def compute_righthind(forecast, hindcast, observation):
"""
Esta função faz a correção do hincast usando
a regressão linear (correção de bias). Mesmo
método utilizado pelo Júnior.
"""
# Calcula média e desvio padrão para todos os pontos da climatologia do modelo
clim_mean = np.nanmean(hindcast, axis=0)
clim_std = np.nanstd(hindcast, axis=0)
# Calcula média e desvio padrão para todos os pontos da climatologia do
# dados observado
obs_mean = np.nanmean(observation, axis=0)
obs_std = np.nanstd(observation, axis=0)
# Calcula variável padronizada do modelo e observado
clim_pad = (hindcast - clim_mean)/clim_std
# obs_pad = (observation - obs_mean)/obs_std
fcst_pad = (forecast - clim_mean)/clim_std
# newhind é o hindcast corrigido
newhind = clim_pad * obs_std + obs_mean
newfcst = fcst_pad * obs_std + obs_mean
return newhind, newfcst
def make_NRCS_image( nobj, bandname, fn='', dir='.', max=np.nan, min=np.nan,
**kwargs):
if not fn:
if 'reduced' in bandname:
fn = bandname[:9]+'.png'
else:
fn = bandname+'.png'
resize(nobj)
try:
s0 = 10.0*np.log10(nobj[bandname])
except:
n_obj.undo()
raise
s0[np.where(np.isinf(s0))]=np.nan
#if nobj.fileName[-2:]=='nc':
# s0 = flipdim(nobj,s0)
caption='dB'
if np.isnan(min):
min = np.nanmedian(s0,axis=None)-2.0*np.nanstd(s0,axis=None)
if np.isnan(max):
max = np.nanmedian(s0,axis=None)+2.0*np.nanstd(s0,axis=None)
nansatFigure(s0, min, max, dir, fn)
nobj.undo()
return fn
def sigma_clip(image, sigma_lo=3, sigma_hi=3, max_iter=5, axis=0):
"""Reference implementation in numpy"""
image = image.copy()
mask = numpy.logical_not(numpy.isfinite(image))
dummies = mask.sum()
image[mask] = numpy.NaN
mean = numpy.nanmean(image, axis=axis, dtype="float64")
std = numpy.nanstd(image, axis=axis, dtype="float64")
for _ in range(max_iter):
if axis == 0:
mean2d = as_strided(mean, image.shape, (0, mean.strides[0]))
std2d = as_strided(std, image.shape, (0, std.strides[0]))
else:
mean2d = as_strided(mean, image.shape, (mean.strides[0], 0))
std2d = as_strided(std, image.shape, (std.strides[0], 0))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
delta = (image - mean2d) / std2d
mask = numpy.logical_or(delta > sigma_hi,
delta < -sigma_lo)
dummies = mask.sum()
if dummies == 0:
break
image[mask] = numpy.NaN
mean = numpy.nanmean(image, axis=axis, dtype="float64")
std = numpy.nanstd(image, axis=axis, dtype="float64")
return mean, std
def predict(self, data=None):
if(self.use_period):
# decomfreq = freq
res = sm.tsa.seasonal_decompose(self.data.tolist(), freq=self.freq, model=self.model)
# res.plot()
median_trend = pd.rolling_median(Series(self.data),window=self.freq, center=True, min_periods=1)
resid = res.observed - res.seasonal - median_trend
else:
resid = self.data
random = Series(resid)
mean_nan = 0
std_nan = 0
# random = res.resid
if (self.mode == 'average'):
mean_nan = np.nanmean(random)
std_nan = np.nanstd(random)
elif (self.mode == 'median'):
rolling_median = pd.rolling_median(random,3,center=True, min_periods=1)
mean_nan = np.nanmean(rolling_median)
std_nan = np.nanstd(rolling_median)
min_val = mean_nan - 4 * std_nan
# max_val = mean(random, na.rm = T) + 4*sd(random, na.rm = T)
max_val = mean_nan + 4 * std_nan
position = Series(resid.tolist(), index=np.arange(resid.shape[0]))
anomaly = position[(position > max_val) | (position < min_val)]
# anomalyL = position[(position<min_val)]
# anomaly = anomalyH.append(anomalyL).drop_duplicates()
point_anomaly_idx = anomaly.index
self.anomaly_idx = point_anomaly_idx
points_anomaly = self.data[point_anomaly_idx]
self.anomalies = points_anomaly
return points_anomaly
def get_night_shifts(offset):
"""Returns the mean APASS-based shifts for all the offsets in the same night."""
shifts = {}
shifts_std = {}
for band in ['r', 'i']:
offset_shifts = []
for sibling in red_groups[offset]:
try:
offset_shifts.append(SHIFTS[sibling][band + 'shift'])
except KeyError:
pass
shifts[band] = np.nanmean(offset_shifts)
shifts_std[band] = np.nanstd(offset_shifts)
if offset not in blue_groups:
for band in ['u', 'g', 'r2']:
shifts[band] = np.nan
else:
for band in ['u', 'g', 'r2']:
offset_shifts = []
for sibling in blue_groups[offset]:
try:
offset_shifts.append(SHIFTS[sibling][band + 'shift'])
except KeyError:
pass
shifts[band] = np.nanmean(offset_shifts)
shifts_std[band] = np.nanstd(offset_shifts)
return shifts, shifts_std
def attenSig( comp , min_var_warn=0 , min_var_fail=0 , sd = False ):
# comp: list of values to run the test on. Therefore, time interval determined by user
# min_var_warn: minimum range of data variation to trip a warning
# min_var_fail: minimum range of data variation to trip a fail
# sd: if true, comp data stdev will be tested against stdev thresholds set by user
if min_var_warn==0 and min_var_fail==0:
raise ValueError ("Please indicate min&max deviance threshold values.")
n=len(comp)
if sd==True:
if np.nanstd(comp) <= min_var_warn:
flag = 3
elif np.nanstd(comp) <= min_var_fail:
flag = 4
else:
flag = 1
if sd==False:
test = abs(max(comp) - min(comp))
if test <= min_var_warn:
flag = 3
elif test <= min_var_fail:
flag = 4
else:
flag = 1
flag_list = [flag*(x/x) for x in np.arange(1,(len(comp)+1))]
return flag_list
def get_variance_map2(a_plus_b, a_minus_b, bias_mask2, pix_mask, gain):
#variance0 = a_minus_b
#a_minus_b = a-b
msk = bias_mask2 | pix_mask | ~np.isfinite(a_minus_b)
from destriper import destriper
variance0 = destriper.get_destriped(a_minus_b,
msk,
pattern=64,
remove_vertical=False,
hori=False)
#variance0 = a_minus_b
# stsci_median cannot be used due to too many array error.
#ss = stsci_median([m1 for m1 in variance0],)
dd1 = np.ma.array(variance0, mask=msk)
ss = np.ma.median(dd1, axis=0)
variance_ = variance0.copy()
variance_[msk] = np.nan
st = np.nanstd(variance_)
st = np.nanstd(variance_[np.abs(variance_) < 3*st])
variance_[np.abs(variance_-ss) > 3*st] = np.nan
import scipy.ndimage as ni
x_std = ni.median_filter(np.nanstd(variance_, axis=0), 11)
variance_map0 = np.zeros_like(variance_) + x_std**2
variance_map = variance_map0 + np.abs(a_plus_b)/gain # add poison noise in ADU
return variance_map
def main():
os.system('modprobe w1-gpio')
os.system('modprobe w1-therm')
print len(sys.argv)
if len(sys.argv) == 1:
number_of_meas = 7
else:
print sys.argv[1]
number_of_meas = int(sys.argv[1])
print "number_of_measurements = " + str(number_of_meas)
print "getting device files and serials..."
THEDICT = _get_w1_tree_and_serials()
print "reading sensors " + str(number_of_meas) + " times ..."
for step in range(int(number_of_meas)):
for sensor_id in THEDICT:
if sensor_id[0:2] == '28' or sensor_id[0:2] == '10':
temp = read_sensor_ds18b20(sensor_id,THEDICT[sensor_id]["path"])
volt = "n.a."
