def process_model(input_csv_file,
output_csv_file,
supplementary_input_csv_file=None):
df1, df2 = get_input_dataframes(input_csv_file,
supplementary_input_csv_file)
# 'periods' hold actual number of periods in the model
periods = get_periods_as_list(df1)
# check if we need lag handling
has_lags = df_has_block(df1, 'value_lag')
# Period 0 is reported data, we store it and not process
values0, multipliers0, equations = get_input_parameters(df1, df2, 0)
values_holder_list = [values0]
# we put multipliers in holder list, because we want to write them down in ouput csv by period
multiplier_holder_list = [multipliers0]
# We start processing from period 1
for p in periods[1:]:
print("-------- Period", p)
values, multipliers, equations = get_input_parameters(df1, df2, p)
if has_lags:
# get system definiton
values_lagged = get_df_block_as_dict(df1, 'value_lag', p)
else:
# The linear system uses lagged variables as an input
# here we construct them from last period
previous_period_values = values_holder_list[-1]
values_lagged = {
k + '_lag': previous_period_values[k]
for k in last_values
}
# solving the system:
x = solve_lin_system(multipliers, equations, values_lagged)
# 'x' must be saved to a new array/dataframe/list holding results for all periods
# GL: We save into a list containing results + multipliers for the
# current period
values_holder_list.append(_get_result_as_dict(x, values))
multiplier_holder_list.append(multipliers)
# save results
# must save array/dataframe/list holding results for all periods
# dump_csv_output() should be dump_csv_output(output_df, output_csv_file)
# GL: we pass lists of values and multipliers
dump_csv_output(values_holder_list,
equations,
multiplier_holder_list,
output_csv_file,
write_lagged=has_lags)
def process_model(input_csv_file, output_csv_file, supplementary_input_csv_file = None):
df1, df2 = get_input_dataframes(input_csv_file, supplementary_input_csv_file)
# 'periods' hold actual number of periods in the model
periods = get_periods_as_list(df1)
# check if we need lag handling
has_lags = df_has_block(df1, 'value_lag')
# Period 0 is reported data, we store it and not process
values0, multipliers0, equations = get_input_parameters(df1, df2, 0)
values_holder_list = [values0]
# we put multipliers in holder list, because we want to write them down in ouput csv by period
multiplier_holder_list = [multipliers0]
# We start processing from period 1
for p in periods[1:]:
print("-------- Period", p)
values, multipliers, equations = get_input_parameters(df1, df2, p)
if has_lags:
# get system definiton
values_lagged = get_df_block_as_dict(df1, 'value_lag', p)
else:
# The linear system uses lagged variables as an input
# here we construct them from last period
previous_period_values = values_holder_list[-1]
values_lagged = {k + '_lag' : previous_period_values[k] for k in last_values}
# solving the system:
x = solve_lin_system(multipliers, equations, values_lagged)
# 'x' must be saved to a new array/dataframe/list holding results for all periods
# GL: We save into a list containing results + multipliers for the
# current period
values_holder_list.append(_get_result_as_dict(x, values))
multiplier_holder_list.append(multipliers)
# save results
# must save array/dataframe/list holding results for all periods
# dump_csv_output() should be dump_csv_output(output_df, output_csv_file)
# GL: we pass lists of values and multipliers
dump_csv_output(values_holder_list,
equations,
multiplier_holder_list,
output_csv_file,
write_lagged=has_lags)
if __name__ == "__main__":
df = get_input_df('input.tab')
multipliers = get_multipliers_as_dict(df)
values = get_values_as_dict(df, 0)
equations = get_equations_as_list(df)
print ('\nMultipliers:')
pprint(multipliers)
print ('\nValues:')
pprint(values)
print ('\nEquations:')
pprint(equations)
# solving the system:
x = solve_lin_system(multipliers, equations, values)
# CHECK 1:
# change dict1 type to nparray
# compare dict_1 to x
data1 = get_ref_array()
print("Reference data:")
print(data1)
print("Found solution:")
print(x.ix[data1.index])
# To actually assert their equality use np.allclose()
print('Matching values?', np.allclose(data1, x.ix[data1.index]))
if __name__ == "__main__":
df = get_input_df('input.tab')
multipliers = get_multipliers_as_dict(df)
values = get_values_as_dict(df, 0)
equations = get_equations_as_list(df)
print('\nMultipliers:')
pprint(multipliers)
print('\nValues:')
pprint(values)
print('\nEquations:')
pprint(equations)
# solving the system:
x = solve_lin_system(multipliers, equations, values)
# CHECK 1:
# change dict1 type to nparray
# compare dict_1 to x
data1 = get_ref_array()
print("Reference data:")
print(data1)
print("Found solution:")
print(x.ix[data1.index])
# To actually assert their equality use np.allclose()
print('Matching values?', np.allclose(data1, x.ix[data1.index]))
