def finder_csv(loci_excel, csv_name, h_excel, added_point, output_excel,
output_fig):
"""
Function returns dataframe with mapped values.
"""
FILE_PTH_INPUT_1 = common.get_local_file_path_withfolder(
file_name=loci_excel, folder_name='Excel')
#FILE_PTH_INPUT_2 = common.get_local_file_path_withfolder(file_name=scan_excel, folder_name='dig_results')
FILE_PTH_INPUT_3 = common.get_local_file_path_withfolder(
file_name=h_excel, folder_name='Excel')
FILE_PTH_OUTPUT_1 = common.get_local_file_path_withfolder(
file_name=output_excel, folder_name='Output_excel')
#FILE_PTH_OUTPUT_2 = common.get_local_file_path_withfolder(file_name=output_fig, folder_name='plots')
loci_df = common.import_excel(FILE_PTH_INPUT_1)
scan_df = CSV_reader_df_maker(
csv_name=csv_name,
csv_folder_name=constants.folder_file_names.dig_result_folder,
object_name=constants.Digsilent.object_name,
param_name_list=constants.Digsilent.param_name_list,
header_list=constants.Digsilent.header_list,
number_or_name=constants.Digsilent.number_or_name_toggle,
param_number_list=constants.Digsilent.param_number_list)
# csv_name, csv_folder_name, object_name, param_name_list, header_list
#scan_df = common.import_excel(FILE_PTH_INPUT_2)
h_df = common.import_excel(FILE_PTH_INPUT_3)
h_df_len = len(h_df)
loci_num = int((len(loci_df.columns)) / 2)
df_list = list()
range_list = list()
# if h_df_len > 0:
# idx = (scan_df['H'].isin(h_df['H'].tolist())) # filters the buses that have GSPs inside the given GSP list
# scan_df = scan_df.loc[idx == True, :]
scan_df['Loci Range'] = np.nan
scan_df['worst_V_R'] = np.nan
scan_df['worst_V_L'] = np.nan
scan_df['worst_V_X'] = np.nan
scan_df['worst_I_R'] = np.nan
scan_df['worst_I_L'] = np.nan
scan_df['worst_I_X'] = np.nan
for i in range(0, int(loci_num) * 2, 2):
df = loci_df.iloc[1:, [i, i + 1]]
df_list.append(df)
range_list.append(df.columns)
for i in range(1, len(scan_df) + 1):
h = float(scan_df.loc[i, 'H'])
for r in range(0, loci_num):
cur_range = range_list[r]
if h >= cur_range[0]:
if h <= cur_range[1]:
scan_df.loc[i, 'Loci Range'] = r
for i in range(1, len(scan_df) + 1):
loci_cur = df_list[int(scan_df.loc[i, 'Loci Range'])]
loci_cur_new = point_adder(loci=loci_cur, p_num=added_point)
r_w = scan_df.loc[i, 'R']
x_w = scan_df.loc[i, 'X']
R_worst_v, X_worst_v, R_worst_i, X_worst_i = worst_point(
loci=loci_cur_new, R=r_w, X=x_w)
if constants.plotting.save_plot:
fig_title = 'h=' + str(scan_df.loc[i, 'H'])
plot_name = output_fig + str(
scan_df.loc[i, 'H']) + constants.plotting.figure_type
FILE_PTH_OUTPUT_2 = common.get_local_file_path_withfolder(
file_name=plot_name, folder_name=constants.plotting.folder)
plotter.loci_scatter_plotter(loci_df=loci_cur_new,
R=r_w,
X=x_w,
R_V=R_worst_v,
X_V=X_worst_v,
R_I=R_worst_i,
X_I=X_worst_i,
save_o=constants.plotting.save_plot,
p_show=constants.plotting.show_plot,
fig_path=FILE_PTH_OUTPUT_2,
fig_name=fig_title)
k = 1
scan_df.loc[i, 'worst_V_R'] = R_worst_v
scan_df.loc[i, 'worst_V_X'] = X_worst_v
scan_df.loc[i, 'worst_I_R'] = R_worst_i
scan_df.loc[i, 'worst_I_X'] = X_worst_i
scan_df.loc[
i, 'worst_V_L'] = (X_worst_v /
(2 * math.pi * 60 * scan_df.loc[i, 'H'])) * 1000
scan_df.loc[
i, 'worst_I_L'] = (X_worst_i /
(2 * math.pi * 60 * scan_df.loc[i, 'H'])) * 1000
if h_df_len > 0:
idx = (scan_df['H'].isin(h_df['H'].tolist())
) # filters the buses that have GSPs inside the given GSP list
scan_df = scan_df.loc[idx == True, :]
scan_df.to_excel(FILE_PTH_OUTPUT_1)
return scan_df
#FILE_NAME_OUTPUT_1 = 'intact.xlsx'
# FILE_PTH_INPUT_1 = common.get_local_file_path_withfolder(file_name=FILE_NAME_INPUT_1, folder_name='Excel')
# FILE_PTH_INPUT_2 = common.get_local_file_path_withfolder(file_name=FILE_NAME_INPUT_2, folder_name='dig_results')
FILE_PTH_INPUT_3 = common.get_local_file_path_withfolder(
file_name=FILE_NAME_INPUT_3, folder_name='Excel')
FILE_PTH_INPUT_4 = common.get_local_file_path_withfolder(
file_name=FILE_NAME_INPUT_4, folder_name='dig_results')
FILE_PTH_INPUT_6 = common.get_local_file_path_withfolder(
file_name=FILE_NAME_INPUT_6, folder_name='dig_results')
# FILE_PTH_OUTPUT_1 = common.get_local_file_path_withfolder(file_name=FILE_NAME_OUTPUT_1, folder_name='Output_excel')
point_number = 20
# loci_df = common.import_excel(FILE_PTH_INPUT_1)
# scan_df = common.import_excel(FILE_PTH_INPUT_2)
h_df = common.import_excel(FILE_PTH_INPUT_3)
h_df_len = len(h_df)
sc_df = common.import_excel(FILE_PTH_INPUT_4)
sc_dig_df = common.import_excel(FILE_PTH_INPUT_6)
scenario_list = list(sc_df['scenario'])
scenario_dig_list = list(sc_dig_df['scenario'])
if csv_or_excel:
sc_num = len(sc_dig_df)
output_fig_list = [
'{}_{}'.format(x, constants.plotting.string_added)
for x in scenario_dig_list
]
else:
sc_num = len(sc_df)
if p_show==True:
plt.show()
plt.close()
return
if __name__ == '__main__':
FILE_NAME_INPUT_1 = 'loci_test.xlsx'
FILE_NAME_INPUT_2='test_plot.png'
#FILE_NAME_INPUT_2 = 'intact_scan.xlsx'
r, x, r_v, x_v, r_i, x_i=13.320539,-73.3139,25,48,12.23809524,41.19047619
plot_name='h=2'
FILE_PTH_INPUT_1 = common.get_local_file_path_withfolder(file_name=FILE_NAME_INPUT_1, folder_name='Excel')
FILE_PTH_INPUT_2 = common.get_local_file_path_withfolder(file_name=FILE_NAME_INPUT_2, folder_name='plots')
loci=common.import_excel(FILE_PTH_INPUT_1)
save=True
figure_show=True
loci_scatter_plotter(loci_df=loci,R=r,X=x,R_V=r_v,X_V=x_v,R_I=r_i,X_I=x_i,save_o=save,p_show=figure_show,fig_path=FILE_PTH_INPUT_2,
fig_name='h=2')
# scan_df_new=CSV_reader_df_maker(csv_name=FILE_NAME_INPUT_5,csv_folder_name=dig_results_folder,
# object_name=object_name_dig,param_name_list=param_name_list_dig,header_list=header_list_new)
y_points = (random.sample(range(upper_limit), number_points))
corners = find_convex_vertices(
x_values=x_points,
y_values=y_points,
max_vertices=constants.LociInputs.unlimited_identifier)
# Confirm all points lie within the Polygon returned by the vertices
polygon = shapely.geometry.polygon.Polygon(list(zip(*corners)))
rand_point = random.randint(0, number_points - 1)
point = shapely.geometry.Point(x_points[rand_point], y_points[rand_point])
if PLOT_FIGURES:
matplotlib.pyplot.plot(x_points, y_points, 'o')
matplotlib.pyplot.plot(corners[0], corners[1], 'r--')
matplotlib.pyplot.show()
FILE_PTH_INPUT_1 = common.get_local_file_path_withfolder(
file_name=FILE_NAME_INPUT_1, folder_name='dig_results')
sc_dig_df = common.import_excel(FILE_PTH_INPUT_1)
scenario_dig_list = list(sc_dig_df['scenario'])
output_excel_dig_list = [
'{}_{}'.format(x, 'points.xlsx') for x in scenario_dig_list
]
scenario_dig_list_V = ['{}_{}'.format(x, 'V') for x in scenario_dig_list]
scenario_dig_list_I = ['{}_{}'.format(x, 'I') for x in scenario_dig_list]
dig_scenarios = scenario_dig_list_V + scenario_dig_list_I
pf_version = '2020'
def loader(scenario_list, project):
"""
Function returns dataframe with mapped values.
"""
sc_num = len(scenario_list)
#import powerfactory
app = powerfactory.GetApplication()
print('PowerFactory Opened')
# activate project
project = app.ActivateProject(project)
prj = app.GetActiveProject()
for i in range(0, sc_num):
scenario_name = scenario_list[i]
size = len(scenario_name)
# # Slice string to remove last 3 characters from string
new_str = scenario_name[:size - 2]
excel_file_name = new_str + '_points.xlsx'
excel_file_path = common.get_local_file_path_withfolder(
file_name=excel_file_name, folder_name='Output_excel')
points_df = common.import_excel(excel_file_path)
L_points_I = points_df.loc[:, 'worst_I_L']
R_points_I = points_df.loc[:, 'worst_I_R']
L_points_V = points_df.loc[:, 'worst_V_L']
R_points_V = points_df.loc[:, 'worst_V_R']
L_I = L_points_I.tolist()
R_I = R_points_I.tolist()
L_V = L_points_V.tolist()
R_V = R_points_V.tolist()
k = 1
folders = prj.GetContents('*.IntPrjfolder')
temp_filtered = filter(
lambda folders: folders.loc_name == 'Study Cases', folders)
studyCases_folder = list(temp_filtered)
studyCases = studyCases_folder[0].GetContents('*.IntCase')
temp_filtered = filter(lambda folders: folders.loc_name == 'Library',
folders)
Library = list(temp_filtered)
Library_temp = Library[0]
Library_subfolders = Library_temp.GetContents('*.')
temp_filtered = filter(
lambda Library_subfolders: Library_subfolders.loc_name ==
'Operational Library', Library_subfolders)
oper_folder = list(temp_filtered)
oper_temp = oper_folder[0]
oper_subfolders = oper_temp.GetContents('*.')
temp_filtered = filter(
lambda oper_subfolders: oper_subfolders.loc_name ==
'Characteristics', oper_subfolders)
char_folder = list(temp_filtered)
char_temp = char_folder[0]
char_subfolders = char_temp.GetContents('*.')
# temp_filtered = filter(lambda char_subfolders: char_subfolders.loc_name == 'Oyster Creek 800MW',
# char_subfolders)
temp_filtered = filter(
lambda char_subfolders: char_subfolders.loc_name ==
'BL England 400MW', char_subfolders)
scen_folder = list(temp_filtered)
scen_temp = scen_folder[0]
scen_subfolders = scen_temp.GetContents('*.')
temp_filtered = filter(
lambda scen_subfolders: scen_subfolders.loc_name == scenario_name,
scen_subfolders)
scen_param_folder = list(temp_filtered)
scen_param_temp = scen_param_folder[0]
scen_param_subvalues = scen_param_temp.GetContents('*.')
# studyCases = folders[4].GetContents('*.IntCase')
temp_filtered_L = filter(
lambda scen_param_subvalues: scen_param_subvalues.loc_name == 'L',
scen_param_subvalues)
temp_filtered_R = filter(
lambda scen_param_subvalues: scen_param_subvalues.loc_name == 'R',
scen_param_subvalues)
L = list(temp_filtered_L)
R = list(temp_filtered_R)
if scenario_name.endswith('_V'):
L[0].vector = L_V
R[0].vector = R_V
if scenario_name.endswith('_I'):
L[0].vector = L_I
R[0].vector = R_I
k = 1
k = 1
return k