if datain%5==0:

dataout=datain

else:

if datain/5<0:

dataout=np.ceil(datain/5)*5

else:

dataout=np.floor(datain/5)*5

"""

Purpose: plot the map used for later contour or image plot.

Note:

return m,lonpro,latpro,latind,lonind

1. return m --> map drawed; lonpro/latpro --> lat/lon transferred

to projection coords; latind/lonind --> index for lat/lon

falling with mapbound

2. lat must be descending and lon must be ascending.

Parameters:

-----------

kwargs: used for basemap.Basemap method.

Example:

>>> fig,ax=g.Create_1Axes()

>>> m,lonpro,latpro,lonind,latind=bmap.gmap(ax,'cyl',mapbound='all',lat=np.arange(89.75,-89.8,-0.5),lon=np.arange(-179.75,179.8,0.5),gridstep=(30,30))

>>> x,y=m(116,40) #plot Beijing

>>> m.scatter(x,y,s=30,marker='o',color='r')

"""

def __init__(self,ax=None,projection='cyl',mapbound='all',lat=None,lon=None,

gridstep=(30,30),**kwargs):

ax = tools._replace_none_axes(ax)

lat = tools._replace_none_by_given(lat,np.arange(89.75,-89.8,-0.5))

lon = tools._replace_none_by_given(lon,np.arange(-179.75,179.8,0.5))

latstep = lat[0] - lat[1]

if latstep <= 0:

raise TypeError("lat input is increasing!")

else:

if latstep == 0.5:

half_degree = True

else:

half_degree = False

if projection=='cyl':

if isinstance(mapbound,dict):

raise ValueError('cannot use dict for cyl projection')

elif mapbound=='all':

lat1=lat[-1]

lat2=lat[0]

lon1=lon[0]

lon2=lon[-1]

#when the data is of half degree resolution, often the lat1 and

#lat2 is in the center of the half degree cell, so we need to

#adjust for the vertices.

if half_degree == True:

if lat1%0.25 == 0:

lat1 = lat1-0.25

if lat2%0.25 == 0:

lat2 = lat2+0.25

if lon1%0.25 == 0:

lon1 = lon1-0.25

if lon2%0.25 == 0:

lon2 = lon2+0.25

if lat1<-85:

lat1=-90.

if lat2>85:

lat2=90.

if lon1<-175:

lon1=-180.

if 185>lon2>175:

lon2=180.

if lon2>355:

lon2=360.

else:

lat1=mapbound[0]

lat2=mapbound[1]

lon1=mapbound[2]

lon2=mapbound[3]

#draw the map, parallels and meridians

m=bmp.Basemap(projection=projection,llcrnrlat=lat1,urcrnrlat=lat2,

llcrnrlon=lon1,urcrnrlon=lon2,resolution='l',ax=ax,

**kwargs)

m.drawcoastlines(linewidth=0.7)

if gridstep!=None and gridstep!=False:

para_range=np.arange(near5even(lat1),near5even(lat2)+0.1,gridstep[0])

meri_range=np.arange(near5even(lon1),near5even(lon2)+0.1,gridstep[1])

m.drawparallels(para_range,labels=[1,0,0,0])

m.drawmeridians(meri_range,labels=[0,0,0,1])

#make the grid

latind=np.nonzero((lat>lat1)&(lat<lat2))[0]

lonind=np.nonzero((lon>lon1)&(lon<lon2))[0]

numlat=len(np.nonzero((lat>lat1)&(lat<lat2))[0])

numlon=len(np.nonzero((lon>lon1)&(lon<lon2))[0])

lonm,latm=m.makegrid(numlon,numlat)

latm=np.flipud(latm)

lonpro,latpro=m(lonm,latm)

elif projection=='npstere':

if not isinstance(mapbound,dict):

raise ValueError('please use dict to specify')

else:

m=bmp.Basemap(projection='npstere',boundinglat=mapbound['blat'],

lon_0=mapbound['lon_0'],resolution='l',ax=ax,

**kwargs)

m.drawcoastlines(linewidth=0.7)

m.fillcontinents(color='0.8',zorder=0)

if gridstep!=None and gridstep!=False:

m.drawparallels(np.arange(mapbound['para0'],91.,gridstep[0]),

labels=[1,0,0,0],fontsize=10)

m.drawmeridians(np.arange(-180.,181.,gridstep[1]),

labels=[0,0,0,0],fontsize=10)

