def plot_modelparam_cdfs(core_dict, df):
import myplotting as myplt
import matplotlib.pyplot as plt
import mystats
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
c_cycle = [cmap(i) for i in np.linspace(0, 0.8, 3)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.5, 5))
parameters = [
'phi_cnm',
'alphaG',
]
fig, axes = plt.subplots(2, 1, sharey=True, figsize=(3.5, 5))
clouds = 'california', 'perseus', 'taurus'
linestyles = ['-', '--', '-.']
include_errors = True
for i, cloud in enumerate(clouds):
phi_cnms = []
phi_cnms_error = []
alphaGs = []
alphaGs_error = []
for core in core_dict:
if core_dict[core]['cloud'] == cloud:
phi_cnms.append(core_dict[core]['krumholz']['phi_cnm'])
alphaGs.append(core_dict[core]['sternberg']['alphaG'])
phi_cnms_error.append(
core_dict[core]['krumholz']['phi_cnm_error'])
alphaGs_error.append(
core_dict[core]['sternberg']['alphaG_error'])
alphaGs = np.array(alphaGs)
alphaGs_error = np.array(alphaGs_error)
phi_cnms = np.array(phi_cnms)
phi_cnms_error = np.array(phi_cnms_error)
if include_errors:
n_sim = 100000
alpha = 1.0 / n_sim
alpha = 0.1
if 0:
for j in xrange(n_sim):
phi_cnm_sim = \
phi_cnms + np.random.normal(scale=phi_cnms_error[:, 0])
alphaG_sim = \
alphaGs + np.random.normal(scale=alphaGs_error[:, 0])
if j == 0:
label = cloud.capitalize()
x = axes[0].plot(
0,
0,
label=label,
color=c_cycle[i],
)
else:
label = None
x = myplt.plot_cdf(phi_cnm_sim,
ax=axes[0],
plot_kwargs={#'label': label,
'color': c_cycle[i],
#'linestyle': linestyles[i],
'alpha': alpha,
})
x = myplt.plot_cdf(alphaG_sim,
ax=axes[1],
plot_kwargs={#'label': label,
'color': c_cycle[i],
#'linestyle': linestyles[i],
'alpha': alpha,
})
else:
label = cloud.capitalize()
linestyle = linestyles[i]
nbins = 20
alpha = 0.3
axes[0].plot(
0,
0,
label=label,
color=c_cycle[i],
)
myplt.plot_cdf_confint(phi_cnms,
data_error=phi_cnms_error[:,0],
ax=axes[0],
nsim=n_sim,
nbins=nbins,
#bin_limits=[0, None],
plot_kwargs_line={'color': c_cycle[i],
'linestyle': linestyle,
'linestyle': linestyles[i],
},
plot_kwargs_fill_between=\
{'color': c_cycle[i],
'alpha': alpha,
'linewidth': 0,
},
)
myplt.plot_cdf_confint(alphaGs,
data_error=alphaGs_error[:,0],
ax=axes[1],
nsim=n_sim,
nbins=nbins,
plot_kwargs_line={'color': c_cycle[i],
'linestyle': linestyle,
},
plot_kwargs_fill_between=\
{'color': c_cycle[i],
'alpha': alpha,
'linewidth': 0,
},
)
else:
x = myplt.plot_cdf(phi_cnms,
ax=axes[0],
plot_kwargs={
'label': cloud.capitalize(),
'color': c_cycle[i],
'linestyle': linestyles[i],
})
x = myplt.plot_cdf(alphaGs,
ax=axes[1],
plot_kwargs={
'label': cloud.capitalize(),
'color': c_cycle[i],
'linestyle': linestyles[i],
})
axes[0].legend(loc='lower right')
#ax.set_xscale('log')
axes[0].set_xlabel(r'$\phi_{\rm CNM}$')
axes[1].set_xlabel(r'$\alpha G$')
axes[0].set_ylabel('Cumulative Distribution')
axes[1].set_ylabel('Cumulative Distribution')
axes[0].set_ylim([0, 1])
axes[1].set_ylim([0, 1])
axes[0].set_xlim([0, 25])
axes[1].set_xlim([0, 60])
#plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/temps_cdf.png')
plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/modelparams_cdf.pdf')
def plot_modelparam_cdfs(core_dict, df):
import myplotting as myplt
import matplotlib.pyplot as plt
import mystats
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
c_cycle = [cmap(i) for i in np.linspace(0, 0.8, 3)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.5, 5))
parameters = ['phi_cnm', 'alphaG',]
fig, axes = plt.subplots(2,1,sharey=True, figsize=(3.5, 5))
clouds = 'california', 'perseus', 'taurus'
linestyles = ['-', '--', '-.']
include_errors = True
for i, cloud in enumerate(clouds):
phi_cnms = []
phi_cnms_error = []
alphaGs = []
alphaGs_error = []
for core in core_dict:
if core_dict[core]['cloud'] == cloud:
phi_cnms.append(core_dict[core]['krumholz']['phi_cnm'])
alphaGs.append(core_dict[core]['sternberg']['alphaG'])
phi_cnms_error.append(core_dict[core]['krumholz']['phi_cnm_error'])
alphaGs_error.append(core_dict[core]['sternberg']['alphaG_error'])
alphaGs = np.array(alphaGs)
alphaGs_error = np.array(alphaGs_error)
phi_cnms = np.array(phi_cnms)
phi_cnms_error = np.array(phi_cnms_error)
if include_errors:
n_sim = 100000
alpha = 1.0 / n_sim
alpha = 0.1
if 0:
for j in xrange(n_sim):
phi_cnm_sim = \
phi_cnms + np.random.normal(scale=phi_cnms_error[:, 0])
alphaG_sim = \
alphaGs + np.random.normal(scale=alphaGs_error[:, 0])
if j == 0:
label = cloud.capitalize()
x = axes[0].plot(0,0,
label=label,
color=c_cycle[i],
)
else:
label = None
x = myplt.plot_cdf(phi_cnm_sim,
ax=axes[0],
plot_kwargs={#'label': label,
'color': c_cycle[i],
#'linestyle': linestyles[i],
'alpha': alpha,
})
x = myplt.plot_cdf(alphaG_sim,
ax=axes[1],
plot_kwargs={#'label': label,
'color': c_cycle[i],
#'linestyle': linestyles[i],
'alpha': alpha,
})
else:
label = cloud.capitalize()
linestyle = linestyles[i]
nbins = 20
alpha = 0.3
axes[0].plot(0,0,
label=label,
color=c_cycle[i],
)
myplt.plot_cdf_confint(phi_cnms,
data_error=phi_cnms_error[:,0],
ax=axes[0],
nsim=n_sim,
nbins=nbins,
#bin_limits=[0, None],
plot_kwargs_line={'color': c_cycle[i],
'linestyle': linestyle,
'linestyle': linestyles[i],
},
plot_kwargs_fill_between=\
{'color': c_cycle[i],
'alpha': alpha,
'linewidth': 0,
},
)
myplt.plot_cdf_confint(alphaGs,
data_error=alphaGs_error[:,0],
ax=axes[1],
nsim=n_sim,
nbins=nbins,
plot_kwargs_line={'color': c_cycle[i],
'linestyle': linestyle,
},
plot_kwargs_fill_between=\
{'color': c_cycle[i],
'alpha': alpha,
'linewidth': 0,
},
)
else:
x = myplt.plot_cdf(phi_cnms,
ax=axes[0],
plot_kwargs={'label': cloud.capitalize(),
'color': c_cycle[i],
'linestyle': linestyles[i],
})
x = myplt.plot_cdf(alphaGs,
ax=axes[1],
plot_kwargs={'label': cloud.capitalize(),
'color': c_cycle[i],
'linestyle': linestyles[i],
})
axes[0].legend(loc='lower right')
#ax.set_xscale('log')
axes[0].set_xlabel(r'$\phi_{\rm CNM}$')
axes[1].set_xlabel(r'$\alpha G$')
axes[0].set_ylabel('Cumulative Distribution')
axes[1].set_ylabel('Cumulative Distribution')
axes[0].set_ylim([0,1])
axes[1].set_ylim([0,1])
axes[0].set_xlim([0,25])
axes[1].set_xlim([0,60])
#plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/temps_cdf.png')
plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/modelparams_cdf.pdf')
def plot_density_cdfs(core_dict, df):
import myplotting as myplt
import matplotlib.pyplot as plt
import mystats
import myscience
# load Stanimirovic temps
filename = \
'/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_temps.npy'
stani_temps = np.loadtxt(filename)
filename = \
'/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_temp_errors.npy'
stani_temp_errors = np.loadtxt(filename)
stani_temp_errors = np.array((stani_temp_errors, stani_temp_errors)).T
filename = \
'/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_int_temps.npy'
stani_tempKs = np.loadtxt(filename)
# load the dict
with open('/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_temps.pickle', 'rb') as f:
temp_dict = pickle.load(f)
# concatenate the temps from each cloud
Ts_list = []
Ts_error_list = []
Ts_avg_list = []
Ts_avg_error_list = []
for cloud in temp_dict:
for Ts in temp_dict[cloud]['Ts_list']:
Ts_list.append(Ts)
for Ts_error in temp_dict[cloud]['Ts_error_list']:
Ts_error_list.append(Ts_error)
for Ts_avg in temp_dict[cloud]['Ts_avg_list']:
#for Ts_avg in temp_dict[cloud]['Tk_list']:
Ts_avg_list.append(Ts_avg)
for Ts_avg_error in temp_dict[cloud]['Ts_avg_error_list']:
#for Ts_avg_error in temp_dict[cloud]['Tk_error_list']:
Ts_avg_error_list.append(Ts_avg_error)
Ts_list = np.array(Ts_list)
Ts_error_list = np.array(Ts_error_list)
Ts_avg_list = np.array(Ts_avg_list)
Ts_avg_error_list = np.array(Ts_avg_error_list)
print '\nNumber of LOS:', len(Ts_avg_list)
# collect data
T_cnms = np.empty(len(core_dict))
T_Hs = np.empty(len(core_dict))
T_cnm_errors = np.empty((len(core_dict), 2))
T_H_errors = np.empty((len(core_dict), 2))
n_cnms = np.empty(len(core_dict))
n_Hs = np.empty(len(core_dict))
n_cnm_errors = np.empty((len(core_dict), 2))
n_H_errors = np.empty((len(core_dict), 2))
for i, core_name in enumerate(core_dict):
core = core_dict[core_name]
T_cnms[i] = core['T_cnm']
T_Hs[i] = core['T_H'] * 1000.0
T_cnm_errors[i] = core['T_cnm_error']
T_H_errors[i] = core['T_H_error'] * 1000.0
n_cnms[i] = core['n_cnm']
n_Hs[i] = core['n_H']
n_cnm_errors[i] = core['n_cnm_error']
n_H_errors[i] = core['n_H_error']
# Make plot
#plt.close;plt.clf()
# Create figure instance
fig = plt.figure(figsize=(3.5, 2.5))
ax = fig.add_subplot(111)
c_cycle = myplt.set_color_cycle(4, cmap_limits=(0.0, 0.9))
#data_list = [stani_temps, stani_tempKs, T_cnms, T_Hs]
data_list = [Ts_list, Ts_avg_list, T_cnms, T_Hs]
data_error_list = [
stani_temp_errors,
np.array(((5), )), T_cnm_errors, T_H_errors
]
if 1:
data_names = [
r'Stanimirovic+14 $n_{\rm s}$', r'Stanimirovic+14 $<n_s>$',
r'$n_{\rm CNM}$', r'$n_H$'
]
else:
data_names = [
r'Stanimirovi$\acute{c}$+14' + '\n' + r'$T_{\rm s}$',
r'Stanimirovi$\acute{c}$+14' + '\n' + r'$<T_{\rm s}>$',
r'$T_{\rm CNM}$', r'$T_H$'
]
linestyles = ['--', '-.', ':', '-']
# Plot MC sim of error?
include_errors = True
# Define pressures
P = 3700.
P_error = [1200., 1200.]
P_low = P - P_error[0]
P_hi = P + P_error[1]
for i, data in enumerate(data_list):
data = np.array(data)
if i < 2:
include_errors = True
else:
include_errors = True
if include_errors:
if i == 0:
#n = P / data
T = Ts_list
T_error = Ts_error_list
n, n_error = myscience.calc_density(
T_H=T,
pressure=P,
pressure_error=P_error[0],
T_H_error=T_error,
calc_error=True,
)
elif i == 1:
#n = P / data
T = Ts_avg_list
T_error = Ts_avg_error_list
n, n_error = myscience.calc_density(
T_H=T,
pressure=P,
pressure_error=P_error[0],
T_H_error=T_error,
calc_error=True,
)
elif i == 2:
n = n_cnms
n_error = n_cnm_errors[:, 0]
T = T_cnms
T_error = T_cnm_errors[:, 0]
elif i == 3:
n = n_Hs
n_error = n_H_errors[:, 0]
T = T_Hs
T_error = T_H_errors[:, 0]
if 0:
n_sim = 100
alpha = 1.0 / n_sim
alpha = 0.1
for j in xrange(n_sim):
if 0:
error = data_error_list[i]
data_sim = data + np.random.normal(scale=error[:, 0])
else:
pressure = P + np.random.normal(scale=(P - P_low),
size=data.size)
data_sim = pressure / (n + np.random.normal(n_error))
x = myplt.plot_cdf(
data_sim,
ax=ax,
plot_kwargs={ #'label': label,
'color': c_cycle[i],
'alpha': alpha
})
else:
linestyle = linestyles[i]
label = data_names[i]
alpha = 0.3
nbins = 20
error = n_error
data = n
n_sim = 100000
mask = (error > data)
data = data[~mask]
error = error[~mask]
if 1:
myplt.plot_cdf_confint(data,
data_error=error,
ax=ax,
nsim=n_sim,
nbins=nbins,
bin_limits=[-20, 300],
plot_kwargs_line={'color': c_cycle[i],
'linestyle': linestyle,
'label': label,
'linewidth' : 1.5,
},
plot_kwargs_fill_between=\
{'color': c_cycle[i],
'alpha': alpha,
'linewidth': 0,
},
)
#if i < 2:
if 0:
x = myplt.plot_cdf(data[data > 0],
ax=ax,
plot_kwargs={
'label': data_names[i],
'color': c_cycle[i],
'linestyle': linestyles[i],
'linewidth': 2,
'zorder': 1000
})
else:
if 0:
x = myplt.plot(
(0, 0),
ax=ax,
plot_kwargs={
'label': data_names[i],
'color': c_cycle[i],
'linestyle': linestyles[i]
})
ax.legend(loc='best')
if 1:
ax.set_xscale('linear')
ax.set_xlim([-10, 200])
else:
ax.set_xscale('log')
ax.set_xlim([7 * 10**-1, 10**4])
ax.set_ylim([0, 1])
ax.set_xlabel(r'Neutral Gas Density [cm$^{-3}$]')
ax.set_ylabel('Cumulative Distribution')
#plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/temps_cdf.png')
plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/density_cdf.pdf')
def plot_density_cdfs(core_dict, df):
import myplotting as myplt
import matplotlib.pyplot as plt
import mystats
import myscience
# load Stanimirovic temps
filename = \
'/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_temps.npy'
stani_temps = np.loadtxt(filename)
filename = \
'/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_temp_errors.npy'
stani_temp_errors = np.loadtxt(filename)
stani_temp_errors = np.array((stani_temp_errors, stani_temp_errors)).T
filename = \
'/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_int_temps.npy'
stani_tempKs = np.loadtxt(filename)
# load the dict
with open('/d/bip3/ezbc/multicloud/data/python_output/tables/' + \
'stanimirovic14_temps.pickle', 'rb') as f:
temp_dict = pickle.load(f)
# concatenate the temps from each cloud
Ts_list = []
Ts_error_list = []
Ts_avg_list = []
Ts_avg_error_list = []
for cloud in temp_dict:
for Ts in temp_dict[cloud]['Ts_list']:
Ts_list.append(Ts)
for Ts_error in temp_dict[cloud]['Ts_error_list']:
Ts_error_list.append(Ts_error)
for Ts_avg in temp_dict[cloud]['Ts_avg_list']:
#for Ts_avg in temp_dict[cloud]['Tk_list']:
Ts_avg_list.append(Ts_avg)
for Ts_avg_error in temp_dict[cloud]['Ts_avg_error_list']:
#for Ts_avg_error in temp_dict[cloud]['Tk_error_list']:
Ts_avg_error_list.append(Ts_avg_error)
Ts_list = np.array(Ts_list)
Ts_error_list = np.array(Ts_error_list)
Ts_avg_list = np.array(Ts_avg_list)
Ts_avg_error_list = np.array(Ts_avg_error_list)
print '\nNumber of LOS:', len(Ts_avg_list)
# collect data
T_cnms = np.empty(len(core_dict))
T_Hs = np.empty(len(core_dict))
T_cnm_errors = np.empty((len(core_dict), 2))
T_H_errors = np.empty((len(core_dict), 2))
n_cnms = np.empty(len(core_dict))
n_Hs = np.empty(len(core_dict))
n_cnm_errors = np.empty((len(core_dict), 2))
n_H_errors = np.empty((len(core_dict), 2))
for i, core_name in enumerate(core_dict):
core = core_dict[core_name]
T_cnms[i] = core['T_cnm']
T_Hs[i] = core['T_H'] * 1000.0
T_cnm_errors[i] = core['T_cnm_error']
T_H_errors[i] = core['T_H_error'] * 1000.0
n_cnms[i] = core['n_cnm']
n_Hs[i] = core['n_H']
n_cnm_errors[i] = core['n_cnm_error']
n_H_errors[i] = core['n_H_error']
# Make plot
#plt.close;plt.clf()
# Create figure instance
fig = plt.figure(figsize=(3.5, 2.5))
ax = fig.add_subplot(111)
c_cycle = myplt.set_color_cycle(4, cmap_limits=(0.0, 0.9))
#data_list = [stani_temps, stani_tempKs, T_cnms, T_Hs]
data_list = [Ts_list, Ts_avg_list, T_cnms, T_Hs]
data_error_list = [stani_temp_errors, np.array(((5),)),
T_cnm_errors, T_H_errors]
if 1:
data_names = [r'Stanimirovic+14 $n_{\rm s}$',
r'Stanimirovic+14 $<n_s>$',
r'$n_{\rm CNM}$',
r'$n_H$']
else:
data_names = [r'Stanimirovi$\acute{c}$+14' + '\n' + r'$T_{\rm s}$',
r'Stanimirovi$\acute{c}$+14' + '\n' + r'$<T_{\rm s}>$',
r'$T_{\rm CNM}$',
r'$T_H$']
linestyles = ['--', '-.', ':', '-']
# Plot MC sim of error?
include_errors = True
# Define pressures
P = 3700.
P_error = [1200., 1200.]
P_low = P - P_error[0]
P_hi = P + P_error[1]
for i, data in enumerate(data_list):
data = np.array(data)
if i < 2:
include_errors = True
else:
include_errors = True
if include_errors:
if i == 0:
#n = P / data
T = Ts_list
T_error = Ts_error_list
n, n_error = myscience.calc_density(T_H=T,
pressure=P,
pressure_error=P_error[0],
T_H_error=T_error,
calc_error=True,
)
elif i == 1:
#n = P / data
T = Ts_avg_list
T_error = Ts_avg_error_list
n, n_error = myscience.calc_density(T_H=T,
pressure=P,
pressure_error=P_error[0],
T_H_error=T_error,
calc_error=True,
)
elif i == 2:
n = n_cnms
n_error = n_cnm_errors[:, 0]
T = T_cnms
T_error = T_cnm_errors[:, 0]
elif i == 3:
n = n_Hs
n_error = n_H_errors[:, 0]
T = T_Hs
T_error = T_H_errors[:, 0]
if 0:
n_sim = 100
alpha = 1.0 / n_sim
alpha = 0.1
for j in xrange(n_sim):
if 0:
error = data_error_list[i]
data_sim = data + np.random.normal(scale=error[:, 0])
else:
pressure = P + np.random.normal(scale=(P - P_low),
size=data.size)
data_sim = pressure / (n + np.random.normal(n_error))
x = myplt.plot_cdf(data_sim,
ax=ax,
plot_kwargs={#'label': label,
'color': c_cycle[i],
'alpha': alpha})
else:
linestyle = linestyles[i]
label = data_names[i]
alpha = 0.3
nbins = 20
error = n_error
data = n
n_sim = 100000
mask = (error > data)
data = data[~mask]
error = error[~mask]
if 1:
myplt.plot_cdf_confint(data,
data_error=error,
ax=ax,
nsim=n_sim,
nbins=nbins,
bin_limits=[-20, 300],
plot_kwargs_line={'color': c_cycle[i],
'linestyle': linestyle,
'label': label,
'linewidth' : 1.5,
},
plot_kwargs_fill_between=\
{'color': c_cycle[i],
'alpha': alpha,
'linewidth': 0,
},
)
#if i < 2:
if 0:
x = myplt.plot_cdf(data[data > 0],
ax=ax,
plot_kwargs={'label': data_names[i],
'color': c_cycle[i],
'linestyle': linestyles[i],
'linewidth': 2,
'zorder': 1000})
else:
if 0:
x = myplt.plot((0,0),
ax=ax,
plot_kwargs={'label': data_names[i],
'color': c_cycle[i],
'linestyle': linestyles[i]})
ax.legend(loc='best')
if 1:
ax.set_xscale('linear')
ax.set_xlim([-10, 200])
else:
ax.set_xscale('log')
ax.set_xlim([7*10**-1, 10**4])
ax.set_ylim([0,1])
ax.set_xlabel(r'Neutral Gas Density [cm$^{-3}$]')
ax.set_ylabel('Cumulative Distribution')
#plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/temps_cdf.png')
plt.savefig('/d/bip3/ezbc/multicloud/figures/temps/density_cdf.pdf')