ')
plt.ylabel('Drought Index - std units')
plt.title(
'{} {} Drought Propagation Chart for The Period {} - {}'.format(
'[1 month]', StationName, MinYear, MaxYear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0, n, 12)
plt.xticks(myTicks, xYears, rotation="vertical")
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax=n)
plt.ylim([-3, 3])
plt.text(len(Spi1), 2.5, 'Wet Conditions', rotation=90)
plt.text(len(Spi1), -0.5, 'Dry Conditions', rotation=90)
plt.subplot(212)
plt.scatter(Spi1, Rain1_data, label='Spi ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
fig.figimage(im, 0)
#plt.show()
#return [os.path.join(os.getcwd(), f) for f in os.listdir(os.getcwd())]
plt.savefig(appfolder + 'static/usercharts/' + stationName +
'SPI_1month_' + userid + '.png')
# <--------------------------------------------PIC ANTONIS
plt.close()
# Note some info inside the chart will be added later
# Spi3 and Rdi3 Chart-------------------------------------------------------------------------------------------
Spi3 = SPIresults["Spi3"]['Spi_result']
plt.figure(figsize=(6 * 3.13, 4 * 3.13))
plt.subplot(211)
plt.axhspan(0,
-0.8,
facecolor='0.5',
alpha=0.5,
color='yellowgreen',
linewidth=True)
plt.axhspan(-0.8,
-1.3,
facecolor='0.5',
alpha=0.5,
color='yellow',
linewidth=True)
plt.axhspan(-1.3,
-1.6,
facecolor='0.5',
alpha=0.5,
color='goldenrod',
linewidth=True)
plt.axhspan(-1.6,
-2,
facecolor='0.5',
alpha=0.5,
color='r',
linewidth=True)
plt.axhspan(-2,
-3,
facecolor='0.5',
alpha=0.5,
color='darkred',
linewidth=True)
n = len(Spi3)
ind = np.arange(n)
width = 0.40
rects1 = plt.bar(ind, Spi3, width, color='b', label='Spi ')
plt.ylabel('Drought Index - std units')
plt.title(
'{} {} Drought Propagation Chart for The Period {} - {}'.format(
'[3 Month]', StationName, MinYear, MaxYear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0, n, 4)
plt.xticks(myTicks, xYears, rotation='vertical')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax=n)
plt.ylim([-3, 3])
plt.text(len(Spi3), 2.5, 'Wet Conditions', rotation=90)
plt.text(len(Spi3), -0.5, 'Dry Conditions', rotation=90)
plt.grid()
#plt.show()
plt.subplot(212)
plt.scatter(Spi3, Rain3_data, label='Spi ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.savefig(appfolder + 'static/usercharts/' + stationName +
'SPI_3month_' + userid + '.png')
plt.close()
# Spi6 and Rdi6 Chart-------------------------------------------------------------------------------------------
plt.figure(figsize=(6 * 3.13, 4 * 3.13))
plt.subplot(211)
Spi6 = SPIresults["Spi6"]['Spi_result']
plt.axhspan(0,
-0.8,
facecolor='0.5',
alpha=0.5,
color='yellowgreen',
linewidth=True)
plt.axhspan(-0.8,
-1.3,
facecolor='0.5',
alpha=0.5,
color='yellow',
linewidth=True)
plt.axhspan(-1.3,
-1.6,
facecolor='0.5',
alpha=0.5,
color='goldenrod',
linewidth=True)
plt.axhspan(-1.6,
-2,
facecolor='0.5',
alpha=0.5,
color='r',
linewidth=True)
plt.axhspan(-2,
-3,
facecolor='0.5',
alpha=0.5,
color='darkred',
linewidth=True)
n = len(Spi6)
ind = np.arange(n)
width = 0.40
rects1 = plt.bar(ind, Spi6, width, color='b', label='Spi')
plt.ylabel('Drought Index - std units')
plt.title(
'{} {} Drought Propagation Chart for The Period {} - {}'.format(
'[6 Month]', StationName, MinYear, MaxYear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0, n, 2)
plt.xticks(myTicks, xYears, rotation='vertical')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax=n)
plt.ylim([-3, 3])
plt.grid()
plt.text(len(Spi6), 2.5, 'Wet Conditions', rotation=90)
plt.text(len(Spi6), -0.5, 'Dry Conditions', rotation=90)
#plt.show()
plt.subplot(212)
plt.scatter(Spi6, Rain6_data, label='Spi ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
plt.savefig(appfolder + 'static/usercharts/' + stationName +
'SPI_6month_' + userid + '.png')
plt.close()
# Spi12 and Rdi12 Chart-------------------------------------------------------------------------------------------
Spi12 = SPIresults["Spi12"]['Spi_result']
plt.figure(figsize=(6 * 3.13, 4 * 3.13))
plt.subplot(211)
n = len(Spi12)
ind = np.arange(n)
width = 0.40
plt.axhspan(0,
-0.8,
facecolor='0.5',
alpha=0.5,
color='yellowgreen',
linewidth=True)
plt.axhspan(-0.8,
-1.3,
facecolor='0.5',
alpha=0.5,
color='yellow',
linewidth=True)
plt.axhspan(-1.3,
-1.6,
facecolor='0.5',
alpha=0.5,
color='goldenrod',
linewidth=True)
plt.axhspan(-1.6,
-2,
facecolor='0.5',
alpha=0.5,
color='r',
linewidth=True)
plt.axhspan(-2,
-3,
facecolor='0.5',
alpha=0.5,
color='darkred',
linewidth=True)
rects1 = plt.bar(ind, Spi12, width, color='b', label='Spi ')
plt.ylabel('Drought Index - std units')
plt.title(
'{} {} Drought Propagation Chart for The Period {} - {}'.format(
'[12 Month]', StationName, MinYear, MaxYear))
#Need to search in net , convert list to list of str
xYears = []
for Year in Years:
xYears.append(str(Year))
myTicks = range(0, n)
plt.xticks(myTicks, xYears, rotation='vertical')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlim(xmax=n)
plt.ylim([-3, 3])
plt.grid()
plt.text(len(Spi12), 2.5, 'Wet Conditions', rotation=90)
plt.text(len(Spi12), -0.5, 'Dry Conditions', rotation=90)
#plt.show()
plt.subplot(212)
plt.scatter(Spi12, Rain12_data, label='Spi ')
plt.legend(bbox_to_anchor=(1.025, 1), loc=2, borderaxespad=0.)
