You want to calculate the overlap of two circles given the position of their centers (x1,y1) and (x2,y2) and their radii r1 and r2.
You know that the overlap is 0 if the distance between the two circles is greater than the sum of their radii.
You also know the overlap is the area of the smaller circle if one circle is contained within the other.
Your colleague wrote some code to calculate the area of the intersection, if the two circles overlap but don't fall in one of the special cases just mentioned (no overlap or fully contained). This is great! Unfortunately your colleague wasn't a fan of functions, so you know only that the area of intersection is:
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rr1=r1**2
rr2=r2**2
phi = (math.acos((rr1 + (d ** 2) - rr2) / (2 * r1 * d))) * 2
theta = (math.acos((rr2 + (d ** 2) - rr1) / (2 * r2 * d))) * 2
area1 = 0.5 * theta * rr2 - 0.5 * rr2 * math.sin(theta)
area2 = 0.5 * phi * rr1 - 0.5 * rr1 * math.sin(phi)
area= area1 + area2
Define a function called interarea() which takes as input the positions and radii of the two circles and returns the area of the intersection.
Feel free to rename your colleague's variables if a different convention makes more sense to you.
Write at least 3 test cases to be sure your functions work for all scenarios!
(There are hints at the end of the exercise's parts.)
In [ ]:
import math
def interarea(x1,y1,r1,x2,y2,r2) :
'''return the overlap area of two circles given the center
points (x1, y1) and (x2, y2) and their radii: r1 and r2.'''
d = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
if (d >= r1 + r2):
# Circle don't intersect or just in one point
return 0
elif ( d <= abs(r1 - r2) and r2>r1):
# Circle 1 is fully contained, returning it's area
return math.pi*r1**2
elif ( d <= abs(r1 - r2) and r2<r1):
# Circle 1 is fully contained, returning it's area
return math.pi*r2**2
else:
# Circle 1 and 2 are intersecting
rr1=r1**2
rr2=r2**2
phi = (math.acos((rr1 + (d ** 2) - rr2) / (2 * r1 * d))) * 2
theta = (math.acos((rr2 + (d ** 2) - rr1) / (2 * r2 * d))) * 2
area1 = 0.5 * theta * rr2 - 0.5 * rr2 * math.sin(theta)
area2 = 0.5 * phi * rr1 - 0.5 * rr1 * math.sin(phi)
# Return area of intersection
return area1 + area2
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# case with no overlap
print interarea(0,0,1,2,2,1)
# case with near total overlap
print interarea(1,1,1.,2.,2.,10.) == math.pi
# overlapping circles
print interarea(1,1,1.,2.,2.,1.5)
You want to know the area of the intersection because you're interested in associating a point source, defined as a position (x1,y1) with an error in the measurement of (x1Err, y1Err), with a known source of position (x2,y2) with a given error on the position measurement of r2Err.
To do that, you've decided to define the "location overlap" fraction as the intersection area divided by the maximum possible area of intersection allowed by the errors in the position measurements.
Write a function overlapFracLoc() which returns the location overlap fraction.
Hint! The overlap fraction should range between 0 and 1.
You can either create your own data set (eg w random.random()) or use the data provided for candidates (source 1) and flares (source 2) at the bottom of this notebook and equivalently in candidates.txt and flare.txt.
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# I admit this was way hard... easy enough with just x1Err...
def overlapFracLoc(x1,y1,r1Err,x2,y2,r2Err):
fracArea=[]
area = interarea(x1,y1,r1Err,x2,y2,r2Err)
fracArea = area / min(math.pi*r1Err**2,math.pi*r2Err**2)
return fracArea
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# case with no overlap
print overlapFracLoc(0,0,1,2,2,1)
# case with near total overlap
print overlapFracLoc(1,1,1.,2.,2.,10.)
# overlapping circles
print overlapFracLoc(1,1,1.,2.,2.,1.5)
The first point source could also actually be an extended source. How exciting!
So in addition to having a radius and thus circle defined by the position error measurement (x1Err, y1Err), it can also have an actual extension measured (r1Ext).
Write another function which calculates the fractional extension overlap, overlapFracExt(), defined to be the ratio of [the intersection of the circle defined by the actual extension (r1Ext) and the position error circle (x2,y2,r2Err)] to the maximum area of either the first or second source.
Of course, not all the sources are extended, but we would still like to define this parameter. It's sensible if one considers the minimum resolvable radius, i.e. the boundary between when we can detect that a source is actually extended.
If the source is a point source, use an optionally settable parameter for the extension (==minimum resolvable radius), and return the ratio relative to the second source's area (defined by r2Err).
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def overlapFracExtforExtended(x1,y1,r1,x2,y2,r2,r2err):
'''
First source must be radio, second is GeV extended source with error in radius.
Will return tuple with overlap fractional extension value and error.
'''
A = r1
B = r2
denom = max(A,B)
#Want to keep the same denom A or B independent of B+error value. Avoid to change denom because of r2err
if A > B:
denom_up = A
denom_low = A
elif A < B:
denom_up = B + r2err
denom_low = B - r2err
else:
denom_up = B + r2err
denom_low = B - r2err
value = interarea(x1,y1,A,x2,y2,B)/(math.pi*(denom**2))
upper = interarea(x1,y1,A,x2,y2,B + r2err)/(math.pi*(denom_up**2)) -value
lower = interarea(x1,y1,A,x2,y2,B - r2err)/(math.pi*(denom_low**2)) -value
error = abs(upper - lower)/2.
return value,error
def overlapFracExtforPoint(x1,y1,r1,x2,y2,r2Err=0.2):
'''
First source must be radio, second is GeV point source.
'''
A = r1
B = 0.2 # Some minimal GeV resolvable size in degrees
denom = A
value = interarea(x1,y1,A,x2,y2,B)/(math.pi*(denom**2))
return value
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# case with no possible extended source overlap
print overlapFracExtforExtended(0,0,1,2,2,2.,3)
# case with near total overlap
print overlapFracExtforPoint(1.9,1.9,1.,2.,2.,)
# overlapping circles
print overlapFracExtforPoint(1,1,1.,2.,2.,1.5)
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d = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
Note the error message when you first try to evaluate d: python doesn't know what math is.
Math turns out to be one of those useful libraries of functions (and variables like pi). You can import them within the scope of a variable you call (as) math with:
In [ ]:
import math as math
Typically we import modules like math globally.
It's also possible to import from the math module just the particular function desired:
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from math import sqrt
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sqrt(25)
We can also rename it as:
In [ ]:
from math import sqrt as squareroot
In [ ]:
squareroot(25)
Note which version of import your colleague used in their code.
A. By definition in Part 1, the maximum area of overlap is when one circle is fully contained within the other. Thus the fractional overlap is defined as the intersection area divided by the minimum of the radii.
B. There are several ways to convert the given error measurement (x1Err, y1Err) into a radius. Consider the relative sizes and their impact on the final fraction calculated. Optionally, allow the user to select which option to use!
Try writing your functions in a file and running the code from the terminal.
Hint: Calling "python filename.py" will execute the code in filename.py.
Write functions to read in the data from files.
Write a wrapper function that takes the data read in from the files and calls the fractional overlap functions.
candidates.txt contains the first sources' information, including position, position error, extension, and extension error (all in degrees, and incidentally in the RA & Dec coordinate system; just assume cartesian geometry!).
flare.txt contains the second sources' information, including position and position error radius (also in degrees and RA,Dec).
Write the results to the terminal and then to a file.
How else might you display the results?
Use matplotlib to display the interesting results!
*(extracted from candidates.txt and flare.txt)
In [ ]:
candidatesData = {'SNR357.0-1.0': {'raErr': 0.271981431953, 'decErr': 0.234572055248, 'radius': 0.0, 'ra': 265.612194957, 'radiusErr': 0.0, 'dec': -32.045488282}, 'SNR305.2+0.4': {'raErr': 0.230708064968, 'decErr': 0.224605379155, 'radius': 0.0, 'ra': 197.711507061, 'radiusErr': 0.0, 'dec': -62.4200203869}, 'SNR34.5-1.8': {'raErr': 0.243113651368, 'decErr': 0.235113062829, 'radius': 0.0, 'ra': 285.178490429, 'radiusErr': 0.0, 'dec': 0.597646829113}, 'SNR179.0+3.4': {'raErr': 0.231809181189, 'decErr': 0.231489134935, 'radius': 0.0, 'ra': 89.1713292269, 'radiusErr': 0.0, 'dec': 31.4951149482}, 'SNR0.9-3.2': {'raErr': 0.287716937271, 'decErr': 0.287716937271, 'radius': 2.68576864989, 'ra': 270.078473699, 'radiusErr': 0.0745858407624, 'dec': -29.7964411324}, 'SNR6.7-0.8': {'raErr': 0.24706826093, 'decErr': 0.24706826093, 'radius': 1.61811862853, 'ra': 270.946260674, 'radiusErr': 0.0362000163985, 'dec': -23.5765462593}, 'SNR37.4-2.6': {'raErr': 0.223437466251, 'decErr': 0.218477218501, 'radius': 0.0, 'ra': 287.193642574, 'radiusErr': 0.0, 'dec': 2.79930826528}, 'SNR206.0-0.4': {'raErr': 0.311777416739, 'decErr': 0.311777416739, 'radius': 3.16518333722, 'ra': 99.3480499716, 'radiusErr': 0.0829090932598, 'dec': 6.04291711301}, 'SNR260.0-1.2': {'raErr': 0.219684652942, 'decErr': 0.219684652942, 'radius': 1.05326352149, 'ra': 127.619604964, 'radiusErr': 0.0241890082999, 'dec': -41.3686479476}, 'SNR4.5+3.1': {'raErr': 0.36342658877, 'decErr': 0.36342658877, 'radius': 3.63289679912, 'ra': 266.081085134, 'radiusErr': 0.0403179887527, 'dec': -23.5162043303}, 'SNR350.8+0.6': {'raErr': 0.226943837147, 'decErr': 0.218266375394, 'radius': 0.0, 'ra': 259.821259092, 'radiusErr': 0.0, 'dec': -36.3657425647}, 'SNR36.6-2.7': {'raErr': 0.213787209434, 'decErr': 0.218556786321, 'radius': 0.698134038916, 'ra': 286.888157707, 'radiusErr': 0.0193633172924, 'dec': 2.03796233627}, 'SNR133.1+2.0': {'raErr': 0.256251963544, 'decErr': 0.256251963544, 'radius': 1.4571202293, 'ra': 35.8303573643, 'radiusErr': 0.0589524317201, 'dec': 63.016863932}, 'SNR0.5-1.0': {'raErr': 0.213520647551, 'decErr': 0.491818560314, 'radius': 3.64836077677, 'ra': 267.714692466, 'radiusErr': 0.0246423475888, 'dec': -29.056555018}, 'SNR21.7-4.6': {'raErr': 0.261238806812, 'decErr': 0.228564707775, 'radius': 0.0, 'ra': 281.834000499, 'radiusErr': 0.0, 'dec': -12.0421945577}, 'SNR187.5+4.3': {'raErr': 0.207130074927, 'decErr': 0.205781790933, 'radius': 1.28816870319, 'ra': 94.7693692753, 'radiusErr': 0.00875163251335, 'dec': 24.5142843902}, 'SNR307.9+1.1': {'raErr': 0.268809096198, 'decErr': 0.268809096198, 'radius': 0.948980341874, 'ra': 203.294861341, 'radiusErr': 0.0688023427086, 'dec': -61.3380483636}, 'SNR26.7-2.9': {'raErr': 0.238906235566, 'decErr': 0.238906235566, 'radius': 0.666441980216, 'ra': 282.554062879, 'radiusErr': 0.0378130702637, 'dec': -6.8920362723}, 'SNR266.6+1.1': {'raErr': 0.253406814737, 'decErr': 0.253406814737, 'radius': 1.71293639428, 'ra': 135.870977376, 'radiusErr': 0.0445868105039, 'dec': -45.1219937402}, 'SNR192.6+1.5': {'raErr': 0.29267141516, 'decErr': 0.257917683615, 'radius': 0.0, 'ra': 94.6639732117, 'radiusErr': 0.0, 'dec': 18.6874706276}, 'SNR292.7+0.6': {'raErr': 0.211669864532, 'decErr': 0.211529641793, 'radius': 0.0, 'ra': 171.769078323, 'radiusErr': 0.0, 'dec': -60.5705794929}, 'SNR348.9-0.4': {'raErr': 0.241375544076, 'decErr': 0.241375544076, 'radius': 1.37703943137, 'ra': 259.425427937, 'radiusErr': 0.0346313902607, 'dec': -38.4677640832}, 'SNR1.3-2.9': {'raErr': 0.276048520226, 'decErr': 0.276048520226, 'radius': 2.89045818583, 'ra': 270.040273019, 'radiusErr': 0.0580412932745, 'dec': -29.2575899789}, 'SNR19.6-0.2': {'raErr': 0.218599592668, 'decErr': 0.215109650286, 'radius': 0.0, 'ra': 276.873652703, 'radiusErr': 0.0, 'dec': -11.8917300079}, 'SNR288.0+0.8': {'raErr': 0.233711496338, 'decErr': 0.233711496338, 'radius': 1.86996638915, 'ra': 163.294239207, 'radiusErr': 0.0508525104263, 'dec': -58.6315983807}, 'SNR313.2+0.8': {'raErr': 0.394070303943, 'decErr': 0.394070303943, 'radius': 1.70579742563, 'ra': 213.932796558, 'radiusErr': 0.0528298804548, 'dec': -60.4095049085}, 'SNR34.9-3.6': {'raErr': 0.298655659691, 'decErr': 0.268422313615, 'radius': 0.0, 'ra': 286.982102109, 'radiusErr': 0.0, 'dec': 0.0681712044775}, 'SNR77.8-10.4': {'raErr': 0.255675770164, 'decErr': 0.255675770164, 'radius': 2.54424199628, 'ra': 317.286698163, 'radiusErr': 0.0432313048963, 'dec': 32.3791351336}, 'SNR356.9-2.2': {'raErr': 0.262974308106, 'decErr': 0.257591367918, 'radius': 0.0, 'ra': 266.748578364, 'radiusErr': 0.0, 'dec': -32.7404998269}, 'SNR33.6-2.0': {'raErr': 0.379497447662, 'decErr': 0.379497447662, 'radius': 2.69385048285, 'ra': 284.957646547, 'radiusErr': 0.102425262102, 'dec': -0.316655458163}, 'SNR353.7+0.4': {'raErr': 0.246005751271, 'decErr': 0.246005751271, 'radius': 1.23307152431, 'ra': 261.999939154, 'radiusErr': 0.0819007090426, 'dec': -33.9988614089}, 'SNR338.4+0.7': {'raErr': 0.251629117163, 'decErr': 0.251629117163, 'radius': 2.8293996792, 'ra': 249.479447695, 'radiusErr': 0.100211831445, 'dec': -45.9863390198}, 'SNR338.5-1.2': {'raErr': 0.238002174149, 'decErr': 0.238002174149, 'radius': 0.935759126861, 'ra': 251.704491372, 'radiusErr': 0.0241741004183, 'dec': -47.181164597}, 'SNR264.4-0.8': {'raErr': 0.22305862071, 'decErr': 0.219152934832, 'radius': 0.0, 'ra': 131.758850198, 'radiusErr': 0.0, 'dec': -44.6702066761}, 'SNR321.8-1.9': {'raErr': 0.218281076948, 'decErr': 0.216582158807, 'radius': 0.0, 'ra': 231.615371743, 'radiusErr': 0.0, 'dec': -58.9345087487}, 'SNR292.3-0.0': {'raErr': 0.225969979162, 'decErr': 0.225969979162, 'radius': 0.927298739579, 'ra': 170.511752225, 'radiusErr': 0.0395919631572, 'dec': -61.0622576573}, 'SNR349.9+0.9': {'raErr': 0.216179967762, 'decErr': 0.213377009531, 'radius': 0.0, 'ra': 258.886658595, 'radiusErr': 0.0, 'dec': -36.8597486164}, 'SNR19.6-3.3': {'raErr': 0.344205084869, 'decErr': 0.344205084869, 'radius': 3.04704002084, 'ra': 279.659282116, 'radiusErr': 0.0545954052338, 'dec': -13.3957517685}, 'SNR3.2-2.2': {'raErr': 0.227442192502, 'decErr': 0.221265865521, 'radius': 0.0, 'ra': 270.352408502, 'radiusErr': 0.0, 'dec': -27.2731176644}, 'SNR326.9-2.1': {'raErr': 0.217548504499, 'decErr': 0.217548504499, 'radius': 0.938827953143, 'ra': 239.409968227, 'radiusErr': 0.0254444041526, 'dec': -56.0366371638}, 'SNR318.7-0.9': {'raErr': 0.235692743113, 'decErr': 0.224930318533, 'radius': 0.0, 'ra': 225.390216638, 'radiusErr': 0.0, 'dec': -59.7206378884}, 'SNR298.8+1.8': {'raErr': 0.225842286172, 'decErr': 0.221696652515, 'radius': 0.0, 'ra': 184.342648857, 'radiusErr': 0.0, 'dec': -60.7649405423}, 'SNR28.3-3.0': {'raErr': 0.228599044991, 'decErr': 0.228599044991, 'radius': 0.840031316494, 'ra': 283.438429381, 'radiusErr': 0.0356132924753, 'dec': -5.50680845713}, 'SNR349.9-0.6': {'raErr': 0.216028596904, 'decErr': 0.213251133676, 'radius': 0.0, 'ra': 260.422638137, 'radiusErr': 0.0, 'dec': -37.7348753207}, 'SNR110.2-0.5': {'raErr': 0.236849515328, 'decErr': 0.230260437312, 'radius': 0.0, 'ra': 346.95051627, 'radiusErr': 0.0, 'dec': 59.81091111}, 'SNR349.0-2.8': {'raErr': 0.29074521768, 'decErr': 0.384293598602, 'radius': 2.83871530095, 'ra': 262.10978964, 'radiusErr': 0.0871497833017, 'dec': -39.7314877386}, 'SNR38.0-1.2': {'raErr': 0.283037547031, 'decErr': 0.283037547031, 'radius': 1.34579950505, 'ra': 286.233309323, 'radiusErr': 0.0532988018448, 'dec': 3.94808854222}, 'SNR19.6-1.5': {'raErr': 0.257374355229, 'decErr': 0.257374355229, 'radius': 1.74060124067, 'ra': 278.047525227, 'radiusErr': 0.0919806094943, 'dec': -12.5523966455}, 'SNR91.5+4.2': {'raErr': 0.263638985711, 'decErr': 0.263638985711, 'radius': 1.47211097459, 'ra': 314.685294819, 'radiusErr': 0.059201947235, 'dec': 52.313575239}, 'SNR21.3-2.3': {'raErr': 0.294407339889, 'decErr': 0.246261917561, 'radius': 0.0, 'ra': 279.548261093, 'radiusErr': 0.0, 'dec': -11.4031668834}, 'SNR75.2+1.0': {'raErr': 0.215353155464, 'decErr': 0.213779870664, 'radius': 0.0, 'ra': 304.349927117, 'radiusErr': 0.0, 'dec': 37.3362577335}, 'SNR185.6-3.4': {'raErr': 0.202794333677, 'decErr': 0.20271688318, 'radius': 0.0, 'ra': 86.4210131902, 'radiusErr': 0.0, 'dec': 22.4377998483}, 'SNR31.0+0.3': {'raErr': 0.248025266891, 'decErr': 0.248025266891, 'radius': 0.607140537348, 'ra': 281.743487302, 'radiusErr': 0.0669140104212, 'dec': -1.55237255283}, 'SNR338.9-0.7': {'raErr': 0.225481752104, 'decErr': 0.225481752104, 'radius': 1.05623055203, 'ra': 251.521101135, 'radiusErr': 0.0446777776714, 'dec': -46.5753551249}, 'SNR330.6-0.0': {'raErr': 0.234954432574, 'decErr': 0.234954432574, 'radius': 0.966485195736, 'ra': 241.813325782, 'radiusErr': 0.0348944403116, 'dec': -52.0696464985}, 'SNR179.7-1.6': {'raErr': 0.295735580209, 'decErr': 0.295735580209, 'radius': 2.2746520516, 'ra': 84.6514062551, 'radiusErr': 0.0627806184125, 'dec': 28.3537157867}, 'SNR314.3+1.0': {'raErr': 0.274438303389, 'decErr': 0.274438303389, 'radius': 1.29691980645, 'ra': 215.909934841, 'radiusErr': 0.0371154850299, 'dec': -59.8191299263}, 'SNR298.4+11.8': {'raErr': 0.313375760068, 'decErr': 0.313375760068, 'radius': 1.76035220138, 'ra': 185.757884938, 'radiusErr': 0.0768070296844, 'dec': -50.8660030744}, 'SNR27.9-0.7': {'raErr': 0.258584479144, 'decErr': 0.258584479144, 'radius': 1.67659641636, 'ra': 281.196489622, 'radiusErr': 0.0440245156953, 'dec': -4.7862936741}, 'SNR24.4-0.1': {'raErr': 0.234521546046, 'decErr': 0.234521546046, 'radius': 1.12957934733, 'ra': 279.072202377, 'radiusErr': 0.0370680188077, 'dec': -7.60274129272}, 'SNR15.4-3.2': {'raErr': 0.236252794145, 'decErr': 0.236252794145, 'radius': 0.904611875572, 'ra': 277.632251388, 'radiusErr': 0.0661079886355, 'dec': -17.1098893637}, 'SNR1.5-1.8': {'raErr': 0.301136570075, 'decErr': 0.350579204193, 'radius': 3.97194529144, 'ra': 269.083870298, 'radiusErr': 0.0106048222889, 'dec': -28.5346640059}, 'SNR27.7-2.1': {'raErr': 0.251936191748, 'decErr': 0.251936191748, 'radius': 1.36738670143, 'ra': 282.350910245, 'radiusErr': 0.0378777867137, 'dec': -5.58498279785}, 'SNR47.3+0.1': {'raErr': 0.232245266658, 'decErr': 0.22595209949, 'radius': 0.0, 'ra': 289.433674631, 'radiusErr': 0.0, 'dec': 12.8215567861}, 'SNR340.0+1.2': {'raErr': 0.222737932148, 'decErr': 0.222737932148, 'radius': 0.511918833626, 'ra': 250.42984654, 'radiusErr': 0.0273043571285, 'dec': -44.4949793168}, 'SNR30.0-1.9': {'raErr': 0.232168094995, 'decErr': 0.232168094995, 'radius': 1.69218827621, 'ra': 283.220120639, 'radiusErr': 0.0330618850719, 'dec': -3.48120201817}, 'SNR7.0-2.0': {'raErr': 0.312222919137, 'decErr': 0.267941316416, 'radius': 0.0, 'ra': 272.286431813, 'radiusErr': 0.0, 'dec': -23.9070487126}, 'SNR44.2-1.5': {'raErr': 0.208068049359, 'decErr': 0.20768641136, 'radius': 0.0, 'ra': 289.366755954, 'radiusErr': 0.0, 'dec': 9.27952062116}, 'SNR340.5-1.4': {'raErr': 0.217865253515, 'decErr': 0.217865253515, 'radius': 1.1099562952, 'ra': 253.750178228, 'radiusErr': 0.026096770521, 'dec': -45.728520612}, 'SNR212.0-0.4': {'raErr': 0.236335938673, 'decErr': 0.233634411825, 'radius': 0.0, 'ra': 102.064573789, 'radiusErr': 0.0, 'dec': 0.585185159102}, 'SNR33.8-0.6': {'raErr': 0.282578061319, 'decErr': 0.282578061319, 'radius': 1.71854086244, 'ra': 283.818949902, 'radiusErr': 0.093282108915, 'dec': 0.518769676872}, 'SNR292.0+3.3': {'raErr': 0.216935056034, 'decErr': 0.215769395535, 'radius': 0.0, 'ra': 172.093936919, 'radiusErr': 0.0, 'dec': -57.8300773879}, 'SNR80.6+0.6': {'raErr': 0.229616117211, 'decErr': 0.229616117211, 'radius': 1.67791494779, 'ra': 308.825443364, 'radiusErr': 0.0342199404282, 'dec': 41.4608292626}, 'SNR113.6-1.9': {'raErr': 0.210205443684, 'decErr': 0.209880072488, 'radius': 0.0, 'ra': 354.133277338, 'radiusErr': 0.0, 'dec': 59.6193946617}, 'SNR31.7-1.4': {'raErr': 0.380821634236, 'decErr': 0.380821634236, 'radius': 3.28938499231, 'ra': 283.57307866, 'radiusErr': 0.0966106582556, 'dec': -1.72169220629}, 'SNR76.5+0.5': {'raErr': 0.239194370141, 'decErr': 0.237042031629, 'radius': 0.0, 'ra': 305.79604969, 'radiusErr': 0.0, 'dec': 38.0899242377}, 'SNR359.9-3.9': {'raErr': 0.370403396666, 'decErr': 0.370403396666, 'radius': 3.40579876002, 'ra': 270.248088067, 'radiusErr': 0.0322331856041, 'dec': -31.0404354684}, 'SNR13.9-2.5': {'raErr': 0.246910688874, 'decErr': 0.230218282388, 'radius': 0.0, 'ra': 276.166824682, 'radiusErr': 0.0, 'dec': -18.0610935207}, 'SNR1.8-4.3': {'raErr': 0.289021434669, 'decErr': 0.289021434669, 'radius': 3.81421375835, 'ra': 271.728824097, 'radiusErr': 0.0379135371627, 'dec': -29.5968366794}, 'SNR356.2+0.3': {'raErr': 0.235796937237, 'decErr': 0.231081834107, 'radius': 0.0, 'ra': 263.780658892, 'radiusErr': 0.0, 'dec': -31.9487883966}, 'SNR284.4-1.1': {'raErr': 0.208806630737, 'decErr': 0.208371410621, 'radius': 0.0, 'ra': 155.495025217, 'radiusErr': 0.0, 'dec': -58.5085968979}, 'SNR10.6+0.2': {'raErr': 0.225499913609, 'decErr': 0.225499913609, 'radius': 1.10449743091, 'ra': 272.032883863, 'radiusErr': 0.0341393645176, 'dec': -19.7032873662}, 'SNR7.2-1.0': {'raErr': 0.212435277411, 'decErr': 0.212435277411, 'radius': 1.15182527401, 'ra': 271.458009475, 'radiusErr': 0.0181587333311, 'dec': -23.2450887576}, 'SNR8.4-3.3': {'raErr': 0.24715969135, 'decErr': 0.244745829424, 'radius': 0.0, 'ra': 274.287183754, 'radiusErr': 0.0, 'dec': -23.2733938363}, 'SNR10.6-0.0': {'raErr': 0.216131293557, 'decErr': 0.216131293557, 'radius': 1.1415765588, 'ra': 272.248039653, 'radiusErr': 0.02081870657, 'dec': -19.7838255334}, 'SNR19.5-0.5': {'raErr': 0.329306643087, 'decErr': 0.329306643087, 'radius': 3.33374746458, 'ra': 277.035347808, 'radiusErr': 0.0638087205202, 'dec': -12.1823823697}, 'SNR29.9-0.1': {'raErr': 0.226943543794, 'decErr': 0.220524036188, 'radius': 0.0, 'ra': 281.586115676, 'radiusErr': 0.0, 'dec': -2.74345446505}, 'SNR333.5+1.0': {'raErr': 0.225792647797, 'decErr': 0.22178170716, 'radius': 0.0, 'ra': 244.032656947, 'radiusErr': 0.0, 'dec': -49.305185678}, 'SNR19.0-3.8': {'raErr': 0.240504129745, 'decErr': 0.240504129745, 'radius': 2.97308953392, 'ra': 279.888971769, 'radiusErr': 0.0297750778352, 'dec': -14.1878114482}, 'SNR19.1-4.5': {'raErr': 0.258761006736, 'decErr': 0.258761006736, 'radius': 1.43509359229, 'ra': 280.599105519, 'radiusErr': 0.0528473369628, 'dec': -14.3515550345}, 'SNR25.1-1.8': {'raErr': 0.229996217496, 'decErr': 0.229996217496, 'radius': 1.03107244152, 'ra': 280.858348847, 'radiusErr': 0.0380107405473, 'dec': -7.82248442903}, 'SNR13.1-1.2': {'raErr': 0.234350036095, 'decErr': 0.226731586895, 'radius': 0.0, 'ra': 274.602218968, 'radiusErr': 0.0, 'dec': -18.1865742766}, 'SNR338.9+0.1': {'raErr': 0.222766347396, 'decErr': 0.218998825863, 'radius': 0.0, 'ra': 250.52920877, 'radiusErr': 0.0, 'dec': -46.0119304027}, 'SNR32.2-0.1': {'raErr': 0.224030013612, 'decErr': 0.224030013612, 'radius': 1.28305718136, 'ra': 282.658087263, 'radiusErr': 0.0542669367717, 'dec': -0.688323988425}, 'SNR33.6+0.4': {'raErr': 0.274322951956, 'decErr': 0.245689538065, 'radius': 0.0, 'ra': 282.788714843, 'radiusErr': 0.0, 'dec': 0.823801501246}, 'SNR0.2-1.1': {'raErr': 0.231727942038, 'decErr': 0.229055997984, 'radius': 0.0, 'ra': 267.577987688, 'radiusErr': 0.0, 'dec': -29.3043856157}, 'SNR50.6-0.8': {'raErr': 0.209175932656, 'decErr': 0.209175932656, 'radius': 1.05319549445, 'ra': 291.900664176, 'radiusErr': 0.0159060774408, 'dec': 15.2813222061}, 'SNR21.1-2.8': {'raErr': 0.243908532658, 'decErr': 0.243908532658, 'radius': 2.39797220885, 'ra': 279.965228189, 'radiusErr': 0.0543193832304, 'dec': -11.7788597994}, 'SNR324.4-0.0': {'raErr': 0.219817336197, 'decErr': 0.218253702216, 'radius': 0.0, 'ra': 233.713747774, 'radiusErr': 0.0, 'dec': -55.9346857956}, 'SNR334.2-1.9': {'raErr': 0.233771109082, 'decErr': 0.233771109082, 'radius': 1.38014246042, 'ra': 248.115723197, 'radiusErr': 0.0490037345498, 'dec': -50.8741175307}, 'SNR338.2-0.9': {'raErr': 0.254174197169, 'decErr': 0.254174197169, 'radius': 1.26483497373, 'ra': 251.025366585, 'radiusErr': 0.0286707253048, 'dec': -47.2488966627}}
flaresData = {'Flare54': {'dec': -6.21, 'radius': 1.8, 'ra': 174.2}, 'Flare55': {'dec': 13.27, 'radius': 1.01, 'ra': 238.56}, 'Flare56': {'dec': 52.7, 'radius': 1.39, 'ra': 121.5}, 'Flare57': {'dec': 78.56, 'radius': 0.68, 'ra': 271.96}, 'Flare50': {'dec': 50.57, 'radius': 0.63, 'ra': 132.32}, 'Flare51': {'dec': 32.07, 'radius': 1.01, 'ra': 350.45}, 'Flare52': {'dec': 4.84, 'radius': 1.39, 'ra': 153.94}, 'Flare53': {'dec': 11.11, 'radius': 1.01, 'ra': 72.07}, 'Flare58': {'dec': -46.67, 'radius': 1.01, 'ra': 41.7}, 'Flare59': {'dec': -1.96, 'radius': 1.39, 'ra': 323.52}, 'Flare138': {'dec': 0.91, 'radius': 1.8, 'ra': 165.18}, 'Flare139': {'dec': -27.99, 'radius': 1.39, 'ra': 34.97}, 'Flare134': {'dec': 35.73, 'radius': 1.39, 'ra': 34.79}, 'Flare135': {'dec': -48.83, 'radius': 1.39, 'ra': 269.92}, 'Flare136': {'dec': 38.28, 'radius': 0.63, 'ra': 249.16}, 'Flare137': {'dec': -29.32, 'radius': 1.39, 'ra': 161.57}, 'Flare130': {'dec': 42.92, 'radius': 0.84, 'ra': 36.29}, 'Flare131': {'dec': -50.17, 'radius': 1.39, 'ra': 137.24}, 'Flare132': {'dec': -21.06, 'radius': 1.8, 'ra': 57.75}, 'Flare133': {'dec': -2.43, 'radius': 1.8, 'ra': 55.26}, 'Flare166': {'dec': -31.93, 'radius': 1.39, 'ra': 155.78}, 'Flare61': {'dec': 45.71, 'radius': 1.01, 'ra': 315.87}, 'Flare60': {'dec': -26.66, 'radius': 1.39, 'ra': 333.6}, 'Flare63': {'dec': -33.7, 'radius': 1.01, 'ra': 258.88}, 'Flare62': {'dec': -12.98, 'radius': 0.84, 'ra': 263.2}, 'Flare65': {'dec': 60.67, 'radius': 0.95, 'ra': 39.28}, 'Flare64': {'dec': -4.97, 'radius': 0.59, 'ra': 203.18}, 'Flare67': {'dec': -25.88, 'radius': 0.63, 'ra': 191.56}, 'Flare66': {'dec': 18.02, 'radius': 1.01, 'ra': 259.88}, 'Flare69': {'dec': -1.49, 'radius': 1.39, 'ra': 65.66}, 'Flare68': {'dec': 45.49, 'radius': 1.39, 'ra': 79.32}, 'Flare161': {'dec': 28.58, 'radius': 0.59, 'ra': 39.5}, 'Flare160': {'dec': -11.54, 'radius': 0.68, 'ra': 18.94}, 'Flare129': {'dec': -53.72, 'radius': 1.39, 'ra': 332.38}, 'Flare128': {'dec': 32.35, 'radius': 0.63, 'ra': 282.58}, 'Flare127': {'dec': 15.5, 'radius': 1.39, 'ra': 30.95}, 'Flare126': {'dec': 2.16, 'radius': 0.63, 'ra': 187.43}, 'Flare125': {'dec': -20.05, 'radius': 1.3, 'ra': 287.56}, 'Flare124': {'dec': 52.1, 'radius': 1.01, 'ra': 265.8}, 'Flare123': {'dec': 4.69, 'radius': 0.74, 'ra': 76.12}, 'Flare122': {'dec': 61.53, 'radius': 1.39, 'ra': 121.02}, 'Flare121': {'dec': -1.77, 'radius': 1.39, 'ra': 75.92}, 'Flare120': {'dec': -83.96, 'radius': 1.39, 'ra': 328.85}, 'Flare165': {'dec': 19.5, 'radius': 1.01, 'ra': 108.05}, 'Flare173': {'dec': -29.58, 'radius': 1.39, 'ra': 207.21}, 'Flare164': {'dec': 67.09, 'radius': 0.49, 'ra': 283.29}, 'Flare78': {'dec': 5.78, 'radius': 1.39, 'ra': 100.11}, 'Flare79': {'dec': -39.31, 'radius': 0.63, 'ra': 270.92}, 'Flare76': {'dec': 16.01, 'radius': 1.8, 'ra': 81.3}, 'Flare77': {'dec': 47.51, 'radius': 1.8, 'ra': 24.81}, 'Flare74': {'dec': -30.49, 'radius': 0.86, 'ra': 328.63}, 'Flare75': {'dec': 37.19, 'radius': 1.39, 'ra': 303.22}, 'Flare72': {'dec': -15.89, 'radius': 0.41, 'ra': 356.35}, 'Flare73': {'dec': -51.19, 'radius': 0.68, 'ra': 32.42}, 'Flare70': {'dec': 48.85, 'radius': 0.59, 'ra': 198.29}, 'Flare71': {'dec': -56.45, 'radius': 1.39, 'ra': 36.99}, 'Flare112': {'dec': 33.76, 'radius': 1.39, 'ra': 305.99}, 'Flare113': {'dec': 49.73, 'radius': 0.68, 'ra': 178.24}, 'Flare110': {'dec': -35.87, 'radius': 1.39, 'ra': 136.3}, 'Flare111': {'dec': 4.69, 'radius': 1.39, 'ra': 162.82}, 'Flare116': {'dec': 31.62, 'radius': 0.59, 'ra': 230.75}, 'Flare117': {'dec': 11.8, 'radius': 0.84, 'ra': 338.35}, 'Flare114': {'dec': -13.9, 'radius': 1.39, 'ra': 149.43}, 'Flare115': {'dec': 50.11, 'radius': 1.39, 'ra': 265.32}, 'Flare118': {'dec': -66.29, 'radius': 1.39, 'ra': 353.25}, 'Flare119': {'dec': -49.79, 'radius': 0.84, 'ra': 352.17}, 'Flare198': {'dec': 34.83, 'radius': 1.8, 'ra': 167.58}, 'Flare199': {'dec': -22.74, 'radius': 1.39, 'ra': 195.04}, 'Flare192': {'dec': 54.96, 'radius': 0.68, 'ra': 115.18}, 'Flare193': {'dec': -47.35, 'radius': 0.74, 'ra': 314.16}, 'Flare190': {'dec': -54.96, 'radius': 1.39, 'ra': 84.37}, 'Flare191': {'dec': -60.37, 'radius': 1.39, 'ra': 66.94}, 'Flare196': {'dec': -39.36, 'radius': 1.39, 'ra': 339.75}, 'Flare197': {'dec': -38.05, 'radius': 0.49, 'ra': 67.02}, 'Flare194': {'dec': 24.37, 'radius': 1.39, 'ra': 149.38}, 'Flare195': {'dec': 50.77, 'radius': 0.74, 'ra': 330.62}, 'Flare167': {'dec': -21.19, 'radius': 0.84, 'ra': 291.03}, 'Flare170': {'dec': -40.44, 'radius': 1.01, 'ra': 353.29}, 'Flare171': {'dec': -31.63, 'radius': 1.8, 'ra': 229.26}, 'Flare172': {'dec': -24.46, 'radius': 0.84, 'ra': 45.65}, 'Flare105': {'dec': -12.27, 'radius': 0.84, 'ra': 132.41}, 'Flare104': {'dec': -36.49, 'radius': 1.39, 'ra': 37.14}, 'Flare107': {'dec': -13.39, 'radius': 0.53, 'ra': 233.37}, 'Flare106': {'dec': 1.77, 'radius': 1.39, 'ra': 33.84}, 'Flare101': {'dec': -55.85, 'radius': 1.8, 'ra': 202.86}, 'Flare100': {'dec': -64.92, 'radius': 0.86, 'ra': 195.99}, 'Flare103': {'dec': 4.69, 'radius': 1.39, 'ra': 203.41}, 'Flare102': {'dec': 1.35, 'radius': 1.8, 'ra': 48.14}, 'Flare174': {'dec': 44.19, 'radius': 1.39, 'ra': 300.26}, 'Flare109': {'dec': -3.85, 'radius': 1.39, 'ra': 350.73}, 'Flare108': {'dec': 30.21, 'radius': 1.39, 'ra': 208.34}, 'Flare175': {'dec': -56.45, 'radius': 1.39, 'ra': 119.83}, 'Flare189': {'dec': -5.5, 'radius': 0.84, 'ra': 170.21}, 'Flare176': {'dec': 45.07, 'radius': 0.84, 'ra': 103.04}, 'Flare200': {'dec': 24.32, 'radius': 1.39, 'ra': 153.47}, 'Flare185': {'dec': 7.23, 'radius': 0.84, 'ra': 83.26}, 'Flare184': {'dec': 16.52, 'radius': 0.44, 'ra': 39.72}, 'Flare187': {'dec': 32.49, 'radius': 0.84, 'ra': 198.25}, 'Flare177': {'dec': 65.88, 'radius': 1.39, 'ra': 148.76}, 'Flare181': {'dec': 1.59, 'radius': 1.39, 'ra': 137.67}, 'Flare180': {'dec': -36.43, 'radius': 1.39, 'ra': 80.08}, 'Flare183': {'dec': -36.21, 'radius': 0.59, 'ra': 60.65}, 'Flare182': {'dec': -0.74, 'radius': 1.01, 'ra': 70.45}, 'Flare18': {'dec': 54.9, 'radius': 1.39, 'ra': 181.36}, 'Flare19': {'dec': 28.17, 'radius': 1.39, 'ra': 141.14}, 'Flare201': {'dec': 1.13, 'radius': 1.08, 'ra': 147.17}, 'Flare10': {'dec': -52.35, 'radius': 1.01, 'ra': 259.7}, 'Flare11': {'dec': -6.03, 'radius': 1.8, 'ra': 341.82}, 'Flare12': {'dec': -30.2, 'radius': 1.8, 'ra': 101.75}, 'Flare13': {'dec': 42.23, 'radius': 0.56, 'ra': 330.56}, 'Flare14': {'dec': 41.0, 'radius': 1.39, 'ra': 250.49}, 'Flare15': {'dec': 14.14, 'radius': 0.56, 'ra': 111.15}, 'Flare16': {'dec': -62.38, 'radius': 1.8, 'ra': 256.22}, 'Flare17': {'dec': 33.08, 'radius': 0.84, 'ra': 110.14}, 'Flare83': {'dec': 10.1, 'radius': 0.68, 'ra': 33.18}, 'Flare82': {'dec': -9.12, 'radius': 0.5, 'ra': 228.34}, 'Flare81': {'dec': 17.72, 'radius': 1.8, 'ra': 326.07}, 'Flare80': {'dec': -70.28, 'radius': 1.39, 'ra': 91.08}, 'Flare87': {'dec': -35.96, 'radius': 1.39, 'ra': 288.58}, 'Flare86': {'dec': -35.57, 'radius': 0.63, 'ra': 224.76}, 'Flare85': {'dec': 24.17, 'radius': 1.39, 'ra': 116.92}, 'Flare84': {'dec': 32.55, 'radius': 1.01, 'ra': 268.36}, 'Flare178': {'dec': 70.23, 'radius': 0.84, 'ra': 266.67}, 'Flare179': {'dec': 36.98, 'radius': 1.39, 'ra': 112.62}, 'Flare89': {'dec': 33.63, 'radius': 0.46, 'ra': 95.65}, 'Flare88': {'dec': 40.93, 'radius': 1.08, 'ra': 308.59}, 'Flare163': {'dec': 1.56, 'radius': 1.8, 'ra': 116.55}, 'Flare2': {'dec': 9.38, 'radius': 1.39, 'ra': 267.78}, 'Flare3': {'dec': -44.26, 'radius': 0.51, 'ra': 84.07}, 'Flare0': {'dec': 39.12, 'radius': 0.74, 'ra': 263.99}, 'Flare1': {'dec': 61.27, 'radius': 0.84, 'ra': 18.12}, 'Flare6': {'dec': 44.87, 'radius': 0.56, 'ra': 206.64}, 'Flare7': {'dec': -7.55, 'radius': 0.56, 'ra': 306.65}, 'Flare4': {'dec': -38.36, 'radius': 1.39, 'ra': 299.07}, 'Flare5': {'dec': -27.82, 'radius': 0.84, 'ra': 343.02}, 'Flare8': {'dec': 48.59, 'radius': 1.39, 'ra': 283.36}, 'Flare9': {'dec': -55.05, 'radius': 1.8, 'ra': 7.28}, 'Flare188': {'dec': 70.77, 'radius': 1.39, 'ra': 131.43}, 'Flare90': {'dec': -46.03, 'radius': 1.8, 'ra': 320.5}, 'Flare91': {'dec': -11.71, 'radius': 0.95, 'ra': 112.24}, 'Flare92': {'dec': 2.49, 'radius': 1.39, 'ra': 122.8}, 'Flare93': {'dec': -40.37, 'radius': 1.39, 'ra': 53.43}, 'Flare94': {'dec': -42.84, 'radius': 1.39, 'ra': 299.19}, 'Flare95': {'dec': -53.17, 'radius': 0.84, 'ra': 159.57}, 'Flare96': {'dec': 29.94, 'radius': 1.39, 'ra': 184.36}, 'Flare97': {'dec': 47.56, 'radius': 1.39, 'ra': 250.29}, 'Flare98': {'dec': -32.97, 'radius': 