def hicat_pa(df_a,df_b):
binwidth = 5
plt.figure(num=None,figsize=(8,10),dpi=80,facecolor='w',edgecolor='k')
plt.subplot(211)
pa_a = (df_a["PA-N [deg]"].values+df_a["PA-S [deg]"].values)/2
pa_b = (df_b["PA-N [deg]"].values+df_b["PA-S [deg]"].values)/2
pa_all = np.append(pa_a,pa_b)
plt.hist(pa_all,bins=np.arange(0,max(pa_all)+binwidth,binwidth),normed=False)
plt.xlim([0,360])
plt.xlabel("Position Angle ($deg.$)")
plt.ylabel("Count")
plt.title("HICAT CMEs Central Position Angle")
plt.text(30,90,r'$\mu=%d$' % np.mean(pa_a))
plt.text(210,90,r'$\mu=%d$' % np.mean(pa_b))
plt.subplot(212)
pa_a = df_a["PA-fit"].values
pa_b = df_b["PA-fit"].values
pa_all = np.append(pa_a,pa_b)
plt.hist(pa_all,bins=np.arange(0,max(pa_all)+binwidth,binwidth),normed=False)
plt.xlim([0,360])
plt.xlabel("Position Angle ($deg.$)")
plt.ylabel("Count")
plt.title("HICAT CMEs Fitted Position Angle")
plt.text(30,65,r'$\mu=%d$' % np.mean(pa_a))
plt.text(210,65,r'$\mu=%d$' % np.mean(pa_b))
plt.tight_layout()
save(path=os.path.join(config.hicat_path,"hicat_pa"),verbose=True)
def display(oneSimulation,pathString, separate=False ,saveOnly=True):
'''
show/save figures
'''
self = oneSimulation
pylab.rc('axes', linewidth=2) # make the axes boundary lines bold
#fig, ax = plt.subplots()
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.7, 0.7]) # left, bottom, width, height (range 0 to 1)
if separate:
rr.plot( self.df_Region, 'red', lw=6, axes=ax, label='DF')
rr.plot( self.cf_Region, 'blue', lw=4, axes=ax, label='CF')
rr.plot( self.fco_Region, 'red', lw=6, axes=ax, label='DF')
rr.plot( self.icf_Region_bigR1, 'blue', lw=4, axes=ax, label='ICF')
rr.plot( self.icf_Region_bigR2, 'blue', lw=4, axes=ax)
rr.plot(self.df_fco_region, 'red', lw=6, label='DF+FCo')
rr.plot(self.cf_icf_region, 'blue', lw=4, label='CF+ICF')
fig.suptitle('$g ={},{},{},{}$'.format(self.g13, self.g14, self.g23, self.g24), fontsize=14, fontweight='bold')
ax.set_title(r'$P_s=P_1=P_2={}, \, P_3={}, \, P_4={}, \, N=1$'.format(self.Ps,
self.P3, self.P4),fontdict=self.font)
ax.set_xlabel('$R_1$', fontdict=self.font)
ax.set_ylabel('$R_2$', fontdict=self.font)
#ax.set_xlim(xmin=0, xmax=3.5)
#ax.set_ylim(ymin=0, ymax=3.5)
ax.legend(loc=0)
nameStr = 'PsP3P4_{}_{}_{}_g13g14g23g24_{}_{}_{}_{}'.format(self.Ps, self.P3, self.P4,
self.g13, self.g14, self.g23, self.g24)
savefig.save(path='{}/plots_CF_ICF__DF_FCo/{}'.format(pathString, nameStr), ext='pdf', close=saveOnly, verbose=True)
def speeds_datetime():
import datetime
import matplotlib.ticker as ticker
time_format_cdaw = "%Y/%m/%dT%H:%M:%S"
time_format_hicat = "%Y-%m-%dT%H:%MZ"
datetimes_cdaw = np.array([datetime.datetime.strptime(x,time_format_cdaw) \
for x in df_cdaw.date.values+'T'+df_cdaw.time.values])
datetimes_hicat = np.array([datetime.datetime.strptime(x[0],time_format_hicat) \
for x in df_hicat[['Date [UTC]']].values])
fig = plt.figure(num=None,
figsize=(10, 7),
dpi=80,
facecolor='w',
edgecolor='k')
ax = fig.add_subplot(111)
ssz = 10
#lin = plt.scatter(datetimes,df_cdaw.lin_speed,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
q_f = plt.scatter(datetimes_cdaw,df_cdaw.quad_speed_final,s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.3)
hi = plt.scatter(datetimes_hicat,df_hicat[['FP speed [kms-1]']],s=ssz,\
facecolor='red',edgecolor='none',alpha=0.3)
plt.ylim([0, 3500])
plt.title("Comparing HICAT with the CDAW LASCO CME Catalog")
plt.ylabel(speeds_label)
plt.xlabel("Time")
#ax = plt.axes()
#ax.xaxis.set_major_locator(ticker.MultipleLocator(365))
#labels=ax.get_xticklabels()
#plt.setp(labels,rotation=40)
#plt.legend([q_f,hi],['CDAW','HICAT'],prop={'size':ledge_sz})
