def fig_ew(outfil=None):
# Init
wvmnx = (1214.5, 1216.8) * u.AA
wave = np.linspace(wvmnx[0].value, wvmnx[1].value, 100)
wave = wave * u.AA
# Single line (Lya)
zabs = 0.0
line = AbsLine(1215.6701 * u.AA)
line.z = zabs
line.attrib['N'] = 16.0
line.attrib['b'] = 30.0
vmodel = xsp.voigt.voigt_model(wave, line, Npix=None) # No smoothing
line.spec = vmodel
# EW
line.analy['WVMNX'] = wvmnx
EW, sigEW = line.ew()
# Plot boundaries
ymnx = (-0.05, 1.1)
if outfil == None:
outfil = 'fig_EW.pdf'
# Start the plot
if outfil != None:
pp = PdfPages(outfil)
# Make the plots
for ss in range(2):
plt.figure(figsize=(8, 5))
plt.clf()
gs = gridspec.GridSpec(1, 1)
# Axes
ax = plt.subplot(gs[0, 0])
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.5))
ax.xaxis.set_major_locator(plt.MultipleLocator(1.))
ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.set_xlim(wvmnx.value)
ax.set_ylim(ymnx)
ax.set_ylabel('Normalized Flux')
ax.set_xlabel(r'Wavelength ($\AA$)')
# Zero line
ax.plot(wvmnx.value, (0., 0.), 'g--')
# Data
lines = ax.step(vmodel.dispersion,
vmodel.flux,
'k',
where='mid',
linewidth=1.5)
#lines = ax.step(vmodel.dispersion, vmodel.flux, 'k', where='mid')#, linewidth=1.5)
# Fill?
if ss > 0:
#ax.fill_between( vmodel.dispersion, vmodel.flux, [1.]*len(wave),
# color='blue', alpha=0.3)
ax.fill_between(lines[0].get_xdata(orig=False),
1,
lines[0].get_ydata(orig=False),
alpha=0.4)
csz = 20.
ax.text(1215.670, 0.5, 'EW', color='black', ha='center', size=csz)
ax.text(1214.8,
0.2,
r'$W_\lambda = \, $' + '{:.2f}'.format(EW.value) +
r' $\AA$',
color='blue',
ha='left',
size=csz)
# Fonts
xputils.set_fontsize(ax, 15.)
# Write
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
pp.savefig()
plt.close()
# Finish
print('tlk_spectra.fig_ew: Wrote {:s}'.format(outfil))
pp.close()
# plot cluster boundaries
FeH_centers = 0.5 * (FeH_bins[1:] + FeH_bins[:-1])
alphFe_centers = 0.5 * (alphFe_bins[1:] + alphFe_bins[:-1])
Xgrid = np.meshgrid(FeH_centers, alphFe_centers)
Xgrid = np.array(Xgrid).reshape((2, 50 * 50)).T
H = clf.predict(scaler.transform(Xgrid)).reshape((50, 50))
for i in range(n_clusters):
Hcp = H.copy()
flag = (Hcp == i)
Hcp[flag] = 1
Hcp[~flag] = 0
ax.contour(FeH_centers,
alphFe_centers,
Hcp, [-0.5, 0.5],
linewidths=1,
colors='k')
ax.xaxis.set_major_locator(plt.MultipleLocator(0.3))
ax.set_xlim(-1.101, 0.101)
ax.set_ylim(alphFe_bins[0], alphFe_bins[-1])
ax.set_xlabel(r'$\rm [Fe/H]$')
ax.set_ylabel(r'$\rm [\alpha/Fe]$')
plt.show()
print "Twinned"
print "GaAs GaSb AlSb CdTe Ge ZnTe InP GaP"
print np.degrees(np.arcsin(np.sqrt(3)*lattice_constants_all/(2*5.43095))) - 74.2068
print "Test"
print np.degrees(np.arcsin(1.2 * np.sin(np.radians(19.4712)))) - 19.4712
plt.hold(True)
plt.axis([0,4.10695300643,0,20])
#plt.pcolormesh(x,y2,strain_epi, cmap=cmap_map(lambda x: x/2.0+0.4, plt.cm.RdBu))
plt.pcolormesh(x,y2,strain_epi, cmap=plt.cm.bwr, vmin=-0.32, vmax=+0.32, rasterized=True)
#plt.colorbar(use_gridspec=True, format="%+1.2f", ticks=[-0.6,-0.45,-0.3,-0.15,0,0.15,0.3,0.45,0.6] )
cb = plt.colorbar(use_gridspec=True, format="%+1.2f")
cb.solids.set_rasterized(True)
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.5))
ax.yaxis.set_major_locator(plt.MultipleLocator(2.5))
ax.yaxis.set_minor_locator(plt.MultipleLocator(1.25))
plt.xlabel('Tilt (Degrees)')
plt.ylabel('Intrinsic Lattice Mismatch (\%)')
#plt.title('Effective Mismatch of Tilted Strained Films')
CS = plt.contour(x,y2,strain_epi,levels=[0], colors='black', linestyles='dashdot', zorder=100)
#Change dash-dot pattern, on, off, on, off
for c in CS.collections:
c.set_dashes([(0, (4.0, 3.0, 1.0, 3.0))])
ax.annotate('Zero Projected Strain', xy=(1.55, 7.45), xytext=(1.75, 5.5),
arrowprops=dict(facecolor='black', shrink=0.1,width=0.05,headwidth=2),
)
yerr=error_FM1,
error_kw=dict(elinewidth=1, ecolor='black'),
label=r'$ FM1-virus $')
plt.bar(gT_lab_fresh - bar_width / 2,
gX31_lab_fresh,
bar_width,
alpha=0.1,
color='black',
yerr=error_lab_fresh,
error_kw=dict(elinewidth=1, ecolor='black'),
label=r'$ (X31-virus) $')
plt.grid(True, which='both')
plt.title(r'$ Original \ Antigenic \ Sin \ (sequential-infection)$',
fontsize=AlvaFontSize)
plt.xlabel(r'$time \ (%s)$' % (timeUnit), fontsize=AlvaFontSize)
plt.ylabel(r'$ Neutralization \ \ titer $', fontsize=AlvaFontSize)
plt.xticks(fontsize=AlvaFontSize * 0.7)
plt.yticks(fontsize=AlvaFontSize * 0.7)
plt.xlim([minT, 2 * 30 * day])
plt.ylim([2**5, 2**14])
plt.yscale('log', basey=2)
# gca()---GetCurrentAxis and Format the ticklabel to be 2**x
plt.gca().yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: int(2**(np.log(x) / np.log(2)))))
plt.gca().xaxis.set_major_locator(plt.MultipleLocator(7))
plt.legend(loc=(1, 0), fontsize=AlvaFontSize)
plt.savefig(save_figure, dpi=100, bbox_inches='tight')
plt.show()
# In[ ]:
fig = plt.figure(figsize=(10, 8))
fig.subplots_adjust(left=0.05,
right=0.95,
wspace=0.05,
bottom=0.1,
top=0.95,
hspace=0.05)
titles = ['PCA components', 'ICA components', 'NMF components']
for i, comp in enumerate(decompositions):
for j in range(n_components):
ax = fig.add_subplot(n_components, 3, 3 * j + 1 + i)
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_major_locator(plt.MultipleLocator(1000))
if j < n_components - 1:
ax.xaxis.set_major_formatter(plt.NullFormatter())
else:
ax.set_xlabel(r'wavelength $(\AA)$')
ax.plot(wavelengths, comp[j], '-k', lw=1)
# plot zero line
xlim = [3000, 7999]
ax.plot(xlim, [0, 0], '-', c='gray', lw=1)
ax.set_xlim(xlim)
if j == 0:
ax.set_title(titles[i], fontsize='medium')
def frame_fiberflat(outfil, qaframe, frame, fiberflat):
""" QA plots for fiber flat
Args:
outfil:
qaframe:
frame:
fiberflat:
Returns:
Stuff?
"""
# Setup
fibermap = frame.fibermap
gdp = fiberflat.mask == 0
nfiber = len(frame.fibers)
xfiber = np.zeros(nfiber)
yfiber = np.zeros(nfiber)
for ii, fiber in enumerate(frame.fibers):
mt = np.where(fiber == fibermap['FIBER'])[0]
xfiber[ii] = fibermap['X_TARGET'][mt]
yfiber[ii] = fibermap['Y_TARGET'][mt]
jet = cm = plt.get_cmap('jet')
# Tile plot(s)
fig = plt.figure(figsize=(8, 5.0))
gs = gridspec.GridSpec(2, 2)
# Mean Flatfield flux in each fiber
ax = plt.subplot(gs[0, 0])
ax.xaxis.set_major_locator(plt.MultipleLocator(100.))
mean_flux = np.mean(frame.flux * gdp, axis=1)
rms_mean = np.std(mean_flux)
med_mean = np.median(mean_flux)
#from xastropy.xutils import xdebug as xdb
#pdb.set_trace()
mplt = ax.scatter(xfiber, yfiber, marker='o', s=9., c=mean_flux, cmap=jet)
mplt.set_clim(vmin=med_mean - 2 * rms_mean, vmax=med_mean + 2 * rms_mean)
cb = fig.colorbar(mplt)
cb.set_label('Mean Flux')
# Mean
ax = plt.subplot(gs[0, 1])
ax.xaxis.set_major_locator(plt.MultipleLocator(100.))
mean_norm = np.mean(fiberflat.fiberflat * gdp, axis=1)
m2plt = ax.scatter(xfiber, yfiber, marker='o', s=9., c=mean_norm, cmap=jet)
m2plt.set_clim(vmin=0.98, vmax=1.02)
cb = fig.colorbar(m2plt)
cb.set_label('Mean of Fiberflat')
# RMS
ax = plt.subplot(gs[1, 0])
ax.xaxis.set_major_locator(plt.MultipleLocator(100.))
rms = np.std(
gdp *
(fiberflat.fiberflat - np.outer(mean_norm, np.ones(fiberflat.nwave))),
axis=1)
rplt = ax.scatter(xfiber, yfiber, marker='o', s=9., c=rms, cmap=jet)
#rplt.set_clim(vmin=0.98, vmax=1.02)
cb = fig.colorbar(rplt)
cb.set_label('RMS in Fiberflat')
# Meta text
ax2 = plt.subplot(gs[1, 1])
ax2.set_axis_off()
show_meta(ax2, qaframe, 'FIBERFLAT', outfil)
"""
xlbl = 0.05
ylbl = 0.85
i0 = outfil.rfind('/')
ax2.text(xlbl, ylbl, outfil[i0+1:], color='black', transform=ax2.transAxes, ha='left')
yoff=0.10
for key in sorted(qaframe.data['FIBERFLAT']['METRICS'].keys()):
if key in ['QA_FIG']:
continue
# Show
ylbl -= yoff
ax2.text(xlbl+0.05, ylbl, key+': '+str(qaframe.data['FIBERFLAT']['METRICS'][key]),
transform=ax2.transAxes, ha='left', fontsize='x-small')
"""
# Finish
plt.tight_layout(pad=0.1, h_pad=0.0, w_pad=0.0)
plt.savefig(outfil)
plt.close()
print('Wrote QA SkyRes file: {:s}'.format(outfil))
for (offset, color, error, linewidth) in zip(offsets, colors,
errors, linewidths):
# compute the PSD
err = np.random.normal(0, error, size=hj.shape)
hj_N = hj + err + offset
fk, PSD = PSD_continuous(tj, hj_N)
# plot the data and PSD
ax1.scatter(tj, hj_N, s=4, c=color, lw=0)
ax1.plot(tj, 0 * tj + offset, '-', c=color, lw=1)
ax2.plot(fk, PSD, '-', c=color, lw=linewidth)
# vertical line marking the expected peak location
ax2.plot([0.5 / np.pi, 0.5 / np.pi], [-0.1, 1], ':k', lw=1)
ax1.set_xlim(-25, 25)
ax1.set_ylim(-0.1, 0.3001)
ax1.set_xlabel('$t$')
ax1.set_ylabel('$h(t)$')
ax1.yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax2.set_xlim(0, 0.8)
ax2.set_ylim(-0.101, 0.801)
ax2.set_xlabel('$f$')
ax2.set_ylabel('$PSD(f)$')
plt.show()
def makeChartsByAquifer(options, DBKeyList):
ThisDate = dt.date.today()
currentTime = dt.datetime.now().time()
timeStr = currentTime.strftime("%H:%M:%S")
timeStamp = ThisDate.strftime("%m/%d/%Y") + " " + timeStr
print timeStamp
myFmt = mdates.DateFormatter('%b %Y')
try:
print 'Establishing Database connection'
cnxn = pyodbc.connect('DSN=WREP; UID=pub; PWD=pub')
except Exception as err:
print err
cursor = cnxn.cursor()
# Define SQL for selecting date ranges by DBKEY
sqlQ = setSQLstring('DBKeyDates', keys=DBKeyList)
for frequency in ('DA', 'RI'):
print 'Selecting date ranges for ' + frequency + ' DBkeys'
DBKeys2WorkWith = fetchSQL(cursor, sqlQ, frequency)
recKnt = len(DBKeys2WorkWith)
# Display Execution progress on screen
steps = recKnt / 10
if steps > 1:
print 'Progress interval: ' + str(
steps) + ' hydrographs created for each 10%'
print 'Total hydrographs: ' + str(recKnt)
i = 0
print 'Building Hydrographs'
print 'Progress for ' + frequency
print ' 1'
print ' 1 2 3 4 5 6 7 8 9 0'
print ' 0 0 0 0 0 0 0 0 0 0 '
print ' [',
else:
steps = 1
print 'Total hydrographs being created for ' + frequency + ': ' + str(
recKnt)
i = 0
print 'Building Hydrographs'
print ' [',
sys.stdout.flush()
for row in DBKeys2WorkWith:
# Define chart title and figure name variables from each row
station = row.STATION.strip(' ')
key = row.DBKEY.strip(' ')
stationDB = station + ' (dbkey[' + key + '])'
dataType = row.DATA_TYPE.strip(' ')
dataSource = row.AGENCY.strip(' ')
i = i + 1
if i % steps == 0:
print 'X',
sys.stdout.flush()
# Frequency codes for Daily and Random require separate queries as the data is pulled from separate tables in DBHydro.
# DMDBASE.DAILY_DATA and DMDBASE.RANDOM_DATA
# Daily query:
if frequency == 'DA':
sql = setSQLstring('DailyData', rowData=row)
sdateDT = row.SDATE
oneStartDate = row.START_DATE.strip(' ')
# Hydrographs for Daily data need an additional query for each set of qualifier codes.
# Currently E for Estimated in one query, all other codes <> M in another.
sqlwithCodes = setSQLstring('DailyCodes', rowData=row)
sqlQualifiers = setSQLstring('DailyQualifiers', rowData=row)
sqlAnnotations = setSQLstring('DailyAnnotations', rowData=row)
sqlDCVPcodes = setSQLstring('DCVPcodes', rowData=row)
sqlDCVPcodesV = setSQLstring('DCVPcodeV', rowData=row)
sqlwithEs = setSQLstring('DailyEcodes', rowData=row)
else:
# Random Interval query:
# anyDailyDBKey= '01081' <--- This DBKey was picked to provide daily dates to random data.
sql = setSQLstring('RandomIntervalData', rowData=row)
# Fetch records for either Daily or Random Interval with sql String
records = fetchSQL(cursor, sql, 'none')
# If query returned 0 records skip the chart processing.
if len(records):
# Process Random Interval and Daily data in a similar fashion
# when retrieving data values and defining the x and y axis
dbhydDF = pd.DataFrame(records, columns=['dateread', 'value'])
dmax = dbhydDF.dateread.max()
ymax = float(dbhydDF.value.max())
ymin = float(dbhydDF.value.min())
yrange = ymax - ymin
ymin = ymin - round((yrange * .05), 2)
ymax = ymax + round((yrange * .05), 2)
yinterval = round((yrange / 8.0), 1)
ys = dbhydDF['value']
# Hydrographs for Daily data need additional query processing for each set of qualifier codes.
if frequency == 'DA':
dmin = sdateDT
xs = pd.date_range(dmin, dmax - dt.timedelta(days=1))
qualiferRecord = fetchSQL(cursor, sqlQualifiers, 'none')
cols = [t[0] for t in cursor.description]
for col, value in zip(cols, qualiferRecord):
qualiferStr = str(value).strip('( )')[:-1]
annotationRecord = fetchSQL(cursor, sqlAnnotations, 'none')
cols = [t[0] for t in cursor.description]
for col, value in zip(cols, annotationRecord):
annotationStr = str(value).strip('( )')[:-1]
# Create dataframe from Qualifier Code cursor
Qcodes = fetchSQL(cursor, sqlwithCodes, 'none')
codesDF = pd.DataFrame(
Qcodes, columns=['dateread', 'value', 'code'])
ycodes = codesDF['value']
dmax2 = codesDF.dateread.max()
xs2 = pd.date_range(dmin, dmax2 - dt.timedelta(days=1))
# Create dataframe from Estimated Code cursor
Ecodes = fetchSQL(cursor, sqlwithEs, 'none')
EcodesDF = pd.DataFrame(
Ecodes, columns=['dateread', 'value', 'code'])
ecodes = EcodesDF['value']
dmax3 = EcodesDF.dateread.max()
xs3 = pd.date_range(dmin, dmax3 - dt.timedelta(days=1))
# Create dataframes from Annoatation and Verification cursors
Acodes = fetchSQL(cursor, sqlDCVPcodes, 'none')
if len(Acodes) > 0:
annoDF = pd.DataFrame(
Acodes, columns=['dateread', 'value', 'code'])
dmax4 = annoDF.dateread.max()
xs4 = pd.date_range(dmin, dmax4 - dt.timedelta(days=1))
CannoCodes = annoDF['value']
Vcodes = fetchSQL(cursor, sqlDCVPcodesV, 'none')
if len(Vcodes) > 0:
annoVDF = pd.DataFrame(
Vcodes, columns=['dateread', 'value', 'code'])
dmax5 = annoVDF.dateread.max()
xs5 = pd.date_range(dmin, dmax5 - dt.timedelta(days=1))
VannoCodes = annoVDF['value']
else:
# Random Interval needs to create a mask for null values with isfinite
dmin = dbhydDF.dateread.min()
xs = pd.date_range(dmin, dmax)
ts2 = np.array(ys.values).astype(np.double)
ts2mask = np.isfinite(ts2)
# Define general chart characteristics, titles and descriptive text:
fig, chart = plt.subplots()
fontP = FontProperties()
fontP.set_size('xx-small')
chart.set_autoscale_on(True)
minorlocator = plt.MultipleLocator(10)
chart.text(0.99, 0.01, 'Plots created: '+ timeStamp + \
'\nReviewed by the Hydrogeology Unit of the Water Supply Bureau',
verticalalignment='bottom', horizontalalignment='right',
transform=chart.transAxes,
color='green', fontsize=7)
if frequency == 'DA':
title= station +'\n' + dataSource +' ' + Aquifer2Process +' Daily Water Level\n' + \
sdateDT.strftime("%m/%d/%Y") + ' thru ' + ThisDate.strftime("%m/%d/%Y")
else:
title= station +'\n' + dataSource +' '+ Aquifer2Process + ' Random Interval Water Level\n' + \
sdateDT.strftime("%m/%d/%Y") + ' thru ' + ThisDate.strftime("%m/%d/%Y")
plt.title(str(title), fontsize=8)
# Create X axis with full date range
chart.set_xlim(sdateDT, ThisDate)
# Plot the Data:
markers = []
for m in Line2D.markers:
try:
if len(m) == 1 and m != ' ':
markers.append(m)
except TypeError:
pass
style6 = markers[6]
style9 = markers[9]
if frequency == 'DA':
chart.plot_date(xs,
ys,
ydate=False,
linestyle='-',
marker='.',
markersize=1,
linewidth=1,
color='blue',
label=stationDB)
Qstr = 'Qualifier List:[' + qualiferStr + ']'
chart.plot_date(xs3,
ecodes,
ydate=False,
linestyle='-',
marker='.',
markersize=1,
linewidth=1,
color='green',
label='Estimated')
chart.plot_date(xs2,
ycodes,
ydate=False,
linestyle='b-',
marker='o',
markersize=4,
linewidth=1,
color='red',
label=Qstr)
if annotationStr <> 'None':
if 'C' in annotationStr:
chart.plot_date(xs4,
CannoCodes,
ydate=False,
linestyle='None',
marker=style9,
markersize=6,
linewidth=1,
color='magenta',
label='Calibration')
if 'V' in annotationStr:
chart.plot_date(xs5,
VannoCodes,
ydate=False,
linestyle='None',
marker=style6,
markersize=4,
linewidth=1,
color='yellow',
label='Verification')
else:
chart.plot(xs[ts2mask],
ys[ts2mask],
linestyle='-',
marker='o',
markersize=2,
linewidth=1,
color='red',
label=stationDB)
# Set xaxis specifics:
if len(xs) < 1095:
majorlocator = plt.MultipleLocator(30.4)
else:
majorlocator = plt.MultipleLocator(365)
chart.xaxis.set_major_formatter(myFmt)
chart.xaxis.set_major_locator(majorlocator)
for label in chart.xaxis.get_ticklabels():
label.set_rotation(300)
label.set_fontsize(7)
# Set yaxis specifics:
major_yloc = plt.MultipleLocator(yinterval)
chart.yaxis.set_major_locator(major_yloc)
if dataType == 'UNHD':
chart.set_ylabel('Uncorrected Head (ft)')
else:
chart.set_ylabel('Head (ft)')
for ylabel in chart.yaxis.get_ticklabels():
ylabel.set_fontsize(7)
chart.legend(loc=3,
bbox_to_anchor=(.01, .01),
prop=fontP,
fancybox=True,
shadow=False)
# Create figure filenames and output chart .png's to the hydrograph directory.
stationKey = station + '_' + key
fname = str(stationKey)
fig = str('{0}.png'.format(fname))
figout = fig.strip()
out = basepath + Aquifer2Process + "\\" + str(figout)
plt.savefig(out)
plt.close()
print ' ] Done!'
def plot(self, rec:str, data:Optional[np.ndarray]=None, ticks_granularity:int=0, leads:Optional[Union[str, List[str]]]=None, same_range:bool=False, waves:Optional[ECGWaveForm]=None, **kwargs) -> NoReturn:
""" finished, checked, to improve,
plot the signals of a record or external signals (units in μV),
with metadata (fs, labels, tranche, etc.),
possibly also along with wave delineations
Parameters
----------
rec: str,
name of the record
data: ndarray, optional,
12-lead ecg signal to plot,
if given, data of `rec` will not be used,
this is useful when plotting filtered data
ticks_granularity: int, default 0,
the granularity to plot axis ticks, the higher the more,
0 (no ticks) --> 1 (major ticks) --> 2 (major + minor ticks)
leads: str or list of str, optional,
the leads to plot
same_range: bool, default False,
if True, forces all leads to have the same y range
waves: ECGWaveForm, optional,
the waves (p waves, t waves, qrs complexes)
kwargs: dict,
TODO:
-----
1. slice too long records, and plot separately for each segment
2. plot waves using `axvspan`
NOTE:
-----
`Locator` of `plt` has default `MAXTICKS` equal to 1000,
if not modifying this number, at most 40 seconds of signal could be plotted once
Contributors: Jeethan, and WEN Hao
"""
if "plt" not in dir():
import matplotlib.pyplot as plt
plt.MultipleLocator.MAXTICKS = 3000
_leads = self._normalize_leads(leads, standard_ordering=True, lower_cases=False)
# lead_list = self.load_ann(rec)["df_leads"]["lead_name"].tolist()
# _lead_indices = [lead_list.index(l) for l in leads]
_lead_indices = [self.all_leads.index(l) for l in _leads]
if data is None:
_data = self.load_data(rec, data_format="channel_first", units="μV")[_lead_indices]
else:
units = self._auto_infer_units(data)
print(f"input data is auto detected to have units in {units}")
if units.lower() == "mv":
_data = 1000 * data
else:
_data = data
if same_range:
y_ranges = np.ones((_data.shape[0],)) * np.max(np.abs(_data)) + 100
else:
y_ranges = np.max(np.abs(_data), axis=1) + 100
if data is None and waves is None:
waves = self.load_ann(rec, leads=_leads)["waves"]
if waves is not None:
pwaves = {l:[] for l in _leads}
qrs = {l:[] for l in _leads}
twaves = {l:[] for l in _leads}
for l, l_w in waves.items():
for w in l_w:
itv = [w.onset, w.offset]
if w.name == self._symbol_to_wavename["p"]:
pwaves[l].append(itv)
elif w.name == self._symbol_to_wavename["N"]:
qrs[l].append(itv)
elif w.name == self._symbol_to_wavename["t"]:
twaves[l].append(itv)
palette = {"pwaves": "green", "qrs": "red", "twaves": "yellow",}
plot_alpha = 0.4
diagnoses = self.load_diagnoses(rec)
nb_leads = len(_leads)
seg_len = self.fs * 25 # 25 seconds
nb_segs = _data.shape[1] // seg_len
t = np.arange(_data.shape[1]) / self.fs
duration = len(t) / self.fs
fig_sz_w = int(round(4.8 * duration))
fig_sz_h = 6 * y_ranges / 1500
fig, axes = plt.subplots(nb_leads, 1, sharex=True, figsize=(fig_sz_w, np.sum(fig_sz_h)))
for idx in range(nb_leads):
lead_name = self.all_leads[_lead_indices[idx]]
axes[idx].plot(t, _data[idx], label=f"lead - {lead_name}")
axes[idx].axhline(y=0, linestyle="-", linewidth="1.0", color="red")
# NOTE that `Locator` has default `MAXTICKS` equal to 1000
if ticks_granularity >= 1:
axes[idx].xaxis.set_major_locator(plt.MultipleLocator(0.2))
axes[idx].yaxis.set_major_locator(plt.MultipleLocator(500))
axes[idx].grid(which="major", linestyle="-", linewidth="0.5", color="red")
if ticks_granularity >= 2:
axes[idx].xaxis.set_minor_locator(plt.MultipleLocator(0.04))
axes[idx].yaxis.set_minor_locator(plt.MultipleLocator(100))
axes[idx].grid(which="minor", linestyle=":", linewidth="0.5", color="black")
# add extra info. to legend
# https://stackoverflow.com/questions/16826711/is-it-possible-to-add-a-string-as-a-legend-item-in-matplotlib
for d in diagnoses:
axes[idx].plot([], [], " ", label=d)
for w in ["pwaves", "qrs", "twaves"]:
for itv in eval(f"{w}["{lead_name}"]"):
axes[idx].axvspan(
itv[0]/self.fs, itv[1]/self.fs,
color=palette[w], alpha=plot_alpha,
)
axes[idx].legend(loc="upper left")
axes[idx].set_xlim(t[0], t[-1])
axes[idx].set_ylim(-y_ranges[idx], y_ranges[idx])
axes[idx].set_xlabel("Time [s]")
axes[idx].set_ylabel("Voltage [μV]")
def plot(self,
min_freq,
output='VEL',
show=True,
label='',
axes=None,
sampling_rate=None,
sym='b-'):
"""
Plot PAZ object's Response
:type min_freq: float
:param min_freq: Lowest frequency to plot.
:type output: str
:param output: Output units. One of:
``"DISP"``
displacement
``"VEL"``
velocity
``"ACC"``
acceleration
:type axes: list of 2 :class:`matplotlib.axes.Axes`
:param axes: List/tuple of two axes instances to plot the
amplitude/phase spectrum into. If not specified, a new figure is
opened.
:type label: str
:param label: Label string for legend.
:type sampling_rate: float
:param sampling_rate: Manually specify sampling rate of time series.
If not given it is attempted to determine it from the information
in the individual response stages. Does not influence the spectra
calculation, if it is not known, just provide the highest frequency
that should be plotted times two.
:type show: bool
:param show: Whether to show the figure after plotting or not. Can be
used to do further customization of the plot before showing it.
:type sym: str
:param sym: symbol to use on plot
"""
paz = self.copy()
if output == 'DISP':
paz.input_units = 'm'
elif output == 'VEL':
paz.input_units = 'm/s'
elif output == 'ACC':
paz.input_units = 'm/s^2'
else:
print(f'Unknown output type: {output}, using VEL')
paz.input_units = 'm/s'
if sampling_rate is None:
sampling_rate = paz.sampling_rate
if sampling_rate is None:
if min_freq < 1:
sampling_rate = 100.
else:
sampling_rate = min_freq * 100.
else:
assert sampling_rate > 2 * min_freq
if axes is not None:
axs = axes
fig = axs[0].figure
npts = 50
else:
fig, axs = plt.subplots(2, 1, sharex=True)
npts = 100
f = np.power(
10.,
np.linspace(np.log10(min_freq), np.log10(sampling_rate / 2), npts))
resp = paz.get_response(f)
axs[0].loglog(f, np.absolute(resp), sym, label=label)
axs[0].set_ylabel(
f'Amplitude ({self.output_units.lower()}/{self.input_units.lower()})'
)
axs[0].grid(True)
axs[0].legend()
axs[1].semilogx(f, np.angle(resp), sym)
axs[1].grid(True)
axs[1].set_ylabel('Phase')
axs[1].yaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))
axs[1].yaxis.set_major_formatter(
plt.FuncFormatter(multiple_formatter()))
axs[1].set_xlabel('Frequency (Hz)')
if show is True:
plt.show()
#----------------------------------------------------------------------
# plot results
fig = plt.figure(figsize=(5, 2.5))
fig.subplots_adjust(left=0.1, right=0.95, wspace=0.25, bottom=0.17, top=0.95)
ax = fig.add_subplot(121)
ax.plot(mGrid, sigGrid, '-k', label='scatter')
ax.plot(mGrid, medGrid, '--k', label='bias')
ax.plot(mGrid, 0 * mGrid, ':k', lw=1)
ax.legend(loc=2)
ax.set_xlabel(r'$m_{\rm obs}$')
ax.set_ylabel('bias/scatter (mag)')
ax.set_ylim(-0.04, 0.21)
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.yaxis.set_minor_locator(plt.MultipleLocator(0.01))
for l in ax.yaxis.get_minorticklines():
l.set_markersize(3)
ax = fig.add_subplot(122)
ax.plot(pErrGrid, med2, '-k', label='$p=2$')
ax.plot(pErrGrid, med4, '--k', label='$p=4$')
ax.legend(loc=2)
ax.set_xlabel(r'$\sigma_\pi / \pi$')
ax.set_ylabel('absolute magnitude bias')
ax.xaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.set_xlim(0.02, 0.301)
ax.set_ylim(0, 0.701)
def plotlines3(left_arrays, right_arrays, oops_arrays, durata, partic, integ,
scale):
# dB_sum_mono = mono_arrays[0]
dB_sum_left = left_arrays[0]
dB_sum_righ = right_arrays[0]
dB_sum_oops = oops_arrays[0]
sample_frames = left_arrays[1]
mk_col = 'k'
mk_col2 = 'g'
mk_coll = 'b'
mk_colr = 'r'
plt.figure(figsize=(15, 10))
fig, ax = plt.subplots(figsize=(15, 5))
# plt.annotate(str(round(np.percentile(dB_sum_mono, 90),1)), (0,np.percentile(dB_sum_mono, 90)))
# plt.annotate(str(round(np.percentile(dB_sum_mono, 50),1)), (0,np.percentile(dB_sum_mono, 50)))
# plt.annotate(str(round(np.percentile(dB_sum_mono, 10),1)), (0,np.percentile(dB_sum_mono, 10)))
# plt.axhline(y=np.percentile(dB_sum_mono, 90), linewidth= .5, linestyle= ':',color=mk_col, label='LA10' )
# plt.axhline(y=np.percentile(dB_sum_mono, 10), linewidth= .5,linestyle= '--',color=mk_col, label='LA90')
# plt.axhline(y=np.percentile(dB_sum_mono, 50), linewidth= .5,linestyle= '-',color=mk_col, label='LA50')
# la10m=str(round(np.percentile(dB_sum_mono, 90),1))
# la50m=str(round(np.percentile(dB_sum_mono, 50),1))
# la90m=str(round(np.percentile(dB_sum_mono, 10),1))
la10l = str(round(np.percentile(dB_sum_left, 90), 1))