#make the grid

lat1=mapbound['blat']

latind=np.nonzero(lat>lat1)[0]

lonind=np.arange(len(lon))

latnew=np.linspace(90, lat1, num=len(latind), endpoint=True)

if lon[-1]>180:

lonnew=np.linspace(0,360,num=len(lonind),endpoint=True)

else:

lonnew=np.linspace(-180,180,num=len(lonind),endpoint=True)

lonm,latm=np.meshgrid(lonnew,latnew)

lonpro,latpro=m(lonm,latm)

else:

raise ValueError('''projection '{0}' not supported'''

.format(projection))

self.m = m

self.lonpro = lonpro

self.latpro = latpro

self.latind = latind

'''

Return [pdata,plotlev,plotlab,extend,trans_base_list];

if data_transform == False, trans_base_list = None.

Notes:

------

pdata: data used for contourf plotting.

plotlev: the levels used in contourf plotting.

extend: the value for parameter extand in contourf.

trans_base_list: cf. mathex.plot_array_transg

'''

if levels==None:

ftuple = (pdata,None,None,'neither')

if data_transform==True:

raise Warning("Strange levels is None but data_transform is True")

else:

if data_transform==True:

#make the data transform before plotting.

pdata_trans,plotlev,plotlab,trans_base_list = \

mathex.plot_array_transg(pdata, levels, copy=True)

if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):

ftuple = (pdata_trans,plotlev[1:-1],plotlab,'both')

elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):

raise ValueError('''only one extreme set as infinitive, please

set both as infinitive if arrow colorbar is wanted.''')

else:

ftuple = (pdata_trans,plotlev,plotlab,'neither')

#data_transform==False

else:

plotlev=pb.iteflat(levels)

plotlab=pb.iteflat(levels)

if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):

#here the levels would be like [np.NINF,1,2,3,np.PINF]

#in following contourf, all values <1 and all values>3 will be

#automatically plotted in the color of two arrows.

#easy to see in this example:

#a=np.tile(np.arange(10),10).reshape(10,10);

#fig,ax=g.Create_1Axes();

#cs=ax.contourf(a,levels=np.arange(2,7),extend='both');

#plt.colorbar(cs)

ftuple = (pdata,plotlev[1:-1],plotlab,'both')

elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):

raise ValueError('''only one extreme set as infinitive, please

set both as infinitive if arrow colorbar is wanted.''')

else:

ftuple = (pdata,plotlev,plotlab,'neither')

datalist = list(ftuple)

if data_transform == True:

datalist.append(trans_base_list)

else:

datalist.append(None)

colorbarlabel=None,

trans_base_list=None,

forcelabel=None,

plotlev=None,

plotlab=None):

'''

Return (colorbar_ticks,colorbar_labels)

'''

#data_transform==True and levels!=None

if data_transform==True:

if colorbarlabel != None:

colorbarlabel=pb.iteflat(colorbarlabel)

transformed_colorbarlabel_ticks,x,y,trans_base_list = \

mathex.plot_array_transg(colorbarlabel, trans_base_list,

copy=True)

#Note if/else blocks are organized in 1st tire by check if the two

#ends are -inf/inf and 2nd tire by check if colorbarlabel is None

if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):

if colorbarlabel!=None:

ftuple = (transformed_colorbarlabel_ticks,colorbarlabel)

else:

ftuple = (plotlev,plotlab[1:-1])

elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):

raise ValueError("It's strange to set only side as infitive")

else:

if colorbarlabel!=None:

ftuple = (transformed_colorbarlabel_ticks,colorbarlabel)

else:

ftuple = (plotlev,plotlab)

#data_transform==False

else:

if np.isneginf(plotlab[0]) and np.isposinf(plotlab[-1]):

#if colorbarlabel is forced, then ticks and ticklabels will be forced.

if colorbarlabel!=None:

ftuple = (colorbarlabel,colorbarlabel)

#This by default will be done, it's maintained here only for clarity.

else:

ftuple = (plotlab[1:-1],plotlab[1:-1])

elif np.isneginf(plotlab[0]) or np.isposinf(plotlab[-1]):

raise ValueError("It's strange to set only side as infitive")

else:

if colorbarlabel!=None:

ftuple = (colorbarlabel,colorbarlabel)

else:

ftuple = (plotlab,plotlab)

ftuple = list(ftuple)

if forcelabel != None:

if len(forcelabel) != len(ftuple[1]):

raise ValueError('''the length of the forcelabel and the

length of labeled ticks is not equal!''')

else:

ftuple[1] = forcelabel

"""

generate smart levels by using the min, percentiles from 5th

to 95th with every 5 as the step, and the max value.

"""

def even_num(num):

if num >= 10:

return int(num)

else:

return round(num,4)

def extract_percentile(array,per):

return even_num(np.percentile(array,per))

def generate_smartlevel_from_1Darray(array):

vmax = even_num(np.max(array))

vmin = even_num(np.min(array))

per_level = map(lambda x:extract_percentile(array,x),

np.arange(5,96,5))

return np.array([vmin]+per_level+[vmax])

if np.isnan(np.sum(pdata)):

pdata = np.ma.masked_invalid(pdata)

if np.ma.isMA(pdata):

array1D = pdata[np.nonzero(~pdata.mask)]

else:

array1D = pdata.flatten()

projection='cyl',

mapbound='all',

gridstep=(30,30),

shift=False,

map_threshold=None,

levels=None,

cmap=None,

smartlevel=None,

data_transform=False,

gmapkw={},

ax=None):

"""

This function makes the map, and transform data for ready

use of m.contourf or m.imshow

"""

if shift==True:

data,lon=bmp.shiftgrid(180,data,lon,start=False)

mgmap=gmap(ax,projection,mapbound,lat,lon,gridstep,**gmapkw)

m,lonpro,latpro,latind,lonind = (mgmap.m, mgmap.lonpro, mgmap.latpro,

mgmap.latind, mgmap.lonind)

pdata = data[latind[0]:latind[-1]+1,lonind[0]:lonind[-1]+1]

#mask by map_threshold

pdata = mathex.ndarray_mask_by_threshold(pdata,map_threshold)

#generate the smartlevel

if smartlevel == True:

if levels != None:

raise ValueError("levels must be None when smartlevel is True!")

else:

levels = _generate_smartlevel(pdata)

data_transform = True

#prepare the data for contourf

pdata,plotlev,plotlab,extend,trans_base_list = \

_transform_data(pdata,levels,data_transform)

return (mgmap,pdata,plotlev,plotlab,extend,

levels=None,

data_transform=False,

colorbarlabel=None,

trans_base_list=None,

forcelabel=None,

show_colorbar=True,

plotlev=None,

plotlab=None,

cbarkw={}):

"""

Wrap the process for setting colorbar.

"""

#handle the colorbar attributes by using dictionary which flexibility.

if show_colorbar == False:

cbar = None

else:

location = colorbardic.get('location','right')

size = colorbardic.get('size','3%')

pad = colorbardic.get('pad','2%')

cbar=m.colorbar(cs,location=location, size=size, pad=pad,**cbarkw)

#set colorbar ticks and colorbar label

if levels==None:

pass

else:

ticks,labels = \

_generate_colorbar_ticks_label(data_transform=data_transform,

colorbarlabel=colorbarlabel,

trans_base_list=trans_base_list,

forcelabel=forcelabel,

plotlev=plotlev,

plotlab=plotlab)

cbar.set_ticks(ticks)

cbar.set_ticklabels(labels)

"""

Purpose: plot a map on 'cyl' or 'npstere' projection.

Arguments:

ax --> An axes instance

projection --> for now two projections have been added:

1. 'cyl' -- for global and regional mapping

2. 'npstere' -- for north polar centered mapping.

lat,lon --> geographic coordinate variables; lat must be in

desceding order and lon must be ascending.

mapbound --> specify the bound for mapping;

1. 'cyl'

tuple containing (lat1,lat2,lon1,lon2); lat1 --> lower

parallel; lat2 --> upper parallel; lon1 --> left meridian;

lon2 --> right meridian; default 'all' means plot

the extent of input lat, lon coordinate variables;

for global mapping, set (-90,90,-180,180) or (-90,90,0,360).

2. 'npstere'

mapbound={'blat':45,'lon_0':0,'para0':40}

blat --> boundinglat in the bmp.Basemap method.

The souther limit for mapping.

lon_0 --> center of desired map domain.

para0 --> souther boundary for parallel ticks, the default

norther limit is 90; default longitude 0-360 (or -180-180)

gridstep --> the step for parallel and meridian grid for the map,

tuple containing (parallel_step, meridian_step).

levels --> default None; levels=[-5,-2,-1,0,1,2,5] ;

or levels=[(-10,-4,-2,-1,-0.4),(-0.2,-0.1,0,0.1,0.2),

(0.4,1,2,4,10)].

1. Anything that can work as input for function pb.iteflat()

will work.

2. If the first and last element of pb.iteflat(levels) is

np.NINF and np.PINF, the colorbar of contourf plot will

use the 'two-arrow' shape.

3. If data_transform==True, the input data will be transformed

from pb.iteflat(levels) to

np.linspace(1,len(pb.iteflat(interval_original)). this can

help to create arbitrary contrasting in the plot.

cf. mathex.plot_array_transg

smartlevel:

1. when True, a "smart" level will be generated by

using the min,max value and the [5th, 10th, ..., 95th]

percentile of the input array.

2. it will be applied after applying the mask_threshold.

data_transform:

1. set as True if increased contrast in the plot is desired.

In this case the function mathex.plot_array_transg will

be called and pb.iteflat(levels) will be used as original

interval for data transformation.

2. In case of data_transform==False, pb.iteflat(levels)

will be used directly in the plt.contour function for

ploting and hence no data transformation is made. The

treatment by this way allows very flexible

(in a mixed way) to set levels.

3. In any case, if np.NINF and np.PINF as used as two

extremes of levels, arrowed colorbar will be returned.

colorbarlabel:

1. used to put customized colorbar label and this will override

using levels as colorbar. IF colorbarlabel!=None,

colorbar ticks and labels will be set using colorbarlabel.

so this means colorbarlabel could only be array or

list of numbers.

2. If data_transform==True, colorbar will also be transformed

accordingly. In this case, the colorbar ticks will use

transformed colorbarlabel data, but colorbar ticklables

will use non-transformed colorbarlabel data. This means

the actual ticks numbers and labels are not the same.

forcelabel --> to force the colorbar label as specified by forcelabel.

This is used in case to set the labels not in numbers but in

other forms (eg. strings).

In case of data_transform = True, levels will be used to

specifiy levels for the original colorbar, colorbarlabel will

be used to create ticks on colrobar which will be labeled,

if forcelabel=None, then colorbarlabel will agined be used

to label the ticks, otherwise forcelabel will be used to

label the ticks on the colorbar. So this means forcelabel will

mainly be list of strings.

data --> numpy array with dimension of len(lat)Xlen(lon)

map_threshold --> dictionary like {'lb':2000,'ub':5000}, data

less than 2000 and greater than 5000 will be masked.

Note this will be applied before data.

transform.

shift --> boolean value. False for longtitude data ranging [-180,180];

for longtitude data ranging [0,360] set shift to True if a

180 east shift is desired. if shift as True, the mapbound

range should be set using shifted longtitude

(use -180,180 rather than 0,360).

colorbardic --> dictionary to specify the attributes for colorbar,

translate all the keys in function bmp.Basemap.colorbar()

into keys in colorbardic to manipulation.

Note:

1. lat must be descending, and lon must be ascending.

2*. NOTE use both data_transform=True and impose unequal

colorbarlabel could be very confusing! Because normaly in

case of data_transform as True the labels are ALREADY

UNEQUALLY distributed!

an example to use colorbarlabel and forcelabel:

data_transform=True,

levels=[0,1,2,3,4,5,6,7,8]

colorbarlabel=[0,2,4,6,8]

forcelabel=['extreme low','low','middle','high','extreme high']

So colorbarlabel will set both ticks and labels, but forcelabel

will further overwrite the labels.

3. This function has been test using data, the script and

generated PNG files are availabe at ~/python/bmaptest

See also:

mathex.plot_array_transg

"""

def __init__(self,data=None,lat=None,lon=None,ax=None,

projection='cyl',mapbound='all',

gridstep=(30,30),shift=False,map_threshold=None,

cmap=None,colorbarlabel=None,forcelabel=None,

show_colorbar=True,

smartlevel=False,

levels=None,data_transform=False,

colorbardic={},

cbarkw={},

gmapkw={}

):

(mgmap,pdata,plotlev,plotlab,extend,

trans_base_list,data_transform) = \

_generate_map_prepare_data(data=data,lat=lat,lon=lon,

projection=projection,

mapbound=mapbound,

gridstep=gridstep,

shift=shift,

map_threshold=map_threshold,

levels=levels,

cmap=cmap,

smartlevel=smartlevel,

data_transform=data_transform,

gmapkw=gmapkw,

ax=ax)

#make the contourf plot

cs=mgmap.m.contourf(mgmap.lonpro,mgmap.latpro,pdata,

levels=plotlev,extend=extend,cmap=cmap)

##handle colorbar

cbar = _set_colorbar(mgmap.m,cs,

colorbardic=colorbardic,

levels=plotlev,

data_transform=data_transform,

colorbarlabel=colorbarlabel,

trans_base_list=trans_base_list,

forcelabel=forcelabel,

plotlev=plotlev,

plotlab=plotlab,

cbarkw=cbarkw,

show_colorbar=show_colorbar)

#return

self.m = mgmap.m

self.cs = cs

self.cbar = cbar

self.plotlev = plotlev

self.plotlab = plotlab

self.ax = mgmap.m.ax

self.trans_base_list = trans_base_list

self.gmap = mgmap

if levels == None:

pass

else:

cbar_ticks,cbar_labels = \

_generate_colorbar_ticks_label(data_transform=data_transform,

colorbarlabel=colorbarlabel,

trans_base_list=trans_base_list,

forcelabel=forcelabel,

plotlev=plotlev,

plotlab=plotlab)

self.cbar_ticks = cbar_ticks

self.cbar_labels = cbar_labels

def colorbar(self,cax=None,**kwargs):

"""

set colorbar on specified cax.

kwargs applies for plt.colorbar

"""

cbar = plt.colorbar(self.cs,cax=cax,**kwargs)

cbar.set_ticks(self.cbar_ticks)

cbar.set_ticklabels(self.cbar_labels)

"""

Purpose: plot a map on cyl projection.

Arguments:

ax --> An axes instance

lat,lon --> geographic coordinate variables;

mapbound --> tuple containing (lat1,lat2,lon1,lon2);

lat1 --> lower parallel; lat2 --> upper parallel;

lon1 --> left meridian; lon2 --> right meridian;

default 'all' means plot the extent of input lat, lon

coordinate variables;

gridstep --> the step for parallel and meridian grid for the map,

tuple containing (parallel_step, meridian_step).

vmin,vmax --> as in plt.imshow function

data --> numpy array with dimension of len(lat)Xlen(lon)

shift --> boolean value. False for longtitude data ranging [-180,180];

for longtitude data ranging [0,360] set shift to True if

a 180 east shift is desired.

"""

def __init__(self,data=None,lat=None,lon=None,ax=None,

projection='cyl',mapbound='all',

gridstep=(30,30),shift=False,map_threshold=None,

cmap=None,colorbarlabel=None,forcelabel=None,

smartlevel=False,

levels=None,data_transform=False,

colorbardic={},

cbarkw={},

gmapkw={},

*args,

**kwargs):

(mgmap,pdata,plotlev,plotlab,extend,

trans_base_list,data_transform) = \

_generate_map_prepare_data(data=data,lat=lat,lon=lon,

projection=projection,

mapbound=mapbound,

gridstep=gridstep,

shift=shift,

map_threshold=map_threshold,

levels=levels,

cmap=cmap,

smartlevel=smartlevel,

data_transform=data_transform,

gmapkw=gmapkw,

ax=ax)

cs=mgmap.m.imshow(pdata,origin='upper',*args,**kwargs)

cbar = _set_colorbar(mgmap.m,cs,

colorbardic=colorbardic,

levels=plotlev,

data_transform=data_transform,

colorbarlabel=colorbarlabel,

trans_base_list=trans_base_list,

forcelabel=forcelabel,

plotlev=plotlev,

plotlab=plotlab,

cbarkw=cbarkw)

self.m = mgmap.m

self.cs = cs

self.cbar = cbar

self.plotlev = plotlev

self.plotlab = plotlab

self.ax = mgmap.m.ax

self.trans_base_list = trans_base_list

gridstep=(30,30),half_degree=True,**kwargs):

"""

Purpose: plot the map used for later contour or image plot.

Note:

return m,lonpro,latpro,latind,lonind

1. return m --> map drawed; lonpro/latpro --> lat/lon transferred

to projection coords; latind/lonind --> index for lat/lon

falling with mapbound

2. lat must be descending and lon must be ascending.

Example:

>>> fig,ax=g.Create_1Axes()

>>> m,lonpro,latpro,lonind,latind=bmap.makemap(ax,'cyl',mapbound='all',lat=np.arange(89.75,-89.8,-0.5),lon=np.arange(-179.75,179.8,0.5),gridstep=(30,30))

>>> x,y=m(116,40) #plot Beijing

>>> m.scatter(x,y,s=30,marker='o',color='r')

"""

print '''!!Deprecate Warning: please use gmap instead'''

if projection=='cyl':

if isinstance(mapbound,dict):

raise ValueError('cannot use dict for cyl projection')

elif mapbound=='all':

lat1=lat[-1]

lat2=lat[0]

lon1=lon[0]

lon2=lon[-1]

#when the data is of half degree resolution, often the lat1 and

#lat2 is in the center of the half degree cell, so we need to

#adjust for the vertices.

if half_degree == True:

if lat1%0.25 == 0:

lat1 = lat1-0.25

if lat2%0.25 == 0:

lat2 = lat2+0.25

if lat1<-85:

lat1=-90.

if lat2>85:

lat2=90.

if lon1<-175:

lon1=-180.

if 185>lon2>175:

lon2=180.

if lon2>355:

lon2=360.

else:

lat1=mapbound[0]

lat2=mapbound[1]

lon1=mapbound[2]

lon2=mapbound[3]

#draw the map, parallels and meridians

m=bmp.Basemap(projection=projection,llcrnrlat=lat1,urcrnrlat=lat2,

llcrnrlon=lon1,urcrnrlon=lon2,resolution='l',ax=ax,

**kwargs)

m.drawcoastlines(linewidth=0.7)

if gridstep!=None:

para_range=np.arange(near5even(lat1),near5even(lat2)+0.1,gridstep[0])

meri_range=np.arange(near5even(lon1),near5even(lon2)+0.1,gridstep[1])

m.drawparallels(para_range,labels=[1,0,0,0])

m.drawmeridians(meri_range,labels=[0,0,0,1])

#make the grid

latind=np.nonzero((lat>lat1)&(lat<lat2))[0]

lonind=np.nonzero((lon>lon1)&(lon<lon2))[0]

numlat=len(np.nonzero((lat>lat1)&(lat<lat2))[0])

numlon=len(np.nonzero((lon>lon1)&(lon<lon2))[0])

lonm,latm=m.makegrid(numlon,numlat)

latm=np.flipud(latm)

lonpro,latpro=m(lonm,latm)

return m,lonpro,latpro,latind,lonind

elif projection=='npstere':

if not isinstance(mapbound,dict):

raise ValueError('please use dict to specify')

else:

m=bmp.Basemap(projection='npstere',boundinglat=mapbound['blat'],

lon_0=mapbound['lon_0'],resolution='l',ax=ax,

**kwargs)

m.drawcoastlines(linewidth=0.7)

m.fillcontinents(color='0.8',zorder=0)

if gridstep not in [None,False]:

m.drawparallels(np.arange(mapbound['para0'],91.,gridstep[0]),

labels=[1,0,0,0],fontsize=10)

m.drawmeridians(np.arange(-180.,181.,gridstep[1]),

labels=[0,0,0,0],fontsize=10)

#make the grid

lat1=mapbound['blat']

latind=np.nonzero(lat>lat1)[0]

lonind=np.arange(len(lon))

latnew=np.linspace(90, lat1, num=len(latind), endpoint=True)

if lon[-1]>180:

lonnew=np.linspace(0,360,num=len(lonind),endpoint=True)

else:

lonnew=np.linspace(-180,180,num=len(lonind),endpoint=True)

lonm,latm=np.meshgrid(lonnew,latnew)

lonpro,latpro=m(lonm,latm)

gridstep=(30,30),shift=False,colorbar=True,

colorbarlabel=None,*args,**kwargs):

"""

Purpose: plot a map on cyl projection.

Arguments:

ax --> An axes instance

lat,lon --> geographic coordinate variables;

mapbound --> tuple containing (lat1,lat2,lon1,lon2);

lat1 --> lower parallel; lat2 --> upper parallel;

lon1 --> left meridian; lon2 --> right meridian;

default 'all' means plot the extent of input lat, lon

coordinate variables;

gridstep --> the step for parallel and meridian grid for the map,

tuple containing (parallel_step, meridian_step).

vmin,vmax --> as in plt.imshow function

indata --> numpy array with dimension of len(lat)Xlen(lon)

shift --> boolean value. False for longtitude data ranging [-180,180];

for longtitude data ranging [0,360] set shift to True if

a 180 east shift is desired.

"""

print "Deprecate Warning! imshowmap replaced by mapimshow"

#handle the case ax==None:

if ax==None:

fig,axt=g.Create_1Axes()

else:

axt=ax

if shift==True:

indata,lon=bmp.shiftgrid(180,indata,lon,start=False)

#make the map and use mapbound to cut the data

m,lonpro,latpro,latind,lonind=makemap(axt, projection, mapbound,

lat, lon, gridstep)

pdata=indata[latind[0]:latind[-1]+1,lonind[0]:lonind[-1]+1]

cs=m.imshow(pdata,origin='upper',*args,**kwargs)

if colorbar==True:

cbar=m.colorbar(cs)

if colorbarlabel!=None:

cbar.set_label(colorbarlabel)

else:

cbar=None

gridstep=(30,30),shift=False,cmap=None,

map_threshold=None,colorbarlabel=None,

levels=None,data_transform=False,

ax=None,colorbardic={}):

"""

contourfmap2 is a wrapper of contourfmap. NO need to set up

a figure and axes before drawing map, return fig,ax,m,cbar.

"""

print "Deprecate Warning 'contourfmap2', use mapcontourf instead."

if ax==None:

fig,axt=g.Create_1Axes()

else:

axt=ax

m,bar=contourfmap(axt,lat,lon,indata,projection=projection,

mapbound=mapbound,gridstep=gridstep,

shift=shift,cmap=cmap,

map_threshold=map_threshold,

colorbarlabel=colorbarlabel,

levels=levels,

data_transform=data_transform,

colorbardic=colorbardic)

if ax==None:

return fig,axt,m,bar

else:

gridstep=(30,30),shift=False,map_threshold=None,

cmap=None,colorbarlabel=None,forcelabel=None,

levels=None,data_transform=False,

return_lev_lab=False,colorbardic={},

cbarkw={}):

"""

Purpose: plot a map on 'cyl' or 'npstere' projection.

Arguments:

ax --> An axes instance

projection --> for now two projections have been added:

1. 'cyl' -- for global and regional mapping

2. 'npstere' -- for north polar centered mapping.

lat,lon --> geographic coordinate variables; lat must be in

desceding order and lon must be ascending.

mapbound --> specify the bound for mapping;

1. 'cyl'

tuple containing (lat1,lat2,lon1,lon2); lat1 --> lower

parallel; lat2 --> upper parallel; lon1 --> left meridian;

lon2 --> right meridian; default 'all' means plot

the extent of input lat, lon coordinate variables;

for global mapping, set (-90,90,-180,180) or (-90,90,0,360).

2. 'npstere'

mapbound={'blat':45,'lon_0':0,'para0':40}

blat --> boundinglat in the bmp.Basemap method.

The souther limit for mapping.

lon_0 --> center of desired map domain.

para0 --> souther boundary for parallel ticks, the default

norther limit is 90; default longitude 0-360 (or -180-180)

gridstep --> the step for parallel and meridian grid for the map,

tuple containing (parallel_step, meridian_step).

levels --> default None; levels=[-5,-2,-1,0,1,2,5] ;

or levels=[(-10,-4,-2,-1,-0.4),(-0.2,-0.1,0,0.1,0.2),

(0.4,1,2,4,10)].

1. Anything that can work as input for function pb.iteflat()

will work.

2. If the first and last element of pb.iteflat(levels) is

np.NINF and np.PINF, the colorbar of contourf plot will

use the 'two-arrow' shape.

3. If data_transform==True, the input data will be transformed

from pb.iteflat(levels) to

np.linspace(1,len(pb.iteflat(interval_original)). this can

help to create arbitrary contrasting in the plot.

cf. mathex.plot_array_transg

data_transform:

1. set as True if increased contrast in the plot is desired.

In this case the function mathex.plot_array_transg will

be called and pb.iteflat(levels) will be used as original

interval for data transformation.

2. In case of data_transform==False, pb.iteflat(levels)

will be used directly in the plt.contour function for

ploting and hence no data transformation is made. The

treatment by this way allows very flexible

(in a mixed way) to set levels.

3. In any case, if np.NINF and np.PINF as used as two

extremes of levels, arrowed colorbar will be returned.