class Instrument(object):
"""Download, load, manage, modify and analyze science data.
Parameters
----------
platform : string
name of platform/satellite.
name : string
name of instrument.
tag : string, optional
identifies particular subset of instrument data.
sat_id : string, optional
identity within constellation
clean_level : {'clean','dusty','dirty','none'}, optional
level of data quality
pad : pandas.DateOffset, or dictionary, optional
Length of time to pad the begining and end of loaded data for
time-series processing. Extra data is removed after applying all
custom functions. Dictionary, if supplied, is simply passed to
pandas DateOffset.
orbit_info : dict
Orbit information, {'index':index, 'kind':kind, 'period':period}.
See pysat.Orbits for more information.
inst_module : module, optional
Provide instrument module directly.
Takes precedence over platform/name.
update_files : boolean, optional
If True, immediately query filesystem for instrument files and store.
temporary_file_list : boolean, optional
If true, the list of Instrument files will not be written to disk.
Prevents a race condition when running multiple pysat processes.
multi_file_day : boolean, optional
Set to True if Instrument data files for a day are spread across
multiple files and data for day n could be found in a file
with a timestamp of day n-1 or n+1.
manual_org : bool
if True, then pysat will look directly in pysat data directory
for data files and will not use default /platform/name/tag
directory_format : str
directory naming structure in string format. Variables such as
platform, name, and tag will be filled in as needed using python
string formatting. The default directory structure would be
expressed as '{platform}/{name}/{tag}'
file_format : str or NoneType
File naming structure in string format. Variables such as year,
month, and sat_id will be filled in as needed using python string
formatting. The default file format structure is supplied in the
instrument list_files routine.
Attributes
----------
data : pandas.DataFrame
loaded science data
date : pandas.datetime
date for loaded data
yr : int
year for loaded data
bounds : (datetime/filename/None, datetime/filename/None)
bounds for loading data, supply array_like for a season with gaps
doy : int
day of year for loaded data
files : pysat.Files
interface to instrument files
meta : pysat.Meta
interface to instrument metadata, similar to netCDF 1.6
orbits : pysat.Orbits
interface to extracting data orbit-by-orbit
custom : pysat.Custom
interface to instrument nano-kernel
kwargs : dictionary
keyword arguments passed to instrument loading routine
Note
----
Pysat attempts to load the module platform_name.py located in
the pysat/instruments directory. This module provides the underlying
functionality to download, load, and clean instrument data.
Alternatively, the module may be supplied directly
using keyword inst_module.
Examples
--------
::
# 1-second mag field data
vefi = pysat.Instrument(platform='cnofs',
name='vefi',
tag='dc_b',
clean_level='clean')
start = pysat.datetime(2009,1,1)
stop = pysat.datetime(2009,1,2)
vefi.download(start, stop)
vefi.load(date=start)
print(vefi['dB_mer'])
print(vefi.meta['db_mer'])
# 1-second thermal plasma parameters
ivm = pysat.Instrument(platform='cnofs',
name='ivm',
tag='',
clean_level='clean')
ivm.download(start,stop)
ivm.load(2009,1)
print(ivm['ionVelmeridional'])
# Ionosphere profiles from GPS occultation
cosmic = pysat.Instrument('cosmic2013',
'gps',
'ionprf',
altitude_bin=3)
# bins profile using 3 km step
cosmic.download(start, stop, user=user, password=password)
cosmic.load(date=start)
"""
def __init__(self,
platform=None,
name=None,
tag=None,
sat_id=None,
clean_level='clean',
update_files=None,
pad=None,
orbit_info=None,
inst_module=None,
multi_file_day=None,
manual_org=None,
directory_format=None,
file_format=None,
temporary_file_list=False,
*arg,
**kwargs):
if inst_module is None:
# use strings to look up module name
if isinstance(platform, str) and isinstance(name, str):
self.platform = platform.lower()
self.name = name.lower()
# look to module for instrument functions and defaults
self._assign_funcs(by_name=True)
elif (platform is None) and (name is None):
# creating "empty" Instrument object with this path
self.name = ''
self.platform = ''
self._assign_funcs()
else:
raise ValueError(
'Inputs platform and name must both be strings, or both None.'
)
else:
# user has provided a module
try:
# platform and name are expected to be part of module
self.name = inst_module.name.lower()
self.platform = inst_module.platform.lower()
except AttributeError:
raise AttributeError(
string.join((
'A name and platform attribute for the ',
'instrument is required if supplying routine module directly.'
)))
# look to module for instrument functions and defaults
self._assign_funcs(inst_module=inst_module)
# more reasonable defaults for optional parameters
self.tag = tag.lower() if tag is not None else ''
self.sat_id = sat_id.lower() if sat_id is not None else ''
self.clean_level = (clean_level.lower()
if clean_level is not None else 'none')
# assign_func sets some instrument defaults, direct info rules all
if directory_format is not None:
self.directory_format = directory_format.lower()
# value not provided by user, check if there is a value provided by
# instrument module
elif self.directory_format is not None:
try:
# check if it is a function
self.directory_format = self.directory_format(tag, sat_id)
except TypeError:
pass
if file_format is not None:
self.file_format = file_format
# value not provided by user, check if there is a value provided by
# instrument module
elif self.file_format is not None:
# check if it is an iterable string. If it isn't formatted
# properly, give a warning and set file_format to None
if (not isinstance(self.file_format, str)
or self.file_format.find("{") < 0
or self.file_format.find("}") < 1):
estr = 'file format set to default, supplied string must be '
estr = '{:s}iteratable [{:}]'.format(estr, self.file_format)
print(estr)
self.file_format = None
# set up empty data and metadata
self.data = DataFrame(None)
self.meta = _meta.Meta()
# function processing class, processes data on load
self.custom = _custom.Custom()
# create arrays to store data around loaded day
# enables padding across day breaks with minimal loads
self._next_data = DataFrame(None)
self._next_data_track = []
self._prev_data = DataFrame(None)
self._prev_data_track = []
self._curr_data = DataFrame(None)
# multi file day, default set by assign_funcs
if multi_file_day is not None:
self.multi_file_day = multi_file_day
# arguments for padding
if isinstance(pad, pds.DateOffset):
self.pad = pad
elif isinstance(pad, dict):
self.pad = pds.DateOffset(**pad)
elif pad is None:
self.pad = None
else:
estr = 'pad must be a dictionary or a pandas.DateOffset instance.'
raise ValueError(estr)
# instantiate Files class
manual_org = False if manual_org is None else manual_org
temporary_file_list = not temporary_file_list
self.files = _files.Files(self,
manual_org=manual_org,
directory_format=self.directory_format,
update_files=update_files,
file_format=self.file_format,
write_to_disk=temporary_file_list)
# set bounds for iteration
# self.bounds requires the Files class
# setting (None,None) loads default bounds
self.bounds = (None, None)
self.date = None
self._fid = None
self.yr = None
self.doy = None
self._load_by_date = False
# initialize orbit support
if orbit_info is None:
if self.orbit_info is None:
# if default info not provided, set None as default
orbit_info = {'index': None, 'kind': None, 'period': None}
else:
# default provided by instrument module
orbit_info = self.orbit_info
self.orbits = _orbits.Orbits(self, **orbit_info)
# store kwargs, passed to load routine
self.kwargs = kwargs
# run instrument init function, a basic pass function is used
# if user doesn't supply the init function
self._init_rtn(self)
def __getitem__(self, key):
"""
Convenience notation for accessing data; inst['name'] is inst.data.name
Examples
--------
::
# By name
inst['name']
# By position
inst[row_index, 'name']
# Slicing by row
inst[row1:row2, 'name']
# By Date
inst[datetime, 'name']
# Slicing by date, inclusive
inst[datetime1:datetime2, 'name']
# Slicing by name and row/date
inst[datetime1:datetime1, 'name1':'name2']
"""
if isinstance(key, tuple):
# support slicing
return self.data.ix[key[0], key[1]]
else:
return self.data[key]
def __setitem__(self, key, new):
"""Convenience method for adding data to instrument.
Examples
--------
::
# Simple Assignment, default metadata assigned
# 'long_name' = 'name'
# 'units' = ''
inst['name'] = newData
# Assignment with Metadata
inst['name'] = {'data':new_data,
'long_name':long_name,
'units':units}
Note
----
If no metadata provided and if metadata for 'name' not already stored
then default meta information is also added,
long_name = 'name', and units = ''.
"""
if isinstance(new, dict):
# metadata should be included in dict
self.data[key] = new.pop('data')
# pass the rest to meta
self.meta[key] = new
else:
if isinstance(key, tuple):
self.data.ix[key[0], key[1]] = new
self.meta[key[1]] = {}
elif isinstance(key, str):
self.data[key] = new
self.meta[key] = {}
elif isinstance(new, DataFrame):
self.data[key] = new[key]
for ke in key:
self.meta[ke] = {}
else:
raise ValueError("No support for supplied input key")
def copy(self):
"""Deep copy of the entire Instrument object."""
return copy.deepcopy(self)
def _pass_func(*args, **kwargs):
pass
def _assign_funcs(self, by_name=False, inst_module=None):
"""Assign all external science instrument methods to Instrument object."""
