value.py

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import numpy as np
import netCDF4

from .base import Creator
from .. import param

from refractor import framework as rf

class CreatorFlaggedValue(Creator):

    value = param.Choice(param.Array(dims=1), param.ArrayWithUnit(dims=1))
    retrieved = param.Scalar(bool, required=False)

    def retrieval_flag(self, **kwargs):
        val = self.value(**kwargs)
        retrieved = self.retrieved()

        if hasattr(val, "value"):
            # ArrayWithUnit
            val_shape = val.value.shape
        else:
            val_shape = val.shape

        if retrieved is None or retrieved:
            return np.ones(val_shape, dtype=bool)
        else:
            return np.zeros(val_shape, dtype=bool)

class CreatorFlaggedValueMultiChannel(Creator):

    value = param.Choice(param.Array(dims=2), param.ArrayWithUnit(dims=2))
    retrieved = param.Iterable(required=False)

    def retrieval_flag(self, **kwargs):
        val = self.value(**kwargs)
        retrieved = self.retrieved()

        if hasattr(val, "value"):
            # ArrayWithUnit
            val_shape = val.value.shape
        else:
            val_shape = val.shape

        if retrieved is None:
            return np.ones(val_shape, dtype=bool)
        else:
            flags = np.empty(val_shape, dtype=bool)
            for chan_idx, chan_flag in enumerate(retrieved):
                flags[chan_idx, :] = chan_flag
            return flags

class ArrayWithUnit(Creator):
    "Create an array with unit class from a numpy array and type string"

    value = param.Array()
    units = param.Scalar(str)

    def create(self, **kwargs):

        value = self.value(**kwargs)
        units = self.units()

        num_dims = len(value.shape)
        if num_dims == 3:
            return rf.ArrayWithUnit_double_3(value, units)
        elif num_dims == 2:
            return rf.ArrayWithUnit_double_2(value, units)
        elif num_dims == 1:
            return rf.ArrayWithUnit_double_1(value, units)
        else:
            raise param.ParamError("Unsupported number of dimensions %s for array" % (num_dims))

class LoadValuesFromHDF(Creator):

    filename = param.Scalar(str)

    def create(self, **kwargs):

        values = {}

        contents = netCDF4.Dataset(self.filename(), "r")

        def extract_datasets(group, values):
            for v_name, v_ds in group.variables.items():
                v_path = (group.path + "/" + v_ds.name).lstrip("/")
                values[v_path] = v_ds[:]
            for g_name, child_group in group.groups.items():
                extract_datasets(child_group, values)

        extract_datasets(contents, values)

        return values

class NamedCommonValue(Creator):

    name = param.Scalar(str)

    def create(self, **kwargs):
        self.register_parameter(self.name(), param.AnyValue())
        return self.param(self.name())