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Log Bins for snowglobes.py #206

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Log Bins for snowglobes.py #206

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0a766c4
Add changes from wrapper
stlgolfer Sep 6, 2022
4c0899e
Working example for log bins error checking
stlgolfer Sep 7, 2022
0374689
Cleanup
stlgolfer Sep 11, 2022
9515f15
Fixed almost all requested changes
stlgolfer Sep 20, 2022
86be5cf
t=0 check for log bins
stlgolfer Sep 20, 2022
ac756e0
Change error message
stlgolfer Sep 20, 2022
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19 changes: 18 additions & 1 deletion python/snewpy/snowglobes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@
import os
import re
import tarfile
import math
from pathlib import Path
from tempfile import TemporaryDirectory

Expand All @@ -40,7 +41,7 @@

logger = logging.getLogger(__name__)

def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None, snmodel_dict={}):
def generate_time_series(model_path, model_type, transformation_type, d, output_filename=None, ntbins=30, deltat=None, snmodel_dict={}, log_bins=False):
"""Generate time series files in SNOwGLoBES format.

This version will subsample the times in a supernova model, produce energy
Expand All @@ -64,6 +65,8 @@ def generate_time_series(model_path, model_type, transformation_type, d, output_
Length of time slices.
snmodel_dict : dict
Additional arguments for setting up the supernova model. See documentation of relevant ``SupernovaModel`` subclass for available options. (Optional)
log_bins : bool
Use logarithmically-spaced time bins

Returns
-------
Expand Down Expand Up @@ -91,6 +94,20 @@ def generate_time_series(model_path, model_type, transformation_type, d, output_
tedges = np.arange(tmin/u.s, tmax/u.s, dt/u.s)*u.s
times = 0.5*(tedges[1:] + tedges[:-1])

# now process log data
if log_bins:
log_edges = np.asarray([])

if tmax <= 0 or tmin <= 0:
raise ValueError("Cannot apply log to time windows that are less than or equal to 0. Consider adjusting model time window")
tstep = math.log10(abs(tmax/tmin))/len(times)

for i in range(0,len(times)):
t = (tmin/u.s)*(10**(i*tstep))
log_edges = np.append(log_edges,t)
log_edges = log_edges*u.s
times = 0.5*(log_edges[1:] + log_edges[:-1])

# Generate output.
if output_filename is not None:
tfname = output_filename + 'kpc.tar.bz2'
Expand Down