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 = minum.array(yy).array
else:
y1 = plotutil.getplotdata(y1)
if isinstance(y2, (int, long, float)):
yy = []
for i in range(dn):
yy.append(y2)
y2 = minum.array(yy).array
else:
y2 = plotutil.getplotdata(y2)
if not where is None:
if isinstance(where, (tuple, list)):
where = minum.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 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 = minum.array(yy).array
else:
y1 = plotutil.getplotdata(y1)
if isinstance(y2, (int, long, float)):
yy = []
for i in range(dn):
yy.append(y2)
y2 = minum.array(yy).array
else:
y2 = plotutil.getplotdata(y2)
if not where is None:
if isinstance(where, (tuple, list)):
where = minum.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 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 = minum.array(x)
if isinstance(y, (list, tuple)):
y = minum.array(y)
if isinstance(polygon, tuple):
x_p = polygon[0]
y_p = polygon[1]
if isinstance(x_p, (list, tuple)):
x_p = minum.array(x_p)
if isinstance(y_p, (list, tuple)):
y_p = minum.array(y_p)
return MIArray(ArrayMath.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 MIArray(ArrayMath.inPolygon(x.array, y.array, polygon))
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 = minum.array(v)
elif isinstance(v, MIArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, MIArray):
d = [d] * n
d = minum.array(d)
dlist[i] = d
data = dlist
if isinstance(data, MIArray):
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, (MIArray, 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 __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 = minum.array(v)
elif isinstance(v, MIArray):
n = len(v)
dlist.append(v)
for i in range(len(dlist)):
d = dlist[i]
if not isinstance(d, MIArray):
d = [d] * n
d = minum.array(d)
dlist[i] = d
data = dlist
if isinstance(data, MIArray):
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, (MIArray, 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, (MIArray, DimArray)):
return data.asarray()
elif isinstance(data, (list, tuple)):
if isinstance(data[0], datetime.datetime):
dd = []
for d in data:
v = miutil.date2num(d)
dd.append(v)
return minum.array(dd).array
else:
return minum.array(data).array
else:
return minum.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 = minum.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, (MIArray, 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 = MIArray(self._series.getData())
self._index = Index.factory(index=self._series.getIndex())
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 = minum.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, (MIArray, 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 = MIArray(self._series.getData())
self._index = Index.factory(index=self._series.getIndex())
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 = minum.array(value)
if isinstance(value, MIArray):
value = value.array
self._dataframe.addColumn(loc, column, value)
def cdf(self, x, *args, **kwargs):
'''
Cumulative distribution function of the given RV.
:param x: (*array_like*) quantiles.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Cumulative distribution function.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, MIArray):
x = x.array
r = DistributionUtil.cdf(dist, x)
return MIArray(r)
def ppf(self, x, *args, **kwargs):
'''
Percent point function (inverse of cdf) at q of the given RV.
:param q: (*array_like*) lower tail probability.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Quantile corresponding to the lower tail probability q.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, MIArray):
x = x.array
r = DistributionUtil.ppf(dist, x)
return MIArray(r)
def pmf(self, x, *args, **kwargs):
'''
Probability mass function (PMF) of the given RV.
:param x: (*array_like*) quantiles.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Probability mas function.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, MIArray):
x = x.array
r = DistributionUtil.pmf(dist, x)
return MIArray(r)
def logpdf(self, x, *args, **kwargs):
'''
Log of the probability density function at x of the given RV.
:param x: (*array_like*) quantiles.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Log of the probability density function.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, MIArray):
x = x.array
r = DistributionUtil.logpdf(dist, x)
return MIArray(r)
def cdf(self, x, *args, **kwargs):
'''
Cumulative distribution function of the given RV.
:param x: (*array_like*) quantiles.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Cumulative distribution function.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, NDArray):
x = x.array
r = DistributionUtil.cdf(dist, x)
return NDArray(r)
def logpdf(self, x, *args, **kwargs):
'''
Log of the probability density function at x of the given RV.
:param x: (*array_like*) quantiles.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Log of the probability density function.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, NDArray):
x = x.array
r = DistributionUtil.logpdf(dist, x)
return NDArray(r)
def ppf(self, x, *args, **kwargs):
'''
Percent point function (inverse of cdf) at q of the given RV.
:param q: (*array_like*) lower tail probability.
:param loc: (*float*) location parameter (default=0).
:param scale: (*float*) scale parameter (default=1).
:returns: Quantile corresponding to the lower tail probability q.
'''
dist = self._create_distribution(*args)
if isinstance(x, (list, tuple)):
x = minum.array(x)
if isinstance(x, NDArray):
x = x.array
r = DistributionUtil.ppf(dist, x)
return NDArray(r)
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 = minum.array(value)
if isinstance(value, MIArray):
value = value.array
self._dataframe.addColumn(loc, column, value)
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 = minum.array(value)
if isinstance(value, MIArray):
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)
def set_data(self, value):
value = minum.array(value)
self._dataframe.setData(value.array)
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, MIArray):
dims = []
for s in data.shape:
dimvalue = minum.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 MeteoInfo')
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, minum.array(hours))
else:
ncfile.write(dimvar, minum.array(dim.getDimValue()))
ncfile.write(var, data)
#Close netCDF file
ncfile.close()
def set_values(self, value):
self._data = minum.array(value)
self._series.setData(self._data.array)
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 = minum.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 scatter(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)
isvalue = False
if len(c) > 1:
if isinstance(c, (MIArray, 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 = minum.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])
#Create graphics
graphics = GraphicFactory.createPoints3D(xdata, ydata, zdata,
c.asarray(), ls)
else:
colors = plotutil.getcolors(c, alpha)
pbs = []
if isinstance(s, int):
pb.setSize(s)
if len(colors) == 1:
pb.setColor(colors[0])
pbs.append(pb)
else:
n = len(colors)
for i in range(0, n):
npb = pb.clone()
npb.setColor(colors[i])
pbs.append(npb)
else:
n = len(s)
if len(colors) == 1:
pb.setColor(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])
pbs.append(npb)
#Create graphics
graphics = GraphicFactory.createPoints3D(xdata, ydata, zdata, pbs)
visible = kwargs.pop('visible', True)
if visible:
self.add_graphic(graphics)
return graphics
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 = midata.addfile_grads(infn)
if not big_endian is None:
f.bigendian(big_endian)
#New netCDF file
ncfile = midata.addfile(outfn, 'c')
#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, minum.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, minum.array([hours]), origin=origin)
for var in variables:
print 'Variable: ' + var.name
if var.ndim == 3:
data = f[str(var.name)][t,:,:]
data[data==minum.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==minum.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 __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 = minum.array(value)
if isinstance(value, MIArray):
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)
def set_data(self, value):
value = minum.array(value)
self._dataframe.setData(value.array)
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 = midata.addfile(infn)
#New netCDF file
ncfile = midata.addfile(outfn, 'c', version=version, largefile=largefile)
#Add dimensions
dims = []
for dim in f.dimensions():
dims.append(ncfile.adddim(dim.getShortName(), dim.getLength()))
#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
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, minum.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, minum.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 ncwrite(fn, data, varname, dims=None, attrs=None, largefile=False):
"""
Write a netCDF data file.
: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: (*list of attributes*) Attribute list.
:param largefile: (*boolean*) Create netCDF as large file or not.
"""
if dims is None:
if isinstance(data, MIArray):
dims = []
for s in data.shape:
dimvalue = minum.arange(s)
dimname = 'dim' + str(len(dims))
dims.append(dimension(dimvalue, dimname))
else:
dims = data.dims
#New netCDF file
ncfile = midata.addfile(fn, 'c')
#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 MeteoInfo')
#Add dimension variables
dimvars = []
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 = ncfile.addvar(dim.getShortName(), 'float', [dim])
var.addattr('axis', 'null')
dimvars.append(var)
#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, attr[key])
#Create netCDF file
ncfile.create()
#Write variable data
for dimvar, dim in zip(dimvars, dims):
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, minum.array(hours))
else:
ncfile.write(dimvar, minum.array(dim.getDimValue()))
ncfile.write(var, data)
#Close netCDF file
ncfile.close()
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, (MIArray, 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 = minum.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 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 = []
for dim in f.dimensions():
dims.append(ncfile.adddim(dim.getShortName(), dim.getLength()))
#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
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, minum.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, minum.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 set_values(self, value):
self._data = minum.array(value)
self._series.setData(self._data.array)
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, minum.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, minum.array([hours]), origin=origin)
for var in variables:
print 'Variable: ' + var.name
if var.ndim == 3:
data = f[str(var.name)][t, :, :]
data[data == minum.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 == minum.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 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 = minum.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 scatter(self, *args, **kwargs):
"""
Make a scatter plot on a map.
:param x: (*array_like*) Input x data.
:param y: (*array_like*) Input y data.
:param z: (*array_like*) Input z data.
: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, different
levels will be plotted in different colors in the order specified.
:param size: (*int of list*) Marker size.
:param marker: (*string*) Marker of the points.
:param fill: (*boolean*) Fill markers or not. Default is True.
:param edge: (*boolean*) Draw edge of markers or not. Default is True.
:param facecolor: (*Color*) Fill color of markers. Default is black.
:param edgecolor: (*Color*) Edge color of markers. Default is black.
:param proj: (*ProjectionInfo*) Map projection of the data. Default is None.
:param zorder: (*int*) Z-order of created layer for display.
:returns: (*VectoryLayer*) Point VectoryLayer.
"""
n = len(args)
if n == 1:
a = args[0]
y = a.dimvalue(0)
x = a.dimvalue(1)
args = args[1:]
else:
x = args[0]
y = args[1]
if not isinstance(x, MIArray):
x = minum.array(x)
if not isinstance(y, MIArray):
y = minum.array(y)
if n == 2:
a = x
args = args[2:]
else:
a = args[2]
if not isinstance(a, MIArray):
a = minum.array(a)
args = args[3:]
ls = kwargs.pop('symbolspec', None)
if ls is None:
isunique = False
colors = kwargs.get('colors', None)
if not colors is None:
if isinstance(colors, (list, tuple)) and len(colors) == len(x):
isunique = True
size = kwargs.get('size', None)
if not size is None:
if isinstance(size, (list, tuple, MIArray)) and len(size) == len(x):
isunique = True
if isunique:
ls = LegendManage.createUniqValueLegendScheme(len(x), ShapeTypes.Point)
else:
ls = plotutil.getlegendscheme(args, a.min(), a.max(), **kwargs)
ls = plotutil.setlegendscheme_point(ls, **kwargs)
if a.size == ls.getBreakNum() and ls.getLegendType() == LegendType.UniqueValue:
layer = DrawMeteoData.createSTPointLayer_Unique(a.array, x.array, y.array, ls, 'layer', 'data')
else:
layer = DrawMeteoData.createSTPointLayer(a.array, x.array, y.array, ls, 'layer', 'data')
proj = kwargs.pop('proj', None)
if not proj is None:
layer.setProjInfo(proj)
avoidcoll = kwargs.pop('avoidcoll', None)
if not avoidcoll is None:
layer.setAvoidCollision(avoidcoll)
# Add layer
isadd = kwargs.pop('isadd', True)
if isadd:
zorder = kwargs.pop('zorder', None)
select = kwargs.pop('select', True)
self.add_layer(layer, zorder, select)
self.axes.setDrawExtent(layer.getExtent().clone())
self.axes.setExtent(layer.getExtent().clone())
return MILayer(layer)
