Config hotfix

c3/c3objs.py

@@ -33,7 +33,11 @@ def __init__(self, name, desc="", comment="", params=None):
                 if isinstance(par, Quantity):
                     self.params[pname] = par
                 else:
-                    self.params[pname] = Quantity(**par)
+                    try:
+                        self.params[pname] = Quantity(**par)
+                    except Exception as exception:
+                        print(f"Error initializing {pname} with\n {par}")
+                        raise exception
 
     def __str__(self) -> str:
         return hjson.dumps(self.asdict())
@@ -69,7 +73,7 @@ class Quantity:
     """
 
     def __init__(
-        self, value, min_val=None, max_val=None, unit="undefined", symbol=r"\alpha"
+        self, value, unit="undefined", min_val=None, max_val=None, symbol=r"\alpha"
     ):
         if "pi" in unit:
             pref = np.pi

c3/experiment.py

@@ -122,6 +122,26 @@ def quick_setup(self, filepath: str) -> None:
 
         self.pmap = ParameterMap(instructions, generator=gen, model=model)
 
+    def read_config(self, filepath: str) -> None:
+        """
+        Load a file and parse it to create a Model object.
+
+        Parameters
+        ----------
+        filepath : str
+            Location of the configuration file
+
+        """
+        with open(filepath, "r") as cfg_file:
+            cfg = hjson.loads(cfg_file.read())
+        model = Model()
+        model.fromdict(cfg["model"])
+        generator = Generator()
+        generator.fromdict(cfg["generator"])
+        pmap = ParameterMap(model=model, generator=generator)
+        pmap.fromdict(cfg["instructions"])
+        self.pmap = pmap
+
     def write_config(self, filepath: str) -> None:
         """
         Write dictionary to a HJSON file.

c3/generator/devices.py

@@ -36,7 +36,11 @@ def __init__(self, **props):
         name = props.pop("name")
         desc = props.pop("desc", "")
         comment = props.pop("comment", "")
-        params = props
+
+        # Because of legacy usage, we might have parameters given withing props iself
+        # or in a "params" field. Here we combine them.
+        params = props.pop("params", {})
+        params.update(props)
         super().__init__(name, desc, comment, params)
         self.signal = {}
 

c3/generator/generator.py

@@ -68,6 +68,9 @@ def read_config(self, filepath: str) -> None:
         """
         with open(filepath, "r") as cfg_file:
             cfg = hjson.loads(cfg_file.read())
+        self.fromdict(cfg)
+
+    def fromdict(self, cfg: dict) -> None:
         for name, props in cfg["Devices"].items():
             props["name"] = name
             dev_type = props.pop("c3type")

c3/libraries/envelopes.py

@@ -21,7 +21,7 @@ def env_reg_deco(func):
 
 
 @env_reg_deco
-def no_drive(t, params):
+def no_drive(t, params=None):
     """Do nothing."""
     return tf.zeros_like(t, dtype=tf.float64)
 
@@ -37,10 +37,10 @@ def pwc(t, params):
 def pwc_symmetric(t, params):
     """symmetic PWC pulse
     This works only for inphase component"""
-    t_bin_start = tf.cast(params["t_bin_end"].get_value(), dtype=tf.float64)
-    t_bin_end = tf.cast(params["t_bin_start"].get_value(), dtype=tf.float64)
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    inphase = tf.cast(params["inphase"].get_value(), dtype=tf.float64)
+    t_bin_start = tf.cast(params["t_bin_end"].get_value(), tf.float64)
+    t_bin_end = tf.cast(params["t_bin_start"].get_value(), tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    inphase = tf.cast(params["inphase"].get_value(), tf.float64)
 
     t_interp = tf.where(tf.greater(t, t_final / 2), -t + t_final, t)
     shape = tf.reshape(
@@ -72,18 +72,16 @@ def fourier_sin(t, params):
 
