The effective area is provided by the ~pyfoxsi.response.Response object. The following code shows how to initialize the object and plot the results.
import matplotlib.pyplot as plt from pyfoxsi.response import Response resp = Response(shutter_state=0)
plt.figure() resp.plot() plt.show()
On initialization we can also define the shutter state so that one of the shutters is in to block the low energy x-rays. The following plot shows a comparison of the areas for each shutter strategy
import matplotlib.pyplot as plt from pyfoxsi.response import Response
resp0 = Response(shutter_state=0) resp1 = Response(shutter_state=1) resp2 = Response(shutter_state=2)
fig = plt.subplots() resp0.effective_area['total'].plot(label='Shutter state 0') resp1.effective_area['total'].plot(label='Shutter state 1') resp2.effective_area['total'].plot(label='Shutter state 2') plt.legend()
It is also possible to take a look at the area provided by the optics only compared to that acted upon by material in the optical path such as the thermal blankets and detectors.
import matplotlib.pyplot as plt from pyfoxsi.response import Response
resp0 = Response(shutter_state=0)
fig = plt.subplots() resp0.optics_effective_area['total'].plot(label='Optics') resp0.effective_area['total'].plot(label='Optics + path') plt.legend()
The optical path is composed of various materials which affect the transmission (or absorption in the case of the detectors). You can inspect how these materials are affecting the path by looking at the factor variable in the effective area.
import matplotlib.pyplot as plt from pyfoxsi.response import Response
resp0 = Response(shutter_state=0)
fig = plt.subplots() resp0.effective_area['factor'].plot() plt.title('This factor multiplies the effective area') plt.show()
To see what materials are in the path check out the optical_path property. The Material object provides it's own plot function.
import matplotlib.pyplot as plt from pyfoxsi.response import Response
resp0 = Response(shutter_state=0)
fig = plt.subplots() resp0.optical_path[0].plot() plt.legend()