def __init__(self, filename, contents=None):
'''
Takes:
* filename : input filename (.azr)
* contents : list of strings (generated from the input file)
'''
# If contents is provided, don't try to read the input file.
if contents is not None:
self.contents = contents.copy()
else:
self.contents = utility.read_input_file(filename)
i = self.contents.index('<segmentsTest>') + 1
j = self.contents.index('</segmentsTest>')
self.all_segments = [] # all segments in the .azr file
for row in self.contents[i:j]:
if row != '':
self.all_segments.append(TestSegment(row))
self.segments = [
] # only the segments to be included in the calculation
for seg in self.all_segments:
if seg.include:
self.segments.append(seg)
self.output_files = []
for seg in self.segments:
self.output_files.append(seg.output_filename)
self.output_files = list(np.unique(self.output_files))
def __init__(self, filename):
self.contents = utility.read_input_file(filename)
i = self.contents.index('<segmentsData>') + 1
j = self.contents.index('</segmentsData>')
# All segments listed in the file.
self.all_segments = []
k = 0
for row in self.contents[i:j]:
if row != '':
self.all_segments.append(Segment(row, k))
k += 1
# All segments included in the calculation.
self.segments = []
for seg in self.all_segments:
if seg.include:
self.segments.append(seg)
# Indices of segments with varied normalization constants.
self.norm_segment_indices = []
for (i, seg) in enumerate(self.all_segments):
if seg.include and seg.vary_norm_factor:
self.norm_segment_indices.append(i)
# Number of data points for each included segment.
self.ns = [seg.n for seg in self.segments]
# Output files that need to be read.
self.output_files = []
for seg in self.segments:
self.output_files.append(seg.output_filename)
# Eliminates repeated output files AND SORTS them:
# (1, 2, 3, ..., TOTAL_CAPTURE)
self.output_files = list(np.unique(self.output_files))
color_index = 0
for file_name in mag_file_name_lst:
with open(folder_name + "/" + file_name) as csvfile:
if (not file_name.__contains__(".csv")):
continue
if (not file_name.__contains__("mag")):
continue
# if (not file_name.__contains__("180-180") and not file_name.__contains__(
# "135-135") and not file_name.__contains__("45-45") and not file_name.__contains__("0-0")):
# continue
if (not file_name.__contains__("l0r0")):
continue
time, raw = ut.read_input_file(csvfile)
raw = raw - np.mean(raw[200:-1, :], axis=0)
all_data.append(raw)
if (file_name.__contains__("45-45")
or file_name.__contains__("0-0")):
label = "left"
if (file_name.__contains__("135-135")
or file_name.__contains__("180-180")):
label = "right"
# all_label.append(label)
all_label.append(file_name)
# import pywt
from utility import read_input_file
from problem import Problem
from algorithms import ucs, bfs, a_star
if __name__ == '__main__':
input_dict = read_input_file()
for key, value in input_dict.items():
print(key, value, sep=':')
for target in input_dict['targets']:
problem = Problem(
init_state=input_dict['state_grid'][input_dict['landing_site'][1]][
input_dict['landing_site'][0]],
state_space=input_dict['state_grid'],
goal_state=input_dict['state_grid'][target[1]][target[0]],
actions=['E', 'W', 'N', 'S', 'NE', 'SE', 'SW', 'NW'],
max_elev_diff=input_dict['max_elevation_diff'])
# print(problem)
if input_dict['algorithm'].lower() == 'bfs':
print(bfs(problem))
elif input_dict['algorithm'].lower() == 'ucs':
print(ucs(problem))
else:
print(a_star(problem))
def __init__(self, input_filename, parameters=None):
self.input_filename = input_filename
self.input_filename = input_filename
self.input_file_contents = utility.read_input_file(input_filename)
self.initial_levels = utility.read_levels(input_filename)
self.data = Data(self.input_filename)
self.test = Test(self.input_filename)
if parameters is None:
self.parameters = []
jpis = []
for group in self.initial_levels:
# grab the J^pi from the first row in the group
jpi = group[0].spin * group[0].parity
# add it to the list
jpis.append(jpi)
for (i, sublevel) in enumerate(group):
spin = sublevel.spin
parity = sublevel.parity
rank = jpis.count(jpi)
if i == 0:
if not sublevel.energy_fixed:
self.parameters.append(
Parameter(spin,
parity,
'energy',
i + 1,
rank=rank))
if not sublevel.width_fixed:
if sublevel.energy < sublevel.separation_energy:
self.parameters.append(
Parameter(spin,
parity,
'width',
i + 1,
rank=rank,
is_anc=True))
else:
self.parameters.append(
Parameter(spin,
parity,
'width',
i + 1,
rank=rank))
else:
self.parameters = parameters
Jpi = [l[0].spin * l[0].parity for l in self.initial_levels]
self.addresses = []
for p in self.parameters:
jpi = p.spin * p.parity
i = Jpi.index(jpi)
i += p.rank - 1 # convert from one-based count to zero-based index
j = p.channel - 1 # convert from one-based count to zero-based index
self.addresses.append([i, j, p.kind])
self.n1 = len(self.parameters)
self.n2 = len(self.data.norm_segment_indices)
# number of free parameters
self.nd = self.n1 + self.n2
self.labels = []
for i in range(self.n1):
self.labels.append(self.parameters[i].label)
for i in self.data.norm_segment_indices:
self.labels.append(self.data.all_segments[i].nf.label)