Skip to content

I
inventario
Commit eff60dd1 by Jose Luis Gordillo Ruiz
Options

quitamos las gráficas de comidas de la app 'estadísticas' 

son las mismas que ya están en 'estadisticas_comidas' y desde ahí ya
se actualizan usando un endpoint.
parent 2d930a2c
Showing with 1 additions and 281 deletions
estadisticas.py
...@@ -14,39 +14,7 @@ import flask ...@@ -14,39 +14,7 @@ import flask
server = flask.Flask(__name__) server = flask.Flask(__name__)
def update_correlacion_macro_iauc():
datos = comidas.sort_values(by="iAUC", ascending=True)
datos = datos[np.abs(stats.zscore(datos["iAUC"], nan_policy='omit'))<3]
datos.reset_index(inplace=True)
metricas = ["hc_total", "lipids_total","protein_total","fiber_total"]
pcts = [ metrica + "_pct" for metrica in metricas]
datos["total"] = datos[metricas].sum(axis=1)
datos = datos[datos["total"]>0]
datos[pcts] = datos[metricas].div(datos["total"], axis=0)
fig = make_subplots(rows=2, cols=4, vertical_spacing=0.25)
lcol = 1
seq = 1
for metrica in metricas:
datosfil = datos[np.abs(stats.zscore(datos[metrica], nan_policy='omit')) < 3]
fig.add_trace(
go.Scatter(x=datosfil["iAUC"], y=datosfil[metrica], name=metrica, mode='markers'),
row=1, col=lcol
)
fig.add_trace(
go.Scatter(x=datosfil["iAUC"], y=datosfil[metrica + "_pct"], name=metrica, mode='markers'),
row=2, col=lcol
)
fig["layout"]['xaxis' + str(seq)]['title']="iAUC"
fig["layout"]['yaxis' + str(seq)]['title']= metrica
fig["layout"]['xaxis' + str(seq + 4)]['title']="iAUC"
fig["layout"]['yaxis' + str(seq + 4)]['title']= "pct de " + metrica
lcol = lcol + 1
seq = seq + 1
return fig
def datavis(datos): def datavis(datos):
medidas = ["imc", "age", "glucose", "hba1c", "ct", "hdl"] medidas = ["imc", "age", "glucose", "hba1c", "ct", "hdl"]
...@@ -254,151 +222,11 @@ def triareas(pacientes): ...@@ -254,151 +222,11 @@ def triareas(pacientes):
) )
return figura return figura
def comidas_iaucs(datos):
columnas = ["pre-AUC", "post-AUC", "iAUC"]
datos["N-alimentos"] = datos["foods"].apply(lambda x: len(x))
fig = make_subplots(cols=3, rows=1, vertical_spacing=0.25)
lcol = 1
seq = 1
for columna in columnas:
datosfil = datos[np.abs(stats.zscore(datos[columna], nan_policy='omit')) < 3]
fig.add_trace(
go.Scatter(y=datosfil[columna], name=columna, mode="markers"),
row=1, col=lcol
)
fig['layout']['yaxis' + str(seq)]['title']=columna
fig['layout']['xaxis' + str(seq)]['title']="Comida ID"
seq = seq + 1
lcol = lcol + 1
fig.update_layout(showlegend=False)
return fig
def comidas_iauc_describe(datos):
columnas = ["pre-AUC", "post-AUC", "iAUC"]
fig = make_subplots(cols=3, rows=1, vertical_spacing=0.25)
seq = 1
lcol = 1
for columna in columnas:
datosfil = datos[np.abs(stats.zscore(datos[columna], nan_policy='omit')) < 3]
fig.add_trace(
go.Histogram(x=datosfil[columna], name=columna),
row=1, col=lcol
)
fig['layout']['yaxis' + str(seq)]['title']=columna
seq = seq + 1
lcol = lcol + 1
fig.update_layout(
showlegend = False,
bargap=0.05, # gap between bars of adjacent location coordinates
bargroupgap=0.05 # gap between bars of the same location coordinates
)
return fig
def comidas_macro_contenido(datos):
columnas = ["hc_total", "lipids_total", "fiber_total", "protein_total"]
datos["N-alimentos"] = datos["foods"].apply(lambda x: len(x))
figura = make_subplots(cols=4, rows=1, vertical_spacing=0.24)
lcol = 1
seq = 1
for columna in columnas:
datosfil = datos[np.abs(stats.zscore(datos[columna], nan_policy='omit')) < 3]
figura.add_trace(
go.Scatter(y=datosfil[columna], name=columna, mode="markers"),
row=1, col=lcol
)
figura['layout']['yaxis' + str(seq)]['title']=columna
figura['layout']['xaxis' + str(seq)]['title']="Comida ID"
seq = seq + 1
lcol = lcol + 1 if lcol < 4 else 1
figura.update_layout(showlegend=False)
return figura
def comidas_macro_describe(datos):
columnas = ["hc_total", "lipids_total", "fiber_total", "protein_total"]
fig = make_subplots(cols=4, rows=1, vertical_spacing=0.25)
seq = 1
lcol = 1
