def calc_temp():
station = "PB12"
channel = "WKI"
from sito.data import IPOC
ipoc = IPOC()
stream = ipoc.getChannelFromClient("2006-01-01", "2012-01-01", station=station, channel=channel)
stream2 = stream.copy()
day = stream[0].stats.starttime
day2 = UTC(day.year, day.month, day.day)
if day2 < day:
day = day2 + 24 * 3600
stream2.trim(day, day + 24 * 3600)
stream2.merge()
data = []
while day < stream[-1].stats.endtime:
st = stream.slice(day, day + 24 * 3600)
st.merge()
if len(st) == 0 or len(st[0].data) < 8640:
print "skip %s" % day.date
day = day + 24 * 3600
continue
a = st[0].data[:8640]
if channel == "WKI":
a = a / 100.0
a = np.ma.masked_outside(a, 0, 50)
elif channel == "WDI":
a = a / 10000.0
a = np.ma.masked_outside(a, 0, 5)
else:
a = np.ma.masked_outside(a, 0, 100)
a = np.ma.masked_invalid(a)
data.append(a)
day = day + 24 * 3600
# from IPython import embed
# embed()
stream2[0].data = np.ma.mean(data, axis=0)
stream2.write(path + "/climate/%s_%s" % (station, channel), "Q")
stream2.plot(method="full")
plt.show()
def calc_strong_motion_Toco():
t = UTC('2007-11-14')
data = IPOC()
channel = 'BHZ'
acs = {}
vels = {}
for station in 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PATCX HMBCX PSGCX MNMCX'.split(
):
for channel in ('BLZ', 'BHZ'):
try:
ms = data.getChannelFromClient(
t - 60,
t + 24 * 3600 + 60,
network='GE' if station == 'LVC' else 'CX',
location='10' if station == 'LVC' else '',
station=station,
channel=channel)
except Exception as ex:
print station, channel, 'sucks'
continue
for tr in ms:
if tr.stats.endtime - tr.stats.starttime < 1000:
ms.remove(tr)
continue
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
tr.stats.filter = ''
tr.detrend()
tr.filter('highpass', freq=0.2)
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
ms.merge(fill_value=0)
if len(ms) == 0:
continue
maxi = float(np.max(np.abs(ms[0].data)))
if 'BH' in channel:
vels[station] = maxi / 629145000.0 * 100
elif 'BL' in channel:
acs[station] = maxi / 427566.942
with open(GM_TOCO, 'w') as f:
yaml.dump({'vels': vels, 'acs': acs}, f, default_flow_style=False)
def calc_strong_motion_Toco():
t = UTC('2007-11-14')
data = IPOC()
channel = 'BHZ'
acs = {}
vels = {}
for station in 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PATCX HMBCX PSGCX MNMCX'.split():
for channel in ('BLZ', 'BHZ'):
try:
ms = data.getChannelFromClient(t - 60, t + 24 * 3600 + 60,
network='GE' if station == 'LVC' else 'CX',
location='10' if station == 'LVC' else '',
station=station, channel=channel)
except Exception as ex:
print station, channel, 'sucks'
continue
for tr in ms:
if tr.stats.endtime - tr.stats.starttime < 1000:
ms.remove(tr)
continue
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
tr.stats.filter = ''
tr.detrend()
tr.filter('highpass', freq=0.2)
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
ms.merge(fill_value=0)
if len(ms) == 0:
continue
maxi = float(np.max(np.abs(ms[0].data)))
if 'BH' in channel:
vels[station] = maxi / 629145000.0 * 100
elif 'BL' in channel:
acs[station] = maxi / 427566.942
with open(GM_TOCO, 'w') as f:
yaml.dump({'vels': vels, 'acs':acs}, f, default_flow_style=False)
from sito.util.main import streamdaygen
import pylab as plt
stations = 'PB01' # PB04 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB16'
channels = 'WKI WDI WII'
#channels = 'WDI_10'
datafile = '/home/richter/Data/climate/2006-2012_%s_%s.npz'
output = '/home/richter/Results/IPOC/climate/%s.pdf'
calculate = False
show = False
if calculate:
ipoc = IPOC()
for station in stations.split():
for channel in channels.split():
stream = ipoc.getChannelFromClient('2006-01-01', '2013-01-01',
station=station, channel=channel)
data = []
dates = []
for day in streamdaygen(stream):
day.merge()
data.append(np.mean(day[0].data))
st = day[0].stats.starttime
et = day[0].stats.endtime
dates.append(st + (et - st) / 2.)
np.savez(datafile % (station, channel), dates=dates, data=data)
else:
#http://stackoverflow.com/questions/7733693/matplotlib-overlay-plots-with-different-scales
fig, ax = plt.subplots()
axes = [ax, ax.twinx(), ax.twinx()]
fig.subplots_adjust(right=0.75)
axes[-1].spines['right'].set_position(('axes', 1.2))
import pylab as plt
stations = 'PB01' # PB04 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB16'
channels = 'WKI WDI WII'
#channels = 'WDI_10'
datafile = '/home/richter/Data/climate/2006-2012_%s_%s.npz'
output = '/home/richter/Results/IPOC/climate/%s.pdf'
calculate = False
show = False
if calculate:
ipoc = IPOC()
for station in stations.split():
for channel in channels.split():
stream = ipoc.getChannelFromClient('2006-01-01',
'2013-01-01',
station=station,
channel=channel)
data = []
dates = []
for day in streamdaygen(stream):
day.merge()
data.append(np.mean(day[0].data))
st = day[0].stats.starttime
et = day[0].stats.endtime
dates.append(st + (et - st) / 2.)
np.savez(datafile % (station, channel), dates=dates, data=data)
else:
#http://stackoverflow.com/questions/7733693/matplotlib-overlay-plots-with-different-scales
fig, ax = plt.subplots()
axes = [ax, ax.twinx(), ax.twinx()]
fig.subplots_adjust(right=0.75)
