From 7dabf08f4abadcd21d407279c32426c3f8e97b66 Mon Sep 17 00:00:00 2001
From: Aaron LI <aaronly.me@outlook.com>
Date: Tue, 26 Apr 2016 19:39:15 +0800
Subject: sbp_fit.py: reorder for better clearance; add new TODO items
---
python/sbp_fit.py | 62 +++++++++++++++++++++++++++++--------------------------
1 file changed, 33 insertions(+), 29 deletions(-)
diff --git a/python/sbp_fit.py b/python/sbp_fit.py
index a22dd19..c22e0c8 100755
--- a/python/sbp_fit.py
+++ b/python/sbp_fit.py
@@ -3,9 +3,12 @@
#
# Aaron LI
# Created: 2016-03-13
-# Updated: 2016-04-21
+# Updated: 2016-04-26
#
# Changelogs:
+# 2016-04-26:
+# * Reorder some methods of classes 'FitModelSBeta' and 'FitModelDBeta'
+# * Change the output file extension from ".txt" to ".json"
# 2016-04-21:
# * Plot another X axis with unit "r500", with R500 values marked
# * Adjust output image size/resolution
@@ -33,6 +36,8 @@
#
# TODO:
# * to allow fit the outer beta component, then fix it, and fit the inner one
+# * to integrate basic information of config file to the output json
+# * to output the ignored radius range in the same unit as input sbp data
#
"""
@@ -59,7 +64,7 @@ model = sbeta
#model = dbeta
# output file to store the fitting results
-outfile = sbpfit.txt
+outfile = sbpfit.json
# output file to save the fitting plot
imgfile = sbpfit.png
@@ -70,7 +75,7 @@ imgfile = sbpfit.png
[sbeta]
# model-related options (OVERRIDE the upper level options)
-outfile = sbpfit_sbeta.txt
+outfile = sbpfit_sbeta.json
imgfile = sbpfit_sbeta.png
#ignore = 0.0-20.0,
#ignore_r500 = 0.0-0.15,
@@ -85,7 +90,7 @@ imgfile = sbpfit_sbeta.png
[dbeta]
-outfile = sbpfit_dbeta.txt
+outfile = sbpfit_dbeta.json
imgfile = sbpfit_dbeta.png
#ignore = 0.0-20.0,
#ignore_r500 = 0.0-0.15,
@@ -100,18 +105,10 @@ imgfile = sbpfit_dbeta.png
-------------------------------------------------
"""
-__version__ = "0.6.1"
-__date__ = "2016-04-21"
+__version__ = "0.6.2"
+__date__ = "2016-04-26"
-import numpy as np
-import lmfit
-import matplotlib.pyplot as plt
-
-from configobj import ConfigObj
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from matplotlib.figure import Figure
-
import os
import sys
import re
@@ -119,6 +116,13 @@ import argparse
import json
from collections import OrderedDict
+import numpy as np
+import lmfit
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib.figure import Figure
+from configobj import ConfigObj
+
plt.style.use("ggplot")
@@ -129,8 +133,8 @@ class FitModel:
The supplied `func' should have the following syntax:
y = f(x, params)
- where the `params' is the parameters to be fitted,
- and should be provided as well.
+ where the `params' is `lmfit.Parameters' instance which contains all
+ the model parameters to be fitted, and should be provided as well.
"""
def __init__(self, name=None, func=None, params=lmfit.Parameters()):
self.name = name
@@ -177,6 +181,10 @@ class FitModelSBeta(FitModel):
("beta", 0.7, True, 0.3, 1.1, None),
("bkg", 1.0e-9, True, 0.0, 1.0e-7, None))
+ def __init__(self):
+ super(self.__class__, self).__init__(name="Single-beta",
+ func=self.sbeta, params=self.params)
+
@staticmethod
def sbeta(r, params):
parvals = params.valuesdict()
@@ -186,10 +194,6 @@ class FitModelSBeta(FitModel):
bkg = parvals["bkg"]
return s0 * np.power((1 + (r/rc)**2), (0.5 - 3*beta)) + bkg
- def __init__(self):
- super(self.__class__, self).__init__(name="Single-beta",
- func=self.sbeta, params=self.params)
-
def plot(self, params, xdata, ax):
"""
Plot the fitted model, as well as the fitted parameters.
@@ -232,6 +236,14 @@ class FitModelDBeta(FitModel):
params.add("beta2", value=0.7, min=0.3, max=1.1)
params.add("bkg", value=1.0e-9, min=0.0, max=1.0e-7)
+ def __init__(self):
+ super(self.__class__, self).__init__(name="Double-beta",
+ func=self.dbeta, params=self.params)
+
+ @classmethod
+ def dbeta(self, r, params):
+ return self.beta1(r, params) + self.beta2(r, params)
+
@staticmethod
def beta1(r, params):
"""
@@ -255,14 +267,6 @@ class FitModelDBeta(FitModel):
beta2 = parvals["beta2"]
return s02 * np.power((1 + (r/rc2)**2), (0.5 - 3*beta2))
- @classmethod
- def dbeta(self, r, params):
- return self.beta1(r, params) + self.beta2(r, params)
-
- def __init__(self):
- super(self.__class__, self).__init__(name="Double-beta",
- func=self.dbeta, params=self.params)
-
def plot(self, params, xdata, ax):
"""
Plot the fitted model, and each beta component,
@@ -450,7 +454,7 @@ class SbpFit:
except TypeError:
self.r500_kpc = None
try:
- self.kpc_per_pix = r500_kpc / r500_pix
+ self.kpc_per_pix = self.r500_kpc / self.r500_pix
except (TypeError, ZeroDivisionError):
self.kpc_per_pix = -1
--
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