def ajax():
reader = codecs.getreader("utf-8")
query = json.load(reader(request.body))
if query['type'] == "question":
if query['method']=='PE':
return methods.PE(float(query['min_interval']),float(query['max_interval']),float(query['proba']), int(query['choice']), str(query['mode']))
elif query['method']=='LE':
return methods.LE(float(query['min_interval']),float(query['max_interval']),float(query['proba']), int(query['choice']), str(query['mode']))
elif query['method']=='CE_Constant_Prob':
return methods.CE(float(query['min_interval']),float(query['max_interval']),float(query['gain']), int(query['choice']), str(query['mode']))
else:
return query['method']
elif query['type'] == "calc_util":
return fit.regressions(query['points'])
elif query['type'] == "k_calculus":
print("on va calculer k_calculus sur le fichier kcalc")
if query['number'] == 3:
return {'k':kcalc.calculk(query['k']['k1'],query['k']['k2'],query['k']['k3'])}
elif query['number'] == 4:
return {'k':kcalc.calculk4(query['k']['k1'],query['k']['k2'],query['k']['k3'],query['k']['k4'])}
elif query['number'] == 5:
return {'k':kcalc.calculk4(query['k']['k1'],query['k']['k2'],query['k']['k3'],query['k']['k4'], query['k']['k5'])}
elif query['type'] == "svg":
dictionary = query['data']
min = query['min']
max = query['max']
liste_cord = query['liste_cord']
return plot.generate_svg_plot(dictionary, min, max, liste_cord)
elif query['type'] == "export_xls":
return export_xls.generate_fichier(query['data'])
def ajax():
if check_passwd(request.get_cookie("mdp"))==False:
return template('authentification', get_url=app.get_url)
reader = codecs.getreader("utf-8")
query = json.load(reader(request.body))
if query['type'] == "question":
if query['method']=='PE':
return methods.PE(float(query['min_interval']),float(query['max_interval']),float(query['proba']), int(query['choice']), str(query['mode']))
elif query['method']=='LE':
return methods.LE(float(query['min_interval']),float(query['max_interval']),float(query['proba']), int(query['choice']), str(query['mode']))
elif query['method']=='CE_Constant_Prob':
return methods.CE(float(query['min_interval']),float(query['max_interval']),float(query['gain']), int(query['choice']), str(query['mode']))
else:
return query['method']
elif query['type'] == "calc_util":
return fit.regressions(query['points'])
elif query['type'] == "k_calculus":
print("on va calculer k_calculus sur le fichier kcalc")
if query['number'] == 2:
return kcalc.calculk2(query['k']['k1'],query['k']['k2'])
elif query['number'] == 3:
return kcalc.calculk3(query['k']['k1'],query['k']['k2'],query['k']['k3'])
elif query['number'] == 4:
return kcalc.calculk4(query['k']['k1'],query['k']['k2'],query['k']['k3'],query['k']['k4'])
elif query['number'] == 5:
return kcalc.calculk5(query['k']['k1'],query['k']['k2'],query['k']['k3'],query['k']['k4'], query['k']['k5'])
elif query['number'] == 6:
return kcalc.calculk6(query['k']['k1'],query['k']['k2'],query['k']['k3'],query['k']['k4'], query['k']['k5'], query['k']['k6'])
elif query['type'] == "utility_calculus_multiplicative":
return kcalc.calculUtilityMultiplicative(query['k'],query['utility'])
elif query['type'] == "utility_calculus_multilinear":
return kcalc.calculUtilityMultilinear(query['k'],query['utility'])
elif query['type'] == "svg":
dictionary = query['data']
min = query['min']
max = query['max']
liste_cord = query['liste_cord']
width=query['width']
return plot.generate_svg_plot(dictionary, min, max, liste_cord, width)
elif query['type'] == "export_xlsx":
return export_xlsx.generate_fichier(query['data'])
elif query['type'] == "export_xlsx_option":
return export_xlsx.generate_fichier_with_specification(query['data'])
def ajax():
if check_passwd(request.get_cookie("mdp")) == False:
return template('authentification', get_url=app.get_url)
reader = codecs.getreader("utf-8")
query = json.load(reader(request.body))
if query['type'] == "question":
if query['method'] == 'PE':
return methods.PE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'LE':
return methods.LE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'CE_Constant_Prob':
return methods.CE(float(query['min_interval']),
float(query['max_interval']),
float(query['gain']), int(query['choice']),
str(query['mode']))
else:
return query['method']
elif query['type'] == "calc_util":
return fit.regressions(query['points'])
elif query['type'] == "calc_util_multi":
return fit.multipoints(query['points'])
elif query['type'] == "k_calculus":
if query['number'] == 2:
return kcalc.calculk2(query['k']['k1'], query['k']['k2'])
elif query['number'] == 3:
return kcalc.calculk3(query['k']['k1'], query['k']['k2'],
query['k']['k3'])
elif query['number'] == 4:
return kcalc.calculk4(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'])
elif query['number'] == 5:
return kcalc.calculk5(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'])
elif query['number'] == 6:
return kcalc.calculk6(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'], query['k']['k6'])
elif query['type'] == "utility_calculus_multiplicative":
return kcalc.calculUtilityMultiplicative(query['k'], query['utility'])
elif query['type'] == "utility_calculus_multilinear":
return kcalc.calculUtilityMultilinear(query['k'], query['utility'])
elif query['type'] == "svg":
dictionary = query['data']
min = float(query['min'])
max = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
return plot.generate_svg_plot(dictionary, min, max, liste_cord, width)
elif query['type'] == "svgg":
dictionary = query['data']
min = float(query['min'])
max = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
choice = query['choice']
return plot.generate_svg_plot2(dictionary, min, max, liste_cord, width,
choice)
elif query['type'] == "svg_QUALI":
dictionary = query['data']
list_names = query['list_names']
width = query['width']
return plot.generate_svg_plot_QUALI(dictionary, list_names, width)
elif query['type'] == "pie_chart":
names = query['names']
probas = query['probas']
return plot.pie_chart(names, probas)
elif query['type'] == "export_xlsx":
return export_xlsx.generate_fichier(query['data'])
elif query['type'] == "export_xlsx_option":
return export_xlsx.generate_fichier_with_specification(query['data'])
elif query['type'] == "latex_render":
return latex_render.render(query['formula'])
elif query['type'] == "tree":
return draw_tree.draw(query['gain'], query['upper_label'],
query['bottom_label'], query['upper_proba'],
query['bottom_proba'], query['assess_type'])
def ajax():
if check_passwd(request.get_cookie("mdp")) == False:
return template('authentification', get_url=app.get_url)
reader = codecs.getreader("utf-8")
query = json.load(reader(request.body))
"""
query = {'type': 'export_xlsx',
'data':
{'attributes':
[{'type': 'Quantitative', 'name': 'pieniche', 'unit': 'euros', 'val_min': 20, 'val_med': ['40', '60', '80'], 'val_max': 100, 'method': 'PE', 'mode': 'Normal', 'completed': 'False', 'checked': True, 'questionnaire': {'number': 3, 'points': {'40': 0.43, '60': 0.86, '80': 0.82}, 'utility': {}}, 'fonction': 'exponential', 'numero': 0, 'pts': {'points': [1, 2, 3], 'coord': [[40, 0.43], [60, 0.86], [80, 0.82], [20, 0], [100, 1]], 'exp': {'a': -1.963679476149352, 'b': 0.028213793872664365, 'c': 1.116885624561104, 'r2': 0.9669098127318985}, 'quad': {'a': -0.5720588230939125, 'b': 0.00016102941154695627, 'c': 0.03182352938563475, 'r2': 0.9609977126945451}, 'pow': {'a': 2.2107646754127557, 'b': 1.3371874567609472, 'c': -4.168783582605269, 'r2': 0.9633217332780892}, 'lin': {'a': 0.0125, 'b': -0.25, 'r2': 0.7480678661997344}, 'expo-power': {'a': -1825.2176950884375, 'b': -7.508435131653395, 'c': 4.5317707067475485e-05, 'r2': 0.9498855834689701}}},
{'type': 'Quantitative', 'name': 'ccxw', 'unit': 'cxw', 'val_min': 10, 'val_med': ['32.5', '55', '77.5'], 'val_max': 100, 'method': 'PE', 'mode': 'Normal', 'completed': 'False', 'checked': True, 'questionnaire': {'number': 3, 'points': {'55': 0.76, '32.5': 0.32, '77.5': 0.96}, 'utility': {}}, 'fonction': '', 'numero': 0}],
'k_calculus':
[{'method': 'multiplicative', 'active': True, 'k': [], 'GK': None, 'GU': None},
{'method': 'multilinear', 'active': False, 'k': [], 'GK': None, 'GU': None}],
'settings':
{'decimals_equations': '2', 'decimals_dpl': '8', 'proba_ce': '0.3', 'proba_le': '0.3', 'language': 'french', 'display': 'trees'}
}
}
"""
if query['type'] == "question":
if query['method'] == 'PE':
return methods.PE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'LE':
return methods.LE(float(query['min_interval']),
float(query['max_interval']),
float(query['proba']), int(query['choice']),
str(query['mode']))
elif query['method'] == 'CE_Constant_Prob':
return methods.CE(float(query['min_interval']),
float(query['max_interval']),
float(query['gain']), int(query['choice']),
str(query['mode']))
else:
return query['method']
elif query['type'] == "calc_util":
return fit.regressions(query['points'])
elif query['type'] == "calc_util_multi":
return fit.multipoints(query['points'])
elif query['type'] == "k_calculus":
if query['number'] == 2:
return kcalc.calculk2(query['k']['k1'], query['k']['k2'])
elif query['number'] == 3:
return kcalc.calculk3(query['k']['k1'], query['k']['k2'],
query['k']['k3'])
elif query['number'] == 4:
return kcalc.calculk4(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'])
elif query['number'] == 5:
return kcalc.calculk5(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'])
elif query['number'] == 6:
return kcalc.calculk6(query['k']['k1'], query['k']['k2'],
query['k']['k3'], query['k']['k4'],
query['k']['k5'], query['k']['k6'])
elif query['type'] == "utility_calculus_multiplicative":
return kcalc.calculUtilityMultiplicative(query['k'], query['utility'],
query['virgule'])
elif query['type'] == "utility_calculus_multilinear":
return kcalc.calculUtilityMultilinear(query['k'], query['utility'],
query['virgule'])
elif query['type'] == "svg":
dictionary = query['data']
min_ = float(query['min'])
max_ = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
liste = query['liste']
return plot.generate_svg_plot(dictionary, min_, max_, liste_cord,
width, liste)
elif query['type'] == "svgg":
dictionary = query['data']
min = float(query['min'])
max = float(query['max'])
liste_cord = query['liste_cord']
width = query['width']
choice = query['choice']
return plot.generate_svg_plot2(dictionary, min, max, liste_cord, width,
choice)
elif query['type'] == "svg_QUALI":
dictionary = query['data']
list_names = query['list_names']
width = query['width']
return plot.generate_svg_plot_QUALI(dictionary, list_names, width)
elif query['type'] == "pie_chart":
names = query['names']
probas = query['probas']
return plot.pie_chart(names, probas)
elif query['type'] == "demande_de_transformation":
return selecteur.selecteur1(query['data'], query['numero'])
elif query['type'] == "export_xlsx":
return export_xlsx.generate_fichier(query['data'])
elif query['type'] == "export_xlsx_option":
return export_xlsx.generate_fichier_with_specification(query['data'])
elif query['type'] == "latex_render":
return latex_render.render(query['formula'])
elif query['type'] == "tree":
return draw_tree.draw(query['gain'], query['upper_label'],
query['bottom_label'], query['upper_proba'],
query['bottom_proba'], query['assess_type'])