     """
     amps = tf.reshape(
-        tf.cast(params["amps"].get_value(), dtype=tf.float64),
-        [params["amps"].shape[0], 1],
+        tf.cast(params["amps"].get_value(), tf.float64), [params["amps"].shape[0], 1]
     )
     freqs = tf.reshape(
-        tf.cast(params["freqs"].get_value(), dtype=tf.float64),
-        [params["freqs"].shape[0], 1],
+        tf.cast(params["freqs"].get_value(), tf.float64), [params["freqs"].shape[0], 1]
     )
     phases = tf.reshape(
-        tf.cast(params["phases"].get_value(), dtype=tf.float64),
+        tf.cast(params["phases"].get_value(), tf.float64),
         [params["phases"].shape[0], 1],
     )
-    t = tf.reshape(tf.cast(t, dtype=tf.float64), [1, t.shape[0]])
+    t = tf.reshape(tf.cast(t, tf.float64), [1, t.shape[0]])
     return tf.reduce_sum(amps * tf.sin(freqs * t + phases), 0)
 
 
@@ -101,21 +99,19 @@ def fourier_cos(t, params):
 
     """
     amps = tf.reshape(
-        tf.cast(params["amps"].get_value(), dtype=tf.float64),
-        [params["amps"].shape[0], 1],
+        tf.cast(params["amps"].get_value(), tf.float64), [params["amps"].shape[0], 1]
     )
     freqs = tf.reshape(
-        tf.cast(params["freqs"].get_value(), dtype=tf.float64),
-        [params["freqs"].shape[0], 1],
+        tf.cast(params["freqs"].get_value(), tf.float64), [params["freqs"].shape[0], 1]
     )
-    t = tf.reshape(tf.cast(t, dtype=tf.float64), [1, t.shape[0]])
+    t = tf.reshape(tf.cast(t, tf.float64), [1, t.shape[0]])
     return tf.reduce_sum(amps * tf.cos(freqs * t), 0)
 
 
 @env_reg_deco
 def rect(t, params):
     """Rectangular pulse. Returns 1 at every time step."""
-    return tf.ones_like(t, dtype=tf.float64)
+    return tf.ones_like(t, tf.float64)
 
 
 @env_reg_deco
@@ -130,10 +126,10 @@ def trapezoid(t, params):
         risefall : float
             Length of the slope
     """
-    risefall = tf.cast(params["risefall"].get_value(), dtype=tf.float64)
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
+    risefall = tf.cast(params["risefall"].get_value(), tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
 
-    envelope = tf.ones_like(t, dtype=tf.float64)
+    envelope = tf.ones_like(t, tf.float64)
     envelope = tf.where(
         tf.less_equal(t, risefall * 2.5), t / (risefall * 2.5), envelope
     )
@@ -159,8 +155,8 @@ def flattop_risefall(t, params):
             with the start of the ramp-up and ends at the end of the ramp-down
 
     """
-    risefall = tf.cast(params["risefall"].get_value(), dtype=tf.float64)
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
+    risefall = tf.cast(params["risefall"].get_value(), tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
     t_up = risefall
     t_down = t_final - risefall
     return (
@@ -186,9 +182,9 @@ def flattop(t, params):
             Length of the ramps.
 
     """
-    t_up = tf.cast(params["t_up"].get_value(), dtype=tf.float64)
-    t_down = tf.cast(params["t_down"].get_value(), dtype=tf.float64)
-    risefall = tf.cast(params["risefall"].get_value(), dtype=tf.float64)
+    t_up = tf.cast(params["t_up"].get_value(), tf.float64)
+    t_down = tf.cast(params["t_down"].get_value(), tf.float64)
+    risefall = tf.cast(params["risefall"].get_value(), tf.float64)
     return (
         (1 + tf.math.erf((t - t_up) / (risefall)))
         / 2
@@ -212,9 +208,9 @@ def flattop_cut(t, params):
             Length of the ramps.
 