THEDICT[sensor_id]["temp"].append(temp)
THEDICT[sensor_id]["volt"].append(0.)
if sensor_id[0:2] == '26':
temp,volt = read_sensor_ds2438(sensor_id,THEDICT[sensor_id]["path"])
THEDICT[sensor_id]["temp"].append(temp)
THEDICT[sensor_id]["volt"].append(volt)
print "step " + str(step) + " " + sensor_id + " " + str(temp) + " " + str(volt)
print "calculating individual and total means:"
MEAN_IND = {}
for sensor_id in THEDICT:
MEAN_IND[sensor_id] = [
np.nanmean(np.array(THEDICT[sensor_id]["temp"])),
np.nanmean(np.array(THEDICT[sensor_id]["volt"]))
]
total_temp = []
total_volt = []
for sensor_id in MEAN_IND:
if sensor_id[0:2] == '28' or sensor_id[0:2] == '10':
total_temp.append(MEAN_IND[sensor_id][0])
if sensor_id[0:2] == '26':
total_volt.append(MEAN_IND[sensor_id][1])
mean_temp = np.nanmean(np.array(total_temp))
mean_volt = np.nanmean(np.array(total_volt))
print "temp mean: " + str(mean_temp) + " +/- " + str(np.nanstd(np.array(total_temp)))
print "volt mean: " + str(mean_volt) + " +/- " + str(np.nanstd(np.array(total_temp)))
print "calculating offsets..."
OFFSETS = {}
for sensor_id in MEAN_IND:
OFFSETS[sensor_id] = [
MEAN_IND[sensor_id][0] - mean_temp,
MEAN_IND[sensor_id][1] - mean_volt
]
print OFFSETS
print "writing offsets..."
write_offset(OFFSETS)
def main():
fname = 'cleaned_data/core_male_cleaned.csv'
# print(filelength(fname))
# fname = 'cleaned_data/core_patients_cleaned.csv'
# code1 = URETHRAL_INJURY_CODES[0]
PENILE_FRACTURE_CODE = '95913'
if not sys.argv[1]:
print('Please enter an argument')
sys.exit(0)
code1 = str(sys.argv[1])
code2 = '95913'
# try:
# data_mat = np.loadtxt('cleaned_data/pxpywt.txt')
# print('Done loading file! Starting analysis.')
# except:
print('cannot load data, going to try generating...')
data_mat = binary_arrays(fname,code1,code2,1)
true_stat = mi(data_mat)
print(true_stat)
# print(leaders(DXLIST))
print('beginning surrogate_stats')
surrogate_stats = surrogate_mi(data_mat)
print('beginning bootstrap_stats')
bootstrap_stats = bootstrap_mi(data_mat)
np.savetxt('cleaned_data/{0}surrogate_stats.txt'.format(str(sys.argv[2]),),surrogate_stats,fmt='%f')
np.savetxt('cleaned_data/{0}bootstrap_stats.txt'.format(str(sys.argv[2]),),bootstrap_stats,fmt='%f')
# plt.hist(bootstrap_stats,50)
# plt.show()
print(wald_test(true_stat, np.nanstd(bootstrap_stats), np.nanmean(surrogate_stats), np.nanstd(surrogate_stats)))
def ddiff_var_wb(lh_ctx_file, rh_ctx_file, sc_file):
import numpy as np
from generic_pipelines.moco_eval import wb_to_tss
tss = wb_to_tss(lh_ctx_file, rh_ctx_file, sc_file)
out_fname = 'ddiff.npz'
np.savez(out_fname,diffvar=np.nanstd(tss,0), ddiffvar=np.nanstd(np.diff(tss,1,0),0))
return out_fname
def radial_contrast_flr(image, xc, yc, seps, zw, coron_thrupt, klip_thrupt=None):
rad_flr_ctc = np.empty((len(seps)))
assert(len(seps) == len(coron_thrupt))
if klip_thrupt is not None:
assert(len(seps) == len(klip_thrupt))
rad_flr_ctc_ktc = np.empty((len(seps)))
else:
rad_flr_ctc_ktc = None
imh = image.shape[0]
imw = image.shape[1]
xs = np.arange(imw) - xc
ys = np.arange(imh) - yc
XXs, YYs = np.meshgrid(xs, ys)
RRs = np.sqrt(XXs**2 + YYs**2)
for si, sep in enumerate(seps):
r_in = np.max([seps[0], sep-zw/2.])
r_out = np.min([seps[-1], sep+zw/2.])
meas_ann_mask = np.logical_and(np.greater_equal(RRs, r_in),
np.less_equal(RRs, r_out))
meas_ann_ind = np.nonzero(np.logical_and(np.greater_equal(RRs, r_in).ravel(),
np.less_equal(RRs, r_out).ravel()))[0]
meas_ann = np.ravel(image)[meas_ann_ind]
rad_flr_ctc[si] = np.nanstd(meas_ann)/coron_thrupt[si]
if rad_flr_ctc_ktc is not None:
rad_flr_ctc_ktc[si] = np.nanstd(meas_ann)/coron_thrupt[si]/klip_thrupt[si]
#pdb.set_trace()
return rad_flr_ctc, rad_flr_ctc_ktc
def plotQE(self, save=False):
'''
Plot the measured and theoretical QE
'''
fig, ax = plt.subplots()
QE_median = np.array([np.nanmedian(QE.flatten()) for QE in self.QE])
QE_upper = np.array([QE_median[ind] + np.nanstd(QE.flatten())
for ind, QE in enumerate(self.QE)])
QE_lower = np.array([QE_median[ind] - np.nanstd(QE.flatten())
for ind, QE in enumerate(self.QE)])
ax.plot(self.wavelengths, QE_median * 100, linewidth=3, color='black',
label=r'Measured')
ax.fill_between(self.wavelengths, QE_lower * 100, QE_upper * 100,
where=QE_upper >= QE_lower, color='green', facecolor='green',
interpolate='True', alpha=0.1)
ax.plot(self.wvl_theory, 100 * self.QE_theory, linestyle='-.', linewidth=2,
color='black', label=r'Theoretical')
ax.set_xlabel(r'Wavelength (nm)')
ax.set_ylabel(r'QE (%)')
ax.legend()
ax.set_xlim([min(self.wavelengths), max(self.wavelengths)])
ax.set_ylim([0, max(QE_upper) * 100 * 1.2])
if save:
file_name = os.path.join(self.out_directory, self.log_file + '.pdf')
plt.savefig(file_name, format='pdf')
plt.close(fig)
else:
plt.show(block=False)
def _get_x_0_stats(self):
x_diff = np.diff(self.x_arr_0, axis=1)
mu_mm = np.nanmean(x_diff)
std_mm = np.nanstd(x_diff)
mu_px_mm = np.nanmean(x_diff / self.aramis_info.n_px_facet_step_x)
std_px_mm = np.nanstd(x_diff / self.aramis_info.n_px_facet_step_x)
return mu_mm, std_mm, mu_px_mm, std_px_mm
def bin_fit(x, y, buckets=3):
assert buckets in [3,25]
xstd=np.nanstd(x)
if buckets==3:
binlimits=[np.nanmin(x), -xstd/2.0,xstd/2.0 , np.nanmax(x)]
elif buckets==25:
steps=xstd/4.0
binlimits=np.arange(-xstd*3.0, xstd*3.0, steps)
binlimits=[np.nanmin(x)]+list(binlimits)+[np.nanmax(x)]
fit_y=[]
err_y=[]
x_values_to_plot=[]
for binidx in range(len(binlimits))[1:]:
lower_bin_x=binlimits[binidx-1]
upper_bin_x=binlimits[binidx]
x_values_to_plot.append(np.mean([lower_bin_x, upper_bin_x]))
y_in_bin=[y[idx] for idx in range(len(y)) if x[idx]>=lower_bin_x and x[idx]<upper_bin_x]
fit_y.append(np.nanmedian(y_in_bin))
err_y.append(np.nanstd(y_in_bin))
## no zeros
return (binlimits, x_values_to_plot, fit_y, err_y)
def checkAbundanceScatterClusters():
# First read the cluster data
cldata= read_clusterdata.read_caldata()
# Read the allStar data to match
# For each of the calibration open clusters, calculate the offset from the
# mean in our FEHTAG and AFETAG
clusters= ['M71','N2158','N2420','N188','M67','N7789','N6819',
'N6791']
fehoffset= []
afeoffset= []
for cluster in clusters:
tdata= cldata[cldata['CLUSTER'] == cluster.upper()]
tdata= tdata[(tdata['TEFF'] < _TEFFMAX)\
*(tdata['TEFF'] > _TEFFMIN)\
*(tdata['LOGG'] < 3.5)]
# Compute the average feh and afe and save the offsets
medianfeh= numpy.median(tdata['FE_H'])
medianafe= numpy.median(tdata[define_rcsample._AFETAG])
fehoffset.extend(tdata['FE_H']-medianfeh)
afeoffset.extend(tdata[define_rcsample._AFETAG]-medianafe)
if cluster == 'M67': print medianfeh, medianafe, len(tdata)
fehoffset= numpy.array(fehoffset)
afeoffset= numpy.array(afeoffset)
print 'FE_H scatter %g' % (numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3]))
print 'A_FE scatter %g' % (numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
gindx= (numpy.fabs(fehoffset) < 0.3)*(numpy.fabs(afeoffset) < 0.3)
print 'FE_H/A_FE correlation %g' % (numpy.mean(afeoffset[gindx]*fehoffset[gindx])/numpy.nanstd(fehoffset[numpy.fabs(fehoffset) < 0.3])/numpy.nanstd(afeoffset[numpy.fabs(afeoffset) < 0.3]))
print 'FE_H robust scatter %g' % (1.4826*numpy.median(numpy.fabs(fehoffset)))
print 'A_FE robust scatter %g' % (1.4826*numpy.median(numpy.fabs(afeoffset)))
bovy_plot.bovy_print()
bovy_plot.bovy_hist(fehoffset,range=[-0.3,0.3],bins=31,histtype='step')
bovy_plot.bovy_hist(afeoffset,range=[-0.3,0.3],bins=31,histtype='step',
overplot=True)
bovy_plot.bovy_end_print('test.png')
return None
def compare_iter(arr_len, n_iter):
"""
Use bubble, quick and merge sorts of random arrays of a set length,
for n_iter times. Then return mean and standard deviations of results
The arrays are limited to values less than 1000.