#plt.legend([lin,q_f],['Linear','Quad. (final)'],prop={'size':ledge_sz})
save(path=os.path.join(config.hicat_path, "compare_speeds_datetimes"),
verbose=True)
def speeds_datetime():
import datetime
import matplotlib.ticker as ticker
time_format_cdaw = "%Y/%m/%dT%H:%M:%S"
time_format_hicat = "%Y-%m-%dT%H:%MZ"
datetimes_cdaw = np.array([datetime.datetime.strptime(x,time_format_cdaw) \
for x in df_cdaw.date.values+'T'+df_cdaw.time.values])
datetimes_hicat = np.array([datetime.datetime.strptime(x[0],time_format_hicat) \
for x in df_hicat[['Date [UTC]']].values])
fig = plt.figure(num=None,figsize=(10,7),dpi=80,facecolor='w',edgecolor='k')
ax = fig.add_subplot(111)
ssz=10
#lin = plt.scatter(datetimes,df_cdaw.lin_speed,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
q_f = plt.scatter(datetimes_cdaw,df_cdaw.quad_speed_final,s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.3)
hi = plt.scatter(datetimes_hicat,df_hicat[['FP speed [kms-1]']],s=ssz,\
facecolor='red',edgecolor='none',alpha=0.3)
plt.ylim([0,3500])
plt.title("Comparing HICAT with the CDAW LASCO CME Catalog")
plt.ylabel(speeds_label)
plt.xlabel("Time")
#ax = plt.axes()
#ax.xaxis.set_major_locator(ticker.MultipleLocator(365))
#labels=ax.get_xticklabels()
#plt.setp(labels,rotation=40)
#plt.legend([q_f,hi],['CDAW','HICAT'],prop={'size':ledge_sz})
#plt.legend([lin,q_f],['Linear','Quad. (final)'],prop={'size':ledge_sz})
save(path=os.path.join(config.hicat_path,"compare_speeds_datetimes"),verbose=True)
def hicat_all_speeds(*args,**kwargs):
# input the speeds from Ahead & Behind of each of the FP, SSE, HM geometrical fittings.
fp_a = args[0]
fp_b = args[1]
sse_a = args[2]
sse_b = args[3]
hm_a = args[4]
hm_b = args[5]
if 'tit' in kwargs:
tit = kwargs['tit']
else:
tit = ""
plt.figure(num=None,figsize=(10,12),dpi=80,facecolor='w',edgecolor='k')
plt.subplot(311)
plt.hist([fp_a,fp_b],bins=np.arange(0,max(fp_a)+binwidth,binwidth),\
stacked=True,normed=False,color=colors[0:2],label=labels[0:2])
plt.title("HICAT CME Speeds\n %s" %tit[0])
plt.xlim(speeds_lim)
plt.ylabel("Count")
plt.legend(prop={'size':ledge_sz})
plt.subplot(312)
plt.hist([sse_a,sse_b],bins=np.arange(0,max(sse_a)+binwidth,binwidth),\
stacked=True,normed=False,color=colors[0:2],label=labels[0:2])
plt.title("%s" %tit[1])
plt.xlim(speeds_lim)
plt.ylabel("Count")
plt.subplot(313)
plt.hist([hm_a,hm_b],bins=np.arange(0,max(hm_a)+binwidth,binwidth),\
stacked=True,normed=False,color=colors[0:2],label=labels[0:2])
plt.title("%s" %tit[2])
plt.xlim(speeds_lim)
plt.xlabel(speeds_label)
plt.ylabel("Count")
plt.tight_layout()
save(path=os.path.join(config.hicat_path,"hicat_speeds_hist"),verbose=True)
def hicact_speeds_datetime(df_hicact_a, df_hicact_b):
import datetime
time_format = "%Y/%m/%d %H:%M"
datetimes_a = np.array([
datetime.datetime.strptime(x, time_format)
for x in df_hicact_a.starttime
])
datetimes_b = np.array([
datetime.datetime.strptime(x, time_format)
for x in df_hicact_b.starttime
])
plt.figure(num=None, figsize=(7, 8), dpi=80, facecolor='w', edgecolor='k')
a = plt.scatter(datetimes_a,
df_hicact_a.v,
s=20,
facecolor='red',
edgecolor='none',
alpha=alph)
b = plt.scatter(datetimes_b,
df_hicact_b.v,
s=20,
facecolor='blue',
edgecolor='none',
alpha=alph)
plt.ylim([0, 2300])
plt.title("HICACTus CMEs")
plt.ylabel(speeds_label)
plt.xlabel("Time")
plt.legend([a, b], ['Ahead', 'Behind'], prop={'size': ledge_sz}, loc=2)
save(path=os.path.join(config.hicact_path, "hicact_speeds_datetimes"),
verbose=True)
def display_TwoHopSchemes(two_hop_schemes, pathString):
'''
Produce a graph that compare ...
'''
self = two_hop_schemes
pylab.rc('axes', linewidth=2) # make the axes boundary lines bold
#fig, ax = plt.subplots()
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.7, 0.7]) # left, bottom, width, height (range 0 to 1)
rr.plot( self.region_CF_ICFsch3, 'red', lw=8, axes=ax, label='CF + ICF')
rr.plot( self.region_DFIntegerCoeff_FCo, 'c', lw=4, axes=ax, label='DF + FCo')
rr.plot( self.region_noInterference, 'blue', lw=2, axes=ax, label='Free Interf.')
fig.suptitle('$g ={},{},{},{}$'.format(self.g13, self.g14, self.g23, self.g24), fontsize=14, fontweight='bold')
ax.set_title(r'$P_s=P_1=P_2={}, \, P_3={}, \, P_4={}, \, N=1$'.format(self.Ps, self.P3, self.P4),fontdict=self.font)
ax.set_xlabel('$R_1$', fontdict=self.font)
ax.set_ylabel('$R_2$', fontdict=self.font)
#ax.set_xlim(xmin=0, xmax=3.5)
#ax.set_ylim(ymin=0, ymax=3.5)
ax.legend(loc=0)
# savefig.save(path='{}compare_TwoHop_Schemes/PsP3P4_{}_{}_{}_g_{}_{}_{}_{}'.format(pathString, self.Ps, self.P3, self.P4, self.g13, self.g14, self.g23, self.g24), ext='pdf', close=False, verbose=True)
nameStr = 'PsP3P4_{}_{}_{}_g13g14g23g24_{}_{}_{}_{}'.format(self.Ps, self.P3, self.P4, self.g13, self.g14, self.g23, self.g24)
savefig.save(path='{}compare_TwoHop_Schemes/{}'.format(pathString, nameStr), ext='pdf', close=False, verbose=True)
def display70(oneSimulation,pathString, separate=False ,saveOnly=True):
'''
show/save figures
'''
self = oneSimulation
tmp = np.asarray(oneSimulation.cf_icf01_region._geometry.boundary)
tmp01= tmp[(0,1,3), :]
tmp = np.asarray(oneSimulation.cf_icf_region._geometry.boundary)
tmp03= tmp[(1,2,3,4), :]
tmp = np.asarray(oneSimulation.df_fco_region._geometry.boundary)
tmpDF = tmp[(0,1,2,3), :]
pylab.rc('axes', linewidth=2) # make the axes boundary lines bold
# fig, ax = plt.subplots()
fig = plt.figure()
fig.set( size_inches=(8.8, 6) )
ax = fig.add_axes([0.1, 0.1, 0.7, 0.7]) # left, bottom, width, height (range 0 to 1)
# if separate:
# rr.plot( self.df_region, 'red', lw=6, axes=ax, label='DF')
# rr.plot( self.cf_region, 'blue', lw=4, axes=ax, label='CF')
# rr.plot( self.fco_region, 'red', lw=6, axes=ax, label='DF')
# rr.plot( self.icf_region_bigR1, 'blue', lw=4, axes=ax, label='ICF')
# rr.plot( self.icf_region_bigR2, 'blue', lw=4, axes=ax)
# rr.plot(self.df_fco_region, 'blue', lw=6, label='DF+FCo')
# rr.plot(self.cf_icf_region, 'red', lw=4, label='CF+ICF Scheme 3')
# rr.plot(self.cf_icf01_region, 'green', lw=2, label='CF+ICF Scheme 1' )
ax.plot(tmpDF[:,0], tmpDF[:,1], 'blue', lw=6, label='DF+FCo')
ax.plot(tmp03[:,0], tmp03[:,1], 'red', lw=4, label='CF+ICF Scheme 3')
ax.plot(tmp01[:,0], tmp01[:,1], 'green', lw=2, label='CF+ICF Scheme 1' )
# fig.suptitle('$g ={},{},{},{}$'.format(self.g13, self.g14, self.g23, self.g24), fontsize=14, fontweight='bold')
ax.set_title(r'$P_s=P_1=P_2={}, \, P_3={}, \, P_4={}, \, N=1$'.format(self.Ps,
self.P3, self.P4),fontdict=self.font)
ax.set_xlabel('$R_1$', fontdict=self.font)
ax.set_ylabel('$R_2$', fontdict=self.font)
#ax.set_xlim(xmin=0, xmax=3.5)
#ax.set_ylim(ymin=0, ymax=3.5)
ax.legend(loc=0)
nameStr = 'PsP3P4_{}_{}_{}_g13g14g23g24_{}_{}_{}_{}'.format(self.Ps, self.P3, self.P4,
self.g13, self.g14, self.g23, self.g24)
savefig.save(path='{}/plots_CF_ICF03__CF_ICF01__DF_FCo/{}'.format(pathString, nameStr), ext='pdf', close=saveOnly, verbose=True)
def hicact_spc_speeds_pa(df_hicact, spc):
plt.scatter(df_hicact.v, df_hicact.pa, s=80, facecolor=colors[spc], edgecolor="none", alpha=alph)
# plt.axhline(y=df_hicact_b.pa.min())
# plt.axhline(y=df_hicact_b.pa.max())
plt.xlim(speeds_lim)
