def h_field(a, b, m, n, omega, lambda_mn, x_min, x_max, dx, y_min, y_max, dy,
z_min, z_max, dz, t_min, t_max, t_step):
for t in drange(t_min, t_max, t_step):
for z in drange(z_min, z_max, dz):
for y in drange(y_min, y_max, dy):
for x in drange(x_min, x_max, dx):
vector_dx = hx(a, b, m, n, omega, lambda_mn, x, y, z, t)
vector_dy = hy(a, b, m, n, omega, lambda_mn, x, y, z, t)
vector_dz = hz(a, b, m, n, omega, lambda_mn, x, y, z, t)
yield gplot_vector3(x, y, z, vector_dx, vector_dy,
vector_dz)
# Returning None to specify the end of one time frame
yield None
import math as m
from mk_galaxy_struc import mk_galaxy_struc
from drange import drange
sample = []
# get galaxy info
galaxies = mk_galaxy_struc()
for i in range(len(galaxies)):
if galaxies[i].ston_I > 30.0 and galaxies[i].ICD_IH <= 0.25:
sample.append([galaxies[i].ICD_IH, galaxies[i].Mass])
sample = np.asarray(sample)
# make the bins
bins = []
for i in drange(7.0, 12.5, 0.1):
bins.append(10.0 ** i)
bins = np.asarray(bins)
# digitize the sample
inds = np.digitize(sample[:, 1], bins)
# put the sample in the postboxes
sorted = []
for i in range(bins.size):
sorted.append([])
for i in range(inds.size):
j = int(inds[i])
sorted[j].append(i)
# match the sample to the icd's
def plot_histogram(xs, filename, title, xlabel, lower, upper, normed=False):
old_lower, old_upper = int(lower * 1000), int(upper * 1000)
# lower/upper is in percentage.
# in order to see what's appropriate, we need to figure out the scale.
# like this?
# http://stackoverflow.com/questions/15177203/how-is-the-pyplot-histogram-bins-interpreted
matching_xs = [x for x in xs if x >= lower and x <= upper]
if len(matching_xs) == 0:
print "Nothing matching for", lower, upper
return
"""
if lower == 75:
i = 0
for x in matching_xs:
print x,
if i % 80 == 0:
print
i+=1
#matching_xs = [99]*20 + [90]*10 + [80]*5
pass
"""
# at least MIN_RANGE steps.
step = upper - lower
if step < MIN_RANGE:
step = float(step) / MIN_RANGE
else:
step = 1.0
bins = [i for i in drange(lower, upper + 0.000001, step)]
#print "bins from lower to upper:", bins
#n, bins, patches = plt.hist(matching_xs, bins=[0, 70,80,90,100], histtype='stepfilled')
#n, bins, patches = plt.hist(xs, bins=100, range=(lower, upper), histtype='stepfilled')
#n, bins, patches = plt.hist(xs, bins=100, histtype='stepfilled')
#n, bins, patches = plt.hist(matching_xs, bins=100, histtype='stepfilled')
n, bins, patches = plt.hist(matching_xs,
bins=bins,
normed=normed,
histtype='stepfilled')
#n, bins, patches = plt.hist(matching_xs, bins=bins, histtype='stepfilled')
#print "n =", n
print "lower, upper =", lower, upper
#print "bins =", bins
#print "patches =", patches
plt.xlabel(xlabel)
plt.ylabel("Handle count / % of max")
plt.title("Histogram of memory area lifetime (%d - %d): %s" %
(old_lower, old_upper, title))
#plt.axis([100, 0, 0, len(bins)*0.75])
plt.axis('tight')
plt.grid(True)
ax = plt.gca()
ax.xaxis.set_major_formatter(
mpl.ticker.FuncFormatter(formatter_x_to_percent))
ymin, ymax = ax.get_ylim()
ax.yaxis.set_major_formatter(
mpl.ticker.FuncFormatter(
lambda y, pos: formatter_max_to_percent(y, pos, ymax)))
#plt.show()
fname = "%s-histogram-%d-%d.pdf" % (filename, old_lower, old_upper)
plt.savefig(fname)
print "Saved histogram:", fname
plt.clf()
plt.cla()
plt.close()
def plot_histogram(xs, filename, title, xlabel, lower, upper, normed=False):
old_lower, old_upper = int(lower*1000), int(upper*1000)
# lower/upper is in percentage.
# in order to see what's appropriate, we need to figure out the scale.
# like this?
# http://stackoverflow.com/questions/15177203/how-is-the-pyplot-histogram-bins-interpreted
matching_xs = [x for x in xs if x >= lower and x <= upper]
if len(matching_xs) == 0:
print "Nothing matching for", lower, upper
return
"""
if lower == 75:
i = 0
for x in matching_xs:
print x,
if i % 80 == 0:
print
i+=1
#matching_xs = [99]*20 + [90]*10 + [80]*5
pass
"""
# at least MIN_RANGE steps.
step = upper-lower
if step < MIN_RANGE:
step = float(step) / MIN_RANGE
else:
step = 1.0
bins = [i for i in drange(lower, upper+0.000001, step)]
#print "bins from lower to upper:", bins
#n, bins, patches = plt.hist(matching_xs, bins=[0, 70,80,90,100], histtype='stepfilled')
#n, bins, patches = plt.hist(xs, bins=100, range=(lower, upper), histtype='stepfilled')
#n, bins, patches = plt.hist(xs, bins=100, histtype='stepfilled')
#n, bins, patches = plt.hist(matching_xs, bins=100, histtype='stepfilled')
n, bins, patches = plt.hist(matching_xs, bins=bins, normed=normed, histtype='stepfilled')
#n, bins, patches = plt.hist(matching_xs, bins=bins, histtype='stepfilled')
#print "n =", n
print "lower, upper =", lower, upper
#print "bins =", bins
#print "patches =", patches
plt.xlabel(xlabel)
plt.ylabel("Handle count / % of max")
plt.title("Histogram of memory area lifetime (%d - %d): %s" % (old_lower, old_upper, title))
#plt.axis([100, 0, 0, len(bins)*0.75])
plt.axis('tight')
plt.grid(True)
ax = plt.gca()
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(formatter_x_to_percent))
ymin, ymax = ax.get_ylim()
ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda y, pos: formatter_max_to_percent(y, pos, ymax)))
#plt.show()
fname = "%s-histogram-%d-%d.pdf" % (filename, old_lower, old_upper)
plt.savefig(fname)
print "Saved histogram:", fname
plt.clf()
plt.cla()
plt.close()
import math as m
from mk_galaxy_struc import mk_galaxy_struc
from drange import drange
sample = []
# get galaxy info
galaxies = mk_galaxy_struc()
for i in range(len(galaxies)):
if galaxies[i].ston_I > 30.0 and galaxies[i].ICD_IH <= 0.25:
sample.append([galaxies[i].ICD_IH,galaxies[i].Mass])
sample = np.asarray(sample)
# make the bins
bins = []
for i in drange(7.0,12.5,0.1):
bins.append(10.0**i)
bins = np.asarray(bins)
# digitize the sample
inds = np.digitize(sample[:,1],bins)
# put the sample in the postboxes
sorted =[]
for i in range(bins.size):
sorted.append([])
for i in range(inds.size):
j = int(inds[i])
sorted[j].append(i)
# match the sample to the icd's
