In [2]:
from wmf import wmf
import numpy as np
import pylab as pl
import pandas as pd
import pickle
import datetime
from datetime import timedelta
%matplotlib inline
import matplotlib.pyplot as plt
import collections
import warnings
warnings.filterwarnings('ignore')
import scipy as scp
import os
import aforos as af
from IPython.display import IFrame
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import xlsxwriter
import codecs
from multiprocessing import Pool
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.patches import PathPatch
import matplotlib.patches as patche
from IPython.core.display import HTML
css = open('style-table.css').read() + open('style-notebook.css').read()
HTML('<style>{}</style>'.format(css))
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In [3]:
def aforo(codigo,DEM='dem_amva60',DIR='dir_amva60',aforo=1):
'''runs aforos module
Parameters
----------
codigo: id in dbase
salida: campain folder name
DEM : dem name
DIR : dir name
aforo : flow measurement number or id
-------
Returns
------
out: self
sets module aforos up
'''
self = af.aforos(codigo,aforo=int(aforo),
nombre = df_informacion.loc[codigo,'FolderName'])
self.ruta_dem = '/media/nicolas/Home/nicolas/01_SIATA/raster/%s.tif'%DEM
self.ruta_dir = '/media/nicolas/Home/nicolas/01_SIATA/raster/%s.tif'%DIR
self.longitud = float(df_informacion.loc[codigo,'Longitud'])
self.latitud = float(df_informacion.loc[codigo,'Latitud'])
self.name = df_informacion.loc[codigo,'Nombre']
self.municipio = df_informacion.loc[codigo,'Municipio']
self.direccion = df_informacion.loc[codigo,'Direccion']
self.barrio = df_informacion.loc[codigo,'Barrio']
self.subcuenca = df_informacion.loc[codigo,'Subcuenca']
return self
def runbyid(dfe_id):
self = aforo(int(dfe.loc[dfe_id,'id_estacion_asociada']),aforo=dfe.loc[dfe_id,'aforo'])
self.fecha = pd.to_datetime(dfe.loc[dfe_id,'fecha'])
self.date = self.fecha.strftime('%Y-%m-%d %H:%M')
return self
In [4]:
hora_inicial = '06:00'
hora_final = '18:00'
df_informacion = pd.read_csv('../information/info_redrio.csv',index_col=0)
#dfe = pd.read_csv('../information/salidas_21y22_junio.csv',index_col=0)
texto = ['% ----------------------------------------------',
'% ---------------------TEXTO AQUÍ---------------',
'% ----------------------------------------------']
#estaciones = pd.read_csv('/media/nicolas/Home/Jupyter/Aforos/informacion/estaciones.csv',index_col=0)
#nombres = pd.read_csv('/media/nicolas/Home/Jupyter/Aforos/informacion/nombres.csv',index_col=0)
In [5]:
fecha = pd.to_datetime('2017-03-09')
dfe = pd.read_csv('../information/20170309_dfe.csv',index_col=0)
nombre_salida = u'Campaña 09 de Marzo'
In [6]:
self = aforo(1061)
self.fecha = fecha+datetime.timedelta(hours=18)
#self.get_rain()
self.simubasin(rute=True,show=False)
rain = self.plot_lluvia_aforo(ax2color='darkslategrey',
fecha=self.fecha.strftime('%Y-%m-%d')+' 18:00',
ruta='%s/%s_rain.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d')),
formato='png')
In [59]:
resumen = df_informacion.loc[dfe['id_estacion_asociada'].values].set_index('Nombre')
resumen['caudal_medio']= dfe['caudal_medio'].values
resumen = resumen.reset_index().drop(2)
resumen.loc[3,'caudal_medio']= 0.767612+0.373506
resumen = resumen.set_index('Nombre')
self.plot_resumen_flow(resumen['caudal_medio'],'%s/%s_flux.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d')))
In [60]:
point = df_informacion.loc[dfe.drop(2)['id_estacion_asociada'].values].sort_values(by='Latitud')
self.Plot_Mapa2(add_scatter=[point['Longitud'].values,point['Latitud'].values],\
title =nombre_salida,Logo='SIATA2.png',\
Drainage='/media/nicolas/maso/Mario/shapes/nets/Puente_Gabino_1061/Puente_Gabino_1061',\
add_stations=map(lambda x:x.decode('utf-8'),point['Nombre'].values),\
georef=[6.52,6.15,-75.725,(-75.725+0.43)],clim=[1300,3400],fontsize=24,\
Basin='/media/nicolas/maso/Mario/shapes/basins/Puente_Gabino_1061/Puente_Gabino_1061',
decimales=33)
self.m.readshapefile('/media/nicolas/maso/Mario/shapes/streams/169/169','drenaje',
color=self.colores_siata[-3],
linewidth=3.0,zorder=5)
plt.savefig('%s/%s_map_campain.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d')),format='png',bbox_inches='tight')
Tabla resultados¶
In [9]:
nombre_salida = 'Campaña 09 de Marzo'
caption = 'Parámetros hidráulicos %s'%nombre_salida
columns = ['Caudal medio','Velocidad media','Área','Perímetro','Altura media','Rádio hidráulico']
cellwidth = [30]+6*[70/6]
df_latex = dfe[['caudal_medio','velocidad_media','area_total','perimetro','altura_media','radio_hidraulico']]
df_latex.index = df_informacion.loc[dfe['id_estacion_asociada'].values]['Nombre'].values
tabla=self.latex_table_from_df(df_latex,caption,'tab:%s'%fecha.strftime('%Y%m%d'),columns,cellwidth,width=5.6)
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def latex_hidrologia(self,caudal,ruta_fig,label):
difiere = round(((caudal/self.caudales_historicos.median().values[0])-1.0)*100,3)
plot_figure = []
plot_figure.append('\\begin{multicols}{2}')
plot_figure.append('La figura \\ref{%s} está construida con datos de aforos históricos realizado sobre la misma sección, el recuadro superior es un histograma de frecuencias, mientras que el recuadro inferior es un resumen con los estadísticos más importantes. En total se cuenta con %d aforos, el caudal observado difiere de la mediana histórica %.3f $[m^3/s]$ en un %.2f porciento.'%(label,self.caudales_historicos.index.size,self.caudales_historicos.median().values[0],difiere))
plot_figure.append('{\\centering')
plot_figure.append('\\includegraphics[width=5.0cm]{{%s}}'%(ruta_fig))
plot_figure.append('\\captionof{figure}{\small Información de Aforos históricos %s}'%self.name)
plot_figure.append('\\label{%s}'%label)
plot_figure.append('}')
plot_figure.append('\\end{multicols}')
return plot_figure
resultados = ['']
resultados.append('\\section{%s}'%nombre_salida)
#--plot map_campain
resultados+=self.latex_figure(17,'%s/%s_map_campain.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d')),'Mapa %s'%nombre_salida,'fig:%smapcamp'%(fecha.strftime('%Y%m%d')))
resultados+=self.latex_figure(17,'%s/%s_rain.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d')),'Lluvia %s'%nombre_salida,'fig:%srain'%(fecha.strftime('%Y%m%d')))
resultados+=self.latex_figure(17,'%s/%s_flux.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d')),'Resultados %s'%nombre_salida,'fig:%sflux'%(fecha.strftime('%Y%m%d')))
resultados+=tabla
df = pd.DataFrame.copy(dfe)
df_alturas = pd.read_csv('../information/%s_heights.csv'%fecha.strftime('%Y%m%d'),index_col=0)
df_alturas.columns = np.array(df_alturas.columns,int)
for i in df.index:
self = runbyid(i)
if df.loc[i,'aforo_especial']==1:
self.dispositivo = '-999'
resultados+= ['\\subsection{%s}\\'%self.name]
if int(df.loc[i,'id_estacion_asociada'])==1061:
resultados+= self.latex_figure(17,'%s/curva_gabino.png'%(fecha.strftime('%Y%m%d')),
'Caudales estimados a partir de curva de calibración',
'fig:%scurva'%(fecha.strftime('%Y%m%d')))
resultados += ['\\clearpage']
else:
self.get_resultados_mysql()
self.dispositivo = self.dfr.loc['dispositivo','Resultado']
self.plot_section(wet=self.verticales,
lev=self.levantamiento,
figsize=(12,4),
fontsize=18,
sepx=0.03,
sepy=0.01,
filepath='%s/%s_section_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre))
resultados += self.latex_results_table('tab:%s_hidroparam_%s'%(fecha.strftime('%Y%m%d'),self.nombre))
lbl = '\\ref{tab:%s_hidroparam_%s}'%(fecha.strftime('%Y%m%d'),self.nombre)
resultados+=['El aforo se realizó en la fecha %s, el caudal obtenido fue de %s $[m^3/s]$. La sección aforada tiene un ancho superficial que mide %s [m], la altura promedio de la lámina de agua es de %s [m], la altura máxima se ubica a %s [m] de la margen izquierda del canal con un valor de %s [m]. Para el cálculo de velocidades se utilizó el dispositivo %s, el valor medio de velocidad obtenido fue de %s $[m/s]$, y el máximo de %s $[m/s]$. La sumatoria de áreas en todas las verticales deja un área total de %s $[m^2]$. Los demás parámetros se pueden observar en la tabla %s.'%(self.fecha.strftime('%Y-%m-%d %H:%M'),round(self.dfr.loc['caudal_medio','Resultado'],3),round(self.dfr.loc['ancho_superficial','Resultado'],3),round(self.dfr.loc['altura_media','Resultado'],3),
round(abs(self.verticales.set_index('x')['y'].argmin()),3),round(abs(self.verticales['y'].min()),3),self.dfr.loc['dispositivo','Resultado'],
round(self.dfr.loc['velocidad_media','Resultado']),round(self.verticales['velocidad04'].max(),3),round(self.dfr.loc['area_total','Resultado'],3),lbl)]
resultados += self.latex_section('%s/%s_section_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre),
'fig:%s_section_%s'%(fecha.strftime('%Y%m%d'),self.nombre))+texto
resultados+=['\\clearpage']
try:
if int(df.loc[i,'id_estacion_asociada'])==1061:
dfe.loc[i,'alturas'] = 0
else:
caudal = dfe.loc[i,'caudal_medio']
caudales = self.get_flow_from_heights(caudal,self.codigo,df_alturas)
self.plot_bars(caudales,rute='%s/%s_heights_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre),decimales=2)
resultados+= self.latex_figure(17,'%s/%s_heights_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre),
'Caudales estimados %s %s'%(nombre_salida,self.name),
'fig:%sheights_%s'%(fecha.strftime('%Y%m%d'),self.nombre))
resultados+=['Partiendo del caudal estimado mediante el aforo y de datos de nivel de la lámina de agua medidos en campo durante la campaña, se estimaron los caudales horarios desde las 6:00 hasta las 18:00, el caudal promedio durante la campaña fue %.3f $[m^3/s]$. Los resultados se encuentran en la Figura \\ref{%s}.'%(caudales.mean(),'fig:%sheights_%s'%(fecha.strftime('%Y%m%d'),self.nombre))]
dfe.loc[i,'alturas'] = 1
except:
dfe.loc[i,'alturas'] = 0
try:
self.plot_compara_historicos('%s/%s_hist_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre))
resultados+=latex_hidrologia(self,dfe.loc[i,'caudal_medio'],'%s/%s_hist_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre),
'fig:%shist%s'%(fecha.strftime('%Y%m%d'),self.nombre))
dfe.loc[i,'historico'] = 1
except:
dfe.loc[i,'historico'] = 0
if df_informacion.loc[self.codigo,'morfo']==1.0:
self.simubasin(rute=True,show=False)
rain = self.plot_lluvia_aforo(ax2color='darkslategrey',
fecha=self.fecha.strftime('%Y-%m-%d')+' 18:00',
ruta='%s/%s_rain_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre),
formato='png')
resultados+= self.latex_figure(17,'%s/%s_rain_%s.png'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre),
'Lluvia %s %s'%(nombre_salida,self.name),
'fig:%srain_%s'%(fecha.strftime('%Y%m%d'),self.nombre))
if max(self.accumulated_rain)>1.0:
resultados+=['La mayor intensidad promedio de lluvia fue registrada en la cuenca en la fecha %s, con un valor de %.3f $[mm/h]$.'%(rain.argmax().strftime('%Y-%m-%d %H:%M'),rain.max())]
resultados+=['La distribución de la lluvia en la cuenca se puede observar en el recuadro izquierdo de la Figura \\ref{%s}, donde se reportó un valor máximo de %.3f $[mm]$.'%('fig:%srain_%s'%(fecha.strftime('%Y%m%d'),self.nombre),max(self.accumulated_rain))]
try:
self.dfr = self.dfr.fillna(-999)
self.verticales = self.verticales.fillna(-999)
self.levantamiento = self.levantamiento.fillna(-999)
self.data_to_excel('%s_data/%s_%s.xlsx'%(fecha.strftime('%Y%m%d'),fecha.strftime('%Y%m%d'),self.nombre))
dfe.loc[i,'datatoexcel']=1
except:
dfe.loc[i,'datatoexcel']=0
np.savetxt('%s.tex'%fecha.strftime('%Y%m%d'),resultados, fmt='%s')
In [11]:
nombre_info = 'borrar09'
includes = ['\\include{Portada}','\\setlength{\\headsep}{40pt}',\
'\\tableofcontents',\
'\\listoffigures',\
'\\listoftables',\
'\\include{introduccion}',\
'\\chapter{Información de las estaciones aforadas}']+texto+\
['\\clearpage']
self.informe(includes+['\\include{20170309}'],\
header_filepath='figuras/header.png',\
header_text='Informe salidas modelación',\
logo_filepath='figuras/logo.png',\
cover_filepath = 'figuras/cover.png',\
foot_filepath = 'figuras/foot.png',\
nombre_info=nombre_info)
IFrame('%s_optimizado.pdf'%nombre_info, width=1000, height=1000)
Out[11]:
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In [93]:
point = df_informacion.loc[dfe.drop(2)['id_estacion_asociada'].values].sort_values(by='Latitud')
self.Plot_Mapa2(add_scatter=[point['Longitud'].values,point['Latitud'].values],\
#title =nombre_salida,Logo='SIATA2.png',\
Drainage='/media/nicolas/maso/Mario/shapes/nets/Puente_Gabino_1061/Puente_Gabino_1061',\
add_stations=map(lambda x:x.decode('utf-8'),point['Nombre'].values),\
georef=[6.52,6.15,-75.725,(-75.725+0.43)],clim=[1300,3400],fontsize=24,\
Basin='/media/nicolas/maso/Mario/shapes/basins/Puente_Gabino_1061/Puente_Gabino_1061',
decimales=33)
self.m.readshapefile('/media/nicolas/maso/Mario/shapes/streams/169/169','drenaje',
color=self.colores_siata[-3],
linewidth=3.0,zorder=5)
plt.savefig('../figures/mapa_ensayo.png',format='png',bbox_inches='tight')
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point = df_informacion.loc[dfe.drop(2)['id_estacion_asociada'].values].sort_values(by='Latitud')
self.Plot_Mapa2(add_scatter=[point['Longitud'].values,point['Latitud'].values],\
#title =nombre_salida,Logo='SIATA2.png',\
Drainage='/media/nicolas/maso/Mario/shapes/nets/Puente_Gabino_1061/Puente_Gabino_1061',\
add_stations=map(lambda x:x.decode('utf-8'),point['Nombre'].values),\
georef=[6.52,6.15,-75.725,(-75.725+0.43)],clim=[1300,3400],fontsize=24,\
Basin='/media/nicolas/maso/Mario/shapes/basins/Puente_Gabino_1061/Puente_Gabino_1061',
decimales=33)
self.m.readshapefile('/media/nicolas/maso/Mario/shapes/streams/169/169','drenaje',
color=self.colores_siata[-3],
linewidth=3.0,zorder=5)
plt.savefig('../figures/mapa_ensayo.pdf',format='pdf',bbox_inches='tight')
In [13]:
cd ../scripts/
In [1]:
import matplotlib
matplotlib.use('Template')
import matplotlib.pyplot as plt
import matplotlib.colors as clr
import matplotlib.font_manager as fm
import matplotlib.gridspec as grid
from matplotlib.patches import Rectangle
from matplotlib.collections import LineCollection
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.patches import PathPatch
from matplotlib.colors import LightSource
import matplotlib as mpl
import pandas as pd
import numpy as np
import datetime as dt
import locale
import scipy as sp
from scipy import stats as st
import glob as gb
from netCDF4 import Dataset
from osgeo import gdal, osr
from osgeo.gdalconst import * # shell utility module to work with os
import os, sys, time
from matplotlib.mlab import griddata
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
from mpl_toolkits.mplot3d import axes3d
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from dateutil.relativedelta import relativedelta
import MySQLdb
import psycopg2
import pickle
#from windrose import WindroseAxes
#Reportlab
from reportlab.lib import colors
from reportlab.lib.pagesizes import letter, landscape, inch
from reportlab.lib.enums import TA_JUSTIFY
from reportlab.platypus import Image, Paragraph, SimpleDocTemplate, Table, TableStyle
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.pdfgen import canvas
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
from StringIO import StringIO
import scipy.ndimage
from reportlab.lib.enums import TA_JUSTIFY
from pyPdf import PdfFileWriter, PdfFileReader
os.system('ln -s /media/nicolas/Home/Jupyter/MarioLoco/Tools/PDFImageSIATA.py %s' %os.getcwd())
from PDFImageSIATA import PdfImageSIATA
from pyPdf import PdfFileWriter, PdfFileReader
In [ ]:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# reportlab.py
locale.setlocale(locale.LC_TIME, ('es_co','utf-8'))
reload (sys)
sys.setdefaultencoding('utf8')
imgTemp = StringIO()
imgDoc = canvas.Canvas(imgTemp)
imagenPDF = PdfImageSIATA ('niveles.pdf' , width = 540, height = 816)
imagenPDF.drawOn (imgDoc, 840,0)
#imagenPDF = PdfImageSIATA ('Figuras/Mapa_ICA_PM25.pdf', width = 750, height = 1000)
#imagenPDF.drawOn (imgDoc, 952,150)
#-----------------------------
# Agregar texto
#~ imgDoc.setFillColorRGB(1, 1, 1)
#~ imgDoc.setFont("AvenirHeavy", 30)
#~ imgDoc.drawString(680,1175,'%s - %s' %(self.Contaminante.index[0].strftime('%d/%m/%Y'),self.Contaminante.index[-1].strftime('%d/%m/%Y')))
#------------------------------
#~ imagenPDF = PdfImageSIATA (,width = 140, height = 140)
#~ imagenPDF.drawOn (imgDoc, 475,5)
#~ imgDoc.setFillColorRGB(0.45, 0.45, 0.45)
#~ imgDoc.drawString(505,150,'Conventions')
#~ imgDoc.drawString(488,168,'Winds Flow From')
#~ if type(self.Hourly) != type(None):
#~ imgDoc.setFont("AvenirHeavy", 18)
#~ imgDoc.drawString(380,80,'Hour %s' %self.Hourly)
#~ # Plantilla
imgDoc.save()
page = PdfFileReader(file("ensayo.pdf" ,"rb")).getPage(0)
overlay = PdfFileReader(StringIO(imgTemp.getvalue())).getPage(0)
page.mergePage(overlay)
#~ #Save the result
output = PdfFileWriter()
output.addPage(page)
#~ output.addPage(page_1)
output.write(file("ejemplomapa.pdf" ,"w"))
#os.system('scp Figuras/Mapa_ICA_test.pdf jhernandezv@siata.gov.co:/var/www/jhernandezv/' )
#578-295
#195 × 295 mm
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IFrame('ejemplomapa.pdf', width=1000, height=1000)
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In [13]:
import MySQLdb
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
import os,sys
import string
import matplotlib.patches as patche
from mpl_toolkits.basemap import Basemap
import gdal
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.patches import PathPatch
from matplotlib import colors
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import MySQLdb
from wmf import wmf
from mpl_toolkits.basemap import Basemap
import pylab as pl
import matplotlib.gridspec as gridspec
from scipy import spatial
import matplotlib.dates as mdates
import matplotlib.font_manager as fm
import matplotlib.colors as mcolors
import codecs
import xlsxwriter
In [14]:
df = pd.read_csv('niveles.csv',index_col=0)
In [21]:
df.index
Out[21]:
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In [15]:
point = df
Plot_Mapa2(self,add_scatter=[point['longitud'].values,point['latitud'].values],\
Drainage='/media/nicolas/maso/Mario/shapes/nets/Puente_Gabino_1061/Puente_Gabino_1061',\
#Logo='../../Aforos/scripts/figuras/JU26to30.png',\
add_stations=map(lambda x:x.decode('utf-8'),point['nombre'].values),\
georef=[6.556,5.975,-75.725,-75.1255],clim=[1300,3400],fontsize=24,\
decimales=4,\
#Basin='/media/nicolas/maso/Mario/shapes/basins/Puente_Gabino_1061/Puente_Gabino_1061',\
textcolor='w')
self.m.readshapefile('/media/nicolas/maso/Mario/shapes/streams/169/169','drenaje',
color=self.colores_siata[-3],
linewidth=3.0,zorder=5)
self.m.readshapefile('/media/nicolas/maso/Mario/shapes/AreaMetropolitana','area',
color=self.colores_siata[-1],
linewidth=3.0,zorder=5)
plt.savefig('ensayo.pdf',format='pdf',bbox_inches='tight')
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self.Setcolor(self.m.drawparallels(np.linspace(np.round(self.georef[1],decimals=decimales),
np.round(self.georef[0],decimals=decimales),3),labels=[1,0,0,0],fmt=coord_format,color=(0.45, 0.45, 0.45),fontsize=self.fontsize,zorder=1,ax=self.ax[0],alpha=1,rotation='vertical',linewidth=0) ,(0.45, 0.45, 0.45) )
self.Setcolor(self.m.drawmeridians(np.linspace(np.round(self.georef[2],decimals=decimales),np.round(self.georef[3],decimals=decimales),3),labels=[0,0,1,0], fmt=coord_format,color=(0.45, 0.45, 0.45),fontsize=self.fontsize,zorder=2,ax=self.ax[0],alpha=1,linewidth=0),(0.45, 0.45, 0.45))
In [129]:
6.556+(6.2655 - 6.556)/2
Out[129]:
In [144]:
75.2775-(75.2775-75.13)/2
Out[144]:
In [141]:
75.425+(-75.425+75.13)/2
Out[141]:
In [ ]:
In [125]:
np.linspace(np.round(self.georef[1],decimals=3),np.round(self.georef[0],decimals=3),3)
Out[125]:
In [136]:
decimales=2
np.linspace(np.round(self.georef[2],decimals=decimales),np.round(self.georef[3],decimals=decimales),3)
Out[136]:
In [9]:
def Plot_Mapa2(self,*args,**kwargs):
'''Función para graficar mapas, tomado de módulo Air.py de jhernandezv@siata.gov.co'''
prop_1 = fm.FontProperties(fname='../Tools/AvenirLTStd-Book.ttf',)
self.figsize = kwargs.get('figsize',11)
self.fontsize = kwargs.get('fontsize',24)
self.textsave = kwargs.get('textsave',None)
self.path = kwargs.get('path','../redrio_salida/')
textcolor = kwargs.get('textcolor',(0.007,0.32,0.36))
decimales = kwargs.get('decimales',1)
coord_format = kwargs.get('coord_format',"%.2f")
self.Read_DEM(path=kwargs['DEM'],ajust=False) if 'DEM' in kwargs.keys() else self.Read_DEM()
self.georef = kwargs.get('georef',[self.DEM.index[0]+(self.DEM.index[0]-self.DEM.index[-1])*.03,self.DEM.index[-1]-(self.DEM.index[0]-self.DEM.index[-1])*.03,self.DEM.columns[0]+(self.DEM.columns[0]-self.DEM.columns[-1])*.03,self.DEM.columns[-1]-(self.DEM.columns[0]-self.DEM.columns[-1])*.03])
self.figsize = (self.figsize,self.figsize*(self.georef[0]-self.georef[1])/(self.georef[3]-self.georef[2]))
plt.close('all')
self.fig = plt.figure(figsize=self.figsize,facecolor='w',edgecolor='w')
self.ax = {}
self.ax[0] = self.fig.add_axes((0.00,0.00,1,1)) #Relieve
self.ax[1] = self.fig.add_axes((1.01,0.22,0.67,0.62))
self.ax[1].patch.set_visible(False)
if 'Logo' in kwargs.keys():
self.logo = plt.imread(kwargs['Logo'])
self.logodim = [self.logo.shape[1]/100./self.figsize[0]* (.7/(self.logo.shape[0]/100./self.figsize[1])), .7]
self.ax[2] = self.fig.add_axes((1 +(.46-self.logodim[0])*.5 if(.46-self.logodim[0])>0 else 1 ,1-self.logodim[1],self.logodim[0],self.logodim[1]))
self.ax[2].imshow(self.logo)
#Mapas
self.ax[7] = self.fig.add_axes((0.48,0.00,0.52,0.33))
self.ax[4] = self.fig.add_axes((1.01-0.505,0.01,0.24,0.24))
self.ax[5] = self.fig.add_axes((1.27-0.505,0.01,0.24,0.24))
# Colorbar
self.ax[3] = self.fig.add_axes((0.0,0.95-0.05,0.34,0.11))
self.ax[6] = self.fig.add_axes((0.0,0.95+0.03,0.3,0.03))
for i in self.ax.keys():
plt.setp(self.ax[i].spines.values(), color='w')
if i > 0: self.ax[i].tick_params(labelleft='off',labelbottom='off',right='off',left='off',bottom='off',top='off',which='both', colors=(0.45, 0.45, 0.45))
self.ax[0].tick_params(labelleft='on',labelbottom='on',right='off',left='on',bottom='on',top='off',which='both', colors=(0.45, 0.45, 0.45))
self.ax[0].spines['bottom'].set_color((0.45, 0.45, 0.45))
self.ax[0].spines['left'].set_color((0.45, 0.45, 0.45))
self.m = Basemap(ax=self.ax[0],projection='merc',llcrnrlat=self.georef[1],urcrnrlat=self.georef[0],llcrnrlon=self.georef[2],urcrnrlon=self.georef[3],resolution='h')
self.Setcolor(self.m.drawparallels([6.12025,6.41075],labels=[1,0,0,0],fmt=coord_format,color=(0.45, 0.45, 0.45),fontsize=self.fontsize,zorder=1,ax=self.ax[0],alpha=1,rotation='vertical',linewidth=0) ,(0.45, 0.45, 0.45) )
self.Setcolor(self.m.drawmeridians([-75.425,-75.20375],labels=[0,0,1,0], fmt=coord_format,color=(0.45, 0.45, 0.45),fontsize=self.fontsize,zorder=2,ax=self.ax[0],alpha=1,linewidth=0),(0.45, 0.45, 0.45))
##############################################################################
#~ #DEM
if 'Municipios' in kwargs.keys():
self.m.readshapefile('../Tools/MunicipiosAntioquia','Municipios',linewidth=.5,color=(0.0078,0.227,0.26),zorder=1)
self.patches = []
for info,shape in zip(self.m.Municipios_info,self.m.Municipios):
if info['nom_munici'] in kwargs['Municipios']: self.patches.append( Polygon(np.array(shape),True))
self.ax[0].add_collection(PatchCollection(self.patches, facecolor= [ 0.96470588, 0.95686275, 0.95294118], edgecolor=(0.0078,0.227,0.26), linewidths=1, zorder=2))
self.X,self.Y = np.meshgrid(self.DEM.columns.values,self.DEM.index.values)
self.Xm,self.Ym = self.m(self.X,self.Y)
#~ #norm=mpl.colors.Normalize(700,3100)
self.cs = self.m.contourf(self.Xm, self.Ym, self.DEM.values, levels=np.linspace(self.DEM.min().min(),self.DEM.max().max(),50) if 'clim' not in kwargs.keys() else np.linspace(kwargs['clim'][0],kwargs['clim'][1],50), cmap=plt.cm.terrain_r,alpha=.5,extend='min',zorder=3)
self.cs.cmap.set_over('b')
self.cs.cmap.set_under('w')
# Colorbar
cbar=plt.colorbar(self.cs,cax=self.ax[6],orientation='horizontal',format='%.f')
cbar.set_label(r"Altitud $[km]$",fontsize=self.fontsize,color=(0.45, 0.45, 0.45))
cbar.set_ticks(np.linspace(self.DEM.min().min(),self.DEM.max().max(),4) if 'clim' not in kwargs.keys() else np.linspace(kwargs['clim'][0],kwargs['clim'][1],4))
cbar.set_ticklabels(np.round(np.linspace(self.DEM.min().min(),self.DEM.max().max(),4)/1000.,decimals=1) if 'clim' not in kwargs.keys() else np.round(np.linspace(kwargs['clim'][0],kwargs['clim'][1],4)/1000.,decimals=1))
cbar.ax.tick_params(labelsize=self.fontsize,colors=(0.45, 0.45, 0.45))
zorder = 0
if 'Basin' in kwargs.keys():
if type(kwargs['Basin']) == type('str'):
self.m.readshapefile(kwargs['Basin'],'Area',linewidth=1.5,color=(0.0078,0.227,0.26),zorder=4)
else:
for basin in kwargs['Basin']:
self.m.readshapefile(basin,'Area',linewidth=1,color=(0.0078,0.227,0.26),zorder=4 + zorder)
zorder +=1
if 'add_shape' in kwargs.keys():
self.m.readshapefile(kwargs['add_shape'],'add_shape',linewidth=.5,color=(0.0078,0.227,0.26),zorder=5 + zorder)
self.patches = []
for info,shape in zip(self.m.add_shape_info,self.m.add_shape):
self.patches.append( Polygon(np.array(shape),True))
self.ax[0].add_collection(PatchCollection(self.patches, facecolor= [ 0.7372549 , 0.99215686, 0.1372549 ], edgecolor=(0.0078,0.227,0.26), linewidths=.5, zorder=6 + zorder)) #[ 0.96470588, 0.95686275, 0.95294118]
if 'Drainage' in kwargs.keys():
self.m.readshapefile(kwargs['Drainage'],'Drainage',linewidth=1,color=[0.21, 0.6, 0.65],zorder=7 + zorder)
if 'add_scatter' in kwargs.keys():
self.X,self.Y = self.m(kwargs['add_scatter'][0] ,kwargs['add_scatter'][1])
self.m.scatter(self.X,self.Y,s=500 + (300 if 'add_stations' in kwargs.keys() else 0),facecolor=(0.0078,0.227,0.26),edgecolor='w',zorder=8+ zorder)
if 'add_stations' in kwargs.keys():
c = 0
for i in range(len(self.X)):
self.ax[0].text(self.X[i],self.Y[i],'%d' %(i+1), ha='center',va='center',
fontsize=self.fontsize-3,color='w',zorder=9+zorder) #,bbox={'facecolor':(0.32,0.71,0.77), 'alpha':0.2, 'pad':2,'edgecolor':'w'})
self.ax[1].text(0,.86-.76/(len(self.X)-1)*i,'%d - %s'%(i+1,kwargs['add_stations'][i] ), ha='left', va='center',color=textcolor,fontsize=self.fontsize,fontproperties=prop_1)
#~ self.ax[0].text(self.m(-75.1,6.472-c)[0],self.m(-75.1,6.472-c)[1],'%d - %s'%(i+1,kwargs['add_stations'][i] ), ha='left', va='center',color=(0.007,0.32,0.36),fontsize=self.fontsize,fontproperties=prop_1)
c += 0.0385
zorder+=1
if kwargs.get('legend',False):
self.legend = {}
plt.rcParams['legend.numpoints']=1
#~ iqr = plt.Line2D((0,1),(0,0),color=(0.875,0.875,0.875),marker='s',linestyle='',fillstyle='full',markeredgecolor='w')#(0.007,0.32,0.36))
if 'Drainage' in kwargs.keys(): self.legend['Red de Drenaje'] = plt.Line2D((0,1),(0,0),color=(0.0078,0.227,0.26),ls='-',lw=1)
if 'Basin' in kwargs.keys(): self.legend['Cuenca Hidrográfica'] = plt.Line2D((0,1),(0,0),color=(0.0078,0.227,0.26),ls='-',lw=4)
if 'add_scatter' in kwargs.keys(): self.legend['Estación IDEAM'] = plt.Line2D((0,1),(0,0),ls='',marker='o',markersize=20,mfc=(0.0078,0.227,0.26),mec='w',lw=1,fillstyle='full')
if 'add_shape' in kwargs.keys(): self.legend[kwargs.get('add_shape_name','Cabecera Urbana')] = plt.Line2D((0,1),(0,0),ls='',marker='s',markersize=20,mfc=[ 0.7372549 , 0.99215686, 0.1372549 ],mec=(0.0078,0.227,0.26),lw=.5,fillstyle='full')
if 'Municipios' in kwargs.keys():
for i in range(len(kwargs['Municipios'])): self.legend[kwargs['Municipios'][i]] = plt.Line2D((0,1),(0,0),ls='',marker='s',markersize=20,mfc=[ 0.96470588, 0.95686275, 0.95294118], mec=(0.0078,0.227,0.26),lw=1,fillstyle='full')
self.leg = self.ax[0].legend(self.legend.values(),self.legend.keys(),bbox_to_anchor=(1,.54),fontsize=self.fontsize-2, loc='upper left',title='Convenciones',labelspacing=.8)
self.leg.get_frame().set_edgecolor('w')
for text in self.leg.get_texts(): plt.setp(text, color = (0.45, 0.45, 0.45))
#~ self.leg.get_title().set_fontsize(self.fontsize)
plt.setp(self.leg.get_title(),fontsize=self.fontsize,weight='bold',color=(0.45, 0.45, 0.45))
#####################################################################
#~ # Texto
if 'title' in kwargs.keys():
self.ax[1].text(.5,.95,kwargs['title'],ha='center',va='center',fontsize=self.fontsize+2,color=(0.45, 0.45, 0.45),weight='bold')
if 'add_text1' in kwargs.keys():
self.ax[1].text(.0,.851,kwargs['add_text1'][0],ha='left',va='bottom',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
self.ax[1].text(.0,.849,kwargs['add_text1'][1],ha='left',va='top',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
if 'add_text2' in kwargs.keys():
self.ax[1].text(.0,.751,kwargs['add_text2'][0],ha='left',va='bottom',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
self.ax[1].text(.0,.749,kwargs['add_text2'][1],ha='left',va='top',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
if 'add_text3' in kwargs.keys():
self.ax[1].text(.0,.651,kwargs['add_text3'][0],ha='left',va='bottom',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
self.ax[1].text(.0,.649,kwargs['add_text3'][1],ha='left',va='top',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
if 'add_text4' in kwargs.keys():
self.ax[1].text(.0,.551,kwargs['add_text4'][0],ha='left',va='bottom',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
self.ax[1].text(.0,.549,kwargs['add_text4'][1],ha='left',va='top',fontsize=self.fontsize-2,color=(0.45, 0.45, 0.45))
self.ax[7].text(.51,.87,u'Ubicación Nacional y Regional',ha='center',va='bottom',fontsize=self.fontsize-1,color=(0.45, 0.45, 0.45),weight='bold')
################################################################################
#colores
#~ Oceano [ 0.71372549, 0.87058824, 0.88235294];
#~ Antioquia [ 0.64705882, 0.97254902, 0.70980392];[ 0.3372549 , 0.91764706, 0.75294118] ;[ 0.44705882, 0.94901961, 0.68235294]
#~ Colombia [ 0.91372549, 0.87843137, 0.87843137];
#~ Valle [ 0.4745098 , 0.52941176, 0.80784314];[ 0.36078431, 0.6745098 , 0.94509804]
print 'Colombia'
# Colombia
self.mc = Basemap(ax=self.ax[4],projection='merc',llcrnrlat=-5,urcrnrlat=13,llcrnrlon=-81.86,urcrnrlon=-65,resolution='h')
self.mc.drawmapboundary(fill_color=[ 0.69019608, 0.89411765, 1. ],zorder=1)
self.mc.fillcontinents(color=[ 0.96470588, 0.95686275, 0.95294118],lake_color=[ 0.69019608, 0.89411765, 1. ],zorder=2)
self.mc.drawcountries(color=(0.0078,0.227,0.26),zorder=3)
self.mc.drawcoastlines(color=(0.0078,0.227,0.26),zorder=4)
self.mc.readshapefile('../Tools/Antioquia','Antioquia',linewidth=0.1,color=(0.0078,0.227,0.26),zorder=5)
self.patches = []
for info,shape in zip(self.mc.Antioquia_info,self.mc.Antioquia):
self.patches.append( Polygon(np.array(shape),True))
self.ax[4].add_collection(PatchCollection(self.patches, facecolor=[0.32, 0.71, 0.77], edgecolor=(0.0078,0.227,0.26), linewidths=.1, zorder=6))
###########################################################################
print 'Antioquia'
# Antioquia
self.ma = Basemap(ax=self.ax[5],projection='merc',llcrnrlat=5.35,urcrnrlat=8.95,llcrnrlon=-77.18,urcrnrlon=-73.76,resolution='f')
self.ma.drawmapboundary(fill_color=[ 0.69019608, 0.89411765, 1. ],zorder=1)
self.ma.fillcontinents(color=[ 0.96470588, 0.95686275, 0.95294118],lake_color=[ 0.69019608, 0.89411765, 1. ],zorder=2)
self.ma.readshapefile('../Tools/Departamentos','Departamentos',linewidth=0.1,color=(0.0078,0.227,0.26),zorder=3)
self.ma.readshapefile('../Tools/Antioquia','Antioquia',linewidth=0.1,color=(0.0078,0.227,0.26),zorder=4)
self.patches = []
for info,shape in zip(self.ma.Antioquia_info,self.ma.Antioquia):
self.patches.append( Polygon(np.array(shape),True))
self.ax[5].add_collection(PatchCollection(self.patches, facecolor=[0.32, 0.71, 0.77], edgecolor=(0.0078,0.227,0.26), linewidths=.1,zorder=5 ))
if 'Basin' in kwargs.keys():
self.ma.readshapefile(kwargs['Basin'],'Area',linewidth=0.5,color=(0.0078,0.227,0.26),zorder=6)
self.patches = []
for info,shape in zip(self.ma.Area_info,self.ma.Area):
self.patches.append( Polygon(np.array(shape),True))
self.ax[5].add_collection(PatchCollection(self.patches, facecolor= [ 0.96470588, 0.95686275, 0.95294118], edgecolor=(0.0078,0.227,0.26), linewidths=.1,zorder=7 )) #fc=[0.21,0.60,0.65]
if 'Arrow' in kwargs.keys():
self.ax[5].annotate('' , xy=self.ma(kwargs['Arrow'][0],kwargs['Arrow'][1]), xytext=self.ma(-75.47,7.15),va='center',ha='center',fontsize=self.fontsize,weight='bold',color=(0.0078,0.227,0.26),arrowprops=dict(facecolor=(0.0078,0.227,0.26),edgecolor=(0.0078,0.227,0.26), shrink=0.00,width=.5,headwidth=5),zorder=8) #bbox=dict(facecolor=colors[0], edgecolor='none', boxstyle='round',alpha=.2))
plt.savefig('%s.pdf' %kwargs.get('textsave','mapa'), bbox_inches='tight')