old_contents: 
  
  
  import pandas as pd
  import os
  from cea.utilities import dbf
  import cea.globalvar
  import cea.config
  import cea.inputlocator
  
  __author__ = "Jimeno A. Fonseca"
  __copyright__ = "Copyright 2017, Architecture and Building Systems - ETH Zurich"
  __credits__ = ["Jimeno A. Fonseca", "Sreepathi Bhargava Krishna"]
  __license__ = "MIT"
  __version__ = "0.1"
  __maintainer__ = "Daren Thomas"
  __email__ = "cea@arch.ethz.ch"
  __status__ = "Production"
  
  def calc_spatio_temporal_visuals(locator, period, variables_to_plot, list_of_buildings, initial_date):
  
      # now the dates in which the building demand is calculated is stored in 'date'
      date = pd.date_range(initial_date, periods=8760, freq='H')[period[0]: period[1]]
      time = date.strftime("%Y%m%d%H%M%S")
  
      # this loop checks if all the buildings are selected and gets the building names from Total demand.csv file
      if 'all' in list_of_buildings:
          building_names = pd.read_csv(locator.get_total_demand())['Name'].values
      else:
          building_names = list_of_buildings
  
      for i, building in enumerate(building_names):
          # importing corresponding variables of each building and then slicing it to take just a single period value
          # i.e a time step
          data = pd.read_csv(locator.SC_results(building))[variables_to_plot][period[0]: period[1]]
          data['date'] = time
          data['Name'] = building
          data['rad_kWh/m2'] = data['radiation_kWh'] / data['Area_SC_m2']
  
          if i == 0:
              final = data
          else:
              final = final.append(data, ignore_index=True)
  
      dbf.dataframe_to_dbf(final, locator.get_4D_sc_plot(period))
  
  def main(config):
  
      locator = cea.inputlocator.InputLocator(scenario=config.scenario)
  
      variables_to_plot = ['Q_SC_gen_kWh', 'radiation_kWh', 'Area_SC_m2']
      initial_date = '1/1/2015'
      list_of_buildings = ['all']  # 'all' for all buildings or else provide a list of building names
      period = [1, 168] # period in hours of the year to visualize
      calc_spatio_temporal_visuals(locator, period, variables_to_plot, list_of_buildings, initial_date)
  
  if __name__ == '__main__':
      main(cea.config.Configuration())
  
  
  
  
new_contents: 
  
  
  import pandas as pd
  import os
  from cea.utilities import dbf
  import cea.globalvar
  import cea.config
  import cea.inputlocator
  
  __author__ = "Jimeno A. Fonseca"
  __copyright__ = "Copyright 2017, Architecture and Building Systems - ETH Zurich"
  __credits__ = ["Jimeno A. Fonseca", "Sreepathi Bhargava Krishna"]
  __license__ = "MIT"
  __version__ = "0.1"
  __maintainer__ = "Daren Thomas"
  __email__ = "cea@arch.ethz.ch"
  __status__ = "Production"
  
  def calc_spatio_temporal_visuals(locator, period, variables_to_plot, list_of_buildings, initial_date):
  
      # now the dates in which the building demand is calculated is stored in 'date'
      date = pd.date_range(initial_date, periods=8760, freq='H')[period[0]: period[1]]
      time = date.strftime("%Y%m%d%H%M%S")
  
      # this loop checks if all the buildings are selected and gets the building names from Total demand.csv file
      if 'all' in list_of_buildings:
          building_names = pd.read_csv(locator.get_total_demand())['Name'].values
      else:
          building_names = list_of_buildings
  
      for i, building in enumerate(building_names):
          # importing corresponding variables of each building and then slicing it to take just a single period value
          # i.e a time step
          data = pd.read_csv(locator.SC_results(building, panel_type='FP'))[variables_to_plot][period[0]: period[1]]
          data['date'] = time
          data['Name'] = building
          data['rad_kWh/m2'] = data['radiation_kWh'] / data['Area_SC_m2']
  
          if i == 0:
              final = data
          else:
              final = final.append(data, ignore_index=True)
  
      dbf.dataframe_to_dbf(final, locator.get_4D_sc_plot(period))
  
  def main(config):
  
      locator = cea.inputlocator.InputLocator(scenario=config.scenario)
  
      variables_to_plot = ['Q_SC_gen_kWh', 'radiation_kWh', 'Area_SC_m2']
      initial_date = '1/1/2015'
      list_of_buildings = ['all']  # 'all' for all buildings or else provide a list of building names
      period = [1, 168] # period in hours of the year to visualize
      calc_spatio_temporal_visuals(locator, period, variables_to_plot, list_of_buildings, initial_date)
  
  if __name__ == '__main__':
      main(cea.config.Configuration())
  
  
  
  
subject: add SC panel_type to 4D plot