Coverage for /usr/local/lib/python3.7/dist-packages/pyrocko/io/datacube.py : 61%

# http://pyrocko.org - GPLv3 

# 

# The Pyrocko Developers, 21st Century 

# ---|P------/S----------~Lg---------- 

# python 2/3 

from __future__ import absolute_import 

import os 

import math 

import logging 

import numpy as num 

from builtins import str as newstr 

from pyrocko import trace, util, plot 

from pyrocko.guts import Object, Int, String, Timestamp 

from . import io_common 

logger = logging.getLogger('pyrocko.io.datacube') 

N_GPS_TAGS_WANTED = 200 # must match definition in datacube_ext.c 

def color(c): 

c = plot.color(c) 

return tuple(x/255. for x in c) 

class DataCubeError(io_common.FileLoadError): 

pass 

class ControlPointError(Exception): 

pass 

def make_control_point(ipos_block, t_block, tref, deltat): 

# reduce time (no drift would mean straight line) 

tred = (t_block - tref) - ipos_block*deltat 

# first round, remove outliers 

q25, q75 = num.percentile(tred, (25., 75.)) 

iok = num.logical_and(q25 <= tred, tred <= q75) 

# detrend 

slope, offset = num.polyfit(ipos_block[iok], tred[iok], 1) 

tred2 = tred - (offset + slope * ipos_block) 

# second round, remove outliers based on detrended tred, refit 

q25, q75 = num.percentile(tred2, (25., 75.)) 

iok = num.logical_and(q25 <= tred2, tred2 <= q75) 

x = ipos_block[iok].copy() 

ipos0 = x[0] 

x -= ipos0 

y = tred[iok].copy() 

(slope, offset), cov = num.polyfit(x, y, 1, cov=True) 

slope_err, offset_err = num.sqrt(num.diag(cov)) 

slope_err_limit = 1.0e-10 

offset_err_limit = 5.0e-3 

if slope_err > slope_err_limit: 

raise ControlPointError('slope error too large') 

if offset_err > offset_err_limit: 

raise ControlPointError('offset error too large') 

ic = ipos_block[ipos_block.size//2] 

tc = offset + slope * (ic - ipos0) 

return ic, tc + ic * deltat + tref 

def analyse_gps_tags(header, gps_tags, offset, nsamples): 

ipos, t, fix, nsvs = gps_tags 

deltat = 1.0 / int(header['S_RATE']) 

tquartz = offset + ipos * deltat 

toff = t - tquartz 

toff_median = num.median(toff) 

n = t.size 

dtdt = (t[1:n] - t[0:n-1]) / (tquartz[1:n] - tquartz[0:n-1]) 

ok = abs(toff_median - toff) < 10. 

xok = num.abs(dtdt - 1.0) < 0.00001 

ok[0] = False 

ok[1:n] &= xok 

ok[0:n-1] &= xok 

ok[n-1] = False 

ipos = ipos[ok] 

t = t[ok] 

fix = fix[ok] 

nsvs = nsvs[ok] 

blocksize = N_GPS_TAGS_WANTED // 2 

try: 

if ipos.size < blocksize: 

raise ControlPointError( 

'could not safely determine time corrections from gps') 

j = 0 

control_points = [] 

tref = num.median(t - ipos*deltat) 

while j < ipos.size - blocksize: 

ipos_block = ipos[j:j+blocksize] 

t_block = t[j:j+blocksize] 

try: 

ic, tc = make_control_point(ipos_block, t_block, tref, deltat) 

control_points.append((ic, tc)) 

except ControlPointError: 

pass 

j += blocksize 

ipos_last = ipos[-blocksize:] 

t_last = t[-blocksize:] 

try: 

ic, tc = make_control_point(ipos_last, t_last, tref, deltat) 

control_points.append((ic, tc)) 

except ControlPointError: 

pass 

if len(control_points) < 2: 

raise ControlPointError( 

'could not safely determine time corrections from gps') 

i0, t0 = control_points[0] 

i1, t1 = control_points[1] 

i2, t2 = control_points[-2] 

i3, t3 = control_points[-1] 

if len(control_points) == 2: 

tmin = t0 - i0 * deltat - offset * deltat 

tmax = t3 + (nsamples - i3 - 1) * deltat 

else: 

icontrol = num.array( 

[x[0] for x in control_points], dtype=num.int64) 

tcontrol = num.array( 

[x[1] for x in control_points], dtype=num.float) 

# robust against steps: 

slope = num.median( 

(tcontrol[1:] - tcontrol[:-1]) 

/ (icontrol[1:] - icontrol[:-1])) 

tmin = t0 + (offset - i0) * slope 

tmax = t2 + (offset + nsamples - 1 - i2) * slope 

if offset < i0: 

control_points[0:0] = [(offset, tmin)] 

if offset + nsamples - 1 > i3: 

control_points.append((offset + nsamples - 1, tmax)) 

icontrol = num.array([x[0] for x in control_points], dtype=num.int64) 

tcontrol = num.array([x[1] for x in control_points], dtype=num.float) 

return tmin, tmax, icontrol, tcontrol, ok 

except ControlPointError: 

tmin = util.str_to_time(header['S_DATE'] + header['S_TIME'], 

format='%y/%m/%d%H:%M:%S') 

idat = int(header['DAT_NO']) 

if idat == 0: 

tmin = tmin + util.gps_utc_offset(tmin) 

else: 

tmin = util.day_start(tmin + idat * 24.*3600.) \ 

+ util.gps_utc_offset(tmin) 

tmax = tmin + (nsamples - 1) * deltat 

icontrol, tcontrol = None, None 

return tmin, tmax, icontrol, tcontrol, None 

def plot_timeline(fns): 

from matplotlib import pyplot as plt 

fig = plt.figure() 

axes = fig.gca() 

h = 3600. 

if isinstance(fns, (str, newstr)): 

fn = fns 

if os.path.isdir(fn): 

fns = [ 

os.path.join(fn, entry) for entry in sorted(os.listdir(fn))] 

ipos, t, fix, nsvs, header, offset, nsamples = \ 

get_timing_context(fns) 

else: 

ipos, t, fix, nsvs, header, offset, nsamples = \ 

get_extended_timing_context(fn) 

else: 

ipos, t, fix, nsvs, header, offset, nsamples = \ 

get_timing_context(fns) 

deltat = 1.0 / int(header['S_RATE']) 

tref = num.median(t - ipos * deltat) 

tref = round(tref / deltat) * deltat 

x = ipos*deltat 

y = (t - tref) - ipos*deltat 

bfix = fix != 0 

bnofix = fix == 0 

tmin, tmax, icontrol, tcontrol, ok = analyse_gps_tags( 

header, (ipos, t, fix, nsvs), offset, nsamples) 

la = num.logical_and 

nok = num.logical_not(ok) 

axes.plot( 

x[la(bfix, ok)]/h, y[la(bfix, ok)], '+', 

ms=5, color=color('chameleon3')) 

axes.plot( 

x[la(bfix, nok)]/h, y[la(bfix, nok)], '+', 

ms=5, color=color('aluminium4')) 

axes.plot( 

x[la(bnofix, ok)]/h, y[la(bnofix, ok)], 'x', 

ms=5, color=color('chocolate3')) 

axes.plot( 

x[la(bnofix, nok)]/h, y[la(bnofix, nok)], 'x', 

ms=5, color=color('aluminium4')) 

tred = tcontrol - icontrol*deltat - tref 

axes.plot(icontrol*deltat/h, tred, color=color('aluminium6')) 

axes.plot(icontrol*deltat/h, tred, 'o', ms=5, color=color('aluminium6')) 

ymin = (math.floor(tred.min() / deltat)-1) * deltat 

ymax = (math.ceil(tred.max() / deltat)+1) * deltat 

# axes.set_ylim(ymin, ymax) 

if ymax - ymin < 1000 * deltat: 

ygrid = math.floor(tred.min() / deltat) * deltat 

while ygrid < ymax: 

axes.axhline(ygrid, color=color('aluminium4'), alpha=0.3) 

ygrid += deltat 

xmin = icontrol[0]*deltat/h 

xmax = icontrol[-1]*deltat/h 

xsize = xmax - xmin 

xmin -= xsize * 0.1 

xmax += xsize * 0.1 

axes.set_xlim(xmin, xmax) 

axes.set_ylim(ymin, ymax) 

axes.set_xlabel('Uncorrected (quartz) time [h]') 

axes.set_ylabel('Relative time correction [s]') 

plt.show() 

g_dir_contexts = {} 

class DirContextEntry(Object): 

path = String.T() 

tstart = Timestamp.T() 

ifile = Int.T() 

class DirContext(Object): 

path = String.T() 

mtime = Timestamp.T() 

entries = DirContextEntry.T() 

def get_entry(self, fn): 

path = os.path.abspath(fn) 

for entry in self.entries: 

if entry.path == path: 

return entry 

raise Exception('entry not found') 

def iter_entries(self, fn, step=1): 

current = self.get_entry(fn) 

by_ifile = dict( 

(entry.ifile, entry) for entry in self.entries 

if entry.tstart == current.tstart) 

icurrent = current.ifile 

while True: 

icurrent += step 

try: 

yield by_ifile[icurrent] 

except KeyError: 

break 

def context(fn): 

from pyrocko import datacube_ext 

dpath = os.path.dirname(os.path.abspath(fn)) 

mtimes = [os.stat(dpath)[8]] 

dentries = sorted([os.path.join(dpath, f) for f in os.listdir(dpath) 

if os.path.isfile(os.path.join(dpath, f))]) 

for dentry in dentries: 

fn2 = os.path.join(dpath, dentry) 

mtimes.append(os.stat(fn2)[8]) 

mtime = float(max(mtimes)) 

if dpath in g_dir_contexts: 

dir_context = g_dir_contexts[dpath] 

if dir_context.mtime == mtime: 

return dir_context 

del g_dir_contexts[dpath] 

entries = [] 

for dentry in dentries: 

fn2 = os.path.join(dpath, dentry) 

if not os.path.isfile(fn2): 

continue 

with open(fn2, 'rb') as f: 

first512 = f.read(512) 

if not detect(first512): 

continue 

with open(fn2, 'rb') as f: 

try: 

header, data_arrays, gps_tags, nsamples, _ = \ 

datacube_ext.load(f.fileno(), 3, 0, -1, None) 

except datacube_ext.DataCubeError as e: 

e = DataCubeError(str(e)) 

e.set_context('filename', fn) 

raise e 

header = dict(header) 

entries.append(DirContextEntry( 

path=os.path.abspath(fn2), 

tstart=util.str_to_time( 

'20' + header['S_DATE'] + ' ' + header['S_TIME'], 

format='%Y/%m/%d %H:%M:%S'), 

ifile=int(header['DAT_NO']))) 

dir_context = DirContext(mtime=mtime, path=dpath, entries=entries) 

return dir_context 

def get_time_infos(fn): 

from pyrocko import datacube_ext 

with open(fn, 'rb') as f: 

try: 

header, _, gps_tags, nsamples, _ = datacube_ext.load( 

f.fileno(), 1, 0, -1, None) 

except datacube_ext.DataCubeError as e: 

e = DataCubeError(str(e)) 

e.set_context('filename', fn) 

raise e 

return dict(header), gps_tags, nsamples 

def get_timing_context(fns): 

joined = [[], [], [], []] 

ioff = 0 

for fn in fns: 

header, gps_tags, nsamples = get_time_infos(fn) 

ipos = gps_tags[0] 

ipos += ioff 

for i in range(4): 

joined[i].append(gps_tags[i]) 

ioff += nsamples 

ipos, t, fix, nsvs = [num.concatenate(x) for x in joined] 

nsamples = ioff 

return ipos, t, fix, nsvs, header, 0, nsamples 

def get_extended_timing_context(fn): 

c = context(fn) 

header, gps_tags, nsamples_base = get_time_infos(fn) 

ioff = 0 

aggregated = [gps_tags] 

nsamples_total = nsamples_base 

if num.sum(gps_tags[2]) == 0: 

ioff = nsamples_base 

for entry in c.iter_entries(fn, 1): 

_, gps_tags, nsamples = get_time_infos(entry.path) 

ipos = gps_tags[0] 

ipos += ioff 

aggregated.append(gps_tags) 

nsamples_total += nsamples 

if num.sum(gps_tags[2]) > 0: 

break 

ioff += nsamples 

ioff = 0 

for entry in c.iter_entries(fn, -1): 

_, gps_tags, nsamples = get_time_infos(entry.path) 

ioff -= nsamples 

ipos = gps_tags[0] 

ipos += ioff 

aggregated[0:0] = [gps_tags] 

nsamples_total += nsamples 

if num.sum(gps_tags[2]) > 0: 

break 

ipos, t, fix, nsvs = [num.concatenate(x) for x in zip(*aggregated)] 

# return ipos, t, fix, nsvs, header, ioff, nsamples_total 

return ipos, t, fix, nsvs, header, 0, nsamples_base 

def iload(fn, load_data=True, interpolation='sinc'): 

from pyrocko import datacube_ext 

from pyrocko import signal_ext 

if interpolation not in ('sinc', 'off'): 

raise NotImplementedError( 

'no such interpolation method: %s' % interpolation) 

with open(fn, 'rb') as f: 

if load_data: 

loadflag = 2 

else: 

loadflag = 1 

try: 

header, data_arrays, gps_tags, nsamples, _ = datacube_ext.load( 

f.fileno(), loadflag, 0, -1, None) 

except datacube_ext.DataCubeError as e: 

e = DataCubeError(str(e)) 

e.set_context('filename', fn) 

raise e 

header = dict(header) 

deltat = 1.0 / int(header['S_RATE']) 

nchannels = int(header['CH_NUM']) 

ipos, t, fix, nsvs, header_, offset_, nsamples_ = \ 

get_extended_timing_context(fn) 

tmin, tmax, icontrol, tcontrol, _ = analyse_gps_tags( 

header_, (ipos, t, fix, nsvs), offset_, nsamples_) 

if icontrol is None: 

logger.warn( 

'No usable GPS timestamps found. Using datacube header ' 

'information to guess time. (file: "%s")' % fn) 

tmin_ip = round(tmin / deltat) * deltat 

if interpolation != 'off': 

tmax_ip = round(tmax / deltat) * deltat 

else: 

tmax_ip = tmin_ip + (nsamples-1) * deltat 

nsamples_ip = int(round((tmax_ip - tmin_ip)/deltat)) + 1 

# to prevent problems with rounding errors: 

tmax_ip = tmin_ip + (nsamples_ip-1) * deltat 

leaps = num.array( 

[x[0] + util.gps_utc_offset(x[0]) for x in util.read_leap_seconds2()], 

dtype=num.float) 

if load_data and icontrol is not None: 

ncontrol_this = num.sum( 

num.logical_and(0 <= icontrol, icontrol < nsamples)) 

if ncontrol_this <= 1: 

logger.warn( 

'Extrapolating GPS time information from directory context ' 

'(insufficient number of GPS timestamps in file: "%s").' % fn) 

for i in range(nchannels): 

if load_data: 

arr = data_arrays[i] 

assert arr.size == nsamples 

if interpolation == 'sinc' and icontrol is not None: 

ydata = num.empty(nsamples_ip, dtype=num.float) 

try: 

signal_ext.antidrift( 

icontrol, tcontrol, 

arr.astype(num.float), tmin_ip, deltat, ydata) 

except signal_ext.Error as e: 

e = DataCubeError(str(e)) 

e.set_context('filename', fn) 

e.set_context('n_control_points', icontrol.size) 

e.set_context('n_samples_raw', arr.size) 

e.set_context('n_samples_ip', ydata.size) 

e.set_context('tmin_ip', util.time_to_str(tmin_ip)) 

raise e 

ydata = num.round(ydata).astype(arr.dtype) 

else: 

ydata = arr 

tr_tmin = tmin_ip 

tr_tmax = None 

else: 

ydata = None 

tr_tmin = tmin_ip 

tr_tmax = tmax_ip 

tr = trace.Trace('', header['DEV_NO'], '', 'p%i' % i, deltat=deltat, 

ydata=ydata, tmin=tr_tmin, tmax=tr_tmax, meta=header) 

bleaps = num.logical_and(tmin_ip <= leaps, leaps < tmax_ip) 

if num.any(bleaps): 

assert num.sum(bleaps) == 1 

tcut = leaps[bleaps][0] 

for tmin_cut, tmax_cut in [ 

(tr.tmin, tcut), (tcut, tr.tmax+tr.deltat)]: 

try: 

tr_cut = tr.chop(tmin_cut, tmax_cut, inplace=False) 

tr_cut.shift( 

util.utc_gps_offset(0.5*(tr_cut.tmin+tr_cut.tmax))) 

yield tr_cut 

except trace.NoData: 

pass 

else: 

tr.shift(util.utc_gps_offset(0.5*(tr.tmin+tr.tmax))) 

yield tr 

header_keys = { 

str: b'GIPP_V DEV_NO E_NAME GPS_PO S_TIME S_DATE DAT_NO'.split(), 

int: b'''P_AMPL CH_NUM S_RATE D_FILT C_MODE A_CHOP F_TIME GPS_TI GPS_OF 

A_FILT A_PHAS GPS_ON ACQ_ON V_TCXO D_VOLT E_VOLT'''.split()} 

all_header_keys = header_keys[str] + header_keys[int] 

def detect(first512): 

s = first512 

if len(s) < 512: 

return False 

if ord(s[0:1]) >> 4 != 15: 

return False 

n = s.find(b'\x80') 

if n == -1: 

n = len(s) 

s = s[1:n] 

s = s.replace(b'\xf0', b'') 

s = s.replace(b';', b' ') 

s = s.replace(b'=', b' ') 

kvs = s.split(b' ') 

if len([k for k in all_header_keys if k in kvs]) == 0: 

return False 

return True 

if __name__ == '__main__': 

import sys 

fns = sys.argv[1:] 

if len(fns) > 1: 

plot_timeline(fns) 

else: 

plot_timeline(fns[0])