This notebook contains a Unit Test for the housekeeping information from the Observatory Simulator.
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."
As a quick diagnostic, we start and stop capture frames.
In [2]:
fpe1.cmd_start_frames()
Out[2]:
In [3]:
fpe1.cmd_stop_frames()
Out[3]:
Now we collect the measurements; this will make 10 sets of samples for heater currents that are between 0 and 150 mA, spaced out by 10 mA.
In [4]:
from tessfpe.data.operating_parameters import operating_parameters
operating_parameters["heater_1_current"]
high = operating_parameters["heater_1_current"]["high"]
In [5]:
measurements = []
for i in range(5,int(high)*10,10):
a = i % high
b = (i + 100) % high
c = abs((high - i) % high)
fpe1.ops.heater_1_current = a
fpe1.ops.heater_2_current = b
fpe1.ops.heater_3_current = c
fpe1.ops.send()
measurements.append({"set": {"heater_1_current": a,
"heater_2_current": b,
"heater_3_current": c},
"measured": {"heater_1_current": fpe1.house_keeping["analogue"]["heater_1_current"],
"heater_2_current": fpe1.house_keeping["analogue"]["heater_2_current"],
"heater_3_current": fpe1.house_keeping["analogue"]["heater_3_current"]}})
len(measurements)
Out[5]:
Make sure to run the following cell so that the heaters can "chill out".
In [6]:
fpe1.ops.heater_1_current = fpe1.ops.heater_1_current.low
fpe1.ops.heater_2_current = fpe1.ops.heater_2_current.low
fpe1.ops.heater_3_current = fpe1.ops.heater_3_current.low
fpe1.ops.send()
Let's first plot the set value on the $x$ axis versus the measured value on the $y$ axis to test how well calibrated the sensors are:
In [7]:
x1 = [i["set"]["heater_1_current"] for i in measurements]
y1 = [j["measured"]["heater_1_current"] for j in measurements]
x2 = [i["set"]["heater_2_current"] for i in measurements]
y2 = [j["measured"]["heater_2_current"] for j in measurements]
x3 = [i["set"]["heater_3_current"] for i in measurements]
y3 = [j["measured"]["heater_3_current"] for j in measurements]
In [8]:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
In [9]:
import numpy as np
import matplotlib.pyplot as plt
In [10]:
f = plt.figure(figsize=(10,10))
ax1 = f.add_subplot(331, aspect='equal')
ax1.set_title('Heater 1')
ax1.scatter(x1,y1,color='blue')
ax1.locator_params(nbins=4)
ax1.set_xlim([0,160])
ax1.set_ylim([0,160])
ax2 = f.add_subplot(332, aspect='equal')
ax2.set_title('Heater 2')
ax2.scatter(x2,y2,color='red')
ax2.locator_params(nbins=4)
ax2.set_xlim([0,160])
ax2.set_ylim([0,160])
ax3 = f.add_subplot(333, aspect='equal')
ax3.set_title('Heater 3')
ax3.scatter(x3,y3,color='green')
ax3.locator_params(nbins=4)
ax3.set_xlim([0,160])
ax3.set_ylim([0,160])
plt.show()
As we can see, the measured heater values from the housekeeping are roughly the same as the values set values, up to an affine transformation. To see this, it is helpful to compute measured - set to see the errors:
In [11]:
err1 = map(lambda y,x: y-x, y1, x1)
err2 = map(lambda y,x: y-x, y2, x2)
err3 = map(lambda y,x: y-x, y3, x3)
In [12]:
f = plt.figure(figsize=(10,10))
ax1 = f.add_subplot(331)
ax1.set_title('Error for Heater 1')
ax1.locator_params(nbins=4)
ax1.set_xlim([-20,160])
ax1.scatter(x1,err1,color='blue')
ax2 = f.add_subplot(332)
ax2.set_title('Error for Heater 2')
ax2.locator_params(nbins=4)
ax2.set_xlim([-20,160])
ax2.scatter(x2,err2,color='red')
ax3 = f.add_subplot(333)
ax3.set_title('Error for Heater 3')
ax3.locator_params(nbins=4)
ax3.set_xlim([-20,160])
ax3.scatter(x3,err3,color='green')
plt.show()
From the above we can infer that in order to correct for the error we need to disregard the measured values when the heaters are set to 0 mA, since they are apparently outliars.
We next turn to calibrating the heaters.
The above analysis only really looks at 3 heaters; we can see that the error measurements have an outliar at 0 mA, but it is of interest to reproduce this analysis for more than N=3 components. This should be as simple as rerunning this notebook, however.
For the time being we are proceeding with calibration by performing a simple linear transformation on the set value so that it will properly correspond with the desired observed value.
To calculate this, we first compute via linear regression the constants $m$ and $c$ in the equation below:
$$ \texttt{measured_value} = m \cdot \texttt{set_value} + c $$We make sure to avoid $\texttt{set_value} = 0$ where outliers have been observed.
In [13]:
def get_set_observed(heater_current, measurements):
return ([i["set"][heater_current] for i in measurements
if i["set"][heater_current] != 0],
[j["measured"][heater_current] for j in measurements
if j["set"][heater_current] != 0])
def calibrate(heater_current, measurements):
from scipy import stats
x,y = get_set_observed(heater_current, measurements)
return stats.linregress(x,y)
Once $m$ and $c$ have been calculated in the calibrate function, we have to make a preadjustment function, which is given by:
$$ preadjustment(x) := \frac{x - c}{m} $$
In [14]:
def error_correction_function(heater_current, measurements):
m, c, r_value, p_value, std_err = calibrate(heater_current, measurements)
return (lambda x: (x - c) / m)
Now that we can compute the error correction function, we turn to looking at the error again, after calibration.
In [15]:
err_corr1 = error_correction_function("heater_1_current", measurements)
err_corr2 = error_correction_function("heater_2_current", measurements)
err_corr3 = error_correction_function("heater_3_current", measurements)
Given these error corrections, we now turn to using them as preadjustments and recompute our measurements that we previously took:
In [16]:
preadjusted_measurements = []
for i in range(5,int(high)*10,10):
a = i % high
b = (i + 100) % high
c = abs((high - i) % high)
if not (0 < err_corr1(a) < high) or not (0 < err_corr2(b) < high) or not (0 < err_corr3(c) < high):
continue
fpe1.ops.heater_1_current = err_corr1(a)
fpe1.ops.heater_2_current = err_corr2(b)
fpe1.ops.heater_3_current = err_corr3(c)
fpe1.ops.send()
analogue_house_keeping = fpe1.house_keeping["analogue"]
preadjusted_measurements.append({"set": {"heater_1_current": a,
"heater_2_current": b,
"heater_3_current": c},
"measured": {"heater_1_current":
analogue_house_keeping["heater_1_current"],
"heater_2_current":
analogue_house_keeping["heater_2_current"],
"heater_3_current":
analogue_house_keeping["heater_3_current"]}})
Smokey the bear warns you to turn off your heaters when you are done:
In [17]:
fpe1.ops.heater_1_current = fpe1.ops.heater_1_current.low
fpe1.ops.heater_2_current = fpe1.ops.heater_2_current.low
fpe1.ops.heater_3_current = fpe1.ops.heater_3_current.low
fpe1.ops.send()
In [18]:
x1 = [i["set"]["heater_1_current"] for i in measurements]
y1 = [err_corr1(j["measured"]["heater_1_current"]) for j in measurements]
x2 = [i["set"]["heater_2_current"] for i in measurements]
y2 = [err_corr2(j["measured"]["heater_2_current"]) for j in measurements]
x3 = [i["set"]["heater_3_current"] for i in measurements]
y3 = [err_corr3(j["measured"]["heater_3_current"]) for j in measurements]
err1 = map(lambda y,x: y-x, y1, x1)
err2 = map(lambda y,x: y-x, y2, x2)
err3 = map(lambda y,x: y-x, y3, x3)
In [19]:
f = plt.figure(figsize=(10,10))
ax1 = f.add_subplot(331)
ax1.set_title('Error for Heater 1')
ax1.locator_params(nbins=4)
ax1.set_xlim([-20,160])
ax1.scatter(x1,err1,color='blue')
ax2 = f.add_subplot(332)
ax2.set_title('Error for Heater 2')
ax2.locator_params(nbins=4)
ax2.set_xlim([-20,160])
ax2.scatter(x2,err2,color='red')
ax3 = f.add_subplot(333)
ax3.set_title('Error for Heater 3')
ax3.locator_params(nbins=4)
ax3.set_xlim([-20,160])
ax3.scatter(x3,err3,color='green')
plt.show()
As we can see, after calibration we have an order of magnitude less error.