def graphSpec(self,graphName):
csv_reader = csv.reader(open(self.specFile,'r'))
cnt = 0
frequencies = None
decibels = None
for line in csv_reader:
if cnt == 0:
frequencies = line
cnt += 1
else:
decibels = line
# decibels = [(decibels)]
print frequencies, decibels
cnt=0
frequencies_floats=[]
for x in frequencies:
frequencies_floats.append(float(x))
cnt+=1
cnt=0
decibels_floats=[]
for x in decibels:
decibels_floats.append(float(x))
cnt+=1
N=len(decibels)
ind = np.arange(N)
width = .35
fig = plt.figure()
plt.bar(ind,decibels_floats,width)
plt.xticks(ind+width/2., frequencies,rotation=40, size='small')
plt.xlabel("Frequency (Hz)")
plt.ylabel("Decibels (db)")
plt.show()
def graphDB(self,graphName):
print self.dbFile
csv_reader = csv.reader(open(self.dbFile,'r'))
bigx = float(-sys.maxint -1)
bigy = float(-sys.maxint -1)
smallx = float(sys.maxint)
smally = float(sys.maxint)
verts = []
for row in csv_reader:
print row
verts.append(row)
x_arr = []
y_arr = []
for vert in verts:
date = vert[0].split(':')
print float(date[0])+(float(date[1])/60)
x_arr.append(float(date[0])+(float(date[1])/60))
y_arr.append(vert[1])
# dates.date2num(x_arr)
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
ax.set_xlabel('Time (hh:mm:ss)')
ax.set_ylabel('Decibels (db)')
#ax.set_xlim()
ax.set_ylim(30,80)
ax.plot(x_arr,y_arr)
plt.show()
plt.savefig(graphName)
def xzCloudPlot(nest,time,plotTemp=True,plotRH=False):
nc = openWRF(nest)
Nx,Ny,Nz,longitude,_lats,_dx,_dy,x_nr,y_nr = getDimensions(nc)
heightground_x,heighthalf_xz = _getHeight(nc, time, Nx, -1, Nz, -1, y_nr)
print 'Model height: ' + str(heightground_x[x_nr])
theta = nc.variables['T'][time,:,y_nr,:] + T_base
P = nc.variables['P'][time,:,y_nr,:] + nc.variables['PB'][time,:,y_nr,:]
T = theta*(P/P_bot)**kappa # Temperatur i halvflatene (Kelvin)
rho = P/(R*T) #[kg/m3]
qcloud_xz = 1000.0*nc.variables['QCLOUD'][time,:,y_nr,:]*rho # regner om til g/m3
qrain_xz = 1000.0*nc.variables['QRAIN'][time,:,y_nr,:]*rho
qsnow_xz = 1000.0*nc.variables['QSNOW'][time,:,y_nr,:]*rho
plt.figure()
plt.set_cmap(cmap_red)
plt.axis([0,Nx-1,0.0,z_max])
print u'Cloud water red, snow blue, rain green ($g/m^3$)'
grid = np.reshape(np.tile(arange(Nx),Nz),(Nz,-1))
plt.contourf(grid, heighthalf_xz, qcloud_xz, alpha=0.9,levels=xz_cloudwater_levels, cmap=cmap_red)#
plt.colorbar()
plt.contourf(grid, heighthalf_xz, qrain_xz, alpha=0.6,levels=xz_rain_levels, cmap=cmap_green)#
plt.colorbar()
plt.contourf(grid, heighthalf_xz, qsnow_xz, alpha=0.6,levels=xz_snow_levels,cmap=cmap_blue)#
plt.colorbar()
if plotTemp:
temp_int = arange(-80.0,50.0,2.0)
cs = plt.contour(grid, heighthalf_xz, T-T_zero, temp_int,colors='black',linestyles='solid')#linewidths=4
plt.clabel(cs, inline=1, fmt='%1.0f', fontsize=12,colors='black')
if plotRH:
rh = _getRH(nc,time,-1,y_nr,T,P)
rh_int = arange(90.,111.,5.)
