def updateSimpleDepthTime(self):
'''
Read the time series of depth values for this activity, simplify it and insert the values in the
SimpleDepthTime table that is related to the Activity.
'''
measurements = m.Measurement.objects.using(self.dbAlias).filter( instantpoint__activity=self.activity)
for meas in measurements:
ems = 1000 * to_udunits(meas.instantpoint.timevalue, 'seconds since 1970-01-01')
d = float(meas.depth)
m.SimpleDepthTime.objects.using(self.dbAlias).create(activity = self.activity, instantpoint = meas.instantpoint, depth = d, epochmilliseconds = ems)
logger.info('Inserted %d values into SimpleDepthTime', len(measurements))
def updateSimpleDepthTime(self):
'''
Read the time series of depth values for this activity, simplify it and insert the values in the
SimpleDepthTime table that is related to the Activity.
'''
measurements = m.Measurement.objects.using(self.dbAlias).filter( instantpoint__activity=self.activity)
for meas in measurements:
ems = 1000 * to_udunits(meas.instantpoint.timevalue, 'seconds since 1970-01-01')
d = float(meas.depth)
m.SimpleDepthTime.objects.using(self.dbAlias).create(activity = self.activity, instantpoint = meas.instantpoint,
depth = d, epochmilliseconds = ems)
logger.info('Inserted %d values into SimpleDepthTime', len(measurements))
def select_profiles(indexurl,
begin=None,
end=None,
urcrnrlat=None,
urcrnrlon=None,
llcrnrlat=None,
llcrnrlon=None):
index = open_url(indexurl)
begin = to_udunits(begin, index.sequence.time.units)
end = to_udunits(end, index.sequence.time.units)
conds = []
if begin:
c = (index.sequence.time > begin) & (index.sequence.time < end)
conds.append(c)
if urcrnrlat:
c = ((index.sequence.latitude > llcrnrlat) &
(index.sequence.latitude < urcrnrlat) &
(index.sequence.longitude > llcrnrlon) &
(index.sequence.longitude < urcrnrlon))
conds.append(c)
prof_ids = index.sequence[reduce(operator.__and__, conds)]
return prof_ids
# read ctls
files = glob.glob("*.ctl")
n = len(files)
bounds = []
data = []
for i, filename in enumerate(files):
f = ctler.CTLReader(filename)
values = f.variables[variable["name in BMGCS"].upper()][:]
data.append(values * adjust_units(variable["units in BMGCS"], variable["desired units"]))
bounds.append(f.variables["time"][0])
data = np.concatenate(data, axis=0)
dt = bounds[1] - bounds[0]
# bounds.insert(0, bounds[0] - dt )
bounds.append(bounds[-1] + dt)
bounds = np.array([to_udunits(v, TIME) for v in bounds])
time = (bounds[:-1] + bounds[1:]) / 2.0
levels = f.variables["levels"][:] * 100.0
cell_methods = variable["cell_methods"] or ""
if re.search("3hr", variable["table"]):
if not re.match("time:\s*mean", cell_methods):
bounds = None
elif re.search("6hr", variable["table"]):
if re.match("time:\s*mean", cell_methods):
data = average_data(data, 2)
time = average_data(time, 2)
bounds = bounds[::2]
else:
data = data[::2] # sample every 6hr
time = time[::2]
# read ctls
files = glob.glob('*.ctl')
n = len(files)
bounds = []
data = []
for i, filename in enumerate(files):
f = ctler.CTLReader(filename)
values = f.variables[variable['name in BMGCS'].upper()][:]
data.append(values * adjust_units(variable['units in BMGCS'],
variable['desired units']))
bounds.append(f.variables['time'][0])
data = np.concatenate(data, axis=0)
dt = (bounds[1] - bounds[0])
#bounds.insert(0, bounds[0] - dt )
bounds.append(bounds[-1] + dt)
bounds = np.array([to_udunits(v, TIME) for v in bounds])
time = (bounds[:-1] + bounds[1:]) / 2.
levels = f.variables['levels'][:] * 100.
cell_methods = variable['cell_methods'] or ''
if re.search('3hr', variable['table']):
if not re.match('time:\s*mean', cell_methods):
bounds = None
elif re.search('6hr', variable['table']):
if re.match('time:\s*mean', cell_methods):
data = average_data(data, 2)
time = average_data(time, 2)
bounds = bounds[::2]
else:
data = data[::2] # sample every 6hr
time = time[::2]
air_2m_mon_mean_url = monthly_monolevel + "air.2m.mon.mean.nc"
precip_rate_url = dods + "gaussian/monolevel/prate.2008.nc"
precip_month_url = dods + "Monthlies/gaussian/monolevel/prate.mon.mean.nc"
#dataset = open_url(air_2m_mon_mean_url)
dataset = open_url(precip_month_url)
varname = "prate"
missing = 32766
secs_per_month = 2592000
print dataset.keys()
alllats = dataset.lat[:]
alllons = dataset.lon[:]
first = to_udunits(firstday, dataset.time.units)
last = to_udunits(lastday, dataset.time.units)
interval = ((first <= dataset.time) & (dataset.time <= last))
def date_str(time):
date = from_udunits(time, dataset.time.units.replace('GMT', '+0:00'))
return '%d-%02d-%02d' % (date.year, date.month, date.day)
def code(n):
if n < 10:
return chr(ord('0')+n)
else:
return chr(ord('A')+n-10)
pass
def main():
form=cgi.FieldStorage()
tqx={}
tqx['reqId']=0
tqx['out']='json'
try:
tqxa=form["tqx"].value
tqxa=tqxa.split(';')
for kv in tqxa:
(k,v)=kv.split(':')
tqx[k]=v
pass
except:
pass
month_start = 0
try:
lat = float(form["lat"].value)
lng = float(form["lng"].value)
except:
warn("lat and lng must be floats.")
return
try:
year_start = int(form["yr0"].value)
except:
year_start = default_year_start
pass
try:
year_end = int(form["yr1"].value)
except:
year_end = date.today().year
pass
try:
month_start = int(form["mo"].value)
except:
pass
try:
month_start = int(form["mo0"].value)
except:
pass
try:
month_end = int(form["mo1"].value)
except:
month_end = 12
pass
try:
q = form["q"].value
config = months20cr[q]
if month_start == 0:
month_start = 1
skip = 1
else:
skip = 12
pass
except:
try:
fi = form["fi"].value
config = {}
config['url'] = "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets20thC_ReanV2/" + \
"gaussian/monolevel/" + fi + "." + str(year_start) + ".nc"
config['url2'] = "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets20thC_ReanV2/" + \
"gaussian/monolevel/" + fi + "." + str(year_end) + ".nc"
config['var'] = fi.split(".")[0]
config['en'] = fi
config['convert'] = lambda data: data[:]
skip = 1
except:
k = months20cr.keys()
warn("q must be one of " + str(k))
return
pass
varname = config['var']
firstday = datetime(year_start,month_start,1, tzinfo=UTC())
if (month_end == 12):
lastday = datetime(year_end,12,31, tzinfo=UTC())
else:
lastday = datetime(year_end,month_end+1,1, tzinfo=UTC())
pass
outdata = []
# At present this code has 1 or 2 urls, but the code here could easily be extended
# to cope with more, just add them to the list 'urls', but make sure they're in the
# correct order, oldest data first.
urls = [config['url']]
try:
urls.append(config['url2'])
except:
pass
for dataurl in urls:
dataset = open_url(dataurl)
first = to_udunits(firstday, dataset.time.units)
last = to_udunits(lastday, dataset.time.units)
(x,y) = toXY(lat,lng)
# Note one or other (or both) of the start or end times could be outside the
# range of a given dataset, that's fine and no problems will result.
a = dataset[varname][(first <= dataset.time) & (dataset.time <= last),y,x]
seq = a.array[::skip]
times = a.time[::skip]
latitude = dataset['lat'][y]
longitude = dataset['lon'][x]
missing = dataset[varname].missing_value
data = numpy.select([seq == missing],[None], default = seq)
data = (data * dataset[varname].scale_factor + dataset[varname].add_offset)
# Get the values and loop over them inserting into the outdata list
# csv and json require slightly different treatment.
values = config['convert'](data).astype('float').tolist()
i = 0
if tqx['out'] == 'csv':
for t in times:
# See comment below.
outdata.append({"date":str(dDate(t,dataset)),"value":values[i]})
i += 1
pass
pass
else:
for t in times:
outdata.append({"date":dDate(t,dataset),"value":values[i]})
i += 1
pass
pass
pass
# Loading it into gviz_api.DataTable
# Default output is 'json'. Ideally this is specified as tqx=out:json
# but even when it isn't, that's what is produced.
# For CSV the date and time is presented as a string, otherwise it would be a
# javascript "new Date()" object.
if tqx['out'] == 'csv':
description = {"date": ("string", "Date"),
"value": ("number", config['en'])}
else:
description = {"date": ("datetime", "Date"),
"value": ("number", config['en'])}
pass
data_table = gviz_api.DataTable(description)
data_table.LoadData(outdata)
if tqx['out'] == 'csv':
print 'Content-type: text/plain\n'
csv = data_table.ToCsv(columns_order=("date", "value"),order_by="date",separator=",")
print csv
else:
json = data_table.ToJSonResponse(columns_order=("date", "value"), order_by="date",
req_id=tqx['reqId'])
print 'Content-type: text/plain\n'
print json
pass
return
def main():
form=cgi.FieldStorage()
tqx={}
tqx['reqId']=0
tqx['out']='json'
try:
tqxa=form["tqx"].value
tqxa=tqxa.split(';')
for kv in tqxa:
(k,v)=kv.split(':')
tqx[k]=v
pass
except:
pass
month_start = 0
try:
lat = float(form["lat"].value)
lng = float(form["lng"].value)
except:
warn("lat and lng must be floats.")
return
try:
year_start = int(form["yr0"].value)
except:
year_start = default_year_start
pass
try:
year_end = int(form["yr1"].value)
except:
year_end = date.today().year
pass
try:
month_start = int(form["mo"].value)
except:
pass
try:
month_start = int(form["mo0"].value)
except:
pass
try:
month_end = int(form["mo1"].value)
except:
month_end = 12
pass
try:
q = form["q"].value
config = months20cr[q]
if month_start == 0:
month_start = 1
skip = 1
else:
skip = 12
pass
except:
try:
fi = form["fi"].value
config = {}
config['url'] = "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets20thC_ReanV2/" + \
"gaussian/monolevel/" + fi + "." + str(year_start) + ".nc"
config['url2'] = "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets20thC_ReanV2/" + \
"gaussian/monolevel/" + fi + "." + str(year_end) + ".nc"
config['var'] = fi.split(".")[0]
config['en'] = fi
config['convert'] = lambda data: data[:]
skip = 1
except:
k = months20cr.keys()
warn("q must be one of " + str(k))
return
pass
varname = config['var']
firstday = datetime(year_start,month_start,1, tzinfo=UTC())
if (month_end == 12):
lastday = datetime(year_end,12,31, tzinfo=UTC())
else:
lastday = datetime(year_end,month_end+1,1, tzinfo=UTC())
pass
outdata = []
urls = [config['url']]
try:
urls.append(config['url2'])
except:
pass
for dataurl in urls:
dataset = open_url(dataurl)
first = to_udunits(firstday, dataset.time.units)
last = to_udunits(lastday, dataset.time.units)
(x,y) = toXY(lat,lng)
a = dataset[varname][(first <= dataset.time) & (dataset.time <= last),y,x]
seq = a.array[::skip]
times = a.time[::skip]
latitude = dataset['lat'][y]
longitude = dataset['lon'][x]
missing = dataset[varname].missing_value
data = numpy.select([seq == missing],[None], default = seq)
data = (data * dataset[varname].scale_factor + dataset[varname].add_offset)
values = config['convert'](data).astype('float').tolist()
i = 0
if tqx['out'] == 'json':
for t in times:
outdata.append({"date":str(dDate(t,dataset)),"value":values[i]})
i += 1
pass
pass
else:
for t in times:
outdata.append({"date":str(dDate(t,dataset)),"value":values[i]})
i += 1
pass
pass
pass
# Loading it into gviz_api.DataTable
if tqx['out'] == 'json':
description = {"date": ("string", "Date"),
"value": ("number", config['en'])}
else:
description = {"date": ("string", "Date"),
"value": ("number", config['en'])}
pass
data_table = gviz_api.DataTable(description)
data_table.LoadData(outdata)
if tqx['out'] == 'json':
# Creating a JSon string
json = data_table.ToJSonResponse(columns_order=("date", "value"), order_by="date",
req_id=tqx['reqId'])
print 'Content-type: text/plain\n'
print json
elif tqx['out'] == 'csv':
print 'Content-type: text/plain\n'
csv = data_table.ToCsv(columns_order=("date", "value"), order_by="date",
separator=", ")
print csv
pass
return
def main():
form=cgi.FieldStorage()
try:
x = int(form["x"].value)
y = int(form["y"].value)
except:
returnJson({"msg":"x and y must be integers"})
return
try:
q = form["q"].value
config = months20cr[q]
except:
k = months20cr.keys()
returnJson({"msg":"q must be one of " + str(k)})
return
try:
year_start = int(form["yr0"].value)
except:
year_start = default_year_start
pass
try:
year_end = int(form["yr1"].value)
except:
year_end = date.today().year
pass
try:
month = int(form["mo"].value)
except:
month = 0 # i.e. default is all months
pass
try:
callback = form["callback"].value
except:
callback = None
pass
dataset = open_url(config['url'])
varname = config['var']
if month != 0:
month_start = month
skip = 12
else:
month_start = 1
skip = 1
pass
firstday = datetime(year_start,month_start,1, tzinfo=UTC())
lastday = datetime(year_end,12,31, tzinfo=UTC())
first = to_udunits(firstday, dataset.time.units)
last = to_udunits(lastday, dataset.time.units)
interval = ((first <= dataset.time) & (dataset.time <= last))
rainrecs = []
a = dataset[varname][interval,y,x]
seq = a.array[::skip]
times = a.time[::skip]
#latitude = a.lat
#longitude = a.lon
latitude = dataset['lat'][y]
longitude = dataset['lon'][x]
missing = dataset[varname].missing_value
data = numpy.select([seq == missing],[None], default = seq)
data = (data * dataset[varname].scale_factor + dataset[varname].add_offset)
values = config['convert'](data).astype('float').tolist()
plot = []
i = 0
for t in times:
plot.append([udDate(t,dataset),values[i]])
i += 1
pass
report = {}
report['attributes'] = dataset.attributes
report['data-url'] = config['url']
report['data-en'] = config['en']
report['lat'] = latitude.astype('float').tolist()
report['lon'] = longitude.astype('float').tolist()
report['data'] = plot
returnJson(report,callback)
return