plt.xlabel("Drought Index")
plt.ylabel("Rainfall (mm)")
plt.grid()
plt.savefig(appfolder + 'static/usercharts/' + stationName +
'SPI_12month_' + userid + '.png')
plt.close()
print "Charts Update Ending"
except Exception, e:
print "Drought Error:", e.message, e.args
def scatter_suspects_probabilities(scatter_dicts,
x_ticks,
net_right_prediction,
subjective_right_prediction,
out,
title='Training Curve',
x_label="Iterations",
legend_box_pos=(1, 1)):
fig, ax1 = plt.subplots()
fig.set_size_inches(10, 10)
# ax1.xaxis.grid(True)
# plt.grid()
# else:
# ax1.xaxis.set_major_formatter(FuncFormatter(ord_to_char))
# ax1.xaxis.set_major_locator(MultipleLocator(1))
ax1.set_ylim(-0.5, 1.5)
ax1.set_xlabel(x_label, fontsize=15)
ax1.set_ylabel("KC Probability", fontsize=15)
ax1.tick_params(labelsize=10)
legends_plots = []
legends_strs = []
for plot_dict in scatter_dicts:
tmp = ax1.scatter(plot_dict.x,
plot_dict.y,
color=plot_dict.color,
marker=plot_dict.marker,
s=plot_dict.markersize,
label=plot_dict.legend)
legends_plots.append(tmp)
legends_strs.append(plot_dict.legend)
ax1.yaxis.set_ticks(np.arange(0, 1.1, 0.1))
ax1.set_xticks(range(len(x_ticks)))
ax1.set_xticklabels(x_ticks, rotation='vertical', fontsize=10)
plt.axhline(y=0, xmin=0, xmax=3, linewidth=1, color='grey')
plt.axhline(y=1, xmin=0, xmax=3, linewidth=1, color='grey')
plt.axhline(y=0.5,
xmin=0,
xmax=3,
linewidth=1.5,
zorder=0,
color='green',
linestyle='dashed',
label="0.5 propability. above: KC. below: Healthy")
plt.axhspan(0.5, 1, facecolor='0.2', alpha=0.2)
plt.axhspan(0, 0.5, facecolor='0.5', alpha=0.2)
for i, right_prediction in enumerate(net_right_prediction):
if right_prediction:
if subjective_right_prediction[i]:
ax1.axvspan(i - 0.5, i + 0.5, alpha=0.1, color='green')
else:
ax1.axvspan(i - 0.5, i + 0.5, alpha=0.2, color='green')
else:
if subjective_right_prediction[i]:
ax1.axvspan(i - 0.5, i + 0.5, alpha=0.2, color='red')
else:
ax1.axvspan(i - 0.5, i + 0.5, alpha=0.1, color='red')
# Adding legend
a = mpatches.Patch(alpha=0.1, color='green', label='both predicted kc')
b = mpatches.Patch(alpha=0.2,
color='green',
label='net: kc. subjective: healthy')
c = mpatches.Patch(alpha=0.1, color='red', label='both predicted healthy')
d = mpatches.Patch(alpha=0.2,
color='red',
label='subjective: kc. net: healthy')
handles, labels = ax1.get_legend_handles_labels()
handles += [a, b, c, d]
lgd = plt.legend(handles=handles, bbox_to_anchor=legend_box_pos)
plt.title(wrap_title(title), fontsize=18)
if out.endswith(".png"):
out = out.replace(".png", "")
fig.set_size_inches(15, 10)
plt.savefig(out,
bbox_extra_artists=(lgd, ),
bbox_inches='tight',
pad_inches=0.5)
plt.close()
print ' * and', NumberOfFrames, 'frames of the video (frame*.tif), thus',\
'a (calculated)', str(round(NumberOfFrames / options.videotime, 2)), 'fps.'
print 'Have fun with this!'
if options.display:
# Get middle frame
filename = os.path.join(
FileSavePath,
"frame_%05d" % (int(round(NumberOfFrames / 2.0))) + ".tif")
# Make image
image = plt.imread(filename)
plt.imshow(image, cmap='bone')
figuretitle = "Snapshot", str(int(round(NumberOfFrames / 2.0))), "of", \
str(NumberOfFrames), "from", FileSavePath,\
"\nwith an exposure time of", str(options.exposuretime), "ms"
if options.preview:
plt.axhspan(ymin=CMOSheight - previewheight,
ymax=CMOSheight,
xmin=0,
xmax=float(previewwidth) / CMOSwidth,
facecolor='r',
alpha=0.5)
plt.xlim([0, CMOSwidth])
plt.ylim([0, CMOSheight])
figuretitle += "| red=preview area",
plt.title(' '.join(figuretitle))
plt.show()
print 80 * "-"
print "done"