def get_start_time(self):
"""Read first datatime from the database table.
Raises:
nc_exc.NoDataException
"""
vname = "datetime"
try:
with self.conn as conn:
with conn.cursor() as cur:
# datetimes in database are returned to python as timezone naive.
cur.execute(
"SELECT {} FROM {} FETCH FIRST 1 ROW ONLY;".format(
vname, self.table))
start_time = cur.fetchone()
if not start_time:
_logger.warning("%s: read %s: no data", conn, vname)
raise nc_exc.NoDataException("read {}".format(vname))
return pytz.utc.localize(start_time[0])
except psycopg2.Error as exc:
_logger.warning("%s: read %s: %s", conn, vname, exc)
RAFDatabase.close_connection(conn)
raise nc_exc.NoDataException("read {}: {}".format(vname, exc))
def read_times(self,
start_time=pytz.utc.localize(datetime.min),
end_time=pytz.utc.localize(datetime.max)):
"""Read datetimes from the table within a range.
Raises:
nc_exc.NoDataException
"""
start_time = start_time.replace(tzinfo=None)
end_time = end_time.replace(tzinfo=None)
# _logger.debug("read_times, table=%s", self.table)
vname = "datetime"
try:
with self.conn as conn:
with conn.cursor() as cur:
# datetimes in database are returned to python as timezone naive.
cur.execute(
"SELECT {} FROM {} WHERE {} >= %s AND {} < %s;".format(
vname, self.table, vname, vname),
(start_time, end_time))
return [pytz.utc.localize(x[0]).timestamp() for x in cur]
except psycopg2.Error as exc:
RAFDatabase.close_connection(conn)
raise nc_exc.NoDataException("read {}: {}".format(vname, exc))
def get_variables(self):
"""Fetch pertinent fields from the 'variable_list' table in
the RAF database, such as the list of variable names, their units, and
missing values.
Raises:
nc_exc.NoDataException
"""
try:
with self.conn as conn:
with conn.cursor() as cur:
cur.execute("\
SELECT name, units, long_name, ndims, dims, missing_value from variable_list;"
)
variables = {}
for var in cur:
dimnames = ["time"]
# make a bold assumption that a second dimension
# is a particle-probe bin number
if var[3] > 1:
dimnames.append("bin")
variables[var[0]] = {
"units": var[1],
"long_name": var[2],
"dimnames": dimnames,
"shape": var[4]
}
return variables
except psycopg2.Error as exc:
# psycopg.connections are thread safe
RAFDatabase.close_connection(conn)
raise nc_exc.NoDataException("No variables found: {}".format(exc))
def __init__(self,
database="real-time-GV",
user="******",
host="eol-rt-data.fl-ext.ucar.edu",
port=5432,
password=None,
table="raf_lrt"):
"""Construct an instance of RAF database connection.
Args:
database, user, host, port, password: Usual parameters
needed to create a PostgreSQL connection.
table: name of table in the database which contains
the time-series data to be read.
Raises:
nc_exc.NoDataException
"""
try:
self.conn = RAFDatabase.get_connection(database=database,
user=user,
host=host,
port=port,
password=password)
self.database = database
self.user = user
self.host = host
self.port = port
self.password = password
self.table = table
except psycopg2.Error as exc:
raise nc_exc.NoDataException(
"Database not available: {}".format(exc))
def post(self, request, *args, project_name, dataset_name, **kwargs):
"""Respond to a POST request where the user has sent back a form.
Using the requested parameters in the form, such as start and end times
and a list of variables, the dataset can be read, and the contents
sent back to the user.
"""
_logger.debug("DatasetView.post(project=%s, dataset=%s)", project_name,
dataset_name)
try:
client_state = get_client_from_session(request.session,
project_name, dataset_name)
except Http404 as exc:
_logger.warning("post: %s", exc)
messages.warning(request, exc)
return redirect('ncharts:dataset',
project_name=project_name,
dataset_name=dataset_name)
dset = get_dataset(client_state)
# dataset name and project name from POST should agree with
# those in the cached dataset.
if dset.name != dataset_name or dset.project.name != project_name:
_logger.error(
"post, old session, project=%s, dataset=%s, "
"url project=%s, dataset=%s", dset.project.name, dset.name,
project_name, dataset_name)
messages.warning(request, "session is for a different dataset")
return redirect('ncharts:dataset',
project_name=project_name,
dataset_name=dataset_name)
proj = nc_models.Project.objects.get(name=project_name)
projs = nc_models.Project.objects.all()
plats = nc_models.Platform.objects.all()
dsets = proj.dataset_set.all()
# vars = [ v.name for v in dset.variables.all() ]
# page-backward or page-forward in time
# TODO: implement a javascript button that manipulates the
# html field directly
if 'submit' in request.POST and request.POST['submit'][0:4] == 'page':
post = request.POST.copy()
try:
# will throw ValidationError if timezone doesn't exist
timezone = nc_models.TimeZone.objects.get(
tz=request.POST['timezone']).tz
start_time = timezone.localize(
datetime.datetime.strptime(request.POST['start_time'],
"%Y-%m-%d %H:%M"))
delt = nc_forms.get_time_length(
request.POST['time_length_0'],
request.POST['time_length_units'])
if request.POST['submit'] == 'page-backward':
start_time = start_time - delt
elif request.POST['submit'] == 'page-forward':
start_time = start_time + delt
post['start_time'] = start_time.replace(tzinfo=None)
except (KeyError, ValueError, dj_exc.ValidationError):
# Will result in invalid form below
post['start_time'] = None
post['track_real_time'] = False
form = nc_forms.DataSelectionForm(post,
dataset=dset,
request=request)
else:
form = nc_forms.DataSelectionForm(request.POST,
dataset=dset,
request=request)
