def plot_model(self,
ax,
I0,
R,
b0,
a0,
model_kind=None,
dimless=True,
rdf=True,
bg=False):
plt.rc('text', usetex=True)
plt.rc('font', family='serif', size=12)
if ax is None:
ax = plt.gca()
if model_kind is None:
model_kind = SprModelRadial._model_spr
q0, qn = self.q[0, 0], self.q[-1, 0]
q = np.linspace(q0, qn, 300).reshape(300, 1)
q1 = np.linspace(0, q0, 50).reshape(50, 1)
I = self.model_q(I0, R, q, model_kind) + a0 * np.interp(
q, self.q.flat, self.b.flat) + b0
I1 = self.model_q(I0, R, q1, model_kind) + a0 * self.b[0, 0] + b0
#q = q.reshape(200,1)
#print("I", I.shape)
#print("q", q.shape)
dq = (7.72525183693770716 - 4.49340945790906418) / (math.pi * R) / 9
if dq < self.dq:
dq = self.dq
qr, v, sv, b, sb, nn = SprModelRadial._rdf(self.qi, dq, self.vi,
self.bi, self.si, self.ei)
if dimless:
q = q * R
q1 = q1 * R
qr = qr * R
ax.set_xlabel("$q$")
else:
#q = self.q
ax.set_xlabel("$q, \mathrm{\AA}^{-1}$")
#idx = I > 0.
ax.semilogy(q, I - b0, 'C0', linewidth=2)
ax.semilogy(q1, I1 - b0, 'C0:', linewidth=2)
if rdf:
idx = v > 0.
ax.semilogy(qr, v - b0, 'C1--', linewidth=2)
if bg:
idx = b > 0.
ax.semilogy(qr, a0 * b, 'C2', linewidth=2)
ax.set_xlim(0, q[-1])
ax.set_ylabel("$I, \mathrm{photons}$")
def plot_model(self,
ax,
I0,
R,
b0,
a0,
model_kind=None,
dimless=True,
rdf=True,
bg=False):
if ax is None:
ax = plt.gca()
if model_kind is None:
model_kind = SprModelRadial._model_spr
q0, qn = self.q[0, 0], self.q[-1, 0]
q = np.linspace(q0, qn, 1000).reshape(1000, 1)
q1 = np.linspace(0, q0, 50).reshape(50, 1)
I = self.model_q(
I0, R, q, model_kind) #+ a0*np.interp(q, self.q.flat, self.b.flat)
I1 = self.model_q(I0, R, q1, model_kind) + a0 * self.b[0, 0]
dq = (7.72525183693770716 - 4.49340945790906418) / (math.pi * R) / 21
if dq < self.dq:
dq = self.dq
qr, v, sv, b, sb, nn = SprModelRadial._rdf(self.qi, dq, self.vi,
self.bi, self.si, self.ei)
if dimless:
q = q * R
q1 = q1 * R
qr = qr * R
ax.set_xlabel("$q$")
else:
#q = self.q
ax.set_xlabel("$q, \mathrm{nm}^{-1}$")
#idx = I > 0.
# ax.semilogy(q, I-b0, 'C0', linewidth=2)
ax.semilogy(q * 10, I, 'C0', linewidth=2)
#ax.semilogy(q1, I1-b0, 'C0:', linewidth=2)
if rdf:
idx = v > 0.
#ax.semilogy(qr, v-b0, 'C1--', linewidth=2)
ax.semilogy(qr * 10, v - b0, 'C1--', linewidth=2)
if bg:
idx = b > 0.
ax.semilogy(qr * 10, a0 * b, 'C2', linewidth=2)
# ax.semilogy(qr, a0*b, 'C2', linewidth=2)
#ax.set_xlim(0, q[-1])
ax.set_ylabel("$I, \mathrm{photons/pixel}$")
def plot(self, ax=None):
x = [*self.times, self.times[-1] + self.values[-1][1]]
y = [*(v for v, d in self.values), self.values[-1][0]]
from matplotlib.pylab import plt
if ax is None:
ax = plt.gca()
ax.step(x, y, where='post')
ax.set_xlabel('Time (seconds)')
y_ticks = list(map(int, plt.yticks()[0]))
ax.set_yticks(y_ticks, list(map(self.format_value.format, y_ticks)))
def plotJoinTree(real_acts, pred_acts):
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
size = 0.45
if (len(real_acts) == 0):
print('real not enough data of this type')
print(real_acts)
return
if (len(pred_acts) == 0):
#pred_acts=pred_acts.append({'StartTime':real_acts.StartTime.iloc[0],'EndTime':real_acts.EndTime.iloc[0],'Activity':real_acts.Activity.iloc[0]},ignore_index=True)
print('pred not enough p data of this type')
print(pred_acts)
return
rstart = [r.begin for r in real_acts]
rend = [r.end for r in real_acts]
ract = [1 + (size / 2) for r in real_acts]
# .apply(lambda x:dataset.activities_map[x.Activity], axis=1).tolist()
pstart = [r.begin for r in pred_acts]
pend = [r.end for r in pred_acts]
pact = [1 - (size / 2) for r in pred_acts]
ax = plt.gca()
_plotActs(ax,
ract,
rstart,
rend,
linewidth=1,
edgecolor='k',
facecolor='g',
size=size,
alpha=.6)
_plotActs(ax,
pact,
pstart,
pend,
linewidth=1,
edgecolor='k',
facecolor='r',
size=size,
alpha=.6)
ax.set_xticks([])
ax.set_xlim(min(pstart + rstart), max(pend + rend))
ax.set_yticks([i for i in [1]])
ax.yaxis.grid(True)
# ax.set_yticklabels([l for l in labels])
ax.set_ylim(1 - size, 1 + size)
plt.margins(0.1)
def plot(self, ax=None, **kwargs):
"""plot using matplotlib
: param ax: The axis to plot on(default is plt.gca())
: param ** kwargs: Additional keyword arguments passed on to plt.Polygon.
"""
if ax is None:
ax = plt.gca()
patch_args = dict(
alpha=0.5,
fill=False,
color='r',
)
patch_args.update(kwargs)
ax.add_patch(plt.Polygon(self.points(), **patch_args))
def plot_rdf(self, ax, R=None):
plt.rc('text', usetex=True)
plt.rc('font', family='serif', size=12)
if ax is None:
ax = plt.gca()
idx = self.v > 0.
if R is None:
ax.loglog(self.q[idx], self.v[idx])
ax.loglog(self.q[idx], self.v[idx] + self.sv[idx] + self.sb[idx])
ax.set_xlabel("$q, \mathrm{\AA}^{-1}$")
else:
ax.loglog(self.q[idx] * R, self.v[idx])
ax.loglog(self.q[idx], self.v[idx] + self.sv[idx] + self.sb[idx])
ax.set_xlabel("$q$")
ax.set_ylabel("$I, \mathrm{photon}/\mathrm{\AA}^{-2}$")
def anim_daily_total(data, file_name, kind="cases", minlim=1):
fig = plt.figure()
FFMpegWriter = manimation.writers['ffmpeg']
metadata = dict(title='Corona virus temp evolution',
artist='Sylvain Guieu',
comment='https://github.com/SylvainGuieu/corona')
writer = FFMpegWriter(fps=6, metadata=metadata)
subset = data
perday = subset.cases.iloc[:, 1:] - np.asarray(subset.cases.iloc[:, 0:-1])
perday = perday.rolling(4, center=True, axis=1).mean()
total = subset.cases.iloc[:, 1:]
mdates = range(2,
len(total.iloc[0]) + 1) # add 1 iddentical graph for pause
axes = plt.gca()
xlm = np.nanmax(np.asarray(total), axis=None) * 2
ylm = np.nanmax(np.asarray(perday), axis=None) * 2
print(xlm)
with writer.saving(fig,
os.path.join(root, file_name) + ".mp4", len(mdates)):
for mdate in mdates:
axes.clear()
pd1 = perday.subset(end=mdate)
ttl = total.subset(end=mdate)
for name, r in pd1.iterrows():
axes.plot(ttl.loc[name], r, label=name, **styles.get(name, {}))
axes.plot(ttl.loc[name].iloc[-1], r.iloc[-1], 'k*')
axes.legend(loc='upper left')
axes.set(yscale="log",
xscale="log",
xlabel="Total %s" % kind,
ylabel="Daily %s" % kind,
xlim=(minlim, xlm),
ylim=(minlim, ylm),
title=r.index[-1])
writer.grab_frame()
print(".", end="")
print('')
subprocess.run(
f"ffmpeg -y -i {root}/{file_name}.mp4 -vf \"fps=10,scale=800:-1:flags=lanczos\" -loop 999 {root}/{file_name}.gif",
shell=True)
def ncp(s2n):
"""
Explore the output of BB as we change the prior on the number of
change points
"""
nncp = logspace(0.5,1.5,9)
f,t = keptoy.lightcurve(s2n=s2n)
sig = zeros(len(t)) + std(f)
for i in range( len(nncp) ):
last,val = find_blocks.pt( t,f,sig,ncp=nncp[i] )
blocks(t,f,last,val)
ax = plt.gca()
ax.set_title('s2n - %.1e, cpts prior - %.2e' % (s2n,nncp[i]) )
fig = plt.gcf()
fig.savefig('frames/s2n-%.1e_%02d.png' % (s2n,i) )
plt.show()
def plot_graph(g, ax=None, with_labels=True):
from matplotlib.pylab import plt
if ax is None:
ax = plt.gca()
ax.set_facecolor('lightgray')
edges = np.array(g.edges())
edge_scores = np.array([a['score'] for a in g.edges().values()])
# edge_scores = np.log(edge_scores)
# edge_scores += edge_scores.min()
order = np.argsort(edge_scores)
nx.draw_networkx_nodes(
g,
nx.get_node_attributes(g, 'xy'),
ax=ax,
node_size=30,
font_size=9,
font_color='blue',
with_labels=with_labels,
)
edges = nx.draw_networkx_edges(g,
nx.get_node_attributes(g, 'xy'),
ax=ax,
edgelist=edges[order].tolist(),
edge_color=edge_scores[order],
edge_cmap=plt.cm.gray_r,
node_size=30,
edge_vmin=edge_scores[order[0]],
edge_vmax=edge_scores[order[-1]],
font_size=9,
font_color='blue',
with_labels=with_labels,
connectionstyle='arc3,rad=0.2')
ax.set_aspect('equal')
return edges
import matplotlib
from matplotlib.pylab import plt #load plot library
matplotlib.use('Agg')
from sys import stdout
import sys
filepath = str(sys.argv[1])
print('Reading file ', filepath)
plt.figure(1, figsize = (50,20))
axes = plt.gca()
plt.gca().set_aspect('auto')
vector_l = 2000
bin_size = 0
x_size = vector_l
y_size = 500
axes.set_xlim(0, x_size)
word_hash = "none"
frequency = 0
seq_len = 0
count = 0
xdata_prev = 0
ydata_prev = 0
import matplotlib
from matplotlib.pylab import plt #load plot library
matplotlib.use('Agg')
from sys import stdout
import sys
filepath = str(sys.argv[1])
print('Reading file ', filepath)
# Frag,562480,166774007,562667,166774194,f,0,187,66,66,0.352941,0.352941,0,0
#SeqX length : 155270560
#SeqY length : 171031299
plt.figure(1, figsize=(8.5, 11))
axes = plt.gca()
plt.gca().set_aspect('equal')
x_size = 1000
y_size = 1000
axes.set_xlim(0, x_size)
axes.set_ylim(0, y_size)
#ratio_x = x_size / 155270560
#ratio_y = y_size / 171031299
count = 0
with open(filepath) as openfileobject:
for line in openfileobject:
if (count == 6):
subset = subset.sample(n=1000)
plt.figure()
plt.scatter(result['g_auto'] - result['i_auto'], result['g_auto'] - result['r_auto'], s=10, c='gray', edgecolor='none', alpha = 0.5)
plt.scatter(subset['g_auto'] - subset['i_auto'], subset['g_auto'] - subset['r_auto'], s=20, c='blue', edgecolor='none')
plt.scatter(GCs['g_auto'] - GCs['i_auto'], GCs['g_auto'] - GCs['r_auto'], s=10, c='red', edgecolor='none')
plt.xlabel('(g - i)')
plt.ylabel('(g - r)')
plt.xlim(-1,4)
plt.ylim(-1,4)
plt.figure()
plt.scatter(subset['g_auto'] - subset['r_auto'], subset['r_auto'], s=30, c='blue', edgecolor='none')
plt.scatter(GCs['g_auto'] - GCs['r_auto'], GCs['i_auto'], s=8, c='red', edgecolor='none')
plt.ylim(13,24)
plt.gca().invert_yaxis()
plt.xlabel('(g - i)')
plt.ylabel('i')
plt.figure()
plt.scatter(result['g_auto'] - result['u_auto'], result['g_auto'] - result['r_auto'], s=10, c='gray', edgecolor='none', alpha = 0.5)
plt.scatter(subset['g_auto'] - subset['u_auto'], subset['g_auto'] - subset['r_auto'], s=20, c='blue', edgecolor='none')
plt.scatter(GCs['g_auto'] - GCs['u_auto'], GCs['g_auto'] - GCs['r_auto'], s=10, c='red', edgecolor='none')
plt.scatter(galaxies['g_auto'] - galaxies['u_auto'], galaxies['g_auto'] - galaxies['r_auto'], s=10, c='green', edgecolor='none')
plt.xlabel('(g - u)')
plt.ylabel('(g - r)')
plt.xlim(-4,3)
plt.ylim(-1,2)
for i in tqdm(range(0,len(subset))):