la50l = str(round(np.percentile(dB_sum_left, 50), 1))
la90l = str(round(np.percentile(dB_sum_left, 10), 1))
la10r = str(round(np.percentile(dB_sum_righ, 90), 1))
la50r = str(round(np.percentile(dB_sum_righ, 50), 1))
la90r = str(round(np.percentile(dB_sum_righ, 10), 1))
la10o = str(round(np.percentile(dB_sum_oops, 90), 1))
la50o = str(round(np.percentile(dB_sum_oops, 50), 1))
la90o = str(round(np.percentile(dB_sum_oops, 10), 1))
plt.axhline(y=np.percentile(dB_sum_left, 90),
linewidth=.7,
linestyle=':',
color=mk_coll,
label='LA10L = ' + la10l)
plt.axhline(y=np.percentile(dB_sum_left, 50),
linewidth=.7,
linestyle='-',
color=mk_coll,
label='LA50L = ' + la50l)
plt.axhline(y=np.percentile(dB_sum_left, 10),
linewidth=.7,
linestyle='--',
color=mk_coll,
label='LA90L = ' + la90l)
plt.axhline(y=np.percentile(dB_sum_righ, 90),
linewidth=.7,
linestyle=':',
color=mk_colr,
label='LA10R = ' + la10r)
plt.axhline(y=np.percentile(dB_sum_righ, 50),
linewidth=.7,
linestyle='-',
color=mk_colr,
label='LA50R = ' + la50r)
plt.axhline(y=np.percentile(dB_sum_righ, 10),
linewidth=.7,
linestyle='--',
color=mk_colr,
label='LA90R = ' + la90r)
print('la10l = ', la10l)
print('la50l = ', la50l)
print('la90l = ', la90l)
print('seconds = ', durata / fs)
print('la10r = ', la10r)
print('la50r = ', la50r)
print('la90r = ', la90r)
print('seconds = ', durata / fs)
# rowm = [partic, integ, round(durata/fs,3), la10m, la50m, la90m, durata]
rowl = [
partic[:-6] + 'left_A', integ,
round(durata / fs, 3), la10l, la50l, la90l, durata
]
rowr = [
partic[:-6] + 'righ_A', integ,
round(durata / fs, 3), la10r, la50r, la90r, durata
]
rowo = [
partic[:-6] + 'oops_A', integ,
round(durata / fs, 3), la10o, la50o, la90o, durata
]
with open('results.csv', 'a') as csvFile:
writer = csv.writer(csvFile)
# writer.writerow(rowm)
writer.writerow(rowl)
writer.writerow(rowr)
writer.writerow(rowo)
csvFile.close()
plt.xticks(sample_frames, fontsize=14)
plt.plot(sample_frames,
dB_sum_oops,
color='k',
linestyle='-.',
linewidth=.5,
label=' LA oops')
# plt.plot(sample_frames, dB_sum_mono, color=mk_col, linestyle= '-.', linewidth= .5, label=' LA mono')
plt.plot(sample_frames,
dB_sum_left,
color='b',
linewidth=.9,
label=' LA left')
plt.plot(sample_frames,
dB_sum_righ,
color='r',
linewidth=.9,
label=' LA right')
plt.legend(loc="lower left", bbox_to_anchor=[0, 0], ncol=3)
if scale == 's':
# seconds
ax.xaxis.set_major_locator(plt.MultipleLocator(44100 * 30))
def format_func(value, tick_number):
return int(value / 44100)
ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel('Seconds')
# min
elif scale == 'm':
ax.xaxis.set_major_locator(plt.MultipleLocator(44100 * 60))
def format_func(value, tick_number):
return int(value / (44100 * 60))
ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel('Minutes')
# ax.xaxis.set_major_locator(plt.MultipleLocator(44100 *5))
#
# def format_func(value, tick_number):
#
# return int(value / 44100)
# ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.title(partic + '_tri_' + '_LA ' + integ)
# plt.xlabel('Seconds')
plt.ylabel('LA_' + integ)
plt.grid(True, which='major', axis='both')
plt.xlim(0, durata)
plt.ylim((35, 80))
fig.tight_layout()
plt.savefig(partic + "_tri_" + integ + '_' + str(durata) + '.eps', dpi=150)
# 0.2 Formatter for inserting commas in y axis labels with magnitudes in the thousands
def func(x, pos): # formatter function takes tick label and tick position
s = '{:0,d}'.format(int(x))
return s
y_format = plt.FuncFormatter(func) # make formatter
# 0.3 format the x axis ticksticks
years2, years4, years5, years10, years15 = dts.YearLocator(2), dts.YearLocator(
4), dts.YearLocator(5), dts.YearLocator(10), dts.YearLocator(15)
# 0.4 y label locator for vertical axes plotting gdp
majorLocator_y = plt.MultipleLocator(3)
majorLocator_shares = plt.MultipleLocator(0.2)
# In[3]:
# 1. Import data
pwt = pd.read_excel('../xslx/pwt90.xlsx', sheet_name='Data')
# In[4]:
# 2. lists of countries, codes, and years
year0 = 1960
country_codes = []
countries = []
years = []
X = fetch_LIGO_bigdog()
t = X['t']
h = X['Hanford']
dt = t[1] - t[0]
Q = np.sqrt(22)
f0 = 1
f0 = 2**np.linspace(5, 8, 50)
f, H = FT_continuous(t, h)
W = np.conj(wavelet_FT(f, 0, f0[:, None], Q))
t, HW = IFT_continuous(f, H * W)
t = t[::100]
HW = HW[:, ::100]
fig = plt.figure(figsize=(5, 3.75))
plt.imshow(abs(HW),
origin='lower',
aspect='auto',
extent=[t[0], t[-1], np.log2(f0[0]),
np.log2(f0[-1])])
plt.colorbar()
plt.gca().yaxis.set_major_locator(plt.MultipleLocator(1))
plt.gca().yaxis.set_major_formatter(
plt.FuncFormatter(lambda x, *args: "%i" % (2**x)))
plt.show()
for key in sorted(reds.keys()):
blue_keys.append(key)
blue_values.append(blues[key].seconds / day_to_seconds[dt.datetime.strptime(key, "%Y-%m-%d").weekday()])
yellow_keys = []
yellow_values = []
for key in sorted(reds.keys()):
yellow_keys.append(key)
yellow_values.append(yellows[key].seconds / day_to_seconds[dt.datetime.strptime(key, "%Y-%m-%d").weekday()])
green_keys = []
green_values = []
for key in sorted(reds.keys()):
green_keys.append(key)
green_values.append(greens[key].seconds / day_to_seconds[dt.datetime.strptime(key, "%Y-%m-%d").weekday()])
data = range(0, max(len(red_values), len(blue_values), len(yellow_values), len(green_values)))
plt.plot(data, red_values, 'r')
plt.plot(data, yellow_values, 'y')
plt.plot(data, blue_values, 'b')
plt.plot(data, green_values, 'g')
ax = plt.axes()
ax.grid()
ax.xaxis.set_major_locator(plt.MultipleLocator(base=7.0))
ax.xaxis.set_minor_locator(plt.MultipleLocator(base=1.0))
ax.set_xticklabels(["Day: ", weekday_string[starting_date.weekday()]])
plt.show()
plt.rc('xtick', labelsize=24) # fontsize of the tick labels
plt.rc('ytick', labelsize=24)
N = len(x)
print N
ind = np.arange(N) # the x locations for the groups
width = 0.15 # the width of the bars
ax = fig.add_subplot(111)
font = {'size': '24'}
rects1 = ax.bar(ind - width, y1, width, color='g', label="B")
rects2 = ax.bar(ind, y2, width, color='r', label="L_Init")
rects3 = ax.bar(ind + width, y3, width, color='b', label="L_Z-ordering")
rects4 = ax.bar(ind + 2 * width, y4, width, color='y', label="L_Building")
rects5 = ax.bar(ind + 3 * width, y5, width, color='pink', label="L_Traversing")
ax.set_ylabel('Time for 1GB data (s)', font)
ax.set_ylim([0, 4])
y_major_locator = plt.MultipleLocator(1)
ax.yaxis.set_major_locator(y_major_locator)
#ax.set_title("RMSE comparison",axis_font)
ax.set_xticks(ind + width)
ax.set_xticklabels(namelist)
ax.set_xlim([-0.3, 7.8])
ax.set_xlabel('Number of AMR levels', font)
ax.legend(loc=0, ncol=5, prop=font)
plt.savefig(name_hat + 'Time_level.pdf', format='pdf', bbox_inches="tight")
plt.show()
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(20, 24))
ax1.plot(win['time'][int(windowStart):int(windowEnd)],
btot_tw[:-1, iEv],
label='B$_{tot}$')
ax1.plot(win['time'][int(windowStart):int(windowEnd)],
bz_tw[:-1, iEv],
label='B$_{z}$')
ax1.axvline(x=win['time'][int(mostart)], color='r', linestyle='--')
ax1.axvline(x=win['time'][int(moend)], color='r', linestyle='--')
# # ax1.set_xlabel('Start Time (' + start_month + '/' + start_day + '/' + start_year + ' ' + start_hr + ':' + start_min + ')')
ax1.set_ylabel('B [nT]')
ax1.set_xlim([win['time'][int(windowStart)], win['time'][int(windowEnd)]])
ax1.xaxis.set_major_locator(
plt.MultipleLocator(0.1)) # want to have ticks every 1/10th day
# define formatter function for tick-labels
fmtr = plt.FuncFormatter(hourMin)
_ = ax1.xaxis.set_major_formatter(fmtr)
ax1.legend(numpoints=1, ncol=2, loc='best')
ax2.plot(win['time'][int(windowStart):int(windowEnd)],
vtot_tw[:-1, iEv],
label='v$_{tot}$')
ax2.axvline(x=win['time'][int(mostart)], color='r', linestyle='--')
ax2.axvline(x=win['time'][int(moend)], color='r', linestyle='--')
# # ax2.set_xlabel('Start Time (' + start_month + '/' + start_day + '/' + start_year + ' ' + start_hr + ':' + start_min + ')')
ax2.set_ylabel('v [km/s]')
ax2.set_xlim([win['time'][int(windowStart)], win['time'][int(windowEnd)]])
def chartTimeSeriesDistributions(tables,
output_dir,
output_base_name,
n_bins=40,
min_pctl=0.05,
max_pctl=99.95,
background_color='#D6D1CA',
font_color='#1B1716',
overwrite=False,
howManyHarmonics=3,
showChart=False,
annotate_harmonic_peaks=True):
# print(tables)
check_dir(output_dir)
bands = []
for table in tables:
band = os.path.splitext(os.path.basename(table))[0].split('_')[-1]
if band not in bands: bands.append(band)
for band in bands:
output_chart_name = os.path.join(
output_dir, output_base_name + '_' + band + '.png')
if not os.path.exists(output_chart_name) or overwrite:
title = ' '.join(output_base_name.split(
'_')) + ' ' + band + ' Distrubution Time Series'
tablesT = [
table for table in tables
if os.path.basename(table).find(band) > -1
]
#Find the name of the band/index
index_name = band
print('Creating time series distribution chart for:', band)
values = []
data = pd.concat([pd.read_csv(t) for t in tablesT], axis=1)
columns = data.columns
values = data.to_numpy()
#Get min and max
flat = values.flatten()
flat = flat[~(np.isnan(flat))]
min = np.percentile(flat, min_pctl) #flat.min()
max = np.percentile(flat, max_pctl) #flat.max()
min_2 = np.percentile(flat, 10)
max_2 = np.percentile(flat, 90)
values = values.clip(min, max)
#Get dates (yyyy-mm-dd)
dates = [i.split('_')[0] for i in columns]
years = np.unique([i.split('-')[0] for i in dates])
print('years', years)
# dates = ['2000-{}-{}'.format(i.split('-')[1],i.split('-')[2]) for i in dates]
#Set up bins for histogram
bin_step = (max - min) / n_bins
bins = np.arange(min, max + bin_step, bin_step)
#Get histograms for each date and clip out outlier frequency values
hist = np.array([
np.histogram(data[column], bins=bins, density=True)[0]
for column in columns
]).transpose()
hist = np.nan_to_num(hist, nan=0)
hist = hist.clip(np.percentile(hist, 10), np.percentile(hist, 99))
#Harmonic regression fitting
#Get table of all xs and ys
table_xs = np.array([])
table_ys = np.array([])
table_all_xs = np.array([])
d0 = date(1969, 12, 31)
percentiles = []
#Set up tables for harmonic regression
for i, column in enumerate(columns):
d = dates[i]
d1 = date(int(d.split('-')[0]), int(d.split('-')[1]),
int(d.split('-')[2]))
delta = d1 - d0
delta_fraction = math.modf(delta.days / 365.25)[0]
decimal_date = int(d.split('-')[0]) + delta_fraction
ys = values[:, i]
ys = ys[~(np.isnan(ys))]
if (len(ys) > 3):
percentiles.append(
np.percentile(ys, [0, 5, 25, 50, 75, 95, 100]))
else:
percentiles.append([
np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
])
xs = np.repeat(decimal_date, len(ys))
table_ys = np.append(table_ys, ys)
table_xs = np.append(table_xs, xs)
table_all_xs = np.append(table_all_xs, decimal_date)
percentiles = np.array(percentiles)
table_all_xs = np.array(table_all_xs).flatten()
table_xs = np.array(table_xs).flatten()
table_ys = np.array(table_ys).flatten()
#Harmonic regression fit model
peak_year_i = 2
if peak_year_i > len(years) - 1: peak_year_i = 0
xs = np.array([table_xs]).T
sin1Term = np.sin(xs * 2 * math.pi)
cos1Term = np.cos(xs * 2 * math.pi)
sin2Term = np.sin(xs * 4 * math.pi)
cos2Term = np.cos(xs * 4 * math.pi)
sin3Term = np.sin(xs * 6 * math.pi)
cos3Term = np.cos(xs * 6 * math.pi)
intTerm = np.ones(xs.shape[0])
harm_1 = np.c_[sin1Term, cos1Term, xs, intTerm]
harm_1_2 = np.c_[sin1Term, cos1Term, sin2Term, cos2Term, xs,
intTerm]
harm_1_2_3 = np.c_[sin1Term, cos1Term, sin2Term, cos2Term,
sin3Term, cos3Term, xs, intTerm]
harm_1_model = np.linalg.lstsq(harm_1, table_ys, rcond=None)
harm_1_2_model = np.linalg.lstsq(harm_1_2, table_ys, rcond=None)
harm_1_2_3_model = np.linalg.lstsq(harm_1_2_3,
table_ys,
rcond=None)
# print(harm_1_model)
peak_1_fraction = math.atan(
harm_1_model[0][0] / harm_1_model[0][1]) / (2 * math.pi)
peak_2_fraction = peak_1_fraction + 0.5
if peak_1_fraction < 0:
peak_1_fraction = 1 + peak_1_fraction
if peak_2_fraction > 1:
peak_2_fraction = peak_2_fraction - 1
peak_1_yr = int(years[peak_year_i]) + peak_1_fraction
peak_2_yr = int(years[peak_year_i]) + peak_2_fraction
peak_1_pred = np.dot([
np.sin(peak_1_yr * 2 * math.pi),
np.cos(peak_1_yr * 2 * math.pi), peak_1_yr, 1
], harm_1_model[0])
peak_2_pred = np.dot([
np.sin(peak_2_yr * 2 * math.pi),
np.cos(peak_2_yr * 2 * math.pi), peak_2_yr, 1
], harm_1_model[0])
peak_1_y = min_2
peak_2_y = max_2
if peak_1_pred > peak_2_pred:
peak_1_y = max_2
peak_2_y = min_2
peak_date = int(peak_1_fraction * 365) + 1
peak_date2 = int(peak_2_fraction * 365) + 1
print(peak_date, peak_date2, peak_1_pred, peak_2_pred,
years[peak_year_i][2:] + f'{peak_date:03}',
years[peak_year_i][2:] + f'{peak_date2:03}')
# print(datetime.datetime.strptime('19'+str(int(peak_date*365)), '%y%j').strftime("%d-%m-%Y"))
peak_date = datetime.datetime.strptime(
years[peak_year_i][2:] + f'{peak_date:03}',
'%y%j').strftime("%m-%d")
peak_date2 = datetime.datetime.strptime(
years[peak_year_i][2:] + f'{peak_date2:03}',
'%y%j').strftime("%m-%d")
# print(peak_date)
# print(peak_date2)
#Apply harm model
xs = np.array([table_all_xs]).T
sin1Term = np.sin(xs * 2 * math.pi)
cos1Term = np.cos(xs * 2 * math.pi)
sin2Term = np.sin(xs * 4 * math.pi)
cos2Term = np.cos(xs * 4 * math.pi)
sin3Term = np.sin(xs * 6 * math.pi)
cos3Term = np.cos(xs * 6 * math.pi)
intTerm = np.ones(xs.shape[0])
harm_1 = np.c_[sin1Term, cos1Term, xs, intTerm]
harm_1_2 = np.c_[sin1Term, cos1Term, sin2Term, cos2Term, xs,
intTerm]
harm_1_2_3 = np.c_[sin1Term, cos1Term, sin2Term, cos2Term,
sin3Term, cos3Term, xs, intTerm]
# print('beta hat:',beta_hat[0],xs)
pred_1 = np.dot(harm_1, harm_1_model[0])
pred_1_2 = np.dot(harm_1_2, harm_1_2_model[0])
pred_1_2_3 = np.dot(harm_1_2_3, harm_1_2_3_model[0])
pred_dict = {'1': pred_1, '2': pred_1_2, '3': pred_1_2_3}
pred = pred_dict[str(howManyHarmonics)]
#Plot
xTickFreq = 8 #int(len(columns)/20)
yTickFreq = (max - min) / 10 #0.1
width = (len(columns) / 25) + 2
if width < 8:
width = 12
xTickFreq = 3
# fig = plt.figure(figsize=(width,6),frameon=True,facecolor='w')
fig, ax = plt.subplots(figsize=(width, 7),
frameon=True,
facecolor='w')
fig.patch.set_facecolor(background_color)
params = {
"ytick.color": font_color,
"xtick.color": font_color,
"axes.labelcolor": font_color,
"axes.edgecolor": font_color,
'legend.fontsize': 7,
'legend.handlelength': 1.2
}
plt.rcParams.update(params)
# ax = fig.add_axes([0.06, 0.15, 0.84, 0.80])
ax.set_title(title)
# fig.annotate('local max', xy=(0.5, 0.5))
cmap = plt.get_cmap('viridis')
cf = plt.pcolormesh(dates, bins, hist, cmap=cmap) #, vmin = 500)
degrees = 45
plt.xticks(rotation=degrees, fontsize=7, ha='right')
# print(list(zip(dates,pred,table_all_xs)))
harm_line = plt.plot(
dates,
pred,
linestyle='-',
color=background_color,
linewidth=2,
label='Harmonic Fit ({})'.format(howManyHarmonics))
# median_line = plt.plot(dates, percentiles[:,3], linestyle = '--', color = font_color, linewidth = 1.5,label = 'Median')
ax.set_ylim([min, max])
ax.set_ylabel('{} Value'.format(index_name), fontsize=10)
ax.set_xlabel('Date', fontsize=10)
#Set up the x and y axis tick frequencies
ax.xaxis.set_major_locator(plt.MultipleLocator(xTickFreq))
ax.xaxis.set_minor_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_locator(plt.MultipleLocator(yTickFreq))
ax.yaxis.set_minor_locator(plt.MultipleLocator(1))
ax.grid(True,
which='major',
axis='y',
linestyle='--',
color=font_color)
ax.grid(True,
which='major',
axis='x',
linestyle='--',
color=font_color)
cbax = fig.add_axes([0.93, 0.11, 0.01, 0.71])
legend = plt.legend(handles=harm_line,
bbox_to_anchor=(-2.5, 1.08),
loc='upper left')
cb = plt.colorbar(cf, cax=cbax, orientation='vertical')
cb.ax.tick_params(labelsize=10)
cb.set_label('Percent of Samples (%)',
rotation=270,
labelpad=15,
fontsize=10,
color=font_color)
if peak_1_pred > max:
peak_1_pred = max
elif peak_1_pred < min:
peak_1_pred = min
if peak_2_pred > max:
peak_2_pred = max
elif peak_2_pred < min:
peak_2_pred = min
if (annotate_harmonic_peaks):
print('Annotating peak dates of harmonics')
try:
yr_dates = [
i for i in dates
if i.split('-')[0] == years[peak_year_i]
]
m_dates = [
i for i in yr_dates
if i.split('-')[1] == peak_date.split('-')[0]
]
if len(m_dates) == 0:
m_dates = [
i for i in yr_dates if int(i.split('-')[1]) ==
int(peak_date.split('-')[0]) - 1
]
print(m_dates)
m_dates = m_dates[0]
ax.annotate(
'{} ({})'.format(peak_date, round(peak_1_pred, 3)),
xy=(m_dates, peak_1_pred),
xycoords='data',
# xytext=(m_dates, peak_1_y), # fraction, fraction
# textcoords='data',
# arrowprops=dict(facecolor='black', shrink=0.05),
color=background_color,
fontsize='10',
path_effects=[
pe.withStroke(linewidth=2.5, foreground=font_color)
])
yr_dates = [
i for i in dates
if i.split('-')[0] == years[peak_year_i]
]
m_dates = [
i for i in yr_dates
if i.split('-')[1] == peak_date2.split('-')[0]
][0]
ax.annotate(
'{} ({})'.format(peak_date2, round(peak_2_pred, 3)),
xy=(m_dates, peak_2_pred),
xycoords='data',
# xytext=(m_dates, peak_2_y), # fraction, fraction
# textcoords='data',
# arrowprops=dict(facecolor='black', shrink=0.05),
color=background_color,
