by Matthew Wampler-Doty and Ed Bokhour
Remember that whenever you power-cycle the Observatory Simulator, you should set preload=True below.
When you are running this notbook and it has not been power cycled, you should set preload=False.
In [1]:
from tessfpe.dhu.fpe import FPE
from tessfpe.dhu.unit_tests import check_house_keeping_voltages
fpe1 = FPE(1, debug=False, preload=False, FPE_Wrapper_version='6.1.1')
print fpe1.version
if check_house_keeping_voltages(fpe1):
print "Wrapper load complete. Interface voltages OK."
We assume that there is a slight global error in the housekeeping that may be corrected by a linear transformation:
$$ f(x) := m \cdot x + c $$To calculus $c$, we can average the biases in the housekeeping, but we must first convert them to ADUs from uA by using the unscale_value function.
Note that because of statistical variation, we collect 50 samples:
In [2]:
def estimate_c_param(fpe,samples=50):
from tessfpe.dhu.house_keeping import unscale_value
from tessfpe.data.housekeeping_channels import housekeeping_channels
sample_data = []
for _ in range(samples):
analogue_house_keeping = fpe.house_keeping["analogue"]
biases = [unscale_value(analogue_house_keeping[k],
16,
housekeeping_channels[k]["low"],
housekeeping_channels[k]["high"])
for k in fpe1.house_keeping["analogue"]
if 'bias' in k]
for b in biases:
sample_data.append(b)
return sum(sample_data) / len(sample_data)
Below run the above estimation; this should be approximately $-4.83$ based on previous calculations
In [3]:
estimate_c_param(fpe1)
Out[3]:
Next we can use the known voltages from the power supply to recover the slope $m$, which is done as follows. Once again, 50 samples are collected because we are scientists at MIT:
In [4]:
def unscale_1_8_f(x):
from tessfpe.data.housekeeping_channels import housekeeping_channels
from tessfpe.dhu.house_keeping import unscale_value
low = housekeeping_channels['+1.8f']['low']
high = housekeeping_channels['+1.8f']['high']
return unscale_value(x, 16, low, high)
def unscale_1_f(x):
from tessfpe.data.housekeeping_channels import housekeeping_channels
from tessfpe.dhu.house_keeping import unscale_value
low = housekeeping_channels['+1f']['low']
high = housekeeping_channels['+1f']['high']
return unscale_value(x, 16, low, high)
def estimate_m_param(fpe,samples=50):
slope_samples = []
for _ in range(samples):
a_ = unscale_1_8_f(1.807)
b_ = unscale_1_f(0.998)
a = unscale_1_8_f(fpe1.house_keeping['analogue']['+1.8f'])
b = unscale_1_f(fpe1.house_keeping['analogue']['+1f'])
slope_samples.append((a_ - b_) / (a - b))
return sum(slope_samples) / len(slope_samples)
Once again, we run the calculation; this should be $\approx 1$
In [5]:
estimate_m_param(fpe1)
Out[5]: