tuplelist = [ (p, d) for p, d in data.items()\
if max(d['cases']) >= 20\
and len(np.where(np.logical_and(np.array(d['times'])<=12,
np.array(d['cases'])>0))[0])\
>= 8
]
colors
tuplelist = sorted([t for t in tuplelist], key=lambda x: -max(x[1]['cases']))
n_fits = len(tuplelist)
n_col = int(np.ceil(np.sqrt(n_fits)))
n_row = 2
n_col = 4
fig, ax = pl.subplots(n_row, n_col, figsize=(8, 3))
ax = ax.flatten()
titlemap = REPL({'mainland_china': 'All w/o Hubei'})
letter = "abcdefg"
roman = ["i", "ii", "iii", "iv", "v", "vi", "vii", "viii", "ix"]
i = -1
for province, pdata in tqdm(tuplelist[2:10]):
i += 1
print(province)
t = np.array(pdata['times'])
cases = np.array(pdata['cases'])
if pdata['dates'][0] == "2020-01-22 12:00:00":
if loaded_fits:
with open(pickle_filename, 'rb') as f:
fit_parameters = pickle.load(f)
else:
fit_parameters = {}
letter = "abcdefg"
roman = ["i", "ii", "iii", "iv", "v", "vi"]
ylims = [[100, 100000], [10, 20000]]
max_dates = ['Feb. 7th', 'Feb. 1st.']
max_dates_pos = [60000, 15000]
max_dates_va = ['bottom'] * 2
i = -1
fig, ax = pl.subplots(1, 2, figsize=(3.5, 2))
curves = {}
for province, pdata in tqdm(tuplelist[:2]):
i += 1
t = np.array(pdata['times'])
cases = np.array(pdata['cases'])
dates = np.array(pdata['dates'], np.datetime64)
if max(cases) <= 20:
continue
i0 = np.where(cases > 0)[0][0]
t = t[i0:]
cases = cases[i0:]
and len(np.where(np.logical_and(np.array(d['times'])<=12,
np.array(d['cases'])>0))[0])\
>= 8
]
tuplelist = sorted([t for t in tuplelist], key=lambda x: -max(x[1]['cases']))
loaded_fits = len(sys.argv) > 1
if loaded_fits:
pickle_filename = sys.argv[1]
n_fits = len(tuplelist)
n_col = int(np.ceil(np.sqrt(n_fits)))
n_row = n_col
n_col = n_col - 1
fig, ax = pl.subplots(n_row, n_col, figsize=(10, 10))
ax = ax.flatten()
titlemap = REPL({'mainland_china': 'All exc. Hubei'})
if loaded_fits:
with open(pickle_filename, 'rb') as f:
fit_parameters = pickle.load(f)
else:
fit_parameters = {}
letter = "abcdefg"
roman = ["i", "ii", "iii", "iv", "v", "vi"]
i = -1
for province, pdata in tqdm(tuplelist):
if data_exists:
data = np.load('data/clustering_data.npz')
clustering_k = data['clustering_k']
clustering_n = data['clustering_n']
clustering_r = data['clustering_r']
clustering_T = data['clustering_T']
data_arrays = [clustering_k, clustering_n, clustering_r, clustering_T]
matplotlib.rcParams['font.size'] = 12
### triangle plots
fig, axes = pl.subplots(
1,
4,
figsize=(12, 3),
#sharey=True
)
##
axes[0].set_xlabel(r'mean degree $\left\langle k\right\rangle$')
axes[0].set_ylabel(r'clustering coefficient $C$')
for ax in axes:
ax.set_yscale('log')
for ax in axes[:2]:
ax.set_xscale('log')
axes[3].set_xscale('log')
### variance plot
n = 10**5
NMAX = 20
rho = np.linspace(0.0, 0.5, 200)
k = 2**np.arange(0, 10, 2)
k = np.array(k, dtype=float)
if not data_exists:
variances = np.zeros((len(rho), len(k)))
for i in range(len(rho)):
for j in range(len(k)):
t = solve_t(k[j], n)
variances[i, j] = variance(t, rho[i], n, NMAX)
fig, ax = pl.subplots(1, 1, figsize=(4, 2.5))
pl.xlabel(r'local edge weight correlation $\rho$')
pl.ylabel(r'node degree variance $\mathrm{Var}(k)$')
for i in range(len(k)):
pl.plot(rho, variances[:, i], c=bp.brewer_qualitative[1])
bp.strip_axis(ax)
ax.set_yscale('log')
#ax.set_xscale('log')
ax.set_xlim([0, 0.5])
fig.tight_layout()
for i in range(len(k)):
bp.add_curve_label(ax,
rho,
letter = "abcdefg"
roman = ["i", "ii", "iii", "iv", "v", "vi"]
ylims = [[0, 64000], [0, 15000]]
max_dates = ['Feb. 7th', 'Feb. 2nd']
hand_len = [2, 0.5]
max_dates_pos = [29000, 9000]
max_dates_va = ['bottom'] * 2
change_dates_pos = [46000, 11000]
change_dates_label_pos = [5000, 4000]
change_dates_label_ha = ['center', 'right']
change_dates_va = ['bottom'] * 2
i = -1
fig, ax = pl.subplots(1, 2, figsize=(8, 3))
curves = {}
for province, pdata in tqdm(tuplelist[:2]):
i += 1
t = np.array(pdata['times'])
cases = np.array(pdata['cases'])
dates = np.array(pdata['dates'], np.datetime64)
if max(cases) <= 20:
continue
i0 = np.where(cases > 0)[0][0]
tswitch = t[-1]
t = t[i0:]
cases = cases[i0:]
tuplelist = [ (p, d) for p, d in data.items()\
if max(d['cases']) >= 20\
and len(np.where(np.logical_and(np.array(d['times'])<=12,
np.array(d['cases'])>0))[0])\
>= 8
]
colors
tuplelist = sorted([t for t in tuplelist], key=lambda x: -max(x[1]['cases']))
n_fits = len(tuplelist)
n_col = int(np.ceil(np.sqrt(n_fits)))
n_row = 1
n_col = 2
fig, ax = pl.subplots(n_row, n_col, figsize=(3, 2))
ax = ax.flatten()
titlemap = REPL({'mainland_china': 'All w/o Hubei'})
letter = "abcdefg"
roman = ["i", "ii", "iii", "iv", "v", "vi", "vii", "viii", "ix"]
i = -1
for province, pdata in tqdm(tuplelist[:2]):
i += 1
print(province)
t = np.array(pdata['times'])
cases = np.array(pdata['cases'])
'x': ax.set_xticks,
'y': ax.set_yticks,
}
for _ax in axes.lower():
lim = get_lims[_ax]()
Ticks = NiceTicks(*lim)
set_lims[_ax](Ticks.lim)
set_ticks[_ax](Ticks.ticks)
if __name__ == "__main__":
import numpy as np
import bfmplot as bp
fig, ax = pl.subplots(1, 1)
x = np.linspace(1, 10, 100)
mus = np.linspace(1, 4, 4)
for mu in mus:
y = x**mu
pl.plot(x, y, c=bp.brewer_qualitative[0])
#pl.xscale('log')
bp.strip_axis(ax, horizontal='left')
pl.yscale('log')
ax.set_xlabel('x')
ax.set_ylabel('y')
pl.gcf().tight_layout()
"""Simplest implementation of a list with periodic boundary conditions"""
def __init__(self,items):
self.items = items
def __getitem__(self,i):
return self.items[i%len(self.items)]
#markers = simple_cycler(['s','d','o','v','*','^','.','>','h','p','P','<','8','H','X'])
markers = simple_cycler(['s','d','o','v','*','^','>','h','p','P','<','8','H','X'])
if __name__ == "__main__":
import numpy as np
seq = wong_order
ncol = int(np.ceil(np.sqrt(len(seq))))
fig, ax = pl.subplots(ncol,ncol,figsize=(8,8))
ax = ax.flatten()
x = np.linspace(0,2*np.pi,1000)
for i in range(len(seq)):
for j in range(i+1):
ax[i].plot(x,np.sin(x*np.sqrt(j+1)),c=seq[j],lw=3)
pl.show()
from load_data import data
import qsuite_config as cf
import numpy as np
import bfmplot as bp
from bfmplot import pl
import matplotlib as mpl
from matplotlib.ticker import MaxNLocator
mpl.rcParams['font.size'] = 9
ks = cf.ks
ns = cf.ns
rhos = cf.rhos
fig, axes = pl.subplots(1, 2, figsize=(8, 2.5))
LEGN = 0
LEGK = 0
RHON = 0
RHOK = 2
labels = ['0', '0.05', '0.15', '0.5']
pos = [67, 3.5, 2, 2]
ax = axes[0]
for iax, ax in enumerate(axes):
for i_n, n in enumerate(ns):
for i_mk, mk in enumerate(ks):
for i_rho, rho in enumerate(rhos):
tuplelist = [ (p, d) for p, d in data.items()\
if max(d['cases']) >= 20\
and len(np.where(np.logical_and(np.array(d['times'])<=12,
np.array(d['cases'])>0))[0])\
>= 8
]
colors
tuplelist = sorted([t for t in tuplelist], key=lambda x: -max(x[1]['cases']))
n_fits = len(tuplelist)
n_col = int(np.ceil(np.sqrt(n_fits)))
n_row = 1
n_col = 2
fig, ax = pl.subplots(n_row, n_col, figsize=(8, 3), sharex=True)
ax = ax.flatten()
titlemap = REPL({'mainland_china': 'all except Hubei'})
letter = "abcdefg"
roman = ["i", "ii", "iii", "iv", "v", "vi", "vii", "viii", "ix"]
i = -1
for province, pdata in tqdm(tuplelist[:2]):
i += 1
print(province)
t = np.array(pdata['times'])
cases = np.array(pdata['cases'])
dates = np.array(pdata['dates'], dtype=np.datetime64)
