heights = np.random.random(years.shape) * 7000 + 3000
fmt = ScalarFormatter(useOffset=False)
ax.xaxis.set_major_formatter(fmt)
for year, h, bc in zip(years, heights, box_colors):
bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")
ax.add_artist(rb_patch)
ax.annotate(r"%d" % (int(h/100.)*100),
(year, h), va="bottom", ha="center")
patch_gradient = BboxImage(ax.bbox,
interpolation="bicubic",
zorder=0.1,
)
gradient = np.zeros((2, 2, 4), dtype=np.float)
gradient[:, :, :3] = [1, 1, 0.]
gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]] # alpha channel
patch_gradient.set_array(gradient)
ax.add_artist(patch_gradient)
ax.set_xlim(years[0]-0.5, years[-1]+0.5)
ax.set_ylim(0, 10000)
fig.savefig('ribbon_box.png')
plt.show()
fmt = ScalarFormatter(useOffset=False)
ax.xaxis.set_major_formatter(fmt)
for year, h, bc in zip(years, heights, box_colors):
bbox0 = Bbox.from_extents(year - 0.4, 0., year + 0.4, h)
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")
ax.add_artist(rb_patch)
ax.annotate(r"%d" % (int(h / 100.) * 100), (year, h),
va="bottom",
ha="center")
patch_gradient = BboxImage(
ax.bbox,
interpolation="bicubic",
zorder=0.1,
)
gradient = np.zeros((2, 2, 4), dtype=float)
gradient[:, :, :3] = [1, 1, 0.]
gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]] # alpha channel
patch_gradient.set_array(gradient)
ax.add_artist(patch_gradient)
ax.set_xlim(years[0] - 0.5, years[-1] + 0.5)
ax.set_ylim(0, 10000)
fig.savefig('ribbon_box.png')
plt.show()
def plotBar(ax= None,
xdata= np.arange(4, 9),
ydata= np.random.random(5),
labels= None,
colors= 'Random',
figName= 'cilindro',
path= None):
'''box_colors: 'random'|'blue'|'red'|'green'|'ligthgreen'|'darkblue'|'hsv'
figure: redunca02|blue|aluminio|cilindro|
'''
try:
IMAGESPATH= os.path.join(wx.GetApp().installDir, 'nicePlot','images')
except:
path1= sys.argv[0]
if os.path.isfile(path1):
path1= os.path.split(path1)[0]
path1= path1.decode( sys.getfilesystemencoding())
if os.path.split( path1)[-1] == 'nicePlot':
IMAGESPATH= os.path.join( path1, 'images')
else:
IMAGESPATH= os.path.join( path1, 'nicePlot', 'images')
if len(xdata) != len(ydata):
raise StandardError('xdata and ydata must have the same len()')
if isinstance(figName,(str,unicode)):
figName = [figName.lower() for i in range(len(xdata))]
else:
figName = [fig.lower() for fig in figName]
##xdata,ydata = orderData(xdata,ydata)
# se generan los colores en forma aleatoria
box_colors = _generatecolors(colors,len(xdata))
fig = plt.gcf()
if ax == None:
ax = plt.gca()
fmt = ScalarFormatter(useOffset= True) #False
ax.xaxis.set_major_formatter(fmt)
if labels== None:
labels = [None for i in ydata]
if path == None:
path= os.path.relpath(os.path.join(IMAGESPATH,'barplot'))
for year, h, bc,label,figi in zip(xdata, ydata, box_colors,labels,figName):
if h < 0: continue
bbox0 = Bbox.from_extents(year-0.5, 0., year+0.5, h) # year-0.4, 0., year+0.4,
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = _RibbonBoxImage(bbox, bc, figi, path, interpolation='bicubic') #bicubic
ax.add_artist(rb_patch)
if isinstance(label,(str,unicode)):
ax.annotate(label, (year, h), va="bottom", ha="center")
if type(labels) == type(1):
ax.annotate(r"%d" % labels,
(year, labels), va="bottom", ha="center")
elif type(labels) == type(1.1):
ax.annotate(r"%f" % labels,
(year, labels), va="bottom", ha="center")
elif str(type(labels)) == "<type 'numpy.int32'>":
ax.annotate(r"%d" % labels,
(year, labels), va="bottom", ha="center")
patch_gradient = BboxImage(ax.bbox,
interpolation= 'bicubic', # "bicubic"
zorder=0.1,
)
gradient = np.zeros((2, 2, 4), dtype=np.float)
gradient[:,:,:3] = [1, 1, 1]
#gradient[:,:,:3] = [0.5, 0.5, 0.5]
gradient[:,:,3] = [[0.2, 0.3],[0.2, 0.5]] # alpha channel
patch_gradient.set_array(gradient)
ax.add_artist(patch_gradient)
ax.set_xlim( min(xdata)-0.5, max(xdata)+0.5)
# se determinan los limites para el eje Y
if max(ydata) > 0.0:
maxYlimit = max(ydata)*1.05
elif max(ydata) == 0.0:
maxYlimit = 1.0
else:
maxYlimit = max(ydata)*(1-0.05)
ax.set_ylim(min(ydata)*(-0.05), maxYlimit)
return (fig, plt)
def plothist(ax= None,
xdata= np.arange(4, 9),
ydata= np.random.random(5),
labels= [],
colors = 'random',
figName='redunca03'):
if len(xdata) != len(ydata):
raise StandardError('xdata and ydata must have the same len()')
try:
IMAGESPATH= os.path.join(wx.GetApp().installDir, 'nicePlot','images')
except:
path1= sys.argv[0]
if os.path.isfile(path1):
path1= os.path.split(path1)[0]
path1= path1.decode( sys.getfilesystemencoding())
if os.path.split( path1)[-1] == 'nicePlot':
IMAGESPATH= os.path.join( path1, 'images')
else:
IMAGESPATH= os.path.join( path1, 'nicePlot', 'images')
# se generan los colores en forma aleatoria
box_colors = _generatecolors( colors,len( xdata))
# se genera la figura
fig = plt.gcf()
if ax == None:
ax = plt.gca()
fmt = ScalarFormatter( useOffset=False)
ax.xaxis.set_major_formatter( fmt)
for year, h, bc in zip( xdata, ydata, box_colors):
bbox0 = Bbox.from_extents(year-0.5, 0.0, year+0.5, h) # year-0.48, 0., year+0.48, year-1 year+1
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = _RibbonBoxImage(bbox, bc, figName,
path= os.path.join(IMAGESPATH, 'histplot'),
interpolation="bicubic")
ax.add_artist(rb_patch)
if 0:
if type(h) == type(1):
ax.annotate(r"%d" % h,
(year, h), va="bottom", ha="center")
elif type(h) == type(1.1):
ax.annotate(r"%f" % h,
(year, h), va="bottom", ha="center")
elif str(type(h)) == "<type 'numpy.int32'>":
ax.annotate(r"%d" % h,
(year, h), va="bottom", ha="center")
patch_gradient = BboxImage( ax.bbox,
interpolation="bicubic",
zorder=0.1,
)
gradient = np.zeros( (2, 2, 4), dtype=np.float)
gradient[:,:,:3] = [1, 1, 1]
gradient[:,:,3] = [[0.2, 0.3],[0.2, 0.5]] # alpha channel
patch_gradient.set_array( gradient)
ax.add_artist( patch_gradient)
ax.set_xlim( xdata[0]-1.0, xdata[-1]+1.0)
# se determinan los limites para el eje Y
try:
ydatamax = ydata.max() # en el caso de informacion proveniente de numpy
except AttributeError:
ydatamax = max(ydata)
if ydatamax > 0.0:
maxYlimit = ydatamax*1.05
elif ydatamax == 0.0:
maxYlimit = 1.0
else:
maxYlimit = ydatamax*(1.0-0.05)
ax.set_ylim( 0, maxYlimit)
return ( fig,plt)
def draw(self, renderer, *args, **kwargs):
bbox = self.get_window_extent(renderer)
stretch_factor = bbox.height / bbox.width
ny = int(stretch_factor*self._ribbonbox.nx)
if self._cached_ny != ny:
arr = self._ribbonbox.get_stretched_image(stretch_factor)
self.set_array(arr)
self._cached_ny = ny
BboxImage.draw(self, renderer, *args, **kwargs)
if 1:
from matplotlib.transforms import Bbox, TransformedBbox
from matplotlib.ticker import ScalarFormatter
fig, ax = plt.subplots()
years = np.arange(2004, 2009)
box_colors = [(0.8, 0.2, 0.2),
(0.2, 0.8, 0.2),
(0.2, 0.2, 0.8),
(0.7, 0.5, 0.8),
(0.3, 0.8, 0.7),
]
heights = np.random.random(years.shape) * 7000 + 3000
fmt = ScalarFormatter(useOffset=False)
ax.xaxis.set_major_formatter(fmt)
for year, h, bc in zip(years, heights, box_colors):
bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)
bbox = TransformedBbox(bbox0, ax.transData)
rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")
ax.add_artist(rb_patch)
ax.annotate(r"%d" % (int(h/100.)*100),
(year, h), va="bottom", ha="center")
patch_gradient = BboxImage(ax.bbox,
interpolation="bicubic",
zorder=0.1,
)
gradient = np.zeros((2, 2, 4), dtype=np.float)
gradient[:,:,:3] = [1, 1, 0.]
gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel
patch_gradient.set_array(gradient)
ax.add_artist(patch_gradient)
ax.set_xlim(years[0]-0.5, years[-1]+0.5)
ax.set_ylim(0, 10000)
fig.savefig('ribbon_box.png')
plt.show()