plt.title("HICACTus STEREO-%s" % labels[spc])
plt.xlabel(speeds_label)
plt.ylabel("Position Angle ($deg$)")
save(path=os.path.join(config.hicact_path, "hicact_speeds_pa_%s" % labels[spc]), verbose=True)
def hicact_speeds_pa():
plt.figure(num=None, figsize=(6, 8), dpi=80, facecolor="w", edgecolor="k")
a = plt.scatter(df_hicact_a.v, df_hicact_a.pa, s=20, facecolor="red", edgecolor="none", alpha=alph)
b = plt.scatter(df_hicact_b.v, df_hicact_b.pa, s=20, facecolor="blue", edgecolor="none", alpha=alph)
plt.xlim(speeds_lim)
plt.ylim([0, 360])
plt.title("HICACTus STEREO CMEs")
plt.xlabel(speeds_label)
plt.ylabel("Position Angle ($deg$)")
plt.legend([a, b], ["Ahead", "Behind"], prop={"size": 8})
save(path=os.path.join(config.hicact_path, "hicact_speeds_pa"), verbose=True)
def hicact_speeds(v_a, v_b):
plt.hist(v_a,bins=np.arange(0,max(v_a)+binwidth,binwidth),histtype='stepfilled',\
normed=False,color='r',label='Ahead')
plt.hist(v_b,bins=np.arange(0,max(v_b)+binwidth,binwidth),histtype='stepfilled',\
normed=False,color='b',alpha=alph,label='Behind')
plt.title("HICACTus CME Speeds")
plt.xlim(speeds_lim)
plt.xlabel(speeds_label)
plt.ylabel("Count")
plt.legend(prop={'size': 8})
save(path=os.path.join(config.hicact_path, "hicact_speeds_hist"),
verbose=True)
def cdaw_hists():
fig = plt.figure(1,figsize=(20,20))
df_cdaw.hist(column=cols_hist,bins=50,grid=False)
save(path=os.path.join(config.cdaw_path,"cdaw_hist"),verbose=True)
#boxplot
fig = plt.figure(2,figsize=(9,6))
ax = fig.add_subplot(111)
df_cdaw.boxplot(column=cols_boxplot,grid=False)
ax.set_xticklabels(labels_boxplot)
plt.ylabel(speeds_label)
plt.title("CDAW LASCO CME Catalog")
plt.tight_layout()
save(path=os.path.join(config.cdaw_path,"cdaw_speeds_boxplot"),verbose=True)
def cdaw_hists():
fig = plt.figure(1, figsize=(20, 20))
df_cdaw.hist(column=cols_hist, bins=50, grid=False)
save(path=os.path.join(config.cdaw_path, "cdaw_hist"), verbose=True)
#boxplot
fig = plt.figure(2, figsize=(9, 6))
ax = fig.add_subplot(111)
df_cdaw.boxplot(column=cols_boxplot, grid=False)
ax.set_xticklabels(labels_boxplot)
plt.ylabel(speeds_label)
plt.title("CDAW LASCO CME Catalog")
plt.tight_layout()
save(path=os.path.join(config.cdaw_path, "cdaw_speeds_boxplot"),
verbose=True)
def hicact_speeds_datetime(df_hicact_a, df_hicact_b):
import datetime
time_format = "%Y/%m/%d %H:%M"
datetimes_a = np.array([datetime.datetime.strptime(x, time_format) for x in df_hicact_a.starttime])
datetimes_b = np.array([datetime.datetime.strptime(x, time_format) for x in df_hicact_b.starttime])
plt.figure(num=None, figsize=(7, 8), dpi=80, facecolor="w", edgecolor="k")
a = plt.scatter(datetimes_a, df_hicact_a.v, s=20, facecolor="red", edgecolor="none", alpha=alph)
b = plt.scatter(datetimes_b, df_hicact_b.v, s=20, facecolor="blue", edgecolor="none", alpha=alph)
plt.ylim([0, 2300])
plt.title("HICACTus CMEs")
plt.ylabel(speeds_label)
plt.xlabel("Time")
plt.legend([a, b], ["Ahead", "Behind"], prop={"size": ledge_sz}, loc=2)
save(path=os.path.join(config.hicact_path, "hicact_speeds_datetimes"), verbose=True)
def hicat_speeds_pa(df_hicat):
plt.figure(num=None,figsize=(8,10),dpi=80,facecolor='w',edgecolor='k')
hm = plt.scatter(df_hicat[['HM speed [kms-1]']],df_hicat[['PA-fit']],s=50,facecolor='green',\
edgecolor='none',alpha=0.35)
sse = plt.scatter(df_hicat[['SSE speed [kms-1]']],df_hicat[['PA-fit']],s=50,facecolor='blue',\
edgecolor='none',alpha=0.35)
fp = plt.scatter(df_hicat[['FP speed [kms-1]']],df_hicat[['PA-fit']],s=50,facecolor='red',\
edgecolor='none',alpha=0.35)
plt.xlim(speeds_lim)
