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