import importlib
# set defaults
self._list_rtn = self._pass_func
self._load_rtn = self._pass_func
self._default_rtn = self._pass_func
self._clean_rtn = self._pass_func
self._init_rtn = self._pass_func
self._download_rtn = self._pass_func
# default params
self.directory_format = None
self.file_format = None
self.multi_file_day = False
self.orbit_info = None
if by_name:
# look for code with filename name, any errors passed up
inst = importlib.import_module(''.join(
('.', self.platform, '_', self.name)),
package='pysat.instruments')
elif inst_module is not None:
# user supplied an object with relevant instrument routines
inst = inst_module
else:
# no module or name info, default pass functions assigned
return
try:
self._load_rtn = inst.load
self._list_rtn = inst.list_files
self._download_rtn = inst.download
except AttributeError:
estr = 'A load, file_list, and download routine are required for '
raise AttributeError('{:s}every instrument.'.format(estr))
try:
self._default_rtn = inst.default
except AttributeError:
pass
try:
self._init_rtn = inst.init
except AttributeError:
pass
try:
self._clean_rtn = inst.clean
except AttributeError:
pass
# look for instrument default parameters
try:
self.directory_format = inst.directory_format
except AttributeError:
pass
try:
self.multi_file_day = inst.self.multi_file_day
except AttributeError:
pass
try:
self.orbit_info = inst.orbit_info
except AttributeError:
pass
return
def _load_data(self, date=None, fid=None):
"""
Load data for an instrument on given date or fid, dependng upon input.
"""
if fid is not None:
# get filename based off of index value
fname = self.files[fid:fid + 1]
elif date is not None:
fname = self.files[date:date + pds.DateOffset(days=1)]
else:
raise ValueError('Must supply either a date or file id number.')
if len(fname) > 0:
load_fname = [os.path.join(self.files.data_path, f) for f in fname]
data, mdata = self._load_rtn(load_fname,
tag=self.tag,
sat_id=self.sat_id,
**self.kwargs)
else:
data = DataFrame(None)
mdata = _meta.Meta()
output_str = '{platform} {name} {tag} {sat_id}'
output_str = output_str.format(platform=self.platform,
name=self.name,
tag=self.tag,
sat_id=self.sat_id)
if not data.empty:
if not isinstance(data, DataFrame):
raise TypeError(
string.join(('Data returned by instrument load',
'routine must be a pandas.DataFrame')))
if not isinstance(mdata, _meta.Meta):
raise TypeError(
'Metadata returned must be a pysat.Meta object')
if date is not None:
output_str = ' '.join(
('Returning', output_str, 'data for', date.strftime('%D')))
else:
if len(fname) == 1:
# this check was zero
output_str = ' '.join(
('Returning', output_str, 'data from', fname[0]))
else:
output_str = ' '.join(
('Returning', output_str, 'data from', fname[0], '::',
fname[-1]))
else:
# no data signal
output_str = ' '.join(
('No', output_str, 'data for', date.strftime('%D')))
# remove extra spaces, if any
output_str = " ".join(output_str.split())
print(output_str)
return data, mdata
def _load_next(self):
"""Load the next days data (or file) without incrementing the date.
Repeated calls will not advance date/file and will produce the same data
Uses info stored in object to either increment the date,
or the file. Looks for self._load_by_date flag.
"""
if self._load_by_date:
next_date = self.date + pds.DateOffset(days=1)
return self._load_data(date=next_date)
else:
return self._load_data(fid=self._fid + 1)
def _load_prev(self):
"""Load the next days data (or file) without decrementing the date.
Repeated calls will not decrement date/file and will produce the same data
Uses info stored in object to either decrement the date,
or the file. Looks for self._load_by_date flag.
"""
if self._load_by_date:
prev_date = self.date - pds.DateOffset(days=1)
return self._load_data(date=prev_date)
else:
return self._load_data(fid=self._fid - 1)
def load(self,
yr=None,
doy=None,
date=None,
fname=None,
fid=None,
verifyPad=False):
"""Load instrument data into Instrument object .data.
Parameters
----------
yr : integer
year for desired data
doy : integer
day of year
date : datetime object
date to load
fname : 'string'
filename to be loaded
verifyPad : boolean
if True, padding data not removed (debug purposes)
Returns
--------
Void. Data is added to self.data
Note
----
Loads data for a chosen instrument into .data. Any functions chosen
by the user and added to the custom processing queue (.custom.add)
are automatically applied to the data before it is available to
user in .data.
"""
if date is not None:
# date supplied getyrdoy checks if it is datetime
year, doy = utils.getyrdoy(date)
self.yr = year
self.doy = doy
self.date = date
self._fid = None
self._load_by_date = True
inc = pds.DateOffset(days=1)
curr = date
elif (yr is not None) & (doy is not None):
# if date not defined but both yr and doy are
self.date = pds.datetime(yr, 1, 1) + pds.DateOffset(days=(doy - 1))
self.yr = yr
self.doy = doy
self._fid = None
self._load_by_date = True
inc = pds.DateOffset(days=1)
curr = self.date
elif fname is not None:
# date will have to be set later by looking at the data
self.date = None
self.yr = None
self.doy = None
self._load_by_date = False
# if no index, called func tries to find file in instrument dir,
# throws error if it fails
self._fid = self.files.get_index(fname)
inc = 1
curr = self._fid.copy()
elif fid is not None:
self._load_by_date = False
self._fid = fid
self.date = None
self.yr = None
self.doy = None
inc = 1
curr = fid
else:
estr = 'Must supply a yr,doy pair, or datetime object, or filename'
estr = '{:s} to load data from.'.format(estr)
raise TypeError(estr)
self.orbits._reset()
# if pad is true, need to have a three day/file load
if (self.pad is not None) | self.multi_file_day:
if self._next_data.empty & self._prev_data.empty:
# data has not already been loaded for previous and next days
# load data for all three
print('Initializing three day/file window')
# using current date or fid
self._prev_data, self._prev_meta = self._load_prev()
self._curr_data, self._curr_meta = \
self._load_data(date=self.date, fid=self._fid)
self._next_data, self._next_meta = self._load_next()
else:
# moving forward in time
if self._next_data_track == curr:
self._prev_data = self._curr_data
self._prev_meta = self._curr_meta
self._curr_data = self._next_data
self._curr_meta = self._next_meta
self._next_data, self._next_meta = self._load_next()
# moving backward in time
elif self._prev_data_track == curr:
self._next_data = self._curr_data
self._next_meta = self._curr_meta
self._curr_data = self._prev_data
self._curr_meta = self._prev_meta
self._prev_data, self._prev_meta = self._load_prev()
# jumped in time/or switched from filebased to date based access
else:
self._prev_data, self._prev_meta = self._load_prev()
self._curr_data, self._curr_meta = \
self._load_data(date=self.date, fid=self._fid)
self._next_data, self._next_meta = self._load_next()
# make sure datetime indices for all data is monotonic
if not self._prev_data.index.is_monotonic_increasing:
self._prev_data.sort_index(inplace=True)
if not self._curr_data.index.is_monotonic_increasing:
self._curr_data.sort_index(inplace=True)
if not self._next_data.index.is_monotonic_increasing:
self._next_data.sort_index(inplace=True)
# make tracking indexes consistent with new loads
self._next_data_track = curr + inc
self._prev_data_track = curr - inc
# attach data to object
if not self._curr_data.empty:
self.data = self._curr_data.copy()
self.meta = self._curr_meta.copy()
else:
self.data = DataFrame(None)
# line below removed as it would delete previous meta, if any
# if you end a seasonal analysis with a day with no data, then
# no meta: self.meta = _meta.Meta()
if self.multi_file_day:
self.data = self.data.ix[self.date:self.date + pds.DateOffset(
hours=23, minutes=59, seconds=59, nanoseconds=99999999)]
# pad data based upon passed parameter
if (not self._prev_data.empty) & (not self.data.empty):
if self.multi_file_day and self._load_by_date:
padLeft = self._prev_data.ix[(
self.date):self._curr_data.index[0]]
else:
padLeft = self._prev_data.ix[(
self._curr_data.index[0] -
self.pad):self._curr_data.index[0]]
#self.data = pds.concat([padLeft[0:-1], self.data])
self.data = pds.concat([padLeft, self.data])
if (not self._next_data.empty) & (not self.data.empty):
if self.multi_file_day and self._load_by_date:
padRight = self._next_data.ix[self.date : (self.date + \
pds.DateOffset(hours=23, minutes=59, seconds=59, nanoseconds=99999999))]
else:
padRight = self._next_data.ix[self._curr_data.index[-1]:(
self._curr_data.index[-1] + self.pad)]
#self.data = pds.concat([self.data, padRight[1:]])
self.data = pds.concat([self.data, padRight])
# drop any possible duplicate index times
#self.data.drop_duplicates(inplace=True)
self.data = self.data[~self.data.index.duplicated()]
# if self.pad is False, load single day
else:
self.data, meta = self._load_data(date=self.date, fid=self._fid)
if not self.data.empty:
self.meta = meta
# check if load routine actually returns meta
if self.meta.data.empty:
self.meta[self.data.columns] = {
'long_name': self.data.columns,
'units': [''] * len(self.data.columns)
}
# if loading by file set the yr, doy, and date
if not self._load_by_date:
temp = self.data.index[0]
temp = pds.datetime(temp.year, temp.month, temp.day)
self.date = temp
self.yr, self.doy = utils.getyrdoy(self.date)
if not self.data.empty:
self._default_rtn(self)
# clean
if (not self.data.empty) & (self.clean_level != 'none'):
self._clean_rtn(self)
# apply custom functions
if not self.data.empty:
self.custom._apply_all(self)
# remove the excess padding, if any applied
if (self.pad is not None) & (not self.data.empty) & (not verifyPad):
self.data = self.data[self._curr_data.index[0]:self._curr_data.
index[-1]]
sys.stdout.flush()
return
def download(self, start, stop, freq='D', user=None, password=None):
"""Download data for given Instrument object from start to stop.
Parameters
----------
start : pandas.datetime
start date to download data
stop : pandas.datetime
stop date to download data
freq : string
Stepsize between dates for season, 'D' for daily, 'M' monthly
(see pandas)
user : string
username, if required by instrument data archive
password : string
password, if required by instrument data archive
Note
----
Data will be downloaded to pysat_data_dir/patform/name/tag
If Instrument bounds are set to defaults they are updated
after files are downloaded.
"""
import errno
# make sure directories are there, otherwise create them
try:
os.makedirs(self.files.data_path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
print('Downloading data to: ', self.files.data_path)
date_array = utils.season_date_range(start, stop, freq=freq)
if user is None:
self._download_rtn(date_array,
tag=self.tag,
sat_id=self.sat_id,
data_path=self.files.data_path)
else:
self._download_rtn(date_array,
tag=self.tag,
sat_id=self.sat_id,
data_path=self.files.data_path,
user=user,
password=password)
# get current file date range
first_date = self.files.start_date
last_date = self.files.stop_date
print('Updating pysat file list')
self.files.refresh()
# if instrument object has default bounds, update them
if len(self.bounds[0]) == 1:
if (self.bounds[0][0] == first_date
and self.bounds[1][0] == last_date):
print('Updating instrument object bounds.')
self.bounds = None
@property
def bounds(self):
"""Boundaries for iterating over instrument object by date or file.
Parameters
----------
start : datetime object, filename, or None (default)
start of iteration, if None uses first data date.
list-like collection also accepted
end : datetime object, filename, or None (default)
end of iteration, inclusive. If None uses last data date.
list-like collection also accepted
Note
----
Both start and stop must be the same type (date, or filename) or None
Examples
--------
::
inst = pysat.Instrument(platform=platform,
name=name,
tag=tag)
start = pysat.datetime(2009,1,1)
stop = pysat.datetime(2009,1,31)
inst.bounds = (start,stop)
start2 = pysat.datetetime(2010,1,1)
stop2 = pysat.datetime(2010,2,14)
inst.bounds = ([start, start2], [stop, stop2])
"""
return self._iter_start, self._iter_stop
@bounds.setter
def bounds(self, value=None):
if value is None:
value = (None, None)
if len(value) < 2:
raise ValueError('Must supply both a start and end date/file' +
'Supply None if you want the first/last possible')
start = value[0]
end = value[1]
# get the frequency, or step size, of season
if len(value) == 3:
step = value[2]
else:
# default do daily
step = 'D'
if (start is None) and (end is None):
# set default
self._iter_start = [self.files.start_date]
self._iter_stop = [self.files.stop_date]
self._iter_type = 'date'
if self._iter_start[0] is not None:
# check here in case Instrument is initialized with no input
self._iter_list = utils.season_date_range(self._iter_start,
self._iter_stop,
freq=step)
elif (hasattr(start, '__iter__') and not isinstance(start, str)) and (
hasattr(end, '__iter__') and not isinstance(end, str)):
base = type(start[0])
for s, t in zip(start, end):
if (type(s) != type(t)) or (type(s) != base):
raise ValueError(
'Start and end items must all be of the same type')
if isinstance(start[0], str):
self._iter_type = 'file'
self._iter_list = self.files.get_file_array(start, end)
elif isinstance(start[0], pds.datetime):
self._iter_type = 'date'
self._iter_list = utils.season_date_range(start,
end,
freq=step)
else:
raise ValueError(
'Input is not a known type, string or datetime')
self._iter_start = start
self._iter_stop = end
elif (hasattr(start, '__iter__') and not isinstance(start, str)) or (
hasattr(end, '__iter__') and not isinstance(end, str)):
raise ValueError(
'Both start and end must be iterable if one bound is iterable')
elif isinstance(start, str) or isinstance(end, str):
if isinstance(start, pds.datetime) or isinstance(
end, pds.datetime):
raise ValueError('Not allowed to mix file and date bounds')
if start is None:
start = self.files[0]
if end is None:
end = self.files.files[-1]
self._iter_start = [start]
self._iter_stop = [end]
self._iter_list = self.files.get_file_array(
self._iter_start, self._iter_stop)
self._iter_type = 'file'
elif isinstance(start, pds.datetime) or isinstance(end, pds.datetime):
if start is None:
start = self.files.start_date
if end is None:
end = self.files.stop_date
self._iter_start = [start]
self._iter_stop = [end]
self._iter_list = utils.season_date_range(start, end, freq=step)
self._iter_type = 'date'
else:
raise ValueError(
'Provided an invalid combination of bounds. ' +
'if specifying by file, both bounds must be by file. Other ' +
'combinations of datetime objects and None are allowed.')
def __iter__(self):
"""Iterates instrument object by loading subsequent days or files.
Note
----
Limits of iteration, and iteration type (date/file)
set by `bounds` attribute.
Default bounds are the first and last dates from files on local system.
Examples
--------
::
inst = pysat.Instrument(platform=platform,
name=name,
tag=tag)
start = pysat.datetime(2009,1,1)
stop = pysat.datetime(2009,1,31)
inst.bounds = (start,stop)
for inst in inst:
print('Another day loaded', inst.date)
"""
if self._iter_type == 'file':
for fname in self._iter_list:
self.load(fname=fname)
yield self
elif self._iter_type == 'date':
for date in self._iter_list:
self.load(date=date)
yield self
def next(self):
"""Manually iterate through the data loaded in Instrument object.
Bounds of iteration and iteration type (day/file) are set by
`bounds` attribute.
Note
----
If there were no previous calls to load then the
first day(default)/file will be loaded.
"""
if self._iter_type == 'date':
if self.date is not None:
idx, = np.where(self._iter_list == self.date)
if (len(idx) == 0) | (idx + 1 >= len(self._iter_list)):
raise StopIteration('Outside the set date boundaries.')
else:
idx += 1
self.load(date=self._iter_list[idx[0]])
else:
self.load(date=self._iter_list[0])
elif self._iter_type == 'file':
if self._fid is not None:
first = self.files.get_index(self._iter_list[0])
last = self.files.get_index(self._iter_list[-1])
if (self._fid < first) | (self._fid + 1 > last):
raise StopIteration('Outside the set file boundaries.')
else:
self.load(fname=self._iter_list[self._fid + 1 - first])
else:
self.load(fname=self._iter_list[0])
def prev(self):
"""Manually iterate backwards through the data in Instrument object.
Bounds of iteration and iteration type (day/file)
are set by `bounds` attribute.
Note
----
If there were no previous calls to load then the
first day(default)/file will be loaded.
"""
if self._iter_type == 'date':
if self.date is not None:
idx, = np.where(self._iter_list == self.date)
if (len(idx) == 0) | (idx - 1 < 0):
raise StopIteration('Outside the set date boundaries.')
else:
idx -= 1
self.load(date=self._iter_list[idx[0]])
else:
self.load(date=self._iter_list[-1])
elif self._iter_type == 'file':
if self._fid is not None:
first = self.files.get_index(self._iter_list[0])
last = self.files.get_index(self._iter_list[-1])
if (self._fid - 1 < first) | (self._fid > last):
raise StopIteration('Outside the set file boundaries.')
else:
self.load(fname=self._iter_list[self._fid - 1 - first])
else:
self.load(fname=self._iter_list[-1])
def to_netcdf4(self, fname=None, format=None):
"""Stores loaded data into a netCDF3/4 file.
Parameters
----------
fname : string
full path to save instrument object to
format : string
format keyword passed to netCDF4 routine
NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC, and NETCDF4
Note
----
Stores 1-D data along dimension 'time' - the date time index.
Stores object data (e.g. dataframes within series) separately
- The name of the series is used to prepend extra variable
dimensions within netCDF, key_2, key_3; first dimension time
- The index organizing the data stored as key_sample_index
- from_netcdf3 uses this naming scheme to reconstruct data structure
The datetime index is stored as 'UNIX time'. netCDF-3 doesn't support
64-bit integers so it is stored as a 64-bit float. This results in a
loss of datetime precision when converted back to datetime index
up to hundreds of nanoseconds. Use netCDF4 if this is a problem.
All attributes attached to instrument meta are written to netCDF attrs.
"""
import netCDF4
if format is None:
format = 'NETCDF3_64BIT'
else:
format = format.upper()
with netCDF4.Dataset(fname, mode='w', format=format) as out_data:
num = len(self.data.index)
out_data.createDimension('time', num)
# write out the datetime index
cdfkey = out_data.createVariable(
'time',
'f8',
dimensions=('time'),
)
cdfkey.units = 'seconds since 1970-1-1 0:0:0'
cdfkey.long_name = 'UNIX time'
cdfkey.calendar = 'standard'
cdfkey[:] = (self.data.index.astype(int) *
1.E-3).astype(int) * 1.E-6
# store all of the data in dataframe columns
for key in self.data.columns:
if self[key].dtype != np.dtype('O'):
# not an object, simple column of data, write it out
if ((self[key].dtype == np.int64) &
(format[:7] == 'NETCDF3')):
self[key] = self[key].astype(np.int32)
cdfkey = out_data.createVariable(
key,
self[key].dtype,
dimensions=('time'),
)
cdfkey.units = self.meta[key].units
cdfkey.long_name = self.meta[key].long_name
cdfkey[:] = self[key].values
else:
# we are dealing with a more complicated object
# presuming a series with a dataframe in each location
dims = np.shape(self[key].iloc[0])
obj_dim_names = []
# don't need to recreate last dimension,
# it covers number of columns
for i, dim in enumerate(dims[:-1]):
obj_dim_names.append(key + '_dim_%i' % (i + 1))
out_data.createDimension(obj_dim_names[-1], dim)
var_dim = tuple(['time'] + obj_dim_names)
#print (key, var_dim)
# iterate over columns and store
try:
iterable = self[key].iloc[0].columns
is_frame = True
except AttributeError:
# looking at a series, which doesn't have columns
iterable = self[key].iloc[0].name
is_frame = False
for col in iterable:
if is_frame:
coltype = self[key].iloc[0][col].dtype
else:
coltype = self[key].iloc[0].dtype
if ((coltype == np.int64) & (format[:7] == 'NETCDF3')):
coltype = np.int32
#elif coltype == np.dtype('O'):
# if isinstance(self[key].iloc[0][col][0], basestring):
# coltype = 'S1'
#print (key+'_' +col, var_dim, coltype)
cdfkey = out_data.createVariable(key + '_' + col,
coltype,
dimensions=var_dim)
cdfkey.long_name = col
cdfkey.units = ''
if is_frame:
for i in xrange(num):
cdfkey[i, :] = self[key].iloc[i][
col].values.astype(coltype)
else:
#print (self[key])
print(np.shape(cdfkey))
for i in xrange(num):
print(i)
cdfkey[i, :] = self[key].iloc[i].values.astype(
coltype)
# store the dataframe index for each time of main dataframe
datetime_flag = False
coltype = self[key].iloc[0].index.dtype
# check for datetime index
if coltype == np.dtype('<M8[ns]'):
coltype = 'f8'
datetime_flag = True
if coltype == np.int64:
coltype = np.int32
#print (key+'_' + '_ample', var_dim, coltype)
cdfkey = out_data.createVariable(key + '_dim_1',
coltype,
dimensions=var_dim)
if datetime_flag:
cdfkey.units = 'seconds since 1970-1-1 0:0:0'
cdfkey.long_name = 'UNIX time'
for i in xrange(num):
cdfkey[i, :] = (self[key].iloc[i].index.astype(int)
* 1.E-3).astype(int) * 1.E-6
else:
cdfkey.units = ''
if self[key].iloc[0].index.name is not None:
cdfkey.long_name = self[key].iloc[0].index.name
else:
cdfkey.long_name = key
for i in xrange(num):
cdfkey[i, :] = self[key].iloc[
i].index.to_native_types()
# store any non standard attributes
base_attrb = dir(Instrument())
this_attrb = dir(self)
adict = {}
for key in this_attrb:
if key not in base_attrb:
if key[0] != '_':
adict[key] = self.__getattribute__(key)
# store any non-standard attributes attached to meta
base_attrb = dir(_meta.Meta())
this_attrb = dir(self.meta)
for key in this_attrb:
if key not in base_attrb:
if key[0] != '_':
adict[key] = self.meta.__getattribute__(key)
adict['pysat_version'] = 1.0
adict['Conventions'] = 'CF-1.6'
# check for binary types
for key in adict.keys():
if isinstance(adict[key], bool):
adict[key] = int(adict[key])
out_data.setncatts(adict)
return
def load_netcdf4(fnames=None,
strict_meta=False,
format=None): #, index_label=None,
# unix_time=False, **kwargs):
"""Load netCDF-3/4 file produced by pysat.
Parameters
----------
fnames : string or array_like of strings
filenames to load
strict_meta : boolean
check if metadata across fnames is the same
format : string
format keyword passed to netCDF4 routine
NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC, and NETCDF4
"""
import netCDF4
import string
import pysat
if fnames is None:
raise ValueError("Must supply a filename/list of filenames")
if isinstance(fnames, basestring):
fnames = [fnames]
if format is None:
format = 'NETCDF3_64BIT'
else:
format = format.upper()
saved_mdata = None
running_idx = 0
running_store = []
two_d_keys = []
two_d_dims = []
for fname in fnames:
with netCDF4.Dataset(fname, mode='r', format=format) as data:
# build up dictionary with all ncattrs
# and add those attributes to a pysat meta object
ncattrsList = data.ncattrs()
mdata = pysat.Meta()
for d in ncattrsList:
if hasattr(mdata, d):
mdata.__setattr__(d + '_', data.getncattr(d))
else:
mdata.__setattr__(d, data.getncattr(d))
# loadup all of the variables in the netCDF
loadedVars = {}
for key in data.variables.keys():
# load up metadata
# from here group unique dimensions and act accordingly, 1D, 2D, 3D
if len(data.variables[key].dimensions) == 1:
# assuming basic time dimension
loadedVars[key] = data.variables[key][:]
if key != 'time':
mdata[key] = {
'long_name': data.variables[key].long_name,
'units': data.variables[key].units
}
# 'nc_dimensions':data.variables[key].dimensions}
if len(data.variables[key].dimensions) == 2:
# part of dataframe within dataframe
two_d_keys.append(key)
two_d_dims.append(data.variables[key].dimensions)
# we now have a list of keys that need to go into a dataframe,
# could be more than one, collect unique dimensions for 2D keys
for dim in set(two_d_dims):
# get the name of the final data column
# dimension naming follows name_dim_number,
# pull out name by finding last _ and tracking back
obj_key_name = dim[1][:-string.find(dim[1][::-1], '_') - 5]
# collect variable names associated with object
obj_var_keys = []
for tkey, tdim in zip(two_d_keys, two_d_dims):
if tdim == dim:
obj_var_keys.append(tkey)
# loop over first object dimension
# preallocate dataframes to hold objects because it is faster
init_frame = DataFrame(None)
loop_list = [init_frame
] * data.variables[obj_var_keys[0]].shape[0]
for i, loop_frame in enumerate(loop_list):
loop_frame = init_frame.copy()
for key in obj_var_keys:
loop_frame[key[len(obj_key_name) +
1:]] = data.variables[key][i, :]
# if the object index uses unix time, process into datetime index
if data.variables[obj_key_name +
'_dim_1'].long_name == 'UNIX time':
# nanosecond resolution from datetime64 can't be stored in netcdf3
# no 64-bit integers
# it is stored as a float, need to undo processing
# due to precision loss, resolution limited to the microsecond
loop_frame.index = pds.to_datetime(
(1E6 * loop_frame['dim_1']).astype(int) * 1000)
loop_frame.index.name = 'time'
else:
loop_frame.index = loop_frame['dim_1']
loop_frame.index.name = data.variables[
obj_key_name + '_dim_1'].long_name
del loop_frame['dim_1']
loop_list[i] = loop_frame
#print (loop_list[i] )
#loop_list.append(loop_frame)
# add object data to loaded data dictionary
loadedVars[obj_key_name] = loop_list
del loop_list
# prepare dataframe index for this netcdf file
loadedVars['time'] = pds.to_datetime(
(loadedVars.pop('time') * 1E6).astype(int) * 1000)
running_store.append(loadedVars)
running_idx += len(loadedVars['time'])
# if index_label is not None:
# if unix_time:
# loadedVars['_index'] = pds.to_datetime((loadedVars.pop(index_label)*1E6).astype(int)*1000)
# else:
# loadedVars['_index'] = loadedVars.pop(index_label)
# running_store.append(loadedVars)
# running_idx += len(loadedVars['_index'])
# else:
# # keep a running integer index if none provided
# num = len(loadedVars[loadedVars.keys()[0]])
# # this only guaranteed to work if all variables share the same
# # first dimension
# loadedVars['_index'] = np.arange(num) + running_idx
# running_store.append(loadedVars)
# running_idx += num
if strict_meta:
if saved_mdata is None:
saved_mdata = copy.deepcopy(mdata)
elif (mdata != saved_mdata):
raise ValueError(
'Metadata across filenames is not the same.')
# combine all of the data loaded across files together
# currently doesn't work if list of dicts of lists is provided
# in other words, only one file at a time
out = DataFrame.from_records(running_store[0], index='time')
return out, mdata