plt.ylim([0,360])
plt.title("HICAT CMEs")
plt.xlabel(speeds_label)
plt.ylabel("Position Angle ($deg$)")
plt.legend([fp,sse,hm],['Fixed-Phi','Self-Similar Exp.','Harmonic Mean'],prop={'size':ledge_sz})
save(path=os.path.join(config.hicat_path,"hicat_speeds_pa"),verbose=True)
def hicat_spc_speeds_pa(df_hicat,spc):
hm = plt.scatter(df_hicat[['HM speed [kms-1]']],df_hicat[['PA-fit']],s=50,facecolor='green',\
edgecolor='none',alpha=0.5)
sse = plt.scatter(df_hicat[['SSE speed [kms-1]']],df_hicat[['PA-fit']],s=50,facecolor='blue',\
edgecolor='none',alpha=0.5)
fp = plt.scatter(df_hicat[['FP speed [kms-1]']],df_hicat[['PA-fit']],s=50,facecolor='red',\
edgecolor='none',alpha=0.5)
#plt.axhline(y=df_hicat_b.pa.min())
#plt.axhline(y=df_hicat_b.pa.max())
plt.xlim(speeds_lim)
plt.title("HICAT STEREO-%s" %labels[spc])
plt.xlabel(speeds_label)
plt.ylabel("Position Angle ($deg$)")
plt.legend([fp,sse,hm],['Fixed-Phi','Self-Similar Exp.','Harmonic Mean'],prop={'size':ledge_sz})
save(path=os.path.join(config.hicat_path,"hicat_speeds_pa_%s" %labels[spc]),verbose=True)
def hicact_spc_speeds_pa(df_hicact, spc):
plt.scatter(df_hicact.v,
df_hicact.pa,
s=80,
facecolor=colors[spc],
edgecolor='none',
alpha=alph)
#plt.axhline(y=df_hicact_b.pa.min())
#plt.axhline(y=df_hicact_b.pa.max())
plt.xlim(speeds_lim)
plt.title("HICACTus STEREO-%s" % labels[spc])
plt.xlabel(speeds_label)
plt.ylabel("Position Angle ($deg$)")
save(path=os.path.join(config.hicact_path,
"hicact_speeds_pa_%s" % labels[spc]),
verbose=True)
def hi_spc_speeds(v, **kwargs):
if kwargs:
print kwargs
if "spc" in kwargs:
spc = kwargs["spc"]
else:
spc = -1
if "tit" in kwargs:
tit = kwargs["tit"]
else:
tit = ""
v = np.array(v.astype("float"))
plt.hist(v, bins=np.arange(0, max(v) + binwidth, binwidth), color=colors[spc])
plt.title("%s STEREO %s" % (tit.upper(), labels[spc]))
plt.xlim(speeds_lim)
plt.xlabel(speeds_label)
save(path=os.path.join(config.hicact_path, "%s_speeds_hist_%s" % (tit, labels[spc])), verbose=True)
def cdaw_pa():
binwidth=5
plt.figure(num=None,figsize=(8,10),dpi=80,facecolor='w',edgecolor='k')
plt.subplot(211)
plt.hist(df_cdaw.cpa.values,bins=np.arange(0,max(df_cdaw.cpa)+binwidth,binwidth),normed=False)
plt.xlim([0,360])
plt.xlabel("Position Angle ($deg.$)")
plt.ylabel("Count")
plt.title("CDAW CMEs Central Position Angle")
plt.subplot(212)
plt.hist(df_cdaw.mpa.values,bins=np.arange(0,max(df_cdaw.mpa)+binwidth,binwidth),normed=False)
plt.xlim([0,360])
plt.xlabel("Position Angle ($deg.$)")
plt.ylabel("Count")
plt.title("CDAW CMEs Measured Position Angle")
plt.tight_layout()
save(path=os.path.join(config.cdaw_path,"cdaw_pa"),verbose=True)
def hi_geom_speeds(v,**kwargs):
if kwargs:
print kwargs
if 'spc' in kwargs:
spc = kwargs['spc']
else:
spc = -1
if 'tit' in kwargs:
tit = kwargs['tit']
else:
tit = ""
v = np.array(v.astype('float'))
plt.hist(v,bins=np.arange(0, max(v) + binwidth, binwidth),color=colors[spc])
plt.title("%s STEREO %s" %(tit,labels[spc]))
#plt.xlim(speeds_lim)
plt.xlabel(speeds_label)
save(path=os.path.join(config.hicat_path,"%s_speeds_hist_%s" %(tit,labels[spc])),verbose=True)
def cdaw_speeds_pa():
plt.figure(figsize=(10,8),dpi=80,facecolor='w')
ssz = 10
#lin = plt.scatter(df_cdaw.lin_speed,df_cdaw.mpa,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
#q_i = plt.scatter(df_cdaw.quad_speed_init,df_cdaw.mpa,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
q_f = plt.scatter(df_cdaw.quad_speed_final,df_cdaw.mpa,s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.3)
plt.xlim([0,3500])
plt.ylim([0,360])
plt.title("CDAW LASCO CME Catalog")
plt.xlabel(speeds_label)
plt.ylabel("Measured Position Angle ($deg.$)")
#plt.legend([lin,q_i,q_f],['Linear','Quad. (init.)','Quad. (final)'],prop={'size':ledge_sz})
#plt.legend([lin,q_f],['Linear','Quad. (final)'],prop={'size':ledge_sz})
plt.legend([q_f],['Quad. (final)'],prop={'size':ledge_sz})
save(path=os.path.join(config.cdaw_path,"cdaw_speeds_pa"),verbose=True)
def cdaw_speeds_pa():
plt.figure(figsize=(10, 8), dpi=80, facecolor='w')
ssz = 10
#lin = plt.scatter(df_cdaw.lin_speed,df_cdaw.mpa,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
#q_i = plt.scatter(df_cdaw.quad_speed_init,df_cdaw.mpa,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
q_f = plt.scatter(df_cdaw.quad_speed_final,df_cdaw.mpa,s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.3)
plt.xlim([0, 3500])
plt.ylim([0, 360])
plt.title("CDAW LASCO CME Catalog")
plt.xlabel(speeds_label)
plt.ylabel("Measured Position Angle ($deg.$)")
#plt.legend([lin,q_i,q_f],['Linear','Quad. (init.)','Quad. (final)'],prop={'size':ledge_sz})
#plt.legend([lin,q_f],['Linear','Quad. (final)'],prop={'size':ledge_sz})
plt.legend([q_f], ['Quad. (final)'], prop={'size': ledge_sz})
save(path=os.path.join(config.cdaw_path, "cdaw_speeds_pa"), verbose=True)
def hicat_speeds_datetime(df_hicat):
import datetime
time_format = "%Y-%m-%dT%H:%MZ"
datetimes = np.array([datetime.datetime.strptime(x[0],time_format) \
for x in df_hicat[['Date [UTC]']].values])
plt.figure(num=None,figsize=(8,6),dpi=80,facecolor='w',edgecolor='k')
ssz=30
hm = plt.scatter(datetimes,df_hicat[['HM speed [kms-1]']],s=ssz,facecolor='green',\
edgecolor='none',alpha=0.35)
sse = plt.scatter(datetimes,df_hicat[['SSE speed [kms-1]']],s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.35)
fp = plt.scatter(datetimes,df_hicat[['FP speed [kms-1]']],s=ssz,facecolor='red',\
edgecolor='none',alpha=0.35)
plt.ylim(speeds_lim)
plt.title("HICAT CMEs")
plt.ylabel(speeds_label)
plt.xlabel("Time")
plt.legend([fp,sse,hm],['Fixed-Phi','Self-Similar Exp.','Harmonic Mean'],prop={'size':ledge_sz},loc=2)
save(path=os.path.join(config.hicat_path,"hicat_speeds_datetimes"),verbose=True)
def display(oneSimulation,pathString ,saveOnly=True):
'''
Produce a graph that compare three ICF schemes
'''
self = oneSimulation
# get the union rate region for plotting
icf_sch1 = rr.union( [self.icf_sch1_bigR1, self.icf_sch1_bigR2] )
icf_sch2 = rr.union( [self.icf_sch2_bigR1, self.icf_sch2_bigR2] )
icf_sch3 = rr.union( [self.icf_sch3_bigR1, self.icf_sch3_bigR2] )
pylab.rc('axes', linewidth=2) # make the axes boundary lines bold
fig, ax = plt.subplots()
rr.plot( icf_sch1, 'g', axes=ax, label='Scheme 1')
rr.plot( icf_sch2, 'b', axes=ax, label='Scheme 2')
rr.plot( icf_sch3, 'r', axes=ax, label='Scheme 3')
# plot the line: R2 = R1
tmp = np.asarray(self.icf_sch1_bigR1._geometry.boundary)
tmp2 = [[0, tmp[1, 0]], [0, tmp[1, 1] ] ]
ax.plot( [0, tmp[1, 0]], [0, tmp[1, 1] ] , 'k--', lw=2)
ax.set_title(r'$ P_3={}, \, P_4={}, \, N=1$'.format(self.P3, self.P4) , fontdict=self.font)
ax.set_xlabel('$R_1$', fontdict=self.font)
ax.set_ylabel('$R_2$', fontdict=self.font)
ax.set_xlim(xmin=0, xmax=3.5)
ax.set_ylim(ymin=0, ymax=3.5)
ax.legend(loc='upper right')
savefig.save(path='{}/compare_three_ICF_Schemes/P3P4_{}_{}'.format(pathString, self.P3, self.P4 ), ext='pdf', close=saveOnly, verbose=True)
if (0):
# add annotations for 3 regions
plt.text(0.7, 2.3, 'capacity region by coherent coding with cardinality-bounding', color='red')
plt.text(1, 1.8, 'non-coherent coding with cardinality-bounding', color='blue')
plt.text(1.3, 1.4, 'capacity region by coherent coding with cardinality-bounding', color='green')
bbox_props = dict(boxstyle="round,pad=0.1", fc="white", ec="g", lw=1)
plt.text(1, 0.5, r'$1$', color='black', bbox=bbox_props)
bbox_props = dict(boxstyle="round,pad=0.1", fc="white", ec="b", lw=1)
plt.text(1.7, 0.4, r'$2$', color='black', bbox=bbox_props)
bbox_props = dict(boxstyle="round,pad=0.1", fc="white", ec="r", lw=1)
plt.text(2.2, 0.3, r'$3$', color='black', bbox=bbox_props)
def hicact_speeds_pa():
plt.figure(num=None, figsize=(6, 8), dpi=80, facecolor='w', edgecolor='k')
a = plt.scatter(df_hicact_a.v,
df_hicact_a.pa,
s=20,
facecolor='red',
edgecolor='none',
alpha=alph)
b = plt.scatter(df_hicact_b.v,
df_hicact_b.pa,
s=20,
facecolor='blue',
edgecolor='none',
alpha=alph)
plt.xlim(speeds_lim)
plt.ylim([0, 360])
plt.title("HICACTus STEREO CMEs")
plt.xlabel(speeds_label)
plt.ylabel("Position Angle ($deg$)")
plt.legend([a, b], ['Ahead', 'Behind'], prop={'size': 8})
save(path=os.path.join(config.hicact_path, "hicact_speeds_pa"),
verbose=True)
def hi_spc_speeds(v, **kwargs):
if kwargs:
print kwargs
if 'spc' in kwargs:
spc = kwargs['spc']
else:
spc = -1
if 'tit' in kwargs:
tit = kwargs['tit']
else:
tit = ""
v = np.array(v.astype('float'))
plt.hist(v,
bins=np.arange(0,
max(v) + binwidth, binwidth),
color=colors[spc])
plt.title("%s STEREO %s" % (tit.upper(), labels[spc]))
plt.xlim(speeds_lim)
plt.xlabel(speeds_label)
save(path=os.path.join(config.hicact_path,
"%s_speeds_hist_%s" % (tit, labels[spc])),
verbose=True)
def cdaw_pa():
binwidth = 5
plt.figure(num=None, figsize=(8, 10), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(211)
plt.hist(df_cdaw.cpa.values,
bins=np.arange(0,
max(df_cdaw.cpa) + binwidth, binwidth),
normed=False)
plt.xlim([0, 360])
plt.xlabel("Position Angle ($deg.$)")
plt.ylabel("Count")
plt.title("CDAW CMEs Central Position Angle")
plt.subplot(212)
plt.hist(df_cdaw.mpa.values,
bins=np.arange(0,
max(df_cdaw.mpa) + binwidth, binwidth),
normed=False)
plt.xlim([0, 360])
plt.xlabel("Position Angle ($deg.$)")
plt.ylabel("Count")
plt.title("CDAW CMEs Measured Position Angle")
plt.tight_layout()
save(path=os.path.join(config.cdaw_path, "cdaw_pa"), verbose=True)
def cdaw_speeds_datetime():
import datetime
import matplotlib.ticker as ticker
time_format = "%Y/%m/%dT%H:%M:%S"
datetimes = np.array([datetime.datetime.strptime(x,time_format) \
for x in df_cdaw.date.values+'T'+df_cdaw.time.values])
plt.figure(num=None,figsize=(10,7),dpi=80,facecolor='w',edgecolor='k')
ssz=10
#lin = plt.scatter(datetimes,df_cdaw.lin_speed,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
q_f = plt.scatter(datetimes,df_cdaw.quad_speed_final,s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.3)
plt.ylim([0,3500])
plt.title("CDAW LASCO CME Catalog")
plt.ylabel(speeds_label)
plt.xlabel("Time")
ax = plt.axes()
ax.xaxis.set_major_locator(ticker.MultipleLocator(365))
labels=ax.get_xticklabels()
plt.setp(labels,rotation=40)
plt.legend([q_f],['Quad. (final)'],prop={'size':ledge_sz})
#plt.legend([lin,q_f],['Linear','Quad. (final)'],prop={'size':ledge_sz})
save(path=os.path.join(config.cdaw_path,"cdaw_speeds_datetimes"),verbose=True)
def cdaw_speeds_datetime():
import datetime
import matplotlib.ticker as ticker
time_format = "%Y/%m/%dT%H:%M:%S"
datetimes = np.array([datetime.datetime.strptime(x,time_format) \
for x in df_cdaw.date.values+'T'+df_cdaw.time.values])
plt.figure(num=None, figsize=(10, 7), dpi=80, facecolor='w', edgecolor='k')
ssz = 10
#lin = plt.scatter(datetimes,df_cdaw.lin_speed,s=ssz,facecolor='red',\