"""
bubble_comps = []
quick_comps = []
merge_comps = []
# Perform sorting the required number of times:
for ind in range(n_iter):
rand_arr = np.random.randint(1000, size = arr_len)
bubble_comps.append(bubble_sort(rand_arr, 0))
quick_comps.append(quick_sort(rand_arr, 0))
merge_comps.append(merge_sort(rand_arr, 0))
# Extract the number of comparisons:
bub_no = np.array([x[0] for x in bubble_comps])
qck_no = np.array([x[0] for x in quick_comps])
mrg_no = np.array([x[0] for x in merge_comps])
# Calculate mean and standard deviations:
bub_mean = np.nanmean(bub_no)
qck_mean = np.nanmean(qck_no)
mrg_mean = np.nanmean(mrg_no)
bub_stddev = np.nanstd(bub_no)
qck_stddev = np.nanstd(qck_no)
mrg_stddev = np.nanstd(mrg_no)
# Return the means and standard deviations
return bub_mean, bub_stddev, qck_mean, qck_stddev, mrg_mean, mrg_stddev
def score_signal_to_noise(a, b, axis=0):
mean_a = np.nanmean(a, axis=axis)
mean_b = np.nanmean(b, axis=axis)
std_a = np.nanstd(a, axis=axis, ddof=1)
std_b = np.nanstd(b, axis=axis, ddof=1)
return (mean_a - mean_b) / (std_a + std_b)
def test_zernikes_rms(nterms=10, size=500):
"""Verify RMS(Zernike[n,m]) == 1."""
assert np.nanstd(zernike.zernike1(1)) == 0.0, "Zernike(j=0) has nonzero RMS"
for j in range(2, nterms):
n, m = zernike.noll_indices(j)
Z = zernike.zernike(n, m, npix=size)
rms = np.nanstd(Z) # exclude masked pixels
assert 1.0 - rms < 0.001, "Zernike(j={}) has RMS value of {}".format(j, rms)
def pickVMIN(dataIn, devs):
try:
if np.nanmean(dataIn) - devs * np.nanstd(dataIn) < np.nanmin(dataIn):
return np.nanmin(dataIn)
else:
return np.nanmean(dataIn) - devs * np.nanstd(dataIn)
except ValueError:
return 1
def ddiff_var_moco(in_file):
import numpy as np
import h5py
ts = h5py.File(in_file,'r')
tss = np.asarray(ts['FMRI/DATA']).T
out_fname = 'ddiff.npz'
np.savez(out_fname,diffvar=np.nanstd(tss,0), ddiffvar=np.nanstd(np.diff(tss,1,0),0))
return out_fname
def estimate_f(self, i, samples = 200):
#minibatch = np.random.choice(X,batch_size,replace=False)
LL_X = np.array([self.f['LL'](i) for s in range(samples if samples != None else self.samples)])
LL_Y = np.array([self.f['LLY'](i) for s in range(samples if samples != None else self.samples)])
return np.nanmean(LL_X, 0)+np.nanmean(LL_Y, 0), np.nanstd(LL_X, 0)+np.nanstd(LL_Y, 0)
def pickVMAX(dataIn, devs):
try:
if np.nanmean(dataIn) + devs * np.nanstd(dataIn) > np.nanmax(dataIn):
return np.nanmax(dataIn)
else:
return np.nanmean(dataIn) + devs * np.nanstd(dataIn)
except ValueError:
return 1
def calc_perf_stats(self):
"""Calculate mean performance based on trimmed time series."""
self.mean_tsr, self.std_tsr = nanmean(self.tsr), nanstd(self.tsr)
self.mean_cp, self.std_cp = nanmean(self.cp), nanstd(self.cp)
self.mean_cd, self.std_cd = nanmean(self.cd), nanstd(self.cd)
self.mean_ct, self.std_ct = nanmean(self.ct), nanstd(self.ct)
self.mean_u_enc = nanmean(self.tow_speed)
self.std_u_enc = nanstd(self.tow_speed)
def test_nanstd(self):
tgt = np.std(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanstd(mat), tgt)
tgt = np.std(self.mat, ddof=1)
for mat in self.integer_arrays():
assert_equal(np.nanstd(mat, ddof=1), tgt)
def normalize(m1, m2, m3, m4): norm1 = (m1 - np.nanmean(m1))/np.nanstd(m1) norm2 = (m2 - np.nanmean(m2))/np.nanstd(m2) norm3 = (m3 - np.nanmean(m3))/np.nanstd(m3) norm4 = (m4 - np.nanmean(m4))/np.nanstd(m4) return norm1, norm2, norm3, norm4
def pretty_gradual_plot(data, concentrations, strain_name_map, drug_name, blank_line=200):
def inner_scatter_plot(mean, std, relative, limiter=4):
series = np.zeros(mean.shape)
cell_type = np.zeros(mean.shape)
for i, name in enumerate(names):
series[i, :] = np.arange(i, c.shape[0]*(len(names)+40)+i, len(names)+40)
cell_type[i, :] = i
plt.scatter(series[i, :], mean[i, :], c=cm(i/float(len(names))), s=35, label=name)
plt.errorbar(series.flatten(), mean.flatten(), yerr=std.flatten(), fmt=None, capsize=0)
plt.xticks(np.mean(series, axis=0), c)
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=len(names)/limiter, mode="expand", borderaxespad=0.,prop={'size':6})
if not relative:
plt.axhline(y=blank_line)
plt.show()
filter = np.all(np.logical_not(np.isnan(data)), axis=(1, 2))
names = [strain_name_map[i] for i in filter.nonzero()[0].tolist()]
c = concentrations[filter, :][0, :]
mean = np.nanmean(data[filter, :, :], axis=-1)
std = np.nanstd(data[filter, :, :], axis=-1)
cm = plt.cm.get_cmap('spectral')
refmean = mean[:, 0].reshape((mean.shape[0], 1))
refstd = std[:, 0].reshape((mean.shape[0], 1))
rel_mean, rel_std = (mean/refmean, np.sqrt(np.power(refstd, 2)+np.power(std, 2))/mean)
inner_scatter_plot(mean, std, False)
inner_scatter_plot(rel_mean, rel_std, True)
mean_mean = np.nanmean(mean, axis=0)
std_mean = np.nanstd(mean, axis=0)
mean_std = np.nanmean(std, axis=0)
total_std = np.sqrt(np.power(std_mean, 2) + np.power(mean_std, 2))
confusables = np.sum(mean - std < blank_line, axis=0) / float(len(names))
rel_mean_mean = np.nanmean(rel_mean, axis=0)
rel_std_mean = np.nanstd(rel_mean, axis=0)
rel_mean_std = np.nanmean(rel_std, axis=0)
rel_total_std = np.sqrt(np.power(rel_std_mean, 2) + np.power(rel_mean_std, 2))
plt.subplot(212)
plt.plot(mean_mean, c=cm(0.00), label='mean of mean')
plt.plot(mean_std, c=cm(.25), label='mean of std')
plt.plot(std_mean, c=cm(.50), label='std of mean')
plt.plot(total_std, c=cm(0.75), label='total std')
# plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, mode="expand", borderaxespad=0.,prop={'size':8})
plt.axhline(y=blank_line)
plt.subplot(211)
plt.plot(rel_mean_mean, c=cm(0.00), label='mean of mean')
plt.plot(rel_mean_std, c=cm(.25), label='mean of std')
plt.plot(rel_std_mean, c=cm(.50), label='std of mean')
plt.plot(rel_total_std, c=cm(0.75), label='total std')
plt.plot(confusables, c=cm(0.9), label='confusable with null')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, mode="expand", borderaxespad=0.,prop={'size':8})
plt.show()
def match_fits(fitsfile1, fitsfile2, header=None, sigma_cut=False,
use_mad_std=True,
return_header=False, **kwargs):
"""
Project one FITS file into another's coordinates
If sigma_cut is used, will try to find only regions that are significant
in both images using the standard deviation
Parameters
----------
fitsfile1: str
Reference fits file name
fitsfile2: str
Offset fits file name
header: pyfits.Header
Optional - can pass a header to projet both images to
sigma_cut: bool or int
Perform a sigma-cut on the returned images at this level
use_mad_std : bool
Use mad_std instead of std dev for stddev estimation
Returns
-------
image1,image2,[header] : Two images projected into the same space, and
optionally the header used to project them
"""
if header is None:
header = load_header(fitsfile1)
image1 = load_data(fitsfile1)
else: # project image 1 to input header coordinates
image1 = project_to_header(fitsfile1, header)
# project image 2 to image 1 coordinates
image2_projected = project_to_header(fitsfile2, header)
if image1.shape != image2_projected.shape:
raise ValueError("Failed to reproject images to same shape.")