cs = plt.contour(grid, heighthalf_xz,rh , rh_int, colors='grey')
plt.clabel(cs, inline=1, fmt='%1.0f', fontsize=12, colors='grey')
plt.plot(arange(Nx),heightground_x,color='black')
plt.fill_between(arange(Nx),heightground_x,0,facecolor='lightgrey')
plt.xticks(np.arange(0,Nx,8),np.round(longitude[Ny/2,::8], 1), fontsize='small')
plt.yticks(np.arange(0,z_max,dz), fontsize='small')
plt.xlabel('Lengdegrad')
plt.ylabel(u'Høyde [m]')
plt.show()
plt.close()
def skewTPlot(nest, time):
"""
This is the method to use from the outside
nest: The nesting level of the nc-file from WRF
time: The time for which to plot
"""
nc = openWRF(nest)
_Nx, _Ny, _Nz, _longs, _lats, _dx, _dy, x, y = getDimensions(nc)
plt.figure()
_isotherms()
_isobars()
_dry_adiabats()
_moist_adiabats()
P = nc.variables['P'][time, :, y, x] + nc.variables['PB'][time, :, y, x]
_windbarbs(nc, time, y, x, P)
_temperature(nc, time, y, x, P)
_dewpoint(nc, time, y, x, P)
plt.axis([-40, 50, P_b, P_t])
plt.xlabel('Temperatur ($^{\circ}\! C$) ved 1000hPa')
xticks = np.arange(-40, 51, 5)
plt.xticks(xticks,
['' if tick % 10 != 0 else str(tick) for tick in xticks])
plt.ylabel('Trykk (hPa)')
yticks = np.arange(P_bot, P_t - 1, -10**4)
plt.yticks(yticks, yticks / 100)
sfcT = nc.variables['T2'][time, y, x] - T_zero
sfcP = nc.variables['PSFC'][time, y, x]
sfcW = nc.variables['Q2'][time, y, x]
sfcTd = td(e(sfcW, sfcP))
plt.suptitle('Bakketemp: %4.1f$^{\circ}\! C$ Duggpunkt: %3.1f$^{\circ}\! C$ Trykk: %5.1f hPa' % (sfcT,sfcTd,0.01*sfcP), \
fontsize=10, x = 0.5, y = 0.03)
plt.show()
plt.close()
def skewTPlot(nest,time):
"""
This is the method to use from the outside
nest: The nesting level of the nc-file from WRF
time: The time for which to plot
"""
nc = openWRF(nest)
_Nx,_Ny,_Nz,_longs,_lats,_dx,_dy,x,y = getDimensions(nc)
plt.figure()
_isotherms()
_isobars()
_dry_adiabats()
_moist_adiabats()
P = nc.variables['P'][time,:,y,x] + nc.variables['PB'][time,:,y,x]
_windbarbs(nc,time,y,x,P)
_temperature(nc,time,y,x,P)
_dewpoint(nc,time,y,x,P)
plt.axis([-40,50,P_b,P_t])
plt.xlabel('Temperatur ($^{\circ}\! C$) ved 1000hPa')
xticks = np.arange(-40,51,5)
plt.xticks(xticks,['' if tick%10!=0 else str(tick) for tick in xticks])
plt.ylabel('Trykk (hPa)')
yticks = np.arange(P_bot,P_t-1,-10**4)
plt.yticks(yticks,yticks/100)
sfcT = nc.variables['T2'][time,y,x]-T_zero
sfcP = nc.variables['PSFC'][time,y,x]
sfcW = nc.variables['Q2'][time,y,x]
sfcTd = td(e(sfcW,sfcP))
plt.suptitle('Bakketemp: %4.1f$^{\circ}\! C$ Duggpunkt: %3.1f$^{\circ}\! C$ Trykk: %5.1f hPa' % (sfcT,sfcTd,0.01*sfcP), \
fontsize=10, x = 0.5, y = 0.03)
plt.show()
plt.close()
def tzCloudPlot(nest, plotMetar=False, offset=0):
nc = openWRF(nest)
Nx, Ny, Nz, _longs, _lats, _dx, _dy, x_nr, y_nr = getDimensions(nc)
heightground_t, heighthalf_tz = _getHeight(nc, Nx, Ny, Nz, x_nr, y_nr)
T_tz = np.zeros((Nz, Nt))
qcloud_tz = np.zeros((Nz, Nt))
qice_tz = np.zeros((Nz, Nt))
qsnow_tz = np.zeros((Nz, Nt))
qrain_tz = np.zeros((Nz, Nt))
for t in arange(Nt):
theta = nc.variables['T'][t, :, y_nr, x_nr] + T_base
P = nc.variables['P'][t, :, y_nr,
x_nr] + nc.variables['PB'][t, :, y_nr, x_nr]
T_tz[:,
t] = theta * (P /
P_bot)**kappa # Temperatur i halvflatene (Kelvin)
rho = P[:] / (R * T_tz[:, t]) # regner om til g/m3
qcloud_tz[:,
t] = 1000.0 * nc.variables['QCLOUD'][t, :, y_nr, x_nr] * rho
qice_tz[:, t] = 1000.0 * nc.variables['QICE'][t, :, y_nr, x_nr] * rho
qsnow_tz[:, t] = 1000.0 * nc.variables['QSNOW'][t, :, y_nr, x_nr] * rho
qrain_tz[:, t] = 1000.0 * nc.variables['QRAIN'][t, :, y_nr, x_nr] * rho
for s in [u'Snø', 'Regn']:
plt.figure()
plt.axis([-offset, Nt - 1, 0.0, z_max])
grid = np.reshape(np.tile(arange(Nt), Nz), (Nz, -1))
if (s == u"Snø"):
var = qsnow_tz
cm = cmap_blue
levs = tz_snow_levels
else:
var = qrain_tz
cm = cmap_green
levs = tz_rain_levels
plt.contourf(grid,
heighthalf_tz,
qcloud_tz,
alpha=0.9,
levels=tz_cloudwater_levels,
cmap=cmap_red) #
plt.colorbar()
plt.contourf(grid, heighthalf_tz, var, alpha=0.6, levels=levs,
cmap=cm) #
plt.colorbar()
cs = plt.contour(grid,
heighthalf_tz,
T_tz - T_zero,
temp_int,
colors='black',
linestyles='solid')
plt.clabel(cs, inline=1, fmt='%1.0f', fontsize=12, colors='black')
plt.fill_between(arange(-offset, Nt),
heightground_t[0],
0,
facecolor='lightgrey')
if plotMetar:
_metar()
print s + ' ($g/m^3$)'
plt.xlabel('Timer etter ' + date + 'T00:00Z')
plt.ylabel(u'Høyde [m]')
plt.yticks(np.arange(0, z_max, dz), fontsize='small')
plt.show()
plt.close()
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_xlim(0, Nt - 1)
ax1.plot(np.sum(qcloud_tz, axis=0), color='black', label=u"skyvann")
ax1.plot(np.sum(qsnow_tz, axis=0), color='green', label=u"snø")
ax1.set_xlabel('Timer etter ' + date + 'T00:00Z')
ax1.set_ylabel(u'Skyvann og snø ($g/m^2$)')
ax2 = ax1.twinx()
ax2.plot(np.sum(qice_tz, axis=0), color='blue', label="is")
ax2.plot(np.sum(qrain_tz, axis=0), color='red', label="regn")
ax2.set_ylabel('Regn og is ($g/m^2$)')
ax1.set_xlim(0, Nt - 1)
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')
plt.show()
plt.close()
def from_file_home_outside_calls():
file1 = "/home/sscepano/D4D res/ORGANIZED/SET3/Clustering/usr res/usr_home_calls.tsv"
file2 = "/home/sscepano/D4D res/ORGANIZED/SET3/Clustering/usr res/usr_outside_calls.tsv"
usr_home_c = n.zeros(500001)
usr_outside_c = n.zeros(500001)
i = 0
f1 = open(file1, 'r')
f2 = open(file2, 'r')
# read the file1
for line in f1:
i = i + 1
u, home_c = line.split('\t')
home_c = float(home_c)
u = int(u)
usr_home_c[u] = home_c
# read the file
for line in f2:
i = i + 1
u, outside_c = line.split('\t')
outside_c = float(outside_c)
u = int(u)
usr_outside_c[u] = outside_c
############################################################################################################################
# THIS is to plot pdf of home calls
############################################################################################################################
fig1 = plt.figure(1)
ax = fig1.add_subplot(211)
nn, bins, rectangles = ax.hist(usr_home_c, 100, normed=True)
#plt.plot(nc_distr_pct, 'ro', linewidth=0.5, label= 'pdf Num of calls')
plt.xlabel('NcH, number of calls from the home subprefecture')
plt.ylabel('P(NcH)')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf rg loglog.png"
#this is a regular plot file, then comment the previous loglog block
#figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf home calls.png"
#print(figure_name)
#plt.savefig(figure_name, format = "png")
############################################################################################################################
# THIS is to plot pdf of outside calls
############################################################################################################################
#fig1 = plt.figure(1)
ax = fig1.add_subplot(212)
nn, bins, rectangles = ax.hist(usr_outside_c, 100, normed=True)
#plt.plot(nc_distr_pct, 'ro', linewidth=0.5, label= 'pdf Num of calls')
plt.xlabel('NcO, number of calls from outside the home subprefecture')
plt.ylabel('P(NcO)')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf home outside calls.png"
# #this is a regular plot file, then comment the previous loglog block
# figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf home outside calls.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
return
# invoke the function for plotting number of calls and frequency probability distribution (percents of users)
#from_file_home_outside_calls()
def from_fq_files_hist_pdf():
file_name= "/home/sscepano/D4D res/ORGANIZED/SET3/Distr of Num and Fq of Calls/new results -- check the same/Users_and_their_total_calls_number.tsv"
file_name2 = "/home/sscepano/D4D res/ORGANIZED/SET3/Distr of Num and Fq of Calls/new results -- check the same/Users_and_their_calling_fq.tsv"
# here we store the num of calls made by a user
usr_and_his_num_calls = n.zeros(500001, dtype=n.int)
nc_distr = n.zeros(max_num_calls, dtype=n.int)
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
u, nc = line.split('\t')
nc = int(nc)
u = int(u)
usr_and_his_num_calls[u] = nc
nc_distr[nc] += 1
mi = min(usr_and_his_num_calls)
mx = max(usr_and_his_num_calls)
print("Minimum number of calls ", mi)
print("Maximum number of calls ", mx)
total_u = float(sum(nc_distr))
print("Total users found ", total_u)
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
# fig1 = plt.figure(1)
# ax = fig1.add_subplot(111)
# nn, bins, rectangles = ax.hist(usr_and_his_num_calls, 100, normed=True)
#
# plt.xlabel('N, num of calls')
# plt.ylabel('P(N)')
# plt.legend()
#
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
# plt.yscale('log')
# plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of num of calls loglog.png"
#
## #this is a regular plot file, then comment the previous loglog block
## figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of num of calls.png"
#
# print(figure_name)
# plt.savefig(figure_name, format = "png")
#
# plt.clf()
###############################################################################################################################
# THIS is to plot fq pdf
###############################################################################################################################
# here we store the num of calls made by a user
usr_and_his_fq = n.zeros(500001)
# a loop where we populate those two arrays from the file
i = 0
f2 = open(file_name2, 'r')
# read the file
for line in f2:
i = i + 1
u, fq = line.split('\t')
fq = float(fq)
u = int(u)
usr_and_his_fq[u] = fq
mi = min(usr_and_his_fq)
mx = max(usr_and_his_fq)
print("Minimum fq of calls ", mi)
print("Maximum fq of calls ", mx)
fig2 = plt.figure(2)
ax = fig2.add_subplot(111)
nn, bins, rectangles = ax.hist(usr_and_his_fq, 100, normed=True)
plt.xlabel('fq of calls')
plt.ylabel('P(fq)')
plt.legend()
figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of fq of calls.png"
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
# plt.yscale('log')
# plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/hist of fq of calls loglog.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
return
def from_file_num_calls(file_name):
# here we store the num of calls made by a user
usr_and_his_num_calls = n.zeros(500001, dtype=n.int)
# here we store the fq of calls made by a user, but we calculate it from the num_calls
fq_distr = n.zeros(max_num_calls)
# from the previous array obtained by counting users who made the same total number of calls
nc_distr = n.zeros(max_num_calls, dtype=n.int)
# here we just save percents of users
nc_distr_pct = n.zeros(max_num_calls)
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
u, nc = line.split('\t')
nc = int(nc)
u = int(u)
usr_and_his_num_calls[u] = nc
nc_distr[nc] += 1
mi = min(usr_and_his_num_calls)
mx = max(usr_and_his_num_calls)
print("Minimum number of calls ", mi)
print("Maximum number of calls ", mx)
total_u = float(sum(nc_distr))
print("Total users found ", total_u)
# test_file_out = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Obtained_num_calls_and_its_pct.tsv"
# fto = open(test_file_out,"w")
for j in range(0, max_num_calls):
nc_distr_pct[j] = (nc_distr[j] / total_u)
# fto.write(str(j) + '\t' + str(nc_distr_pct[j]) + '\n')
# # I was just checking that the total percents sums up to 100 and they do but it looked funny as we have so small values
# total = 0
# for i in range (max_num_calls):
# total += percent_users[i]
# print ("Check ", total)
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(1)
plt.plot(nc_distr_pct, 'r', linewidth=0.5, label= 'distribution of N')
plt.xlabel('N, num of calls')
plt.ylabel('% Users')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of num of calls loglog.png"
#this is a regular plot file, then comment the previous loglog block
figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/distr of num of calls2.png"
print(figure_name)
plt.savefig(figure_name, format = "png", pdi=300)
#plt.show()
###############################################################################################################################
# THIS is to plot fq pdf
###############################################################################################################################
plt.figure(2)
# fq = []
#
# for j in range(max_num_calls):
# fq.append( float(j / 3360.0))
#
# ffq = []
#
# for j in range(max_num_calls):
# ffq.append(nc_distr_pct[j])
# for j in range(0, max_num_calls):
# nc_distr_pct[j] = (nc_distr[j] / total_u)
fq = []
for j in range(max_num_calls):
fq.append( float(j / 3360.0))
ffq = []
for j in range(max_num_calls):
ffq.append(nc_distr_pct[j])
# test_file_out2 = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Calculated_fq_calls_and_its_pct2.tsv"
# fto2 = open(test_file_out2,"w")
# for j in range(0, max_num_calls):
# fto2.write(str(fq[j]) + '\t' + str(ffq[j]) + '\n')
# Finally understood here -- when I give two arrays: x, y (at least append values IN ORDER like here) -- pyplot will plot y versus x
plt.plot(fq, ffq, 'g', linewidth=0.3, label= 'distribution of Fq')
plt.xlabel('Fq of calls')
plt.ylabel('% Users')
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/distr of fq of calls2.png"
# # this is a regular plot file, then comment the previous loglog block
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of fq of calls.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
return
def save_to_plot(avg_usr_traj):
nits = []
its = []
# a loop where we populate those two arrays from the file
i = 0
# f = open(file_name, 'r')
# # read the file
for usr in range(500001):
i = i + 1
nits.append(int(avg_usr_traj[usr]))
its.append(usr)
mi = min(nits)
mx = max(nits)
print("Minimum radius of gyr ", mi)
print("Maximum radius of gyr ", mx)
total_nit = float(sum(nits))
print("Total radius of gyr ", total_nit)
pdf_nits = defaultdict(int)
for j in range(0, len(nits)):
pdf_nits[nits[j]] += 1
ordered = OrderedDict(sorted(pdf_nits.items(), key=lambda t: t[0]))
nits7s = []
its7s = []
test = 0
for j in ordered.iterkeys():
nits7s.append(ordered[j]/500000.0)
test += ordered[j]/500000.0
its7s.append(j)
print test
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(7)
plt.plot(its7s, nits7s, 'o', linewidth=0.5, label= 'distribution of Rg')
plt.xlabel('rg [km]')
plt.ylabel('P(rg)')
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/traj/avg daily/avg_daily_traj_total.png"
print(figure_name)
plt.savefig(figure_name, format = "png", dpi=300)
return
# coding: utf-8
import numerical1 as num
from defns import *
def anal(t, g):
x=np.exp(-g/2. t)*np.cos(np.sqrt(4-g**2)/2. t)
p=np.exp(-g/2. t)*np.sin(np.sqrt(4-g**2)/2. t)
return x,p
def anal(t, g):
x=np.exp(-g/2.*t)*np.cos(np.sqrt(4-g**2)/2.*t)
p=np.exp(-g/2.*t)*np.sin(np.sqrt(4-g**2)/2.*t)
return x,p
get_ipython().set_next_input(u't=np.arange');get_ipython().magic(u'pinfo np.arange')
t=np.arange(0., 100., 0.5)
x,p=anl(t, g)
x,p=anal(t, g)
x
import pyplot as plt
import matplotlib.pyplot as plt
plt.figure()
plt.plot(t, x, t, p)
plt.show()
get_ipython().magic(u'save "commands.py"')
def from_file_radius_gyr3(file_name, subpref):
users_list = rd.read_in_subpref_users(subpref)
total = float(rd.read_in_subpref_num_users()[subpref])
if total > 0:
nits = []
its = []
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
it, nit = line.split('\t')
nit = float(nit)
it = int(it)
if users_list[it] == 1:
nit = int(nit)
nits.append(nit)
its.append(it)
mi = min(nits)
mx = max(nits)
print("Minimum radius of gyr ", mi)
print("Maximum radius of gyr ", mx)
total_nit = float(sum(nits))
print("Total radius of gyr ", total_nit)
pdf_nits = defaultdict(int)
for j in range(0, len(nits)):
pdf_nits[nits[j]] += 1
ordered = OrderedDict(sorted(pdf_nits.items(), key=lambda t: t[0]))
nits7s = []
its7s = []
test = 0
#total = 500000.0
for j in ordered.iterkeys():
nits7s.append(ordered[j]/total)
test += ordered[j]/total
its7s.append(j)
print test
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(7)
plt.plot(its7s, nits7s, 'o', linewidth=0.5, label= 'distribution of Rg')
plt.xlabel('rg [km]')
plt.ylabel('P(rg)')
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/rg/1/rg_" + str(subpref) + ".png"
print(figure_name)
plt.savefig(figure_name, format = "png", dpi=300)
plt.clf()
return
def from_file_radius_gyr(file_name):
usr_rg = nn.zeros(500001)
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name, 'r')
# read the file
for line in f:
i = i + 1
u, rg = line.split('\t')
rg = float(rg)
u = int(u)
usr_rg[u] = rg
mi = min(usr_rg)
mx = max(usr_rg)
print("Minimum number of calls ", mi)
print("Maximum number of calls ", mx)
# total_u = float(sum(nc_distr))
# print("Total users found ", total_u)
# test_file_out = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Obtained_num_calls_and_its_pct.tsv"
# fto = open(test_file_out,"w")
# for j in range(0, max_num_calls):
# nc_distr_pct[j] = (nc_distr[j] / total_u) * 100
# fto.write(str(j) + '\t' + str(nc_distr_pct[j]) + '\n')
# # I was just checking that the total percents sums up to 100 and they do but it looked funny as we have so small values
# total = 0
# for i in range (max_num_calls):
# total += percent_users[i]
# print ("Check ", total)
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
fig1 = plt.figure(1)
ax = fig1.add_subplot(111)
n, bins, rectangles = ax.hist(usr_rg, 100, normed=True)
#plt.plot(nc_distr_pct, 'ro', linewidth=0.5, label= 'pdf Num of calls')
plt.xlabel('rg [km]')
plt.ylabel('P(rg)')
plt.legend()
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale('log')
plt.xscale('log')
figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf rg loglog.png"
# #this is a regular plot file, then comment the previous loglog block
# figure_name = "/home/sscepano/D4D res/allstuff/rg/pdf rg.png"
print(figure_name)
plt.savefig(figure_name, format = "png")
################################################################################################################################
## THIS is to plot fq pdf
################################################################################################################################
#
# plt.figure(2)
#
# fq = []
#
# for j in range(max_num_calls):
# fq.append( float(j / 3360.0))
#
# ffq = []
#
# for j in range(max_num_calls):
# ffq.append(nc_distr_pct[j])
#
#
## test_file_out2 = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/Calculated_fq_calls_and_its_pct2.tsv"
## fto2 = open(test_file_out2,"w")
## for j in range(0, max_num_calls):
## fto2.write(str(fq[j]) + '\t' + str(ffq[j]) + '\n')
#
#
# # Finally understood here -- when I give two arrays: x, y (at least append values IN ORDER like here) -- pyplot will plot y versus x
# plt.plot(fq, ffq, 'g.', linewidth=0.3, label= 'pdf fq of calls')
#
# plt.xlabel('fq of calls')
# plt.ylabel('% Users')
# plt.legend()
#
# # this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
# plt.yscale('log')
# plt.xscale('log')
# figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of fq of calls loglog.png"
#
## # this is a regular plot file, then comment the previous loglog block
## figure_name = "/home/sscepano/D4D res/allstuff/SET3 frequent callers from python/1/SET3 distr of fq of calls.png"
#
# print(figure_name)
# plt.savefig(figure_name, format = "png")
return
def yzCloudPlot(nest, time, plotTemp=True, plotRH=False):
nc = openWRF(nest)
Nx, Ny, Nz, _longs, latitude, _dx, _dy, x_nr, y_nr = getDimensions(nc)
heightground_y, heighthalf_yz = _getHeight(nc, time, -1, Ny, Nz, x_nr, -1)
print 'Model height: ' + str(heightground_y[y_nr])
theta = nc.variables['T'][time, :, :, x_nr] + T_base
P = nc.variables['P'][time, :, :, x_nr] + nc.variables['PB'][time, :, :,
x_nr]
T = theta * (P / P_bot)**kappa # Temperatur i halvflatene (Kelvin)
rho = P / (R * T) #[kg/m3]
qcloud_yz = 1000.0 * nc.variables['QCLOUD'][
time, :, :, x_nr] * rho # regner om til g/m3
qrain_yz = 1000.0 * nc.variables['QRAIN'][time, :, :, x_nr] * rho
qsnow_yz = 1000.0 * nc.variables['QSNOW'][time, :, :, x_nr] * rho
plt.figure()
plt.set_cmap(cmap_red)
plt.axis([0, Ny - 1, 0.0, z_max])
print u'Cloud water red, snow blue, rain green ($g/m^3$)'
grid = np.reshape(np.tile(arange(Ny), Nz), (Nz, -1))
plt.contourf(grid,
heighthalf_yz,
qcloud_yz,
alpha=0.9,
levels=xz_cloudwater_levels,
cmap=cmap_red) #
plt.colorbar()
plt.contourf(grid,
heighthalf_yz,
qrain_yz,
alpha=0.6,
levels=xz_rain_levels,
cmap=cmap_green) #
plt.colorbar()
plt.contourf(grid,
heighthalf_yz,
qsnow_yz,
alpha=0.6,
levels=xz_snow_levels,
cmap=cmap_blue) #
plt.colorbar()
if plotTemp:
cs = plt.contour(grid,
heighthalf_yz,
T - T_zero,
temp_int,
colors='black',
linestyles='solid') #linewidths=4
plt.clabel(cs, inline=1, fmt='%1.0f', fontsize=12, colors='black')
if plotRH:
rh = _getRH(nc, time, x_nr, -1, T, P)
rh_int = arange(90., 111., 5.)
cs = plt.contour(grid, heighthalf_yz, rh, rh_int, colors='grey')
plt.clabel(cs, inline=1, fmt='%1.0f', fontsize=12, colors='grey')
plt.plot(arange(Ny), heightground_y, color='black')
plt.fill_between(arange(Ny), heightground_y, 0, facecolor='lightgrey')
plt.xticks(np.arange(0, Ny, 8),
np.round(latitude[::8, Nx / 2], 1),
fontsize='small')
plt.yticks(np.arange(0, z_max, dz), fontsize='small')
plt.xlabel('Breddegrad')
plt.ylabel(u'Høyde [m]')
plt.show()
plt.close()
def from_files_usr_movements():
file_name1 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/user_number_of_edges_v1.tsv"
file_name2 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/user_number_of_nodes_v1.tsv"
file_name3 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/user_number_of_displacements_v1.tsv"
nits = []
its = []
# a loop where we populate those two arrays from the file
i = 0
f = open(file_name3, "r")
# read the file
for line in f:
i = i + 1
it, nit = line.split("\t")
nit = int(nit)
it = int(it)
nit = int(nit)
nits.append(nit)
its.append(it)
mi = min(nits)
mx = max(nits)
print ("Minimum # edges ", mi)
print ("Maximum # edges ", mx)
total_nit = float(sum(nits))
print ("Total # edges ", total_nit)
pdf_nits = defaultdict(int)
for j in range(0, len(nits)):
pdf_nits[nits[j]] += 1
ordered = OrderedDict(sorted(pdf_nits.items(), key=lambda t: t[0]))
nits7s = []
its7s = []
test = 0
for j in ordered.iterkeys():
nits7s.append(ordered[j] / 500000.0)
test += ordered[j] / 500000.0
its7s.append(j)
print test
############################################################################################################################
# THIS is to plot number of users pdf
############################################################################################################################
plt.figure(7)
plt.plot(its7s, nits7s, "o", linewidth=0.5, label="distr. of # displacements ")
plt.xlabel("# displacements")
plt.ylabel("P(# displacements)")
plt.legend()
# this is if we want loglog lot, otheriwse comment and uncomment next line for regular plot file
plt.yscale("log")
plt.xscale("log")
# figure_name1 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/usr_num_edges.png"
# figure_name2 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/usr_num_nodes.png"
figure_name3 = "/home/sscepano/D4D res/allstuff/USER GRAPHS stats/usr_num_displacements.png"
print (figure_name3)
plt.savefig(figure_name3, format="png", dpi=300)
return
pred=conv_net(x,weights,biases,keep_prob)
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))
init=tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
step=1
while step*batch_size<training_iters:
batch_x,batch_y=mnist.train.next_batch(batch_size)
test=batch_x[0]
fig=plt.figure()
plt.imshow(test.reshape((28,28),order='C'),cmap='Greys',interpolation='nearest')
print(weights['wc1'].eval()[0])
plt.imshow(weights['wc1'].eval()[0][0].reshape(4,8),cmap='Greys',interpolation='nearest')
sess.run(optimizer,feed_dict={x:batch_x,y:batch_y,keep_prob:dropout})
if step%display_step==0:
loss,acc=sess.run([cost,accuracy],feed_dict={x:batch_x,y:batch_y,keep_prob:1.})
print('Iter'+str(step*batch_size)+',Minibatch Loss='+'{:.6f}'.format(loss)+'+,Training Accuracy='+'{:.5f}'.format(acc))
step+=1
print('Optimization Finished!')
print('Testing Accuracy:',sess.run(accuracy,feed_dict={x:minist.test.images[:256],
y:minist.test.images[:256],
keep_prob:1.}))
# load the dataset
import pyplot as plt
data = read_csv('brain activity.txt', header=None)
# retrieve data as numpy array
values = data.values
# create a subplot for each time series
plt.figure()
for i in range(values.shape[1]):
plt.subplot(values.shape[1], 1, i+1)
plt.plot(values[:, i])
plt.show()