def main():
shifts = [[-1, 1], [0, 1], [1, 1], [-1, 0], [1, 0], [-1, -1], [0, -1],
[1, -1]]
num_atoms = 100
num_dims = 2 # dimensions
coords = pl.random((num_atoms, num_dims))
chosen = pl.random_integers(num_atoms) # from 1 to num_atoms
chosen -= 1 # from 0 to num_atoms - 1
for i in range(len(shifts)):
coords = pl.vstack((coords, coords[:num_atoms] + shifts[i]))
num_atoms *= 9 # after 8 shifts added
max_distance = 0.9
for i in range(num_atoms):
if i != chosen:
dx = coords[chosen, 0] - coords[i, 0]
dy = coords[chosen, 1] - coords[i, 1]
distance = pl.sqrt(dx * dx + dy * dy)
if distance < max_distance:
pl.plot([coords[i, 0]], [coords[i, 1]], "bo")
else:
pl.plot([coords[i, 0]], [coords[i, 1]], "ko")
# plot last for visibility
pl.plot([coords[chosen, 0]], [coords[chosen, 1]], "ro")
pl.grid(True)
pl.show()
def main(): shifts = [ [-1, 1], [0, 1], [1, 1], [-1, 0], [1, 0], [-1, -1], [0, -1], [1, -1] ] num_atoms = 100 num_dims = 2 # dimensions coords = pl.random((num_atoms, num_dims)) chosen = pl.random_integers(num_atoms) # from 1 to num_atoms chosen -= 1 # from 0 to num_atoms - 1 for i in range(len(shifts)): coords = pl.vstack((coords, coords[:num_atoms] + shifts[i])) num_atoms *= 9 # after 8 shifts added max_distance = 0.9 for i in range(num_atoms): if i != chosen: dx = coords[chosen, 0] - coords[i, 0] dy = coords[chosen, 1] - coords[i, 1] distance = pl.sqrt(dx*dx + dy*dy) if distance < max_distance: pl.plot([coords[i, 0]], [coords[i, 1]], "bo") else: pl.plot([coords[i, 0]], [coords[i, 1]], "ko") # plot last for visibility pl.plot([coords[chosen, 0]], [coords[chosen, 1]], "ro") pl.grid(True) pl.show()
def main(): num_atoms = 64 num_dims = 2 # dimensions coords = pl.random((num_atoms, num_dims)) axis_limits = [0.0, 1.0] points = plot_atoms(coords, num_atoms, axis_limits) update_limit = 16 update_count = 0 while True: chosen = pl.random_integers(num_atoms) - 1 # [0, num_atoms - 1] new_x, new_y = new_xy(coords, chosen, axis_limits) energy_old, energy_new = energies(coords, chosen, num_atoms, new_x, new_y) if energy_new < energy_old: coords[chosen, 0] = new_x coords[chosen, 1] = new_y points[chosen].set_data([new_x, new_y]) update_count += 1 if not update_count < update_limit: pl.draw() update_count = 0 """
def main():
num_atoms = 64
num_dims = 2 # dimensions
coords = pl.random((num_atoms, num_dims))
axis_limits = [0.0, 1.0]
points = plot_atoms(coords, num_atoms, axis_limits)
update_limit = 16
update_count = 0
while True:
chosen = pl.random_integers(num_atoms) - 1 # [0, num_atoms - 1]
new_x, new_y = new_xy(coords, chosen, axis_limits)
energy_old, energy_new = energies(coords, chosen, num_atoms, new_x,
new_y)
if energy_new < energy_old:
coords[chosen, 0] = new_x
coords[chosen, 1] = new_y
points[chosen].set_data([new_x, new_y])
update_count += 1
if not update_count < update_limit:
pl.draw()
update_count = 0
"""
def make_scatter():
f = open('accel_glitches.pkl')
times = pickle.load(f)
f.close()
dt = timedelta(minutes=10)
fig = pl.figure()
which_times = pl.random_integers(0, len(times) - 1, 5)
for i in which_times:
t = times[i]
start = sptz.localize(t - dt)
end = sptz.localize(t + dt)
try:
fig = acc_corr_plots(start, end, fig)
except RuntimeError, err:
print 'Fail! ' + str(err)
def fasta_random_sample(a):
if a.num:
num = a.num
else:
num = '100'
if a.ofile:
out = a.ofile
else:
out = a.ifile.split('.')[0] + '_random' + num + '.fa'
fa = list(parse(a.ifile, 'fasta'))
rand = random_integers(0, len(fa), int(num))
n = 0
with open(out, 'w') as f:
for rec in fa:
if n in rand:
f.write('>' + rec.id + '\n')
f.write(str(rec.seq) + '\n')
n += 1
n = double(data[:, 2])
# Only keep first M papers to record >=N neurons
M = 10
idx = []
for i in range(0, len(t)):
if sum(t[i] > t[n >= n[i]]) < M:
idx.append(i)
idx = asarray(idx)
# Exponential fit with bootstrap standard error (could use statsmodels here)
(ar, br) = polyfit(t[idx], log(n[idx]), 1)
arb = zeros(1000)
brb = zeros(1000)
for i in range(0, 1000):
sidx = idx[random_integers(0, len(idx) - 1, len(idx))]
(arb[i], brb[i]) = polyfit(t[sidx], log(n[sidx]), 1)
t0 = linspace(min(t) - 1, max(t) + 1, 100)
nhat = polyval([ar, br], t0)
x = t[idx]
y = log(n[idx])
yhat = br + x * ar
ci = (
stats.t.isf(0.05 / 2, len(x) - 2)
* sqrt(sum(pow(y - yhat, 2)) / (len(y) - 2))
* sqrt(1 / len(y) + pow(t0 - mean(x), 2) / sum(pow(x - mean(x), 2)))
)
sns.set_style("ticks")
sns.set_palette(sns.color_palette("deep"))
