def create_mesh(spectrum, mplTime):
total = len(spectrum)
width = len(spectrum[min(spectrum)])
x = numpy.empty((width, total + 1)) * numpy.nan
y = numpy.empty((width, total + 1)) * numpy.nan
z = numpy.empty((width, total + 1)) * numpy.nan
j = 1
for ys in spectrum:
time = utc_to_mpl(ys) if mplTime else ys
xs, zs = split_spectrum(spectrum[ys])
for i in range(len(xs)):
x[i, j] = xs[i]
y[i, j] = time
z[i, j] = zs[i]
j += 1
x[:, 0] = x[:, 1]
if mplTime:
y[:, 0] = y[:, 1] - seconds(1)
else:
y[:, 0] = y[:, 1] - 1
z[:, 0] = z[:, 1]
return x, y, z
def create_mesh(spectrum, mplTime):
total = len(spectrum)
width = len(spectrum[min(spectrum)])
x = numpy.empty((width, total + 1)) * numpy.nan
y = numpy.empty((width, total + 1)) * numpy.nan
z = numpy.empty((width, total + 1)) * numpy.nan
j = 1
for ys in spectrum:
time = utc_to_mpl(ys) if mplTime else ys
xs, zs = split_spectrum(spectrum[ys])
for i in range(len(xs)):
x[i, j] = xs[i]
y[i, j] = time
z[i, j] = zs[i]
j += 1
x[:, 0] = x[:, 1]
if mplTime:
y[:, 0] = y[:, 1] - seconds(1)
else:
y[:, 0] = y[:, 1] - 1
z[:, 0] = z[:, 1]
return x, y, z
def plotData(card, label, data, ax):
x = []
y = []
for datum in data:
if datum["data"] != None and label in datum["data"]:
x.append((dt.seconds(datum["datetime"]) + 365.25 * 1969))
y.append(datum["data"][label])
ax.plot_date(x, y, "-o", label=card + " " + label)
def pps_import_file(filename):
fsize = os.path.getsize(filename) # size of the file in bytes
numheaders = int(fsize / (PAGESIZE + HEADERSIZE)) + 1 # number of headers in the data (+1 for first header that is always present)
rawsize = fsize - (numheaders * HEADERSIZE) # size of sample data without headers in bytes
samples = int(rawsize / SAMPLESIZE) # number of samples in the sample data
data = np.recarray((samples,), dtype=desc_pps)
f = open(filename, "rb")
i = 0 # overall sample index
j = 0 # page sample index
k = 0 # header index
headernext = True # header indicator
while i < samples:
if headernext:
bs = f.read(HEADERSIZE)
bs = unpack("%sB"%len(bs), bs)
tme = pps_convtime(bs[0], bs[1], bs[2], bs[3])
l1 = (bs[4]<<8)+(bs[5])
l2 = (bs[6]<<8)+(bs[7])
temp = (bs[8]<<24)+(bs[9]<<16)+(bs[10]<<8)+(bs[11])
press = (bs[12]<<24)+(bs[13]<<16)+(bs[14]<<8)+(bs[15])
hum = (bs[16]<<24)+(bs[17]<<16)+(bs[18]<<8)+(bs[19])
headernext = False
k += 1
elif k < numheaders:
bs = f.read(PAGESIZE)
bs = unpack("%sB"%len(bs), bs)
for x in range(int(PAGESIZE/SAMPLESIZE)):
data[i] = np.array((tme + seconds(x * (1 / SAMPLERATE)), (bs[0+(x*SAMPLESIZE)]<<8)+(bs[1+(x*SAMPLESIZE)]), (bs[2+(x*SAMPLESIZE)]<<8)+(bs[3+(x*SAMPLESIZE)]), (bs[4+(x*SAMPLESIZE)]<<8)+(bs[5+(x*SAMPLESIZE)]), (bs[6+(x*SAMPLESIZE)]<<8)+(bs[7+(x*SAMPLESIZE)]), (bs[8+(x*SAMPLESIZE)]<<8)+(bs[9+(x*SAMPLESIZE)]), (bs[10+(x*SAMPLESIZE)]<<8)+(bs[11+(x*SAMPLESIZE)]), l1, l2, temp, press, hum), dtype=desc_pps)
i += 1
headernext = True
else:
bs = f.read(SAMPLESIZE)
bs = unpack("%sB"%len(bs), bs)
data[i] = np.array((tme + seconds(j * (1 / SAMPLERATE)), (bs[0]<<8)+(bs[1]), (bs[2]<<8)+(bs[3]), (bs[4]<<8)+(bs[5]), (bs[6]<<8)+(bs[7]), (bs[8]<<8)+(bs[9]), (bs[10]<<8)+(bs[11]), l1, l2, temp, press, hum), dtype=desc_pps)
i += 1
j += 1
progress(i, samples)
f.close()
if i > 0:
return data
else:
return []
def bridge_historical_and_present_ticks(stocks_data_arr, ticks_data,
end_time_obj):
min_length = 0
for stock_index, stock_data in enumerate(stocks_data_arr):
chart_ds = stock_data.get_historical_short_term_chart_data_set()
ticks_series = ticks_data[stock_index]
most_recent_price = chart_ds.get_most_recent_price()
most_recent_date = chart_ds.get_most_recent_dt()
most_recent_dt = dates.num2date(most_recent_date).replace(tzinfo=None)
seconds_left = (end_time_obj - most_recent_dt).seconds
stock_data.set_is_storing_short_term()
for i in range(1, seconds_left + 1):
tick_date = most_recent_date + dates.seconds(i)
next_tick_date = most_recent_date + dates.seconds(i + 1)
# Find price of tick series with same second
if ticks_series is not None:
recent_ticks_data = ticks_series[ticks_series[:,
0] >= tick_date]
if len(recent_ticks_data) != 0:
recent_ticks_data = recent_ticks_data[
recent_ticks_data[:, 0] < next_tick_date]
if len(recent_ticks_data) != 0:
most_recent_price = recent_ticks_data[0][1]
# Store price as stick
new_tick = np.array([tick_date, most_recent_price])
chart_ds.add_tick_with_datetime_tick(new_tick)
stock_data.set_finished_storing()
ticks_data[stock_index] = None
stock_data.set_bootstrap_is_completed()
length = len(chart_ds.get_ticks())
if min_length == 0 or length < min_length:
min_length = length
truncate_short_term_ticks_to_length(stocks_data_arr, min_length)
def bridge_historical_and_present_ticks(stocks_data_arr, ticks_data, end_time_obj):
min_length = 0
for stock_index, stock_data in enumerate(stocks_data_arr):
chart_ds = stock_data.get_historical_short_term_chart_data_set()
ticks_series = ticks_data[stock_index]
most_recent_price = chart_ds.get_most_recent_price()
most_recent_date = chart_ds.get_most_recent_dt()
most_recent_dt = dates.num2date(most_recent_date).replace(tzinfo=None)
seconds_left = (end_time_obj - most_recent_dt).seconds
stock_data.set_is_storing_short_term()
for i in range(1, seconds_left+1):
tick_date = most_recent_date + dates.seconds(i)
next_tick_date = most_recent_date + dates.seconds(i+1)
# Find price of tick series with same second
if ticks_series is not None:
recent_ticks_data = ticks_series[ticks_series[:,0] >= tick_date]
if len(recent_ticks_data) != 0:
recent_ticks_data = recent_ticks_data[recent_ticks_data[:,0] < next_tick_date]
if len(recent_ticks_data) != 0:
most_recent_price = recent_ticks_data[0][1]
# Store price as stick
new_tick = np.array([tick_date, most_recent_price])
chart_ds.add_tick_with_datetime_tick(new_tick)
stock_data.set_finished_storing()
ticks_data[stock_index] = None
stock_data.set_bootstrap_is_completed()
length = len(chart_ds.get_ticks())
if min_length==0 or length < min_length:
min_length = length
truncate_short_term_ticks_to_length(stocks_data_arr, min_length)
def test_seconds(self):
self.assertEqual(dates.seconds(self.seconds), float(self.expectedDays))
idx_init = idx_init[:-1]
idx_stop = idx_init + t_opt + n_extr
dd0 = ['01 march 2011, 05:46:00', '06 march 2011']
label1 = 'Fit residuals'
label2 = 'Reduced gravity signals A'
fig, ax1 = plt.subplots(1, figsize=(15,8))
ax1.grid()
ax2 = ax1.twiny()
ax1.plot(24*3600*(t[idx_thk - t_opt + 1: idx_thk + 1 + n_extr] - tEQ), res, 'y')
ax2.plot(t[idx_thk - t_opt + 1: idx_thk + 1 + n_extr], res, 'y')
for iw, wd0 in enumerate(dd0):
tt = np.array([dates.seconds(item) for item in idx_stop])
tt += dates.datestr2num(wd0)
ax2.plot(tt, opt_full[:,iw], lw=2, c='b')
ax2.axvline(x=tEQ, c='k', ymin=0.1, ymax=0.9, lw=2)
ax2.axhline(y=a_thk, c='k', lw=1, ls='--')
ax2.axhline(y=-a_thk, c='k', lw=1, ls='--')
locator = dates.AutoDateLocator()
formatter = dates.AutoDateFormatter(locator)
formatter.scaled = {1. : '%d/%m', 1./24. : '%H:%M:%S', 1./(24.*60.) : '%H:%M:%S'}
ax2.xaxis.set_major_locator(locator)
ax2.xaxis.set_major_formatter(formatter)
ax1.set_xlabel('Time (in seconds from main shock)', fontsize=17, labelpad=12)