# edgecolor='none',alpha=0.3)
q_f = plt.scatter(datetimes,df_cdaw.quad_speed_final,s=ssz,facecolor='blue',\
edgecolor='none',alpha=0.3)
plt.ylim([0, 3500])
plt.title("CDAW LASCO CME Catalog")
plt.ylabel(speeds_label)
plt.xlabel("Time")
ax = plt.axes()
ax.xaxis.set_major_locator(ticker.MultipleLocator(365))
labels = ax.get_xticklabels()
plt.setp(labels, rotation=40)
plt.legend([q_f], ['Quad. (final)'], prop={'size': ledge_sz})
#plt.legend([lin,q_f],['Linear','Quad. (final)'],prop={'size':ledge_sz})
save(path=os.path.join(config.cdaw_path, "cdaw_speeds_datetimes"),
verbose=True)
def hicact_speeds(v_a, v_b):
plt.hist(
v_a,
bins=np.arange(0, max(v_a) + binwidth, binwidth),
histtype="stepfilled",
normed=False,
color="r",
label="Ahead",
)
plt.hist(
v_b,
bins=np.arange(0, max(v_b) + binwidth, binwidth),
histtype="stepfilled",
normed=False,
color="b",
alpha=alph,
label="Behind",
)
plt.title("HICACTus CME Speeds")
plt.xlim(speeds_lim)
plt.xlabel(speeds_label)
plt.ylabel("Count")
plt.legend(prop={"size": 8})
save(path=os.path.join(config.hicact_path, "hicact_speeds_hist"), verbose=True)
def hicat_stacked_speeds(): wp3_speeds=df_hicat[['FP speed [kms-1]','SSE speed [kms-1]','HM speed [kms-1]']] wp3_speeds.plot(kind='hist',stacked=True,bins=100) save(path=os.path.join(config.hicat_path,"hicat_speeds_stacked"),verbose=True)
df_cdaw.describe()
# Generate some initial plots for CDAW
#df_cdaw.hist()
#save(path=os.path.join(config.wp3_path,"cdaw_cme_catalog/cdaw_hist"),verbose=True)
#plt.show()
hicact_a_speeds = df_hicact_a[['v']]
hicact_b_speeds = df_hicact_b[['v']]
binwidth = 50
v_a = np.array(df_hicact_a[['v']].astype('float'))
plt.hist(v_a,bins=np.arange(0, max(v_a) + binwidth, binwidth))
plt.title("HICACTus STEREO-Ahead")
save(path=os.path.join(config.hicact_path,"hicact_a_speeds_hist"),verbose=True)
v_b = np.array(df_hicact_b[['v']].astype('float'))
plt.hist(v_b,bins=np.arange(0,max(v_b)+binwidth,binwidth))
plt.title("HICACTus STEREO-Behind")
save(path=os.path.join(config.hicact_path,"hicact_b_speeds_hist"),verbose=True)
plt.hist(v_a,bins=np.arange(0,max(v_a)+binwidth,binwidth),histtype='stepfilled',\
normed=False,color='b',label='Ahead')
plt.hist(v_b,bins=np.arange(0,max(v_b)+binwidth,binwidth),histtype='stepfilled',\
normed=False,color='r',alpha=0.5,label='Behind')
plt.title("HICACTus CME Speeds")
plt.xlabel("Speed [kms-1]")
plt.ylabel("Count")
plt.legend(prop={'size':8})
save(path=os.path.join(config.hicact_path,"hicact_speeds_hist"),verbose=True)
data1 = pd.read_csv('Images/means_err_dataJan27.csv', header=False, delim_whitespace=True)
CnM_test_list = data1['CnM']
CM_test_list = data1['CM']
CR_test_list = data1['CR']
C_test_list = data1['C']
num_runs = 15
means_list = [sum(C_test_list)/float(num_runs), sum(CnM_test_list)/float(num_runs), sum(CM_test_list)/float(num_runs),sum(CR_test_list)/float(num_runs)]
objects = ('C', 'CnM', 'CM', 'CR')
y_pos = np.arange(len(objects))
std_err_C = np.std(C_test_list)
std_err_CnM = np.std(CnM_test_list)
std_err_CM = np.std(CM_test_list)
std_err_CR = np.std(CR_test_list)
errors = [std_err_C, std_err_CnM, std_err_CM, std_err_CR]
plt.bar(y_pos, means_list, align='center', width=0.7, yerr=errors, alpha=0.6 )
plt.xlabel('Condition for Evolution')
plt.xticks(y_pos, objects)
plt.ylabel('Average Error for Categorization')
plt.ylim([0, max(means_list)+0.1])
plt.title('Average Error')
save('/Users/np/Desktop/Bullet/bullet_make/Demos/RagdollDemo/Debug/Images/Means_werr'+datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H:%M:%S')+'Jan27')
