Esempio n. 1
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    def read_excel(self, fname):
        """Read Excel file data and load into the scenario.

        Parameters
        ----------
        fname : string
            path to file
        """
        funcs = {
            'set': self.add_set,
            'par': self.add_par,
        }

        dfs = pd_read(fname, sheet_name=None)

        # get item-type mapping
        df = dfs['ix_type_mapping']
        ix_types = dict(zip(df['item'], df['ix_type']))

        # fill in necessary items first (only sets for now)
        col = 0  # special case for prefill set Series

        def is_prefill(x):
            return dfs[x].columns[0] == col and len(dfs[x].columns) == 1

        prefill = [x for x in dfs if is_prefill(x)]
        for name in prefill:
            data = list(dfs[name][col])
            if len(data) > 0:
                ix_type = ix_types[name]
                funcs[ix_type](name, data)

        # fill all other pars and sets, skipping those already done
        skip_sheets = ['ix_type_mapping'] + prefill
        for sheet_name, df in dfs.items():
            if sheet_name not in skip_sheets and not df.empty:
                ix_type = ix_types[sheet_name]
                funcs[ix_type](sheet_name, df)
Esempio n. 2
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    def read_excel(self, fname, add_units=False, commit_steps=False):
        """Read Excel file data and load into the scenario.

        Parameters
        ----------
        fname : string
            path to file
        add_units : bool
            add missing units, if any,  to the platform instance.
            default: False
        commit_steps : bool
            commit changes after every data addition.
            default: False
        """
        funcs = {
            'set': self.add_set,
            'par': self.add_par,
        }

        logger().info('Reading data from {}'.format(fname))
        dfs = pd_read(fname, sheet_name=None)

        # get item-type mapping
        df = dfs['ix_type_mapping']
        ix_types = dict(zip(df['item'], df['ix_type']))

        # fill in necessary items first (only sets for now)
        col = 0  # special case for prefill set Series

        def is_prefill(x):
            return dfs[x].columns[0] == col and len(dfs[x].columns) == 1

        prefill = [x for x in dfs if is_prefill(x)]
        for name in prefill:
            data = list(dfs[name][col])
            if len(data) > 0:
                ix_type = ix_types[name]
                logger().info('Loading data for {}'.format(name))
                funcs[ix_type](name, data)
        if commit_steps:
            self.commit('Loaded initial data from {}'.format(fname))
            self.check_out()

        # fill all other pars and sets, skipping those already done
        skip_sheets = ['ix_type_mapping'] + prefill
        for sheet_name, df in dfs.items():
            if sheet_name not in skip_sheets and not df.empty:
                logger().info('Loading data for {}'.format(sheet_name))
                if add_units and 'unit' in df.columns:
                    # add missing units
                    units = set(self.platform.units())
                    missing = set(df['unit'].unique()) - units
                    for unit in missing:
                        logger().info('Adding missing unit: {}'.format(unit))
                        self.platform.add_unit(unit)
                # load data
                ix_type = ix_types[sheet_name]
                funcs[ix_type](sheet_name, df)
                if commit_steps:
                    self.commit('Loaded {} from {}'.format(sheet_name, fname))
                    self.check_out()
Esempio n. 3
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def make_obs(fname, exp, **kwargs):
    utils.pd_write(exp, fname, index=False)
    obs = utils.pd_read(fname, **kwargs)
    os.remove(fname)
    return obs
Esempio n. 4
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def make_obs(fname, exp, **kwargs):
    utils.pd_write(exp, fname, index=False)
    obs = utils.pd_read(fname, **kwargs)
    os.remove(fname)
    return obs