if sigma_cut:
std1 = stats.mad_std(image1, ignore_nan=True) if use_mad_std else np.nanstd(image1)
std2 = stats.mad_std(image2_projected, ignore_nan=True) if use_mad_std else np.nanstd(image2_projected)
corr_image1 = image1*(image1 > std1*sigma_cut)
corr_image2 = image2_projected*(image2_projected > std2*sigma_cut)
OK = np.isfinite(corr_image1) & np.isfinite(corr_image2)
if (corr_image1[OK]*corr_image2[OK]).sum() == 0:
print("Could not use sigma_cut of %f because it excluded all valid data" % sigma_cut)
corr_image1 = image1
corr_image2 = image2_projected
else:
corr_image1 = image1
corr_image2 = image2_projected
returns = corr_image1, corr_image2
if return_header:
returns = returns + (header,)
return returns
def calc_sn(self, w1=4700., w2=6000.):
idx = np.logical_and(self.w[self.goodpixels] >=w1,
self.w[self.goodpixels] <=w2)
self.res = self.galaxy[self.goodpixels] - self.bestfit[self.goodpixels]
# Using robust method to calculate noise using median deviation
self.noise = np.nanstd(self.res[idx])
self.signal = np.nanstd(self.galaxy[self.goodpixels][idx])
self.sn = self.signal / self.noise
return
def normalize_raster(src_raster, axis=None):
''' This assumes that multi-band rasters are dimesioned (bands, rows, cols) '''
fail = False
message = {'a':"Normalization will occur using the entire raster",
'b':"Normalization will occur by band",
'p':"Normalization will occur across pixels",
'r':"Normalization will occur across rows",
'c':"Normalization will occur across columns"}
rast_dims = src_raster.shape
num_dims = len(rast_dims)
if num_dims > 3:
print("Warning:function can only normalize two and three dimension rasters")
return False
else:
if axis is None:
axis = 'a'
print(message[axis])
elif axis is int():
if axis >= num_dims:
alt_msg = " (axis value exceeds the raster's dimensions)"
fail = True
elif axis < 0:
alt_msg = ''
fail = True
elif num_dims == 2:
axis = {0:'r', 1:'c'}[axis]
else:
axis = {0:'p', 1:'r', 2:'c'}[axis]
if fail or axis not in message:
print("Warning:invalid axis value%s." % alt_msg)
print("\tChoose a value from the following list:")
for k, v in message.iteritems() : print("\t\t'%s' for %s" % (k, v.replace('will occur ', '').lower()))
return False
axis = axis.lower()[0]
if num_dims == 3:
axes= {'a':None, 'p':0, 'c':1, 'r':2}
shape = {'a':rast_dims, 'p':rast_dims[0], 'c':rast_dims[1], 'r':rast_dims[2]}
else:
axes= {'a':None, 'b':None, 'p':None, 'c':0, 'r':1}
shape = {'a':rast_dims, 'p':rast_dims, 'c':rast_dims[0], 'r':rast_dims[1]}
#d, nRows, nCols = (0,) + rast_dims
if axis == 'p':
print("%s %s" %
("Warning:two-dimensional rasters can not be normalized at the pixel-level.", message['a']))
print("Normalizing %s" % message[axis][25:])
if (num_dims == 3) & (axis == 'b'):
mean_=np.array([np.tile(np.nanmean(src_raster[b]), rast_dims[1:]) for b in range(rast_dims[0])])
std_=np.array([np.tile(np.nanstd(src_raster[b]), rast_dims[1:]) for b in range(rast_dims[0])])
else:
mean_ = np.tile(np.nanmean(src_raster, axis=axes[axis]), shape[axis]).reshape(rast_dims)
std_ = np.tile(np.nanstd(src_raster, axis=axes[axis]), shape[axis]).reshape(rast_dims)
if (std_ == 0).any():
print("Warning:Divide by zero error (there is a zero value in the standard deviation)")
return False
else:
norm_img = (src_raster - mean_)/std_
return norm_img
def init_data_avg(data_regs, flmts, avg='a'):
"""Average region FWHM data that has already been retrieved
Currently reporting standard error of mean as errors
but, for small sample sizes, we must use Student's t-distribution.
For 70% confidence interval, multiply by 1.963 for dof=1 (n=2)
For 90% confidence interval, multiply by 6.3 for dof=1 (n=2) (!!!)
Input:
data_all (dict): keyed by region number, w/ kevs/data/eps/inds (use init_data)
flmts (dict): filament number paired w/ constituent region numbers
avg (str): 'a' or 'g' for arithmetic, geometric averages & errors
Output:
new dict, keyed by filament numbers, w/ averaged data
also includes n_pts (number of points averaged in each band)
"""
data_avgd = {}
for n_flmt, n_regs in flmts.items():
kevs = []
data_all = []
for m in n_regs:
kevs_r, data_r, eps_r, inds_r = data_regs[m]
if len(kevs) > 0 and not np.array_equal(kevs, kevs_r):
raise Exception('Discrepancy in stored kevs')
kevs = kevs_r
data_all.append(data_r)
data_all = np.array(data_all)
n_pts = np.sum(~np.isnan(data_all),axis=0) # Number of usable FWHMs in each energy band
inds = np.where(n_pts)[0]
# Lots of unsafe computation -- but, if n=0 we will throw it out anyways
if avg == 'a':
data = np.nanmean(data_all, axis=0)
std = np.nanstd(data_all, axis=0, ddof=1)
std[np.where(np.isnan(std))] = np.nanmax(std) # Catch n=1 cases (stdev ill-defined)
eps = std / np.sqrt(n_pts) # Standard error of the mean
elif avg == 'g':
# Compute errors in log space first, before setting NaNs in data to 1
std_log = np.nanstd(np.log(data_all), axis=0, ddof=1)
std_log[np.where(np.isnan(std_log))] = np.nanmax(std_log) # Catch n=1 cases (stdev ill-defined)
eps_log = std_log / np.sqrt(n_pts) # Convert to standard err in log space
data_all[np.isnan(data_all)] = 1 # Identity for multiplying
data = np.power(np.prod(data_all, axis=0), 1./n_pts)
# Convert back to original parameter space
eps_upper = np.exp(np.log(data) + eps_log) - data
eps_lower = data - np.exp(np.log(data) - eps_log)
# We need to provide a symmetric error for fitting
eps = np.maximum(eps_upper, eps_lower)
data_avgd[n_flmt] = kevs, data, eps, inds, n_pts
return data_avgd
def thing(retstrip):
avg=np.nanmean(retstrip)
std=np.nanstd(retstrip)
length=len([x for x in retstrip if not np.isnan(x)])
return (avg, std, length, std/(length**.5))
for j, penalty in enumerate(penalties):
LR = LogReg(penalty)
LR.set_learning_params(a=a, b=b)
for k in range(N_repetitions):
try:
LR.fit(X_train,
Z_train,
n_minibatches,
n_epochs,
std_beta=std_beta)
Z_pred = LR.classify(X_test)
accuracy[j, k] = ACC(Z_test, Z_pred)
except:
print(f"Crash with penalty = {penalty}.")
accuracy[j, k] = np.nan
np.seterr(all='ignore')
plt.errorbar(x=penalties,
y=np.nanmean(accuracy, axis=1),
yerr=np.nanstd(accuracy, axis=1),
fmt="o",
capsize=5,
ms=5)
plt.xscale("log")
plt.xlabel(f"penalty ($\lambda$)", fontsize=12)
plt.ylabel("accuracy", fontsize=12)
plt.title("Accuracy score", fontsize=15)
plt.savefig("Figures/LR_acc_penalties.png", dpi=300)
plt.show()
plt.plot(x_data[ssp_option, model_i], y_data[ssp_option, model_i], marker=model_shapes[model_i], color='b', markersize=20, mew=5)
# observational constraint
obs_data_model = obs_data[model_i+(ssp_option*n_models)]
# saving x_data and y_data
flat_x_array = x_data.flatten()
flat_y_array = y_data.flatten()
flat_x_array = flat_x_array[flat_x_array==flat_x_array]
flat_y_array = flat_y_array[flat_y_array==flat_y_array]
# std of constrained values
x_obs = np.nanmean(obs_data)
dx_obs = np.nanstd(obs_data)
# creating constrained data line
x_line = np.linspace(-xmin_limit, xmax_limit, 100)
global_array = np.zeros([100,1])
global_array = np.squeeze(global_array)
for b in range(0,100):
global_array[b] = x_obs
plt.plot(global_array, x_line, color='darkgreen', linewidth=2, alpha=1)
plt.axvspan(x_obs-dx_obs, x_obs+dx_obs, color='lightgreen', alpha=0.8, zorder=20)
# calculating the constrained values
x_values = np.loadtxt("saved_data/combined_x_"+str(min_temperature)+"_degree_warming_cmip6.csv", delimiter=",")
y_values = np.loadtxt("saved_data/combined_y_"+str(min_temperature)+"_degree_warming_cmip6.csv", delimiter=",")
def featureFunc(self,vals,ts):
return np.nanstd(vals)
def featureextraction(print_):
# Amount of people per embarkment location
(train,test) = importdata()
full_data = pd.concat([train,test])
#fill in missing observations with most observed S
full_data['Embarked'] = full_data['Embarked'].replace(np.nan, "S", regex=True)
if print_==True:
print("changed missing data to S")
full_data.loc[full_data['Embarked']=='C','Embarked']=0
full_data.loc[full_data['Embarked']=='Q','Embarked']=1
full_data.loc[full_data['Embarked']=='S','Embarked']=2
if print_==True:
print('\nchanged C,Q,S to 0,1,2')
if print_==True:
print('\nchanging Age feature')
mean = np.nanmean(train['Age'])
st_dev = np.nanstd(train['Age'])
if print_==True:
print('mean: ',np.round(mean,3), '\nSt_dev: ',np.round(st_dev,3))
missing_obs = full_data['Age'].isnull().sum()
if print_==True:
print('filling in missing data with mean and st_dev')
random_ages = np.round(np.random.normal(mean,st_dev,missing_obs)).astype(int)
if print_==True:
print(random_ages[random_ages<=0])
random_ages[random_ages<=0] = np.random.uniform(len(random_ages[random_ages<=0]))
if print_==True:
print(random_ages)
if print_==True:
sns.distplot(random_ages)
plt.show()
full_data.loc[np.isnan(full_data['Age']),'Age'] = random_ages
train['AgeBand'] = pd.cut(train['Age'], 5, precision = 0)
if print_==True:
print(train[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True))
full_data.loc[ full_data['Age'] <= 16, 'Age'] = 0
full_data.loc[(full_data['Age'] > 16) & (full_data['Age'] <= 32), 'Age'] = 1
full_data.loc[(full_data['Age'] > 32) & (full_data['Age'] <= 48), 'Age'] = 2
full_data.loc[(full_data['Age'] > 48) & (full_data['Age'] <= 64), 'Age'] = 3
full_data.loc[ full_data['Age'] > 64, 'Age'] = 4
if print_==True:
print(full_data.sample(8))
if print_==True:
print('Cutting on Fare')
train['Fareband'] = pd.cut(train['Fare'], 5, precision = 0)
if print_==True:
print(train[['Fareband', 'Survived']].groupby(['Fareband'], as_index=False).mean().sort_values(by='Fareband', ascending=True))
full_data.loc[ full_data['Fare'] <= 102, 'Fare'] = 0
full_data.loc[(full_data['Fare'] > 102) & (full_data['Fare'] <= 205), 'Fare'] = 1
full_data.loc[(full_data['Fare'] > 205) & (full_data['Fare'] <= 307), 'Fare'] = 2
full_data.loc[(full_data['Fare'] > 307) & (full_data['Fare'] <= 410), 'Fare'] = 3
full_data.loc[ full_data['Fare'] > 410, 'Fare'] = 4
full_data.loc[full_data.PassengerId==1044,'Fare'] = 0
if print_==True:
print(full_data.sample(8))
if print_==True:
print('Creating family feature')
train['FamilySize'] = train['SibSp'] + train['Parch'] + 1
if print_==True:
print(train[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False))
full_data['FamilySize'] = full_data['SibSp'] + full_data['Parch'] + 1
if print_==True:
print('extracting title')
dataset_title = [i.split(",")[1].split(".")[0].strip() for i in full_data["Name"]]
full_data["Title"] = pd.Series(dataset_title)
if print_==True:
full_data["Title"].head()
if print_==True:
print('converting to categorical data')
full_data["Title"] = full_data["Title"].replace(['Lady', 'the Countess','Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
full_data["Title"] = full_data["Title"].map({"Master":0, "Miss":1, "Ms" : 1 , "Mme":1, "Mlle":1, "Mrs":1, "Mr":2, "Rare":3})
full_data["Title"] = full_data["Title"].astype(int)
if print_==True:
g = sns.factorplot(x="Title",y="Survived",data=full_data,kind="bar")
g = g.set_xticklabels(["Master","Miss-Mrs","Mr","Rare"])
g = g.set_ylabels("survival probability")
if print_==True:
print('converting male-female to 0-1')
full_data = full_data.drop(labels = ["Name","Cabin", 'Ticket'], axis = 1)
full_data.loc[ full_data['Sex'] == 'male', 'Sex'] = 0
full_data.loc[ full_data['Sex'] == 'female', 'Sex'] = 1
test = full_data.loc[full_data['PassengerId'].isin(test['PassengerId'])]
test = test.drop(labels=["Survived"],axis = 1)
train = full_data.loc[full_data['PassengerId'].isin(train['PassengerId'])]
return train, test, full_data
def load_cfs_ensemble(fcst_dir,init_date,par,numdays,bli,tri,
intervals,des_inds,returnpar='mean',daily=True):
"""
Returns the the ensemble mean averaged forecast for the first [numdays]
days of the forecast. [init_date] is the forecast initialization date and
the ensemble (4 members) includes the 00Z, 06Z, 12Z, and 18Z runs.
[returnpar] specifies whether to return the ensemble mean, ensemble spread,
ensemble stdv, or the individual members. The averaging intervals are
specified by [intervals], which is a list of integers specifiying the
number of weeks to average the forecast (0weeks = 1day).
"""
import pygrib as pyg
datestr = init_date.strftime('%Y%m%d')
ls_fils = '{}/{}.{}*.grb2'.format(fcst_dir,check_input_var(par),datestr)
fils = check_output(['ls -1a '+ls_fils],shell=True).split()
# if not len(fils)==4:
# raise IOError('There are only {} files for this day!!'.format(len(fils)))
if returnpar=='mem1':
fils = [fils[0]]
mems = len(fils)
ensemble = np.empty((mems,len(intervals),len(des_inds),bli[0]-tri[0],tri[1]-bli[1]))
m = 0
for cfs_file in fils:
try:
hh = int(cfs_file[-12:-10])
print(' member{}: {}Z'.format(m+1,cfs_file[-12:-10]))
grbs = pyg.open(cfs_file)
if par in ['U200', 'U850']:
if hh == 18:
grb = grbs.select(name='U component of wind',forecastTime=range(numdays*24+1))
else:
grb = grbs.select(name='U component of wind',forecastTime=range((numdays+1)*24+1))
elif par in ['V200', 'V850']:
if hh == 18:
grb = grbs.select(name='V component of wind',forecastTime=range(numdays*24+1))
else:
grb = grbs.select(name='V component of wind',forecastTime=range((numdays+1)*24+1))
else:
if hh == 18:
grb = grbs.select(forecastTime=range(numdays*24+1))
else:
grb = grbs.select(forecastTime=range((numdays+1)*24+1))
grbs.close()
if daily:
# Take daily average
fcst_daily, ftims, skp = daily_ave(np.array([g.values for g in grb]), hh)
if par == 'PRATE':
fcst_daily *= 60*60*24
# Loop over all the different averaging intervals (0-6 weeks)
for n in range(len(intervals)):
numints = intervals[n]
# Compute the mean for the interval at each fcst time
if numints == 0:
fcst_mean = fcst_daily[des_inds,:,:]
else:
ave_interval = numints*7
#average over the time axis
fcst_mean = np.array([np.nanmean(fcst_daily[j:(j+ave_interval),\
:,:],axis=0) for j in des_inds])
ensemble[m,n,:,:,:] = fcst_mean[:,tri[0]:bli[0],bli[1]:tri[1]]
except ValueError:
print('Houston, we have a ValueError')
raise ValueError('There was a ValueError')
ensemble[m,n,:,:,:] = np.nan
except subprocess.CalledProcessError:
raise IOError('There was a CalledProcessError!!')
m += 1
if returnpar=='members':
return ensemble[0,:,:],ensemble[1,:,:],ensemble[2,:,:],ensemble[3,:,:]
elif returnpar in ['mean','mem1']:
return np.nanmean(ensemble,axis=0)
elif returnpar=='stdv':
return np.nanstd(ensemble,axis=0)
elif returnpar=='spread':
return np.amax(ensemble,axis=0) - np.amin(ensemble,axis=0)
else:
raise IOError('Invalid return parameter specified.')
#import random #conditioner=[pd.Series([random.gauss(0.0, 1.0) for unused in ehup.index], ehup.index) for ehup in conditioner] futreturns=[list(x.values) for x in dependent] condvariable=[list(x.values) for x in conditioner] futreturns=sum(futreturns, []) condvariable=sum(condvariable, []) cmean=np.nanmean(condvariable) cstd=np.nanstd(condvariable) condreturns_pair=[(fr,cv) for fr,cv in zip(futreturns, condvariable) if not np.isnan(fr) and not np.isnan(cv)] upper_bound1=cmean+4*cstd lower_bound1=cmean+cstd condreturns=[fr for fr,cv in condreturns_pair if cv<upper_bound1 and cv>lower_bound1] condreturns_cond=[cv for fr,cv in condreturns_pair if cv<=upper_bound1 and cv>lower_bound1] upper_bound2=cmean+cstd lower_bound2=cmean-cstd condreturns2=[fr for fr,cv in condreturns_pair if cv<upper_bound2 and cv>lower_bound2] condreturns_cond2=[cv for fr,cv in condreturns_pair if cv<=upper_bound2 and cv>lower_bound2]
def nanstderr(array):
n = np.sum(np.logical_not(np.isnan(array)))
return np.nanstd(array) / np.sqrt(n)
##plt.scatter(opt_aS[1,:], opt_aR[1,:], marker='+', label='sigmoid P2')
##plt.scatter(opt_aS[2,:], opt_aR[2,:], marker='+', label='ReLU P1')
##plt.scatter(opt_aS[3,:], opt_aR[3,:], marker='+', label='ReLU P2')
##plt.scatter(0.342000, 0.684000, c='yellow', marker='o', label='P1')
# plt.scatter(0.684000, 0.342000, c='yellow', marker='o', label='P2')
# plt.xlabel('alpha_s')
# plt.ylabel('alpha_r')
# plt.legend()
# plt.title('Failed learning: dummy case')
# plt.plot()
# plt.savefig('figures_results/dummy_case.eps', format='eps', dpi=DPI)
"stats"
mean_opt_aR = np.nanmean(opt_aR, axis=1)
mean_opt_aS = np.nanmean(opt_aS, axis=1)
std_opt_aR = np.nanstd(opt_aR, axis=1)
std_opt_aS = np.nanstd(opt_aS, axis=1)
# "Significancy: independent t-tes for the two time constants"
#
## target time constants 0.34, 0.68
# t_S1,p_S1 = stats.ttest_ind(opt_aS[0,:], opt_aS[2,:], equal_var=False, nan_policy='omit')
# t_R1,p_R1 = stats.ttest_ind(opt_aR[0,:], opt_aR[2,:], equal_var=False, nan_policy='omit')
#
## target time constants 0.68, 0.34
# t_S2,p_S2 = stats.ttest_ind(opt_aS[1,:], opt_aS[3,:], equal_var=False, nan_policy='omit')
# t_R2,p_R2 = stats.ttest_ind(opt_aR[1,:], opt_aR[3,:], equal_var=False, nan_policy='omit')
# print('Significancy: independent t-tes for the two time constants')
# print('target time constants s=0.34, r=0.68:')
# print('alpha_s: p=%lf' %p_S1)
# print('alpha_r: p=%lf' %p_R1)
def qualityControl(raw_df,
time_col='charttime',
name_col='label',
val_col='value',
ref_cov_list=None,
fig_dir='.',
fig_format='pdf',
plot=False,
savepath=None):
# Filter for NaNs, any vitals measurements more than 3 standard deviations from the population mean.
if ref_cov_list is None:
ref_cov_list = raw_df[name_col].unique()
cov_stat_df = pd.DataFrame(columns=[
'covariate', 'raw_mean', 'raw_std', 'raw_min', 'raw_max', 'qc_mean',
'qc_std', 'qc_min', 'qc_max'
])
sub_data_df = pd.DataFrame(columns=raw_df.columns)
for cov_name, cov_df in raw_df.groupby(name_col):
print(cov_name)
if (cov_name in ref_cov_list):
raw_cov_val = cov_df[val_col].values
cov_mean = np.nanmean(raw_cov_val)
cov_std = np.nanstd(raw_cov_val)
qc_cov_df = cov_df[~np.isnan(cov_df[val_col])]
qc_cov_df = cov_df[cov_df[val_col] > 0]
qc_cov_df = cov_df[cov_df[val_col] <= 999.0]
qc_cov_df = qc_cov_df[cov_df[val_col] >= (cov_mean - 3 * cov_std)]
qc_cov_df = qc_cov_df[qc_cov_df[val_col] <= (cov_mean +
3 * cov_std)]
qc_cov_val = qc_cov_df[val_col].values
cov_stat_df = cov_stat_df.append(
{
'covariate': cov_name,
'raw_mean': np.nanmean(raw_cov_val),
'raw_std': np.nanstd(raw_cov_val),
'raw_min': np.nanmin(raw_cov_val),
'raw_max': np.nanmax(raw_cov_val),
'qc_mean': np.nanmean(qc_cov_val),
'qc_std': np.nanstd(qc_cov_val),
'qc_min': np.nanmin(qc_cov_val),
'qc_max': np.nanmax(qc_cov_val)
},
ignore_index=True)
if plot:
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
sns.distplot(raw_cov_val[~np.isnan(raw_cov_val)])
plt.title(cov_name + ': before qc')
plt.subplot(1, 2, 2)
sns.distplot(qc_cov_val)
plt.title(cov_name + ': after qc')
plt.savefig(
os.path.join(fig_dir,
'qc_hist_{}.{}'.format(cov_name, fig_format)))
sub_data_df = sub_data_df.append(qc_cov_df)
if (savepath != None):
pickle.dump((cov_stat_df, sub_data_df), open(savepath, 'wb'))
return cov_stat_df, sub_data_df
sns.boxplot(x=data_df.columns[1],
y=data_df.columns[0],
data=data_df,
ax=axes[1, 0])
sns.barplot(x=data_df.columns[1],
y=data_df.columns[0],
data=data_df,
ax=axes[1, 1])
#
# plt.show()
#Gaussian
x = data_n
mu = np.nanmean(x)
sigma = np.nanstd(x)
#print(mu, sigma)
def gauss_fun(x, mu, sigma):
p = (1 / (np.sqrt(2 * np.pi * np.square(sigma)))) * np.exp(
-(np.square(x - mu)) / (2 * np.square(sigma)))
return p
#print(gauss_fun(data_n, mu, sigma))
# Gaussian logL
def gausslogl(x):
logL = np.log(gauss_fun(x, mu, sigma))
def scatter_plots():
def func(x, a, b):
return (a * x)**b
Ic_Ni = []
Ic_Ejecta = []
Ic_tp = []
Ic_Lp = []
Ic_BL_Ni = []
Ic_BL_Ejecta = []
Ic_BL_tp = []
Ic_BL_Lp = []
Ib_Ni = []
Ib_Ejecta = []
Ib_tp = []
Ib_Lp = []
IIb_Ni = []
IIb_Ejecta = []
IIb_tp = []
IIb_Lp = []
GRB_SN_Ni = []
GRB_SN_Ejecta = []
GRB_SN_tp = []
GRB_SN_Lp = []
plt.figure()
plt.ylabel('Ejecta Mass [$M_{\odot}$]')
plt.xlabel('$Ni^{56}$ Mass [$M_{\odot}$]')
plt.title(
'Plot of Ni Mass versus Ejecta Mass for Different Types of Supernovae')
plt.xscale('log')
plt.yscale('log')
plt.xlim(10**-2, 10**0.5)
plt.ylim(10**-0.3, 10**1.2)
plt.minorticks_on()
for i in range(len(Ejecta_Masses) - 1):
if Type[i] == 'Ic':
Ic_Ni.append(Ni_Masses[i])
Ic_Ejecta.append(Ejecta_Masses[i])
Ic = plt.scatter(Ni_Masses[i],
Ejecta_Masses[i],
s=80,
marker='d',
color='red')
plt.errorbar(Ni_Masses[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Ni_Masses_Err_[i],
color='red',
elinewidth=1)
if Type[i] == 'Ic-BL':
Ic_BL_Ni.append(Ni_Masses[i])
Ic_BL_Ejecta.append(Ejecta_Masses[i])
Ic_BL = plt.scatter(Ni_Masses[i],
Ejecta_Masses[i],
s=80,
marker='<',
color='blue')
plt.errorbar(Ni_Masses[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Ni_Masses_Err_[i],
color='blue',
elinewidth=1)
if Type[i] == 'Ib':
Ib_Ni.append(Ni_Masses[i])
Ib_Ejecta.append(Ejecta_Masses[i])
Ib = plt.scatter(Ni_Masses[i],
Ejecta_Masses[i],
s=80,
marker='o',
color='green')
plt.errorbar(Ni_Masses[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Ni_Masses_Err_[i],
color='green',
elinewidth=1)
if Type[i] == 'IIb':
IIb_Ni.append(Ni_Masses[i])
IIb_Ejecta.append(Ejecta_Masses[i])
IIb = plt.scatter(Ni_Masses[i],
Ejecta_Masses[i],
s=80,
marker='>',
color='orange')
plt.errorbar(Ni_Masses[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Ni_Masses_Err_[i],
color='orange',
elinewidth=1)
if Type[i] == 'GRB-SN':
GRB_SN_Ni.append(Ni_Masses[i])
GRB_SN_Ejecta.append(Ejecta_Masses[i])
GRB_SN = plt.scatter(Ni_Masses[i],
Ejecta_Masses[i],
s=100,
marker='*',
color='palevioletred')
plt.errorbar(Ni_Masses[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Ni_Masses_Err_[i],
color='palevioletred',
elinewidth=1)
cri = plt.scatter(Ni_Masses[-1],
Ejecta_Masses[-1],
s=80,
marker='^',
color='lawngreen')
plt.errorbar(Ni_Masses[-1],
Ejecta_Masses[-1],
Ejecta_Masses_Err_[-1],
Ni_Masses_Err_[-1],
color='lawngreen',
elinewidth=1)
Ni_Masses_1 = Ni_Masses[np.logical_not(np.isnan(Ni_Masses))]
Ejecta_Masses_1 = Ejecta_Masses[np.logical_not(np.isnan(Ni_Masses))]
Ejecta_Masses_2 = Ejecta_Masses_1[np.logical_not(
np.isnan(Ejecta_Masses_1))]
Ni_Masses_2 = Ni_Masses_1[np.logical_not(np.isnan(Ejecta_Masses_1))]
plt.legend([Ic, Ic_BL, Ib, IIb, cri, GRB_SN],
['Ic', 'Ic_BL', 'Ib', 'IIb', '2019cri', 'GRB-SN'],
loc='upper left')
plt.show()
plt.close()
R_coeff_1 = np.nansum(
(Ni_Masses - np.nanmean(Ni_Masses) / np.nanstd(Ni_Masses)) *
(Ejecta_Masses - np.nanmean(Ejecta_Masses) / np.nanstd(Ejecta_Masses))
) / (len(Ejecta_Masses) - 1)
# Second
plt.figure()
plt.xlabel('$t_{p}$ [Days]')
plt.ylabel('Log($L_{p}$) [$erg\ s^{-1}$]')
plt.xscale('linear')
plt.xlim(10**0.8, 10**1.88)
plt.title(
'Plot of the Log(Peak Luminosity) versus Rise Time for Different Types of Supernovae'
)
plt.minorticks_on()
for i in range(len(Ejecta_Masses) - 1):
if Type[i] == 'Ic':
Ic_tp.append(t_p[i])
Ic_Lp.append(Log_Lp[i])
Ic = plt.scatter(t_p[i], Log_Lp[i], s=80, marker='d', color='red')
plt.errorbar(t_p[i],
Log_Lp[i],
Log_Lp_Err_[i],
t_p_Err_[i],
color='red',
elinewidth=1)
if Type[i] == 'Ic-BL':
Ic_BL_tp.append(t_p[i])
Ic_BL_Lp.append(Log_Lp[i])
Ic_BL = plt.scatter(t_p[i],
Log_Lp[i],
s=80,
marker='<',
color='blue')
plt.errorbar(t_p[i],
Log_Lp[i],
Log_Lp_Err_[i],
t_p_Err_[i],
color='blue',
elinewidth=1)
if Type[i] == 'Ib':
Ib_tp.append(t_p[i])
Ib_Lp.append(Log_Lp[i])
Ib = plt.scatter(t_p[i],
Log_Lp[i],
s=80,
marker='o',
color='green')
plt.errorbar(t_p[i],
Log_Lp[i],
Log_Lp_Err_[i],
t_p_Err_[i],
color='green',
elinewidth=1)
if Type[i] == 'IIb':
IIb_tp.append(t_p[i])
IIb_Lp.append(Log_Lp[i])
IIb = plt.scatter(t_p[i],
Log_Lp[i],
s=80,
marker='>',
color='orange')
plt.errorbar(t_p[i],
Log_Lp[i],
Log_Lp_Err_[i],
t_p_Err_[i],
color='orange',
elinewidth=1)
if Type[i] == 'GRB-SN':
GRB_SN_tp.append(t_p[i])
GRB_SN_Lp.append(Log_Lp[i])
GRB_SN = plt.scatter(t_p[i],
Log_Lp[i],
s=100,
marker='*',
color='palevioletred')
plt.errorbar(t_p[i],
Log_Lp[i],
Log_Lp_Err_[i],
t_p_Err_[i],
color='palevioletred',
elinewidth=1)
cri = plt.scatter(t_p[-1], Log_Lp[-1], s=80, marker='^', color='lawngreen')
plt.errorbar(t_p[-1],
Log_Lp[-1],
Log_Lp_Err_[-1],
t_p_Err_[-1],
color='lawngreen',
elinewidth=1)
plt.legend([Ic, Ic_BL, Ib, IIb, cri, GRB_SN],
['Ic', 'Ic_BL', 'Ib', 'IIb', '2019cri', 'GRB_SN'],
loc='upper right')
plt.show()
plt.close()
# Third
plt.figure()
plt.xlabel('Log($L_{p}$) [$erg\ s^{-1}$]')
plt.ylabel('Ejecta Mass [$M_{\odot}$]')
plt.yscale('log')
plt.ylim(10**-0.3, 10**1.3)
plt.xlim(41.7, 43.8)
plt.title(
'Plot of the Log(Peak Luminosity) versus Ejecta Mass for Different Types of Supernovae'
)
plt.minorticks_on()
for i in range(len(Ejecta_Masses) - 1):
if Type[i] == 'Ic':
Ic = plt.scatter(Log_Lp[i],
Ejecta_Masses[i],
s=80,
marker='d',
color='red')
plt.errorbar(Log_Lp[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Log_Lp_Err_[i],
color='red',
elinewidth=1)
if Type[i] == 'Ic-BL':
Ic_BL = plt.scatter(Log_Lp[i],
Ejecta_Masses[i],
s=80,
marker='<',
color='blue')
plt.errorbar(Log_Lp[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Log_Lp_Err_[i],
color='blue',
elinewidth=1)
if Type[i] == 'Ib':
Ib = plt.scatter(Log_Lp[i],
Ejecta_Masses[i],
s=80,
marker='o',
color='green')
plt.errorbar(Log_Lp[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Log_Lp_Err_[i],
color='green',
elinewidth=1)
if Type[i] == 'IIb':
IIb = plt.scatter(Log_Lp[i],
Ejecta_Masses[i],
s=80,
marker='>',
color='orange')
plt.errorbar(Log_Lp[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Log_Lp_Err_[i],
color='orange',
elinewidth=1)
if Type[i] == 'GRB-SN':
GRB_SN = plt.scatter(Log_Lp[i],
Ejecta_Masses[i],
s=100,
marker='*',
color='palevioletred')
plt.errorbar(Log_Lp[i],
Ejecta_Masses[i],
Ejecta_Masses_Err_[i],
Log_Lp_Err_[i],
color='palevioletred',
elinewidth=1)
cri = plt.scatter(Log_Lp[-1],
Ejecta_Masses[-1],
s=80,
marker='^',
color='lawngreen')
plt.errorbar(Log_Lp[-1],
Ejecta_Masses[-1],
Ejecta_Masses_Err_[-1],
Log_Lp_Err_[-1],
color='lawngreen',
elinewidth=1)
plt.legend([Ic, Ic_BL, Ib, IIb, cri, GRB_SN],
['Ic', 'Ic_BL', 'Ib', 'IIb', '2019cri', 'GRB_SN'],
loc='upper left')
plt.show()
plt.close()
R_coeff_2 = np.nansum(
((Log_Lp - np.mean(Log_Lp)) / np.nanstd(Log_Lp)) *
((Ejecta_Masses - np.nanmean(Ejecta_Masses)) /
np.nanstd(Ejecta_Masses))) / (len(Ejecta_Masses) - 1)
# Fourth
plt.figure()
plt.xlabel('Log($L_{p}$) [$erg\ s^{-1}$]')
plt.ylabel('$Ni^{56}$ Mass [$M_{\odot}$]')
plt.yscale('log')
plt.ylim(10**-2, 10**0.5)
plt.title(
'Plot of the Log(Peak Luminosity) versus $Ni^{56}$ Mass for Different Types of Supernovae'
)
plt.minorticks_on()
for i in range(len(Ejecta_Masses) - 1):
if Type[i] == 'Ic':
Ic = plt.scatter(Log_Lp[i],
Ni_Masses[i],
s=80,
marker='d',
color='red')
plt.errorbar(Log_Lp[i],
Ni_Masses[i],
Ni_Masses_Err_[i],
Log_Lp_Err_[i],
color='red',
elinewidth=1)
if Type[i] == 'Ic-BL':
Ic_BL = plt.scatter(Log_Lp[i],
Ni_Masses[i],
s=80,
marker='<',
color='blue')
plt.errorbar(Log_Lp[i],
Ni_Masses[i],
Ni_Masses_Err_[i],
Log_Lp_Err_[i],
color='blue',
elinewidth=1)
if Type[i] == 'Ib':
Ib = plt.scatter(Log_Lp[i],
Ni_Masses[i],
s=80,
marker='o',
color='green')
plt.errorbar(Log_Lp[i],
Ni_Masses[i],
Ni_Masses_Err_[i],
Log_Lp_Err_[i],
color='green',
elinewidth=1)
if Type[i] == 'IIb':
IIb = plt.scatter(Log_Lp[i],
Ni_Masses[i],
s=80,
marker='>',
color='orange')
plt.errorbar(Log_Lp[i],
Ni_Masses[i],
Ni_Masses_Err_[i],
Log_Lp_Err_[i],
color='orange',
elinewidth=1)
if Type[i] == 'GRB-SN':
GRB_SN = plt.scatter(Log_Lp[i],
Ni_Masses[i],
s=100,
marker='*',
color='palevioletred')
plt.errorbar(Log_Lp[i],
Ni_Masses[i],
Ni_Masses_Err_[i],
Log_Lp_Err_[i],
color='palevioletred',
elinewidth=1)
cri = plt.scatter(Log_Lp[-1],
Ni_Masses[-1],
s=80,
marker='^',
color='lawngreen')
plt.errorbar(Log_Lp[-1],
Ni_Masses[-1],
Ni_Masses_Err_[-1],
Log_Lp_Err_[-1],
color='lawngreen',
elinewidth=1)
Ni_Masses_1 = Ni_Masses[np.logical_not(np.isnan(Ni_Masses))]
Log_Lp_1 = Log_Lp[np.logical_not(np.isnan(Ni_Masses))]
Ni_Masses_Err_1 = Ni_Masses_Err_[np.logical_not(np.isnan(Ni_Masses))]
Log_Lp_Err_1 = Log_Lp_Err_[np.logical_not(np.isnan(Ni_Masses))]
popt, pcov = curve_fit(func, Log_Lp_1, Ni_Masses_1, sigma=1 / Log_Lp_Err_1)
x_new_1 = np.linspace(41.8, 43.5, 10000)
y_new_1 = (popt[0] * x_new_1)**popt[1]
plt.plot(x_new_1, y_new_1, 'r-')
plt.legend([Ic, Ic_BL, Ib, IIb, cri, GRB_SN],
['Ic', 'Ic_BL', 'Ib', 'IIb', '2019cri', 'GRB_SN'],
loc='upper left')
plt.show()
plt.close()
Cal_Ni_Mass = (popt[0] * Log_Lp_1)**popt[1]
print(f'Fit Equation: Ni_Mass = {popt[0]} * Log(L_p) ** {popt[1]}')
Ni_Diff = ((Ni_Masses_1 - Cal_Ni_Mass) / Cal_Ni_Mass) * 100
Mean_Diff = mean(Ni_Diff)
Median_Diff = statistics.median(Ni_Diff)
Stdev_Diff = statistics.stdev(Ni_Diff, Mean_Diff)
plt.figure()
plt.hist(Ni_Diff[0:45], bins=13, facecolor='g', edgecolor='k', alpha=0.75)
plt.hist(Ni_Diff[-1],
bins=1,
facecolor='purple',
edgecolor='k',
alpha=0.75)
plt.xlabel('Percentage [%]')
plt.ylabel('Frequency')
plt.title('Histogram of Percentage Differnce in Calculated $Ni^{56}$ Mass')
plt.text(
30, 9,
f'$\mu$ = {Mean_Diff} % \n \n $\sigma$ = {Stdev_Diff} % \n \n Median = {Median_Diff} % '
)
plt.axvline(Median_Diff, color='k', linestyle='dashed', linewidth=2)
plt.text(130, 4, 'SN2019cri ')
plt.axvline(Ni_Diff[-1], color='k', linestyle='dashed', linewidth=0.9)
plt.minorticks_on()
return
def std_ratio(pred, obs):
return np.nanstd(pred)/np.nanstd(obs)
def std(array):
return np.nanstd(array)
def normout(a):
meancalc=np.nanmean(a)
stdcalc=np.nanstd(a)
normout=(np.tanh(((a-meancalc)/stdcalc)))
return normout
def estimate_vario(mp, npoints=2000, max_dist=None, interval=None):
""" Given a Multipoint input, estimate a spatial variogram. By default, a
variogram is quickly estimated. If npoints is None, then the full variogram
is calculated.
Parameters:
-----------
points : a Multipoint instance
npoints : number of values use for variogram appromixation. If npoints is
None, the full variogram is calculated.
max_dist : the maximum lag
interval : the lag interval
Returns:
--------
lags : ndarray
variogram : ndarray
"""
if len(mp.data) != len(mp.vertices):
raise Exception('estimate_variogram() requires a Multipoint with '
'scalar data')
if npoints > len(mp):
npoints = len(mp)
irand = random.sample(np.arange(len(mp)), npoints)
verts = mp.get_vertices()[irand]
z = np.array([mp.data[i] for i in irand])
if isinstance(mp.crs, GeographicalCRS):
import warnings
import pyproj
warnings.warn(
"For improved performance, consider projecting data to an "
"approximately equidistant reference system first.")
geod = pyproj.Geod(ellps="WGS84")
def distfunc(a, b):
return geod.inv(a[0], a[1], b[0], b[1])[2]
else:
distfunc = "euclidean"
dist = pdist(verts, distfunc)
I, J = ppairs(z)
diff = z[I] - z[J]
if max_dist is None:
max_dist = dist.max()
else:
max_dist = float(max_dist)
if interval is None:
interval = max_dist / 10.0
else:
interval = float(interval)
lags = np.arange(0, max_dist, interval)
sigma_variance = np.empty_like(lags)
for i, lag in enumerate(lags):
band = (lag <= dist) * (dist < lag + interval)
sigma_variance[i] = 0.5 * np.nanstd(diff[band])**2
return lags, sigma_variance