     """
-    t_up = tf.cast(params["t_up"].get_value(), dtype=tf.float64)
-    t_down = tf.cast(params["t_down"].get_value(), dtype=tf.float64)
-    risefall = tf.cast(params["risefall"].get_value(), dtype=tf.float64)
+    t_up = tf.cast(params["t_up"].get_value(), tf.float64)
+    t_down = tf.cast(params["t_down"].get_value(), tf.float64)
+    risefall = tf.cast(params["risefall"].get_value(), tf.float64)
     shape = tf.math.erf((t - t_up) / (risefall)) * tf.math.erf(
         (-t + t_down) / (risefall)
     )
@@ -236,9 +232,9 @@ def flattop_cut_center(t, params):
             Length of the ramps.
 
     """
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    width = tf.cast(params["width"].get_value(), dtype=tf.float64)
-    risefall = tf.cast(params["risefall"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    width = tf.cast(params["width"].get_value(), tf.float64)
+    risefall = tf.cast(params["risefall"].get_value(), tf.float64)
     t_up = t_final / 2 - width / 2
     t_down = t_final / 2 + width / 2
     shape = tf.math.erf((t - t_up) / risefall) * tf.math.erf((-t + t_down) / risefall)
@@ -251,11 +247,11 @@ def slepian_fourier(t, params):
     """
     ----
     """
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    width = tf.cast(params["width"].get_value(), dtype=tf.float64)
-    fourier_coeffs = tf.cast(params["fourier_coeffs"].get_value(), dtype=tf.float64)
-    offset = tf.cast(params["offset"].get_value(), dtype=tf.float64)
-    amp = tf.cast(params["amp"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    width = tf.cast(params["width"].get_value(), tf.float64)
+    fourier_coeffs = tf.cast(params["fourier_coeffs"].get_value(), tf.float64)
+    offset = tf.cast(params["offset"].get_value(), tf.float64)
+    amp = tf.cast(params["amp"].get_value(), tf.float64)
     shape = tf.zeros_like(t)
     for n, coeff in enumerate(fourier_coeffs):
         shape += coeff * (
@@ -290,8 +286,8 @@ def gaussian_sigma(t, params):
             Width of the Gaussian.
 
     """
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    sigma = tf.cast(params["sigma"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    sigma = tf.cast(params["sigma"].get_value(), tf.float64)
     gauss = tf.exp(-((t - t_final / 2) ** 2) / (2 * sigma ** 2))
     norm = tf.sqrt(2 * np.pi * sigma ** 2) * tf.math.erf(
         t_final / (np.sqrt(8) * sigma)
@@ -321,6 +317,55 @@ def gaussian(t, params):
     return gaussian_sigma(t, params)
 
 
+@env_reg_deco
+def cosine(t, params):
+    """
+    Cosine-shaped envelope. Maximum value is 1, area is given by length.
+
+    Parameters
+    ----------
+    params : dict
+        t_final : float
+            Total length of the Gaussian.
+        sigma: float
+            Width of the Gaussian.
+
+    """
+    # TODO Add zeroes for t>t_final
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    cos = 0.5 * (1 - tf.cos(2 * np.pi * t / t_final))
+    return cos
+
+
+@env_reg_deco
+def cosine_flattop(t, params):
+    """
+    Cosine-shaped envelope. Maximum value is 1, area is given by length.
+
+    Parameters
+    ----------
+    params : dict
+        t_final : float
+            Total length of the Gaussian.
+        sigma: float
+            Width of the Gaussian.
+
+    """
+    t_rise = tf.cast(params["t_rise"].get_value(), tf.float64)
+    dt = t[1] - t[0]
+    n_rise = tf.cast(t_rise / dt, tf.int32)
+    n_flat = len(t) - 2 * n_rise
+    cos_flt = tf.concat(
+        [
+            0.5 * (1 - tf.cos(np.pi * t[:n_rise] / t_rise)),
+            tf.ones(n_flat, dtype=tf.float64),
+            0.5 * (1 + tf.cos(np.pi * t[:n_rise] / t_rise)),
+        ],
+        axis=0,
+    )
+    return cos_flt
+
+
 @env_reg_deco
 def gaussian_nonorm(t, params):
     """
@@ -336,7 +381,7 @@ def gaussian_nonorm(t, params):
 
     """
     # TODO Add zeroes for t>t_final
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
     sigma = params["sigma"].get_value()
     gauss = tf.exp(-((t - t_final / 2) ** 2) / (2 * sigma ** 2))
     return gauss
@@ -345,8 +390,8 @@ def gaussian_nonorm(t, params):
 @env_reg_deco
 def gaussian_der_nonorm(t, params):
     """Derivative of the normalized gaussian (ifself not normalized)."""
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    sigma = tf.cast(params["sigma"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    sigma = tf.cast(params["sigma"].get_value(), tf.float64)
     gauss_der = (
         tf.exp(-((t - t_final / 2) ** 2) / (2 * sigma ** 2))
         * (t - t_final / 2)
@@ -358,8 +403,8 @@ def gaussian_der_nonorm(t, params):
 @env_reg_deco
 def gaussian_der(t, params):
     """Derivative of the normalized gaussian (ifself not normalized)."""
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    sigma = tf.cast(params["sigma"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    sigma = tf.cast(params["sigma"].get_value(), tf.float64)
     gauss_der = (
         tf.exp(-((t - t_final / 2) ** 2) / (2 * sigma ** 2))
         * (t - t_final / 2)
@@ -374,8 +419,8 @@ def gaussian_der(t, params):
 @env_reg_deco
 def drag_sigma(t, params):
     """Second order gaussian."""
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    sigma = tf.cast(params["sigma"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    sigma = tf.cast(params["sigma"].get_value(), tf.float64)
     drag = tf.exp(-((t - t_final / 2) ** 2) / (2 * sigma ** 2))
     norm = tf.sqrt(2 * np.pi * sigma ** 2) * tf.math.erf(
         t_final / (np.sqrt(8) * sigma)
@@ -400,8 +445,8 @@ def drag(t, params):
 @env_reg_deco
 def drag_der(t, params):
     """Derivative of second order gaussian."""
-    t_final = tf.cast(params["t_final"].get_value(), dtype=tf.float64)
-    sigma = tf.cast(params["sigma"].get_value(), dtype=tf.float64)
+    t_final = tf.cast(params["t_final"].get_value(), tf.float64)
+    sigma = tf.cast(params["sigma"].get_value(), tf.float64)
     norm = tf.sqrt(2 * np.pi * sigma ** 2) * tf.math.erf(
         t_final / (np.sqrt(8) * sigma)
     ) - t_final * tf.exp(-(t_final ** 2) / (8 * sigma ** 2))

c3/parametermap.py

@@ -86,6 +86,9 @@ def read_config(self, filepath: str) -> None:
         """
         with open(filepath, "r") as cfg_file:
             cfg = hjson.loads(cfg_file.read())
+        self.fromdict(cfg)
+
+    def fromdict(self, cfg: dict) -> None:
         for key, gate in cfg.items():
             if "mapto" in gate.keys():
                 instr = copy.deepcopy(self.instructions[gate["mapto"]])

c3/signal/pulse.py

@@ -41,20 +41,20 @@ def __init__(
             self.shape = envelopes[shape]
         else:
             self.shape = shape
-        params_default = {
+        default_params = {
             "amp": Qty(value=0.0, min_val=-1.0, max_val=+1.0, unit="V"),
             "delta": Qty(value=0.0, min_val=-5.0, max_val=+5.0, unit="V"),
             "freq_offset": Qty(value=0.0, min_val=-1.0, max_val=+1.0, unit="Hz 2pi"),
             "xy_angle": Qty(value=0.0, min_val=-1.0, max_val=+1.0, unit="rad"),
             "sigma": Qty(value=5e-9, min_val=-2.0, max_val=+2.0, unit="s"),
             "t_final": Qty(value=0.0, min_val=-1.0, max_val=+1.0, unit="s"),
         }
-        params_default.update(params)
+        default_params.update(params)
         super().__init__(
             name=name,
             desc=desc,
             comment=comment,
-            params=params_default,
+            params=default_params,
         )
 
     def write_config(self, filepath: str) -> None: