fdi Quick Start
Contents:
The following demostrates important dataset and pal functionalities. It was made by running fdi/resources/example.py with command elpy-shell-send-group-and-step [c-c c-y c-g] in emacs.
You can copy the code from code blocks by clicking the copy icon on the top-right, with he prompts and results removed.
>>> # import these first.
... import copy
... import getpass
... import os
... from datetime import datetime
... import logging
... from fdi.dataset.product import Product
... from fdi.dataset.metadata import Parameter, NumericParameter, MetaData
... from fdi.dataset.finetime import FineTime1, utcobj
... from fdi.dataset.dataset import ArrayDataset, TableDataset, Column
... from fdi.pal.context import Context, MapContext
... from fdi.pal.productref import ProductRef
... from fdi.pal.query import MetaQuery
... from fdi.pal.poolmanager import PoolManager, DEFAULT_MEM_POOL
... from fdi.pal.productstorage import ProductStorage
dataset
ArrayDataset
>>> # Creation
... a1 = [1, 4.4, 5.4E3, -22, 0xa2] # a 1D array of data
... v = ArrayDataset(data=a1, unit='ev', description='5 elements')
... v
ArrayDataset{ [1, 4.4, 5400.0, -22, 162] <ev>, description = "5 elements", meta = MetaData{[], listeners = []}}
>>> # data access
... v[2]
5400.0
>>> v.unit
'ev'
>>> v.unit = 'm'
... v.unit
'm'
>>> # iteration
... for m in v:
... print(m)
1
4.4
5400.0
-22
162
>>> [m**3 for m in v if m > 0 and m < 40]
[1, 85.18400000000003]
>>> # slice
... v[1:3]
[4.4, 5400.0]
>>> v[2:-1]
[5400.0, -22]
>>> v.data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
... v[0:2]
[[1, 2, 3], [4, 5, 6]]
>>> # Run this to see a demo of the ``toString()`` function::
... # make a 4-D array: a list of 2 lists of 3 lists of 4 lists of 5 elements.
... s = [[[[i + j + k + l for i in range(5)] for j in range(4)]
... for k in range(3)] for l in range(2)]
... x = ArrayDataset(data=s)
... print(x.toString())
# ArrayDataset
# description = "UNKNOWN"
# meta = MetaData{[], listeners = []}
# unit = "None"
# data =
0 1 2 3
1 2 3 4
2 3 4 5
3 4 5 6
4 5 6 7
1 2 3 4
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8
6 7 8 9
#=== dimension 4
1 2 3 4
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8
6 7 8 9
3 4 5 6
4 5 6 7
5 6 7 8
6 7 8 9
7 8 9 10
#=== dimension 4
TableDataset
>>> # Creation
... a1 = [dict(name='col1', unit='eV', column=[1, 4.4, 5.4E3]),
... dict(name='col2', unit='cnt', column=[0, 43.2, 2E3])
... ]
... v = TableDataset(data=a1)
... v
TableDataset{ description = "UNKNOWN", meta = MetaData{[], listeners = []}, data = "OD{'col1':Column{ [1, 4.4, 5400.0] <eV>, description = "UNKNOWN", meta = MetaData{[], listeners = []}}, 'col2':Column{ [0, 43.2, 2000.0] <cnt>, description = "UNKNOWN", meta = MetaData{[], listeners = []}}}"}
>>> # many other ways to create a TableDataset
... v3 = TableDataset(data=[('col1', [1, 4.4, 5.4E3], 'eV'),
... ('col2', [0, 43.2, 2E3], 'cnt')])
... v == v3
True
>>> # quick and dirty. data are list of lists without names or units
... a5 = [[1, 4.4, 5.4E3], [0, 43.2, 2E3]]
... v5 = TableDataset(data=a5)
... print(v5.toString())
# TableDataset
# description = "UNKNOWN"
# meta = MetaData{[], listeners = []}
# data =
# col1 col2
# None None
1 0
4.4 43.2
5400.0 2000.0
>>> # access
... # get names of all column
... v5.data.keys()
odict_keys(['col1', 'col2'])
>>> # get a list of all columns' data
... [c.data for c in v5.data.values()] # == a5
[[1, 4.4, 5400.0], [0, 43.2, 2000.0]]
>>> # get column by name
... c_1 = v5['col1']
... c_1
Column{ [1, 4.4, 5400.0] <None>, description = "UNKNOWN", meta = MetaData{[], listeners = []}}
>>> # indexOf
... v5.indexOf('col1') # == u.indexOf(c_1)
0
>>> v5.indexOf(c_1)
0
>>> # get a cell
... v5['col2'][1] # 43.2
43.2
>>> # set cell value
... v5['col2'][1] = 123
... v5['col2'][1] # 123
123
>>> v5.setValueAt(aValue=42, rowIndex=1, columnIndex=1)
... v5.getValueAt(rowIndex=1, columnIndex=1) # 42
42
>>> # unit access
... v3['col1'].unit # == 'eV'
'eV'
>>> # add, set, and replace columns and rows
... # column set / get
... u = TableDataset()
... c1 = Column([1, 4], 'sec')
... u.addColumn('col3', c1)
... u.columnCount # 1
1
>>> # for non-existing names set is addColum.
... c2 = Column([2, 3], 'eu')
... u['col4'] = c2
... u['col4'][0] # 2
2
>>> u.columnCount # 2
2
>>> # replace column for existing names
... c3 = Column([5, 7], 'j')
... u['col4'] = c3
... u['col4'][0] # c3.data[0]
5
>>> # addRow
... u.rowCount # 2
2
>>> cc = copy.deepcopy(c1)
... c33, c44 = 3.3, 4.4
... cc.append(c33)
... u.addRow({'col4': c44, 'col3': c33})
... u.rowCount # 3
3
>>> u['col3'] # cc
Column{ [1, 4, 3.3] <sec>, description = "UNKNOWN", meta = MetaData{[], listeners = []}}
>>> # removeRow
... u.removeRow(u.rowCount - 1) # [c33, c44]
[3.3, 4.4]
>>> u.rowCount # 2
2
>>> # syntax ``in``
... [c for c in u] # list of column names ['col1', 'col2']
['col3', 'col4']
>>> # run this to see ``toString()``
... ELECTRON_VOLTS = 'eV'
... SECONDS = 'sec'
... t = [x * 1.0 for x in range(10)]
... e = [2 * x + 100 for x in t]
... # creating a table dataset to hold the quantified data
... x = TableDataset(description="Example table")
... x["Time"] = Column(data=t, unit=SECONDS)
... x["Energy"] = Column(data=e, unit=ELECTRON_VOLTS)
... print(x.toString())
# TableDataset
# description = "Example table"
# meta = MetaData{[], listeners = []}
# data =
# Time Energy
# sec eV
0.0 100.0
1.0 102.0
2.0 104.0
3.0 106.0
4.0 108.0
5.0 110.0
6.0 112.0
7.0 114.0
8.0 116.0
9.0 118.0
Parameter
>>> # Creation
... # standard way -- with keyword arguments
... a1 = 'a test parameter'
... a2 = 300
... a3 = 'integer'
... v = Parameter(description=a1, value=a2, type_=a3)
... v.description # == a1
'a test parameter'
>>> v.value # == a2
300
>>> v.type_ # == a3
'integer'
>>> # with no argument
... v = Parameter()
... v.description # == 'UNKNOWN# inherited from Anotatable
'UNKNOWN'
>>> v.value # is None
>>> v.type_ # == ''
''
>>> # make a blank one then set attributes
... v = Parameter(description=a1)
... v.description # == a1
'a test parameter'
>>> v.value # is None
>>> v.type_ # == ''
''
>>> v.setValue(a2)
... v.setType(a3)
... v.description # == a1
'a test parameter'
>>> v.value # == a2
300
>>> v.type_ # == a3
'integer'
>>> # test equivalence of v.setXxxx(a) and v.xxx = a
... a1 = 'test score'
... a2 = 98
... v = Parameter()
... v.description = a1
... v.value = a2
... v.description # == a1
'test score'
>>> v.value # == a2
98
>>> # test equals
... b1 = ''.join(a1) # make a new string copy
... b2 = a2 + 0 # make a copy
... v1 = Parameter(description=b1, value=b2)
... v.equals(v1)
True
>>> v == v1
True
>>> v1.value = -4
... v.equals(v1) # False
False
>>> v != v1 # True
True
Metadata
>>> # Creation
... a1 = 'age'
... a2 = NumericParameter(description='since 2000',
... value=20, unit='year', type_='integer')
... v = MetaData()
... v.set(a1, a2)
... v.get(a1) # == a2
NumericParameter{ 20 (year) <integer>, "since 2000"}
>>> # add more parameter
... a3 = 'Bob'
... v.set(name='name', newParameter=Parameter(a3))
... v.get('name').value # == a3
'Bob'
>>> # access parameters in metadata
... v = MetaData()
... # a more readable way to set a parameter
... v[a1] = a2 # DRM doc case
... # a more readable way to get a parameter
... v[a1] # == a2
NumericParameter{ 20 (year) <integer>, "since 2000"}
>>> v.get(a1) # == a2
NumericParameter{ 20 (year) <integer>, "since 2000"}
>>> v['date'] = Parameter(description='take off at',
... value=FineTime1.datetimeToFineTime(datetime.now(tz=utcobj)))
... # names of all parameters
... [n for n in v] # == [a1, 'date']
['age', 'date']
>>> print(v.toString())
MetaData{[age = NumericParameter{ 20 (year) <integer>, "since 2000"}, date = Parameter{ 108120221290 <integer>, "take off at"}, ], listeners = []}
>>> # remove parameter
... v.remove(a1) # inherited from composite
... print(v.size()) # == 1
1
Product
>>> # Creation:
... x = Product(description="product example with several datasets",
... instrument="Crystal-Ball", modelName="Mk II")
... x.meta['description'].value # == "product example with several datasets"
'product example with several datasets'
>>> x.instrument # == "Crystal-Ball"
'Crystal-Ball'
>>> # ways to add datasets
... i0 = 6
... i1 = [[1, 2, 3], [4, 5, i0], [7, 8, 9]]
... i2 = 'ev' # unit
... i3 = 'image1' # description
... image = ArrayDataset(data=i1, unit=i2, description=i3)
... x["RawImage"] = image
... x["RawImage"].data # == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> # no unit or description. different syntax but same function as above
... x.set('QualityImage', ArrayDataset(
... [[0.1, 0.5, 0.7], [4e3, 6e7, 8], [-2, 0, 3.1]]))
... x["QualityImage"].unit # is None
>>> # add a tabledataset
... s1 = [('col1', [1, 4.4, 5.4E3], 'eV'),
... ('col2', [0, 43.2, 2E3], 'cnt')]
... x["Spectrum"] = TableDataset(data=s1)
... print(x["Spectrum"].toString())
# TableDataset
# description = "UNKNOWN"
# meta = MetaData{[], listeners = []}
# data =
# col1 col2
# eV cnt
1 0
4.4 43.2
5400.0 2000.0
>>> # mandatory properties are also in metadata
... # test mandatory BaseProduct properties that are also metadata
... x.creator = ""
... a0 = "Me, myself and I"
... x.creator = a0
... x.creator # == a0
'Me, myself and I'
>>> # metada by the same name is also set
... x.meta["creator"].value # == a0
'Me, myself and I'
>>> # change the metadata
... a1 = "or else"
... x.meta["creator"] = Parameter(a1)
... # metada changed
... x.meta["creator"].value # == a1
'or else'
>>> # so did the property
... x.creator # == a1
'or else'
>>> # Demo ``toString()`` function. The result should be ::
... print(x.toString())
# Product
# description = "product example with several datasets"
# meta = MetaData{[description = Parameter{ product example with several datasets <string>, "Description of this product"}, type = Parameter{ Product <string>, "Product Type identification. Fully qualified Python class name or CARD."}, creator = Parameter{ or else <string>, "UNKNOWN"}, creationDate = Parameter{ 2017-01-01T00:00:00.000000 TAI(0) <finetime>, "Creation date of this product"}, rootCause = Parameter{ UNKOWN <string>, "Reason of this run of pipeline."}, schema = Parameter{ 0.3 <string>, "Version of product schema"}, startDate = Parameter{ 2017-01-01T00:00:00.000000 TAI(0) <finetime>, "Nominal start time of this product."}, endDate = Parameter{ 2017-01-01T00:00:00.000000 TAI(0) <finetime>, "Nominal end time of this product."}, instrument = Parameter{ Crystal-Ball <string>, "Instrument that generated data of this product"}, modelName = Parameter{ Mk II <string>, "Model name of the instrument of this product"}, mission = Parameter{ _AGS <string>, "Name of the mission."}, ], listeners = []}
# History
# description = "UNKNOWN"
# meta = MetaData{[], listeners = []}
# data =
# data =
# [ RawImage ]
# ArrayDataset
# description = "image1"
# meta = MetaData{[], listeners = []}
# unit = "ev"
# data =
1 4 7
2 5 8
3 6 9
# [ QualityImage ]
# ArrayDataset
# description = "UNKNOWN"
# meta = MetaData{[], listeners = []}
# unit = "None"
# data =
0.1 4000.0 -2
0.5 60000000.0 0
0.7 8 3.1
# [ Spectrum ]
# TableDataset
# description = "UNKNOWN"
# meta = MetaData{[], listeners = []}
# data =
# col1 col2
# eV cnt
1 0
4.4 43.2
5400.0 2000.0
pal
Store a Product in a Pool and Get a Reference Back
Create a product and a productStorage with a pool registered
>>> # disable debugging messages
... logger = logging.getLogger('')
... logger.setLevel(logging.WARNING)
>>> # a pool for demonstration will be create here
... demopoolpath = '/tmp/demopool_' + getpass.getuser()
... demopool = 'file://' + demopoolpath
... # clean possible data left from previous runs
... os.system('rm -rf ' + demopoolpath)
... PoolManager.getPool(DEFAULT_MEM_POOL).removeAll()
... PoolManager.removeAll()
>>> # create a prooduct and save it to a pool
... x = Product(description='in store')
... # add a tabledataset
... s1 = [('energy', [1, 4.4, 5.6], 'eV'), ('freq', [0, 43.2, 2E3], 'Hz')]
... x["Spectrum"] = TableDataset(data=s1)
... # create a product store
... pstore = ProductStorage(pool=demopool)
... pstore
ProductStorage { pool= OD{'file:///tmp/demopool_mh':LocalPool { pool= file:///tmp/demopool_mh }} }
>>> # save the product and get a reference
... prodref = pstore.save(x)
... print(prodref)
ProductRef{ ProductURN=urn:file:///tmp/demopool_mh:fdi.dataset.product.Product:0, meta=MetaData{[description = Parameter{ in store <string>, "Description of this product"}, type = Parameter{ Product <string>, "Product Type identificat...}
>>> # get the urn string
... urn = prodref.urn
... print(urn) # urn:file:///tmp/demopool_mh:fdi.dataset.product.Product:0
urn:file:///tmp/demopool_mh:fdi.dataset.product.Product:0
>>> newp = ProductRef(urn).product
... # the new and the old one are equal
... print(newp == x) # == True
True
Context: a Product with References
>>> # the reference can be stored in another product of Context class
... p1 = Product(description='p1')
... p2 = Product(description='p2')
... # create an empty mapcontext that can carry references with name labels
... map1 = MapContext(description='product with refs 1')
... # A ProductRef created from a lone product will use a mempool
... pref1 = ProductRef(p1)
... pref1
ProductRef{ ProductURN=urn:mem:///default:fdi.dataset.product.Product:0, meta=None}
>>> # A productStorage with a pool on disk
... pref2 = pstore.save(p2)
... pref2
ProductRef{ ProductURN=urn:file:///tmp/demopool_mh:fdi.dataset.product.Product:1, meta=MetaData{[description = Parameter{ p2 <string>, "Description of this p...
>>> # how many prodrefs do we have? (do not use len() due to classID, version)
... map1['refs'].size() # == 0
0
>>> len(pref1.parents) # == 0
0
>>> len(pref2.parents) # == 0
0
>>> # add a ref to the contex. every ref has a name in mapcontext
... map1['refs']['spam'] = pref1
... # add the second one
... map1['refs']['egg'] = pref2
... # how many prodrefs do we have? (do not use len() due to classID, version)
... map1['refs'].size() # == 2
2
>>> len(pref2.parents) # == 1
1
>>> pref2.parents[0] == map1
True
>>> pref1.parents[0] == map1
True
>>> # remove a ref
... del map1['refs']['spam']
... # how many prodrefs do we have? (do not use len() due to classID, version)
... map1.refs.size() # == 1
1
>>> len(pref1.parents) # == 0
0
>>> # add ref2 to another map
... map2 = MapContext(description='product with refs 2')
... map2.refs['also2'] = pref2
... map2['refs'].size() # == 1
1
>>> # two parents
... len(pref2.parents) # == 2
2
>>> pref2.parents[1] == map2
True
Query a ProductStorage
>>> # clean possible data left from previous runs
... defaultpoolpath = '/tmp/pool_' + getpass.getuser()
... newpoolpath = '/tmp/newpool_' + getpass.getuser()
... os.system('rm -rf ' + defaultpoolpath)
... os.system('rm -rf ' + newpoolpath)
... PoolManager.getPool(DEFAULT_MEM_POOL).removeAll()
... PoolManager.removeAll()
... # make a productStorage
... defaultpool = 'file://'+defaultpoolpath
... pstore = ProductStorage(defaultpool)
... # make another
... newpoolname = 'file://' + newpoolpath
... pstore2 = ProductStorage(newpoolname)
>>> # add some products to both storages
... n = 7
... for i in range(n):
... a0, a1, a2 = 'desc %d' % i, 'fatman %d' % (i*4), 5000+i
... if i < 3:
... x = Product(description=a0, instrument=a1)
... x.meta['extra'] = Parameter(value=a2)
... elif i < 5:
... x = Context(description=a0, instrument=a1)
... x.meta['extra'] = Parameter(value=a2)
... ...
... x = MapContext(description=a0, instrument=a1)
... x.meta['extra'] = Parameter(value=a2)
... x.meta['time'] = Parameter(value=FineTime1(a2))
... if i < 4:
... r = pstore.save(x)
... else:
... r = pstore2.save(x)
... print(r.urn)
... # Two pools, 7 products
... # [P P P C] [C M M]
urn:file:///tmp/pool_mh:fdi.dataset.product.Product:0
urn:file:///tmp/pool_mh:fdi.dataset.product.Product:1
urn:file:///tmp/pool_mh:fdi.dataset.product.Product:2
urn:file:///tmp/pool_mh:fdi.pal.context.Context:0
urn:file:///tmp/newpool_mh:fdi.pal.context.Context:0
urn:file:///tmp/newpool_mh:fdi.pal.context.MapContext:0
urn:file:///tmp/newpool_mh:fdi.pal.context.MapContext:1
>>> # register the new pool above to the 1st productStorage
... pstore.register(newpoolname)
... len(pstore.getPools()) # == 2
2
>>> # make a query on product metadata, which is the variable 'm'
... # in the query expression, i.e. ``m = product.meta; ...``
... # But '5000 < m["extra"]' does not work. see tests/test.py.
... q = MetaQuery(Product, 'm["extra"] > 5001 and m["extra"] <= 5005')
... # search all pools registered on pstore
... res = pstore.select(q)
... # [2,3,4,5]
... len(res) # == 4
... [r.product.description for r in res]
['desc 2', 'desc 3', 'desc 4', 'desc 5']
>>> def t(m):
... # query is a function
... import re
... return re.match('.*n.1.*', m['instrument'].value)
>>> q = MetaQuery(Product, t)
... res = pstore.select(q)
... # [3,4]
... [r.product.instrument for r in res]
['fatman 12', 'fatman 16']
>>> # same as above but query is on the product. this is slow.
... q = AbstractQuery(Product, 'p', '"n 1" in p.instrument')
... res = pstore.select(q)
... # [3,4]
... [r.product.instrument for r in res]
['fatman 12', 'fatman 16']
>>>
pns
See the installation and testing sections of the pns page.