# Have to set the choices for variables and soundings
# before the form is validated.
soundings = []
sounding_choices = []
dsetvars = {}
dsetstns = []
try:
dsetstns = dset.get_station_names()
form.set_station_choices(dsetstns)
dsetvars = dset.get_variables()
form.set_variable_choices(dsetvars)
form.set_yvariable_choices(dsetvars)
if dset.dset_type == "sounding":
# all soundings in the dataset
soundings = dset.get_series_tuples(
series_name_fmt=SOUNDING_NAME_FMT)
sounding_choices = dset.get_series_names(
series_name_fmt=SOUNDING_NAME_FMT)
sounding_choices = [(s, s) for s in sounding_choices]
form.fields['soundings'].choices = sounding_choices
except (nc_exc.NoDataException) as exc:
_logger.warning("%s, %s: get_variables: %s", project_name, dset,
exc)
form.no_data("No variables found in {}: {}".format(dset, exc))
if not form.is_valid():
_logger.warning('User form is not valid!: %s', repr(form.errors))
if dset.dset_type == "sounding":
sounding_choices = []
start_time = form.get_cleaned_start_time()
tdelta = form.get_cleaned_time_length()
if start_time and tdelta:
sounding_choices = dset.get_series_names(
series_name_fmt=SOUNDING_NAME_FMT,
start_time=start_time,
end_time=start_time + tdelta)
sounding_choices = [(s, s) for s in sounding_choices]
if form.clean_method_altered_data:
post = request.POST.copy()
post['start_time'] = form.cleaned_data['start_time']
post['track_real_time'] = form.cleaned_data['track_real_time']
form = nc_forms.DataSelectionForm(post, dataset=dset)
form.set_station_choices(dsetstns)
form.set_variable_choices(dsetvars)
form.set_yvariable_choices(dsetvars)
if dset.dset_type == "sounding":
form.fields['soundings'].choices = sounding_choices
return render(
request, self.template_name, {
'version': _version,
'form': form,
'dataset': dset,
'datasets': dsets,
'variables': dsetvars,
'soundings': mark_safe(json.dumps(soundings)),
'projects': projs,
'platforms': plats
})
# Save the client state from the form
sel_stns = form.cleaned_data['stations']
sel_vars = form.cleaned_data['variables']
sel_soundings = form.cleaned_data['soundings']
yvar = form.cleaned_data['yvariable']
tdelta = form.get_cleaned_time_length()
start_time = form.get_cleaned_start_time()
end_time = start_time + tdelta
client_state.stations = json.dumps(sel_stns)
client_state.variables = json.dumps(sel_vars)
client_state.start_time = start_time
client_state.timezone = form.cleaned_data['timezone']
client_state.time_length = tdelta.total_seconds()
client_state.track_real_time = form.cleaned_data['track_real_time']
client_state.soundings = json.dumps(sel_soundings)
client_state.yvariable = yvar
client_state.save()
# Re-create form if any values have been altered
if form.clean_method_altered_data:
post = request.POST.copy()
post['start_time'] = form.cleaned_data['start_time']
post['track_real_time'] = form.cleaned_data['track_real_time']
form = nc_forms.DataSelectionForm(post, dataset=dset)
form.set_station_choices(dsetstns)
form.set_variable_choices(dsetvars)
form.set_yvariable_choices(dsetvars)
if dset.dset_type == "sounding":
# set sounding choices for selected time period
# soundings between the start and end time
sounding_choices = dset.get_series_names(
series_name_fmt=SOUNDING_NAME_FMT,
start_time=client_state.start_time,
end_time=client_state.start_time + \
datetime.timedelta(seconds=client_state.time_length))
sounding_choices = [(s, s) for s in sounding_choices]
form.fields['soundings'].choices = sounding_choices
if yvar != "":
if yvar not in dsetvars.keys():
exc = nc_exc.NoDataException(
"variable {} not found in {}".format(yvar, dset))
_logger.warning(repr(exc))
form.no_data(repr(exc))
return render(
request, self.template_name, {
'version': _version,
'form': form,
'dataset': dset,
'datasets': dsets,
'variables': dsetvars,
'soundings': mark_safe(json.dumps(soundings)),
'projects': projs,
'platforms': plats
})
if yvar not in sel_vars:
sel_vars.append(yvar)
series_name_fmt = None
if dset.dset_type == "sounding":
series_name_fmt = SOUNDING_NAME_FMT
else:
sel_soundings = None
# If variables exists in the dataset, get their
# attributes there, otherwise from the actual dataset.
if dset.variables.all():
variables = {
k: {
'units': dset.variables.get(name=k).units,
'long_name': dset.variables.get(name=k).long_name
}
for k in sel_vars
}
else:
variables = {k: dsetvars[k] for k in sel_vars}
stndims = {"station": [int(stn) for stn in sel_stns]}
try:
if isinstance(dset, nc_models.FileDataset):
ncdset = dset.get_netcdf_dataset()
indata = ncdset.read_time_series(
sel_vars,
start_time=start_time,
end_time=end_time,
selectdim=stndims,
series=sel_soundings,
series_name_fmt=series_name_fmt)
else:
dbcon = dset.get_connection()
indata = dbcon.read_time_series(sel_vars,
start_time=start_time,
end_time=end_time)
except nc_exc.TooMuchDataException as exc:
_logger.warning("%s, %s: %s", project_name, dataset_name, exc)
form.too_much_data(repr(exc))
return render(
request, self.template_name, {
'version': _version,
'form': form,
'dataset': dset,
'datasets': dsets,
'variables': dsetvars,
'soundings': mark_safe(json.dumps(soundings)),
'projects': projs,
'platforms': plats
})
except (OSError, nc_exc.NoDataException) as exc:
_logger.warning("%s, %s: %s", project_name, dataset_name, exc)
form.no_data(repr(exc))
return render(
request, self.template_name, {
'version': _version,
'form': form,
'dataset': dset,
'datasets': dsets,
'variables': dsetvars,
'soundings': mark_safe(json.dumps(soundings)),
'projects': projs,
'platforms': plats
})
time0 = {}
vsizes = {}
for series_name in indata:
ser_data = indata[series_name]
vsizes[series_name] = {}
if series_name == "":
for vname in sel_vars:
vsizes[series_name][vname] = 0
try:
# works for any shape, as long as time is the
# first dimension
vindex = ser_data['vmap'][vname]
vsizes[series_name][vname] = ser_data['data'][
vindex].size
lastok = np.where(
~np.isnan(ser_data['data'][vindex]))[0][-1]
time_last_ok = ser_data['time'][lastok]
except IndexError: # all data is nan
time_last_ok = (start_time - \
datetime.timedelta(seconds=0.001)).timestamp()
except KeyError: # variable not in vmap
continue
try:
time_last = ser_data['time'][-1]
except IndexError: # no data
time_last = time_last_ok
client_state.save_data_times(vname, time_last_ok,
time_last)
# A simple compression, subtract first time from all times,
# reducing the number of characters sent.
time0[series_name] = 0
if ser_data['time']:
time0[series_name] = ser_data['time'][0]
# subtract off time0
ser_data['time'] = [x - time0[series_name] for \
x in ser_data['time']]
json_time0 = mark_safe(json.dumps(time0))
json_time = mark_safe(
json.dumps({sn: indata[sn]['time']
for sn in indata}))
json_data = mark_safe(
json.dumps({sn: indata[sn]['data']
for sn in indata},
cls=NChartsJSONEncoder))
json_vmap = mark_safe(
json.dumps({sn: indata[sn]['vmap']
for sn in indata},
cls=NChartsJSONEncoder).replace("'", r"\u0027"))
json_dim2 = mark_safe(
json.dumps({sn: indata[sn]['dim2']
for sn in indata},
cls=NChartsJSONEncoder).replace("'", r"\u0027"))
# indata may not have stnnames element
json_stns = mark_safe(
json.dumps(
{
sn: (indata[sn]['stnnames']
if 'stnnames' in indata[sn] else {})
for sn in indata
},
cls=NChartsJSONEncoder).replace("'", r"\u0027"))
def type_by_dims(dimnames):
"""Crude function to return a plot type, given a dimension.
"""
if len(dimnames) == 1:
return 'time-series'
elif len(dimnames) == 2:
if dimnames[1] == "station":
return 'time-series'
return 'heatmap'
else:
return 'none'
plot_types = set()
if len(indata) == 1 and '' in indata:
# one series, named ''
for vname, var in variables.items():
ptype = "time-series"
if vname in indata['']['vmap']:
vindex = indata['']['vmap'][vname]
vdimnames = dsetvars[vname]["dimnames"]
# print("vname=",vname,",shape=",str(indata['']['data'][vindex].shape))
# print("vname=",vname,",nbytes=",str(indata['']['data'][vindex].nbytes))
# print("vname=",vname,",ndim=",str(indata['']['data'][vindex].ndim))
# print("dsetvars[",vname,"]['dimnames']=",str(dsetvars[vname]["dimnames"]))
ptype = type_by_dims(vdimnames)
var['plot_type'] = ptype
plot_types.add(ptype)
else:
plot_types.add("sounding-profile")
# Create plot groups dictionary, for each
# group, the variables in the group, their units, long_names, plot_type
# Use OrderedDict so the plots come out in this order
plot_groups = collections.OrderedDict()
# loop over plot_types
grpid = 0
for ptype in plot_types:
# _logger.debug("ptype=%s", ptype)
# For a heatmap, one plot per variable.
if ptype == 'heatmap':
for vname in sorted(variables): # returns sorted keys
var = variables[vname]
if vsizes[''][vname] > 0 and var['plot_type'] == ptype:
plot_groups['g{}'.format(grpid)] = {
'series':
"",
'variables':
mark_safe(
json.dumps([vname]).replace("'", r"\u0027")),
'units':
mark_safe(
json.dumps([var['units']
]).replace("'", r"\u0027")),
'long_names':
mark_safe(
json.dumps([var['long_name']
]).replace("'", r"\u0027")),
'plot_type':
mark_safe(ptype),
}
grpid += 1
elif ptype == 'sounding-profile':
# one profile plot per series name
for series_name in sorted(indata.keys()):
vnames = sorted([v for v in variables])
units = [variables[v]['units'] for v in vnames]
long_names = [(variables[v]['long_name'] \
if 'long_name' in variables[v] else v) for v in vnames]
plot_groups['g{}'.format(grpid)] = {
'series':
series_name,
'variables':
mark_safe(json.dumps(vnames).replace("'", r"\u0027")),
'units':
mark_safe(json.dumps(units).replace("'", r"\u0027")),
'long_names':
mark_safe(
json.dumps(long_names).replace("'", r"\u0027")),
'plot_type':
mark_safe(ptype),
}
grpid += 1
else:
# unique units, in alphabetical order by the name of the
# first variable which uses it. In this way the plots
# are in alphabetical order on the page by the first plotted variable.
uunits = []
# sorted(dict) becomes a list of sorted keys
for vname in sorted(variables):
units = ''
if 'units' in variables[vname]:
units = variables[vname]['units']
else:
variables[vname]['units'] = units
if units not in uunits:
uunits.append(units)
# unique units
for units in uunits:
uvars = sorted([vname for vname, var in variables.items() \
if vsizes[''][vname] > 0 and var['plot_type'] == ptype and var['units'] == units])
# uvars is a sorted list of variables with units and this plot type.
# Might be empty if the variable is of a different plot type
if uvars:
plot_groups['g{}'.format(grpid)] = {
'series': "",
'variables': mark_safe(
json.dumps(uvars).replace("'", r"\u0027")),
'units': mark_safe(json.dumps(
[(variables[v]['units'] if 'units' in variables[v] else '') \
for v in uvars]).replace("'", r"\u0027")),
'long_names': mark_safe(json.dumps(
[(variables[v]['long_name'] \
if 'long_name' in variables[v] else '') \
for v in uvars]).replace("'", r"\u0027")),
'plot_type': mark_safe(ptype),
}
grpid += 1
# log the request
if len(variables) > 2:
logvars = sorted(variables)[:2] + ['...']
else:
logvars = variables
_request_logger.info(
"%s, %s, %s, #recs=%d, real_time=%s, vars=%s, #vars=%d, stns=%s, snding=%s, plot=%s, fromaddr=%s",
dset.project.name, dset.name, start_time, len(ser_data['time']),
client_state.track_real_time, ' '.join(logvars), len(variables),
' '.join(["%s" % s for s in sel_stns
]), ' '.join(["%s" % s for s in soundings]),
' '.join(plot_types), request.META['REMOTE_ADDR'])
return render(
request, self.template_name, {
'version': _version,
'form': form,
'dataset': dset,
'datasets': dsets,
'variables': dsetvars,
'plot_groups': plot_groups,
'time0': json_time0,
'time': json_time,
'data': json_data,
'vmap': json_vmap,
'dim2': json_dim2,
'stations': json_stns,
'time_length': client_state.time_length,
'soundings': mark_safe(json.dumps(soundings)),
'yvariable': yvar.replace("'", r"\u0027"),
'projects': projs,
'platforms': plats
})
def read_time_series(self,
variables=(),
start_time=pytz.utc.localize(datetime.min),
end_time=pytz.utc.localize(datetime.max),
size_limit=1000 * 1000 * 1000):
"""Read times and variables from the table within a time period.
For each variable, its missing_value will be read from the
variable_list table. Values read from the time series table
which match the missing_value will be set to float('nan').
Args:
variables: list or tuple of variable names to read.
start_time: starting datetime of data to be read.
end_time: ending datetime of data to be read.
size_limit: attempt to screen outrageous requests.
Returns:
A one element dict, compatible with that returned by
netcdf.read_time_series(), containing for a series_name of '':
{
'time' : list of UTC timestamps,
'data': lists of numpy.ndarray containing
the data for each variable,
'vmap': dict by variable name,
containing the index into the series data for the variable,
'dim2': dict by variable name, of values for second
dimension of the data, such as height.