for columna in columnas:
datosfil = datos[np.abs(stats.zscore(datos[columna], nan_policy='omit')) < 3]
fig.add_trace(
go.Histogram(x=datosfil[columna], name=columna),
row=1, col=lcol
)
fig['layout']['yaxis' + str(seq)]['title']=columna
seq = seq + 1
lcol = lcol + 1 if lcol < 4 else 1
fig.update_layout(
showlegend = False,
bargap=0.05, # gap between bars of adjacent location coordinates
bargroupgap=0.05 # gap between bars of the same location coordinates
)
return fig
def comidas_porcentajes(datos):
columnas = ["hc_total", "fiber_total", "protein_total", "lipids_total"]
datos["total"] = datos[columnas].sum(axis=1)
datos = datos[datos["total"]>0]
datos[columnas] = datos[columnas].div(datos["total"], axis=0)
figura = ff.create_distplot([ datos[columna] for columna in columnas], columnas, show_hist = False, show_rug=False)
return figura
def comidas_principales(datos):
dominantes = datos.groupby("dominante").count()
dominantes.sort_values(by="patient", ascending=False, inplace=True)
dominantes = dominantes[dominantes["patient"] > 9]
figura = make_subplots(cols=1, rows=1, subplot_titles= ["Alimentos"])
figura.add_trace(
go.Scatter(x=dominantes.index, y=dominantes["patient"], mode='markers', name="# de comidas"),
col=1, row=1
)
figura["layout"]['yaxis']['title']= "# de comidas"
# figura.update_layout(height=2000, width=800)
return figura
def comidas_principales_categorias(datos):
categorias = datos.groupby("dominante_category").count()
categorias.sort_values(by="patient", ascending=False, inplace=True)
categorias = categorias[categorias["patient"] > 9]
figura = make_subplots(cols=1, rows=1, subplot_titles=["Categorías de alimentos"])
figura.add_trace(
go.Scatter(x=categorias.index, y=categorias["patient"], mode='markers'),
col=1, row=1
)
figura["layout"]['yaxis']['title']= "# de comidas"
return figura
def categorias_promedio_aucs():
categorias = comidas.groupby("dominante_category")[["iAUC","hc_total"]].mean()
print(categorias)
categorias["tamaño"] = comidas.groupby("dominante_category")["iAUC"].count()
categorias.sort_values(ascending=False, inplace=True, by="iAUC")
# figura = make_subplots(cols=1, rows=1, subplot_titles=["Categorías de alimentos"])
# figura.add_trace(
# go.Scatter(x=categorias.index, y=categorias["iAUC"], mode="markers")
# )
figura = px.scatter(categorias["iAUC"], size=categorias["tamaño"])
return figura
def categorias_aucs(datos):
datos_fil = datos[np.abs(stats.zscore(datos["iAUC"], nan_policy='omit')) < 3]
figura = px.box(datos_fil, x="dominante_category", y="iAUC")
return figura
def aucs_paciente(datos):
figura = px.box(datos, x="patient", y="iAUC")
return figura
def heatmap(datos): def heatmap(datos):
...@@ -418,63 +246,8 @@ def heatmap(datos): ...@@ -418,63 +246,8 @@ def heatmap(datos):
) )
return fig return fig
def comidas_paciente_datos(datos):
columnas = ["hc_total", "kcal_total", "fiber_total", "protein_total", "lipids_total"]
figura = make_subplots(cols=5, rows=4, subplot_titles=("hc","kcal","fiber", "protein", "lipids"))
lcol = 1
for columna in columnas:
datosfil = datos[np.abs(stats.zscore(datos[columna], nan_policy='omit')) < 3]
figura.add_trace(
go.Scatter(y=datosfil.groupby("patient")[columna].sum(), name="total_" + columna, mode= "markers"),
row=1, col=lcol
)
figura.add_trace(
go.Histogram(x=datosfil.groupby("patient")[columna].sum(), name="total_" + columna),
row=2, col=lcol
)
figura.add_trace(
go.Scatter(y=datosfil.groupby("patient")[columna].mean(), name="promedio_" + columna, mode= "markers"),
row=3, col=lcol
)
figura.add_trace(
go.Histogram(x=datosfil.groupby("patient")[columna].mean(), name="promedio_" + columna),
row=4, col=lcol
)
lcol = lcol + 1
figura.update_layout(showlegend = False)
return figura
def comidas_auc_paciente(datos):
columnas = ["pre-AUC", "post-AUC", "iAUC"]
figura = make_subplots(cols = 4, rows = 2, subplot_titles=("pre-AUC","post-AUC", "iAUC", "N-Comidas"))
lcol = 1
for columna in columnas:
datosfil = datos[np.abs(stats.zscore(datos[columna], nan_policy='omit')) < 3]
figura.add_trace(
go.Scatter(y=datosfil.groupby("patient")[columna].mean(), name="promedio_" + columna, mode= "markers"),
row=1, col=lcol
)
figura.add_trace(
go.Histogram(x=datosfil.groupby("patient")[columna].mean(), name="Distr. " + columna),
row=2, col=lcol
)
lcol = lcol + 1
figura.add_trace(
go.Scatter(y=datosfil.groupby("patient")["iAUC"].count(), name="N-comidas", mode= "markers"),
row = 1, col = 4
)
figura.add_trace(
go.Histogram(x=datosfil.groupby("patient")["iAUC"].count(), name="Distr. N-comidas"),
row = 2, col = 4
)
figura.update_layout(
showlegend = False,
bargap=0.05, # gap between bars of adjacent location coordinates
bargroupgap=0.05 # gap between bars of the same location coordinates
)
return figura
# algunos parámetros globales # algunos parámetros globales
url = 'https://nutricion.c3.unam.mx/nd/' url = 'https://nutricion.c3.unam.mx/nd/'
...@@ -647,59 +420,6 @@ app.layout = html.Div(children=[ ...@@ -647,59 +420,6 @@ app.layout = html.Div(children=[
# html.Div(children= [html.H1("Mapa de calor de iAUC/alimentos controlados", className = "header-title2")]), # html.Div(children= [html.H1("Mapa de calor de iAUC/alimentos controlados", className = "header-title2")]),
# dcc.Graph(figure=heatmap(visitas_incluidos)) # dcc.Graph(figure=heatmap(visitas_incluidos))
# ,]), # ,]),
# datos de comidas
html.Div(children= [
html.Div(children= [html.H1("iAUCs de las comidas reportadas", className = "header-title2")]),
dcc.Graph(figure=comidas_iaucs(comidas)),
dcc.Graph(figure=comidas_iauc_describe(comidas))
,]),
# datos de comidas
html.Div(children= [
html.Div(children= [html.H1("Contenido de macronutrientes de las comidas", className = "header-title2")]),
dcc.Graph(figure=comidas_macro_contenido(comidas))
,]),
# descripcion de comidas
html.Div(children= [
html.Div(children= [html.H1("Distribución de Macronutrientes de comidas", className = "header-title2")]),
dcc.Graph(figure=comidas_macro_describe(comidas))
,]),
# descripcion de componentes de macronutrientes de comidas
html.Div(children= [
html.Div(children= [html.H1("Distribución de %s de Macronutrientes de comidas", className = "header-title2")]),
dcc.Graph(figure=comidas_porcentajes(comidas))
,]),
# relación de macronutrientes de comidas con sus iAUC
html.Div(children= [
html.Div(children= [html.H1("Relación de macros / iAUCs de las comidas ", className = "header-title2")]),
dcc.Graph(figure=update_correlacion_macro_iauc())
,]),
# # de comidas por componente dominante
html.Div(children= [
html.Div(children= [html.H1("Distribución de num. de comidas por componente principal", className = "header-title2")]),
dcc.Graph(figure=comidas_principales(comidas)),
dcc.Graph(figure=comidas_principales_categorias(comidas)),
dcc.Graph(figure=categorias_promedio_aucs()),
dcc.Graph(figure=categorias_aucs(comidas))
,]),
# descripcion de comidas por paciente
html.Div(children= [
html.Div(children= [html.H1("Macronutrientes de comidas por paciente", className = "header-title2")]),
dcc.Graph(figure=comidas_paciente_datos(comidas))
,]),
# descripcion de aucs comidas por paciente
html.Div(children= [
html.Div(children= [html.H1("AUCs promedio de comidas por paciente", className = "header-title2")]),
dcc.Graph(figure=comidas_auc_paciente(comidas))
,]),
# descripcion de aucs comidas por paciente
html.Div(children= [
html.Div(children= [html.H1("iAUCs de comidas por paciente", className = "header-title2")]),
dcc.Graph(figure=aucs_paciente(comidas))
,]),
]) # cierre del layout ]) # cierre del layout
...@@ -751,5 +471,5 @@ def update_correlacion(controlado): ...@@ -751,5 +471,5 @@ def update_correlacion(controlado):
return figura return figura
if __name__ == "__main__": if __name__ == "__main__":
app.run_server(debug=True, port=8051, host='0.0.0.0') app.run_server(debug=True, port=8052, host='0.0.0.0')
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment