def OnSave(self, e):
"""Write data to file."""
convert_to = None
if e.Id == 201:
convert_to = "photoabsorption"
elif e.Id == 202:
convert_to = "refractive_index"
logger.info("Save")
fd = wx.FileDialog(self, style=wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT)
if fd.ShowModal()==wx.ID_OK:
metadata = {"Density": float(self.DensityText.GetValue()), "Molecular Formula":self.StoichiometryText.GetValue(),"Formula Mass":data.calculate_FormulaMass(self.Stoichiometry)}
data.export_data(fd.GetPath(), numpy.transpose(numpy.vstack((self.Full_E,self.KK_Real_Spectrum,data.coeffs_to_ASF(self.Full_E,self.Imaginary_Spectrum)))), header_info=metadata, convert_to=convert_to)
def search_data(query, category, intermediate_value):
"""
Searches <intermediate_value> based on user-specified <query> and <category>.
Args:
query (str): A search query.
category (str): A category ('all' / 'spam' / 'ham').
intermediate_value (str): A string of data to process in JSON format.
Returns:
A list of matching data, and a string in the format "<length of data> items matched".
"""
status_code, data = parse_json(intermediate_value, category)
download_link = file_download_link('output.csv')
anchor = html.A(html.Button('Export Results', id='exportBtn', n_clicks=0),
href=download_link,
download=download_link)
if status_code == 1:
# Display empty table, and show the error message
return [], data, anchor
data = get_data(query, data)
output = export_data(data_dict=data)
save_file('output.csv', output)
return data, "{} items matched.".format(len(data)), anchor
def calc_yearly_diff(rc,rid,load_saved=[True,True],save_file=[False,False],
filename=['MaleRankCountDiffIndex.csv','MaleRankCountDiffCount.csv'],verbose=False):
# loading data
isloaded = [False,False]
if len(load_saved) == 2 and len(filename) == 2:
if verbose: print("Trying to import from "+filename[0]+" and "+filename[1])
if load_saved[0]:
rc_diffi = data.import_csv(file=filename[0])
if rc_diffi.__len__() > 0:
isloaded[0] = True
if load_saved[1]:
rc_diffc = data.import_csv(file=filename[1])
if rc_diffc.__len__() > 0:
isloaded[1] = True
if isloaded[0] and isloaded[1]:
if verbose: print("Import successful")
return rc_diffi, rc_diffc
if not isloaded[0]: rc_diffi = pd.DataFrame(index=rc.index,columns=rc.columns[1:],dtype='int')
if not isloaded[1]: rc_diffc = pd.DataFrame(index=rc.index,columns=rc.columns[1:],dtype='int')
prev_yr = rc.columns[0]
# Calculating first index of NaN values to improve looping efficiency when searching previous year data
lens = rc.count(axis=0)
# Starting calculations
for cur_yr in rc.columns[1:]:
for i in rc.index[:lens[cur_yr]]:
prev_count = 0
cur_id = rid[cur_yr][i]
res = rid[prev_yr][rid[prev_yr]==cur_id].index
if res.size:
prev_count = rc[prev_yr][res[0]]
else:
res = [lens[cur_yr]]
rc_diffi[cur_yr][i] = i - res[0]
rc_diffc[cur_yr][i] = rc[cur_yr][i] - prev_count
if verbose: print("Year: "+str(cur_yr))
prev_yr = cur_yr
#Saving data
if len(save_file) == 2:
if save_file[0]:
data.export_data(df=rc_diffi,filename=filename[0],path='data')
if save_file[1]:
data.export_data(df=rc_diffi,filename=filename[1],path='data')
return rc_diffi,rc_diffc
def save_dfs(dfs,filenames,save_file,verbose):
if len(save_file) == len(dfs) and len(filenames) == len(dfs):
if verbose: print("\nSaving.")
for i in range(len(dfs)):
if save_file[i]:
data.export_data(df=dfs[i],filename=filenames[i],path='data')
import face_recognition
import cv2
import pyttsx3
import time
import data
import ai
print('loads trained data')
known_face_names = []
for person in data.export_data():
known_face_names.append([person[0], person[1], None])
known_face_encodings = []
for face_encoding in known_face_names:
known_face_encodings.append(face_encoding[1])
print('done loading')
engine = pyttsx3.init()
cap = cv2.VideoCapture(1)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
im = cap.read()[1] #because reasons
r = cv2.selectROI(im)
process_this_frame = True
def import_file(n_clicks, filename, contents, chantier, type_doc, nom_capteur, date):
if n_clicks > 0:
if contents is None:
return ""
else:
df = read_data(contents, filename)
if type_doc == 1:
'''type : mesures topographiques globales'''
filename_archive = f"topo_{date}.csv"
filename_actif = "topo.csv"
export_data(df, chantier, "actif", "topographie", filename_actif)
export_data(df, chantier, "archive", "topographie", filename_archive)
elif type_doc == 2:
'''type : mesures associées à UN inclinomètre defini'''
filename_archive = f"{nom_capteur}_{date}.csv"
filename_actif = f"{nom_capteur}.csv"
export_data(df, chantier, "actif", "inclinometrie", filename_actif)
export_data(df, chantier, "archive", "inclinometrie", filename_archive)
elif type_doc == 3:
'''type : mesures associées à UN piezomètre defini '''
filename_archive = f"{nom_capteur}_{date}.csv"
filename_actif = f"{nom_capteur}.csv"
export_data(df, chantier, "actif", "piezometrie", filename_actif)
export_data(df, chantier, "archive", "piezometrie", filename_archive)
elif type_doc == 4:
'''type : mesures tirant globales'''
filename_archive = f"tirant_{date}.csv"
filename_actif = "tirant.csv"
export_data(df, chantier, "actif", "tirant", filename_actif)
export_data(df, chantier, "archive", "tirant", filename_archive)
elif type_doc == 5:
'''type : mesures jauge globales'''
filename_archive = f"jauge_{date}.csv"
filename_actif = "jauge.csv"
export_data(df, chantier, "actif", "jauge", filename_actif)
export_data(df, chantier, "archive", "jauge", filename_archive)
return "Le fichier à bien été importé"
else:
return ""
