167 lines
6.4 KiB
Python
Executable File
167 lines
6.4 KiB
Python
Executable File
x = to_array([0,1,2,3,4,5,6,7,8,9,10],'i')
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y = to_array([1,2,3,4,5,10, 50, 11, 4,3,1],'i')
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def to_array(obj, type = 'o'):
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"""Convert Python list to Java array.
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Args:
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obj(list): Original data.
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type(str): array type 'b' = byte, 'h' = short, 'i' = int, 'l' = long, 'f' = float, 'd' = double,
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'c' = char, 'z' = boolean, 's' = String, 'o' = Object
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Returns:
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Java array.
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"""
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if type[0] == '[':
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type = type[1:]
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arrayType = class_types.get("["+type)
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if obj is None:
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return None
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if isinstance(obj,java.util.List):
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obj = obj.toArray(java.lang.reflect.Array.newInstance(Class.forName(class_types.get(type)),0))
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if type != 'o':
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obj = Convert.toPrimitiveArray(obj)
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if isinstance(obj,PyArray):
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if obj.typecode != type:
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print "DIF", obj.typecode, type
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ret = java.lang.reflect.Array.newInstance(Class.forName(class_types.get(type)),len(obj))
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if type == 's':
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for i in range(len(obj)): ret[i] = str(obj[i])
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elif type == 'c':
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for i in range(len(obj)): ret[i] = chr(obj[i])
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else:
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for i in range(len(obj)): ret[i] = obj[i]
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obj = ret
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if type not in ['o', 's']:
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obj = Convert.toPrimitiveArray(obj)
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return obj
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if is_list(obj):
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if type=='o' or type== 's':
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ret = java.lang.reflect.Array.newInstance(Class.forName(class_types.get(type)),len(obj))
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for i in range (len(obj)):
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if is_list(obj):
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ret[i] = to_array(obj[i],type)
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elif type == 's':
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ret[i] = str(obj[i])
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else:
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ret[i] = obj[i]
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return ret
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if len(obj)>0 and is_list(obj[0]):
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if len(obj[0])>0 and is_list(obj[0][0]):
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ret = java.lang.reflect.Array.newInstance(Class.forName(arrayType),len(obj),len(obj[0]))
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for i in range(len(obj)):
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ret[i]=to_array(obj[i], type)
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return ret
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else:
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ret = java.lang.reflect.Array.newInstance(Class.forName(arrayType),len(obj))
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for i in range(len(obj)):
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ret[i]=to_array(obj[i], type)
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return ret
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return jarray.array(obj,type)
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return obj
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def plot(data, name = None, xdata = None, ydata=None, title=None):
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"""Request one or multiple plots of user data (1d, 2d or 3d)
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Args:
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data: array or list of values. For multiple plots, array of arrays or lists of values.
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name(str or list of str, optional): plot name or list of names (if multiple plots).
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xdata: array or list of values. For multiple plots, array of arrays or lists of values.
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ydata: array or list of values. For multiple plots, array of arrays or lists of values.
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title(str, optional): plotting context name.
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Returns:
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ArrayList of Plot objects.
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"""
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if isinstance(data, ch.psi.pshell.data.Table):
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if is_list(xdata):
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xdata = to_array(xdata, 'd')
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return get_context().plot(data,xdata,name,title)
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if isinstance(data, ch.psi.pshell.scan.ScanResult):
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return get_context().plot(data,title)
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if (name is not None) and is_list(name):
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if len(name)==0:
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name=None;
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else:
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if (data==None):
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data = []
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for n in name:
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data.append([])
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plots = java.lang.reflect.Array.newInstance(Class.forName("ch.psi.pshell.data.PlotDescriptor"), len(data))
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for i in range (len(data)):
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plotName = None if (name is None) else name[i]
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x = xdata
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if is_list(x) and len(x)>0 and (is_list(x[i]) or isinstance(x[i] , java.util.List) or isinstance(x[i],PyArray)):
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x = x[i]
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y = ydata
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if is_list(y) and len(y)>0 and (is_list(y[i]) or isinstance(y[i] , java.util.List) or isinstance(y[i],PyArray)):
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y = y[i]
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plots[i] = PlotDescriptor(plotName , to_array(data[i], 'd'), to_array(x, 'd'), to_array(y, 'd'))
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return get_context().plot(plots,title)
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else:
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plot = PlotDescriptor(name, to_array(data, 'd'), to_array(xdata, 'd'), to_array(ydata, 'd'))
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return get_context().plot(plot,title)
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from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list, Gaussian,fit_harmonic,HarmonicOscillator
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import java.awt.Color as Color
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def fit(ydata, xdata = None):
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if xdata is None:
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xdata = frange(0, len(ydata), 1)
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max_y= max(ydata)
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index_max = ydata.index(max_y)
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max_x= xdata[index_max]
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print "Max index:" + str(index_max),
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print " x:" + str(max_x),
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print " y:" + str(max_y)
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gaussians = fit_gaussians(ydata, xdata, [index_max,])
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print "PLOT"
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ydata =to_array(ydata,'d')
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xdata= to_array(xdata,'d')
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p = plot([ydata],["data"],[xdata], title="Fit" )[0]
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print "OK"
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if gaussians[0] is None: #Fitting error
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p.addMarker(max_x, None, "Max="+str(round(max_x,2)), Color.GRAY)
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print "Fitting error - using max value"
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return (None, max_x, None)
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(norm, mean, sigma) = gaussians[0]
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fitted_gaussian_function = Gaussian(norm, mean, sigma)
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scale_x = [float(min(xdata)), float(max(xdata)) ]
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points = max((len(xdata)+1), 100)
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resolution = (scale_x[1]-scale_x[0]) / points
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fit_y = []
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fit_x = frange(scale_x[0],scale_x[1],resolution, True)
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for x in fit_x:
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fit_y.append(fitted_gaussian_function.value(x))
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p.addSeries(LinePlotSeries("fit"))
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p.getSeries(1).setData(fit_x, fit_y)
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if abs(mean - xdata[index_max]) < ((scale_x[0] + scale_x[1])/2):
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print "Mean -> " + str(mean)
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p.addMarker(mean, None, "Mean="+str(round(norm,2)), Color.MAGENTA.darker())
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return (norm, mean, sigma)
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else:
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p.addMarker(max_x, None, "Max="+str(round(max_x,2)), Color.GRAY)
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print "Invalid gaussian fit: " + str(mean) + " - using max value"
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return (None, max_x, None)
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fit(y,x) |