1.39, 'ra': 268.0}, 'Flare99': {'dec': 10.49, 'radius': 0.35, 'ra': 226.28}, 'Flare169': {'dec': -34.21, 'radius': 1.8, 'ra': 84.89}, 'Flare168': {'dec': 31.46, 'radius': 1.39, 'ra': 329.9}, 'Flare186': {'dec': 41.63, 'radius': 0.84, 'ra': 50.15}, 'Flare156': {'dec': -64.43, 'radius': 1.8, 'ra': 171.21}, 'Flare157': {'dec': 68.58, 'radius': 0.84, 'ra': 255.11}, 'Flare154': {'dec': 4.41, 'radius': 1.01, 'ra': 190.12}, 'Flare155': {'dec': 79.94, 'radius': 1.39, 'ra': 57.74}, 'Flare152': {'dec': -21.06, 'radius': 0.51, 'ra': 278.63}, 'Flare153': {'dec': 60.94, 'radius': 0.63, 'ra': 158.35}, 'Flare150': {'dec': -70.17, 'radius': 1.01, 'ra': 202.32}, 'Flare151': {'dec': 6.21, 'radius': 1.39, 'ra': 160.17}, 'Flare206': {'dec': 40.39, 'radius': 1.39, 'ra': 351.55}, 'Flare207': {'dec': -22.6, 'radius': 0.74, 'ra': 42.88}, 'Flare204': {'dec': 30.37, 'radius': 0.68, 'ra': 30.87}, 'Flare205': {'dec': 11.16, 'radius': 1.39, 'ra': 309.08}, 'Flare202': {'dec': -48.41, 'radius': 0.59, 'ra': 83.24}, 'Flare203': {'dec': -75.45, 'radius': 0.68, 'ra': 327.06}, 'Flare158': {'dec': 43.38, 'radius': 0.51, 'ra': 257.7}, 'Flare159': {'dec': 22.39, 'radius': 1.8, 'ra': 52.48}, 'Flare25': {'dec': -2.74, 'radius': 1.8, 'ra': 136.96}, 'Flare24': {'dec': -2.3, 'radius': 1.39, 'ra': 7.85}, 'Flare27': {'dec': 0.84, 'radius': 1.39, 'ra': 129.98}, 'Flare26': {'dec': 13.97, 'radius': 1.01, 'ra': 84.5}, 'Flare21': {'dec': -8.21, 'radius': 0.63, 'ra': 122.14}, 'Flare20': {'dec': 71.14, 'radius': 0.56, 'ra': 109.46}, 'Flare23': {'dec': 58.34, 'radius': 0.68, 'ra': 16.04}, 'Flare22': {'dec': 39.92, 'radius': 0.84, 'ra': 176.37}, 'Flare29': {'dec': 44.56, 'radius': 0.68, 'ra': 140.16}, 'Flare28': {'dec': -5.35, 'radius': 1.8, 'ra': 4.77}, 'Flare162': {'dec': 9.36, 'radius': 1.39, 'ra': 352.57}, 'Flare47': {'dec': 23.04, 'radius': 1.39, 'ra': 18.03}, 'Flare46': {'dec': -7.87, 'radius': 1.3, 'ra': 337.38}, 'Flare45': {'dec': -33.63, 'radius': 1.39, 'ra': 199.72}, 'Flare44': {'dec': -11.17, 'radius': 0.74, 'ra': 207.78}, 'Flare43': {'dec': -21.46, 'radius': 1.39, 'ra': 352.31}, 'Flare42': {'dec': -20.18, 'radius': 0.84, 'ra': 97.19}, 'Flare41': {'dec': 32.52, 'radius': 1.8, 'ra': 53.44}, 'Flare40': {'dec': 16.21, 'radius': 0.53, 'ra': 343.57}, 'Flare49': {'dec': -19.23, 'radius': 0.68, 'ra': 172.33}, 'Flare48': {'dec': -23.56, 'radius': 0.49, 'ra': 74.06}, 'Flare149': {'dec': 21.37, 'radius': 0.51, 'ra': 186.22}, 'Flare148': {'dec': 27.29, 'radius': 1.39, 'ra': 265.0}, 'Flare141': {'dec': 81.38, 'radius': 1.39, 'ra': 161.49}, 'Flare140': {'dec': 32.2, 'radius': 1.01, 'ra': 18.41}, 'Flare143': {'dec': -5.82, 'radius': 0.51, 'ra': 194.27}, 'Flare142': {'dec': -46.74, 'radius': 1.39, 'ra': 105.88}, 'Flare145': {'dec': 56.84, 'radius': 1.01, 'ra': 276.32}, 'Flare144': {'dec': 37.89, 'radius': 1.39, 'ra': 165.75}, 'Flare147': {'dec': -61.6, 'radius': 0.84, 'ra': 38.73}, 'Flare146': {'dec': 20.45, 'radius': 0.74, 'ra': 133.77}, 'Flare211': {'dec': -17.3, 'radius': 1.8, 'ra': 30.96}, 'Flare210': {'dec': 29.51, 'radius': 0.63, 'ra': 180.23}, 'Flare213': {'dec': -25.73, 'radius': 0.74, 'ra': 246.58}, 'Flare212': {'dec': 4.21, 'radius': 1.39, 'ra': 127.78}, 'Flare208': {'dec': 48.34, 'radius': 1.39, 'ra': 254.18}, 'Flare209': {'dec': 34.34, 'radius': 0.68, 'ra': 347.93}, 'Flare32': {'dec': 10.4, 'radius': 0.74, 'ra': 47.18}, 'Flare33': {'dec': -52.33, 'radius': 1.39, 'ra': 276.98}, 'Flare30': {'dec': -25.2, 'radius': 1.39, 'ra': 55.7}, 'Flare31': {'dec': 77.17, 'radius': 1.8, 'ra': 256.91}, 'Flare36': {'dec': 55.3, 'radius': 1.39, 'ra': 198.03}, 'Flare37': {'dec': 35.99, 'radius': 0.74, 'ra': 214.86}, 'Flare34': {'dec': -80.23, 'radius': 1.39, 'ra': 287.49}, 'Flare35': {'dec': 21.6, 'radius': 1.39, 'ra': 83.23}, 'Flare38': {'dec': -14.4, 'radius': 0.68, 'ra': 339.35}, 'Flare39': {'dec': -41.83, 'radius': 0.63, 'ra': 217.09}}
NB: candidatesData and flaresData are dictionaries. The data in dicts is accessible via the keys, eg candidatesData[candidateName][variableName] :
In [ ]:
candidatesData['SNR357.0-1.0']['raErr']
In [42]:
from IPython.display import Image
overlapImage1 = Image(filename = 'SNR347.3-00.5_GeVext_overlap.png')
overlapImage1
Out[42]:
In [43]:
overlapImage2 = Image(filename = 'SNR111.7-02.1_GeVext_overlap.png')
overlapImage2
Out[43]:
In [ ]: