def load_clean_set(path):
data_set = parse_binary_llh(path)
data_set = [convert_to_date(x) for x in data_set]
data_set = list(sorted(data_set, key=lambda x: x.time))
baseline = data_set[0].time
#make data relative to baseline
data_set = [date_relative_days(x, baseline) for x in data_set]
#manufacture time series
size = len(data_set)
data = np.zeros([size, 7])
days = [x.time for x in data_set]
ew = np.array([x.pos[0] for x in data_set])
ns = np.array([x.pos[1] for x in data_set])
ud = np.array([x.pos[2] for x in data_set])
ewe = np.array([x.err[0] for x in data_set])
nse = np.array([x.err[1] for x in data_set])
ude = np.array([x.err[2] for x in data_set])
data[:, 0] = days
data[:, 1] = ew
data[:, 2] = ns
data[:, 3] = ud
data[:, 4] = ewe
data[:, 5] = nse
data[:, 6] = ude
return outlierdet(data, 50, 20), baseline
def load_set(file_name):
stations_names = open(file_name).readlines()
stations = dict()
for name in stations_names:
set = sorted(parse_binary_llh(r'conv/'+name[0:4]+'.tseries.neu'), key=lambda x: x.time)
if (get_date(set[-1])-get_date(set[0])).days >= 1000:
print(name[0:4])
print(get_date(set[-1]))
print(get_date(set[0]))
stations[name[0:4]] = set
min_date = max([get_date(stations[key][0]) for key in stations])
max_date = min([get_date(stations[key][-1]) for key in stations])
print(min_date)
print(max_date)
return stations, min_date, max_date
return conv
def seismic_act(data):
scipy.io.savemat("data.mat", {'data': data})
eng = matlab.engine.start_matlab()
eng.quakedet()
quakes = scipy.io.loadmat("earthquakes.mat")['earthquakes']
return quakes
if __name__ == "__main__":
path = "C:\\Users\\Wim Jodehl\\Desktop\\TAaS\\Project2-Code\\conv"
os.chdir(path)
collection = parse_binary_llh(path + "\\KUAL.tseries.neu")
series = sorted(collection, key=lambda x: x.time)
locationsx, locationsy, locationsz, times = [], [], [], []
init_time = series[0].time
init_year = init_time // 1000
init_days = init_time - init_year * 1000
init_date = datetime.date.fromordinal(
datetime.date(init_year, 1, 1).toordinal() + init_days - 1)
for i in range(len(series)):
ts = series[i].time
year = ts // 1000
days = ts - year * 1000
date = datetime.date.fromordinal(
import geoplotlib
from graphing import parse_binary_llh
from geoplotlib.layers import DelaunayLayer
import sys, os
from math import degrees
from geoplotlib.colors import colorbrewer
from geoplotlib.utils import epoch_to_str, BoundingBox, read_csv
if __name__ == "__main__":
path = sys.argv[1]
files = os.listdir(path)
stations = []
for file in files:
stations.append(parse_binary_llh(path + '/' + file)[0])
print(stations[-1].name)
file = open('tmp.csv', 'w')
file.write('name,lat,lon\n')
for stat in stations:
name, lon, lat = (stat.name, degrees(stat.pos[0]),
degrees(stat.pos[1]))
print(name, lon, lat)
file.write(','.join([name, str(lon), str(lat)]) + '\n')
file.close()
data = read_csv('tmp.csv')
print(type(data))
geoplotlib.delaunay(data, cmap="hot_r")
geoplotlib.labels(data,
'name',
color=[0, 0, 255, 255],
def conv_data(data,n):
conv_mat = np.ones(n)/n
d = data[:,1]
d_ave = np.convolve(d,conv_mat,mode = "same")
return ""
#path to converted files
path = os.path.dirname(os.path.realpath(__file__))+'/conv'
os.chdir(path)
#get locations and scaled velocity line of all stations from files
Locations = []
for file in os.listdir():
if fnmatch.fnmatch(file, '*.neu'):
Sname,T,N = file.split('.')
Collection = parse_binary_llh(path+'/'+file)
series = sorted(Collection, key=lambda x: x.time)
#apply outlier detection and paste data into array
Lat_locations,Lon_locations,times = [],[],[]
for i in range(len(series)):
ComT = series[i].time
Year = (ComT-(ComT%1000))/1000
Day = ComT%1000
Year_len = 365*24*3600+6*3600
Day_len = 24*3600
Timestamp = int(((Year-1970)*Year_len) + (Day*Day_len))
times.append(Timestamp)
Lat_locations.append(deg(series[i].pos[0]))
Lon_locations.append(deg(series[i].pos[1]))