<commit_before>from __future__ import division
from __future__ import print_function

import plotly.graph_objs as go
import pandas as pd
from plotly.offline import plot
import cea.plots.solar_technology_potentials
from cea.plots.variable_naming import LOGO, COLOR, NAMING

__author__ = "<NAME>"
__copyright__ = "Copyright 2018, Architecture and Building Systems - ETH Zurich"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "MIT"
__version__ = "0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Production"


class PVPotentialPlot(cea.plots.solar_technology_potentials.SolarTechnologyPotentialsPlotBase):
    """Implement the pv-electricity-potential plot"""
    name = "PV Electricity Potential"
    expected_parameters = {
        'buildings': 'plots:buildings',
        'scenario-name': 'general:scenario-name',
        'timeframe': 'plots:timeframe',
        'normalization': 'plots:normalization',
    }

    def __init__(self, project, parameters, cache):
        super(PVPotentialPlot, self).__init__(project, parameters, cache)
        self.timeframe = self.parameters['timeframe']
        self.normalization = self.parameters['normalization']
        self.input_files = [(self.locator.PV_totals, [])] + [(self.locator.PV_results, [building])
                                                             for building in self.buildings]

    def calc_titles(self):
        if self.normalization == "gross floor area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "net floor area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "air conditioned floor area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "surface area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "building occupancy":
            titley = 'PV Electricity [kWh/pax]'
        else:
            titley = 'PV Electricity [MWh]'
        return titley

    @property<commit_msg>naming?<commit_after>from __future__ import division
from __future__ import print_function

import plotly.graph_objs as go
import pandas as pd
from plotly.offline import plot
import cea.plots.solar_technology_potentials
from cea.plots.variable_naming import LOGO, COLOR, NAMING

__author__ = "<NAME>"
__copyright__ = "Copyright 2018, Architecture and Building Systems - ETH Zurich"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "MIT"
__version__ = "0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Production"


class PVPotentialPlot(cea.plots.solar_technology_potentials.SolarTechnologyPotentialsPlotBase):
    """Implement the pv-electricity-potential plot"""

    name = "PV Electricity Potential"

    expected_parameters = {
        'buildings': 'plots:buildings',
        'scenario-name': 'general:scenario-name',
        'timeframe': 'plots:timeframe',
        'normalization': 'plots:normalization',
    }

    def __init__(self, project, parameters, cache):
        super(PVPotentialPlot, self).__init__(project, parameters, cache)
        self.timeframe = self.parameters['timeframe']
        self.normalization = self.parameters['normalization']
        self.input_files = [(self.locator.PV_totals, [])] + [(self.locator.PV_results, [building])
                                                             for building in self.buildings]

    def calc_titles(self):
        if self.normalization == "gross floor area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "net floor area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "air conditioned floor area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "surface area":
            titley = 'PV Electricity [kWh/m2]'
        elif self.normalization == "building occupancy":
            titley = 'PV Electricity [kWh/pax]'
        else:
            titley = 'PV Electricity [MWh]'
        return titley

    @property
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{'dir': 'git-commits-cleaned', 'id': '2137085', 'n_tokens_mistral': 1251, 'n_tokens_neox': 1152, 'n_words': 291}