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visualisation.py
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visualisation.py
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import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import os
@st.cache
def get_variable(file):
"""
This function reads an Excel file and extracts the data for variables that have a count greater than 1.
Args:
file (str): The path to the Excel file.
Returns:
dict: A dictionary containing the data for each variable. The keys are the variable names, and the values are pandas DataFrames containing the data for each variable.
"""
a = pd.read_excel(file)
data_variable = {}
# Extract the names of the variables that have a count greater than 1
for var in a[(a["Type"]=='variable')&(a["Count"]>1)]['Name']:
# Read the data for each variable from the Excel file
data_variable[var] = pd.read_excel(file, sheet_name=var, header=2)
return data_variable
def get_parameter(file):
"""
This function reads an Excel file and extracts the data for parameters that have a count greater than 1.
Args:
file (str): The path to the Excel file.
Returns:
dict: A dictionary containing the data for each parameter. The keys are the parameter names, and the values are pandas DataFrames containing the data for each parameter.
"""
a = pd.read_excel(file)
data_parameter = {}
# Extract the names of the parameters that have a count greater than 1
for var in a[(a["Type"]=='parameter')&(a["Count"]>1)]['Name']:
# Read the data for each parameter from the Excel file
data_parameter[var] = pd.read_excel(file, sheet_name=var, header=2)
return data_parameter
def get_set(file):
"""
This function reads an Excel file and extracts the data for sets that have a count greater than 1.
Args:
file (str): The path to the Excel file.
Returns:
dict: A dictionary containing the data for each set. The keys are the set names, and the values are pandas DataFrames containing the data for each set.
"""
a = pd.read_excel(file)
data_set = {}
# Extract the names of the sets that have a count greater than 1
for var in a[(a["Type"]=='set')&(a["Count"]>1)]['Name']:
# Read the data for each set from the Excel file
data_set[var] = pd.read_excel(file, sheet_name=var, header=2)
return data_set
def comments(file):
"""
This function reads the 'comments' sheet in an Excel file and displays the comments in a markdown format.
Args:
file (str): The path to the Excel file.
"""
with st.expander("Commentaires",expanded=False):
try:
# Read the comments from the 'comments' sheet in the Excel file
comments = pd.read_excel(file,sheet_name='comments').iloc[0,0]
# Display the comments in a markdown format
st.markdown(comments)
except:
# If there are no comments, display a message
st.markdown("Pas de commentaires")
color_map = {
'p1' : 'red',
'p2' : 'blue',
'p3' : 'green'
}
color_map_light = {
'p1' : 'lightcoral',
'p2' : 'lightblue',
'p3' : 'lightgreen'
}
def etat_RDE(data_variable,Z,Margin=False):
"""
This function creates a plot showing the state of the water distribution network.
Args:
data_variable (dict): A dictionary containing the data for each variable. The keys are the variable names, and the values are pandas DataFrames containing the data for each variable.
Z (float): The cost of the water distribution network.
Margin (bool, optional): Whether to add a margin to the top of the plot. Default is False.
Returns:
plotly.graph_objects.Figure: A plot showing the state of the water distribution network.
"""
fig = go.Figure()
# Add a bar plot for each unique value of 'n' in the 'v' DataFrame
for r in data_variable['v']['n'].unique():
fig.add_trace(
go.Bar(
x = data_variable['v'][data_variable['v']['n']==r]['t'],
y = data_variable['v'][data_variable['v']['n']==r]['Value'],
name= r
)
)
# Add a scatter plot for each unique pair of values of 'c' and 'd' in the 'Ppompe' DataFrame
for c in data_variable['Ppompe']['c'].unique():
for d in data_variable['Ppompe']['d'].unique():
fig.add_trace(
go.Scatter(
x = data_variable['Ppompe'][(data_variable['Ppompe']['c']==c)&(data_variable['Ppompe']['d']==d)]['t'],
y = data_variable['Ppompe'][(data_variable['Ppompe']['c']==c)&(data_variable['Ppompe']['d']==d)]['Value'],
yaxis="y2",
name= f"({c},{d})"
)
)
# Update the layout of the plot
fig.update_layout(
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
hovermode='x',
yaxis=dict(
title="m3" ,
# range=[0,df["Consommation (MW)"].max()*1.1]
),
yaxis2=dict(title="kW",
# range=[df["Coût MWh"].min()*0.95,df["Coût MWh"].max()*1.1],
anchor="free",
overlaying="y",
side="right",
position=1
),
title = f"Etat du réseau de distribution d'eau - Coût : {Z} €"
)
# If Margin is True, add a margin to the top of the plot
if Margin:
fig.update_layout(
margin_t = 120
)
return fig
def Pompe_RDE(data_variable,Z,Margin=False):
"""
This function creates a plot showing the state of the pumps in the water distribution network.
Args:
data_variable (dict): A dictionary containing the data for each variable. The keys are the variable names, and the values are pandas DataFrames containing the data for each variable.
Z (float): The cost of the water distribution network.
Margin (bool, optional): Whether to add a margin to the top of the plot. Default is False.
Returns:
plotly.graph_objects.Figure: A plot showing the state of the pumps in the water distribution network.
"""
color_map = {
'p1' : 'red',
'p2' : 'blue',
'p3' : 'green',
'p4' : 'magenta',
}
color_map_light = {
'p1' : 'lightcoral',
'p2' : 'lightblue',
'p3' : 'lightgreen',
'p4' : 'plum'
}
fig = go.Figure()
fig.add_trace(
go.Scatter(
x = data_variable['Charge'][data_variable['Charge']['n']=='s']['t'],
y = data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'],
marker_color = 'black',
line_dash = 'dash',
name = f"Charge à s"
)
)
for c in data_variable['Qpompe']['c'].unique():
for d in data_variable['Qpompe']['d'].unique():
fig.add_trace(
go.Scatter(
x = data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['t'],
y = data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['Value'],
marker_color = color_map[d],
name = f"Débit {c},{d}",
stackgroup='one'
)
)
fig.update_layout(
hovermode='x',
title = f"Etat des pompes du réseau de distribution d'eau - Coût : {Z} € "
)
if Margin:
fig.update_layout(
margin_t = 150
)
return fig
def Charge_RDE(data_variable,Z):
"""
This function creates a plot showing the charge in the water distribution network.
Args:
data_variable (dict): A dictionary containing the data for each variable. The keys are the variable names, and the values are pandas DataFrames containing the data for each variable.
Z (float): The cost of the water distribution network.
Returns:
plotly.graph_objects.Figure: A plot showing the charge in the water distribution network.
"""
fig = go.Figure()
for n in data_variable['Charge']['n'].unique():
fig.add_trace(
go.Scatter(
x = data_variable['Charge'][data_variable['Charge']['n']==n]['t'],
y = data_variable['Charge'][data_variable['Charge']['n']==n]['Value'],
name = n
)
)
fig.update_layout(
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
hovermode='x',
title = f"Charge dans le réseau de distribution d'eau - Coût : {Z} € ",
yaxis_title = "Charge (m)"
)
return fig
def Etat_reservoir(data_variable,data_set,data_parameter,reservoir):
"""
This function creates a plot showing the state of a given reservoir in the water distribution network.
Args:
data_variable (dict): A dictionary containing the data for each variable. The keys are the variable names, and the values are pandas DataFrames containing the data for each variable.
data_set (dict): A dictionary containing the data for each set. The keys are the set names, and the values are pandas DataFrames containing the data for each set.
data_parameter (dict): A dictionary containing the data for each parameter. The keys are the parameter names, and the values are pandas DataFrames containing the data for each parameter.
reservoir (str): The name of the reservoir to plot.
Returns:
plotly.graph_objects.Figure: A plot showing the state of the given reservoir in
"""
fig = go.Figure()
demand = pd.merge(data_set['t'][['dim1']].rename(columns={'dim1':'t'}),data_parameter['demand'][data_parameter['demand']['r']==reservoir],on=['t'],how='left')
demand['Value'] = demand['Value'].fillna(0)
fig.add_trace(
go.Scatter(
x = demand['t'],
y = -demand['Value'],
stackgroup='one',
name = 'Demande'
)
)
fig.add_trace(
go.Scatter(
x = data_set['t']['dim1'],
y = sum(data_variable['Qpipe'][(data_variable['Qpipe']['n.1']==reservoir)&(data_variable['Qpipe']['n']==n)]['Value'] for n in data_set['l'][data_set['l']['n.1']==reservoir]['n']),
stackgroup='two',
name = 'Approvisionnement'
)
)
fig.add_trace(
go.Scatter(
x = data_set['t']['dim1'],
y = data_variable['v'][data_variable['v']['n']==reservoir]['Value']-data_variable['v'][data_variable['v']['n']==reservoir]['Lowerbound'],
name = 'Volume - Volume min'
)
)
fig.update_layout(
hovermode='x',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
title = reservoir,
margin_t = 200,
height=700
)
return fig
def path_to_n(n,data_set):
"""
This function returns the path from the given node to the source node in the water distribution network.
Args:
n (str): The name of the node.
data_set (dict): A dictionary containing the data for each set. The keys are the set names, and the values are pandas DataFrames containing the data for each set.
Returns:
list: A list containing the names of the nodes from the given node to the source node.
"""
path = [n]
while n !='s':
n = data_set['l'][data_set['l']['n.1']==n]['n'].iloc[0]
path.append(n)
return path
def Chemin_charge(data_parameter,data_variable,data_set,n):
"""
This function creates a plot showing the height and charge at each node along a path from the source node to a given node in the water distribution network.
Args:
data_parameter (dict): A dictionary containing the data for each parameter. The keys are the parameter names, and the values are pandas DataFrames containing the data for each parameter.
data_variable (dict): A dictionary containing the data for each variable. The keys are the variable names, and the values are pandas DataFrames containing the data for each variable.
data_set (dict): A dictionary containing the data for each set. The keys are the set names, and the values are pandas DataFrames containing the data for each set.
n (str): The name of the node to end at.
Returns:
plotly.graph_objects.Figure: A plot showing the height and charge at each node along the path from the source node to the given node.
"""
# Initialize a Figure object
fig = go.Figure()
# Get the path from the source node to the given node
path = path_to_n(n,data_set)
path = path[::-1]
fig.add_trace(
go.Scatter(
x = path,
y = pd.concat([pd.DataFrame([['s',0,0]],columns=data_parameter['height'].columns),data_parameter['height'][data_parameter['height']['n'].isin(path)]])['Value'],
name = 'hauteur'
)
)
for t in data_set['t']['dim1']:
# if t == 't1':
fig.add_trace(
go.Scatter(
x = path,
y = data_variable['Charge'][(data_variable['Charge']['n'].isin(path))&(data_variable['Charge']['t']==t)]['Value'],
name = t,
# visible='legendonly'
)
)
fig.update_layout(
hovermode='x',
height = 700,
title = n
)
return fig
def Relaxation_courbe(data_variable,data_parameter):
Qmax = int(data_variable['Qpompe']['Value'].max()*1.1)
X = np.linspace(0,Qmax,Qmax)
Y = data_parameter['psi'][data_parameter['psi']['degree']==2]['Value'].iloc[0]*X**2 + data_parameter['psi'][data_parameter['psi']['degree']==0]['Value'].iloc[0]
color_map = {
'small' : 'blue',
'large' : 'red'
}
fig = go.Figure()
for c in data_variable['Qpompe']['c'].unique():
fig.add_trace(
go.Scatter(
x = X,
y = data_parameter['psi'][(data_parameter['psi']['degree']==2)&(data_parameter['psi']['c']==c)]['Value'].iloc[0]*X**2 + data_parameter['psi'][(data_parameter['psi']['degree']==0)&(data_parameter['psi']['c']==c)]['Value'].iloc[0],
marker_color = color_map[c],
name = f'Courbe de gain de charge théorique {c}'
)
)
for c in data_variable['Qpompe']['c'].unique():
for d in data_variable['Qpompe']['d'].unique():
fig.add_trace(
go.Scatter(
x = data_variable['Qpompe'][(data_variable['Qpompe']['c']==c)&(data_variable['Qpompe']['d']==d)]['Value'],
y = data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'],
marker_color = color_map[c],
name = f"Débit {c},{d}",
mode='markers'
)
)
fig.update_layout(
height= 800,
hovermode='y',
yaxis_title = "Charge",
xaxis_title = "Débit",
title = "Charge en fonction du débit à chaque instant"
)
return fig
def Relaxation_boxplot(data_variable,data_parameter):
color_map = {
'p1' : 'red',
'p2' : 'blue',
'p3' : 'green',
'p4' : 'magenta',
}
débit_pompe = {}
for c in data_variable['Qpompe']['c'].unique():
for d in data_variable['Qpompe'][data_variable['Qpompe']['c']==c]['d'].unique():
débit_pompe[(c,d)] = (data_variable['Charge'][data_variable['Charge']['n']=='s']['Value'].to_numpy()/(data_parameter['psi'].query(f"c=='{c}' & degree==0")['Value'].iloc[0] + data_parameter['psi'].query(f"c=='{c}' & degree==2")['Value'].iloc[0]*data_variable['Qpompe'].query(f"c=='{c}' & d=='{d}'")['Value'].to_numpy()**2) - 1)
fig = go.Figure()
for pompe in débit_pompe:
fig.add_trace(
go.Box(
y=débit_pompe[pompe][data_variable['Qpompe'].query(f"c=='{pompe[0]}' & d=='{pompe[1]}'")['Value'].to_numpy() > 0], name=f"{pompe}",
marker_color=color_map[pompe[1]],
boxpoints='all'
))
fig.update_layout(title = "Ecart relatif sur la charge des pompes induit par la relaxation convexe")
return fig
st.set_page_config(layout="wide")
comparaison = st.checkbox("Comparaison")
if comparaison:
col_1,col_2 = st.columns(2)
with col_1:
file = "./data_results/" + st.selectbox("Résultat 1",options=[file for file in os.listdir('./data_results') if file.endswith('.xlsx')])
Z = np.round(pd.read_excel(file,sheet_name='Scalar').iloc[8,2],4)
data_variable = get_variable(file)
data_parameter = get_parameter(file)
comments(file)
st.plotly_chart(etat_RDE(data_variable,Z,True),use_container_width=True)
st.plotly_chart(Pompe_RDE(data_variable,Z,True),use_container_width=True)
st.plotly_chart(Charge_RDE(data_variable,Z),use_container_width=True)
st.plotly_chart(Relaxation_courbe(data_variable=data_variable,data_parameter=data_parameter),use_container_width=True)
st.plotly_chart(Relaxation_boxplot(data_variable=data_variable,data_parameter=data_parameter),use_container_width=True)
with col_2:
file2 = "./data_results/" + st.selectbox("Résultat 2",options=[file for file in os.listdir('./data_results') if file.endswith('.xlsx')])
Z2 = np.round(pd.read_excel(file2,sheet_name='Scalar').iloc[8,2],4)
data_variable2 = get_variable(file2)
data_parameter2 = get_parameter(file2)
comments(file2)
st.plotly_chart(etat_RDE(data_variable2,Z2,True),use_container_width=True)
st.plotly_chart(Pompe_RDE(data_variable2,Z2,True),use_container_width=True)
st.plotly_chart(Charge_RDE(data_variable2,Z2),use_container_width=True)
st.plotly_chart(Relaxation_courbe(data_variable=data_variable2,data_parameter=data_parameter2),use_container_width=True)
st.plotly_chart(Relaxation_boxplot(data_variable=data_variable2,data_parameter=data_parameter2),use_container_width=True)
else:
file = "./data_results/" + st.selectbox("Résultat",options=[file for file in os.listdir('./data_results') if file.endswith('.xlsx')])
Z = np.round(pd.read_excel(file,sheet_name='Scalar').iloc[8,2],4)
data_variable = get_variable(file)
data_parameter = get_parameter(file)
data_set = get_set(file)
comments(file)
st.plotly_chart(etat_RDE(data_variable,Z),use_container_width=True)
with st.expander(label="Aide",expanded=False):
st.markdown(
'''
Ce graphique permet de visualiser en fonction du temps l'état des réservoirs et des pompes. L'état des réservoir est décrit par le volume dans les réservoirs. L'état des pompe est décrit par la consommation électrique de chaque pompe.
''',
unsafe_allow_html=True
)
st.plotly_chart(Pompe_RDE(data_variable,Z),use_container_width=True)
with st.expander(label="Aide", expanded=False):
st.markdown(
'''
Ce graphique détaille l'état des pompes du réseau dans le temps. Il montre le débit cumulé des pompes. Il est à noté que les pompes sont branchées en parallèles, il n'y a donc qu'une seul charge possible au borne de ces pompes.<br>
En théorie, le débit des pompes d'un même type est identique. Cependant les relaxations convexes du modèle peuvent conduire à des débits différents pour des pompes du même type.
''',
unsafe_allow_html=True
)
st.plotly_chart(Charge_RDE(data_variable,Z),use_container_width=True)
with st.expander(label="Aide", expanded=False):
st.markdown(
'''
Ce graphique détaille la charge dans le temps de tous les noeuds du réseau. Lorsqu'il n'y a pas de débit dans le réseau, la charge dans le rseau est libre (le réseau est à l'arrêt).
''',
unsafe_allow_html=True
)
with st.expander('Données',expanded=False):
variable = st.selectbox("Variable",options=data_variable.keys())
index = st.multiselect("Ligne",data_variable[variable].columns)
column = st.multiselect("Colonne",data_variable[variable].columns)
value = st.selectbox("Valeur",data_variable[variable].columns)
# st.dataframe(data_variable[variable])
try:
st.dataframe(pd.pivot_table(data_variable[variable], values=value, index=index,columns=column, aggfunc=np.sum))
except:
st.text("Paramètres mal choisis")
st.markdown('## Etat dans chaque réservoir')
with st.expander(label='Aide',expanded=False):
st.markdown(
'''
Ci-dessous, chaque réservoir a deux graphiques associés. Le premier donnee l'évolution des entrées et des sorties d'eaux du réservoir ainsi que l'évolution du volume d'eau (par rapport au volume minimum). En-dessous du premier graphique, un deuxième graphique montre pour le même réservoir l'évolution de la charge dans le réseau de la source jusqu'au réservoir pour des instants fixés. Ainsi que la hauteur dans chaque noeud de ce chemin (pour comparaison). La charge est nécessairement décroissante dans le chemin de la source au réservoir. Une des contraintes est que la charge doit toujours être supérieure à la hauteur.
''',
unsafe_allow_html=True
)
liste_reservoir = data_set['r']['n']
k = len(liste_reservoir)//4
# st.text(f"{liste_reservoir[0:4]}")
for s in range(k):
print(s)
liste_columns = st.columns(4)
for i,reservoir in enumerate(liste_reservoir[s*4:(s+1)*4]):
with liste_columns[i%4]:
st.plotly_chart(Etat_reservoir(data_variable,data_set,data_parameter,reservoir),use_container_width=True)
st.plotly_chart(Chemin_charge(data_parameter,data_variable,data_set,reservoir),use_container_width=True)
st.plotly_chart(Relaxation_courbe(data_variable=data_variable,data_parameter=data_parameter),use_container_width=True)
st.plotly_chart(Relaxation_boxplot(data_variable=data_variable,data_parameter=data_parameter),use_container_width=True)