def fill_between(self, x, y1, y2=0, where=None, **kwargs):
"""
Make filled polygons between two curves (y1 and y2) where ``where==True``.
:param x: (*array_like*) An N-length array of the x data.
:param y1: (*array_like*) An N-length array (or scalar) of the y data.
:param y2: (*array_like*) An N-length array (or scalar) of the y data.
:param where: (*array_like*) If None, default to fill between everywhere. If not None, it is an
N-length boolean array and the fill will only happen over the regions where ``where==True``.
"""
#Get dataset
global gca
#Add data series
label = kwargs.pop('label', 'S_0')
dn = len(x)
xdata = plotutil.getplotdata(x)
if isinstance(y1, (int, long, float)):
yy = []
for i in range(dn):
yy.append(y1)
y1 = np.array(yy).array
else:
y1 = plotutil.getplotdata(y1)
if isinstance(y2, (int, long, float)):
yy = []
for i in range(dn):
yy.append(y2)
y2 = np.array(yy).array
else:
y2 = plotutil.getplotdata(y2)
if not where is None:
if isinstance(where, (tuple, list)):
where = np.array(where)
where = where.asarray()
#Set plot data styles
if not 'fill' in kwargs:
kwargs['fill'] = True
if not 'edge' in kwargs:
kwargs['edge'] = False
pb, isunique = plotutil.getlegendbreak('polygon', **kwargs)
pb.setCaption(label)
#Create graphics
offset = kwargs.pop('offset', 0)
zdir = kwargs.pop('zdir', 'z')
if zdir == 'xy':
y = kwargs.pop('y', x)
ydata = plotutil.getplotdata(y)
graphics = GraphicFactory.createFillBetweenPolygons(xdata, ydata, y1, y2, where, pb, \
offset, zdir)
else:
graphics = GraphicFactory.createFillBetweenPolygons(xdata, y1, y2, where, pb, \
offset, zdir)
visible = kwargs.pop('visible', True)
if visible:
self.add_graphic(graphics)
return graphics
def run(self, context):
# Turn raw python array into ndarray for easier math
if self.norm_device:
data = np.array(context.getData(self.pos_device, self.sig_device, self.norm_device))
x = data[0]
y = data[1]
n = data[2]
print "x = ", x
print "y = ", y
print "n = ", n
print "norm: ", self.norm_value
y = y * float(self.norm_value) / n
context.logData("normalized", y.nda)
else:
data = np.array(context.getData(self.pos_device, self.sig_device))
x = data[0]
y = data[1]
print "x = ", x
print "y = ", y
# Compute fit
g = Gaussian.fromCentroid(x, y)
print g
fit = g.values(x)
# Log the 'fit' data for later comparison with raw data
context.logData("fit", fit.nda)
# Set PVs with result
context.write(self.pv_pos, g.center)
context.write(self.pv_height, g.height)
context.write(self.pv_width, g.width)
def inpolygon(x, y, polygon):
'''
Check if x/y points are inside a polygon or not.
:param x: (*array_like*) X coordinate of the points.
:param y: (*array_like*) Y coordinate of the points.
:param polygon: (*PolygonShape list*) The polygon list.
:returns: (*boolean array*) Inside or not.
'''
if isinstance(x, numbers.Number):
return GeoComputation.pointInPolygon(polygon, x, y)
if isinstance(x, (list, tuple)):
x = np.array(x)
if isinstance(y, (list, tuple)):
y = np.array(y)
if isinstance(polygon, tuple):
x_p = polygon[0]
y_p = polygon[1]
if isinstance(x_p, (list, tuple)):
x_p = np.array(x_p)
if isinstance(y_p, (list, tuple)):
y_p = np.array(y_p)
return np.NDArray(GeoComputation.inPolygon(x._array, y._array, x_p._array, y_p._array))
else:
if isinstance(polygon, MILayer):
polygon = polygon.shapes()
elif isinstance(polygon, PolygonShape):
polygon = [polygon]
return np.NDArray(GeoComputation.inPolygon(x._array, y._array, polygon))
def upcast(*args):
"""Returns the nearest supported sparse dtype for the
combination of one or more types.
upcast(t0, t1, ..., tn) -> T where T is a supported dtype
Examples
--------
>>> upcast('int32')
<type 'numpy.int32'>
>>> upcast('bool')
<type 'numpy.int8'>
>>> upcast('int32','float32')
<type 'numpy.float64'>
>>> upcast('bool',complex,float)
<type 'numpy.complex128'>
"""
sample = np.array([0],dtype=args[0])
for t in args[1:]:
sample = sample + np.array([0],dtype=t)
upcast = sample.dtype
for t in supported_dtypes:
if np.can_cast(sample.dtype,t):
return t
raise TypeError,'no supported conversion for types: %s' % args
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 run(self, context):
# Turn raw python array into ndarray for easier math
if self.norm_device:
data = np.array(
context.getData(self.pos_device, self.sig_device,
self.norm_device))
x = data[0]
y = data[1]
n = data[2]
print "x = ", x
print "y = ", y
print "n = ", n
print "norm: ", self.norm_value
y = y * float(self.norm_value) / n
context.logData("normalized", y.nda)
else:
data = np.array(context.getData(self.pos_device, self.sig_device))
x = data[0]
y = data[1]
print "x = ", x
print "y = ", y
# Compute fit
g = Gaussian.fromCentroid(x, y)
print g
fit = g.values(x)
# Log the 'fit' data for later comparison with raw data
context.logData("fit", fit.nda)
# Set PVs with result
context.write(self.pv_pos, g.center)
context.write(self.pv_height, g.height)
context.write(self.pv_width, g.width)
def __init__(self, data=None, index=None, columns=None, dataframe=None):
if dataframe is None:
if not data is None:
if isinstance(data, dict):
columns = data.keys()
dlist = []
n = 1
for v in data.values():
if isinstance(v, (list, tuple)):
n = len(v)
v = np.array(v)
elif isinstance(v, NDArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, NDArray):
d = [d] * n
d = np.array(d)
dlist[i] = d
data = dlist
if isinstance(data, NDArray):
n = len(data)
data = data._array
else:
dlist = []
n = len(data[0])
for dd in data:
dlist.append(dd._array)
data = dlist
if index is None:
index = range(0, n)
else:
if n != len(index):
raise ValueError('Wrong length of index!')
if isinstance(index, (NDArray, DimArray)):
index = index.tolist()
if isinstance(index, Index):
self._index = index
else:
self._index = Index.factory(index)
if data is None:
self._dataframe = MIDataFrame(self._index._index)
else:
self._dataframe = MIDataFrame(data, self._index._index,
columns)
else:
self._dataframe = dataframe
self._index = Index.factory(index=self._dataframe.getIndex())
def getplotdata(data):
if isinstance(data, np.NDArray):
return data.asarray()
elif isinstance(data, (list, tuple)):
if isinstance(data[0], datetime.datetime):
dd = []
for d in data:
v = np.miutil.date2num(d)
dd.append(v)
return np.array(dd)._array
else:
return np.array(data)._array
else:
return np.array([data])._array
def __init__(self, data=None, index=None, name=None, series=None):
'''
One-dimensional array with axis labels (including time series).
:param data: (*array_like*) One-dimensional array data.
:param index: (*list*) Data index list. Values must be unique and hashable, same length as data.
:param name: (*string*) Series name.
'''
if series is None:
if isinstance(data, (list, tuple)):
data = np.array(data)
if index is None:
index = range(0, len(data))
else:
if len(data) != len(index):
raise ValueError('Wrong length of index!')
if isinstance(index, (NDArray, DimArray)):
index = index.tolist()
if isinstance(index, Index):
self._index = index
else:
self._index = Index.factory(index)
self._data = data
self._series = MISeries(data._array, self._index._index, name)
else:
self._series = series
self._data = NDArray(self._series.getData())
self._index = Index.factory(index=self._series.getIndex())
def run(self, context):
print("ComputeAverage of %s into %s" % (self.data_pv, self.avg_pv))
# Fetching data for N channels returns N x samples
data = np.array(context.getData(self.data_pv))
# Get values for the first (only) channel
values = data[0]
print("Data : " + str(values))
if len(values) > 0:
avg = np.sum(values) / len(values)
else:
avg = np.nan
print("Average: " + str(avg))
context.write(self.avg_pv, avg)
def insert(self, loc, column, value):
'''
Insert column into DataFrame at specified location.
:param loc: (*int*) Insertation index.
:param column: (*string*) Label of inserted column.
:param value: (*array_like*) Column values.
'''
if isinstance(value, datetime.datetime):
value = miutil.jdatetime(value)
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
value = np.array(value)
if isinstance(value, NDArray):
value = value._array
self._dataframe.addColumn(loc, column, value)
def fir(data, fir, zero_edge=True):
"""FIR filter
@param data: Array data
@param fir: FIR filter elements
@param zero_edge: Set edge elements in result to 0, or preserve original data?
@return: Filtered array
"""
L = len(fir)
N = len(data)
mid = int(len(fir)/2)
if zero_edge:
result = np.zeros(N)
else:
result = np.array(data)
for i in range(mid, N-mid):
# index into 'data' at left edge of filter
i0 = i-mid
# print fir, data[i0:(i0+L)], fir * data[i0:(i0+L)], sum(fir * data[i0:(i0+L)])
result[i] = sum(fir * data[i0:(i0+L)])
return result
def run(self, context):
# print "\nWrite data for %s to %s" % (self.device, self.pv)
if self.norm_device:
data = np.array(context.getData(self.device, self.norm_device))
d = data[0]
n = data[1]
try:
normed = d * float(self.norm_value) / n
# Convert np.array back into Java array for 'write'
data = array(normed, 'd')
except:
# On zero division use original data
data = context.getData(self.device)[0]
else:
data = context.getData(self.device)
data = data[0]
# print "Data: ", data.__class__.__name__
try:
context.write(self.pv, data, False)
except Exception, e:
print "WriteDataToPV(%s, %s) exception:" % (self.pv, data)
print e
def run(self, context):
# print "\nWrite data for %s to %s" % (self.device, self.pv)
if self.norm_device:
data = np.array(context.getData(self.device, self.norm_device))
d = data[0]
n = data[1]
try:
normed = d * float(self.norm_value) / n
# Convert np.array back into Java array for 'write'
data = array(normed, 'd')
except:
# On zero division use original data
data = context.getData(self.device)[0]
else:
data = context.getData(self.device)
data = data[0]
# print "Data: ", data.__class__.__name__
try:
context.write(self.pv, data, False)
except Exception, e:
print "WriteDataToPV(%s, %s) exception:" % (self.pv, data)
print e
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 grads2nc(infn, outfn, big_endian=None, largefile=False):
"""
Convert GrADS data file to netCDF data file.
:param infn: (*string*) Input GrADS data file name.
:param outfn: (*string*) Output netCDF data file name.
:param big_endian: (*boolean*) Is GrADS data big_endian or not.
:param largefile: (*boolean*) Create netCDF as large file or not.
"""
#Open GrADS file
f = addfile_grads(infn)
if not big_endian is None:
f.bigendian(big_endian)
#New netCDF file
ncfile = addfile(outfn, 'c', largefile=largefile)
#Add dimensions
dims = []
for dim in f.dimensions():
dims.append(ncfile.adddim(dim.getShortName(), dim.getLength()))
xdim = f.finddim('X')
ydim = f.finddim('Y')
tdim = f.finddim('T')
xnum = xdim.getLength()
ynum = ydim.getLength()
tnum = tdim.getLength()
#Add global attributes
ncfile.addgroupattr('Conventions', 'CF-1.6')
for attr in f.attributes():
ncfile.addgroupattr(attr.getName(), attr.getValues())
#Add dimension variables
dimvars = []
for dim in dims:
dname = dim.getShortName()
if dname == 'T':
var = ncfile.addvar('time', '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', dname)
tvar = var
elif dname == 'Z':
var = ncfile.addvar('level', 'float', [dim])
var.addattr('axis', dname)
else:
var = ncfile.addvar(dim.getShortName(), 'float', [dim])
if 'Z' in dname:
var.addattr('axis', 'Z')
else:
var.addattr('axis', dname)
dimvars.append(var)
#Add variables
variables = []
for var in f.variables():
print 'Variable: ' + var.getShortName()
vdims = []
for vdim in var.getDimensions():
for dim in dims:
if vdim.getShortName() == dim.getShortName():
vdims.append(dim)
#print vdims
nvar = ncfile.addvar(var.getShortName(), var.getDataType(), vdims)
nvar.addattr('fill_value', -9999.0)
for attr in var.getAttributes():
nvar.addattr(attr.getName(), attr.getValues())
variables.append(nvar)
#Create netCDF file
ncfile.create()
#Write variable data
for dimvar, dim in zip(dimvars, f.dimensions()):
if dim.getShortName() != 'T':
ncfile.write(dimvar, np.array(dim.getDimValue()))
sst = datetime.datetime(1900, 1, 1)
for t in range(0, tnum):
st = f.gettime(t)
print st.strftime('%Y-%m-%d %H:00')
hours = (st - sst).total_seconds() // 3600
origin = [t]
ncfile.write(tvar, np.array([hours]), origin=origin)
for var in variables:
print 'Variable: ' + var.name
if var.ndim == 3:
data = f[str(var.name)][t, :, :]
data[data == np.nan] = -9999.0
origin = [t, 0, 0]
shape = [1, ynum, xnum]
data = data.reshape(shape)
ncfile.write(var, data, origin=origin)
else:
znum = var.dims[1].getLength()
for z in range(0, znum):
data = f[str(var.name)][t, z, :, :]
data[data == np.nan] = -9999.0
origin = [t, z, 0, 0]
shape = [1, 1, ynum, xnum]
data = data.reshape(shape)
ncfile.write(var, data, origin=origin)
#Close netCDF file
ncfile.close()
print 'Convert finished!'
def convert2nc(infn,
outfn,
version='netcdf3',
writedimvar=False,
largefile=False):
"""
Convert data file (Grib, HDF...) to netCDF data file.
:param infn: (*string or DimDataFile*) Input data file (or file name).
:param outfn: (*string*) Output netCDF data file name.
:param writedimvar: (*boolean*) Write dimension variables or not.
:param largefile: (*boolean*) Create netCDF as large file or not.
"""
if isinstance(infn, DimDataFile):
f = infn
else:
#Open input data file
f = addfile(infn)
#New netCDF file
ncfile = addfile(outfn, 'c', version=version, largefile=largefile)
#Add dimensions
dims = []
dimnames = []
for dim in f.dimensions():
dimname = dim.getShortName()
if not dimname in dimnames:
dims.append(ncfile.adddim(dimname, dim.getLength()))
dimnames.append(dimname)
#Add global attributes
for attr in f.attributes():
ncfile.addgroupattr(attr.getName(), attr.getValues())
#Add dimension variables
tvar = None
if writedimvar:
dimvars = []
for i in range(len(f.dimensions())):
dim = f.dimensions()[i]
dname = dim.getShortName()
if dim.getDimType() == DimensionType.T:
var = ncfile.addvar(dname, 'int', [dims[i]])
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 dim.getDimType() == DimensionType.Z:
var = ncfile.addvar(dname, 'float', [dims[i]])
var.addattr('long_name', 'Level')
var.addattr('axis', 'Z')
elif dim.getDimType() == DimensionType.Y:
var = ncfile.addvar(dname, 'float', [dims[i]])
var.addattr('long_name', dname)
var.addattr('axis', 'Y')
elif dim.getDimType() == DimensionType.X:
var = ncfile.addvar(dname, 'float', [dims[i]])
var.addattr('long_name', dname)
var.addattr('axis', 'X')
else:
var = ncfile.addvar(dname, 'float', [dims[i]])
var.addattr('long_name', dname)
var.addattr('axis', dname)
dimvars.append(var)
#Add variables
variables = []
for var in f.variables():
#print 'Variable: ' + var.getShortName()
if var.hasNullDimension():
continue
if var.getDataType() == DataType.STRUCTURE:
continue
vdims = []
missdim = False
for vdim in var.getDimensions():
isvalid = False
for dim in dims:
if dim.getShortName() == vdim.getShortName():
vdims.append(dim)
isvalid = True
break
if not isvalid:
missdim = True
break
if missdim:
continue
nvar = ncfile.addvar(var.getShortName(), var.getDataType(), vdims)
for attr in var.getAttributes():
nvar.addattr(attr.getName(), attr.getValues())
variables.append(nvar)
#Create netCDF file
ncfile.create()
#Write dimension variable data
if writedimvar:
for dimvar, dim in zip(dimvars, f.dimensions()):
if dim.getDimType() != DimensionType.T:
ncfile.write(dimvar, np.array(dim.getDimValue()))
#Write time dimension variable data
if writedimvar and not tvar is None:
sst = datetime.datetime(1900, 1, 1)
tnum = f.timenum()
hours = []
for t in range(0, tnum):
st = f.gettime(t)
hs = (st - sst).total_seconds() // 3600
hours.append(hs)
ncfile.write(tvar, np.array(hours))
#Write variable data
for var in variables:
print 'Variable: ' + var.name
data = f[str(var.name)].read()
ncfile.write(var, data)
#Close netCDF file
ncfile.close()
print 'Convert finished!'
def stem(self,
x,
y,
z,
s=8,
c='b',
marker='o',
alpha=None,
linewidth=None,
verts=None,
**kwargs):
"""
Make a 3D scatter plot of x, y and z, where x, y and z are sequence like objects of the same lengths.
:param x: (*array_like*) Input x data.
:param y: (*array_like*) Input y data.
:param z: (*array_like*) Input z data.
:param s: (*int*) Size of points.
:param c: (*Color*) Color of the points. Or z vlaues.
:param alpha: (*int*) The alpha blending value, between 0 (transparent) and 1 (opaque).
:param marker: (*string*) Marker of the points.
:param label: (*string*) Label of the points series.
:param levs: (*array_like*) Optional. A list of floating point numbers indicating the level
points to draw, in increasing order.
:returns: Points legend break.
"""
#Add data series
label = kwargs.pop('label', 'S_0')
xdata = plotutil.getplotdata(x)
ydata = plotutil.getplotdata(y)
zdata = plotutil.getplotdata(z)
#Set plot data styles
pb, isunique = plotutil.getlegendbreak('point', **kwargs)
pb.setCaption(label)
pstyle = plotutil.getpointstyle(marker)
pb.setStyle(pstyle)
bottom = kwargs.pop('bottom', 0)
samestemcolor = kwargs.pop('samestemcolor', False)
isvalue = False
if len(c) > 1:
if isinstance(c, (np.NDArray, DimArray)):
isvalue = True
elif isinstance(c[0], (int, long, float)):
isvalue = True
if isvalue:
ls = kwargs.pop('symbolspec', None)
if ls is None:
if isinstance(c, (list, tuple)):
c = np.array(c)
levels = kwargs.pop('levs', None)
if levels is None:
levels = kwargs.pop('levels', None)
if levels is None:
cnum = kwargs.pop('cnum', None)
if cnum is None:
ls = plotutil.getlegendscheme([], c.min(), c.max(),
**kwargs)
else:
ls = plotutil.getlegendscheme([cnum], c.min(), c.max(),
**kwargs)
else:
ls = plotutil.getlegendscheme([levels], c.min(), c.max(),
**kwargs)
ls = plotutil.setlegendscheme_point(ls, **kwargs)
if isinstance(s, int):
for lb in ls.getLegendBreaks():
lb.setSize(s)
else:
n = len(s)
for i in range(0, n):
ls.getLegendBreaks()[i].setSize(s[i])
linefmt = kwargs.pop('linefmt', None)
if linefmt is None:
linefmt = PolylineBreak()
linefmt.setColor(Color.black)
else:
linefmt = plotutil.getlegendbreak('line', **linefmt)[0]
#Create graphics
graphics = GraphicFactory.createStems3D(xdata, ydata, zdata, c.asarray(), \
ls, linefmt, bottom, samestemcolor)
else:
colors = plotutil.getcolors(c, alpha)
pbs = []
if isinstance(s, int):
pb.setSize(s)
if len(colors) == 1:
pb.setColor(colors[0])
pb.setOutlineColor(colors[0])
pbs.append(pb)
else:
n = len(colors)
for i in range(0, n):
npb = pb.clone()
npb.setColor(colors[i])
npb.setOutlineColor(colors[i])
pbs.append(npb)
else:
n = len(s)
if len(colors) == 1:
pb.setColor(colors[0])
pb.setOutlineColor(colors[0])
for i in range(0, n):
npb = pb.clone()
npb.setSize(s[i])
pbs.append(npb)
else:
for i in range(0, n):
npb = pb.clone()
npb.setSize(s[i])
npb.setColor(colors[i])
npb.setOutlineColor(colors[i])
pbs.append(npb)
linefmt = kwargs.pop('linefmt', None)
if linefmt is None:
linefmt = PolylineBreak()
linefmt.setColor(colors[0])
else:
linefmt = plotutil.getlegendbreak('line', **linefmt)[0]
#Create graphics
graphics = GraphicFactory.createStems3D(xdata, ydata, zdata, pbs, linefmt, \
bottom, samestemcolor)
visible = kwargs.pop('visible', True)
if visible:
self.add_graphic(graphics[0])
self.add_graphic(graphics[1])
return graphics[0], graphics[1]
def plot(self, x, y, z, *args, **kwargs):
"""
Plot 3D lines and/or markers to the axes. *args* is a variable length argument, allowing
for multiple *x, y* pairs with an optional format string.
:param x: (*array_like*) Input x data.
:param y: (*array_like*) Input y data.
:param z: (*array_like*) Input z data.
:param style: (*string*) Line style for plot.
:returns: Legend breaks of the lines.
The following format string characters are accepted to control the line style or marker:
========= ===========
Character Description
========= ===========
'-' solid line style
'--' dashed line style
'-.' dash-dot line style
':' dotted line style
'.' point marker
',' pixel marker
'o' circle marker
'v' triangle_down marker
'^' triangle_up marker
'<' triangle_left marker
'>' triangle_right marker
's' square marker
'p' pentagon marker
'*' star marker
'x' x marker
'D' diamond marker
========= ===========
The following color abbreviations are supported:
========= =====
Character Color
========= =====
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
========= =====
"""
xdata = plotutil.getplotdata(x)
ydata = plotutil.getplotdata(y)
zdata = plotutil.getplotdata(z)
style = None
if len(args) > 0:
style = args[0]
#Set plot data styles
label = kwargs.pop('label', 'S_1')
mvalues = kwargs.pop('mvalues', None)
if mvalues is None:
if style is None:
line = plotutil.getlegendbreak('line', **kwargs)[0]
line.setCaption(label)
else:
line = plotutil.getplotstyle(style, label, **kwargs)
colors = kwargs.pop('colors', None)
if not colors is None:
colors = plotutil.getcolors(colors)
cbs = []
for color in colors:
cb = line.clone()
cb.setColor(color)
cbs.append(cb)
else:
ls = kwargs.pop('symbolspec', None)
if ls is None:
if isinstance(mvalues, (list, tuple)):
mvalues = np.array(mvalues)
levels = kwargs.pop('levs', None)
if levels is None:
levels = kwargs.pop('levels', None)
if levels is None:
cnum = kwargs.pop('cnum', None)
if cnum is None:
ls = plotutil.getlegendscheme([], mvalues.min(),
mvalues.max(), **kwargs)
else:
ls = plotutil.getlegendscheme([cnum], mvalues.min(),
mvalues.max(), **kwargs)
else:
ls = plotutil.getlegendscheme([levels], mvalues.min(),
mvalues.max(), **kwargs)
ls = plotutil.setlegendscheme_line(ls, **kwargs)
#Add graphics
if mvalues is None:
if colors is None:
graphics = GraphicFactory.createLineString3D(
xdata, ydata, zdata, line)
else:
graphics = GraphicFactory.createLineString3D(
xdata, ydata, zdata, cbs)
else:
mdata = plotutil.getplotdata(mvalues)
graphics = GraphicFactory.createLineString3D(
xdata, ydata, zdata, mdata, ls)
visible = kwargs.pop('visible', True)
if visible:
self.add_graphic(graphics)
return graphics
def set_data(self, value):
value = np.array(value)
self._dataframe.setData(value._array)
def set_values(self, value):
self._data = np.array(value)
self._series.setData(self._data._array)
def __setitem__(self, key, value):
if isinstance(value, datetime.datetime):
value = miutil.jdatetime(value)
if isinstance(value, (list, tuple)):
if isinstance(value[0], datetime.datetime):
value = miutil.jdatetime(value)
value = np.array(value)
if isinstance(value, NDArray):
value = value._array
if isinstance(key, basestring):
if isinstance(value, series.Series):
value = value.values._array
self._dataframe.setColumn(key, value)
return
hascolkey = True
if isinstance(key, tuple):
ridx = key[0]
cidx = key[1]
if isinstance(ridx, int) and isinstance(cidx, int):
if ridx < 0:
ridx = self.shape[0] + ridx
if cidx < 0:
cidx = self.shape[1] + cidx
self._dataframe.setValue(ridx, cidx, value)
return
elif isinstance(ridx, int) and isinstance(cidx, basestring):
if ridx < 0:
ridx = self.shape[0] + ridx
self._dataframe.setValue(ridx, cidx, value)
return
else:
key = (key, slice(None))
hascolkey = False
k = key[0]
if isinstance(k, int):
if k < 0:
k = self.shape[0] + k
rowkey = k
elif isinstance(k, basestring):
sidx = self._index.index(k)
if sidx < 0:
return None
eidx = sidx
step = 1
rowkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
if isinstance(k.start, basestring):
sidx = self._index.index(k.start)
if sidx < 0:
sidx = 0
else:
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[0] + sidx
if isinstance(k.stop, basestring):
eidx = self._index.index(k.stop)
if eidx < 0:
eidx = self.shape[0] + eidx
else:
eidx = self.shape[0] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[0] + eidx
step = 1 if k.step is None else k.step
rowkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
rowkey = k
else:
tlist = []
for tstr in k:
idx = self._index.index(tstr)
if idx >= 0:
tlist.append(idx)
rowkey = tlist
else:
return
if not hascolkey:
colkey = Range(0, self.shape[1] - 1, 1)
else:
k = key[1]
if isinstance(k, int):
sidx = k
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = sidx
step = 1
colkey = Range(sidx, eidx, step)
elif isinstance(k, slice):
sidx = 0 if k.start is None else k.start
if sidx < 0:
sidx = self.shape[1] + sidx
eidx = self.shape[1] - 1 if k.stop is None else k.stop - 1
if eidx < 0:
eidx = self.shape[1] + eidx
step = 1 if k.step is None else k.step
colkey = Range(sidx, eidx, step)
elif isinstance(k, list):
if isinstance(k[0], int):
colkey = k
else:
colkey = self.columns.indexOfName(k)
elif isinstance(k, basestring):
col = self.columns.indexOf(k)
colkey = Range(col, col + 1, 1)
else:
return
self._dataframe.setValues(rowkey, colkey, value)