fontsize='10',
path_effects=[
pe.withStroke(linewidth=2.5, foreground=font_color)
])
except Exception as e:
print(e)
# ax.annotate('Peak Date 2',
# xy=('2019-{}'.format(peak_date2), 0.3), xycoords='data'
# )
# plt.text(0.5, 1.1, 'Test', color=font_color,
# bbox=dict(facecolor='none', edgecolor=font_color))
fig.savefig(output_chart_name)
if showChart:
plt.show()
plt.close()
else:
print('Already produced:', output_chart_name)
def locator(self):
return plt.MultipleLocator(self.number / self.denominator)
ax=ax4,
scales=[2],
ec='k',
fc='gray',
alpha=0.2)
titles = [
'True Distribution', 'Noisy Distribution',
'Extreme Deconvolution\n resampling',
'Extreme Deconvolution\n cluster locations'
]
ax = [ax1, ax2, ax3, ax4]
for i in range(4):
ax[i].xaxis.set_major_locator(plt.MultipleLocator(4))
ax[i].yaxis.set_major_locator(plt.MultipleLocator(5))
ax[i].text(0.05,
0.95,
titles[i],
ha='left',
va='top',
transform=ax[i].transAxes)
if i in (0, 1):
ax[i].xaxis.set_major_formatter(plt.NullFormatter())
else:
ax[i].set_xlabel('x')
def SolveProblem(LoadCase, ConstitutiveModel, BCsType, FinalRelativeStretch, RelativeStepSize, Dimensions, NumberElements, Mesh, V, u, du, v, Ic, J, F, Psi, Plot = False, Paraview = False):
if LoadCase == 'Compression':
# Load case
[u_0, u_1, InitialState, Direction, Normal, NumberSteps, DeltaStretch] = LoadCaseDefinition(LoadCase, FinalRelativeStretch, RelativeStepSize, Dimensions, BCsType)
elif LoadCase == 'Tension':
# Load case
[u_0, u_1, InitialState, Direction, Normal, NumberSteps, DeltaStretch] = LoadCaseDefinition(LoadCase, FinalRelativeStretch, RelativeStepSize, Dimensions, BCsType)
elif LoadCase == 'SimpleShear':
# Load case
[u_0, u_1, InitialState, Direction, Normal, NumberSteps, DeltaStretch] = LoadCaseDefinition(LoadCase, FinalRelativeStretch*2, RelativeStepSize*2, Dimensions, BCsType)
# Boundary conditions
[BoundaryConditions, ds] = BCsDefinition(Dimensions, Mesh, V, u_0, u_1, LoadCase, BCsType)
# Estimation of the displacement field using Neo-Hookean model (necessary for Ogden)
u = Estimate(Ic, J, u, v, du, BoundaryConditions, InitialState, u_1)
# Reformulate the problem with the correct constitutive model
Pi = Psi * fe.dx
# First directional derivative of the potential energy
Fpi = fe.derivative(Pi,u,v)
# Jacobian of Fpi
Jac = fe.derivative(Fpi,u,du)
# Define option for the compiler (optional)
ffc_options = {"optimize": True, \
"eliminate_zeros": True, \
"precompute_basis_const": True, \
"precompute_ip_const": True, \
"quadrature_degree": 2, \
"representation" : "uflacs" }
# Define the problem
Problem = fe.NonlinearVariationalProblem(Fpi, u, BoundaryConditions, Jac, form_compiler_parameters=ffc_options)
# Define the solver
Solver = fe.NonlinearVariationalSolver(Problem)
# Set solver parameters (optional)
Prm = Solver.parameters
Prm['nonlinear_solver'] = 'newton'
Prm['newton_solver']['linear_solver'] = 'cg' # Conjugate gradient
Prm['newton_solver']['preconditioner'] = 'icc' # Incomplete Choleski
Prm['newton_solver']['krylov_solver']['nonzero_initial_guess'] = True
# Data frame to store values
cols = ['Stretches','P']
df = pd.DataFrame(columns=cols, index=range(int(NumberSteps)+1), dtype='float64')
if Paraview == True:
# Results File
Output_Path = os.path.join('OptimizationResults', BCsType, ConstitutiveModel)
ResultsFile = xdmffile = fe.XDMFFile(os.path.join(Output_Path, str(NumberElements) + 'Elements_' + LoadCase + '.xdmf'))
ResultsFile.parameters["flush_output"] = True
ResultsFile.parameters["functions_share_mesh"] = True
if Plot == True:
plt.rc('figure', figsize=[12,7])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Set the stretch state to initial state
StretchState = InitialState
# Loop to solve for each step
for Step in range(int(NumberSteps+1)):
# Update current state
u_1.s = StretchState
# Compute solution and save displacement
Solver.solve()
# First Piola Kirchoff (nominal) stress
P = fe.diff(Psi, F)
# Nominal stress vectors normal to upper surface
p = fe.dot(P,Normal)
# Reaction force on the upper surface
f = fe.assemble(fe.inner(p,Direction)*ds(2))
# Mean nominal stress on the upper surface
Pm = f/fe.assemble(1*ds(2))
# Save values to table
df.loc[Step].Stretches = StretchState
df.loc[Step].P = Pm
# Plot
if Plot == True:
ax.cla()
ax.plot(df.Stretches, df.P, color = 'r', linestyle = '--', label = 'P', marker = 'o', markersize = 8, fillstyle='none')
ax.set_xlabel('Stretch ratio (-)')
ax.set_ylabel('Stresses (kPa)')
ax.xaxis.set_major_locator(plt.MultipleLocator(0.02))
ax.legend(loc='upper left', frameon=True, framealpha=1)
display(fig)
clear_output(wait=True)
if Paraview == True:
# Project the displacement onto the vector function space
u_project = fe.project(u, V, solver_type='cg')
u_project.rename('Displacement (mm)', '')
ResultsFile.write(u_project,Step)
# Compute nominal stress vector
p_project = fe.project(p, V)
p_project.rename("Nominal stress vector (kPa)","")
ResultsFile.write(p_project,Step)
# Update the stretch state
StretchState += DeltaStretch
return df
def quality_map(experiment,
file_format=None,
ki=None,
urange=None,
ustep=1,
colored=True,
fill=False,
with_title=True,
signature=True,
q_full_scale=True,
start_calc=1,
cull=None):
"""Plots the log scale quality map for a given kNN[j] of all sample test sequences.
Plots all or ppi (calc, sim) points between each major tick interval
Args:
experiment (str): insights experiment results full file path
file_format (str): One of the file extensions supported by the active backend.
Most backends support png, pdf, ps, eps and svg.
if is None, the plot is displayed and not saved.
ki (int): zero-based index of the NN in kNN list, if None all kNNs are plotted.
ustep (int): a different color is chosen in the color palette for every `ustep` number of updates;
irrelevant for colored=False.
urange (tuple): range of updates to plot given as (start, end) where both is inclusive;
if given as (start, ), max update is used for end;
if None, all updates are plotted.
colored (bool): If True, each update is plotted in a different color; otherwise all plotted black,
fill (bool): if True, propagates the quality for the intermediate calc values;
if False, plots only the given points provided as sparse list.
with_title (bool): if True, shows the figure title.
signature (bool): If True, name of the plotting function is also displayed.
q_full_scale (bool): if True, quality (i.e. y) axis starts from 0.0; otherwise minimum quality is used.
start_calc (int): The start value for the calculations (i.e. x) axis.
cull (float): The percentage (.0, 1.] to cull the data points to be plotted
Returns:
None
Note:
Start: 20190918, End:20191023
"""
# Create a figure
plt.figure(num=1) # , figsize=(10, 8))
# Read experiment data
result = common.load_obj(experiment)
knn_calc_sim_history = result.data.knn_calc_sim_history
k = len(knn_calc_sim_history[0]) - 1 # k of kNN
max_update = max([test[0] for test in knn_calc_sim_history])
if urange is None:
urange = (1, max_update)
elif len(urange) == 1 or urange[1] > max_update:
urange = (urange[0], max_update)
max_X = 0
# Fill in plot data
CALCS = np.array([])
QUALITY = np.array([])
UPDATE = np.array([])
knn_calc_sim_history = sorted(knn_calc_sim_history,
key=lambda point: point[0],
reverse=False) # sort by updates
for test_history in knn_calc_sim_history:
# print(test_history)
update = test_history[0]
if urange[0] <= update <= urange[1]:
# print("update: ", update)
for nn_ind, nn_i_history in enumerate(test_history[1:]):
if ki is None or nn_ind == ki:
points = pdp.quality(nn_i_history)
# print(points)
X, Y = helper.to_arr(points, fill=fill)
# Eliminate (0, 0.0) entries
if X[0] == 0:
X = X[1:]
if Y[0] == 0:
Y = Y[1:]
if max(X) > max_X:
max_X = max(X)
# Eliminate the entries < start_calc
X = X[X >= start_calc]
Y = Y[-len(X):]
CALCS = np.concatenate((CALCS, X))
QUALITY = np.concatenate((QUALITY, Y))
# UPDATE = np.concatenate((UPDATE, np.full((len(X),), math.ceil(update / ustep), dtype=np.int)))
UPDATE = np.concatenate(
(UPDATE, np.full((len(X), ), update, dtype=np.int)))
if cull:
CALCS, ind_removed = helper.cull_arr(CALCS, pct=cull)
QUALITY, _ = helper.cull_arr(QUALITY, ind=ind_removed)
UPDATE, _ = helper.cull_arr(UPDATE, ind=ind_removed)
if colored:
# Color palette
cmap = "autumn" # "autumn" "tab20" # "Blues_r"
cmap_size = math.ceil(max_update / ustep)
my_palette = plt.cm.get_cmap(cmap, cmap_size)
_ = plt.scatter(CALCS,
QUALITY,
marker=".",
s=1,
c=UPDATE,
cmap=my_palette,
vmin=1,
vmax=max_update,
alpha=1.)
cbar = plt.colorbar(orientation="vertical")
cbar.set_label("updates")
else:
_ = plt.scatter(CALCS, QUALITY, marker=".", s=1, c="black")
ax = plt.gca()
ax.yaxis.set_major_locator(plt.MultipleLocator(.1))
ax.yaxis.set_minor_locator(plt.MultipleLocator(.05))
plt.grid(True,
which="major",
linestyle="-",
linewidth=1,
color="lightgrey")
plt.grid(True,
which="minor",
linestyle=":",
linewidth=1,
color="lightgrey")
xticks_ = plt_common.get_ticks_log_scale(max_X, start=start_calc)
y_min = 0.0 if q_full_scale else math.floor(
np.nanmin(QUALITY[start_calc - 1:]) * 10) / 10
yticks_ = np.arange(y_min, 1.01, .1)
plt.rcParams['axes.axisbelow'] = True
plt.xscale('log')
plt.xticks(xticks_)
plt.xlim(left=start_calc, right=max(xticks_))
plt.yticks(yticks_)
if signature:
plt_common.sign_plot(plt, quality_map.__name__)
fn_wo_ext = common.file_name_wo_ext(experiment)
lbl_ki = str(ki if ki is not None else list([0, k - 1])) # zero-based
lbl_update = str(list(urange) if urange[0] != urange[1] else urange[0])
if with_title:
title_ = "Quality Map\n"
title_ = "{}Exp: {}\n".format(title_, fn_wo_ext)
title_ = "{}ki:{}, update:{}".format(title_, lbl_ki, lbl_update)
if colored:
title_ = "{}, color step:{}".format(title_, ustep)
if cull:
title_ = "{}, cull:{:.0%}".format(title_, cull)
plt.title(title_ + "\n\n")
save_fn = "QUALITY_MAP_{}_ki_{}_u_{}{}{}{}{}{}".format(
fn_wo_ext, lbl_ki, lbl_update,
"_s_{}".format(ustep) if colored else "", "_f" if fill else "",
"_t" if with_title else "", "_c_{:.2f}".format(cull) if cull else "",
"_z" if not q_full_scale else "")
# axis labels
matplotlib.rcParams['text.usetex'] = True # Allow LaTeX in text
ax.set_xlabel("$\#$ of similarity calculations ($c$)")
ax.set_ylabel(r"quality ($\mathcal{Q}_c$)")
# Tight layout
plt.tight_layout()
if file_format:
save_fpath = os.path.join(common.APP.FOLDER.FIGURE,
"{}.{}".format(save_fn, file_format))
plt.savefig(save_fpath, dpi=300, bbox_inches="tight")
print("Quality Map figure saved into '{}'.".format(save_fpath))
else:
# Update the title of the plot window
plt.gcf().canvas.set_window_title(save_fn)
plt.show()
plt.close()
return None
def obj_fig(simz_tab, objtype, summ_stats, outfile=None):
"""Generate QA plot for a given object type
"""
from astropy.stats import sigma_clip
logs = get_logger()
gdz_tab = slice_simz(simz_tab,objtype=objtype, survey=True,goodz=True, all_zwarn0=True)
if objtype == 'ELG':
allgd_tab = slice_simz(simz_tab,objtype=objtype, survey=False,goodz=True, all_zwarn0=True)
if len(gdz_tab) <= 1:
logs.info("Not enough objects of type {:s} for QA".format(objtype))
return
# Plot
sty_otype = get_sty_otype()
fig = plt.figure(figsize=(8, 6.0))
gs = gridspec.GridSpec(2,2)
# Title
fig.suptitle('{:s}: Summary'.format(sty_otype[objtype]['lbl']),
fontsize='large')
# Offset
for kk in range(4):
yoff = 0.
ax= plt.subplot(gs[kk])
if kk == 0:
yval = calc_dzsig(gdz_tab)
ylbl = (r'$(z_{\rm red}-z_{\rm true}) / \sigma(z)$')
ylim = 5.
# Stats with clipping
clip_y = sigma_clip(yval, sigma=5.)
rms = np.std(clip_y)
redchi2 = np.sum(clip_y**2)/np.sum(~clip_y.mask)
#
xtxt = 0.05
ytxt = 1.0
for req_tst in ['EFF','CAT_RATE']:
ytxt -= 0.12
if summ_stats[objtype]['REQ_INDIV'][req_tst] == 'FAIL':
tcolor='red'
else:
tcolor='green'
ax.text(xtxt, ytxt, '{:s}: {:.3f}'.format(req_tst,
summ_stats[objtype][req_tst]), color=tcolor,
transform=ax.transAxes, ha='left', fontsize='small')
# Additional
ytxt -= 0.12
ax.text(xtxt, ytxt, '{:s}: {:.3f}'.format('RMS:', rms),
color='black', transform=ax.transAxes, ha='left', fontsize='small')
ytxt -= 0.12
ax.text(xtxt, ytxt, '{:s}: {:.3f}'.format(r'$\chi^2_\nu$:',
redchi2), color='black', transform=ax.transAxes,
ha='left', fontsize='small')
else:
yval = calc_dz(gdz_tab)
if kk == 1:
ylbl = (r'$(z_{\rm red}-z_{\rm true}) / (1+z)$')
else:
ylbl = r'$\delta v_{\rm red-true}$ [km/s]'
ylim = max(5.*summ_stats[objtype]['RMS_DZ'],1e-5)
if (np.median(summ_stats[objtype]['MEDIAN_DZ']) >
summ_stats[objtype]['RMS_DZ']):
yoff = summ_stats[objtype]['MEDIAN_DZ']
if kk==1:
# Stats
xtxt = 0.05
ytxt = 1.0
dx = ((ylim/2.)//0.0001 +1)*0.0001
ax.xaxis.set_major_locator(plt.MultipleLocator(dx))
for stat in ['RMS_DZ','MEAN_DZ', 'MEDIAN_DZ']:
ytxt -= 0.12
try:
pfail = summ_stats[objtype]['REQ_INDIV'][stat]
except KeyError:
tcolor='black'
else:
if pfail == 'FAIL':
tcolor='red'
else:
tcolor='green'
ax.text(xtxt, ytxt, '{:s}: {:.5f}'.format(stat,
summ_stats[objtype][stat]), color=tcolor,
transform=ax.transAxes, ha='left', fontsize='small')
# Histogram
if kk < 2:
binsz = ylim/10.
#i0, i1 = int( np.min(yval) / binsz) - 1, int( np.max(yval) / binsz) + 1
i0, i1 = int(-ylim/binsz) - 1, int( ylim/ binsz) + 1
rng = tuple( binsz*np.array([i0,i1]) )
nbin = i1-i0
# Histogram
hist, edges = np.histogram(yval, range=rng, bins=nbin)
xhist = (edges[1:] + edges[:-1])/2.
#ax.hist(xhist, color='black', bins=edges, weights=hist)#, histtype='step')
ax.hist(xhist, color=sty_otype[objtype]['color'], bins=edges, weights=hist)#, histtype='step')
ax.set_xlabel(ylbl)
ax.set_xlim(-ylim, ylim)
else:
if kk == 2:
lbl = r'$z_{\rm true}$'
xval = gdz_tab['TRUEZ']
xmin,xmax=np.min(xval),np.max(xval)
dx = np.maximum(1,(xmax-xmin)//0.5)*0.1
ax.xaxis.set_major_locator(plt.MultipleLocator(dx))
#xmin,xmax=0.6,1.65
elif kk == 3:
if objtype == 'ELG':
lbl = r'[OII] Flux ($10^{-16}$)'
#xval = gdz_tab['OIIFLUX']*1e16
xval = allgd_tab['OIIFLUX']*1e16
yval = calc_dz(allgd_tab)
# Avoid NAN
gdy = np.isfinite(yval)
xval = xval[gdy]
yval = yval[gdy]
xmin,xmax=0.5,20
ax.set_xscale("log", nonposx='clip')
elif objtype == 'QSO':
lbl = 'g (Mag)'
xval = 22.5 - 2.5 * np.log10(gdz_tab['FLUX_G'])
xmin,xmax=np.min(xval),np.max(xval)
else:
lbl = 'r (Mag)'
xval = 22.5 - 2.5 * np.log10(gdz_tab['FLUX_R'])
xmin,xmax=np.min(xval),np.max(xval)
# Labels
ax.set_xlabel(lbl)
ax.set_ylabel(ylbl)
ax.set_xlim(xmin,xmax)
v_ylim = ylim * C_LIGHT # redshift to km/s
ax.set_ylim(-v_ylim+yoff, v_ylim+yoff)
# Points
ax.plot([xmin,xmax], [0.,0], '--', color='gray')
#ax.scatter(xval, yval, marker='o', s=1, label=objtype,
# color=sty_otype[objtype]['color'])
cm = plt.get_cmap(sty_otype[objtype]['pcolor'])
if objtype == 'ELG':
xbins = 10**np.linspace(np.log10(xmin), np.log10(xmax), 20)
else:
xbins = np.linspace(xmin, xmax, 20)
ybins = np.linspace(-v_ylim+yoff, v_ylim+yoff, 40) # km/s
#import pdb; pdb.set_trace()
counts, xedges, yedges = np.histogram2d(xval, yval * C_LIGHT, bins=(xbins, ybins))
max_c = np.max(counts)
#if kk == 3:
ax.pcolormesh(xedges, yedges, counts.transpose(), cmap=cm, vmin=0, vmax=max_c/5.)
#ax.hist2d(xval, yval, bins=20, cmap=cm)
#ax.scatter(xval, yval, marker='o', s=1, label=objtype,
# color=sty_otype[objtype]['color'])
# Finish
plt.tight_layout(pad=0.2,h_pad=0.2,w_pad=0.3)
plt.subplots_adjust(top=0.92)
if outfile is not None:
plt.savefig(outfile, dpi=700)
plt.close()
print("Wrote {:s}".format(outfile))
ax1 = fig.add_subplot(121)
ax1.hist(data,
bins=np.linspace(0, 10, 11),
density=True,
histtype='stepfilled',
fc='gray',
alpha=0.5)
ax1.plot(x, px, '-k')
ax1.set_xlim(-1, 11)
ax1.set_ylim(0, 0.18)
ax1.set_xlabel('$x$')
ax1.set_ylabel('$p(x)$')
# right panel: construct and plot the likelihood
ax2 = fig.add_subplot(122)
ax2.xaxis.set_major_locator(plt.MultipleLocator(0.01))
a = np.linspace(-0.01, 0.02, 1000)
Npts = (500, 100, 20)
styles = ('-k', '--b', '-.g')
for n, s in zip(Npts, styles):
logL = linprob_logL(data[:n], a, xmin, xmax)
logL = np.exp(logL - logL.max())
logL /= logL.sum() * (a[1] - a[0])
ax2.plot(a, logL, s, label=r'$\rm %i\ pts$' % n)
ax2.legend(loc=2, prop=dict(size=8))
ax2.set_xlim(-0.011, 0.02)
def visual_state_action(self):
"""This method visualizes the state_action pair lively, with the red arrow
referring to the maximum Q_value state_action pair."""
q_dict = self.q_table_dict
plt.figure(dpi=220, figsize=(7, 7))
ax = plt.axes()
ax.set(xlim=[0, 10], ylim=[0, 10])
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0)) # 设置x主坐标间隔 1
ax.yaxis.set_major_locator(plt.MultipleLocator(1.0)) # 设置y主坐标间隔 1
ax.grid(True, linestyle="-", color="0.6", linewidth="1")
# ax.scatter(8.5, 7.5)
keys = sorted(q_dict.keys())
x, y, i = 0.5, 9.5, 1
for key in keys:
# print("key: " + str(key))
while key[0] * 10 + key[1] != i - 1:
i = i + 1
x = x + 1
if x == 10.5:
x = 0.5
y = y - 1
if key == self.goal_state:
ax.scatter(x, y)
i = i + 1
x = x + 1
continue
if np.average(q_dict[key]) == 0:
i = i + 1
x = x + 1
if x == 10.5:
x = 0.5
y = y - 1
continue
if q_dict[key].index(np.max(q_dict[key])) == 0:
plt.annotate('',
xy=(x - 0.5, y),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3",
color='red'))
else:
plt.annotate('',
xy=(x - 0.5, y),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"))
if q_dict[key].index(np.max(q_dict[key])) == 1:
plt.annotate('',
xy=(x, y + 0.5),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3",
color='red'))
else:
plt.annotate('',
xy=(x, y + 0.5),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"))
if q_dict[key].index(np.max(q_dict[key])) == 2:
plt.annotate('',
xy=(x + 0.5, y),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3",
color='red'))
else:
plt.annotate('',
xy=(x + 0.5, y),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"))
if q_dict[key].index(np.max(q_dict[key])) == 3:
plt.annotate('',
xy=(x, y - 0.5),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3",
color='red'))
else:
plt.annotate('',
xy=(x, y - 0.5),
xytext=(x, y),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3"))
x = x + 1
if x == 10.5:
x = 0.5
y = y - 1
i = i + 1
# 设置刻度标记的大小
plt.tick_params(axis='both', labelsize=10)
plt.show()
ts2= np.array(ys.values).astype(np.double)
ts2mask = np.isfinite(ts2)
# print len(ts2mask), len(ts2), len(xs), len(ys)
# Define general chart characteristics, titles and descriptive text:
# fig = plt.figure()
# chart = fig.add_subplot(111)
fig, chart = plt.subplots()
fontP = FontProperties()
fontP.set_size('xx-small')
chart.set_autoscale_on(True)
minorlocator = plt.MultipleLocator(10)
chart.text(0.99, 0.01, 'Plots created: '+ timeStamp + \
'\nReviewed by the Hydrogeology Unit of the Water Supply Bureau',
verticalalignment='bottom', horizontalalignment='right',
transform=chart.transAxes,
color='green', fontsize=7)
if frequency == 'DA':
title= station +'\n' + dataSource +' ' + options.Aquifer +' Daily Water Level\n' + \
sdateDT.strftime("%m/%d/%Y") + ' thru ' + ThisDate.strftime("%m/%d/%Y")
else:
title= station +'\n' + dataSource +' '+ options.Aquifer + ' Random Interval Water Level\n' + \
sdateDT.strftime("%m/%d/%Y") + ' thru ' + ThisDate.strftime("%m/%d/%Y")
plt.title(str(title),fontsize = 8)
zorder=1,
extent=xlim + ylim)
im.set_clim(0, 1.5)
ax.contour(xx, yy, Z, [0.5], colors='k')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_xlabel('$u-g$')
ax.set_ylabel('$g-r$')
# Plot completeness vs Ncolors
ax = plt.subplot(222)
ax.plot(Ncolors, completeness, 'o-k', ms=6)
ax.xaxis.set_major_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.set_ylabel('completeness')
ax.set_xlim(0.5, 4.5)
ax.set_ylim(-0.1, 1.1)
ax.grid(True)
# Plot contamination vs Ncolors
ax = plt.subplot(224)
ax.plot(Ncolors, contamination, 'o-k', ms=6)
ax.xaxis.set_major_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%i'))
return 'h'
elif x == -1:
return '-h'
else:
return '%ih' % x
for i, kernel in enumerate(
['gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear', 'cosine']):
axi = ax.ravel()[i]
log_dens = KernelDensity(kernel=kernel).fit(X_src).score_samples(X_plot)
axi.fill(X_plot[:, 0], np.exp(log_dens), '-k', fc='#AAAAFF')
axi.text(-2.6, 0.95, kernel)
axi.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
axi.xaxis.set_major_locator(plt.MultipleLocator(1))
axi.yaxis.set_major_locator(plt.NullLocator())
axi.set_ylim(0, 1.05)
axi.set_xlim(-2.9, 2.9)
ax[0, 1].set_title('Available Kernels')
#----------------------------------------------------------------------
# Plot a 1D density example
N = 100
np.random.seed(1)
X = np.concatenate((np.random.normal(0, 1, int(0.3 * N)),
np.random.normal(5, 1, int(0.7 * N))))[:, np.newaxis]
X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis]
x, y = np.meshgrid(
time_series,
np.logspace(np.log10(fmin), np.log10(fmax), scalogram.shape[0]))
fig, ax = plt.subplots(figsize=(9, 9))
ax.pcolormesh(x, 1 / y, np.abs(scalogram), cmap=CBname_map)
plt.xlabel('Time around ' + 'Sdiff' + ' (s)', fontsize=18)
plt.ylabel('Period (s)', fontsize=18)
ax.set_yscale('log') # set linear(detail in 1-2-3) or log(even)
ax.set_ylim(Tmin, Tmax)
majors = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ax.yaxis.set_major_locator(plt.FixedLocator(majors))
# ax.yaxis.set_minor_locator(plt.NullLocator())
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.yaxis.set_minor_formatter(NullFormatter())
ax.xaxis.set_major_locator(plt.MultipleLocator(20))
ax.xaxis.set_minor_locator(plt.MultipleLocator(5))
# ax.set_yticks([1/10,1/5,1/3,1/2,1], ['10','5','3','2','1'])
plt.gca().invert_yaxis()
ax2 = ax.twinx()
ax2.plot(time_series, stack_data_series / stack_data_series.max(),
'k') # plot the waveform at the bottom
ax2.set_ylim([-1, 50])
ax2.set_yticklabels([])
plt.xlim([StartTime, EndTime])
plt.title('Wavelet Spectrum at Azimuth ' + str(select_azi) +
';\n Stacking ' + str(count) + ' Traces')
# plt.show()
def fig_ionstate(outfil=None):
# Init COS-Halos sightline
cos_halos = COSHalos()
cos_halos.load_single(('J1016+4706', '274_6'))
cgm_abs = cos_halos.cgm_abs[0]
FeH = -1.
# CUBA
cuba = CUBA()
npt = 30
xeV = np.linspace(13.6, 130., npt) * u.eV
phi = np.zeros(npt)
for kk, ixeV in enumerate(xeV):
phi[kk] = cuba.phi(0.2, min_energy=ixeV).value
# Ions
Zions = [
(1, 1), # HI
(6, 2), # CII
(6, 3), # CIII
(7, 2), # NII
(7, 3), # NIII
(7, 5), # NV
(8, 6), # OVI
(12, 1), # MgI
(12, 2), # MgII
(14, 2), # SiII
(14, 3), # SiIII
(14, 4), # SiIV
]
# Start the plot
if outfil is None:
outfil = 'fig_ionstate.pdf'
pp = PdfPages(outfil)
# Lya spec
lclr = 'red'
pclr = 'blue'
xmnx = (0., 130.) # eV
ymnx = (-1.5, 2.)
for ss in range(5):
plt.figure(figsize=(7, 5))
plt.clf()
gs = gridspec.GridSpec(1, 1)
ax = plt.subplot(gs[0, 0])
#ax.xaxis.set_minor_locator(plt.MultipleLocator(0.5))
ax.yaxis.set_major_locator(plt.MultipleLocator(1.))
#ax.get_xaxis().get_major_formatter().set_useOffset(False)
ax.set_xlim(xmnx)
ax.set_ylim(ymnx)
ax.set_xlabel('Ionization Potential (eV)')
ax.set_ylabel(
r'$\log N_{\rm ion} - \log N_{\rm HI} + 12 - \epsilon_\odot$ - [Fe/H]'
)
ax.minorticks_on()
# Zero line
ax.plot(xmnx, (0., 0.), '--', color='gray')
# Data
HIclm = cgm_abs.abs_sys.ions[(1, 1)]['CLM']
for Zion in Zions:
# Column and flag
try:
clm = cgm_abs.abs_sys.ions[Zion]['CLM']
except KeyError:
print('Zion = {:d},{:d} not analyzed'.format(Zion[0], Zion[1]))
continue
else:
flg_clm = cgm_abs.abs_sys.ions[Zion]['FLG_CLM']
sig_clm = cgm_abs.abs_sys.ions[Zion]['SIG_CLM']
# IP
elm = ELEMENTS[Zion[0]]
IP = elm.ionenergy[Zion[1] - 1]
# Solar abund
abund = xsolar.abund(Zion[0])
yval = clm - HIclm + 12 - abund
if Zion != (1, 1):
yval = yval + FeH
# Plot
if flg_clm == 1:
mark = 'o'
pclr = 'blue'
yoff = 0.1
elif flg_clm == 2:
mark = (3, 0, 0)
pclr = 'red'
yoff = -0.3
elif flg_clm == 3:
mark = (3, 0, 180)
pclr = 'gray'
yoff = 0.1
ax.scatter(IP, yval, marker=mark, color=pclr, s=35.)
# Label
ion_name = xion.ion_name(Zion)
ax.text(IP,
yval + yoff,
ion_name,
color=pclr,
fontsize=17.,
ha='center')
#ax.text(1480., 0.4, cgm_abs.field, ha='left', fontsize=21., color=lclr)
if ss >= 1: # Add EUVB
ax_euvb = ax.twinx()
y0 = np.log10(phi[0])
ax_euvb.plot(xeV, np.log10(phi) - y0, 'k-') # Normalize 1 Ryd to 1
ax_euvb.set_ylabel(r'$\log \; \Phi_{\rm EUVB} ({\rm E > IP})$')
xputils.set_fontsize(ax_euvb, 17.)
ax_euvb.set_xlim(xmnx)
ax_euvb.set_ylim(-1.2, 0.)
ax_euvb.text(100., -1., 'EUVB', color='black', size=17.)
if ss >= 2: # Add shading
xrng = np.array([10., 55])
ax.fill_between(xrng, [ymnx[0]] * 2, [ymnx[1]] * 2,
color='blue',
alpha=0.3)
ax.text(np.mean(xrng),
-1.2,
'Photoionization\n' + r'$U \equiv \Phi/cn_{\rm H}$',
color='black',
size=19.,
ha='center')
if ss >= 3: # Add shading
xrng = np.array([70., 120.])
ax.fill_between(xrng, [ymnx[0]] * 2, [ymnx[1]] * 2,
color='red',
alpha=0.3)
ax.text(np.mean(xrng),
-1.2,
'Another phase',
color='black',
size=19.,
ha='center')
if ss >= 4: # EUV lines
Zions2 = [(10, 8), (16, 4), (16, 5), (8, 2), (8, 3), (8, 5)]
for Zion2 in Zions2:
elm = ELEMENTS[Zion2[0]]
IP = elm.ionenergy[Zion2[1] - 1]
yval = 0.2
yoff = 0.1
ax.scatter(IP, yval, marker='o', color='black', s=35.)
# Label
try:
ion_name = xion.ion_name(Zion2)
except TypeError:
ion_name = 'NeVIII'
ax.text(IP,
yval + yoff,
ion_name,
color='black',
fontsize=17.,
ha='center')
#ax.set_xlim(0.,200)
# Fonts
xputils.set_fontsize(ax, 17.)
# Write
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
pp.savefig()
plt.close()
# Finish
print('tlk_coshalos: Wrote {:s}'.format(outfil))
pp.close()