}
Raises:
nc_exc.NoDataException
"""
total_size = 0
start_time = start_time.replace(tzinfo=None)
end_time = end_time.replace(tzinfo=None)
vtime = self.read_times(start_time=start_time, end_time=end_time)
# _logger.debug("read_times, len=%d", len(vtime))
total_size += sys.getsizeof(vtime)
if total_size > size_limit:
raise nc_exc.TooMuchDataException(
"too many time values requested, size={0} MB".\
format(total_size/(1000 * 1000)))
vdata = []
vmap = {}
vdim2 = {}
try:
with self.conn as conn:
with conn.cursor() as cur:
for vname in variables:
operation = "read variable_list"
# _logger.debug("vname=%s",vname)
cur.execute(
"SELECT dims, missing_value from variable_list where name=%s;",
(vname, ))
vinfo = cur.fetchall()
# _logger.debug("vinfo=%s",vinfo)
dims = vinfo[0][0]
dims[0] = len(vtime)
missval = vinfo[0][1]
if len(dims) > 1:
# In initial CSET data, dims for CUHSAS_RWOOU
# in variable_list was [1,99]
# Seems that the 99 should have been 100,
# which is what is returned by this:
operation = "read dimension of {}".format(vname)
cur.execute("\
SELECT array_upper({},1) FROM {} FETCH FIRST 1 ROW ONLY;\
".format(vname, self.table))
dimsx = cur.fetchall()[0]
dims[1] = dimsx[0]
# _logger.debug("vname=%s, dims=%s, dimsx=%s", vname, dims, dimsx)
operation = "read {}".format(vname)
cur.execute(
"\
SELECT {} FROM {} WHERE datetime >= %s AND datetime < %s;\
".format(vname, self.table), (start_time, end_time))
cdata = np.ma.masked_values(np.ndarray(
shape=dims,
buffer=np.array([v for v in cur], dtype=float)),
value=missval)
if isinstance(cdata, np.ma.core.MaskedArray):
# _logger.debug("is MaskedArray")
cdata = cdata.filled(fill_value=float('nan'))
total_size += sys.getsizeof(cdata)
if total_size > size_limit:
raise nc_exc.TooMuchDataException(
"too many values requested, size={0} MB".\
format(total_size/(1000 * 1000)))
vindex = len(vdata)
vdata.append(cdata)
vmap[vname] = vindex
if len(dims) > 1:
vdim2[vname] = {
"data": [i for i in range(dims[1])],
"name": "bin",
"units": ""
}
return {
'': {
'time': vtime,
'data': vdata,
'vmap': vmap,
'dim2': vdim2,
}
}
except psycopg2.Error as exc:
RAFDatabase.close_connection(conn)
raise nc_exc.NoDataException((operation + ": {}").format(exc))
def read_time_series(
self,
variables=(),
start_time=pytz.utc.localize(datetime.min),
end_time=pytz.utc.localize(datetime.max),
selectdim=None,
size_limit=1000 * 1000 * 1000,
series=None,
series_name_fmt=None):
""" Read a list of time-series variables from this fileset.
Args:
variables: A list of strs containing time series variable
names to be read.
start_time: A datetime, which is timezone aware, of the start
time of the series to read.
end_time: A datetime, timezone aware, end time of series to read.
selectdim: A dict containing for each dimension name of type
string, the indices of the dimension to read.
For example: {"station":[3,4,5]} to read indices 3,4 and 5
(indexed from 0) of the station dimension for variables
which have that dimension.
size_limit: Limit on the total size in bytes to read, used to
screen huge requests.
series: A list of series to be read by name. For soundings
a series name is something like "Aug23_0000Z", as
created by series_fmt from the time associated with
a file. In this way the data read can be split into
named series. If series_fmt is None, the series name
should be a list of one empty string, [''],
and all data are concatenated together in time order.
series_fmt: a datetime.strftime format to create a
series name for the data found in each file, based
on the time associated with the file.
If series_name_fmt is None, all data is put in a dictionary
element named ''.
Returns:
A dict containing, by series name:
'time' : list of UTC timestamps,
'data': list of numpy.ndarray containing the data for
each variable,
'vmap': dict by variable name,
containing the index into the series data for the variable,
'dim2': dict by variable name, of values for second dimension
of the data, such as height,
'stnnames': dict by variable name, of the list of the
station names for the variable that were read,
as selected by selectdim. A list of length 1 containing
an empty string indicates the variable does not have
a station dimension.
Raises:
OSError
nc_exc.NoDataException
The 'data' element in the returned dict is a list of numpy arrays,
and not a dict by variable name. The 'vmap' element provides the
mapping from a variable name to an index into 'data'. The data object
is typically JSON-ified and sent to a browser. If it were a dict,
the variable names may contain characters which cause headaches with
JSON and javascript in django templates. For example, the JSON-ified
string is typically passed to javascript in a django template by
surrounding it with single quotes:
var data = jQuery.parseJSON('{{ data }}');
A single quote within the data JSON string causes grief, and we want
to support single quotes in variable names. The only work around I
know of is to convert the single quotes within the string to '\u0027'.
This is, of course, a time-consuming step we want to avoid when
JSON-ifying a large chunk of data. It is less time-consuming to
replace the quotes in the smaller vmap.
The series names will not contain single quotes.
"""
debug = False
dsinfo = self.get_dataset_info()
if not dsinfo['time_name']:
self.scan_files()
dsinfo = self.get_dataset_info()
dsinfo_vars = dsinfo['variables']
if not selectdim:
selectdim = {}
vshapes = self.resolve_variable_shapes(variables, selectdim)
res_data = {}
total_size = 0
ntimes = 0
files = self.get_files(start_time, end_time)
if debug:
_logger.debug(
"len(files)=%d, series_name_fmt=%s",
len(files), series_name_fmt)
if series_name_fmt:
file_tuples = [(f.time.strftime(series_name_fmt), f.path) \
for f in files]
else:
file_tuples = [("", f.path) for f in files]
for (series_name, ncpath) in file_tuples:
if series and not series_name in series:
continue
if debug:
_logger.debug("series=%s", str(series))
_logger.debug("series_name=%s ,ncpath=%s", series_name, ncpath)
# the files might be in the process of being moved, deleted, etc
fileok = False
exc = None
for itry in range(0, 3):
try:
ncfile = netCDF4.Dataset(ncpath)
fileok = True
break
except (OSError, RuntimeError) as excx:
exc = excx
time.sleep(itry)
if not fileok:
_logger.error("%s: %s", ncpath, exc)
continue
if not series_name in res_data:
res_data[series_name] = {
'time': [],
'data': [],
'vmap': {},
'dim2': {},
'stnnames': {},
}
otime = res_data[series_name]['time']
odata = res_data[series_name]['data']
ovmap = res_data[series_name]['vmap']
odim2 = res_data[series_name]['dim2']
ostns = res_data[series_name]['stnnames']
try:
size1 = sys.getsizeof(otime)
# times are apended to otime
time_slice = self.read_times(
ncfile, ncpath, start_time, end_time, otime,
size_limit - total_size)
# time_slice.start is None if nothing to read
if time_slice.start is None or \
time_slice.stop <= time_slice.start:
continue
total_size += sys.getsizeof(otime) - size1
for exp_vname in variables:
# skip if variable is not a time series or
# doesn't have a selected dimension
if not exp_vname in dsinfo_vars or not exp_vname in vshapes:
continue
# selected shape of this variable
vshape = vshapes[exp_vname]
vsize = reduce_(
operator.mul, vshape, 1) * \
dsinfo_vars[exp_vname]["dtype"].itemsize
if not vsize:
continue
if total_size + vsize > size_limit:
raise nc_exc.TooMuchDataException(
"too much data requested, will exceed {} mbytes".
format(size_limit/(1000 * 1000)))
dim2 = {}
stnnames = []
vdata = self.read_time_series_data(
ncfile, ncpath, exp_vname, time_slice, vshape,
selectdim, dim2, stnnames)
if vdata is None:
continue
# dim2 will be empty if variable is not found in file
if dim2 and not exp_vname in odim2:
odim2[exp_vname] = dim2
# stnnames will be empty if variable is not found in file
if stnnames and not exp_vname in ostns:
ostns[exp_vname] = stnnames
if not exp_vname in ovmap:
size1 = 0
vindex = len(odata)
odata.append(vdata)
ovmap[exp_vname] = vindex
else:
if debug:
_logger.debug(
"odata[%s].shape=%s, vdata.shape=%s",
exp_vname, odata[vindex].shape, vdata.shape)
vindex = ovmap[exp_vname]
size1 = sys.getsizeof(odata[vindex])
time_index = dsinfo_vars[exp_vname]["time_index"]
odata[vindex] = np.append(
odata[vindex], vdata, axis=time_index)
total_size += sys.getsizeof(odata[vindex]) - size1
finally:
ncfile.close()
ntimes += len(otime)
if ntimes == 0:
exc = nc_exc.NoDataException(
"No data between {} and {}".
format(
start_time.isoformat(),
end_time.isoformat()))
# _logger.warning("%s: %s", str(self), repr(exc))
raise exc
if debug:
for series_name in res_data:
for exp_vname in res_data[series_name]['vmap']:
var_index = res_data[series_name]['vmap'][exp_vname]
_logger.debug(
"res_data[%s]['data'][%d].shape=%s, exp_vname=%s",
series_name, var_index,
repr(res_data[series_name]['data'][var_index].shape),
exp_vname)
_logger.debug(
"total_size=%d", total_size)
return res_data
def scan_files(
self,
time_names=('time', 'Time', 'time_offset')):
""" Scan the set of files, accumulating information about the dataset in a dict,
with the following keys:
file_mod_times: dictionary of file modification times by file name, of each
file just before it was last scanned.
base_time: name of base_time variable
time_dim_name: name of time dimension
time_name: name of time variable
nstations: size of the station dimension
station_dim: name of the station dimension
station_names: names of the stations
sites: dictionary of names of sites long names by the site short
names extracted from the exported names
of those variables not associated with a numbered station
variables: dictionary of information for each variable.
If an element in file_mod_times exists for a file, that file is not scanned if
its current modification time has not been updated.
The names of the variables in the dataset are converted to an exported
form. If a variable has a 'short_name' attribute, it is used for the
variable name, otherwise the exported name is set to the NetCDF variable
name.
Note, we don't read every file. May want to have
MAX_NUM_FILES_TO_PRESCAN be an attribute of the dataset.
Args:
time_names: List of allowed names for time variable.
Raises:
OSError
nc_exc.NoDataException
"""
dsinfo = self.get_dataset_info()
# Note: dsinfo_vars is a reference. Modificatons to it
# are also modifications to dsinfo.
dsinfo_vars = dsinfo['variables']
sitedict = dsinfo['sites']
files = self.get_files(
start_time=self.start_time,
end_time=self.end_time)
# typically get_files() also returns the file before start_time
# We may want that in reading a period of data, but not
# in assembling the variables for the dataset
filepaths = [f.path for f in files if f.time >= self.start_time and f.time < self.end_time]
skip = 1
if len(filepaths) > NetCDFDataset.MAX_NUM_FILES_TO_PRESCAN:
skip = len(filepaths) / NetCDFDataset.MAX_NUM_FILES_TO_PRESCAN
# Read at most MAX_NUM_FILES_TO_PRESCAN, including latest file.
# Files are scanned in a backwards sequence
pindex = len(filepaths) - 1
n_files_read = 0
while pindex >= 0:
ncpath = filepaths[int(pindex)]
pindex -= skip
# The files might be in the process of being moved, deleted, etc,
# so if we get an exception in this open, try a few more times.
# Testing indicates that with a truncated file (artificially
# truncated with dd), the underlying C code will cause a crash
# of python from an assert() rather than raising an exception
# that could be caught.
# If the netcdf library is compiled with -DNDEBUG, then the
# the open and parse of the truncated header succeeds, but
# still no exception.
# If the file is artificially corrupted by removing an
# initial portion of the file:
# dd if=test.nc of=bad.nc bs=1014 count=100 skip=1
# then an exception is raised (this was with -DNDEBUG):
# RuntimeError bad.nc: NetCDF: Unknown file format
# To make this robust, it would be good to run a king's
# taster process on each file first to reduce the possibility
# of a server death. The king's taster would not use NDEBUG,
# but perhaps the python server would. Complicated.
fileok = False
skip_file = False
exc = None
siteset = set()
site_sn = []
site_ln = []
for itry in range(0, 3):
try:
curr_mod_time = get_file_modtime(ncpath)
if ncpath in dsinfo['file_mod_times']:
prev_mod_time = dsinfo['file_mod_times'][ncpath]
if curr_mod_time <= prev_mod_time:
skip_file = True
fileok = True
break
dsinfo['file_mod_times'][ncpath] = curr_mod_time
# _logger.debug("ncpath=%s",ncpath)
ncfile = netCDF4.Dataset(ncpath)
fileok = True
break
except (OSError, RuntimeError) as excx:
exc = excx
time.sleep(itry)
if not fileok:
_logger.error("%s: %s", ncpath, exc)
continue
n_files_read += 1
if skip_file:
continue
try:
if not dsinfo['base_time'] and 'base_time' in ncfile.variables:
dsinfo['base_time'] = 'base_time'
tdim = None
# look for a time dimension
for tname in ['time', 'Time']:
if tname in ncfile.dimensions:
tdim = ncfile.dimensions[tname]
break
if not tdim:
continue
# check for tdim.is_unlimited?
if not dsinfo['time_dim_name']:
dsinfo['time_dim_name'] = tdim.name
if STATION_DIMENSION_NAME in ncfile.dimensions:
if dsinfo['nstations'] is None:
dsinfo['nstations'] = len(ncfile.dimensions[STATION_DIMENSION_NAME])
dsinfo['station_dim'] = STATION_DIMENSION_NAME
if STATION_DIMENSION_NAME in ncfile.variables:
dsinfo['station_names'] = []
var = ncfile.variables[STATION_DIMENSION_NAME]
if var.datatype == np.dtype('S1'):
snms = [str(netCDF4.chartostring(v)) for v in var]
dsinfo['station_names'].extend(snms)
elif not dsinfo['nstations'] == \
len(ncfile.dimensions[STATION_DIMENSION_NAME]):
_logger.warning(
"%s: station dimension (%d) is "
"different than that of other files (%d)",
ncpath,
len(ncfile.dimensions[STATION_DIMENSION_NAME]),
dsinfo['nstations'])
# look for a time variable
if not dsinfo['time_name']:
for tname in time_names:
if tname in ncfile.variables:
if tdim.name in ncfile.variables[tname].dimensions:
dsinfo['time_name'] = tname
break
if not dsinfo['time_name'] or \
not dsinfo['time_name'] in ncfile.variables:
# time variable not yet found or not in this file
continue
if not tdim.name in ncfile.variables[dsinfo['time_name']].dimensions:
# time variable in this file doesn't have a time dimension
continue
# pylint: disable=no-member
for (nc_vname, var) in ncfile.variables.items():
if nc_vname == "site_long_name" and \
var.datatype == np.dtype('S1'):
site_ln = [str(netCDF4.chartostring(v)) for v in var]
if nc_vname == "sites" and \
var.datatype == np.dtype('S1'):
site_sn = [str(netCDF4.chartostring(v)) for v in var]
# looking for time series variables
if not dsinfo['time_dim_name'] in var.dimensions:
continue
# time variable
if nc_vname == dsinfo['time_name']:
continue
# exported variable name
if hasattr(var, 'short_name'):
exp_vname = getattr(var, 'short_name')
else:
exp_vname = nc_vname
# var.dimensions is a tuple of dimension names
time_index = var.dimensions.index(dsinfo['time_dim_name'])
# Check if we have found this variable in a earlier file
if not exp_vname in dsinfo_vars:
# New variable
varinfo = {}
varinfo['netcdf_name'] = nc_vname
varinfo['shape'] = var.shape
varinfo['dimnames'] = var.dimensions
varinfo['dtype'] = var.dtype
varinfo['time_index'] = time_index
# Grab certain attributes
for att in ['units', 'long_name']:
if hasattr(var, att):
varinfo[att] = getattr(var, att)
# Set default units to ''
if 'units' not in varinfo:
varinfo['units'] = ''
# For non-station variables, parse the name to
# determine the possible site
if not dsinfo['station_dim'] or \
not dsinfo['station_dim'] in var.dimensions:
site = get_isfs_site(exp_vname)
if site:
varinfo['site'] = site
siteset.add(site)
# dsinfo['sites'].add(site)
else:
dsinfo['has_station_variables'] = True
dsinfo_vars[exp_vname] = varinfo
continue
varinfo = dsinfo_vars[exp_vname]
# variable has been found in an earlier ncfile
# check for consistancy across files
if varinfo['shape'][1:] != var.shape[1:]:
# the above check works even if either shape
# has length 1
if len(varinfo['shape']) != \
len(var.shape):
# changing number of dimensions, punt
_logger.error(
"%s: %s: number of "
"dimensions is not consistent: %d and %d. "
"Skipping this variable.",
ncpath, nc_vname, len(var.shape),
len(varinfo['shape']))
del dsinfo_vars[exp_vname]
continue
# here we know that shapes have same length and
# they must have len > 1. Allow final dimension
# to change.
ndim = len(var.shape)
if (varinfo['shape'][1:(ndim-1)] !=
var.shape[1:(ndim-1)]):
_logger.error(
"%s: %s: incompatible shapes: "
"%s and %s. Skipping this variable.",
ncpath, nc_vname, repr(var.shape),
repr(varinfo['shape']))
del dsinfo_vars[exp_vname]
continue
# set shape to max shape (leaving the problem
# for later...)
varinfo['shape'] = tuple(
[max(i, j) for (i, j) in zip(
varinfo['shape'], var.shape)])
if varinfo['dtype'] != var.dtype:
_logger.error(
"%s: %s: type=%s is different than "
"in other files",
ncpath, nc_vname, repr(var.dtype))
if varinfo['time_index'] != time_index:
_logger.error(
"%s: %s: time_index=%d is different than "
"in other files. Skipping this variable.",
ncpath, nc_vname, time_index)
del dsinfo_vars[exp_vname]
for att in ['units', 'long_name']:
if hasattr(var, att) and att in varinfo:
if getattr(var, att) != varinfo[att]:
_logger.info(
"%s: %s: %s=%s is different than previous value=%s",
ncpath, nc_vname, att, getattr(var, att),
varinfo[att])
varinfo[att] = getattr(var, att)
finally:
for site in siteset:
try:
i = site_sn.index(site)
if i < len(site_ln):
sitedict[site] = site_ln[i]
except ValueError:
pass
if site not in sitedict:
sitedict[site] = ''
ncfile.close()
if not n_files_read:
msg = self.path + ": No files found"
raise nc_exc.NoDataException(msg)
# Remove the station names if no variables have a station dimension
if not dsinfo['has_station_variables']:
dsinfo['station_names'] = []
else:
# create station names if a "station" variable is not found
# in NetCDF files. Names are S1, S2, etc for dimension index 0, 1
if dsinfo['nstations'] and not dsinfo['station_names']:
dsinfo['station_names'].extend(\
['S{}'.format(i+1) for i in range(dsinfo['nstations'])])
# cache dsinfo
self.save_dataset_info(dsinfo)