from builtins import range
from builtins import object
from wmf import wmf
import numpy as np
import plotly.graph_objs as go
import pandas as pd
from plotly import tools
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
class PlotRainfall(object):
def __init__(self, pathRain):
self.path_rainBin, self.path_rainHdr = wmf.__Add_hdr_bin_2route__(pathRain)
self.rainData = wmf.read_rain_struct(self.path_rainHdr)
def Plot_Rainfall(self, pathFigure):
'''Hace el plot de la serie de tiempo de lluvia e incluye en esta toda la info necesaria'''
#obtiene los records
Rs = self.rainData.copy()
Records = ['Record: %d'%i for i in Rs[' Record'].values]
#Datos de lluvia
trace1 = go.Scatter(
x = Rs.index.to_pydatetime(),
y = Rs[' Lluvia'].values,
text = Records,
name = 'Lluvia [mm]',
line = {'width':3},
fill='tozeroy'
)
#Datos de lluvia acumulada
trace2 = go.Scatter(
x = Rs.index.to_pydatetime(),
y = Rs[' Lluvia'].values.cumsum(),
name = 'Acumulado [mm]',
line = {'width':3},
yaxis = 'y2'
)
#Datos y formato de la figura
data = [trace1, trace2]
layout = dict(
width=1020,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
yaxis=dict(
title='Precipitacion',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=-90,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),
),
xaxis = dict(
tickfont = dict(
size = 16
),
),
yaxis2=dict(
title='Acumulada',
titlefont=dict(
color='rgb(255, 153, 51)',
size = 15
),
tickangle=90,
tickfont=dict(
color='rgb(255, 153, 51)',
size = 16,
),
overlaying='y',
side='right',
range = [0,Rs[' Lluvia'].values.sum()]
))
#Figura
fig = dict(data=data, layout=layout)
#Guarda el html
plot(fig,filename=pathFigure, auto_open = False)
def Plot_Histogram(self, pathFigure):
'''Hace un plot del histograma de la distribucion de la lluvia en la cuenca.'''
#Hace una cipia de la informacion
Data = self.rainData.copy()
Data = Data[' Lluvia'].values
Data = Data[Data>0]
step = (np.percentile(Data,95) - np.percentile(Data,5))/7.
#Genera los datos de la figura
trace1 = go.Histogram(
x = Data,
name = 'Lluvia [mm]',
xbins = dict(
start = np.percentile(Data,5),
end = np.percentile(Data,95),
size = step)
)
#Establece la configuracion de la misma
layout = dict(
width=400,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=70,
t=50,
pad=4
),
yaxis=dict(
title='PDF',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=45,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),),
xaxis = dict(
title = 'Lluvia [mm]',
titlefont =dict(
color='rgb(0, 102, 153)',
size = 15
),
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
)
)
)
#Monta la figura
data = [trace1]
fig = dict(data = data, layout = layout)
plot(fig,filename=pathFigure, auto_open = False)
def Plot_MediaMensual(self, pathFigure):
'''Hace el plot de la media mensual multi-anual de la serie de lluvia de la cuenca'''
Rs = self.rainData.copy()
Rain = Rs[' Lluvia'].resample('M').sum()
#Obtiene media y desviqacion
Mean = []
Desv = []
for i in range(1,13):
Mean.append(Rain[Rain.index.month == i].mean())
Desv.append(np.std(Rain[Rain.index.month == i]))
Mean = np.array(Mean)
Desv = np.array(Desv)
#Hace la figura
trace1 = go.Scatter(
x = np.arange(1,13,1),
y = Mean,
name = 'Media',
line = {'width':5,
'color':('rgb(0, 51, 102)')},
)
trace2 = go.Scatter(
x = np.arange(1,13,1),
y = Mean + Desv,
name = 'm + s',
line = dict(color = ('rgb(255, 153, 51)'),
width = 3),
)
trace3 = go.Scatter(
x = np.arange(1,13,1),
y = Mean - Desv,
name = 'm - s',
fill='tonexty',
line = dict( width=3,
color = ('rgb(255, 153, 51)')),
)
data = [trace2, trace3, trace1]
layout = dict(
width=800,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
xaxis=dict(
dtick=1,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16),
ticktext = ['Ene','Feb','Mar','Abr','May','Jun','Jul','Ago','Sep','Oct','Nov','Dic'],
tickvals = list(range(1,13))
),
yaxis=dict(
title='Precipitacion',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=-90,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),
),
)
fig = dict(data=data, layout=layout)
plot(fig,filename=pathFigure, auto_open = False)
class PlotCaudal(object):
def __init__(self):
self.a = 0
def Plot_Caudal(self, pathFigure,DataQ,id_est,colorline):
'''Hace el plot de la serie de tiempo de lluvia e incluye en esta toda la info necesaria'''
#obtiene los records
Q = DataQ[id_est].copy()
#Datos de lluvia
trace1 = go.Scatter(
x = Q.index.to_pydatetime(),
y = Q.values,
name = 'Caudal Observado',
line = dict(color = (colorline),width=3),
fill='tozeroy'
)
#Datos y formato de la figura
data = [trace1]
layout = dict(
width=1020,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
yaxis=dict(
title='Caudal Observado [m3/s]',
titlefont=dict(
color='black',
size = 15
),
tickangle=-90,
tickfont=dict(
color='black',
size = 16,
),
),
xaxis = dict(
tickfont = dict(
size = 16
),
),
)
#Figura
fig = dict(data=data, layout=layout)
#Guarda el html
plot(fig,filename=pathFigure, auto_open = False)
def Plot_Caudal_Simu(self,pathFigure,dfQobs,dfQsim,rain_mean):
trace_high = go.Scatter(
x=dfQsim.index,
y=dfQsim.values,
name = "Q Simulado",
line = dict(color = 'red'),
opacity = 0.8)
trace_low = go.Scatter(
x=dfQsim.index,
y=dfQobs.values,
name = "Q Observado",
line = dict(color = 'blue'),
opacity = 0.8)
trace_inv = go.Scatter(
x=dfQsim.index,
y=rain_mean.values,
name = "Lluvia",
line = dict(color = 'blue'),
opacity = 0.8,
yaxis='y2',
fill='tozeroy',
fillcolor='rgba(0, 102, 153,0.2)')
data = [trace_high,trace_low,trace_inv]
layout = dict(showlegend = False,
xaxis = dict(
title='Fechas'),
yaxis=dict(
title="Caudal [m3/s]"),
yaxis2=dict(autorange="reversed",
title="Caudal [m3/s]",
overlaying ='y',
side='right')
)
fig = dict(data=data, layout=layout)
#Guarda el html
plot(fig,filename=pathFigure, auto_open = False)
def Plot_CDC_caudal(self,pathFigure,dfQobs,dfQsim):
Qs=np.sort(np.array(dfQsim.values))
Qo=np.sort(np.array(dfQobs.values))
porcen_s=[]
porcen_o=[]
for i in range(len(Qo)):
porcen_o.append((len(Qo[Qo>Qo[i]]))/float(len(Qo))*100)
for i in range(len(Qs)):
porcen_s.append((len(Qs[Qs>Qs[i]]))/float(len(Qs))*100)
trace_high = go.Scatter(
x=porcen_s,
y=Qs,
name = "Q simulado",
line = dict(color = 'red'),
opacity = 0.8)
trace_low = go.Scatter(
x=porcen_o,
y=Qo,
name = "Q observado",
line = dict(color = 'blue'),
opacity = 0.8)
data = [trace_high,trace_low]
layout = dict(showlegend = False,
width=500,
height=400,
xaxis = dict(
title='Porcentaje de Excedencia'),
yaxis=dict(
title='$Caudal [m^3/s]$')
)
fig = dict(data=data, layout=layout)
#Guarda el html
plot(fig,filename=pathFigure, auto_open = False)
def Plot_Anual_Caudal(self,pathFigure,dfQobs,dfQsim):
media_obs = dfQobs.groupby(dfQobs.index.month).mean()
media_sim = dfQsim.groupby(dfQsim.index.month).mean()
desv_obs = dfQobs.groupby(dfQobs.index.month).std()
desv_sim = dfQsim.groupby(dfQsim.index.month).std()
'''Hace el plot de la media mensual multi-anual de las series de Caudales'''
#Hace la figura
trace1 = go.Scatter(
x = np.arange(1,13,1),
y = media_obs,
name = 'Media_obs',
line = {'width':5,
'color':('red')},
)
trace2 = go.Scatter(
x = np.arange(1,13,1),
y = media_obs + desv_obs,
name = 'mo + s',
line = dict(color = ('rgb(255, 153, 128)'),
width = 3),
)
trace3 = go.Scatter(
x = np.arange(1,13,1),
y = media_obs - desv_obs,
name = 'mo - s',
fill='tonexty',
line = dict( width=3,
color = ('rgb(255, 153, 128)')),
)
trace4 = go.Scatter(
x = np.arange(1,13,1),
y = media_sim,
name = 'Media_sim',
line = {'width':5,
'color':('blue')},
)
trace5 = go.Scatter(
x = np.arange(1,13,1),
y = media_sim + desv_sim,
name = 'ms + s',
line = dict(color = ('rgb(204, 255, 255)'),
width = 3),
)
trace6 = go.Scatter(
x = np.arange(1,13,1),
y = media_sim - desv_sim,
name = 'ms - s',
fill='tonexty',
line = dict( width=3,
color = ('rgb(204, 255, 255)')),
)
data = [trace2, trace3, trace1,trace4,trace5,trace6]
layout = dict(
width=800,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
xaxis=dict(
dtick=1,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16),
ticktext = ['Ene','Feb','Mar','Abr','May','Jun','Jul','Ago','Sep','Oct','Nov','Dic'],
tickvals = list(range(1,13))
),
yaxis=dict(
title='Caudal [m^3/s]',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=-90,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),
),
)
fig = dict(data=data, layout=layout)
plot(fig,filename=pathFigure, auto_open = False)
def Plot_Series_Sedimentos(self,pathFigure,dfArenas,dfLimos,dfArcillas,dfSed_total,dfDataQobs):
trace_high = go.Scatter(
x=dfArenas.index,
y=dfArenas.values,
name = "Arenas",
line = dict(color = 'rgb(255, 255, 0)',dash = 'dashdot'),
opacity = 0.8)
trace_1 = go.Scatter(
x=dfArenas.index,
y=dfLimos.values,
name = "Limos",
line = dict(color = 'rgb(179, 143, 0)',dash = 'dash'),
opacity = 0.8)
trace_2 = go.Scatter(
x=dfArenas.index,
y=dfArcillas.values,
name = "Arcillas",
line = dict(color = 'rgb(85, 128, 0)',dash = 'dot'),
opacity = 0.8)
trace_3 = go.Scatter(
x=dfArenas.index,
y=dfSed_total.values,
name = "Qsed total",
line = dict(color = 'red',width=3),
opacity = 0.8)
trace_4 = go.Scatter(
x=dfArenas.index,
y=dfDataQobs.values,
name = "Qsed Observado",
line = dict(color = ('rgb(255, 71, 26)'),width=3),
opacity = 0.8)
data = [trace_high,trace_1,trace_2,trace_3,trace_4]
layout = dict(showlegend = False,
xaxis = dict(
title='Fechas'),
yaxis=dict(
title="Caudal Solido [m3/s]"),
)
fig = dict(data=data, layout=layout)
#Guarda el html
plot(fig,filename=pathFigure, auto_open = False)
class PlotGeomorphology(object):
def __init__(self):
self.a = 0
def VarHistogram(self, var, pathFigure, ncells):
'''Hace el plot de un histograma de una variable seleccionada'''
#Seleccion de datos
if ncells > 10000:
pos = np.random.choice(len(var), 10000)
else:
pos = list(range(10000))
#Obtiene la pdf y cdf de la variable
var = var[pos]
p1 = np.percentile(var, 0.1)
p99 = np.percentile(var, 99.9)
h,b = np.histogram(var, bins=np.linspace(p1,p99,10))
pdf = h.astype(float)/h.sum()
cdf = pdf.cumsum()
b = (b[:-1]+b[1:])/2.
#Create a trace
trace1 = go.Scatter(
x = b,
y = pdf,
name = 'PDF',
line = {'width':3},
fill='tozeroy'
)
trace2 = go.Scatter(
x = b,
y = cdf,
name = 'CDF',
line = {'width':3},
yaxis = 'y2'
)
#Datos del plot
data = [trace1, trace2]
#Configuracion del plot
layout = dict(
width=400,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
xaxis = dict(
titlefont =dict(
color='rgb(0, 102, 153)',
size = 15
),
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
)
),
yaxis=dict(
title='PDF',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=-90,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),
),
yaxis2=dict(
title='CDF',
titlefont=dict(
color='rgb(255, 153, 51)',
size = 15
),
tickangle=90,
tickfont=dict(
color='rgb(255, 153, 51)',
size = 16,
),
overlaying='y',
side='right',
))
#Figura y guardado
fig = dict(data=data, layout=layout)
plot(fig,filename=pathFigure, auto_open = False)
def TiempoConcentracion(self, DicTiempos, pathFigure):
'''Plot de los tiempos de concentracion de la cuenca'''
#Los datos
Names = [i[0][:7] for i in list(DicTiempos.items())]
Texto = [i[0] for i in list(DicTiempos.items())]
Times = ['%.3f'%i[1] for i in list(DicTiempos.items())]
#El plot
data = [go.Bar(
x = Names,
y = Times,
text = Texto
)]
#Configuracion del plot
layout = dict(
width=600,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
xaxis = dict(
title = 'Variable',
titlefont =dict(
color='rgb(0, 102, 153)',
size = 15
),
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
)
),
yaxis=dict(
title='Tiempo viaje [hrs]',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=0,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),
),)
#Figura y guardado
fig = dict(data=data, layout=layout)
plot(fig,filename=pathFigure, auto_open = False)
def StreamProfileHipsometric(self, pathFigure, HipsoData, StreamData):
'''Hace el plot del perfil longitudinal y de la curva hipsometrica'''
#plot# Create a trace
trace1 = go.Scatter(
x = HipsoData[0],
y = HipsoData[1],
name = 'Curva hipso',
line = {'width':3},
)
x = StreamData[0]
y = StreamData[1]
trace2 = go.Scatter(
x = x[30:],
y = y[30:],
name = 'Perfil',
line = {'width':3},
xaxis = 'x2'
)
data = [trace1, trace2]
layout = dict(
width=600,
height=400,
showlegend = False,
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
),
xaxis = dict(
title = 'Porcentaje del area [%]',
titlefont =dict(
color='rgb(0, 102, 153)',
size = 15
),
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
)
),
yaxis=dict(
title='Elevacion [m.s.n.m]',
titlefont=dict(
color='rgb(0, 102, 153)',
size = 15
),
tickangle=-90,
tickfont=dict(
color='rgb(0, 102, 153)',
size = 16,
),
),
xaxis2=dict(
title='Distancia a la salida [km]',
titlefont=dict(
color='rgb(255, 153, 51)',
size = 15
),
tickfont=dict(
color='rgb(255, 153, 51)',
size = 16,
),
overlaying='x',
side='top',
))
fig = dict(data=data, layout=layout)
plot(fig,filename=pathFigure, auto_open = False)
class PlotStorage(object):
def __init__(self):
self.foo = 1
def Plot_Storages(self, StoragePath, PathFigure):
'''Hace un plot del storage en el periodo de simulacion'''
#Lee los datos
Data = pd.read_csv(StoragePath, skiprows=4, index_col=6, parse_dates=True)
#Hace la figura
fig = tools.make_subplots(rows=5, cols=1)
for c,key in enumerate(Data.columns.values[1:].tolist()):
trace1 = go.Scatter(
x = Data.index.to_pydatetime(),
y = Data[key].values,
name = key,
line = {'width':3},
fill='tozeroy',
)
fig.append_trace(trace1, c+1, 1)
fig['layout'].update(height=600, width=600,
showlegend = False,
yaxis=dict(title='Estado [mm]',),
margin=dict(
l=50,
r=50,
b=50,
t=50,
pad=4
))
plot(fig,filename=PathFigure, auto_open = False)
