def oneparamcov_noautoflush(save_coverage=False, in_memory=False):
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a set of ParameterContext objects to define the parameters in the coverage, add each to the ParameterDictionary
t_ctxt = ParameterContext('time', param_type=QuantityType(value_encoding=np.dtype('int64')))
t_ctxt.axis = AxisTypeEnum.TIME
t_ctxt.uom = 'seconds since 01-01-1970'
pdict.add_context(t_ctxt)
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [0]), scrs, MutabilityEnum.IMMUTABLE) # 0d spatial topology (station/trajectory)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
scov = SimplexCoverage('test_data', create_guid(), 'sample coverage_model', pdict, tdom, sdom, in_memory, auto_flush_values=False)
# Insert some timesteps (automatically expands other arrays)
nt = 100
scov.insert_timesteps(nt)
# Add data for the parameter
scov.set_parameter_values('time', value=np.arange(nt))
if in_memory and save_coverage:
SimplexCoverage.pickle_save(scov, 'test_data/sample.cov')
return scov
def cov_io(self, context, value_array, comp_val=None):
pdict = ParameterDictionary()
time = ParameterContext(name='time', param_type=QuantityType(value_encoding=np.float64))
pdict.add_context(context)
pdict.add_context(time, True)
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [0]), scrs, MutabilityEnum.IMMUTABLE) # 0d spatial topology (station/trajectory)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
cov = SimplexCoverage('test_data', create_guid(), 'sample coverage_model', parameter_dictionary=pdict, temporal_domain=tdom, spatial_domain=sdom)
cov.insert_timesteps(len(value_array))
cov.set_parameter_values('test', tdoa=slice(0,len(value_array)), value=value_array)
comp_val = comp_val if comp_val is not None else value_array
testval = cov.get_parameter_values('test')
try:
np.testing.assert_array_equal(testval, comp_val)
except:
print repr(value_array)
raise
def nospatialcov(save_coverage=False, in_memory=False):
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a set of ParameterContext objects to define the parameters in the coverage, add each to the ParameterDictionary
t_ctxt = ParameterContext('quantity_time', param_type=QuantityType(value_encoding=np.dtype('int64')), variability=VariabilityEnum.TEMPORAL)
t_ctxt.axis = AxisTypeEnum.TIME
t_ctxt.uom = 'seconds since 01-01-1970'
pdict.add_context(t_ctxt)
quant_ctxt = ParameterContext('quantity', param_type=QuantityType(value_encoding=np.dtype('float32')))
quant_ctxt.long_name = 'example of a parameter of type QuantityType'
quant_ctxt.uom = 'degree_Celsius'
pdict.add_context(quant_ctxt)
const_ctxt = ParameterContext('constant', param_type=ConstantType())
const_ctxt.long_name = 'example of a parameter of type ConstantType'
pdict.add_context(const_ctxt)
arr_ctxt = ParameterContext('array', param_type=ArrayType())
arr_ctxt.long_name = 'example of a parameter of type ArrayType with base_type ndarray (resolves to \'object\')'
pdict.add_context(arr_ctxt)
arr2_ctxt = ParameterContext('array2', param_type=ArrayType())
arr2_ctxt.long_name = 'example of a parameter of type ArrayType with base_type object'
pdict.add_context(arr2_ctxt)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
scov = SimplexCoverage('test_data', create_guid(), 'sample coverage_model', pdict, temporal_domain=tdom, in_memory_storage=in_memory)
# Insert some timesteps (automatically expands other arrays)
nt = 20
scov.insert_timesteps(nt)
# Add data for each parameter
scov.set_parameter_values('quantity_time', value=np.arange(nt))
scov.set_parameter_values('quantity', value=np.random.random_sample(nt)*(26-23)+23)
scov.set_parameter_values('constant', value=20)
arrval = []
arr2val = []
for x in xrange(nt): # One value (which IS an array) for each member of the domain
arrval.append(np.random.bytes(np.random.randint(1,20)))
arr2val.append(np.random.random_sample(np.random.randint(1,10)))
scov.set_parameter_values('array', value=arrval)
scov.set_parameter_values('array2', value=arr2val)
if in_memory and save_coverage:
SimplexCoverage.pickle_save(scov, 'test_data/ptypes.cov')
return scov
def samplecov(save_coverage=False, in_memory=False):
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a set of ParameterContext objects to define the parameters in the coverage, add each to the ParameterDictionary
t_ctxt = ParameterContext('time', param_type=QuantityType(value_encoding=np.dtype('int64')))
t_ctxt.uom = 'seconds since 01-01-1970'
pdict.add_context(t_ctxt, is_temporal=True)
lat_ctxt = ParameterContext('lat', param_type=QuantityType(value_encoding=np.dtype('float32')))
lat_ctxt.axis = AxisTypeEnum.LAT
lat_ctxt.uom = 'degree_north'
pdict.add_context(lat_ctxt)
lon_ctxt = ParameterContext('lon', param_type=QuantityType(value_encoding=np.dtype('float32')))
lon_ctxt.axis = AxisTypeEnum.LON
lon_ctxt.uom = 'degree_east'
pdict.add_context(lon_ctxt)
temp_ctxt = ParameterContext('temp', param_type=QuantityType(value_encoding=np.dtype('float32')))
temp_ctxt.uom = 'degree_Celsius'
pdict.add_context(temp_ctxt)
cond_ctxt = ParameterContext('conductivity', param_type=QuantityType(value_encoding=np.dtype('float32')))
cond_ctxt.uom = 'unknown'
pdict.add_context(cond_ctxt)
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [0]), scrs, MutabilityEnum.IMMUTABLE) # 0d spatial topology (station/trajectory)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
scov = SimplexCoverage('test_data', create_guid(), 'sample coverage_model', pdict, tdom, sdom, in_memory)
# Insert some timesteps (automatically expands other arrays)
nt = 30
scov.insert_timesteps(nt)
# Add data for each parameter
scov.set_parameter_values('time', value=np.arange(nt))
scov.set_parameter_values('lat', value=45)
scov.set_parameter_values('lon', value=-71)
# make a random sample of 10 values between 23 and 26
# Ref: http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.random_sample.html#numpy.random.random_sample
# --> To sample multiply the output of random_sample by (b-a) and add a
tvals=np.random.random_sample(nt)*(26-23)+23
scov.set_parameter_values('temp', value=tvals)
scov.set_parameter_values('conductivity', value=np.random.random_sample(nt)*(110-90)+90)
if in_memory and save_coverage:
SimplexCoverage.pickle_save(scov, 'test_data/sample.cov')
return scov
def benchmark_value_setting(num_params=10, num_insertions=100, repeat=1, bulk_ts_insert=False):
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a set of ParameterContext objects to define the parameters in the coverage, add each to the ParameterDictionary
t_ctxt = ParameterContext('time', param_type=QuantityType(value_encoding=np.dtype('int64')))
t_ctxt.axis = AxisTypeEnum.TIME
t_ctxt.uom = 'seconds since 01-01-1970'
pdict.add_context(t_ctxt)
for i in xrange(num_params-1):
pdict.add_context(ParameterContext('param_{0}'.format(i)))
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [0]), scrs, MutabilityEnum.IMMUTABLE) # 0d spatial topology (station/trajectory)
import time
counter = 1
insert_times = []
per_rep_times = []
full_time = time.time()
for r in xrange(repeat):
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
cov = SimplexCoverage('test_data', create_guid(), 'empty sample coverage_model', parameter_dictionary=pdict, temporal_domain=tdom, spatial_domain=sdom)
rep_time = time.time()
if bulk_ts_insert:
cov.insert_timesteps(num_insertions)
for x in xrange(num_insertions):
in_time = time.time()
if not bulk_ts_insert:
cov.insert_timesteps(1)
slice_ = slice(cov.num_timesteps - 1, None)
cov.set_parameter_values('time', 1, tdoa=slice_)
for i in xrange(num_params-1):
cov.set_parameter_values('param_{0}'.format(i), 1.1, tdoa=slice_)
in_time = time.time() - in_time
insert_times.append(in_time)
counter += 1
rep_time = time.time() - rep_time
per_rep_times.append(rep_time)
cov.close()
print 'Average Value Insertion Time (%s repetitions): %s' % (repeat, sum(insert_times) / len(insert_times))
print 'Average Total Expansion Time (%s repetitions): %s' % (repeat, sum(per_rep_times) / len(per_rep_times))
print 'Full Time (includes cov creation/closing): %s' % (time.time() - full_time)
return cov
def cov_io(self, context, value_array, comp_val=None):
pdict = ParameterDictionary()
time = ParameterContext(
name='time', param_type=QuantityType(value_encoding=np.float64))
pdict.add_context(context)
pdict.add_context(time, True)
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs,
MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial',
[0]), scrs, MutabilityEnum.IMMUTABLE
) # 0d spatial topology (station/trajectory)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
cov = SimplexCoverage('test_data',
create_guid(),
'sample coverage_model',
parameter_dictionary=pdict,
temporal_domain=tdom,
spatial_domain=sdom)
self.addCleanup(shutil.rmtree, cov.persistence_dir)
cov.insert_timesteps(len(value_array))
cov.set_parameter_values('test',
tdoa=slice(0, len(value_array)),
value=value_array)
comp_val = comp_val if comp_val is not None else value_array
testval = cov.get_parameter_values('test')
try:
np.testing.assert_array_equal(testval, comp_val)
except:
print repr(value_array)
raise
def ncstation2cov(save_coverage=False, in_memory=False):
# Open the netcdf dataset
ds = Dataset('test_data/usgs.nc')
# Itemize the variable names that we want to include in the coverage
var_names = ['time','lat','lon','z','streamflow','water_temperature',]
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a ParameterContext object for each of the variables in the dataset and add them to the ParameterDictionaryl
for v in var_names:
var = ds.variables[v]
ptype = QuantityType(var.dtype.char)
if v in ('lat','lon','z'):
ptype=ConstantType(ptype)
pcontext = ParameterContext(v, param_type=ptype)
if 'units' in var.ncattrs():
pcontext.uom = var.getncattr('units')
if 'long_name' in var.ncattrs():
pcontext.description = var.getncattr('long_name')
if '_FillValue' in var.ncattrs():
pcontext.fill_value = var.getncattr('_FillValue')
# Set the axis for the coordinate parameters
if v == 'time':
pcontext.variability = VariabilityEnum.TEMPORAL
pcontext.axis = AxisTypeEnum.TIME
elif v == 'lat':
pcontext.axis = AxisTypeEnum.LAT
elif v == 'lon':
pcontext.axis = AxisTypeEnum.LON
elif v == 'z':
pcontext.axis = AxisTypeEnum.HEIGHT
pdict.add_context(pcontext)
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS.standard_temporal()
scrs = CRS.lat_lon_height()
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [0]), scrs, MutabilityEnum.IMMUTABLE) # 1d spatial topology (station/trajectory)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
scov = SimplexCoverage('test_data', create_guid(), 'sample station coverage_model', pdict, tdom, sdom, in_memory)
# Insert the timesteps (automatically expands other arrays)
tvar=ds.variables['time']
scov.insert_timesteps(tvar.size)
# Add data to the parameters - NOT using setters at this point, direct assignment to arrays
for v in var_names:
var = ds.variables[v]
var.set_auto_maskandscale(False)
if v in ('lat','lon','z'):
scov._range_value[v][0] = make_range_expr(var[0])
else:
scov._range_value[v][:] = var[:]
if in_memory and save_coverage:
SimplexCoverage.pickle_save(scov, 'test_data/usgs.cov')
return scov
def ncgrid2cov(save_coverage=False, in_memory=False):
if True:
raise NotImplementedError('Multidimensional support is not available at this time')
# Open the netcdf dataset
ds = Dataset('test_data/ncom.nc')
# Itemize the variable names that we want to include in the coverage
var_names = ['time','lat','lon','depth','water_u','water_v','salinity','water_temp',]
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a ParameterContext object for each of the variables in the dataset and add them to the ParameterDictionary
for v in var_names:
var = ds.variables[v]
pcontext = ParameterContext(v, param_type=QuantityType(value_encoding=ds.variables[v].dtype.char))
if 'units' in var.ncattrs():
pcontext.uom = var.getncattr('units')
if 'long_name' in var.ncattrs():
pcontext.description = var.getncattr('long_name')
if '_FillValue' in var.ncattrs():
pcontext.fill_value = var.getncattr('_FillValue')
# Set the axis for the coordinate parameters
if v == 'time':
pcontext.variability = VariabilityEnum.TEMPORAL
pcontext.axis = AxisTypeEnum.TIME
elif v == 'lat':
pcontext.axis = AxisTypeEnum.LAT
elif v == 'lon':
pcontext.axis = AxisTypeEnum.LON
elif v == 'depth':
pcontext.axis = AxisTypeEnum.HEIGHT
pdict.add_context(pcontext)
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT, AxisTypeEnum.HEIGHT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal'), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [34,57,89]), scrs, MutabilityEnum.IMMUTABLE) # 3d spatial topology (grid)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
scov = SimplexCoverage('test_data', create_guid(), 'sample grid coverage_model', pdict, tdom, sdom, in_memory)
# Insert the timesteps (automatically expands other arrays)
tvar=ds.variables['time']
scov.insert_timesteps(tvar.size)
# Add data to the parameters - NOT using setters at this point, direct assignment to arrays
for v in var_names:
log.debug('Assign values to %s', v)
var = ds.variables[v]
var.set_auto_maskandscale(False)
arr = var[:]
# TODO: Sort out how to leave these sparse internally and only broadcast during read
if v == 'depth':
z,_,_ = my_meshgrid(arr,np.zeros([57]),np.zeros([89]),indexing='ij',sparse=True)
scov._range_value[v][:] = z
elif v == 'lat':
_,y,_ = my_meshgrid(np.zeros([34]),arr,np.zeros([89]),indexing='ij',sparse=True)
scov._range_value[v][:] = y
elif v == 'lon':
_,_,x = my_meshgrid(np.zeros([34]),np.zeros([57]),arr,indexing='ij',sparse=True)
scov._range_value[v][:] = x
else:
scov._range_value[v][:] = var[:]
if in_memory and save_coverage:
SimplexCoverage.pickle_save(scov, 'test_data/ncom.cov')
return scov
def ptypescov(save_coverage=False, in_memory=False):
# Construct temporal and spatial Coordinate Reference System objects
tcrs = CRS([AxisTypeEnum.TIME])
scrs = CRS([AxisTypeEnum.LON, AxisTypeEnum.LAT])
# Construct temporal and spatial Domain objects
tdom = GridDomain(GridShape('temporal', [0]), tcrs, MutabilityEnum.EXTENSIBLE) # 1d (timeline)
sdom = GridDomain(GridShape('spatial', [0]), scrs, MutabilityEnum.IMMUTABLE) # 0d spatial topology (station/trajectory)
# Instantiate a ParameterDictionary
pdict = ParameterDictionary()
# Create a set of ParameterContext objects to define the parameters in the coverage, add each to the ParameterDictionary
quant_t_ctxt = ParameterContext('quantity_time', param_type=QuantityType(value_encoding=np.dtype('int64')), variability=VariabilityEnum.TEMPORAL)
quant_t_ctxt.axis = AxisTypeEnum.TIME
quant_t_ctxt.uom = 'seconds since 01-01-1970'
pdict.add_context(quant_t_ctxt)
cnst_int_ctxt = ParameterContext('const_int', param_type=ConstantType(QuantityType(value_encoding=np.dtype('int32'))), variability=VariabilityEnum.NONE)
cnst_int_ctxt.long_name = 'example of a parameter of type ConstantType, base_type int32'
cnst_int_ctxt.axis = AxisTypeEnum.LAT
cnst_int_ctxt.uom = 'degree_north'
pdict.add_context(cnst_int_ctxt)
cnst_flt_ctxt = ParameterContext('const_float', param_type=ConstantType(), variability=VariabilityEnum.NONE)
cnst_flt_ctxt.long_name = 'example of a parameter of type QuantityType, base_type float (default)'
cnst_flt_ctxt.axis = AxisTypeEnum.LON
cnst_flt_ctxt.uom = 'degree_east'
pdict.add_context(cnst_flt_ctxt)
# func_ctxt = ParameterContext('function', param_type=FunctionType(QuantityType(value_encoding=np.dtype('float32'))))
# func_ctxt.long_name = 'example of a parameter of type FunctionType'
# pdict.add_context(func_ctxt)
quant_ctxt = ParameterContext('quantity', param_type=QuantityType(value_encoding=np.dtype('float32')))
quant_ctxt.long_name = 'example of a parameter of type QuantityType'
quant_ctxt.uom = 'degree_Celsius'
pdict.add_context(quant_ctxt)
arr_ctxt = ParameterContext('array', param_type=ArrayType())
arr_ctxt.long_name = 'example of a parameter of type ArrayType, will be filled with variable-length \'byte-string\' data'
pdict.add_context(arr_ctxt)
rec_ctxt = ParameterContext('record', param_type=RecordType())
rec_ctxt.long_name = 'example of a parameter of type RecordType, will be filled with dictionaries'
pdict.add_context(rec_ctxt)
# Instantiate the SimplexCoverage providing the ParameterDictionary, spatial Domain and temporal Domain
scov = SimplexCoverage('test_data', create_guid(), 'sample coverage_model', pdict, tdom, sdom, in_memory)
# Insert some timesteps (automatically expands other arrays)
nt = 20
scov.insert_timesteps(nt)
# Add data for each parameter
scov.set_parameter_values('quantity_time', value=np.arange(nt))
scov.set_parameter_values('const_int', value=45.32) # Set a constant directly, with incorrect data type (fixed under the hood)
scov.set_parameter_values('const_float', value=make_range_expr(-71.11)) # Set with a properly formed constant expression
scov.set_parameter_values('quantity', value=np.random.random_sample(nt)*(26-23)+23)
# # Setting three range expressions such that indices 0-2 == 10, 3-7 == 15 and >=8 == 20
# scov.set_parameter_values('function', value=make_range_expr(10, 0, 3, min_incl=True, max_incl=False, else_val=-999.9))
# scov.set_parameter_values('function', value=make_range_expr(15, 3, 8, min_incl=True, max_incl=False, else_val=-999.9))
# scov.set_parameter_values('function', value=make_range_expr(20, 8, min_incl=True, max_incl=False, else_val=-999.9))
arrval = []
recval = []
letts='abcdefghijklmnopqrstuvwxyz'
while len(letts) < nt:
letts += 'abcdefghijklmnopqrstuvwxyz'
for x in xrange(nt):
arrval.append(np.random.bytes(np.random.randint(1,20))) # One value (which is a byte string) for each member of the domain
d = {letts[x]: letts[x:]}
recval.append(d) # One value (which is a dict) for each member of the domain
scov.set_parameter_values('array', value=arrval)
scov.set_parameter_values('record', value=recval)
if in_memory and save_coverage:
SimplexCoverage.pickle_save(scov, 'test_data/ptypes.cov')
return scov
def repair(self,
backup=True,
copy_over=True,
keep_temp=False,
reanalyze=False,
analyze_bricks=False,
detailed_analysis=False):
"""
Heavy repair tool that recreates a blank persisted Coverage from the broken coverage's
original construction parameters, then reconstructs the Master and Parameter metadata
files by inspection of the ION objects and "valid" brick files.
@return:
"""
if self._ar is None or reanalyze:
self._ar = self._do_analysis(analyze_bricks=analyze_bricks,
detailed_analysis=detailed_analysis)
if self._ar.is_corrupt:
if len(self._ar.get_brick_corruptions()) > 0:
raise NotImplementedError(
'Brick corruption. Cannot repair at this time!!!')
else:
# Repair the Master and Parameter metadata files
# Need the ParameterDictionary, TemporalDomain and SpatialDomain
pdict = ParameterDictionary.load(
self._dso.parameter_dictionary)
tdom = GridDomain.load(self._dso.temporal_domain)
sdom = GridDomain.load(self._dso.spatial_domain)
# Set up the working directory for the recovered coverage
tempcov_dir = tempfile.mkdtemp('covs')
# Create the temporary Coverage
tempcov = SimplexCoverage(root_dir=tempcov_dir,
persistence_guid=self._guid,
name=self._guid,
parameter_dictionary=pdict,
spatial_domain=sdom,
temporal_domain=tdom)
# Handle to persistence layer for tempcov
pl = tempcov._persistence_layer
# Set up the original and temporary coverage path strings
orig_dir = os.path.join(self.cov_pth, self._guid)
temp_dir = os.path.join(tempcov.persistence_dir,
tempcov.persistence_guid)
# Insert same number of timesteps into temporary coverage as in broken coverage
brick_domains_new, new_brick_list, brick_list_spans, tD, bD, min_data_bound, max_data_bound = self.inspect_bricks(
self.cov_pth, self._guid, 'time')
empty_cov = brick_list_spans is None # If None, there are no brick files --> no timesteps, empty coverage!
if not empty_cov: # If None, there are no brick files --> no timesteps, empty coverage!
bls = [s.value for s in brick_list_spans]
maxes = [sum(b[3]) for b in new_brick_list.values()]
tempcov.insert_timesteps(sum(maxes))
# Replace metadata is the Master file
pl.master_manager.brick_domains = brick_domains_new
pl.master_manager.brick_list = new_brick_list
# Repair ExternalLinks to brick files
f = h5py.File(pl.master_manager.file_path, 'a')
for param_name in pdict.keys():
del f[param_name]
f.create_group(param_name)
for brick in bls:
link_path = '/{0}/{1}'.format(param_name, brick[0])
brick_file_name = '{0}.hdf5'.format(brick[0])
brick_rel_path = os.path.join(
pl.parameter_metadata[param_name].root_dir.
replace(tempcov.persistence_dir,
'.'), brick_file_name)
log.debug('link_path: %s', link_path)
log.debug('brick_rel_path: %s', brick_rel_path)
pl.master_manager.add_external_link(
link_path, brick_rel_path, brick[0])
pl.flush_values()
pl.flush()
tempcov.close()
# Remove 'rtree' dataset from Master file if it already exists (post domain expansion)
# to make way for reconstruction
f = h5py.File(pl.master_manager.file_path, 'a')
if 'rtree' in f.keys():
del f['rtree']
f.close()
# Reconstruct 'rtree' dataset
# Open temporary Coverage and PersistenceLayer objects
fixed_cov = AbstractCoverage.load(tempcov.persistence_dir,
mode='a')
pl_fixed = fixed_cov._persistence_layer
# Call update_rtree for each brick using PersistenceLayer builtin
brick_count = 0
if not empty_cov:
for brick in bls:
rtree_extents, brick_extents, brick_active_size = pl_fixed.calculate_extents(
brick[1][1], bD, tD)
pl_fixed.master_manager.update_rtree(brick_count,
rtree_extents,
obj=brick[0])
brick_count += 1
# Update parameter_bounds property based on each parameter's brick data using deep inspection
valid_bounds_types = [
'BooleanType', 'ConstantType', 'QuantityType',
'ConstantRangeType'
]
if not empty_cov:
for param in pdict.keys():
if pdict.get_context(
param
).param_type.__class__.__name__ in valid_bounds_types:
brick_domains_new, new_brick_list, brick_list_spans, tD, bD, min_data_bound, max_data_bound = self.inspect_bricks(
self.cov_pth, self._guid, param)
# Update the metadata
pl_fixed.update_parameter_bounds(
param, [min_data_bound, max_data_bound])
pl_fixed.flush()
fixed_cov.close()
# Create backup copy of original Master and Parameter files
if backup:
import datetime
orig_master_file = os.path.join(
self.cov_pth, '{0}_master.hdf5'.format(self._guid))
# Generate the timestamp
tstamp_format = '%Y%m%d%H%M%S'
tstamp = datetime.datetime.now().strftime(tstamp_format)
backup_master_file = os.path.join(
self.cov_pth,
'{0}_master.{1}.hdf5'.format(self._guid, tstamp))
shutil.copy2(orig_master_file, backup_master_file)
for param in pdict.keys():
param_orig = os.path.join(orig_dir, param,
'{0}.hdf5'.format(param))
param_backup = os.path.join(
orig_dir, param,
'{0}.{1}.hdf5'.format(param, tstamp))
shutil.copy2(param_orig, param_backup)
# Copy Master and Parameter metadata files back to original/broken coverage (cov_pth) location
if copy_over == True:
shutil.copy2(
os.path.join(tempcov.persistence_dir,
'{0}_master.hdf5'.format(self._guid)),
os.path.join(self.cov_pth,
'{0}_master.hdf5'.format(self._guid)))
for param in pdict.keys():
shutil.copy2(
os.path.join(temp_dir, param,
'{0}.hdf5'.format(param)),
os.path.join(orig_dir, param,
'{0}.hdf5'.format(param)))
# Reanalyze the repaired coverage
self._ar = self._do_analysis(analyze_bricks=True)
# Verify repair worked, clean up if not
if self._ar.is_corrupt:
# If the files were backed up then revert
if backup:
# Remove backed up files and clean up the repair attempt
log.info(
'Repair attempt failed. Reverting to pre-repair state.'
)
# Use backup copy to replace post-repair file.
shutil.copy2(backup_master_file, orig_master_file)
# Delete the backup
os.remove(backup_master_file)
# Iterate over parameters and revert to pre-repair state
for param in pdict.keys():
param_orig = os.path.join(orig_dir, param,
'{0}.hdf5'.format(param))
param_backup = os.path.join(
orig_dir, param,
'{0}.{1}.hdf5'.format(param, tstamp))
# Use backup copy to replace post-repair file.
shutil.copy2(param_backup, param_orig)
# Delete the backup
os.remove(param_backup)
raise ValueError(
'Coverage repair failed! Revert to stored backup version, if possible.'
)
# Remove temporary coverage
if keep_temp == False:
shutil.rmtree(tempcov_dir)
else:
return tempcov_dir
else:
log.info('Coverage is not corrupt, nothing to repair!')
def repair(self, backup=True, copy_over=True, keep_temp=False, reanalyze=False, analyze_bricks=False, detailed_analysis=False):
"""
Heavy repair tool that recreates a blank persisted Coverage from the broken coverage's
original construction parameters, then reconstructs the Master and Parameter metadata
files by inspection of the ION objects and "valid" brick files.
@return:
"""
if self._ar is None or reanalyze:
self._ar = self._do_analysis(analyze_bricks=analyze_bricks, detailed_analysis=detailed_analysis)
if self._ar.is_corrupt:
if len(self._ar.get_brick_corruptions()) > 0:
raise NotImplementedError('Brick corruption. Cannot repair at this time!!!')
else:
# Repair the Master and Parameter metadata files
# Need the ParameterDictionary, TemporalDomain and SpatialDomain
pdict = ParameterDictionary.load(self._dso.parameter_dictionary)
tdom = GridDomain.load(self._dso.temporal_domain)
sdom = GridDomain.load(self._dso.spatial_domain)
# Set up the working directory for the recovered coverage
tempcov_dir = tempfile.mkdtemp('covs')
# Create the temporary Coverage
tempcov = SimplexCoverage(root_dir=tempcov_dir, persistence_guid=self._guid, name=self._guid, parameter_dictionary=pdict, spatial_domain=sdom, temporal_domain=tdom)
# Handle to persistence layer for tempcov
pl = tempcov._persistence_layer
# Set up the original and temporary coverage path strings
orig_dir = os.path.join(self.cov_pth, self._guid)
temp_dir = os.path.join(tempcov.persistence_dir, tempcov.persistence_guid)
# Insert same number of timesteps into temporary coverage as in broken coverage
brick_domains_new, new_brick_list, brick_list_spans, tD, bD, min_data_bound, max_data_bound = self.inspect_bricks(self.cov_pth, self._guid, 'time')
empty_cov = brick_list_spans is None # If None, there are no brick files --> no timesteps, empty coverage!
if not empty_cov: # If None, there are no brick files --> no timesteps, empty coverage!
bls = [s.value for s in brick_list_spans]
maxes = [sum(b[3]) for b in new_brick_list.values()]
tempcov.insert_timesteps(sum(maxes))
# Replace metadata is the Master file
pl.master_manager.brick_domains = brick_domains_new
pl.master_manager.brick_list = new_brick_list
# Repair ExternalLinks to brick files
with HDFLockingFile(pl.master_manager.file_path, 'r+') as f:
for param_name in pdict.keys():
del f[param_name]
f.create_group(param_name)
for param_name in pdict.keys():
for brick in bls:
link_path = '/{0}/{1}'.format(param_name, brick[0])
brick_file_name = '{0}.hdf5'.format(brick[0])
brick_rel_path = os.path.join(pl.parameter_metadata[param_name].root_dir.replace(tempcov.persistence_dir, '.'), brick_file_name)
log.debug('link_path: %s', link_path)
log.debug('brick_rel_path: %s', brick_rel_path)
pl.master_manager.add_external_link(link_path, brick_rel_path, brick[0])
pl.flush_values()
pl.flush()
tempcov.close()
# Remove 'rtree' dataset from Master file if it already exists (post domain expansion)
# to make way for reconstruction
with HDFLockingFile(pl.master_manager.file_path, 'r+') as f:
if 'rtree' in f.keys():
del f['rtree']
# Reconstruct 'rtree' dataset
# Open temporary Coverage and PersistenceLayer objects
fixed_cov = AbstractCoverage.load(tempcov.persistence_dir, mode='r+')
pl_fixed = fixed_cov._persistence_layer
# Call update_rtree for each brick using PersistenceLayer builtin
brick_count = 0
if not empty_cov:
for brick in bls:
rtree_extents, brick_extents, brick_active_size = pl_fixed.calculate_extents(brick[1][1],bD,tD)
pl_fixed.master_manager.update_rtree(brick_count, rtree_extents, obj=brick[0])
brick_count += 1
# Update parameter_bounds property based on each parameter's brick data using deep inspection
valid_bounds_types = [
'BooleanType',
'ConstantType',
'QuantityType',
'ConstantRangeType'
]
if not empty_cov:
for param in pdict.keys():
if pdict.get_context(param).param_type.__class__.__name__ in valid_bounds_types:
brick_domains_new, new_brick_list, brick_list_spans, tD, bD, min_data_bound, max_data_bound = self.inspect_bricks(self.cov_pth, self._guid, param)
# Update the metadata
pl_fixed.update_parameter_bounds(param, [min_data_bound, max_data_bound])
pl_fixed.flush()
fixed_cov.close()
# Create backup copy of original Master and Parameter files
if backup:
import datetime
orig_master_file = os.path.join(self.cov_pth, '{0}_master.hdf5'.format(self._guid))
# Generate the timestamp
tstamp_format = '%Y%m%d%H%M%S'
tstamp = datetime.datetime.now().strftime(tstamp_format)
backup_master_file = os.path.join(self.cov_pth, '{0}_master.{1}.hdf5'.format(self._guid, tstamp))
shutil.copy2(orig_master_file, backup_master_file)
for param in pdict.keys():
param_orig = os.path.join(orig_dir, param, '{0}.hdf5'.format(param))
param_backup = os.path.join(orig_dir, param, '{0}.{1}.hdf5'.format(param, tstamp))
shutil.copy2(param_orig, param_backup)
# Copy Master and Parameter metadata files back to original/broken coverage (cov_pth) location
if copy_over == True:
shutil.copy2(os.path.join(tempcov.persistence_dir, '{0}_master.hdf5'.format(self._guid)), os.path.join(self.cov_pth, '{0}_master.hdf5'.format(self._guid)))
for param in pdict.keys():
shutil.copy2(os.path.join(temp_dir, param, '{0}.hdf5'.format(param)), os.path.join(orig_dir, param, '{0}.hdf5'.format(param)))
# Reanalyze the repaired coverage
self._ar = self._do_analysis(analyze_bricks=True)
# Verify repair worked, clean up if not
if self._ar.is_corrupt:
# If the files were backed up then revert
if backup:
# Remove backed up files and clean up the repair attempt
log.info('Repair attempt failed. Reverting to pre-repair state.')
# Use backup copy to replace post-repair file.
shutil.copy2(backup_master_file, orig_master_file)
# Delete the backup
os.remove(backup_master_file)
# Iterate over parameters and revert to pre-repair state
for param in pdict.keys():
param_orig = os.path.join(orig_dir, param, '{0}.hdf5'.format(param))
param_backup = os.path.join(orig_dir, param, '{0}.{1}.hdf5'.format(param, tstamp))
# Use backup copy to replace post-repair file.
shutil.copy2(param_backup, param_orig)
# Delete the backup
os.remove(param_backup)
raise ValueError('Coverage repair failed! Revert to stored backup version, if possible.')
# Remove temporary coverage
if keep_temp == False:
shutil.rmtree(tempcov_dir)
else:
return tempcov_dir
else:
log.info('Coverage is not corrupt, nothing to repair!')
