diff options
Diffstat (limited to 'calc_pei.py')
| -rwxr-xr-x | calc_pei.py | 15 |
1 files changed, 8 insertions, 7 deletions
diff --git a/calc_pei.py b/calc_pei.py index 961c19c..bb5879d 100755 --- a/calc_pei.py +++ b/calc_pei.py @@ -2,9 +2,11 @@ # # Aaron LI # Created: 2016-04-29 -# Updated: 2016-05-18 +# Updated: 2016-06-25 # # Change log: +# 2016-06-25: +# * Use 'InterpolatedUnivariateSpline' instead of 'interp1d' # 2016-05-18: # * Roughly implement the PEI uncertainty estimation # * Fix/Update PEI Y positions determination @@ -34,8 +36,8 @@ import json from collections import OrderedDict import numpy as np -import scipy.interpolate -import scipy.integrate +import scipy.interpolate as interpolate +import scipy.integrate as integrate import matplotlib.pyplot as plt from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas @@ -85,9 +87,8 @@ def calc_pei(data, r500, interp_np=101, pei_pos=None): # data points within the PEI range mask_pei = np.logical_and(x >= pei_xmin, x <= pei_xmax) y_pei = y[mask_pei] - # interpolate the power spectrum - f_interp = scipy.interpolate.interp1d(x, y, kind="linear", - assume_sorted=True) + # interpolate the power spectrum by fitting a smoothing spline + f_interp = interpolate.InterpolatedUnivariateSpline(x, y) x_interp = np.linspace(pei_xmin, pei_xmax, num=interp_np) y_interp = f_interp(x_interp) # determine the Y positions of PEI rectangle @@ -111,7 +112,7 @@ def calc_pei(data, r500, interp_np=101, pei_pos=None): y_interp[y_interp < pei_ymin] = pei_ymin # calculate the PEI area_total = (pei_xmax - pei_xmin) * (pei_ymax - pei_ymin) - area_below = scipy.integrate.trapz((y_interp-pei_ymin), x_interp) + area_below = integrate.trapz((y_interp-pei_ymin), x_interp) pei_value = area_below / area_total results = { "pei_xmin": pei_xmin, |