From a563a00bc269f7cb4441aceb1c5cc5b9b41add6e Mon Sep 17 00:00:00 2001
From: Sebastien Bourdeauducq <sb@m-labs.hk>
Date: Sat, 2 Apr 2016 22:46:29 +0800
Subject: [PATCH] examples: add remote exec demo

---
 .../master/repository/remote_exec_demo.py     | 72 +++++++++++++++++++
 .../repository/remote_exec_processing.py      | 30 ++++++++
 2 files changed, 102 insertions(+)
 create mode 100644 examples/master/repository/remote_exec_demo.py
 create mode 100644 examples/master/repository/remote_exec_processing.py

diff --git a/examples/master/repository/remote_exec_demo.py b/examples/master/repository/remote_exec_demo.py
new file mode 100644
index 000000000..81a632e0a
--- /dev/null
+++ b/examples/master/repository/remote_exec_demo.py
@@ -0,0 +1,72 @@
+import time
+import inspect
+
+from artiq.experiment import *
+
+import remote_exec_processing
+
+
+class RemoteExecDemo(EnvExperiment):
+    def build(self):
+        self.setattr_device("camera_sim")
+        self.setattr_device("scheduler")
+        self.setattr_argument("remote_exec", BooleanValue(False))
+        self.setattr_argument("show_picture", BooleanValue(True), "Local options")
+        self.setattr_argument("enable_fit", BooleanValue(True), "Local options")
+        if self.remote_exec:
+            self.setattr_device("camera_sim_rexec")
+
+    def prepare(self):
+        if self.remote_exec:
+            self.camera_sim_rexec.add_code(
+                inspect.getsource(remote_exec_processing))
+
+    def set_dataset(self, name, *args, **kwargs):
+        EnvExperiment.set_dataset(self, "rexec_demo." + name,
+                                  *args, **kwargs)
+
+    def transfer_parameters(self, parameters):
+        w, h, cx, cy = parameters
+        self.set_dataset("gaussian_w", w, save=False, broadcast=True)
+        self.set_dataset("gaussian_h", h, save=False, broadcast=True)
+        self.set_dataset("gaussian_cx", cx, save=False, broadcast=True)
+        self.set_dataset("gaussian_cy", cy, save=False, broadcast=True)
+
+    def fps_meter(self):
+        t = time.monotonic()
+        if hasattr(self, "last_pt_update"):
+            self.iter_count += 1
+            dt = t - self.last_pt_update
+            if dt >= 5:
+                pt = dt/self.iter_count
+                self.set_dataset("picture_pt", pt, save=False, broadcast=True)
+                self.last_pt_update = t
+                self.iter_count = 0
+        else:
+            self.last_pt_update = t
+            self.iter_count = 0
+
+    def run_local(self):
+        while True:
+            self.fps_meter()
+            data = self.camera_sim.get_picture()
+            if self.show_picture:
+                self.set_dataset("picture", data, save=False, broadcast=True)
+            if self.enable_fit:
+                self.transfer_parameters(remote_exec_processing.fit(data))
+            self.scheduler.pause()
+
+    def run_remote(self):
+        while True:
+            self.fps_meter()
+            self.transfer_parameters(self.camera_sim_rexec.call("get_and_fit"))
+            self.scheduler.pause()
+
+    def run(self):
+        try:
+            if self.remote_exec:
+                self.run_remote()
+            else:
+                self.run_local()
+        except TerminationRequested:
+            pass
diff --git a/examples/master/repository/remote_exec_processing.py b/examples/master/repository/remote_exec_processing.py
new file mode 100644
index 000000000..6d02b14bf
--- /dev/null
+++ b/examples/master/repository/remote_exec_processing.py
@@ -0,0 +1,30 @@
+import numpy as np
+from numba import jit
+from scipy.optimize import least_squares
+
+
+@jit(nopython=True)
+def compute_gaussian(r, img_w, img_h,
+                     gaussian_w, gaussian_h,
+                     gaussian_cx, gaussian_cy):
+    for y in range(img_h):
+        for x in range(img_w):
+            ds = ((gaussian_cx-x)/gaussian_w)**2 
+            ds += ((gaussian_cy-y)/gaussian_h)**2
+            r[x, y] = np.exp(-ds/2)
+
+
+def fit(data):
+    img_w, img_h = data.shape
+    def err(parameters):
+        r = np.empty((img_w, img_h))
+        compute_gaussian(r, img_w, img_h, *parameters)
+        r -= data
+        return r.ravel()
+    guess = [12, 15, img_w/2, img_h/2]
+    res = least_squares(err, guess)
+    return res.x
+
+
+def get_and_fit():
+    return fit(controller_driver.get_picture())