Cleanup dev

# import all the required packages

from pyehub.excel_to_request_format import convert as ex_req_convert

from pyehub.outputter import pretty_print

from pyehub.energy_hub.ehub_model import EHubModel

import pickle

# from pyehub import plots

# specify the input file

# input_file = 'C:\\Users\\User\\Desktop\\JupyterNB\\Notebooks\\test_tt.xlsx' # or "my_input.xlsx" you just created

input_file = 'tutorials/test_file.xlsx'

# form the model

# model = EHubModel(excel=input_file)

request = ex_req_convert(input_file)

model = EHubModel(request=request)

di = model._public_attributes()

# with open('dict.dd','rb') as dd:

#     ddo=pickle.load(dd)

# print(ddo)

# for i in range(17505):

#     ddo[i]=float(ddo[i])

# print(type(ddo[0]))

# model.time=[x for x in range(20)]

# model.TIME_SERIES['Elec']={x:0.394 for x in range(20)}

#

# model.TIME_SERIES['Heat']={x:20.0 for x in range(20)}

# model.TIME_SERIES['Irradiation']={x:0.019 for x in range(20)}

# model.LOADS['Elec']=model.TIME_SERIES['Elec']

# model.LOADS['Heat']=model.TIME_SERIES['Heat']

# solve the model

result = model.solve()

# with open('model1.results', 'wb') as results_file:

#     pickle.dump(result, results_file)

# print(model.is_installed)

# print(model.technologies)

# output the results

# plots.plot_energy_balance(result)

pretty_print(result)

# print(result['solution']['LOADS']['Elec'])

# convert python dict to json

# import json

# with open('result.json', 'w') as fp:

#     json.dump(<name of python dict>, fp)

# multiple_hubs(False, 'out.xlsx','tests/network_tests/hub' ,'tests/network_tests/network2.xlsx',24.5, 3)

import pickle

with open('model1.results','wb') as dd:

    pickle.dump(result,dd)

with open('model1.mdl','wb') as md:

    pickle.dump(model,md)

 No newline at end of file

# import all the required packages

from pyehub.outputter import pretty_print

from pyehub.energy_hub.ehub_model import EHubModel

import pickle

# from pyehub import plots

# specify the input file

# input_file = 'C:\\Users\\User\\Desktop\\JupyterNB\\Notebooks\\test_tt.xlsx' # or "my_input.xlsx" you just created

input_file = 'tutorials/test_file.xlsx'

# form the model

model = EHubModel(excel=input_file)

# with open('dict.dd','rb') as dd:

#     ddo=pickle.load(dd)

# for i in range(17505):

#     ddo[i]=float(ddo[i])

# print(type(ddo[0]))

# model.time=[x for x in range(20)]

# model.TIME_SERIES['Elec']={x:0.394 for x in range(20)}

#

# model.TIME_SERIES['Heat']=[20.0 for x in range(20)]

# model.TIME_SERIES['Irradiation']=[0.019 for x in range(20)]

# model.LOADS['Elec']=model.TIME_SERIES['Elec']

# model.LOADS['Heat']=model.TIME_SERIES['Heat']

# solve the model

result = model.solve()

# with open('model1.results', 'wb') as results_file:

#     pickle.dump(result, results_file)

# print(model.is_installed)

# print(model.technologies)

# output the results

# plots.plot_energy_balance(result)

pretty_print(result)

# print(result['solution']['LOADS']['Elec'])

# convert python dict to json

# import json

# with open('result.json', 'w') as fp:

#     json.dump(<name of python dict>, fp)

# multiple_hubs(False, 'out.xlsx','tests/network_tests/hub' ,'tests/network_tests/network2.xlsx',24.5, 3)

from pyehub.outputter import plot_storages

from pyehub.outputter import plot_energy_balance

import pickle

import pandas as pd

import matplotlib.pyplot as plt

from pyehub.energy_hub.ehub_model import EHubModel

from pyehub.outputter import pretty_print

# import seaborn as sns

# sns.set()

def main():

    # with open('model1.results', 'rb') as results_file:

    #     results = pickle.load(results_file)

    # with open('model1.mdl', 'rb') as md:

    #     model = pickle.load(md)

    input_file = 'tutorials/test_file.xlsx'

    model = EHubModel(excel=input_file)

    model1 =EHubModel(excel=input_file)

    results = model.solve()

    results1=model1.solve()

    # print(results['solution'] == results1['solution'])

    plot_storages(results)

    plot_energy_balance(model1, results)

    # pretty_print(results)

    # print(results['solution'] == results1['solution'])

if __name__ == '__main__':

    main()

    # current_palette = sns.color_palette()

    # sns.palplot(current_palette)

# df_storage_state = attributes['storage_level']

# df_storage_state.plot(kind='line', y=storage, ax=ax, xticks=attributes['time'], yticks=ser_storage_state)

# df_gross_charge = attributes['energy_to_storage']

# df_gross_charge.plot(kind='line', y=storage, ax=ax, yticks=ser_gross_charge)

# df_discharging = -attributes['energy_from_storage']

# df_discharging.plot(kind='line', y=storage, ax=ax, yticks=df_discharging[storage])

# # df.plot(kind='line', y='Hot Water Tank', color='red', ax=ax)

# plt.show()

# list_for_yticks = ser_storage_state.tolist() + ser_gross_charge.tolist() + ser_gross_discharge.tolist()

        # list_for_yticks = list(dict.fromkeys(list_for_yticks))

# list_for_yticks = list_for_yticks/capacity * 100

# df.drop(['storage_state'], axis=1).plot(drawstyle='steps-post',color=('green','red'),linewidth=2, ax=ax,)

 # overlay.set_xlim(right=10)

        # overlay.grid(marker='x')