def testFIR(self):
smooth = np.array([0.25, 0.5, 0.25])
d = np.arange(10)
filtered = fir(d, smooth, zero_edge=False)
print d, '--', smooth, '(keep edge) -->', filtered
self.assertTrue(np.all( filtered == d))
filtered = fir(d, smooth, zero_edge=True)
print d, '--', smooth, '-->', filtered
self.assertEquals(0, filtered[9])
d = np.zeros(10)
d[5] = 10
filtered = fir(d, smooth)
print d, '--', smooth, '-->', filtered
self.assertEquals(5.0, filtered[5])
d = np.zeros(10)
d[5:10] = np.ones(5)
filtered = fir(d, smooth)
print d, '--', smooth, '-->', filtered
self.assertEquals(0.0, filtered[3])
self.assertEquals(0.25, filtered[4])
self.assertEquals(0.75, filtered[5])
self.assertEquals(1.0, filtered[6])
diff = np.array([-0.5, 0, 0.5])
filtered = fir(d, diff)
print d, '--', diff, '-->', filtered
def ncwrite(fn,
data,
varname,
dims=None,
attrs=None,
gattrs=None,
largefile=False):
"""
Write a netCDF data file from an array.
:param: fn: (*string*) netCDF data file path.
:param data: (*array_like*) A numeric array variable of any dimensionality.
:param varname: (*string*) Variable name.
:param dims: (*list of dimensions*) Dimension list.
:param attrs: (*dict*) Variable attributes.
:param gattrs: (*dict*) Global attributes.
:param largefile: (*boolean*) Create netCDF as large file or not.
"""
if dims is None:
if isinstance(data, NDArray):
dims = []
for s in data.shape:
dimvalue = np.arange(s)
dimname = 'dim' + str(len(dims))
dims.append(dimension(dimvalue, dimname))
else:
dims = data.dims
#New netCDF file
ncfile = addfile(fn, 'c', largefile=largefile)
#Add dimensions
ncdims = []
for dim in dims:
ncdims.append(ncfile.adddim(dim.getShortName(), dim.getLength()))
#Add global attributes
ncfile.addgroupattr('Conventions', 'CF-1.6')
ncfile.addgroupattr('Tools', 'Created using meteothink')
if not gattrs is None:
for key in gattrs:
ncfile.addgroupattr(key, gattrs[key])
#Add dimension variables
dimvars = []
wdims = []
for dim, midim in zip(ncdims, dims):
dimtype = midim.getDimType()
dimname = dim.getShortName()
if dimtype == DimensionType.T:
var = ncfile.addvar(dimname, 'int', [dim])
var.addattr('units', 'hours since 1900-01-01 00:00:0.0')
var.addattr('long_name', 'Time')
var.addattr('standard_name', 'time')
var.addattr('axis', 'T')
tvar = var
elif dimtype == DimensionType.Z:
var = ncfile.addvar(dimname, 'float', [dim])
var.addattr('axis', 'Z')
elif dimtype == DimensionType.Y:
var = ncfile.addvar(dimname, 'float', [dim])
var.addattr('axis', 'Y')
elif dimtype == DimensionType.X:
var = ncfile.addvar(dimname, 'float', [dim])
var.addattr('axis', 'X')
else:
var = None
if not var is None:
dimvars.append(var)
wdims.append(midim)
#Add variable
var = ncfile.addvar(varname, data.dtype, ncdims)
if attrs is None:
var.addattr('name', varname)
else:
for key in attrs:
var.addattr(key, attrs[key])
#Create netCDF file
ncfile.create()
#Write variable data
for dimvar, dim in zip(dimvars, wdims):
if dim.getDimType() == DimensionType.T:
sst = datetime.datetime(1900, 1, 1)
tt = miutil.nums2dates(dim.getDimValue())
hours = []
for t in tt:
hours.append((t - sst).total_seconds() // 3600)
ncfile.write(dimvar, np.array(hours))
else:
ncfile.write(dimvar, np.array(dim.getDimValue()))
ncfile.write(var, data)
#Close netCDF file
ncfile.close()
def imshow(self, *args, **kwargs):
"""
Display an image on the 3D axes.
:param x: (*array_like*) Optional. X coordinate array.
:param y: (*array_like*) Optional. Y coordinate array.
:param z: (*array_like*) 2-D or 3-D (RGB) z value array.
:param levs: (*array_like*) Optional. A list of floating point numbers indicating the level curves
to draw, in increasing order.
:param cmap: (*string*) Color map string.
:param colors: (*list*) If None (default), the colormap specified by cmap will be used. If a
string, like ‘r’ or ‘red’, all levels will be plotted in this color. If a tuple of matplotlib
color args (string, float, rgb, etc), different levels will be plotted in different colors in
the order specified.
:returns: (*RasterLayer*) RasterLayer created from array data.
"""
n = len(args)
cmap = plotutil.getcolormap(**kwargs)
fill_value = kwargs.pop('fill_value', -9999.0)
xaxistype = None
isrgb = False
if n <= 2:
if isinstance(args[0], (list, tuple)):
isrgb = True
rgbdata = args[0]
if isinstance(rgbdata[0], np.NDArray):
x = np.arange(0, rgbdata[0].shape[1])
y = np.arange(0, rgbdata[0].shape[0])
else:
x = rgbdata[0].dimvalue(1)
y = rgbdata[0].dimvalue(0)
elif args[0].ndim > 2:
isrgb = True
rgbdata = args[0]
if isinstance(rgbdata, np.NDArray):
x = np.arange(0, rgbdata.shape[1])
y = np.arange(0, rgbdata.shape[0])
else:
x = rgbdata.dimvalue(1)
y = rgbdata.dimvalue(0)
else:
a = args[0]
if isinstance(a, DimArray):
y = a.dimvalue(0)
x = a.dimvalue(1)
else:
yn, xn = a.shape
x = np.arange(xn)
y = np.arange(yn)
args = args[1:]
elif n <= 4:
x = args[0]
y = args[1]
a = args[2]
if isinstance(a, (list, tuple)):
isrgb = True
rgbdata = a
elif a.ndim > 2:
isrgb = True
rgbdata = a
else:
args = args[3:]
offset = kwargs.pop('offset', 0)
zdir = kwargs.pop('zdir', 'z')
interpolation = kwargs.pop('interpolation', None)
if isrgb:
if isinstance(rgbdata, (list, tuple)):
rgbd = []
for d in rgbdata:
rgbd.append(d.asarray())
rgbdata = rgbd
else:
rgbdata = rgbdata.asarray()
x = plotutil.getplotdata(x)
y = plotutil.getplotdata(y)
graphics = GraphicFactory.createImage(x, y, rgbdata, offset, zdir,
interpolation)
ls = None
else:
if len(args) > 0:
level_arg = args[0]
if isinstance(level_arg, int):
cn = level_arg
ls = LegendManage.createImageLegend(a._array, cn, cmap)
else:
if isinstance(level_arg, np.NDArray):
level_arg = level_arg.aslist()
ls = LegendManage.createImageLegend(
a._array, level_arg, cmap)
else:
ls = plotutil.getlegendscheme(args, a.min(), a.max(), **kwargs)
ls = ls.convertTo(ShapeTypes.Image)
plotutil.setlegendscheme(ls, **kwargs)
if zdir == 'xy':
sepoint = kwargs.pop('sepoint', [0, 0, 1, 1])
else:
sepoint = None
graphics = GraphicFactory.createImage(x._array, y._array, a._array,
ls, offset, zdir, sepoint,
interpolation)
visible = kwargs.pop('visible', True)
if visible:
self.add_graphic(graphics)
return graphics
def contourf(self, *args, **kwargs):
"""
Plot filled contours.
:param x: (*array_like*) Optional. X coordinate array.
:param y: (*array_like*) Optional. Y coordinate array.
:param z: (*array_like*) 2-D z value array.
:param levs: (*array_like*) Optional. A list of floating point numbers indicating the level curves
to draw, in increasing order.
:param cmap: (*string*) Color map string.
:param colors: (*list*) If None (default), the colormap specified by cmap will be used. If a
string, like ‘r’ or ‘red’, all levels will be plotted in this color. If a tuple of matplotlib
color args (string, float, rgb, etc), different levels will be plotted in different colors in
the order specified.
:param smooth: (*boolean*) Smooth countour lines or not.
:returns: (*VectoryLayer*) Contour VectoryLayer created from array data.
"""
n = len(args)
cmap = plotutil.getcolormap(**kwargs)
fill_value = kwargs.pop('fill_value', -9999.0)
offset = kwargs.pop('offset', 0)
xaxistype = None
if n <= 2:
a = args[0]
if isinstance(a, DimArray):
y = a.dimvalue(0)
x = a.dimvalue(1)
else:
yn, xn = a.shape
x = np.arange(xn)
y = np.arange(yn)
args = args[1:]
elif n <= 4:
x = args[0]
y = args[1]
a = args[2]
args = args[3:]
if len(args) > 0:
level_arg = args[0]
if isinstance(level_arg, int):
cn = level_arg
ls = LegendManage.createLegendScheme(a.min(), a.max(), cn,
cmap)
else:
if isinstance(level_arg, np.NDArray):
level_arg = level_arg.aslist()
ls = LegendManage.createLegendScheme(a.min(), a.max(),
level_arg, cmap)
else:
ls = LegendManage.createLegendScheme(a.min(), a.max(), cmap)
ls = ls.convertTo(ShapeTypes.Polygon)
edge = kwargs.pop('edge', None)
if edge is None:
kwargs['edge'] = False
else:
kwargs['edge'] = edge
plotutil.setlegendscheme(ls, **kwargs)
smooth = kwargs.pop('smooth', True)
zdir = kwargs.pop('zdir', 'z')
if zdir == 'xy':
sepoint = kwargs.pop('sepoint', [0, 0, 1, 1])
igraphic = GraphicFactory.createContourPolygons(x._array, y._array, a._array, offset, zdir, ls, smooth, \
sepoint)
else:
igraphic = GraphicFactory.createContourPolygons(
x._array, y._array, a._array, offset, zdir, ls, smooth)
visible = kwargs.pop('visible', True)
if visible:
self.add_graphic(igraphic)
return igraphic