class BaseScanParameters:
def __init__(self, job_config):
dynamical_system_dict = job_config['dynamical_system']
self.dynamical_system = DynamicalSystem(
*dynamical_system_dict.values())
intg_precision_exp10 = job_config['trajectory_integration'][
'precision']
self.intg_precision = 10**intg_precision_exp10
self.intg_limit = job_config['trajectory_integration']['limit']
scan_resolution_exp10 = job_config['phase_space_scan']['resolution']
self.scan_resolution = 10**scan_resolution_exp10
scan_min_x = job_config['phase_space_scan']['min_x']
scan_max_x = job_config['phase_space_scan']['max_x']
scan_min_y = job_config['phase_space_scan']['min_y']
scan_max_y = job_config['phase_space_scan']['max_y']
self.scan_space = DiscretePlane(scan_min_x, scan_max_x,
self.scan_resolution, scan_min_y,
scan_max_y, self.scan_resolution)
self.scan_range = self.scan_space.range()
self.total_iterations = self.compute_total_iterations()
def compute_total_iterations(self):
initial_positions = self.scan_space.range()
# cannot reuse the attribute cause iterables can only be traversed once!
return sum([1 for xy in initial_positions])
def __init__(self, scan_params):
map_resolution = 10**-2
min_x, max_x, min_y, max_y = scan_params.scan_space.extent()
self.map_space = DiscretePlane(min_x, max_x, map_resolution, min_y,
max_y, map_resolution)
# inbound is marked as 0, outbound is marked as 1.
self.tendency_map = np.zeros(self.map_space.dimension(), dtype=int)
# to normalize at the end.
self.counts_map = np.zeros(self.map_space.dimension(), dtype=int)
def from_dict(cls, dict):
if '__heatmap__' not in dict:
raise RuntimeError('not a Heatmap dict')
restored_heatmap = cls.__new__(cls)
restored_heatmap.map_space = DiscretePlane.from_dict(dict['map_space'])
restored_heatmap.incidence_map = np.array(dict['incidence_map'])
return restored_heatmap
def from_dict(cls, dict):
if '__interloop_scan_map__' not in dict:
raise RuntimeError('not an InterloopScanMap dict')
restored_scan_map = cls.__new__(cls)
restored_scan_map.map_space = DiscretePlane.from_dict(
dict['map_space'])
restored_scan_map.tendency_map = np.array(dict['tendency_map'])
restored_scan_map.counts_map = np.array(dict['counts_map'])
return restored_scan_map
def __init__(self, job_config):
dynamical_system_dict = job_config['dynamical_system']
self.dynamical_system = DynamicalSystem(
*dynamical_system_dict.values())
intg_precision_exp10 = job_config['trajectory_integration'][
'precision']
self.intg_precision = 10**intg_precision_exp10
self.intg_limit = job_config['trajectory_integration']['limit']
scan_resolution_exp10 = job_config['phase_space_scan']['resolution']
self.scan_resolution = 10**scan_resolution_exp10
scan_min_x = job_config['phase_space_scan']['min_x']
scan_max_x = job_config['phase_space_scan']['max_x']
scan_min_y = job_config['phase_space_scan']['min_y']
scan_max_y = job_config['phase_space_scan']['max_y']
self.scan_space = DiscretePlane(scan_min_x, scan_max_x,
self.scan_resolution, scan_min_y,
scan_max_y, self.scan_resolution)
self.scan_range = self.scan_space.range()
self.total_iterations = self.compute_total_iterations()
def __init__(self, job_config):
super().__init__(job_config)
projection_min_x = job_config['heatmap']['min_x']
projection_max_x = job_config['heatmap']['max_x']
resolution_x_exp10 = job_config['heatmap']['resolution_x']
projection_res_x = 10 ** resolution_x_exp10
projection_min_y = job_config['heatmap']['min_y']
projection_max_y = job_config['heatmap']['max_y']
resolution_y_exp10 = job_config['heatmap']['resolution_y']
projection_res_y = 10 ** resolution_y_exp10
self.projection_space = DiscretePlane(
projection_min_x, projection_max_x, projection_res_x,
projection_min_y, projection_max_y, projection_res_y
)
class InterloopScanMap:
def __init__(self, scan_params):
map_resolution = 10**-2
min_x, max_x, min_y, max_y = scan_params.scan_space.extent()
self.map_space = DiscretePlane(min_x, max_x, map_resolution, min_y,
max_y, map_resolution)
# inbound is marked as 0, outbound is marked as 1.
self.tendency_map = np.zeros(self.map_space.dimension(), dtype=int)
# to normalize at the end.
self.counts_map = np.zeros(self.map_space.dimension(), dtype=int)
def project(self, x, y, bit):
#logging.info(f'{x}, {y}: {bit}')
coordinates = self.map_space.projection_coordinates(x, y)
if coordinates is not None:
map_x, map_y = coordinates
self.tendency_map[map_x, map_y] += bit
self.counts_map[map_x, map_y] += 1
def to_dict(self):
return {
'__interloop_scan_map__': True,
'map_space': self.map_space.to_dict(),
'tendency_map': self.tendency_map.tolist(),
'counts_map': self.counts_map.tolist()
}
@classmethod
def from_dict(cls, dict):
if '__interloop_scan_map__' not in dict:
raise RuntimeError('not an InterloopScanMap dict')
restored_scan_map = cls.__new__(cls)
restored_scan_map.map_space = DiscretePlane.from_dict(
dict['map_space'])
restored_scan_map.tendency_map = np.array(dict['tendency_map'])
restored_scan_map.counts_map = np.array(dict['counts_map'])
return restored_scan_map
def plot(self, save_path=""):
averaged_map = np.divide(self.tendency_map, self.counts_map)
im = plt.imshow(averaged_map.transpose(),
cmap='viridis',
aspect='auto',
origin='lower',
extent=self.map_space.extent())
plt.xlabel('x')
plt.ylabel('y')
plt.colorbar(im)
if save_path:
plt.savefig(save_path)
else:
plt.show()