import numpy
from tables import *

class Particle(IsDescription):
    ADCcount    = Int16Col()              # signed short integer
    TDCcount    = UInt8Col()              # unsigned byte
    grid_i      = Int32Col()              # integer
    grid_j      = Int32Col()              # integer
    idnumber    = Int64Col()              # signed long long
    name        = StringCol(16, dflt="")  # 16-character String
    pressure    = Float32Col(shape=2)     # float  (single-precision)
    temperature = Float64Col()            # double (double-precision)

Particle2 = {
    # You can also use any of the atom factories, i.e. the one which
    # accepts a PyTables type.
    "ADCcount": Col.from_type("int16"),    # signed short integer
    "TDCcount": Col.from_type("uint8"),    # unsigned byte
    "grid_i": Col.from_type("int32"),    # integer
    "grid_j": Col.from_type("int32"),    # integer
    "idnumber": Col.from_type("int64"),    # signed long long
    "name": Col.from_kind("string", 16),  # 16-character String
    "pressure": Col.from_type("float32", (2,)), # float  (single-precision)
    "temperature": Col.from_type("float64"),  # double (double-precision)
}

# The name of our HDF5 filename
filename = "table-tree.h5"

# Open a file in "w"rite mode
h5file = open_file(filename, mode = "w")

# Create a new group under "/" (root)
group = h5file.create_group("/", 'detector')

# Create one table on it
#table = h5file.create_table(group, 'table', Particle, "Title example")
# You can choose creating a Table from a description dictionary if you wish
table = h5file.create_table(group, 'table', Particle2, "Title example")

# Create a shortcut to the table record object
particle = table.row

# Fill the table with 10 particles
for i in xrange(10):
    # First, assign the values to the Particle record
    particle['name']  = 'Particle: %6d' % (i)
    particle['TDCcount'] = i % 256
    particle['ADCcount'] = (i * 256) % (1 << 16)
    particle['grid_i'] = i
    particle['grid_j'] = 10 - i
    particle['pressure'] = [float(i*i), float(i*2)]
    particle['temperature'] = float(i**2)
    particle['idnumber'] = i * (2 ** 34)  # This exceeds integer range
    # This injects the Record values.
    particle.append()

# Flush the buffers for table
table.flush()

# Get actual data from table. We are interested in column pressure.
pressure = [ p['pressure'] for p in table.iterrows() ]
print "Last record ==>", p
print "Column pressure ==>", numpy.array(pressure)
print "Total records in table ==> ", len(pressure)
print

# Create a new group to hold new arrays
gcolumns = h5file.create_group("/", "columns")
print "columns ==>", gcolumns, pressure
# Create an array with this info under '/columns' having a 'list' flavor
h5file.create_array(gcolumns, 'pressure', pressure,
                   "Pressure column")
print "gcolumns.pressure type ==> ", gcolumns.pressure.atom.dtype

# Do the same with TDCcount, but with a numpy object
TDC = [ p['TDCcount'] for p in table.iterrows() ]
print "TDC ==>", TDC
print "TDC shape ==>", numpy.array(TDC).shape
h5file.create_array('/columns', 'TDC', numpy.array(TDC), "TDCcount column")

# Do the same with name column
names = [ p['name'] for p in table.iterrows() ]
print "names ==>", names
h5file.create_array('/columns', 'name', names, "Name column")
# This works even with homogeneous tuples or lists (!)
print "gcolumns.name shape ==>", gcolumns.name.shape
print "gcolumns.name type ==> ", gcolumns.name.atom.dtype

print "Table dump:"
for p in table.iterrows():
    print p

# Save a recarray object under detector
r = numpy.rec.array("a"*300, formats='f4,3i4,a5,i2', shape=3)
recarrt = h5file.create_table("/detector", 'recarray', r, "RecArray example")
r2 = r[0:3:2]
# Change the byteorder property
recarrt = h5file.create_table("/detector", 'recarray2', r2,
                             "Non-contiguous recarray")
print recarrt
print

print h5file.root.detector.table.description
# Close the file
h5file.close()

#sys.exit()

# Reopen it in append mode
h5file = open_file(filename, "a")

# Ok. let's start browsing the tree from this filename
print "Reading info from filename:", h5file.filename
print

# Firstly, list all the groups on tree
print "Groups in file:"
for group in h5file.walk_groups("/"):
    print group
print

# List all the nodes (Group and Leaf objects) on tree
print "List of all nodes in file:"
print h5file

# And finally, only the Arrays (Array objects)
print "Arrays in file:"
for array in h5file.walk_nodes("/", classname="Array"):
    print array
print

# Get group /detector and print some info on it
detector = h5file.get_node("/detector")
print "detector object ==>", detector

# List only leaves on detector
print "Leaves in group", detector, ":"
for leaf in h5file.list_nodes("/detector", 'Leaf'):
    print leaf
print

# List only tables on detector
print "Tables in group", detector, ":"
for leaf in h5file.list_nodes("/detector", 'Table'):
    print leaf
print

# List only arrays on detector (there should be none!)
print "Arrays in group", detector, ":"
for leaf in h5file.list_nodes("/detector", 'Array'):
    print leaf
print

# Get "/detector" Group object
group = h5file.root.detector
print "/detector ==>", group

# Get the "/detector/table
table = h5file.get_node("/detector/table")
print "/detector/table ==>", table

# Get metadata from table
print "Object:", table
print "Table name:", table.name
print "Table title:", table.title
print "Rows saved on table: %d" % (table.nrows)

print "Variable names on table with their type:"
for name in table.colnames:
    print "  ", name, ':=', table.coldtypes[name]
print

# Read arrays in /columns/names and /columns/pressure

# Get the object in "/columns pressure"
pressureObject = h5file.get_node("/columns", "pressure")

# Get some metadata on this object
print "Info on the object:", pressureObject
print "  shape ==>", pressureObject.shape
print "  title ==>", pressureObject.title
print "  type ==> ", pressureObject.atom.dtype
print "  byteorder ==> ", pressureObject.byteorder

# Read the pressure actual data
pressureArray = pressureObject.read()
print "  data type ==>", type(pressureArray)
print "  data ==>", pressureArray
print

# Get the object in "/columns/names"
nameObject = h5file.root.columns.name

# Get some metadata on this object
print "Info on the object:", nameObject
print "  shape ==>", nameObject.shape
print "  title ==>", nameObject.title
print "  type ==> " % nameObject.atom.dtype


# Read the 'name' actual data
nameArray = nameObject.read()
print "  data type ==>", type(nameArray)
print "  data ==>", nameArray

# Print the data for both arrays
print "Data on arrays name and pressure:"
for i in range(pressureObject.shape[0]):
    print "".join(nameArray[i]), "-->", pressureArray[i]
print


# Finally, append some new records to table
table = h5file.root.detector.table

# Append 5 new particles to table (yes, tables can be enlarged!)
particle = table.row
for i in xrange(10, 15):
    # First, assign the values to the Particle record
    particle['name']  = 'Particle: %6d' % (i)
    particle['TDCcount'] = i % 256
    particle['ADCcount'] = (i * 256) % (1 << 16)
    particle['grid_i'] = i
    particle['grid_j'] = 10 - i
    particle['pressure'] = [float(i*i), float(i*2)]
    particle['temperature'] = float(i**2)
    particle['idnumber'] = i * (2 ** 34)  # This exceeds integer range
    # This injects the Row values.
    particle.append()

# Flush this table
table.flush()

print "Columns name and pressure on expanded table:"
# Print some table columns, for comparison with array data
for p in table:
    print p['name'], '-->', p['pressure']
print

# Put several flavors
oldflavor = table.flavor
print table.read(field="ADCcount")
table.flavor = "numpy"
print table.read(field="ADCcount")
table.flavor = oldflavor
print table.read(0, 0, 1, "name")
table.flavor = "python"
print table.read(0, 0, 1, "name")
table.flavor = oldflavor
print table.read(0, 0, 2, "pressure")
table.flavor = "python"
print table.read(0, 0, 2, "pressure")
table.flavor = oldflavor

# Several range selections
print "Extended slice in selection: [0:7:6]"
print table.read(0, 7, 6)
print "Single record in selection: [1]"
print table.read(1)
print "Last record in selection: [-1]"
print table.read(-1)
print "Two records before the last in selection: [-3:-1]"
print table.read(-3, -1)

# Print a recarray in table form
table = h5file.root.detector.recarray2
print "recarray2:", table
print "  nrows:", table.nrows
print "  byteorder:", table.byteorder
print "  coldtypes:", table.coldtypes
print "  colnames:", table.colnames

print table.read()
for p in table.iterrows():
    print p['f1'], '-->', p['f2']
print

result = [ rec['f1'] for rec in table if rec.nrow < 2 ]
print result

# Test the File.rename_node() method
#h5file.rename_node(h5file.root.detector.recarray2, "recarray3")
h5file.rename_node(table, "recarray3")
# Delete a Leaf from the HDF5 tree
h5file.remove_node(h5file.root.detector.recarray3)
# Delete the detector group and its leaves recursively
#h5file.remove_node(h5file.root.detector, recursive=1)
# Create a Group and then remove it
h5file.create_group(h5file.root, "newgroup")
h5file.remove_node(h5file.root, "newgroup")
h5file.rename_node(h5file.root.columns, "newcolumns")

print h5file

# Close this file
h5file.close()