This is a simple notebook to test the accuracy and precision of the pick and place tooling/software being used in FPIX module assembly.
the procedure to acquire the data is the following:
Picker tool from the tool rack.Picker tool in contact with the surface of the HDI.Picker tool 5mm, bringing the HDI with it.Picker tool back to 0.Picker tool over the "target position"*. Picker tool to match the target rotation.Picker tool 5mmPicker tool back to 0.Picker tool to the tool rack.*The target position is shifted 1mm in x and y with respect to the initial position so the HDI doesn't catch the edges of the stencil.
In [2]:
import matplotlib
%matplotlib notebook
from matplotlib import rc
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
plt.style.use('fivethirtyeight')
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
plt.rcParams['figure.figsize'] = [12,8]
from pick_and_place_data import data
In [3]:
class Observation:
def __init__(self, i, x, y, z , t):
self.i = i
self.x = x*1000
self.y = y*1000
self.z = z*1000
self.t = t
def __repr__(self):
fmt = ("Observation {}:\n\tx={:7.3f}\n\ty={:7.3f}"
"\n\tz={:7.3f}\n\tt={:+7.3f}\n")
return fmt.format(self.i, self.x, self.y, self.z, self.t)
obs = {key: [Observation(i, *val[i*4:(i+1)*4]) for i in range(len(val)//4)] for key,val in data.items()}
def net_phi(*quat):
from Quaternion import Quat
quat = [float(q) for q in quat]
quat = Quat(quat)
v = np.array([1,0,0])
v = quat.transform @ v
return np.arctan2(v[1],v[0]) * 180/np.pi
target = Observation(0,632.521120,551.126947,63.003312, net_phi(0.000000,-0.000000,0.000662,-1.000000))
print(target)
In [4]:
def delta_summary(data):
fig, axs = plt.subplots(nrows=2, ncols=2)
def histo(data, ax, xlabel, xlim=None, ymax=None):
hist, bins = np.histogram(data, bins=15)
width = 0.8 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
ax.bar(center, hist, align='center', width=width)
ax.set_xlabel(xlabel)
if xlim is None:
xlim = (bins[0], bins[-1])
ax.set_xlim(xlim)
if ymax is not None:
ax.set_ylim((0,ymax))
fmt = "N={}\n$\mu$={:.3f}\n$\sigma=${:.3f}"
label = fmt.format(len(data), np.mean(data), np.std(data))
ax.text(xlim[0], 10, label)
xs, ys, zs, ts = zip(*[(ob.x-target.x,
ob.y-target.y,
ob.z-target.z,
ob.t-target.t) for ob in data])
histo(xs, axs[0][0], r'$\Delta$X($\mu$m)', (-50,50), 15)
histo(ys, axs[0][1], r'$\Delta$Y($\mu$m)', (-50,50), 15)
histo(zs, axs[1][0], r'$\Delta$Z($\mu$m)', (-50,50), 15)
histo(ts, axs[1][1], r'$\Delta\theta$(degrees)', (-.07, .07), 15)
plt.tight_layout()
delta_summary(obs[50.1])
delta_summary(obs[50])
delta_summary(obs[49.8])
In [5]:
def stepwise_summary(data):
fig, (ax1, ax2) = plt.subplots(ncols=2)
ax1.set_aspect(1)
ax2.set_aspect(1)
cmap = cm.get_cmap('viridis')
for ob1, ob2 in zip(data[:-1],data[1:]):
color = cmap(ob1.i/len(data))
ax1.arrow(ob1.x-target.x, ob1.y-target.y,
ob2.x-ob1.x, ob2.y-ob1.y,
head_width=0.25, head_length=.5, fc=color, ec=color,
length_includes_head=True)
ax2.arrow(0, 0,
ob2.x-ob1.x, ob2.y-ob1.y,
head_width=0.25, head_length=.5, fc=color, ec=color,
length_includes_head=True)
ax1.autoscale()
ax2.autoscale()
ax1.set_xlim((-25, 25))
ax1.set_ylim((-25, 25))
ax2.set_xlim((-25, 25))
ax2.set_ylim((-25, 25))
fig.suptitle("Stepwise deltas")
stepwise_summary(obs[50.1])
stepwise_summary(obs[50])
stepwise_summary(obs[49.8])
In [6]:
from scatter_hist import scatter_hist
def delta_summary(data):
fig = plt.figure()
xs, ys, zs, ts = zip(*[(ob.x-target.x,
ob.y-target.y,
ob.z-target.z,
ob.t-target.t) for ob in data])
scatter_hist(xs,ys, fig, plot_mean=True)
delta_summary(obs[50.1])
delta_summary(obs[50])
delta_summary(obs[49.8])
In [8]:
fig, axs = plt.subplots(nrows=2, ncols=2)
def proc_data(data):
f = lambda xs: (np.mean(xs), np.std(xs))
return map(f,zip(*[(ob.x-target.x, ob.y-target.y, ob.z-target.z, ob.t-target.t) for ob in data]))
xdat, ydat, zdat, tdat = zip(*map(proc_data, [obs[50.1], obs[50], obs[49.8]]))
pts, err = zip(*xdat)
axs[0,0].errorbar([50.1, 50, 49.8], pts, yerr=err, fmt='o')
axs[0,0].set_xlim(49.75,50.15)
axs[0,0].set_ylabel("X error ($\\mu$m)")
pts, err = zip(*ydat)
axs[0,1].errorbar([50.1, 50, 49.8], pts, yerr=err, fmt='o')
axs[0,1].set_xlim(49.75,50.15)
axs[0,1].set_ylabel("Y error ($\\mu$m)")
pts, err = zip(*zdat)
axs[1,0].errorbar([50.1, 50, 49.8], pts, yerr=err, fmt='o')
axs[1,0].set_xlim(49.75,50.15)
axs[1,0].set_ylabel("Z error ($\\mu$m)")
pts, err = zip(*tdat)
axs[1,1].errorbar([50.1, 50, 49.8], pts, yerr=err, fmt='o')
axs[1,1].set_xlim(49.75,50.15)
axs[1,1].set_ylabel("$\\theta$ error ($\\mu$m)")
Out[8]: