def moving_average(y, window_length):
"""
Compute the moving average of y with specified window length.
Args:
y: an 1-d pylab array with length N, representing the y-coordinates of
the N sample points
window_length: an integer indicating the window length for computing
moving average
Returns:
an 1-d pylab array with the same length as y storing moving average of
y-coordinates of the N sample points
"""
mov_avg_list = [] #create list to store moving average
for i in range(len(y)):
if i < window_length - 1: #when we don't have enough previous value
ith_avg = pylab.average(
y[:i + 1]) #average current value with previous values
mov_avg_list.append(ith_avg)
else:
ith_avg = pylab.average(y[i + 1 - window_length:i +
1]) #average of the values in window
mov_avg_list.append(ith_avg)
mov_avg = pylab.array(mov_avg_list) #convert to array
return mov_avg
def main():
# read in the data
args = parseCMD()
fileName = args.fileN
mcSteps, Es, Ms, E2 = pl.loadtxt(fileName, unpack=True)
# get parameters from header of data file
estFile = open(fileName,'r')
estLines = estFile.readlines();
T = float(estLines[0].split()[-1])
L = float(estLines[1].split()[-1])
H = float(estLines[2].split()[-1])
# Compute specific heat
Cv = (pl.average(E2) - (pl.average(Es))**2)/(L*L*T*T)
print 'C_v: ',Cv
# plot energy
fig1 = pl.figure(1)
ax = fig1.add_subplot(111)
pl.ylabel('Energy', fontsize=20)
pl.xlabel('MC steps', fontsize=20)
ax.plot(mcSteps,Es, marker='o', linewidth=0,
markerfacecolor='None', markeredgecolor='Orange')
# plot macroscopic magnetization
fig2 = pl.figure(2)
bx = fig2.add_subplot(111)
pl.ylabel('Magnetization', fontsize=20)
pl.xlabel('MC steps', fontsize=20)
bx.plot(mcSteps,Ms, marker='o', linewidth=0,
markerfacecolor='None', markeredgecolor='Lime')
pl.show()
def makeplot(filename):
T0 = 2452525.374416
P = 0.154525
X = pl.load(filename)
x = X[:,0]
y = X[:,1]
print x[0] # check for HJD faults
#orbital phase
p = (x-T0)/P
pl.figure(figsize=(6,4))
pl.subplots_adjust(hspace=0.47,left=0.16)
pl.subplot(211)
pl.scatter(p,y,marker='o',s=0.1,color='k')
pl.ylim(-0.06,0.06)
pl.xlim(pl.average(p)-1.25,pl.average(p)+1.25)
pl.ylabel('Intensity')
pl.xlabel('Orbital Phase')
pl.subplot(212)
f,a = ast.signal.dft(x,y,0,4000,1)
pl.plot(f,a,'k')
pl.ylabel('Amplitude')
pl.xlabel('Frequency (c/d)')
#pl.ylim(yl[0],yl[1])
#pl.vlines(3636,0.002,0.0025,color='k',linestyle='solid')
#pl.vlines(829,0.002,0.0025,color='k',linestyle='solid')
#pl.text(3500,0.00255,'DNO',fontsize=11)
#pl.text(700,0.00255,'lpDNO',fontsize=11)
pl.ylim(0.0,0.004)
pl.savefig('%spng'%filename[:-3])
def flow_rate_hist(sheets):
ant_rates = []
weights = []
for sheet in sheets:
ants, seconds, weight = flow_rate(sheet)
ant_rate = seconds / ants
#ant_rate = ants / seconds
ant_rates.append(ant_rate)
weights.append(float(weight))
#weights.append(seconds)
weights = pylab.array(weights)
weights /= sum(weights)
#print "ants per second"
print "seconds per ant"
mu = pylab.mean(ant_rates)
print "mean", pylab.mean(ant_rates)
wmean = pylab.average(ant_rates, weights=weights)
print "weighted mean", wmean
print "median", pylab.median(ant_rates)
print "std", pylab.std(ant_rates, ddof=1)
ant_rates = pylab.array(ant_rates)
werror = (ant_rates - mu) * weights
print "weighted std", ((sum(werror ** 2))) ** 0.5
print "weighted std 2", (pylab.average((ant_rates - mu)**2, weights=weights)) ** 0.5
pylab.figure()
pylab.hist(ant_rates)
pylab.savefig('ant_flow_rates.pdf', format='pdf')
pylab.close()
def AT(s_ij=0, s_ik=0, s_jk=0, S=0):
'''
Calculates: SEFD = (2k/S) * (s_ij*s_ik)/(s_jk)
'''
kb = 1.38e3 # Boltzmann's Constant in Jy m^2 K^-1
s_ij = pl.average(s_ij)
s_ik = pl.average(s_ik)
s_jk = pl.average(s_jk)
return (2 * kb / S) * (s_ij * s_ik) / (s_jk - s_ij * s_ik)
def AT(s_ij=0, s_ik=0, s_jk=0, S=0): ''' Calculates: SEFD = (2k/S) * (s_ij*s_ik)/(s_jk) ''' kb = 1.38e3 # Boltzmann's Constant in Jy m^2 K^-1 s_ij = pl.average(s_ij) s_ik = pl.average(s_ik) s_jk = pl.average(s_jk) return (2*kb/S)*(s_ij*s_ik)/(s_jk-s_ij*s_ik)
def visualize ():
sample_rate, snd = load_sample(".\\hh-closed\\dh9.WAV")
print snd.dtype
data = normalize(snd)
print data.shape
n = data.shape[0]
length = float(n)
print length / sample_rate, "s"
timeArray = arange(0, length, 1)
timeArray = timeArray / sample_rate
timeArray = timeArray * 1000 #scale to milliseconds
ion()
if False:
plot(timeArray, data, color='k')
ylabel('Amplitude')
xlabel('Time (ms)')
raw_input("press enter")
exit()
p = fft(data) # take the fourier transform
nUniquePts = ceil((n+1)/2.0)
print nUniquePts
p = p[0:nUniquePts]
p = abs(p)
p = p / float(n) # scale by the number of points so that
# the magnitude does not depend on the length
# of the signal or on its sampling frequency
p = p**2 # square it to get the power
# multiply by two (see technical document for details)
# odd nfft excludes Nyquist point
if n % 2 > 0: # we've got odd number of points fft
p[1:len(p)] = p[1:len(p)] * 2
else:
p[1:len(p) -1] = p[1:len(p) - 1] * 2 # we've got even number of points fft
print p
freqArray = arange(0, nUniquePts, 1.0) * (sample_rate / n);
plot(freqArray/1000, 10*log10(p), color='k')
xlabel('Frequency (kHz)')
ylabel('Power (dB)')
raw_input("press enter")
m = average(freqArray, weights = p)
v = average((freqArray - m)**2, weights= p)
r = sqrt(mean(data**2))
s = var(data**2)
print "mean freq", m #TODO: IMPORTANT: this is currently the mean *power*, not the mean freq. What we want is mean freq weighted by power
print "var freq", v
print "rms", r
print "squared variance", s
def hist_values(parameter, group, strategy, decay_type, label, y_limit=None):
values = group[parameter]
values = list(values)
binsize = 0.05
if 'zoom' in label:
binsize = 0.01
if y_limit == None:
y_limit = max(values)
cutoff = 1
#weights = np.ones_like(values)/float(len(values))
weights = group['num_lines'] / sum(group['num_lines'])
weights = np.array(weights)
mu = pylab.average(values, weights=weights)
sigma2 = pylab.var(values)
pylab.figure()
pylab.hist(values, weights=weights, bins=np.arange(0, cutoff + binsize, binsize))
title_items = []
title_items.append('%s maximum likelihood values %s %s %s' % (parameter, strategy, decay_type, label))
title_items.append('mean of estimates = %f' % mu)
title_items.append('variance of estimates = %f' % sigma2)
title_str = '\n'.join(title_items)
#pylab.title(parameter + ' maximum likelihood values ' + str(strategy) + ' ' + str(outname))
#pylab.title(title_str)
print title_str
pylab.xlabel('%s mle' % parameter, fontsize=20)
pylab.ylabel('weighted proportion', fontsize=20)
pylab.xlim((0, 1))
pylab.ylim((0, y_limit))
pylab.savefig('repair_ml_hist_%s_%s_%s_%s.pdf' % (parameter, strategy, decay_type, label), format='pdf')
pylab.close()
def show_grey_channels(I):
K = average(I, axis=2)
for i in range(3):
J = zeros_like(I)
J[:, :, i] = K
figure(i+10)
imshow(J)
def gen_cities_avg(climate, multi_cities, years):
"""
Compute the average annual temperature over multiple cities.
Args:
climate: instance of Climate
multi_cities: the names of cities we want to average over (list of str)
years: the range of years of the yearly averaged temperature (list of
int)
Returns:
a pylab 1-d array of floats with length = len(years). Each element in
this array corresponds to the average annual temperature over the given
cities for a given year.
"""
avg_temp_list = [] #list of the average temperature each year
for year in years:
temp_sum_over_cities = 0 #total sum of all the average temperature of each city
for city in multi_cities:
#get an array of the temperatures at this city and year
year_temps = climate.get_yearly_temp(city, year)
avg_year_temp = pylab.average(
year_temps) #find year average of this city
temp_sum_over_cities += avg_year_temp
#average the yearly averages from each city
avg_over_cities = temp_sum_over_cities / len(multi_cities)
avg_temp_list.append(avg_over_cities)
return pylab.array(avg_temp_list) #return pylab 1-d array
def gen_std_devs(climate, multi_cities, years):
"""
For each year in years, compute the standard deviation over the averaged yearly
temperatures for each city in multi_cities.
Args:
climate: instance of Climate
multi_cities: the names of cities we want to use in our std dev calculation (list of str)
years: the range of years to calculate standard deviation for (list of int)
Returns:
a pylab 1-d array of floats with length = len(years). Each element in
this array corresponds to the standard deviation of the average annual
city temperatures for the given cities in a given year.
"""
std_dev = []
for year in years:
yearly_temps = None
for city in multi_cities:
if yearly_temps is None:
yearly_temps = climate.get_yearly_temp(city, year)
else:
yearly_temp = climate.get_yearly_temp(city, year)
yearly_temps = pylab.vstack((yearly_temps, yearly_temp))
if yearly_temps.ndim > 1:
yearly_temps = pylab.average(yearly_temps, axis=0)
std_dev.append(pylab.std(yearly_temps))
return pylab.array(std_dev)
def time_test(trials):
"""
finds time ratio for 1000 points and 1000 cycles to get an average
"""
ratio = []
for i in range(int(trials)):
st = rejection("sine", 1000, 0, pl.pi)[1]
ut = rejection("uniform", 1000, 0, pl.pi)[1]
ratio.append(st / ut)
return pl.average(ratio)
def movingaverage(x,L):
ma = pl.zeros(len(x),dtype='Float64')
# must take the lead-up zone into account (prob slow)
for i in range(0,L):
ma[i] = pl.average(x[0:i+1])
for i in range(L,len(x)):
ma[i] = ma[i-1] + 1.0/L*(x[i]-x[i-L])
return ma
def properties(self, n=100):
"""
Prints the statistical properties (maximal and average degree) of the results.
It goes through the labels we have set with `self.set_labels`.
Parameters:
`n`: optional
the number of the generated networks to create statistics.
Example::
>>> r.set_labels("200_020")
['200_020']
>>> r.properties(n=100)
====================
label = 200_020
number of generated networks = 100
divs = [0.81104014013270886, 1.0],
probs=[[ 0.27609856 0.23149258]
[ 0.23149258 0.26091629]]
avg max deg = 9.87+-1.0115993937, avg average deg=3.5651+-0.175231419857
"""
out = Output(os.path.join(self.project_dir, "properties.txt"))
assert isinstance(n, int) and n > 0
if not isinstance(self.labels, list) or not self.labels:
raise NoLabelsError
for label in self.labels:
run = self.runs[label]
doubleline = "=" * 20
out.write(doubleline)
out.write("label = %s" % label)
divs, probs = run["divs"], run["probs"]
pm = mfng.ProbMeasure(divs, probs)
K = run.get("K") or run.get(
"k") # Older files have k instead of K.
ipm = pm.iterate(K=K)
out.write(
"number of generated networks = %s\ndivs = %s,\nprobs=%s" %
(n, divs, probs))
maxdegs = []
avgdegs = []
for i in range(n):
nw = ipm.generate(n=run["n"])
deg = nw.degree()
maxdegs.append(max(deg))
avgdegs.append(pylab.average(deg))
avgmaxdeg, avgmaxdegsigma = avg_sigma(maxdegs)
avgavgdeg, avgavgdegsigma = avg_sigma(avgdegs)
out.write("\navg max deg = %5s+-%5s, avg average deg=%5s+-%5s\n" %
(avgmaxdeg, avgmaxdegsigma, avgavgdeg, avgavgdegsigma))
del out
def img2ascii(filename, map_array=None):
a = imread(filename)
print "Converting ..."
# useful only when reading .jpg files.
# PIL is used for jpegs; converting PIL image to numpy array messes up.
# a = a[::-1, :]
# convert image to grayscale.
if len(a.shape) > 2:
a = 0.21 * a[:,:,0] + 0.71 * a[:,:,1] + 0.07 * a[:,:,2]
a_r, a_c = a.shape[:2]
a_max = float(a.max())
blk_siz = 1 #size of block
if map_array == None:
# just linearly map gray level to characters.
# giving lowest gray level to space character.
out_file = open(filename + 'lin' + str(blk_siz) + '.txt', 'w')
print "File %s opened" %out_file.name
for i in range(0, a_r, blk_siz*2):
for j in range(0, a_c, blk_siz):
b = a[i:i+2*blk_siz, j:j+blk_siz]
b_char = chr(32+int((1-average(b))*94))
out_file.write(b_char)
out_file.write("\n")
out_file.close()
else:
# map based on visual density of characters.
out_file = open(filename + 'arr' + str(blk_siz) + '.txt', 'w')
print "File %s opened" %out_file.name
for i in range(0, a_r, blk_siz*2):
for j in range(0, a_c, blk_siz):
b = a[i:i+2*blk_siz, j:j+blk_siz]
b_mean = int(average(b)/a_max*(len(map_array)-1))
b_char = chr(map_array[b_mean])
out_file.write(b_char)
out_file.write("\n")
out_file.close()
print "%s Converted! \nWritten to %s" %(filename, out_file.name)
def ScaleDelayError(ScaleNDelay):
# x1 and x2 must exist in calling namespace
scale = ScaleNDelay[0]
delay = ScaleNDelay[1]
t1 = arange(len(x1))
t2 = arange(len(x2))
t1s = scale * t1 + delay
# resample the scaled x1 onto t2
x1r = interp(t2, t1s, x1)
err = x1r - x2
RMSError = sqrt(average(err**2))
return RMSError
def sigclip(im,nsig):
# returns min and max values of image inside nsig sigmas
temp = im.ravel()
sd = pl.std(temp)
m = pl.average(temp)
gt = temp > m-nsig*sd
lt = temp < m+nsig*sd
temp = temp[gt*lt]
mini = min(temp)
maxi = max(temp)
return mini,maxi
def plot(self):
"""generate the plot formatting"""
if self.data == None:
print "Must load and parse data first"
sys.exit()
for k,v in self.data.iteritems():
for type, data in v.iteritems():
pylab.clf()
height = int(self.height)
width = int(self.width)
pylab.figure()
ax = pylab.gca()
ax.set_xlabel('<--- Width = %s wells --->' % str(width))
ax.set_ylabel('<--- Height = %s wells --->' % str(height))
ax.set_yticks([0,height/10])
ax.set_xticks([0,width/10])
ax.set_yticklabels([0,height])
ax.set_xticklabels([0,width])
ax.autoscale_view()
pylab.jet()
#color = self.makeColorMap()
#remove zeros for calculation of average
flattened = []
for i in data:
for j in i:
flattened.append(j)
flattened = filter(lambda x: x != 0.0, flattened)
Aver=pylab.average(flattened)
name = type.replace(" ", "_")
fave = ("%.2f") % Aver
pylab.title(k.strip().split(" ")[-1] + " Heat Map (Average = "+fave+"%)")
ticks = None
vmax = None
if type == "Region DR":
ticks = [0.0,0.2,0.4,0.6,0.8,1.0]
vmax = 1.0
else:
ticks = [0.0,0.4,0.8,1.2,1.6,2.0]
vmax = 2.0
pylab.imshow(data, vmin=0, vmax=vmax, origin='lower')
pylab.colorbar(format='%.2f %%',ticks=ticks)
pylab.vmin = 0.0
pylab.vmax = 2.0
#pylab.colorbar()
if self.savePath is None:
save = "%s_heat_map_%s.png" % (name,k)
else:
save = path.join(self.savePath,"%s_heat_map_%s.png" % (name,k))
pylab.savefig(save)
pylab.clf()
def moving_average(y, window_length):
"""
Compute the moving average of y with specified window length.
Args:
y: an 1-d pylab array with length N, representing the y-coordinates of
the N sample points
window_length: an integer indicating the window length for computing
moving average
Returns:
an 1-d pylab array with the same length as y storing moving average of
y-coordinates of the N sample points
"""
out = []
for i in range(1, len(y) + 1):
if i < window_length:
out.append(pylab.average(y[:i]))
else:
out.append(pylab.average(y[i - window_length:i]))
return pylab.array(out)
def avg_sigma(numlist):
"""Returns with the average and sigma of the listed numbers.
Parameter:
`numlist`: list or array
"""
average = pylab.average(numlist)
numlist = pylab.array(numlist)
sum2 = sum((numlist - average)**2)
sigma = pylab.sqrt(sum2 / (len(numlist) - 1))
return average, sigma
def avg_sigma(numlist):
"""Returns with the average and sigma of the listed numbers.
Parameter:
`numlist`: list or array
"""
average = pylab.average(numlist)
numlist = pylab.array(numlist)
sum2 = sum((numlist - average)**2)
sigma = pylab.sqrt(sum2 / (len(numlist)-1))
return average, sigma
def properties(self, n=100):
"""
Prints the statistical properties (maximal and average degree) of the results.
It goes through the labels we have set with `self.set_labels`.
Parameters:
`n`: optional
the number of the generated networks to create statistics.
Example::
>>> r.set_labels("200_020")
['200_020']
>>> r.properties(n=100)
====================
label = 200_020
number of generated networks = 100
divs = [0.81104014013270886, 1.0],
probs=[[ 0.27609856 0.23149258]
[ 0.23149258 0.26091629]]
avg max deg = 9.87+-1.0115993937, avg average deg=3.5651+-0.175231419857
"""
out = Output(os.path.join(self.project_dir, "properties.txt"))
assert isinstance(n, int) and n>0
if not isinstance(self.labels, list) or not self.labels:
raise NoLabelsError
for label in self.labels:
run = self.runs[label]
doubleline = "=" * 20
out.write(doubleline)
out.write("label = %s" % label)
divs, probs = run["divs"], run["probs"]
pm = mfng.ProbMeasure(divs, probs)
K = run.get("K") or run.get("k") # Older files have k instead of K.
ipm = pm.iterate(K=K)
out.write("number of generated networks = %s\ndivs = %s,\nprobs=%s" % (n,divs, probs))
maxdegs = []
avgdegs = []
for i in range(n):
nw = ipm.generate(n=run["n"])
deg = nw.degree()
maxdegs.append(max(deg))
avgdegs.append(pylab.average(deg))
avgmaxdeg, avgmaxdegsigma = avg_sigma(maxdegs)
avgavgdeg, avgavgdegsigma = avg_sigma(avgdegs)
out.write("\navg max deg = %5s+-%5s, avg average deg=%5s+-%5s\n" %
(avgmaxdeg, avgmaxdegsigma, avgavgdeg, avgavgdegsigma))
del out
def process_window(sample_rate, data):
# print "processing window"
# print data.dtype
# print data.shape
n = data.shape[0]
length = float(n)
# print length / sample_rate, "s"
p = fft(data) # take the fourier transform
nUniquePts = ceil((n+1)/2.0)
p = p[0:nUniquePts]
p = abs(p)
p = p / float(n) # scale by the number of points so that
# the magnitude does not depend on the length
# of the signal or on its sampling frequency
p = p**2 # square it to get the power
# multiply by two (see technical document for details)
# odd nfft excludes Nyquist point
if n % 2 > 0: # we've got odd number of points fft
p[1:len(p)] = p[1:len(p)] * 2
else:
p[1:len(p) -1] = p[1:len(p) - 1] * 2 # we've got even number of points fft
freqArray = arange(0, nUniquePts, 1.0) * (sample_rate / n);
if sum(p) == 0:
raise Silence
m = average(freqArray, weights = p)
v = sqrt(average((freqArray - m)**2, weights= p))
r = sqrt(mean(data**2))
s = var(data**2)
print "mean freq", m #TODO: IMPORTANT: this is currently the mean *power*, not the mean freq. What we want is mean freq weighted by power
# print freqArray
# print (freqArray - m)
# print p
print "var freq", v
print "rms", r
print "squared variance", s
return [m, v, r, s]
def writeMetricsFile(self, filename):
'''Writes the lib_cafie.txt file'''
if self.data == None:
print "Must load and parse data first"
sys.exit()
cafie_out = open(filename,'w')
for k,v in self.data.iteritems():
for type, data in v.iteritems():
flattened = []
for i in data:
for j in i:
flattened.append(j)
flattened = filter(lambda x: x != 0.0, flattened)
Aver=pylab.average(flattened)
name = type.replace(" ", "_")
if 'LIB' in k:
if len(flattened)==0:
cafie_out.write('%s = %s\n' % (name,0.0))
Aver = 0
else:
cafie_out.write('%s = %s\n' % (name,Aver))
Aver=pylab.average(flattened)
cafie_out.close()
def amptime(uv, baseline="0_1", pol="xx", applycal=False):
'''
Plots Amp vs Time for a single baseline.
'''
fig = pl.figure()
ax = fig.add_subplot(111)
aipy.scripting.uv_selector(uv, baseline, pol)
for preamble, data, flags in uv.all(raw=True):
uvw, t, (i, j) = preamble
ax.plot(t, pl.average(pl.absolute(data)), 'ks', mec='None', alpha=0.2, ms=5)
hfmt = dates.DateFormatter('%m/%d %H:%M')
ax.xaxis.set_major_locator(dates.HourLocator())
ax.xaxis.set_major_formatter(hfmt)
ax.set_ylim(bottom = 0)
pl.xticks(rotation='vertical')
def calcTDData(self,tdDatas):
#tdDatas is a a 3d array of measurements, along with their uncertainties
#meantdData is the weighted sum of the different measurements
#meantdData,sumofweights=py.average(tdDatas[:,:,1:3],axis=0,weights=1.0/tdDatas[:,:,3:]**2,returned=True)
meantdData=py.average(tdDatas[:,:,1:3],axis=0)
#use error propagation formula
noise=py.sqrt(py.mean(self.getAllPrecNoise()[0]**2))
if tdDatas.shape[0]==1:
rep = py.zeros((len(tdDatas[0,:,0]),2))
else:
rep = py.std(tdDatas[:,:,1:3],axis=0, ddof=1)/py.sqrt(self.numberOfDataSets)
unc = py.sqrt(rep**2+noise**2)
#unc=py.sqrt(1.0/sumofweights)
#time axis are all equal
return py.column_stack((tdDatas[0][:,0],meantdData,unc))
def r_squared(y, estimated):
"""
Calculate the R-squared error term.
Args:
y: 1-d pylab array with length N, representing the y-coordinates of the
N sample points
estimated: an 1-d pylab array of values estimated by the regression
model
Returns:
a float for the R-squared error term
"""
SSres = ((y - estimated)**2).sum()
SStot = ((y - pylab.average(y))**2).sum()
return 1 - (SSres / SStot)
def calculateScores(arr, HEIGHT, WIDTH):
rowlen,collen = arr.shape
scores = pylab.zeros(((rowlen/INCREMENT),(collen/INCREMENT)))
score = []
for row in range(rowlen/INCREMENT):
for column in range(collen/INCREMENT):
keypassed,size = getAreaScore(row,column,arr, HEIGHT, WIDTH)
scores[row,column] = round(float(keypassed)/float(size)*100,2)
if keypassed > 2:
score.append(round(float(keypassed)/float(size)*100,2))
#scores[0,0] = 0
#scores[HEIGHT/INCREMENT -1,WIDTH/INCREMENT -1] = 100
print scores
flattened = []
for i in score:
flattened.append(i)
flattened = filter(lambda x: x != 0.0, flattened)
average=pylab.average(flattened)
return score, scores, average
def runSimulation(num_robots, speed, width, height, min_coverage, num_trials,
robot_type):
"""
Runs NUM_TRIALS trials of the simulation and returns the mean number of
time-steps needed to clean the fraction MIN_COVERAGE of the room.
The simulation is run with NUM_ROBOTS robots of type ROBOT_TYPE, each with
speed SPEED, in a room of dimensions WIDTH x HEIGHT.
num_robots: an int (num_robots > 0)
speed: a float (speed > 0)
width: an int (width > 0)
height: an int (height > 0)
min_coverage: a float (0 <= min_coverage <= 1.0)
num_trials: an int (num_trials > 0)
robot_type: class of robot to be instantiated (e.g. StandardRobot or
RandomWalkRobot)
"""
test_result = []
for t in range(num_trials):
# anim = ps2_visualize.RobotVisualization(num_robots, width, height, 0.5)
test_space = RectangularRoom(width, height)
list_robots = []
for item in range(num_robots):
list_robots.append(robot_type(test_space, speed))
count_step = 0
while test_space.getNumCleanedTiles() < (min_coverage *
test_space.getNumTiles()):
# anim.update(test_space, list_robots)
for item in list_robots:
item.updatePositionAndClean()
count_step += 1
# anim.done()
test_result.append(count_step)
return pylab.average(test_result)
def plot_effect_num_cashiers_on_cust_wait_time(customersPerMinute = 10, numCashiersToTestUpTo = 12):
assert customersPerMinute > 0
assert numCashiersToTestUpTo > 0
assert type(customersPerMinute) == type(numCashiersToTestUpTo) == int
store = Store(customersPerMinute)
worstCase = []
averageCase = []
rangeOfNumCashiers = range(1, numCashiersToTestUpTo + 1)
for i in rangeOfNumCashiers:
store.run_simulation(i)
timeOnLineData = [x.timeOnLine / 60. for x in store.completedCustomers]
averageCase.append(pylab.average(timeOnLineData))
worstCase.append(max(timeOnLineData))
store.reset_store()
pylab.plot(rangeOfNumCashiers, worstCase, label='Longest Time on Line')
pylab.plot(rangeOfNumCashiers, averageCase, label = 'Average Time on Line')
pylab.title('Effect of Adding Additional Cashiers \n on Customer Wait Time')
pylab.xlabel('Number of Cashiers')
pylab.ylabel('Customer Wait Time in Minutes \n (if store receives {} customers per minute)'.format(store.customersPerMinute))
pylab.legend()
pylab.xticks(rangeOfNumCashiers)
pylab.show()
def gen_cities_avg(climate, multi_cities, years):
"""
Compute the average annual temperature over multiple cities.
Args:
climate: instance of Climate
multi_cities: the names of cities we want to average over (list of str)
years: the range of years of the yearly averaged temperature (list of
int)
Returns:
a pylab 1-d array of floats with length = len(years). Each element in
this array corresponds to the average annual temperature over the given
cities for a given year.
"""
yearly_avgs = pylab.array([])
for year in years:
cities_temps = pylab.array([])
for city in multi_cities:
city_temps = climate.get_yearly_temp(city, year)
cities_temps = pylab.hstack((cities_temps, city_temps))
yearly_avg = pylab.average(cities_temps)
yearly_avgs = pylab.hstack((yearly_avgs, yearly_avg))
return yearly_avgs
if __name__ == '__main__':
climate_data = Climate('data.csv') #initialize data
# Part A.4
years = pylab.array(TRAINING_INTERVAL) #the x values of the dataset
temp_list = [
] #use climate class to obtain the temperature on Jan 10th in NYC that year
for year in TRAINING_INTERVAL:
temp_list.append(climate_data.get_daily_temp('NEW YORK', 1, 10, year))
temp = pylab.array(temp_list) #array of temperature (y) values
models = generate_models(years, temp, [1]) #fit to degree one
evaluate_models_on_training(years, temp, models) #plot AI
#now want annual temperature
ann_temp_list = [] #use climate data to obtain annual average temperature
for year in TRAINING_INTERVAL:
year_temperatures = climate_data.get_yearly_temp('NEW YORK', year)
average_year_temp = pylab.average(year_temperatures)
ann_temp_list.append(average_year_temp)
ann_temp = pylab.array(ann_temp_list)
models_ann = generate_models(years, ann_temp, [1])
evaluate_models_on_training(years, ann_temp, models_ann) #plot AII
# Part B
#national yearly temperatures
#get national average temperature in the years
nat_avg_temp = gen_cities_avg(climate_data, CITIES, TRAINING_INTERVAL)
#generate model
nat_temp_model = generate_models(years, nat_avg_temp, [1]) #fit with deg 1
evaluate_models_on_training(years, nat_avg_temp, nat_temp_model) #plot B
# Part C
#take moving average of national average temperature
mov_nat_temp = moving_average(
nat_avg_temp, 5) #window size of 5 on the national temperatures
def Froudenumber(flmlname):
print("\n********** Calculating the Froude number\n")
# warn user about assumptions
print(
"Froude number calculations makes three assumptions: \n i) domain height = 0.1m \n ii) mid point domain is at x = 0.4 \n iii) initial temperature difference is 1.0 degC"
)
domainheight = 0.1
domainmid = 0.4
rho_zero, T_zero, alpha, g = le_tools.Getconstantsfromflml(flmlname)
gprime = rho_zero * alpha * g * 1.0 # this has assumed the initial temperature difference is 1.0 degC
# get list of vtus
filelist = le_tools.GetFiles('./')
logs = [
'diagnostics/logs/time.log', 'diagnostics/logs/X_ns.log',
'diagnostics/logs/X_fs.log'
]
try:
# if have extracted information already just use that
os.stat('diagnostics/logs/time.log')
os.stat('diagnostics/logs/X_ns.log')
os.stat('diagnostics/logs/X_fs.log')
time = le_tools.ReadLog('diagnostics/logs/time.log')
X_ns = [
x - domainmid
for x in le_tools.ReadLog('diagnostics/logs/X_ns.log')
]
X_fs = [
domainmid - x
for x in le_tools.ReadLog('diagnostics/logs/X_fs.log')
]
except OSError:
# otherwise get X_ns and X_fs and t from vtus
time, X_ns, X_fs = le_tools.GetXandt(filelist)
f_time = open('./diagnostics/logs/time.log', 'w')
for t in time:
f_time.write(str(t) + '\n')
f_time.close()
f_X_ns = open('./diagnostics/logs/X_ns.log', 'w')
for X in X_ns:
f_X_ns.write(str(X) + '\n')
f_X_ns.close()
f_X_fs = open('./diagnostics/logs/X_fs.log', 'w')
for X in X_fs:
f_X_fs.write(str(X) + '\n')
f_X_fs.close()
# shift so bot X_ns and X_fs are
# distance of front from
#initial position (mid point of domain)
X_ns = [x - domainmid for x in X_ns]
X_fs = [domainmid - x for x in X_fs]
# Calculate U_ns and U_fs from X_ns, X_fs and t
U_ns = le_tools.GetU(time, X_ns)
U_fs = le_tools.GetU(time, X_fs)
U_average = [[], []]
# If possible average
# (if fronts have not travelled far enough then will not average)
start_val, end_val, average_flag_ns = le_tools.GetAverageRange(
X_ns, 0.2, domainheight)
if average_flag_ns == True:
U_average[0].append(pylab.average(U_ns[start_val:end_val]))
start_val, end_val, average_flag_fs = le_tools.GetAverageRange(
X_fs, 0.25, domainheight)
if average_flag_fs == True:
U_average[1].append(pylab.average(U_fs[start_val:end_val]))
# plot
fs = 18
pylab.figure(num=1, figsize=(16.5, 11.5))
pylab.suptitle('Front speed', fontsize=fs)
pylab.subplot(221)
pylab.plot(time, X_ns, color='k')
pylab.axis([0, 45, 0, 0.4])
pylab.grid('on')
pylab.xlabel('$t$ (s)', fontsize=fs)
pylab.ylabel('$X$ (m)', fontsize=fs)
pylab.title('no-slip', fontsize=fs)
pylab.subplot(222)
pylab.plot([x / domainheight for x in X_ns],
[U / math.sqrt(gprime * domainheight) for U in U_ns],
color='k')
pylab.axis([0, 4, 0, 0.6])
pylab.grid('on')
pylab.axhline(0.406, color='k')
pylab.axhline(0.432, color='k')
pylab.text(3.95,
0.396,
'Hartel 2000',
bbox=dict(facecolor='white', edgecolor='black'),
va='top',
ha='right')
pylab.text(3.95,
0.442,
'Simpson 1979',
bbox=dict(facecolor='white', edgecolor='black'),
ha='right')
pylab.xlabel('$X/H$', fontsize=fs)
pylab.ylabel('$Fr$', fontsize=fs)
pylab.title('no-slip', fontsize=fs)
if average_flag_ns == True:
pylab.axvline(2.0, color='k')
pylab.axvline(3.0, color='k')
pylab.text(
0.05,
0.01,
'Average Fr = ' + '{0:.2f}'.format(
U_average[0][0] / math.sqrt(gprime * domainheight)) +
'\nvertical lines indicate the range \nover which the average is taken',
bbox=dict(facecolor='white', edgecolor='black'))
pylab.subplot(223)
pylab.plot(time, X_fs, color='k')
pylab.axis([0, 45, 0, 0.4])
pylab.grid('on')
pylab.xlabel('$t$ (s)', fontsize=fs)
pylab.ylabel('$X$ (m)', fontsize=fs)
pylab.title('free-slip', fontsize=fs)
pylab.subplot(224)
pylab.plot([x / domainheight for x in X_fs],
[U / math.sqrt(gprime * domainheight) for U in U_fs],
color='k')
pylab.axis([0, 4, 0, 0.6])
pylab.grid('on')
pylab.axhline(0.477, color='k')
pylab.text(3.95,
0.467,
'Hartel 2000',
va='top',
bbox=dict(facecolor='white', edgecolor='black'),
ha='right')
pylab.xlabel('$X/H$', fontsize=fs)
pylab.ylabel('$Fr$', fontsize=fs)
pylab.title('free-slip', fontsize=fs)
if average_flag_fs == True:
pylab.text(
0.05,
0.01,
'Average Fr = ' + '{0:.2f}'.format(
U_average[1][0] / math.sqrt(gprime * domainheight)) +
'\nvertical lines indicate the range \nover which the average is taken',
bbox=dict(facecolor='white', edgecolor='black'))
pylab.axvline(2.5, color='k')
pylab.axvline(3.0, color='k')
pylab.savefig('diagnostics/plots/front_speed.png')
return
requestor_rep.append(requestor_avg)
time.append(x + 1)
# Add new workers
for y in xrange(random.randint(1, 20)):
workers.append(Worker(x + 1, getAverageRep(workers)))
for y in xrange(random.randint(1, 5)):
requestors.append(Requestor(x + 1, getAverageRep(requestors)))
#Create lines for plot of average overall worker and requestor reputation over time
work_line.set_xdata(time)
work_line.set_ydata(worker_rep)
req_line.set_ydata(requestor_rep)
req_line.set_xdata(time)
ax.relim()
ax.autoscale_view()
plt.draw()
plt.show()
#Plot histogram of all worker reputations
P.figure()
all_reputations = [x.get_reputation() for x in workers]
sigma = P.std(all_reputations)
mu = P.average(all_reputations)
n, bins, patches = P.hist(all_reputations,
20,
normed=1,
histtype='step',
cumulative=True)
y = P.normpdf(bins, mu, sigma)
P.plot(bins, y)
requestor.start_job(workers)
worker_rep.append(worker_avg)
requestor_rep.append(requestor_avg)
time.append(x+1)
# Add new workers
for y in xrange(random.randint(1,20)):
workers.append(Worker(x+1,getAverageRep(workers)))
for y in xrange(random.randint(1,5)):
requestors.append(Requestor(x+1,getAverageRep(requestors)))
#Create lines for plot of average overall worker and requestor reputation over time
work_line.set_xdata(time)
work_line.set_ydata(worker_rep)
req_line.set_ydata(requestor_rep)
req_line.set_xdata(time)
ax.relim()
ax.autoscale_view()
plt.draw()
plt.show()
#Plot histogram of all worker reputations
P.figure()
all_reputations = [x.get_reputation() for x in workers]
sigma = P.std(all_reputations)
mu = P.average(all_reputations)
n, bins, patches = P.hist(all_reputations, 20, normed=1, histtype='step',cumulative=True)
y = P.normpdf(bins, mu, sigma)
P.plot(bins, y)
#x = (x - T0) / P
ft = []
date = []
peaks = []
f0 = 3000
f1 = 4000
for i in range(0,N,int(fitlength/2.0)):
if i + fitlength/2.0 <= len(x):
print 'somewhere'
date.append(pl.average(x[i:i + fitlength]))
f,a = ast.signal.dft(x[i:i+fitlength],y[i:i+fitlength],f0,f1,1)
ft.append(a)
#sort,argsort = sci.fastsort(a)
#peaks.append(f[argsort[-1]])
# / len(x[i:i+fitlength]))
print i, i+fitlength
else:
print 'finally'
#x = fitwave(y[i:len(t)+1],t[i:len(t)+1],freq)
f,a = ast.signal.dft(x[i:len(x)+1],y[i:len(x)+1],f0,f1,1)
ft.append(a)
#sort,argsort = sci.fastsort(a)
#peaks.append(f[argsort[-1]])
date.append(pl.average(x[i:-1]))# / len(x[i:-1]))
def plot_sigTau_driven(self,th1_cs,slip,figNum=11,plotBekker=False):
th_m = self._thm
th2 = self._th2
r = self._radius
b = self._width
k1 = self._k1
k2 = self._k2
n = self._n
phi = self._phi
K = self._K
c = self._coh
incr = (th1_cs - th2) / 100.0 # plot increment
th_arr = py.arange(0,th1_cs, incr) # find sigma, tau at these discrete vals
sig_arr = py.zeros(len(th_arr))
tau_arr = py.zeros(len(th_arr))
slip_arr = py.zeros(len(th_arr))
for idx in range(0,len(th_arr)):
th = th_arr[idx]
if(th <= th_m):
# we're in the bototm region
sig_curr = sig_2(th,th_m,th1_cs,th2,r,b,n,k1,k2)
tau_curr = tau_d2(th,th_m,th1_cs,th2,r,b,n,k1,k2,c,K,phi,slip)
slip_j = jdriven(th,th1_cs,r,slip)
sig_arr[idx] = sig_curr
tau_arr[idx] = tau_curr
slip_arr[idx] = slip_j
else:
# we're in the top region ()
sig_curr = sig_1(th, th1_cs,r,b,n,k1,k2)
tau_curr = tau_d1(th,th1_cs,r,b,n,k1,k2,c,phi,K,slip)
slip_j = jdriven(th,th1_cs,r,slip)
sig_arr[idx] = sig_curr
tau_arr[idx] = tau_curr
slip_arr[idx] = slip_j
if( self._plots):
fig = plt.figure()
ax = fig.add_subplot(211,title='Fig.'+str(figNum) )
ax.plot(radToDeg(th_arr),sig_arr,radToDeg(th_arr),tau_arr,linewidth=1.5)
ax.set_xlabel('theta [deg]')
ax.set_ylabel('stress [psi]')
ax.legend((r'$\sigma$',r'$\tau$'))
ax.grid(True)
# can also plot Bekker's solution
if(plotBekker):
th_bek, sig_bek, tau_bek = self.get_sigTau_Bekker_driven(slip)
ax.plot(radToDeg(th_bek),sig_bek, radToDeg(th_bek), tau_bek, linewidth=1.5)
ax.legend((r'$\sigma$',r'$\tau$',r'$\sigma_bek$',r'$\tau_bek$'))
# take a look at what I"m using for slip displacement also
ax = fig.add_subplot(212)
ax.plot(radToDeg(th_arr),slip_arr,linewidth=1.5)
ax.set_xlabel('theta [deg]')
if( self._units == 'ips'):
ax.set_ylabel('slip disp.[in]')
else:
ax.set_ylabel('slip disp.[m]')
ax.grid(True)
# polar plots
fig=plt.figure()
ax=fig.add_subplot(111,projection='polar')
ax.plot(th_arr,sig_arr,'b',linewidth=1.5)
ax.plot(th_arr,tau_arr,'r--',linewidth=1.5)
# fix the axes
ax.grid(True)
if( self._units == 'ips'):
leg = ax.legend((r'$\sigma$ [psi]',r'$\tau$'))
else:
leg = ax.legend((r'$\sigma$ [Pa]',r'$\tau$'))
leg.draggable()
ax.set_theta_zero_location('S')
# also, draw the tire
polar_r_offset = py.average(sig_arr)
theta = py.arange(0.,2.*py.pi+0.05,0.05)
tire_pos = py.zeros(len(theta))
ax.plot(theta,tire_pos,color='k',linewidth=1.0)
ax.set_rmin(-polar_r_offset)
ax.set_title(r'driven wheel stresses, $\theta_1$ = %4.3f [rad]' %th1_cs)
ax.set_thetagrids([-10,0,10,20,30,40,50,60])
#print '\nDone...\n'
# bin spectra together in 0.1 phase bins
im3 = []
klist = []
k = 0
temp = pl.zeros(len(pf.getdata(ff[0])[xx]),dtype=float)
for i in range(0,100):
for j in range(len(ff)):
if phase[j] >= i*0.01 and phase[j] < (i+1)*0.01:
temp += pf.getdata(ff[i])[xx]-pl.median(pf.getdata(ff[i])[xx])#pf.getdata(ff[argsort[j]])
k+=1
ave = pl.average(temp)
im3.append(temp)
temp = pl.zeros(len(pf.getdata(ff[0])[xx]),dtype=float)
klist.append(k)
k = 0
#print sum(klist)
#print len(klist)
pl.figure()
pl.gray()
pl.imshow(im3, interpolation='nearest', aspect='auto',cmap=pl.cm.gray_r,extent=(6500,6625,1,0))
#ax = pl.axes()
# try to flatten lightcurves using Fourier Filter
import scipy.signal as sci
import pylab as pl
import os
X = pl.load('speclc_Ha.dat')
x = X[:,0]
y = X[:,1]
fft = sci.fft(y-pl.average(y))
l = len(fft)
dt = x[1]-x[0]
# kill the negative frequencies
#fft[l/2:] = 0.0
# kill the low frequency bits > ~ 1hour
k = int(24.0*l*dt)
print k
fft[0:5] = 0.0
fft[-5:] = 0.0
yy = sci.ifft(fft)
climate = Climate('data.csv')
pla_training_years = pylab.array(TRAINING_INTERVAL)
pla_testing_years = pylab.array(TESTING_INTERVAL)
# Part A.4 I
temps = []
for year in TRAINING_INTERVAL:
temp = climate.get_daily_temp('NEW YORK', 1, 10, year)
temps.append(temp)
pla_temps = pylab.array(temps)
models = generate_models(pla_training_years, pla_temps, [1])
evaluate_models_on_training(pla_training_years, pla_temps, models)
# Part A.4 II
temps = []
for year in TRAINING_INTERVAL:
temp = pylab.average(climate.get_yearly_temp('NEW YORK', year))
temps.append(temp)
pla_temps = pylab.array(temps)
nyc_avg_models = generate_models(pla_training_years, pla_temps, [1])
evaluate_models_on_training(pla_training_years, pla_temps, nyc_avg_models)
# Part B
cities_avg = gen_cities_avg(climate, CITIES, pla_training_years)
avg_models = generate_models(pla_training_years, cities_avg, [1])
evaluate_models_on_training(pla_training_years, cities_avg, avg_models)
print('avg_models', avg_models)
# Part C
cities_avg = gen_cities_avg(climate, CITIES, pla_training_years)
mvg_avg = moving_average(cities_avg, 5)
mvg_avg_models = generate_models(pla_training_years, mvg_avg, [1])
outer_pass2.append( pl.arange(len(fib.flux))*0 )
outer_pass2.append( pl.arange(len(fib.flux))*0 )
n = 100
tmp0 = fibers[0]
tmpn = fibers[n]
fibers[0] = tmpn
fibers[n] = tmp0
stack_x = fibers[0].xaxis
stack_f = fibers[0].flux
for f in fibers:
# FINDS THE EMISSION LINES, WHERE FLUX IS ABOVE THE AVERAGE
flx_above_medi0 = pl.find( f.flux > pl.average(f.flux) )
flx_above_medi1 = []
# FINDS THE EMISSION THAT HAVE MORE THAN 5 PIXELS ABOVE
# AVERAGE FLUX. THEN RECORDS THE AVERAGE X-COORD FOR THE LINE.
tmp = [ flx_above_medi0[0] ]
for i in range(1,len(flx_above_medi0)):
if flx_above_medi0[i] - tmp[-1] < 2: tmp.append( flx_above_medi0[i] )
else:
if len(tmp)>=5: f.xpeaks.append( pl.average(tmp) )
tmp = [ flx_above_medi0[i] ]
if f==fibers[0]: continue
# NOW WE NEED TO FIND THE RESCALING FACTOR (s) AND OFFSET (m)
f.s = pl.average( (pl.array(fibers[0].xpeaks)-fibers[0].xpeaks[0]+1) / (pl.array(f.xpeaks)-f.xpeaks[0]+1) )
def plot_smf_with_fit(sp, phi, elo, ehi, color_i):
inds = phi > 0
x_data = smfdata.lmass[inds]
y_data = phi[inds]
elo_data = elo[inds]
ehi_data = ehi[inds]
sp.errorbar(x_data,
y_data,
xerr=0.25 / 2,
yerr=[elo_data, ehi_data],
ls='',
marker='o',
mew=2,
ms=11,
mfc='none',
mec=color_i,
ecolor=color_i,
zorder=10)
### fitting toy models
scale_factors = pylab.zeros(simulation.n_timesteps)
chi2s = pylab.zeros(simulation.n_timesteps)
log_errors = (pylab.log10(y_data / (y_data - elo_data)) + pylab.log10(
(y_data + ehi_data) / y_data)) / 2.
weights = 1. / ((elo_data + ehi_data) / 2.)**2
weights = 1. / log_errors**2
bestfit_models = numpy.zeros((simulation.n_timesteps, len(x_data)))
for i_model in range(simulation.n_timesteps):
y_model = simulation.SMFs[i_model]
y_model = numpy.interp(x_data, simulation.lmassbars, y_model)
numer = pylab.sum(y_data * weights)
denom = pylab.sum(y_model * weights)
scale = numer / denom
scale_factors[i_model] = scale
bestfit_models[i_model] = y_model * scale
chi2s[i_model] = pylab.sum(
(pylab.log10(y_data) - pylab.log10(y_model * scale))**2 /
log_errors**2)
Pchi = 1. / chi2s**2
Pchi = pylab.exp(-0.5 * chi2s)
### plotting best-fit model SMF
i_best = pylab.find(Pchi == Pchi.max())[0]
sp.plot(x_data, bestfit_models[i_best], color='r', lw=1.5)
### plotting marginalized best-fit SMF
best_smf = pylab.average(bestfit_models, axis=0, weights=Pchi)
sp.plot(x_data, best_smf, color='k', lw=5, zorder=1)
sp.plot(x_data, best_smf, color='gray', lw=2, zorder=1)
### plotting stats
best_f_icl = pylab.average(simulation.get_f_ICL(),
axis=0,
weights=Pchi)
### plotting info text
t = 'f$_{ICL}$ = %.1f%%' % (best_f_icl * 100)
t += '\nN$_{initial}$ = %i' % simulation.ngal_initial
t += '\nN$_{best-fit}$ = %i' % pylab.average(
simulation.ngal_timesteps, axis=0, weights=Pchi)
t += ' (%.1f%%)' % (100. * pylab.average(
simulation.ngal_timesteps, axis=0, weights=Pchi) /
simulation.ngal_initial)
sp.text(0.03,
0.03,
t,
horizontalalignment='left',
verticalalignment='bottom',
transform=sp.transAxes)
# resample zwift power onto edge
CrossPlotFig = plt.figure()
sc = plt.scatter(edge_power_x, zwift_power_r_r, s=5, c=base_t, \
cmap=plt.get_cmap('brg'), edgecolors='face' )
plt.colorbar(orientation='horizontal')
plt.title('Infocrank Vs PowerTap P1 Over Time (sec)\n(delay removed)')
plt.xlabel('PowerTap P1 (w)')
plt.ylabel('Infocrank via Zwift (w)')
plt.grid(b=True, which='major', axis='both')
a = plt.axis()
plt.axis([0, a[1], 0, a[3]])
plt.show()
#
# linear regression
#
from pylab import polyfit, average, ones, where, logical_and, nonzero
ii = nonzero( logical_and( base_t>=0, \
logical_and(edge_power_x>50, \
edge_power_x<1000) ))
x = edge_power_x[ii]
y = zwift_power_r_r[ii]
coef = polyfit(x, y, deg=1)
slope = coef[0]
offset = coef[1]
print 'slope = %5.3f, offset = %i' % (slope, offset)
y_fit = slope * x + offset
color = average(edge_t[ii]) * ones(len(edge_t[ii]))
plt.plot(x, y_fit, 'k-')
plt.show()
def nvnReportPipe(
self): #Executes functions that generate result.html file
try:
steps = int(self.paraSteps.get())
hrr = int(self.paraHrr.get())
except:
self.printer(
"You need to enter integer values for HRR and step parameters.\n"
)
if self.listbox.curselection() != ():
query = self.genelist[int(self.listbox.curselection()[0])][0]
#### Plot expression profile function
try:
plotDatabase = codecs.open(
open('dbconf.txt', 'r').read().rstrip().split(".")[0] +
".plt",
mode='r',
encoding='ASCII',
errors='ignore').readlines()
ticka = [""] + plotDatabase[0].replace(
",", "\n").lstrip().rstrip().split("\t")
for i in plotDatabase:
if query in i:
query = i
data = query.split()[1:]
temp = []
for i in range(len(data)):
temp.append([
map(float, data[i].replace("-",
"\t").rstrip().split()),
average(
map(float, data[i].replace("-",
"\t").rstrip().split()))
])
fig = plt.figure(figsize=(12, 7))
ax = fig.add_subplot(111)
plt.subplots_adjust(left=0.1, right=0.97, top=0.93, bottom=0.3)
ax.set_ylabel("Signal value")
ax.set_title(query.split()[0])
ax.grid(True)
plt.xticks(range(len(ticka) + 1),
ticka,
rotation=90,
fontsize="small",
horizontalalignment="center")
ax.plot([0], [0])
crossX = []
crossY = []
for i in range(len(temp)):
ax.plot([i + 1] * len(temp[i][0]), temp[i][0], "g.")
crossX.append([i + 1])
crossY.append(temp[i][1])
ax.plot(crossX, crossY, "-ro")
ax.plot([i + 2], [0])
canvas = FigureCanvasAgg(fig)
canvas.print_figure("profile.png")
plt.clf()
except:
self.printer(
"Failed to generate an expression profile of your gene of interes.\nThe expression matrix used for plotting of expression profiles must be present and named "
+ open('dbconf.txt', 'r').read().rstrip().split(".")[0] +
".plt!")
###Call network creator
try:
networkViewer.makeNetwork(query.split()[0], steps, hrr)
except:
self.printer(
"Failed to generate an co-expression network of your gene of interes.\nThe HRR network file used must be present named "
+ open('dbconf.txt', 'r').read().rstrip().split(".")[0] +
".hrr!")
### Calculate PCC of a gene to all genes in database
try:
query = self.queries[int(
self.listbox.curselection()[0])].split("\t")
expVector = map(float, query[5:])
expVector = numpy.array(expVector)
nomi = expVector - (numpy.sum(expVector) / len(expVector))
denomi = numpy.sqrt(numpy.sum(nomi**2))
rValues = numpy.dot(self.nominator, nomi) / numpy.dot(
self.denominator, denomi)
displayList = []
for i in range(len(rValues)):
displayList.append([rValues[i], self.annoDict[i]])
displayList.sort(reverse=True)
except:
displayList = []
self.printer(
"Failed to calculate Pearson correlation co-efficient list.\n"
)
###Create html document with results
header = '<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2//EN">\n<html>\n<head>\n<!-- blarg -->\n</head>\n<body>\n'
try:
header += '<big><b>Summary page for: %s</b></big>\n<br><b>Expression profile:</b>\n<IMG SRC="profile.png"><br>\n' % (
displayList[0][1].split()[0] + "\t" +
displayList[0][1].split()[1])
except:
pass
header += '<b>HRR based co-expression network:</b>\nGreen, organge and red edges represent HRR values of %s, %s and %s, respectively.</b><br>\n' % (
int(hrr) / 3, (int(hrr) / 3) * 2, hrr)
header += '<embed src="network.svg" width="1200" height="1200" type="image/svg+xml" pluginspage="http://www.adobe.com/svg/viewer/install/" />\n<br>'
#header += '<iframe src="network.svg" width="1500" height="1500">\n</iframe>\n'
header += "<br><b>MapMan ontology analysis of the above network:</b>\n"
try:
header += open("mapRes.mapman", "r").read()
except:
pass
header += "\n<br><b>Pearson correlation based co-expression analysis:</b>\n<pre>"
v = open("result.html", "w")
v.close()
v = open("result.html", "a")
v.write(header)
for i in range(len(self.annoDict)):
v.write(
str(displayList[i][0])[:6] + "\t" + displayList[i][1] +
"\n")
v.write("</pre>")
v.close()
self.printer(
"Probeset specific result calculated and available in result.html file.\n"
)
def xforce_stat(sf): stat = fluidity_tools.stat_parser(sf) time_avg_xforce = pylab.average(stat["0"]["Velocity"]["force%1"][-20:]) return time_avg_xforce
sa = []
for f in files:
print 'Reading %s ' % f
name, eph = string.split(f)
T0 = ephemeris[eph]
P = 0.154525
X = pl.load(name)
x = (X[:,2] - T0)/P
xx.append(x - int(x[0]))
aa.append(X[:,0])
# let phase at 0.8 -> 1.0
tpp = X[:,1]
tpp -= pl.average(tpp[0])
#tpp += 1.0
pp.append(tpp)
sa.append(X[:,3])
sp.append(X[:,4])
# now sort observations in terms of orbital phase
xx = pl.array([i for i in pl.flatten(xx)])
pp = pl.array([i for i in pl.flatten(pp)])
aa = pl.array([i for i in pl.flatten(aa)])
sa = pl.array([i for i in pl.flatten(sa)])
sp = pl.array([i for i in pl.flatten(sp)])
arg = xx.argsort()
print runlen
runlen = max(runlen)+0.4
##############################################################################################
pl.figure(figsize=(6,4))
pl.subplots_adjust(hspace=0.47,left=0.16)
pl.subplot(211)
pl.scatter(x1,y1,marker='o',s=0.1,color='k')
pl.ylim(-0.06,0.06)
pl.xlim(pl.average(x1)-runlen/2,pl.average(x1)+runlen/2)
pl.ylabel('Intensity')
pl.xlabel('Orbital Phase')
pl.subplot(212)
#f,a = ast.signal.dft(x1,y1,0,4000,1)
pl.plot(f1,a1,'k')
pl.ylabel('Amplitude')
pl.xlabel('Frequency (c/d)')
pl.ylim(0.0,0.007)
pl.vlines(3560,0.002,0.0025,color='k',linestyle='solid')
pl.vlines(950,0.002,0.0025,color='k',linestyle='solid')
pl.text(3425,0.00255,'DNO',fontsize=11)
pl.text(750,0.00255,'lpDNO',fontsize=11)
pl.ylim(0.0,0.007)
def completeness():#measure completeness on final image
#combinepath=bcdpath+'pbcd/Combine/output_apex_step2'
combinepath=bcdpath+'pbcd/Combine/apex_1frame_step2'
os.chdir(combinepath)
file='mosaic_extract_final.tbl'
input=open(file,'r')
xgal=[]#positions of previous detections with snr > 3
ygal=[]
fap4gal=[]
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('\\') > -1: #skip lines with '#' in them
continue
if line.find('|') > -1: #skip lines with '#' in them
continue
t=line.split()
xgal.append(float(t[8]))
ygal.append(float(t[10]))
fap4gal.append(float(t[28]))
input.close()
xgal=N.array(xgal,'f')
ygal=N.array(ygal,'f')
fsimall=[]
matchflagsimall=[]
f2all=[]
f3all=[]
f4all=[]
deblendsimall=[]
snrsimall=[]
myminmag=24.75
mymaxmag=27.4
myfmin=10.**((25.-mymaxmag)/2.5)#ZP=25
myfmax=10.**((25.-myminmag)/2.5)#ZP=25
#below is loop to create image w/artificial sources, extract, and compare
for k in range(100):
createflag=1.#create image w/artificial sources?
detectflag=1.#detect sources in image?
if createflag > 0.1:
xnoise=[]
ynoise=[]
infile=open('noisecoords.dat','r')
for line in infile:
t=line.split()
xnoise.append(float(t[0]))
ynoise.append(float(t[1]))
infile.close()
nstar=10
xsim=N.zeros(nstar,'d')
ysim=N.zeros(nstar,'d')
msim=N.zeros(nstar,'d')
outfile=open('stars.coords.dat','w')
for i in range(nstar):
#j=int(round(1.*len(xnoise)*random.uniform(0,1)))
#xsim[i]=xnoise[j]
#ysim[i]=ynoise[j]
j=0
for j in range(10000):
xt=int(round(random.uniform(5.,125.)))
yt=int(round(random.uniform(5.,140.)))
d=pylab.sqrt((xt-xgal)**2+(yt-ygal)**2)#make sure sim galaxies are not near real galaxies
if min(d) > -1.:
d2=pylab.sqrt((xt-xsim)**2+(yt-ysim)**2)#make sure sim points are not on top of each other
if min(d2) > 5.:
print i,'got a good point after ',j,' tries',xt,yt
break
j=j+1
xsim[i]=xt
ysim[i]=yt
k=random.uniform(myfmin,myfmax)
msim[i]=25.-2.5*pylab.log10(k)
#print k,msim[i]
s='%8.2f %8.2f %8.2f \n' % (xsim[i],ysim[i],msim[i])
outfile.write(s)
outfile.close()
#os.system('rm stars.coords.dat')
#iraf.starlist('stars.coords.dat',nstars=100,spatial='uniform',xmax=130,ymax=145,luminosity='uniform',minmag=22.,maxmag=30.0,mzero=22.0,sseed='INDEF',power=0.6,alpha=0.74,lseed='INDEF')
os.system('rm mosaic-completeness.fits')
#iraf.mkobjects(input='mosaic_minus_median_extract.fits',output='mosaic-completeness.fits',objects='stars.coords.dat',radius=1.13,magzero=25.,background=0.,gain=5.,rdnoise=0.,poisson='no')#don't convolve w/PRF
#os.system('cp ../cal/MIPS24_PRF_HD_center.fits .')#convolve star w/SSC PRF
os.system('cp ../cal/mips24_prf_mosaic_2.45_4x.fits .')#convolve star w/SSC PRF
iraf.mkobjects(input='mosaic_minus_median_extract.fits',output='mosaic-completeness.fits',objects='stars.coords.dat',radius=14,star='mips24_prf_mosaic_2.45_4x.fits',magzero=25.,background=0.,gain=5.,rdnoise=0.,poisson='no')
#os.system('cp ../cal/PRF_estimate.fits .')#convolve gaussian w/measured PRF
#iraf.mkobjects(input='mosaic_minus_median_extract.fits',output='mosaic-completeness.fits',objects='stars.coords.dat',radius=15,star='PRF_estimate.fits',magzero=25.,background=0.,gain=5.,rdnoise=0.,poisson='no')
os.system('ls *.fits')
os.system('pwd')
iraf.display('mosaic_minus_median_extract.fits',1,contrast=0.01)
iraf.display('mosaic-completeness.fits',2,contrast=0.01)
iraf.tvmark(1,'stars.coords.dat')
iraf.tvmark(2,'stars.coords.dat')
fsim=10.**((25.-msim)/2.5)#ZP=25
if createflag < .1:#read in positions and magnitudes of artdata sources
xsim=[]
ysim=[]
msim=[]
infile=open('stars.coords.dat','r')
for line in infile:
if line.find('#') > -1:
continue
t=line.split()
xsim.append(float(t[0]))
ysim.append(float(t[1]))
msim.append(float(t[2]))
infile.close()
xsim=N.array(xsim,'f')
ysim=N.array(ysim,'f')
msim=N.array(msim,'f')
fsim=10.**((25.-msim)/2.5)#ZP=25
if detectflag > 0.1:#now run detection on mosaic-completeness.fits
combinepath=bcdpath+'pbcd/Combine/'
os.chdir(combinepath)
print combinepath
#os.system('apex_1frame.pl -n apex_1frame_MIPS24_step2.nl -i output_apex_step2/mosaic-completeness.fits')
#combinepath=bcdpath+'pbcd/Combine/output_apex_step2'
os.system('apex_1frame.pl -n apex_1frame_step2all.nl -i apex_1frame_step2/mosaic-completeness.fits')
combinepath=bcdpath+'pbcd/Combine/apex_1frame_step2'
os.chdir(combinepath)
print combinepath
file='mosaic-completeness_extract_raw.tbl'
input=open(file,'r')
ngal=0
for line in input:
if line.find('Conversion') > -1:
t=line.split('=')
convfactor=float(t[1])#conversion from ADU to uJy
#aperr=aveaperr*convfactor #convert noise in ADU to uJy using conv factor from apex
print "Conversion Factor = ",convfactor
#print "aveaperr = ",aveaperr
#print "aperr = ",aperr
continue
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('\\') > -1: #skip lines with '#' in them
continue
if line.find('|') > -1: #skip lines with '#' in them
continue
ngal=ngal+1
input.close()
id24 = N.zeros(ngal,'f')
imagex24 = N.zeros(ngal,'f')
imagey24 = N.zeros(ngal,'f')
ra24 = N.zeros(ngal,'f')
dec24 = N.zeros(ngal,'f')
f24 = N.zeros(ngal,'d')#flux
errf24 = N.zeros(ngal,'d')
fap1 = N.zeros(ngal,'d')#flux in aperture 1 (1,1.5,2,2.6,3,3.5,4,4.5,5.,5.5) pixels
fap2 = N.zeros(ngal,'d')#flux
fap3 = N.zeros(ngal,'d')#flux
fap4 = N.zeros(ngal,'d')#flux in ap 4 - this is one w/ap cor of 1.67 (Calzetti et al 2007)
fap5 = N.zeros(ngal,'d')#flux
fap6 = N.zeros(ngal,'d')#flux
fap7 = N.zeros(ngal,'d')#flux
fap8 = N.zeros(ngal,'d')#flux
fap9 = N.zeros(ngal,'d')#flux
fap10 = N.zeros(ngal,'d')#flux
snr24 = N.zeros(ngal,'d')#SNR calculated by mopex
deblend = N.zeros(ngal,'f')#SNR calculated by mopex
input=open(file,'r')
i=0
output=open('xy24raw.dat','w')
for line in input:
if line.find('#') > -1: #skip lines with '#' in them
continue
if line.find('\\') > -1: #skip lines with '#' in them
continue
if line.find('|') > -1: #skip lines with '#' in them
continue
t=line.split()
#print "length of t = ",len(t)
#print (t[8]),(t[10]),(t[13]),(t[14]),(t[18]),(t[2]),(t[23]),(t[24]),(t[25]),(t[26]),(t[27]),(t[28]),(t[29]),(t[30]),(t[31]),(t[32])
(imagex24[i],imagey24[i],f24[i],errf24[i],snr24[i],deblend[i],fap1[i],fap2[i],fap3[i],fap4[i],fap5[i],fap6[i],fap7[i],fap8[i],fap9[i],fap10[i])=(float(t[8]),float(t[10]),float(t[13]),float(t[14]),float(t[18]),float(t[2]),float(t[25]),float(t[26]),float(t[27]),float(t[28]),float(t[29]),float(t[30]),float(t[31]),float(t[32]),float(t[33]),float(t[34]))
s='%6.2f %6.2f \n'%(imagex24[i],imagey24[i])
output.write(s)
i=i+1
input.close()#44 -> 43
output.close()
iraf.tvmark(1,'xy24raw.dat',color=204,radi=2)
iraf.tvmark(2,'xy24raw.dat',color=204,radi=2)
delta=1.#max number of pixels for a match
#get rid of objects that were detected in original image. Otherwise, matching will think any object near a sim galaxy is the sim galaxy. A faint galaxy placed on type of a pre-existing bright galaxy will be detected.
newgalflag=N.ones(len(imagex24),'i')
for i in range(len(imagex24)):
(imatch, matchflag,nmatch)=findnearest(imagex24[i],imagey24[i],xgal,ygal,delta)
if matchflag > 0.:
dflux=abs(fap4gal[imatch] - fap4[i])/fap4[i]
if dflux < .1:#position of real galaxy, flux difference less than 10% -> not a new galaxy
newgalflag[i] = 0
#keep only galaxies that are new
imagex24 = N.compress(newgalflag,imagex24)
imagey24 = N.compress(newgalflag,imagey24)
fap1 = N.compress(newgalflag,fap1)
fap2 = N.compress(newgalflag,fap2)
fap3 = N.compress(newgalflag,fap3)
fap4 = N.compress(newgalflag,fap4)
fap5 = N.compress(newgalflag,fap5)
fap6 = N.compress(newgalflag,fap6)
fap7 = N.compress(newgalflag,fap7)
fap8 = N.compress(newgalflag,fap8)
fap9 = N.compress(newgalflag,fap9)
fap10 =N.compress(newgalflag,fap10)
snr24 =N.compress(newgalflag,snr24)
deblend = N.compress(newgalflag,deblend)
delta=2.#max number of pixels for a match
matchflagsim=N.zeros(len(xsim),'i')
fmeas1=N.zeros(len(xsim),'f')
fmeas2=N.zeros(len(xsim),'f')
fmeas3=N.zeros(len(xsim),'f')
fmeas4=N.zeros(len(xsim),'f')
fmeas5=N.zeros(len(xsim),'f')
fmeas6=N.zeros(len(xsim),'f')
fmeas7=N.zeros(len(xsim),'f')
fmeas8=N.zeros(len(xsim),'f')
fmeas9=N.zeros(len(xsim),'f')
fmeas10=N.zeros(len(xsim),'f')
fmeas24=N.zeros(len(xsim),'f')
deblendsim=N.zeros(len(xsim),'f')
snrsim=N.zeros(len(xsim),'f')
for i in range(len(xsim)):
(imatch, matchflag,nmatch)=findnearest(xsim[i],ysim[i],imagex24,imagey24,delta)
matchflagsim[i]=matchflag
if matchflag > .1:
fmeas1[i]=fap1[int(imatch)]
fmeas2[i]=fap2[int(imatch)]
fmeas3[i]=fap3[int(imatch)]
fmeas4[i]=fap4[int(imatch)]
fmeas5[i]=fap5[int(imatch)]
fmeas6[i]=fap6[int(imatch)]
fmeas7[i]=fap7[int(imatch)]
fmeas8[i]=fap8[int(imatch)]
fmeas9[i]=fap9[int(imatch)]
fmeas10[i]=fap10[int(imatch)]
fmeas24[i]=f24[int(imatch)]
deblendsim[i]=deblend[int(imatch)]
snrsim[i]=snr24[int(imatch)]
fsimall=fsimall+list(fsim)
matchflagsimall=matchflagsimall+list(matchflagsim)
f2all=f2all+list(fmeas2)
f3all=f3all+list(fmeas3)
f4all=f4all+list(fmeas4)
deblendsimall=deblendsimall+list(deblendsim)
snrsimall=snrsimall+list(snrsim)
fsim=N.array(fsimall,'f')
matchflagsim=N.array(matchflagsimall,'f')
fmeas2=N.array(f2all,'f')
fmeas3=N.array(f3all,'f')
fmeas4=N.array(f4all,'f')
deblendsim=N.array(deblendsimall,'f')
snrsim=N.array(snrsimall,'f')
#make plots using all realizations
pylab.cla()
pylab.clf()
fsim=fsim*convfactor
fs=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fsim)
#f1=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas1)
f2=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas2)
f3=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas3)
f4=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas4)
#f242=pylab.compress((matchflagsim > 0.1) & (deblendsim < 1.5),fmeas24)
r4=pylab.median(fs/f4)
r3=pylab.median(fs/f3)
r2=pylab.median(fs/f2)
print "average ratios ap 4",pylab.average(fs/f4),r4,pylab.std((fs/f4)/pylab.average(fs/f2))
print "average ratios ap 3",pylab.average(fs/f3),pylab.median(fs/f3),pylab.std((fs/f3)/pylab.average(fs/f3))
print "average ratios ap 2",pylab.average(fs/f2),pylab.median(fs/f2),pylab.std((fs/f2)/pylab.average(fs/f2))
s='f4 w/apcor = %3.2f(%4.2f)'%(r4,pylab.average(abs(fs-f4*r4)/fs))
pylab.plot(fs,f4*r4,'b.',label=s)
pylab.plot(fs,f4,'bo',label='f4')
s='f3 w/apcor = %3.2f(%4.2f)'%(r3,pylab.average(abs(fs-f3*r3)/fs))
pylab.plot(fs,f3*r3,'g.',label=s)
pylab.plot(fs,f3,'go',label='f3')
s='f2 w/apcor = %3.2f(%4.2f)'%(r2,pylab.average(abs(fs-f2*r2)/fs))
pylab.plot(fs,f2*r2,'r.',label=s)
pylab.plot(fs,f2,'ro',label='f2')
#pylab.plot(fs,f1,'co',label='f1')
#pylab.plot(fs,f242,'k.',label='f24')
pylab.legend(loc='best')
x=N.arange(0.,max(fs),10.)
y=x
pylab.plot(x,y,'k-')
#y=2.*x
#pylab.plot(x,y,'k--')
#y=3.*x
#pylab.plot(x,y,'k--')
#y=4.*x
#pylab.plot(x,y,'k--')
#y=5.*x
#pylab.plot(x,y,'k--')
pylab.xlabel('F(24) Input')
pylab.ylabel('F(24) measured')
#pylab.axis([0.,50.,0.,50.])
s=str(prefix)+'fluxcomp.eps'
pylab.savefig(s)
pylab.cla()
pylab.clf()
nbins=20
fmin=10.#min(fsim)
fmax=max(fsim)
df=5.#(fmax-fmin)/(1.*nbins)
bins=N.arange(fmin,(fmax+df),df)
(xbin,ybin,ybinerr)=mystuff.completeness(bins,fsim,matchflagsim)
s=str(prefix)+'FracComplvsFlux.dat'
outdat=open(s,'w')
print "Completeness vs Input Flux"
for i in range(len(xbin)):
print i, xbin[i],ybin[i],ybinerr[i]
t='%8.2f %8.4f %8.4f\n'%(xbin[i],ybin[i],ybinerr[i])
outdat.write(t)
outdat.close()
#for i in range(len(fsim)):
#if snrsim[i] > 3.:
# print i, fsim[i],matchflagsim[i],deblendsim[i],abs(fsim[i]-fmeas4[i]*1.67)/fsim[i],snrsim[i]
#(xbin,ybin2,ybin2err)=mystuff.scipyhist2(bins,fmeas4)
#pylab.plot(xbin,ybin,'bo')
#pylab.plot(xbin,ybin2,'ro')
#s=str(prefix)+'NDetectvsFlux.eps'
#pylab.savefig(s)
pylab.cla()
pylab.clf()
pylab.plot(xbin,ybin,'ko')
pylab.errorbar(xbin,ybin,yerr=ybinerr,fmt=None,ecolor='k')
s=str(prefix)+'FracComplvsFlux.eps'
pylab.axhline(y=1.0,ls='-')
pylab.axhline(y=.8,ls='--')
pylab.axvline(x=80.0,ls=':',color='b')
pylab.xlabel('Input Flux (uJy)')
pylab.ylabel('Completeness')
pylab.axis([0.,max(xbin)+df,-.05,1.05])
pylab.savefig(s)
os.system('cp *.eps /Users/rfinn/clusters/spitzer/completeness/.')
os.system('cp *vsFlux.dat /Users/rfinn/clusters/spitzer/completeness/.')
f = string.strip(f)
if f[-4:] == '.dat':
if f[-8:] != 'FFOP.dat':
print f
X = pl.load(f)
x = X[:,2]
# limit orbital phase between 0.8 and 1.2
lt = x < 1.2
gt = x > 0.8
x = x[lt*gt]
if len(x) != 0:
a = X[:,0][lt*gt]
p = X[:,1][lt*gt] - pl.average(X[:,1][lt*gt])
sa = X[:,3][lt*gt]
sp = X[:,4][lt*gt]
# get outliers to fall in respectable range
lt = p < -1.0
p[lt] += 1.0
gt = p > 0.5
p[gt] -= 1.0
anew.append(a)
pnew.append(p)
#instanciation of pop2 neurons
pop2.append(pop2n.pop2n(CpES=CpES2, CpCS=CpCS, g_x2x2=g_x2x2, g_x1x2=g_x1x2, I2=I2, g_x2x2_slow=g_x2x2_slow, c2=c2, noise=noise2))
pop2[j].x2=x2_init[j]
pop2[j].y2=y2_init[j]
#connections between neurons
for i in range(nbn1):
pop1[i].connect_syn_pop2n(pop2[:])
pop1[i].connect_gap(pop1[:])
for j in range(nbn2):
pop2[j].connect_syn_pop1n(pop1[:])
pop2[j].connect_syn_pop2n(pop2[:])
pop2[j].connect_gap(pop2[:])
x1bar=pb.average(x1_init)
x2bar=pb.average(x2_init)
zbar=pb.average(z_init)
#get pop2 fixed point coordinates. (pop1 need to be computed at each timestep)
############################
# Integration loop
############################
count_samples=0
for ti in pb.arange(t_stop/dt):
for i in range(nbn1):
#pop1[i].x0=x0_variable[ti]
#pop1[i].CpES=CpES_variable[ti]
x1_nsamples[i,count_samples],y1_nsamples[i,count_samples],z_nsamples[i,count_samples]=pop1[i].euler(dt, 0, x1bar,x2bar,zbar, ti) # 4th parameter: x2bar
def ami_ensemble_stats(indata, nlat, nlon):
"""generates ensemble mean, max, and min for the ami data generated with get_amiuk.py
the stats code is incredibly simple iteration with missing value handling, because
it used to be fortran code, nothing clever or optimised."""
import numpy as np
from pylab import average
mean = np.zeros((149, nlat, nlon))
meanobs = np.zeros((61, nlat, nlon))
minm = np.zeros((149, nlat, nlon))
minobs = np.zeros((61, nlat, nlon))
maxm = np.zeros((149, nlat, nlon))
maxobs = np.zeros((61, nlat, nlon))
#mean map
for j in range(0, nlat):
for k in range(0, nlon):
for i in range(0, 149):
mcount = 0
for h in range(0, 10):
if indata[h, i, j, k] != -1.0:
mean[i, j, k] = mean[i, j, k] + indata[h, i, j, k]
mcount += 1
if mcount > 0:
mean[i, j, k] = mean[i, j, k] / mcount
else:
mean[i, j, k] = np.nan
ocount = 0
for i in range(0, 61):
if indata[11, i, j, k] != -1.0:
meanobs[i, j, k] = average(indata[11, i, j, k])
ocount += 1
if ocount > 0:
meanobs[i, j, k] = meanobs[i, j, k] / ocount
else:
meanobs[i, j, k] = np.nan
#minimal map
for j in range(0, nlat):
for k in range(0, nlon):
for i in range(0, 149):
minm[i, j, k] = min(indata[0:10, i, j, k])
mac = np.ma.count(indata[11, :, j, k])
for i in range(0, 61):
# check to see if any values left after mask, if none, use missing value
if mac < 2:
minobs[i, j, k] = np.nan
else:
minobs[i, j, k] = min(indata[11, :, j, k])
#maximal map
for j in range(0, nlat):
for k in range(0, nlon):
for i in range(0, 149):
maxm[i, j, k] = max(indata[0:10, i, j, k])
mac = np.ma.count(indata[11, :, j, k])
for i in range(0, 61):
# check to see if any values left after mask, if none, use missing value
if mac < 2:
maxobs[i, j, k] = np.nan
else:
maxobs[i, j, k] = max(indata[11, :, j, k])
return (mean, meanobs, maxm, maxobs, minm, minobs)
def Froudenumber(flmlname):
print "\n********** Calculating the Froude number\n"
# warn user about assumptions
print "Froude number calculations makes three assumptions: \n i) domain height = 0.1m \n ii) mid point domain is at x = 0.4 \n iii) initial temperature difference is 1.0 degC"
domainheight = 0.1
domainmid = 0.4
rho_zero, T_zero, alpha, g = le_tools.Getconstantsfromflml(flmlname)
gprime = rho_zero*alpha*g*1.0 # this has assumed the initial temperature difference is 1.0 degC
# get list of vtus
filelist = le_tools.GetFiles('./')
logs = ['diagnostics/logs/time.log','diagnostics/logs/X_ns.log','diagnostics/logs/X_fs.log']
try:
# if have extracted information already just use that
os.stat('diagnostics/logs/time.log')
os.stat('diagnostics/logs/X_ns.log')
os.stat('diagnostics/logs/X_fs.log')
time = le_tools.ReadLog('diagnostics/logs/time.log')
X_ns = [x-domainmid for x in le_tools.ReadLog('diagnostics/logs/X_ns.log')]
X_fs = [domainmid-x for x in le_tools.ReadLog('diagnostics/logs/X_fs.log')]
except OSError:
# otherwise get X_ns and X_fs and t from vtus
time, X_ns, X_fs = le_tools.GetXandt(filelist)
f_time = open('./diagnostics/logs/time.log','w')
for t in time: f_time.write(str(t)+'\n')
f_time.close()
f_X_ns = open('./diagnostics/logs/X_ns.log','w')
for X in X_ns: f_X_ns.write(str(X)+'\n')
f_X_ns.close()
f_X_fs = open('./diagnostics/logs/X_fs.log','w')
for X in X_fs: f_X_fs.write(str(X)+'\n')
f_X_fs.close()
# shift so bot X_ns and X_fs are
# distance of front from
#initial position (mid point of domain)
X_ns = [x-domainmid for x in X_ns]
X_fs = [domainmid-x for x in X_fs]
# Calculate U_ns and U_fs from X_ns, X_fs and t
U_ns = le_tools.GetU(time, X_ns)
U_fs = le_tools.GetU(time, X_fs)
U_average = [[],[]]
# If possible average
# (if fronts have not travelled far enough then will not average)
start_val, end_val, average_flag_ns = le_tools.GetAverageRange(X_ns, 0.2, domainheight)
if average_flag_ns == True: U_average[0].append(pylab.average(U_ns[start_val:end_val]))
start_val, end_val, average_flag_fs = le_tools.GetAverageRange(X_fs, 0.25, domainheight)
if average_flag_fs == True: U_average[1].append(pylab.average(U_fs[start_val:end_val]))
# plot
fs = 18
pylab.figure(num=1, figsize = (16.5, 11.5))
pylab.suptitle('Front speed', fontsize = fs)
pylab.subplot(221)
pylab.plot(time,X_ns, color = 'k')
pylab.axis([0,45,0,0.4])
pylab.grid('on')
pylab.xlabel('$t$ (s)', fontsize = fs)
pylab.ylabel('$X$ (m)', fontsize = fs)
pylab.title('no-slip', fontsize = fs)
pylab.subplot(222)
pylab.plot([x/domainheight for x in X_ns],[U/math.sqrt(gprime*domainheight) for U in U_ns], color = 'k')
pylab.axis([0,4,0,0.6])
pylab.grid('on')
pylab.axhline(0.406, color = 'k')
pylab.axhline(0.432, color = 'k')
pylab.text(3.95,0.396,'Hartel 2000',bbox=dict(facecolor='white', edgecolor='black'), va = 'top', ha = 'right')
pylab.text(3.95,0.442,'Simpson 1979',bbox=dict(facecolor='white', edgecolor='black'), ha = 'right')
pylab.xlabel('$X/H$', fontsize = fs)
pylab.ylabel('$Fr$', fontsize = fs)
pylab.title('no-slip', fontsize = fs)
if average_flag_ns == True:
pylab.axvline(2.0, color = 'k')
pylab.axvline(3.0, color = 'k')
pylab.text(0.05, 0.01, 'Average Fr = '+'{0:.2f}'.format(U_average[0][0]/math.sqrt(gprime*domainheight))+'\nvertical lines indicate the range \nover which the average is taken', bbox=dict(facecolor='white', edgecolor='black'))
pylab.subplot(223)
pylab.plot(time,X_fs, color = 'k')
pylab.axis([0,45,0,0.4])
pylab.grid('on')
pylab.xlabel('$t$ (s)', fontsize = fs)
pylab.ylabel('$X$ (m)', fontsize = fs)
pylab.title('free-slip', fontsize = fs)
pylab.subplot(224)
pylab.plot([x/domainheight for x in X_fs],[U/math.sqrt(gprime*domainheight) for U in U_fs], color = 'k')
pylab.axis([0,4,0,0.6])
pylab.grid('on')
pylab.axhline(0.477, color = 'k')
pylab.text(3.95,0.467,'Hartel 2000', va = 'top',bbox=dict(facecolor='white', edgecolor='black'), ha = 'right')
pylab.xlabel('$X/H$', fontsize = fs)
pylab.ylabel('$Fr$', fontsize = fs)
pylab.title('free-slip', fontsize = fs)
if average_flag_fs == True:
pylab.text(0.05, 0.01, 'Average Fr = '+'{0:.2f}'.format(U_average[1][0]/math.sqrt(gprime*domainheight))+'\nvertical lines indicate the range \nover which the average is taken', bbox=dict(facecolor='white', edgecolor='black'))
pylab.axvline(2.5, color = 'k')
pylab.axvline(3.0, color = 'k')
pylab.savefig('diagnostics/plots/front_speed.png')
return
summary(net)
M=net.ecount()
N = net.vcount()
Mmax = N*(N-1)/2
Mmax
M/Mmax
p = 1.*M/Mmax
net.diameter()
net.components()
cc=net.components() # (összefüggő) komponensek, (connected) components
ccs = cc.sizes()
max(ccs)
average(ccs)
css # Így kifut.
array(ccs) # Így jobban néz ki, csak pylabbal vagy numpy-vel
len(ccs) # ipython parancssorban a zárójel elhagyható
sizedist = [ccs.count(i) for i in range(max(ccs)+1)]
array(sizedist)
plot(sizedist, "o")
grid()
"A komponensek méreteloszlása".decode("utf-8") # unicode sztring lesz.
title("A komponensek méretei".decode("utf-8"))
xlabel("")
title("A komponensek méretgyakorisága (Erdős-Rényi 1000,0.001)".decode("utf-8"))
xlabel("méret (csúcsok száma)".decode("utf-8"))
ylabel("darabszám".decode("utf-8"))
savefig("meretgyakorisag_ER1000_001.pdf")
def xforce_stat(sf):
stat = fluidity_tools.stat_parser(sf)
time_avg_xforce = pylab.average(stat["0"]["Velocity"]["force%1"][-20:])
return time_avg_xforce
def main():
args = parseCMD()
# Check if our data file exists, if not: write one.
# Otherwise, open the file and plot.
check = glob.glob('*JackKnifeData_Cv.dat*')
if check == []:
fileNames = args.fileNames
skip = args.skip
temps,Cvs,CvsErr = pl.array([]),pl.array([]),pl.array([])
Es, EsErr = pl.array([]),pl.array([])
ET, ETerr = pl.array([]),pl.array([])
# open new data file, write headers
fout = open('JackKnifeData_Cv.dat', 'w')
fout.write('#%15s\t%16s\t%16s\t%16s\t%16s\t%16s\t%16s\n'% (
'T', 'Ecv', 'EcvErr', 'Et', 'EtErr','Cv', 'CvErr'))
# perform jackknife analysis of data, writing to disk
for fileName in fileNames:
temp = float(fileName[-40:-34])
temps = pl.append(temps, temp)
# grab and analyze energy data
Ecv,Eth = pl.loadtxt(fileName, unpack=True, usecols=(4,-5))
E = pl.average(Ecv)
Et = pl.average(Eth)
EErr = pl.std(Ecv)/pl.sqrt(float(len(Ecv)))
EtErr = pl.std(Eth)/pl.sqrt(float(len(Eth)))
Es = pl.append(Es,E)
ET = pl.append(ET, Et)
EsErr = pl.append(EsErr, EErr)
ETerr = pl.append(ETerr, EtErr)
# grab and analyze specific heat data
EEcv, Ecv, dEdB = pl.loadtxt(fileName, unpack=True, usecols=(11,12,13))
jkAve, jkErr = jk.jackknife(EEcv[skip:],Ecv[skip:],dEdB[skip:])
Cvs = pl.append(Cvs,jkAve)
CvsErr = pl.append(CvsErr,jkErr)
fout.write('%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\t%16.8E\n' %(
temp, E, EErr, Et, EtErr, jkAve, jkErr))
print 'T = ',str(temp),' complete.'
fout.close()
else:
print 'Found existing data file in CWD.'
temps,Es,EsErr,ET,ETerr,Cvs,CvsErr = pl.loadtxt(
'JackKnifeData_Cv.dat', unpack=True)
# plot specific heat for QHO
tempRange = pl.arange(0.01,1.0,0.01)
Eanalytic = 0.5/pl.tanh(1.0/(2.0*tempRange))
CvAnalytic = 1.0/(4.0*(tempRange*pl.sinh(1.0/(2.0*tempRange)))**2)
pl.figure(1)
ax1 = pl.subplot(211)
pl.plot(tempRange,CvAnalytic, label='Exact')
pl.errorbar(temps,Cvs,CvsErr, label='PIMC',color='Violet',fmt='o')
pl.ylabel('Specific Heat',fontsize=20)
pl.title('1D QHO -- 1 boson',fontsize=20)
pl.legend(loc=2)
pl.setp(ax1.get_xticklabels(), visible=False)
ax2 = pl.subplot(212, sharex=ax1)
pl.plot(tempRange,Eanalytic, label='Exact')
pl.errorbar(temps,Es,EsErr, label='PIMC virial',color='Lime',fmt='o')
pl.errorbar(temps,ET,ETerr, label='PIMC therm.',color='k',fmt='o')
#pl.scatter(temps,Es, label='PIMC')
pl.xlabel('Temperature [K]',fontsize=20)
pl.ylabel('Energy [K]',fontsize=20)
pl.legend(loc=2)
pl.savefig('1Dqho_largerCOM_800000bins_CvANDenergy.pdf',
format='pdf', bbox_inches='tight')
pl.show()
def plot_sigTau_towed(self,th1_cs,skid,figNum=11):
th0 = self._th0
th2 = self._th2
r = self._radius
b = self._width
k1 = self._k1
k2 = self._k2
n = self._n
phi = self._phi
K = self._K
c = self._coh
incr = (th1_cs - th2) / 100.0 # plot increment
th_arr = py.arange(0,th1_cs + incr, incr) # find sigma, tau at these discrete vals
sig_arr = py.zeros(len(th_arr))
tau_arr = py.zeros(len(th_arr))
slip_arr = py.zeros(len(th_arr))
for idx in range(0,len(th_arr)):
th = th_arr[idx]
if(th < th0):
# we're in the bototm region
sig_curr = sig_2(th,th0,th1_cs,th2,r,b,n,k1,k2)
tau_curr = -tau_t2(th,th0,th1_cs,th2,r,b,n,k1,k2,c,K,phi,skid)
slip_j = j2(th,th0,r,skid)
sig_arr[idx] = sig_curr
tau_arr[idx] = tau_curr
slip_arr[idx] = slip_j
else:
# we're in the top region ()
sig_curr = sig_1(th, th1_cs,r,b,n,k1,k2)
tau_curr = tau_t1(th,th0,th1_cs,r,b,n,k1,k2,c,K,phi,skid)
slip_j = j1(th,th0,th1_cs,r,skid)
sig_arr[idx] = sig_curr
tau_arr[idx] = tau_curr
slip_arr[idx] = slip_j
if( self._plots):
fig = plt.figure()
ax = fig.add_subplot(211,title='Fig. ' + str(figNum) +' skid=' + str(skid))
ax.plot(radToDeg(th_arr),sig_arr,radToDeg(th_arr),tau_arr,linewidth=1.5)
ax.set_xlabel('theta [deg]')
if( self._units == 'ips'):
ax.set_ylabel('stress [psi]')
else:
ax.set_ylabel('stress [Pa]')
ax.legend((r'$\sigma$($\theta$)',r'$\tau$($\theta$)'))
ax.grid(True)
# take a look at what I"m using for slip displacement also
ax = fig.add_subplot(212)
ax.plot(radToDeg(th_arr),slip_arr,linewidth=1.5)
ax.set_xlabel('theta [deg]')
if( self._units == 'ips'):
ax.set_ylabel('slip disp.[in]')
else:
ax.set_ylabel('slip disp.[m]')
ax.grid(True)
# polar plots
fig=plt.figure()
ax=fig.add_subplot(111,projection='polar')
ax.plot(th_arr,sig_arr/1000.,'b',linewidth=1.5)
ax.plot(th_arr,tau_arr/1000.,'r--',linewidth=1.5)
# fix the axes
ax.grid(True)
if( self._units == 'ips'):
leg = ax.legend((r'$\sigma$ [kip]',r'$\tau$'))
else:
leg = ax.legend((r'$\sigma$ [kPa]',r'$\tau$'))
leg.draggable()
ax.set_theta_zero_location('S')
# also, draw the tire
polar_r_offset = py.average(sig_arr)/1000.
theta = py.arange(0.,2.*py.pi+0.05,0.05)
tire_pos = py.zeros(len(theta))
ax.plot(theta,tire_pos,color='k',linewidth=1.0)
ax.set_rmin(-polar_r_offset)
ax.set_title(r'towed wheel stresses, $\theta_1$ = %4.3f [rad]' %th1_cs)
ax.set_thetagrids([-10,0,10,20,30,40,50,60])
return [sig_arr, tau_arr]
# write average subtracted spectrum to new fits file
#pf.writeto('avesub%s'%i,data=data,header=head)
start = head['CRVAL1']
step = head['CDELT1']
length = head['NAXIS1']
x = start + pl.arange(0,length)*step
# hydrogen alpha
dl = v/c*6563.0
w1 = x > 6563 - dl
w2 = x < 6563 + dl
imHa.append(data[w1*w2]-pl.average(data[w1*w2]))
#imHa.append((data[w1*w2]))
#imHa.append((data[w1*w2]-pl.average(data[w1*w2])-(ave[w1*w2]-pl.average(ave[w1*w2]))))
dl = v/c*4860.0
w1 = x > 4860 - dl
w2 = x < 4860 + dl
#data = pf.getdata(i)
imHb.append(data[w1*w2]-pl.average(data[w1*w2]))
#imHb.append((data[w1*w2]-pl.average(data[w1*w2])-(ave[w1*w2]-pl.average(ave[w1*w2]))))
#imHb.append(data[w1*w2])
dl = v/c*4686
w1 = x > 4686 - dl
w2 = x < 4686 + dl
#data = pf.getdata(i)
def main():
args = parseCMD()
# Check if our data file exists, if not: write one.
# Otherwise, open the file and plot.
check = glob.glob('*JackKnifeData_Cv.dat*')
if check == []:
fileNames = args.fileNames
skip = args.skip
temps, Cvs, CvsErr = pl.array([]), pl.array([]), pl.array([])
timeSteps = pl.array([])
# open new data file, write headers
fout = open('JackKnifeData_Cv.dat', 'w')
fout.write('#%09s\t%10s\t%10s\t%10s\n' % ('T', 'tau', 'Cv', 'CvErr'))
# perform jackknife analysis of data, writing to disk
for fileName in fileNames:
tau = float(fileName[-21:-14])
temp = float(fileName[-40:-34])
timeSteps = pl.append(timeSteps, tau)
temps = pl.append(temps, temp)
EEcv, Ecv, dEdB = pl.loadtxt(fileName,
unpack=True,
usecols=(11, 12, 13))
jkAve, jkErr = aTools.jackknife(EEcv[skip:], Ecv[skip:],
dEdB[skip:])
print '<est> = ', jkAve, ' +/- ', jkErr
Cvs = pl.append(Cvs, jkAve)
CvsErr = pl.append(CvsErr, jkErr)
fout.write('%10s\t%10s\t%10s\t%10s\n' % (temp, tau, jkAve, jkErr))
fout.close()
else:
print 'Found existing data file in CWD.'
temps, timeSteps, Cvs, CvsErr = pl.loadtxt('JackKnifeData_Cv.dat',
unpack=True)
# make array of energies
Es, EsErr = pl.array([]), pl.array([])
ET, ETerr = pl.array([]), pl.array([])
for fileName in args.fileNames:
Ecv, Eth = pl.loadtxt(fileName, unpack=True, usecols=(4, -5))
Es = pl.append(Es, pl.average(Ecv))
ET = pl.append(ET, pl.average(Eth))
EsErr = pl.append(EsErr, pl.std(Ecv) / pl.sqrt(float(len(Ecv))))
ETerr = pl.append(ETerr, pl.std(Eth) / pl.sqrt(float(len(Eth))))
# plot specific heat for QHO
tempRange = pl.arange(0.01, 1.0, 0.01)
Eanalytic = 0.5 / pl.tanh(1.0 / (2.0 * tempRange))
CvAnalytic = 1.0 / (4.0 * (tempRange * pl.sinh(1.0 /
(2.0 * tempRange)))**2)
if args.timeStepScaling:
pl.figure(1)
pl.plot(timeSteps, 0.5 / pl.tanh(1.0 / (2.0 * (temps))), label='Exact')
pl.errorbar(timeSteps,
Es,
EsErr,
label='PIMC virial',
color='Lime',
fmt='o')
pl.errorbar(timeSteps,
ET,
ETerr,
label='PIMC therm.',
color='Navy',
fmt='o')
pl.xlabel('Time Step [1/K]')
pl.ylabel('Energy [K]')
pl.title('1D QHO -- 1 boson -- T=%s' % temps[0])
pl.legend(loc=1)
pl.figure(2)
pl.plot(timeSteps,
1.0 / (4.0 * (temps * pl.sinh(1.0 / (2.0 * temps)))**2),
label='Exact')
pl.errorbar(timeSteps,
Cvs,
CvsErr,
label='PIMC virial',
color='Navy',
fmt='o')
pl.xlabel('Time Step [1/K]')
pl.ylabel('Specific Heat [K]')
pl.title('1D QHO -- 1 boson -- T=%s' % temps[0])
pl.legend(loc=1)
else:
pl.figure(1)
pl.plot(tempRange, CvAnalytic, label='Exact')
pl.errorbar(temps, Cvs, CvsErr, label='PIMC', color='Violet', fmt='o')
pl.xlabel('Temperature [K]')
pl.ylabel('Specific Heat')
pl.title('1D QHO -- 1 boson')
pl.legend(loc=2)
pl.figure(2)
pl.plot(tempRange, Eanalytic, label='Exact')
pl.errorbar(temps,
Es,
EsErr,
label='PIMC virial',
color='Lime',
fmt='o')
pl.errorbar(temps, ET, ETerr, label='PIMC therm.', color='k', fmt='o')
#pl.scatter(temps,Es, label='PIMC')
pl.xlabel('Temperature [K]')
pl.ylabel('Energy [K]')
pl.title('1D QHO -- 1 boson')
pl.legend(loc=2)
pl.show()
def edge_length_stats():
df = pd.read_csv('neuromorpho_lengths.csv', names=['name', 'points', 'mean_edge_length'])
print pylab.average(df['mean_edge_length'], weights=df['points'])