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Update target.py #55

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Update target.py
redeaglekr Sep 13, 2025
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redeaglekr Dec 8, 2025
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4 changes: 2 additions & 2 deletions src/pysyd/target.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1595,8 +1595,8 @@ def numax_smooth(self, sm_par=None):
sm_par = self.params['sm_par']
else:
sm_par = 4.*(self.params['numax']/self.constants['numax_sun'])**0.2
if sm_par < 1.:
sm_par = 1.
if sm_par < 2.:
sm_par = 2.
sig = (sm_par*(self.params['dnu']/self.params['resolution']))/np.sqrt(8.0*np.log(2.0))
self.pssm = convolve_fft(np.copy(self.random_pow), Gaussian1DKernel(int(sig)))
self.pssm_bgcorr = self.pssm-models.background(self.frequency, self.params['pars'], noise=self.params['noise'])
Expand Down
185 changes: 146 additions & 39 deletions src/pysyd/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -817,7 +817,8 @@ def _save_parameters(star):
"""
if star.params['save']:
results = star.params['results']['parameters']
df = pd.DataFrame(results)
print("results", results)
df = pd.DataFrame.from_dict(results,orient='index').transpose()
star.df = df.copy()
new_df = pd.DataFrame(columns=['parameter','value'])
for c, col in enumerate(df.columns.values.tolist()):
Expand Down Expand Up @@ -968,6 +969,35 @@ def _max_elements(x, y, npeaks, distance=None, exp_dnu=None):
return list(peaks_x), list(peaks_y), weights


##def _return_max(x, y, exp_dnu=None,):
## """Return max
##
## Return the peak (and/or the index of the peak) in a given 2D array
##
## Parameters
## x, y : numpy.ndarray, numpy.ndarray
## the independent and dependent axis, respectively
## exp_dnu : Required[float]
## the expected dnu. Default value is `None`.
##
## Returns
## idx : int
## **New**: *always* returns the index first, followed by the corresponding peak from the x+y arrays
## xx[idx], yy[idx] : Union[int, float], Union[int, float]
## corresponding peak in the x and y arrays
##
## """
## xx, yy = np.copy(x), np.copy(y)
## if list(yy) != []:
## if exp_dnu is not None:
## lst = list(np.absolute(xx-exp_dnu))
## idx = lst.index(min(lst))
## else:
## lst = list(yy)
## idx = lst.index(max(lst))
## else:
## return None, np.nan, np.nan
## return idx, xx[idx], yy[idx]
def _return_max(x, y, exp_dnu=None,):
"""Return max

Expand All @@ -989,52 +1019,129 @@ def _return_max(x, y, exp_dnu=None,):
xx, yy = np.copy(x), np.copy(y)
if list(yy) != []:
if exp_dnu is not None:
lst = list(np.absolute(xx-exp_dnu))
idx = lst.index(min(lst))

new_idx = np.argmax(yy)
best_x = xx[new_idx]
if (best_x >= 0.75*exp_dnu) and (best_x <= 1.25*exp_dnu):
idx = new_idx
else:
lst = list(np.absolute(xx-exp_dnu))
idx = lst.index(min(lst))
else:
lst = list(yy)
idx = lst.index(max(lst))
else:
return None, np.nan, np.nan
return idx, xx[idx], yy[idx]


def _bin_data(x, y, width, log=False, mode='mean'):
"""Bin data
##def _bin_data(x, y, width, log=False, mode='mean'):
## """Bin data
##
## Bins 2D series of data
##
## Parameters
## x, y : numpy.ndarray, numpy.ndarray
## the x and y values of the data
## width : float
## bin width (typically in :math:`\\rm \\mu Hz`)
## log : bool, default=False
## creates equal bin sizes in logarithmic space when `True`
##
## Returns
## bin_x, bin_y, bin_yerr : numpy.ndarray, numpy.ndarray, numpy.ndarray
## binned arrays (and error is computed using the standard deviation)
##
## """
## #fres= x[1]- x[0]
## #width = width/fres
## print("fres", fres)
## print("np", width/fres)
## #width = width/fres
## fres = np.median(np.diff(x))
## print("bin size", fres)
## width = width*fres
## print("width", width)
## if log:
##
## mi = np.log10(min(x))
## ma = np.log10(max(x))
## no = int(np.ceil((max(x)-min(x))/width))
## bins = np.logspace(mi, ma+width, no)
## print("no", no)
## else:
## print("yes")
## bins = np.arange(min(x), max(x)+width, width)
##
## digitized = np.digitize(x, bins)
## if mode == 'mean':
## bin_x = np.array([x[digitized == i].mean() for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
## bin_y = np.array([y[digitized == i].mean() for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
## elif mode == 'median':
## bin_x = np.array([np.median(x[digitized == i]) for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
## bin_y = np.array([np.median(y[digitized == i]) for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
## else:
## pass
## bin_yerr = np.array([y[digitized == i].std()/np.sqrt(len(y[digitized == i])) for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
## for i in range(len(bin_yerr)):
## if bin_yerr[i] == 0:
## bin_yerr[i] = 0.1*bin_y[i]
## print("from func", bin_x, bin_y, bin_yerr)
## return bin_x, bin_y, bin_yerr



################## my version##########################

def _bin_data(x, y, width, log, mode='mean'):

if log is False:

x = np.array(x)
y = np.array(y)
no = width
arr = np.arange(0, len(x), 1, dtype = int)
sect = len(arr)/no
#x_bin_start = 8
#x_max = 12
bin_x =[]
bin_y =[]
bin_yerr =[]
gh = np.array_split(arr, sect)
for i in gh:
num_bin = x[i]
frac_bin = y[i]

bin_y.append(np.nanmean(frac_bin))
bin_yerr.append(np.nanstd(frac_bin)/(np.sqrt(len(frac_bin))))
bin_x.append((num_bin[0] + num_bin[-1])/2)
bin_x = np.array(bin_x)
bin_y = np.array(bin_y)
bin_yerr = np.array(bin_yerr)

Bins 2D series of data

Parameters
x, y : numpy.ndarray, numpy.ndarray
the x and y values of the data
width : float
bin width (typically in :math:`\\rm \\mu Hz`)
log : bool, default=False
creates equal bin sizes in logarithmic space when `True`

Returns
bin_x, bin_y, bin_yerr : numpy.ndarray, numpy.ndarray, numpy.ndarray
binned arrays (and error is computed using the standard deviation)

"""
if log:
mi = np.log10(min(x))
ma = np.log10(max(x))
no = int(np.ceil((ma-mi)/width))
bins = np.logspace(mi, mi+(no+1)*width, no)
else:
bins = np.arange(min(x), max(x)+width, width)

digitized = np.digitize(x, bins)
if mode == 'mean':
bin_x = np.array([x[digitized == i].mean() for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
bin_y = np.array([y[digitized == i].mean() for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
elif mode == 'median':
bin_x = np.array([np.median(x[digitized == i]) for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
bin_y = np.array([np.median(y[digitized == i]) for i in range(1, len(bins)) if len(x[digitized == i]) > 0])
else:
pass
bin_yerr = np.array([y[digitized == i].std()/np.sqrt(len(y[digitized == i])) for i in range(1, len(bins)) if len(x[digitized == i]) > 0])

x = np.asarray(x)
y = np.asarray(y)

# Create logarithmic bins
log_bins = np.logspace(np.log10(np.min(x)), np.log10(np.max(x)), num=len(x)//width + 1)

# Initialize a list to hold binned values
binned_y = []
binned_x = []
binned_yerr = []

# Loop over the bins and collect points
for i in range(len(log_bins) - 1):
# Get indices of points in the current bin range
indices = np.where((x >= log_bins[i]) & (x < log_bins[i + 1]))[0]
binned_x.append((x[indices][-1] + x[indices][0])/2)
binned_y.append(np.nanmean(y[indices]))
binned_yerr.append(np.nanstd(y[indices])/np.sqrt(len(y[indices])))
bin_x = np.array(binned_x)
bin_y = np.array(binned_y)
bin_yerr = np.array(binned_yerr)

return bin_x, bin_y, bin_yerr


Expand Down Expand Up @@ -1268,4 +1375,4 @@ def get_output(fun=False):
if len(sentence) > width:
value = int(np.ceil((counts[0]-width)/2.))
sentence = sentence[value:-value]
print(sentence.center(width,' '))
print(sentence.center(width,' '))