amda.py
43 lines
| 1.5 KiB
| text/x-python
|
PythonLexer
| r1340 | import sys | |||
| import os | ||||
| import numpy as np | ||||
| import datetime | ||||
| import time | ||||
| os.environ['TZ'] = 'UTC' | ||||
| epoch_2000 = np.datetime64('2000-01-01T00:00:00',tzinfo=datetime.timezone.utc) | ||||
| def load_scalar(fname): | ||||
| with open(fname, "r") as f: | ||||
| return [[ | ||||
| datetime.datetime(*(time.strptime(line.split()[0], '%Y-%m-%dT%H:%M:%S.%f')[0:6]), | ||||
| tzinfo=datetime.timezone.utc), | ||||
| float(line.split()[1])] | ||||
| for line in f if "#" not in line] | ||||
| r1341 | def extract_vector(variable): | |||
| return zip(*[(pt.x, pt.value(0), pt.value(1), pt.value(2)) for pt in variable]) | ||||
| r1340 | """ | |||
| Copied from myAMDA should be factored in somehow | ||||
| """ | ||||
| def generate_data(tstart, tstop, dt): | ||||
| delta = np.timedelta64(dt, 's') | ||||
| vector_size = int(np.round((tstop-tstart)/delta)) + 1 | ||||
| t = [tstart+i*delta for i in range(vector_size)] | ||||
| x0 = tstart-epoch_2000 | ||||
| x = [(x0 + i * delta).astype('float')/1000000 for i in range(vector_size)] | ||||
| y = [(x0 + (i+1) * delta).astype('float')/1000000 for i in range(vector_size)] | ||||
| z = [(x0 + (i+2) * delta).astype('float')/1000000 for i in range(vector_size)] | ||||
| return t,x,y,z | ||||
| r1341 | ||||
| def compare_with_ref(var, ref): | ||||
| t_ref, x_ref, y_ref, z_ref = ref | ||||
| t,x,y,z = extract_vector(var) | ||||
| return all([ | ||||
| all([t_ref[i].astype(float)/1000000 == t[i] for i in range(len(t))]), | ||||
| all([x_ref[i] == x[i] for i in range(len(x))]), | ||||
| all([y_ref[i] == y[i] for i in range(len(y))]), | ||||
| all([z_ref[i] == z[i] for i in range(len(z))]) | ||||
| ]) | ||||
