Backup from old computer.

Author: evgkanias

.vscode/settings.json

@@ -26,6 +26,7 @@ def deep_update(obj: dict, path: str, value: str):
         t = type(obj[key])
         obj[key] = t(value)
 
+
 def run_experiment(args):
     if args.slow_ode:
         pim.math.slow_solver = True

cli.py

@@ -1,7 +1,7 @@
 from pim.models.new.stone import trials
 import pickle
 
-headings, velocities = trials.generate_route(T = 1500, vary_speed = True)
+headings, velocities = trials.generate_route(T=1500, vary_speed=True)
 
 with open("../fastbee/path.pickle", "wb") as f:
     pickle.dump((headings.tolist(), velocities.tolist()), f)

genpath.py

@@ -35,6 +35,7 @@ def save(self, setup: str, timestamp: datetime, results_dir="results"):
             pickle.dump(output, f)
         logger.info("saved experiment results as {}", filename)
 
+
 class Experiment:
     @abstractmethod
     def run(self, name: str, config_id: str) -> ExperimentResults:

lib/pim/experiment.py

@@ -4,26 +4,31 @@
 from ..network import InputLayer, Network, Output, RecurrentForwardNetwork, FunctionLayer, IdentityLayer, Layer
 from .constants import *
 
+
 def cl1_output(inputs):
     "Invert compass"
     tl2, = inputs
     return tl2 + np.pi
 
+
 def tb1_output(inputs):
     """Sinusoidal response to solar compass."""
     cl1, tb1 = inputs
     return (1.0 + np.cos(cl1 - column_angles)) / 2.0
 
+
 def tn1_output(inputs):
     """Linearly inverse sensitive to forwards and backwards motion."""
     flow, = inputs
     return np.clip((1.0 - flow) / 2.0, 0, 1)
 
+
 def tn2_output(inputs):
     """Linearly sensitive to forwards motion only."""
     flow, = inputs
     return np.clip(flow, 0, 1)
 
+
 class CPU4Layer(Layer):
     def __init__(self, TB1, TN1, TN2, gain):
         self.TB1 = TB1
@@ -64,6 +69,7 @@ def internal(self) -> Output:
     def output(self, dt: float) -> Output:
         return self.memory
 
+
 def cpu1_output(inputs):
     tb1, cpu4 = inputs
     """Offset CPU4 columns by 1 column (45 degrees) left and right
@@ -74,6 +80,7 @@ def cpu1_output(inputs):
                                     np.roll(cpu4_reshaped[0], -1)])
     return cpu1.reshape(-1)
 
+
 def motor_output(inputs):
     cpu1, = inputs
     random_std=0.05

lib/pim/models/basic.py

@@ -83,6 +83,7 @@ def output(self, network: Network) -> Output:
             mem_update -= np.dot(self.W_TB1, tb1)
             return np.clip(noisy_sigmoid(mem_update, self.slope, self.bias, self.noise) * self.gain + beta, 0, 1)
 
+
 class CPU4Layer(Layer):
     def __init__(self, TB1, TN1, TN2, W_TN, W_TB1, gain, slope, bias, noise, holonomic=False):
         self.TB1 = TB1
@@ -120,6 +121,7 @@ def output(self, network: Network) -> Output:
         return noisy_sigmoid(self.memory, self.slope,
                              self.bias, self.noise)
 
+
 class CPU4PontineLayer(CPU4Layer):
     def internal(self):
         return [self.memory, self.memory]
@@ -148,6 +150,7 @@ def step(self, network: Network, dt: float):
             cpu4_mem_reshaped += self.gain * mem_update
             self.memory = np.clip(cpu4_mem_reshaped.reshape(-1), 0.0, 1.0)
 
+
 W_CL1_TB1 = np.tile(np.eye(N_TB1), 2)
 W_TB1_TB1 = gen_tb_tb_weights()
 W_TB1_CPU1a = np.tile(np.eye(N_TB1), (2, 1))[1:N_CPU1A+1, :]
@@ -206,6 +209,7 @@ def step(self, network: Network, dt: float):
 ])
 W_CPU4_pontine = np.eye(N_CPU4)
 
+
 def tl2_output(noise):
     """Just a dot product with preferred angle and current heading""" # bad description
     def f(inputs):
@@ -214,13 +218,15 @@ def f(inputs):
         return noisy_sigmoid(output, tl2_slope_tuned, tl2_bias_tuned, noise)
     return f
 
+
 def cl1_output(noise):
     """Takes input from the TL2 neurons and gives output."""
     def f(inputs):
         tl2, = inputs
         return noisy_sigmoid(-tl2, cl1_slope_tuned, cl1_bias_tuned, noise)
     return f
 
+
 def tb1_output(noise):
     """Ring attractor state on the protocerebral bridge."""
     def f(inputs):
@@ -232,6 +238,7 @@ def f(inputs):
         return noisy_sigmoid(output, tb1_slope_tuned, tb1_bias_tuned, noise)
     return f
 
+
 def tn1_output(noise):
     def f(inputs):
         flow, = inputs
@@ -241,6 +248,7 @@ def f(inputs):
         return np.clip(output, 0.0, 1.0)
     return f
 
+
 def tn2_output(noise):
     def f(inputs):
         flow, = inputs
@@ -250,6 +258,7 @@ def f(inputs):
         return np.clip(output, 0.0, 1.0)
     return f
 
+
 def cpu1a_output(noise):
     def f(inputs):
         """The memory and direction used together to get population code for
@@ -259,6 +268,7 @@ def f(inputs):
         return noisy_sigmoid(inputs, cpu1_slope_tuned, cpu1_bias_tuned, noise)
     return f
 
+
 def cpu1b_output(noise):
     def f(inputs):
         """The memory and direction used together to get population code for
@@ -276,6 +286,7 @@ def f(inputs):
 #        return np.hstack([cpu1b[-1], cpu1a, cpu1b[0]])
 #    return f
 
+
 def motor_output(noise):
     """outputs a scalar where sign determines left or right turn."""
     def f(inputs):
@@ -286,13 +297,15 @@ def f(inputs):
         return output
     return f
 
+
 def pontine_output(noise):
     def f(inputs):
         cpu4, = inputs
         inputs = np.dot(W_CPU4_pontine, cpu4)
         return noisy_sigmoid(inputs, pontine_slope_tuned, pontine_bias_tuned, noise)
     return f
 
+
 def cpu1a_pontine_output(noise):
     def f(inputs):
         """The memory and direction used together to get population code for
@@ -307,6 +320,7 @@ def f(inputs):
         return noisy_sigmoid(inputs, cpu1_pontine_slope_tuned, cpu1_pontine_bias_tuned, noise)
     return f
 
+
 def cpu1b_pontine_output(noise):
     def f(inputs):
         """The memory and direction used together to get population code for
@@ -322,7 +336,6 @@ def f(inputs):
     return f
 
 
-
 def build_network(params, CPU4LayerClass = CPU4Layer) -> Network:
     noise = params.get("noise",0.1)
     cpu4_mem_gain = params.get("cpu4_mem_gain",0.005)
@@ -381,6 +394,7 @@ def build_network(params, CPU4LayerClass = CPU4Layer) -> Network:
         )
     })
 
+
 def build_network_pontine(params) -> Network:
     # TODO: allow noisy weights
     noise = params.get("noise",0.1)

lib/pim/models/rate.py

@@ -13,7 +13,7 @@ def __init__(self, layers: Dict[str, "Layer"]):
         some network types (such as the forward network) relies on the order of this dictionary,
         which is a reliable property of dictionaries since Python 3.7."""
         self.layers = layers
-        self.context = None # type: Any
+        self.context = None  # type: Any
 
     def reset(self):
         for name, layer in self.layers.items():
@@ -82,6 +82,7 @@ def postprocess_layers(self):
     def output(self, layer) -> Output:
         return self._previous_outputs[layer]
 
+
 class RecurrentForwardNetwork(Network):
     """Forward network that allows backward connections."""
 
@@ -144,7 +145,7 @@ def resolve(self, network: Network):
 
 
 class Layer:
-    def __init__(self, initial = np.array([0.0])):
+    def __init__(self, initial=np.array([0.0])):
         self.initial = initial
         self.reset()
 
@@ -198,7 +199,7 @@ def IdentityLayer(input):
 
 
 class InputLayer(Layer):
-    def __init__(self, initial = np.array([0.0])):
+    def __init__(self, initial=np.array([0.0])):
         super().__init__(initial)
         self.input = Trap()