def test_data(self):
types = ["Python", "Ruby", "Lua", "CSS", "Java", "JavaScript"]
for i in range(0, 100, 5):
title = str(i) + ".Pastebin_for_test_code"
type = random.choice(types)
time = fmt_time()
sql = (
'INSERT INTO posts(title, poster, type, content, time, password) \
values("%s", "test", "%s", "#!/usr/bin/env python","%s","1")'
% (title, type, time)
)
self.cur.execute(sql)
self.conn.commit()
def post(self,id):
if self.check_login(id):
id = int(id)
url = '/detail/' + str(id)
title = xhtml_escape(self.get_argument('title', ''))
poster = xhtml_escape(self.get_argument('poster', ''))
password = xhtml_escape(self.get_argument('password', ''))
type = xhtml_escape(self.get_argument('syntax', 'other'))
content = xhtml_escape(self.get_argument('content', ''))
time = fmt_time()
Post.modify(id, title, poster, type, content, time, password)
self.redirect(url)
else:
self.redirect('/detail/' + str(id))
def post(self):
title = xhtml_escape(self.get_argument('title', ''))
poster = xhtml_escape(self.get_argument('poster', ''))
password = xhtml_escape(self.get_argument('password', ''))
type = xhtml_escape(self.get_argument('syntax', 'other'))
content = xhtml_escape(self.get_argument('content', ''))
time = fmt_time()
Post.add(title, poster, type, content, time, password)
self.redirect('/')
def today():
return fmt_time('%Y-%m-%d')
def fmt(seconds, f='%Y-%m-%d %H:%M:%S'):
return fmt_time(f, seconds)
def main(profile=False, sds_fn=None):
if not PROFILE:
end = input("Begin simulation? ('n' to break) ") == 'n'
if end: sys.exit()
print("\nBEGINNING MAIN ROUTINE")
if sds_fn is None:
# Generate list of Superdroplets
sds = [Superdroplet(xi_i, r**3., 0.) for r in r_grid]
else:
# Load from sds_fn
with open(sds_fn, "rb") as f:
sds = pickle.load(f)
sds = to_sd_array(sds)
wm0 = np.sum([s.multi * s.mass for s in sds]) / 1e3
sdss = [
sort_sds(sds),
]
print("Initial water mass = ", wm0)
results = []
c_step = partial(cython_step, t_c=t_c, delta_V=delta_V, kern=kernel)
t, ti = 0., 0
n_drops = len(sds)
n_init = n_drops * 1
wms = [
np.sum([s.multi * s.mass for s in sds]) / 1e3,
]
xi_s = [
np.sum([s.multi for s in sds]),
]
color_map = OrderedDict(initial='k')
while t < t_end:
ti += 1
t = ti * t_c # diagnose time from step to avoid accumulating
# floating point errors which screw things up
minutes, seconds = s_to_min_s(t)
if profile and ti > 10:
break
print("STEP %d (%s)" % (ti, fmt_time(minutes, seconds)))
print(ti, out_dt, ti % out_dt, is_output_step(ti))
print(minutes, seconds)
sds = c_step(sds)
# sds = recycle(sds)
sds = to_sd_array(sds)
print(len(sds))
if len(sds) < n_drops:
print(" Lost %d superdrops" % (n_drops - len(sds), ))
n_drops = len(sds)
else:
print(" No change in superdrop number")
if is_output_step(ti):
if DIAG_PLOTS:
minutes_str = fmt_time(minutes, seconds, True)
xx, yy = kde_plot(sds)
plt.figure(1)
lines = plt.plot(xx, yy, lw=2.5, label=minutes_str)
l = lines[0]
results.append(yy)
r_centers, gyt = \
bin_mass_density(sds, True, n_bins=41,
log_r_min=-7, log_r_max=np.log(5e-2))
r_centers *= 1e6 * 10
plt.plot(
r_centers,
gyt,
ls='--',
color=l.get_color(),
# label='%s [binned]' % minutes_str
)
color_map[minutes_str] = l.get_color()
plt.draw()
if SAVE_SDS:
save_fn = "{}_{}m_{}s.sds".format(casename, minutes, seconds)
with open(save_fn, 'wb') as f:
pickle.dump(sds, f)
if n_drops < n_init / (2**3):
if DIAG_PLOTS:
xx, yy = kde_plot(sds)
plt.figure(1)
plt.plot(xx, yy, label='crash')
plt.draw()
print("Superdroplet number dropped too low")
break
if is_output_step(ti):
wms.append(np.sum([s.multi * s.mass for s in sds]) / 1e3)
xi_s.append(np.sum([s.multi for s in sds]))
sdss.append(sort_sds(sds))
print("--" * 40)
if DIAG_PLOTS:
xx, yy = kde_plot(sds)
plt.figure(1)
# plt.plot(xx, yy, '-b', label='final')
plt.legend(loc='best')
ax = plt.gca()
colortext_legend(color_map,
ax,
fontsize=14,
loc='upper left',
bbox_to_anchor=(-0.1, 1.0))
plt.draw()
## Additional diagnostics -
## 1) total water mass, black (should be flat)
## 2) total SD number, red (should decrease over time)
# plt.figure(2)
# plt.clf()
# plt.plot(wms, color='k')
# plt.twinx()
# plt.plot(xi_s, color='r')
# plt.legend()
plt.show()
return wms, xi_s, sdss
def main(profile=False):
print()
print("BEGINNING MAIN ROUTINE")
# Generate list of Superdroplets
sds = [Superdroplet(xi_i, r**3., 0.) for r in r_grid]
sds = to_sd_array(sds)
wm0 = np.sum([s.multi * s.mass for s in sds]) / 1e3
sdss = [
sort_sds(sds),
]
print("Initial water mass = ", wm0)
if not PROFILE:
end = input("Begin simulation? ('n' to break) ") == 'n'
if end: sys.exit()
results = []
c_step = partial(cython_step, t_c=t_c, delta_V=delta_V, kern=kernel)
t, ti = 0., 0
n_drops = len(sds)
n_init = n_drops * 1
wms = [
np.sum([s.multi * s.mass for s in sds]) / 1e3,
]
xi_s = [
np.sum([s.multi for s in sds]),
]
color_map = OrderedDict(initial='k')
while t < t_end:
minutes, seconds = s_to_min_s(t)
if profile and ti > 10:
break
print("STEP %d (%s)" % (ti, fmt_time(minutes, seconds)))
print(ti, out_dt, ti % out_dt, is_output_step(ti))
print(minutes, seconds)
sds = c_step(sds)
# sds = recycle(sds)
sds = to_sd_array(sds)
print(len(sds))
if len(sds) < n_drops:
print(" Lost %d superdrops" % (n_drops - len(sds), ))
n_drops = len(sds)
else:
print(" No change in superdrop number")
print()
if (is_output_step(ti) or is_plot_step(ti)) and \
(DIAG_PLOTS or WRITE_OUT):
# pre-compute KDE data for plotting/saving
xx, yy = kde_plot(sds, delta_V=delta_V)
if is_output_step(ti) and WRITE_OUT and (ti > 0):
# save KDE-estimate of droplet size distribution
output_df = pd.DataFrame({'size': xx, 'wm': yy})
output_df.to_csv("out_{:04d}.csv".format(int(t)), index=False)
wms.append(np.sum([s.multi * s.mass for s in sds]) / 1e3)
xi_s.append(np.sum([s.multi for s in sds]))
sdss.append(sort_sds(sds))
if DIAG_PLOTS and is_plot_step(ti) and (ti > 0):
minutes_str = fmt_time(minutes, seconds, True)
plt.figure(1)
lines = plt.plot(xx, yy, lw=2.5, label=minutes_str)
l = lines[0]
results.append(yy)
r_centers, gyt = \
bin_mass_density(sds, True, n_bins=41,
log_r_min=-7, log_r_max=np.log(5e-2),
delta_V=delta_V)
r_centers *= 1e6 * 10
plt.plot(
r_centers,
gyt,
ls='--',
color=l.get_color(),
# label='%s [binned]' % minutes_str
)
color_map[minutes_str] = l.get_color()
plt.draw()
if n_drops < n_init / (2**3):
if DIAG_PLOTS:
xx, yy = kde_plot(sds, delta_V=delta_V)
plt.figure(1)
plt.plot(xx, yy, label='crash')
plt.draw()
print("Superdroplet number dropped too low")
break
print("--" * 40)
ti += 1
t = ti * t_c # diagnose time from step to avoid accumulating
# floating point errors which screw things up
if DIAG_PLOTS:
xx, yy = kde_plot(sds, delta_V=delta_V)
plt.figure(1)
# plt.plot(xx, yy, '-b', label='final')
plt.legend(loc='best')
ax = plt.gca()
colortext_legend(color_map,
ax,
fontsize=14,
loc='upper left',
bbox_to_anchor=(-0.1, 1.0))
plt.draw()
## Additional diagnostics -
## 1) total water mass, black (should be flat)
## 2) total SD number, red (should decrease over time)
# plt.figure(2)
# plt.clf()
# plt.plot(wms, color='k')
# plt.twinx()
# plt.plot(xi_s, color='r')
# plt.legend()
plt.show()
return wms, xi_s, sdss
def main(profile=False):
if not PROFILE:
end = raw_input("Begin simulation? ('n' to break) ") == 'n'
if end: sys.exit()
print
print "BEGINNING MAIN ROUTINE"
# Generate list of Superdroplets
sds = [Superdroplet(xi_i, r**3., 0.) for r in r_grid]
sds = to_sd_array(sds)
wm0 = np.sum([s.multi*s.mass for s in sds])/1e3
sdss = [sort_sds(sds), ]
print "Initial water mass = ", wm0
results = []
c_step = partial(cython_step, t_c=t_c, delta_V=delta_V,
kern=kernel)
t, ti = 0., 0
n_drops = len(sds)
n_init = n_drops*1
wms = [np.sum([s.multi*s.mass for s in sds])/1e3, ]
xi_s = [np.sum([s.multi for s in sds]), ]
color_map = OrderedDict(initial='k')
while t < t_end:
ti += 1
t = ti * t_c # diagnose time from step to avoid accumulating
# floating point errors which screw things up
minutes, seconds = s_to_min_s(t)
if profile and ti > 10:
break
print "STEP %d (%s)" % (ti, fmt_time(minutes, seconds))
print ti, out_dt, ti % out_dt, is_output_step(ti)
print minutes, seconds
sds = c_step(sds)
sds = recycle(sds)
# sds = to_sd_array(sds)
print len(sds)
if len(sds) < n_drops:
print " Lost %d superdrops" % (n_drops - len(sds), )
n_drops = len(sds)
else:
print " No change in superdrop number"
print
if is_output_step(ti):
if DIAG_PLOTS:
minutes_str = fmt_time(minutes, seconds, True)
xx, yy = kde_plot(sds)
plt.figure(1)
lines = plt.plot(xx, yy, lw=2.5, label=minutes_str)
l = lines[0]
results.append(yy)
r_centers, gyt = \
bin_mass_density(sds, True, n_bins=41,
log_r_min=-7, log_r_max=np.log(5e-2))
r_centers *= 1e6 * 10
plt.plot(r_centers, gyt, ls='--', color=l.get_color(),
# label='%s [binned]' % minutes_str
)
color_map[minutes_str] = l.get_color()
plt.draw()
if n_drops < n_init/(2**3):
if DIAG_PLOTS:
xx, yy = kde_plot(sds)
plt.figure(1)
plt.plot(xx, yy, label='crash')
plt.draw()
print "Superdroplet number dropped too low"
break
if is_output_step(ti):
wms.append(np.sum([s.multi*s.mass for s in sds])/1e3)
xi_s.append(np.sum([s.multi for s in sds]))
sdss.append(sort_sds(sds))
print "--"*40
if DIAG_PLOTS:
xx, yy = kde_plot(sds)
plt.figure(1)
# plt.plot(xx, yy, '-b', label='final')
plt.legend(loc='best')
ax = plt.gca()
colortext_legend(color_map, ax, fontsize=14,
loc='upper left',
bbox_to_anchor=(-0.1, 1.0))
plt.draw()
## Additional diagnostics -
## 1) total water mass, black (should be flat)
## 2) total SD number, red (should decrease over time)
# plt.figure(2)
# plt.clf()
# plt.plot(wms, color='k')
# plt.twinx()
# plt.plot(xi_s, color='r')
# plt.legend()
plt.show()
return wms, xi_s, sdss
def request_time_info(self):
return '<!-- generated when %s, use %fms -->' % (
fmt_time(), self.request.request_time())
def today():
return fmt_time('%Y-%m-%d')
def fmt(seconds, f='%Y-%m-%d %H:%M:%S'):
return fmt_time(f, seconds)
def request_time_info(self):
return '<!-- generated when %s, use %fms -->' % (fmt_time(), self.request.request_time())
