update curve_fit function - still is not usable

meteoinfo-lab/milconfig.xml

@@ -1,34 +1,34 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <MeteoInfo File="milconfig.xml" Type="configurefile">
-  <Path OpenPath="D:\Temp\HYSPLIT">
+  <Path OpenPath="D:\Working\MIScript\Jython\mis\plot_types\contour">
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\grads"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\satellite\himawari"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\LaSW"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\meteo\wrf"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\meteo"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\meteo\calc"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\traj"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\traj\TrajStat"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\micaps"/>
     <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\plot"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl"/>
-    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d"/>
     <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\no_opengl"/>
     <RecentFolder Folder="D:\Temp\HYSPLIT"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\gui"/>
+    <RecentFolder Folder="D:\MyProgram\java\MeteoInfoDev\toolbox\meteoview3d"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\common_math"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\common_math\fitting"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\common_math\optimize"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\particles"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\surf"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\3d"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types"/>
+    <RecentFolder Folder="D:\Working\MIScript\Jython\mis\plot_types\contour"/>
   </Path>
   <File>
     <OpenedFiles>
       <OpenedFile File="D:\Working\MIScript\Jython\mis\LaSW\typhoon_map_slice.py"/>
-      <OpenedFile File="D:\Working\MIScript\Jython\mis\micaps\micaps_4_8.py"/>
+      <OpenedFile File="D:\MyProgram\java\MeteoInfoDev\toolbox\meteoview3d\mainGUI.py"/>
-      <OpenedFile File="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\plot\plot3_multi_color_1.py"/>
+      <OpenedFile File="D:\Working\MIScript\Jython\mis\common_math\optimize\curve_fit_2.py"/>
-      <OpenedFile File="D:\Temp\HYSPLIT\hys_lekima.py"/>
+      <OpenedFile File="D:\Working\MIScript\Jython\mis\plot_types\contour\conout_negtive_value.py"/>
     </OpenedFiles>
     <RecentFiles>
       <RecentFile File="D:\Working\MIScript\Jython\mis\LaSW\typhoon_map_slice.py"/>
-      <RecentFile File="D:\Working\MIScript\Jython\mis\micaps\micaps_4_8.py"/>
+      <RecentFile File="D:\MyProgram\java\MeteoInfoDev\toolbox\meteoview3d\mainGUI.py"/>
-      <RecentFile File="D:\Working\MIScript\Jython\mis\plot_types\3d\jogl\plot\plot3_multi_color_1.py"/>
+      <RecentFile File="D:\Working\MIScript\Jython\mis\common_math\optimize\curve_fit_2.py"/>
-      <RecentFile File="D:\Temp\HYSPLIT\hys_lekima.py"/>
+      <RecentFile File="D:\Working\MIScript\Jython\mis\plot_types\contour\conout_negtive_value.py"/>
     </RecentFiles>
   </File>
   <Font>
@@ -36,5 +36,5 @@
   </Font>
   <LookFeel DockWindowDecorated="true" LafDecorated="true" Name="FlatDarkLaf"/>
   <Figure DoubleBuffering="true"/>
-  <Startup MainFormLocation="-7,0" MainFormSize="1342,730"/>
+  <Startup MainFormLocation="-7,-7" MainFormSize="1293,685"/>
 </MeteoInfo>

meteoinfo-lab/pylib/milab_debug.py

@@ -6,5 +6,6 @@ sys.path.append(toolbox_path)
 sys.path.append(os.path.join(toolbox_path, 'miml_dev'))
 sys.path.append(os.path.join(toolbox_path, 'EMIPS'))
 sys.path.append(os.path.join(toolbox_path, 'IMEP'))
+sys.path.append(os.path.join(toolbox_path, 'meteoview3d'))
 
 mipylib.migl.mifolder = 'D:/MyProgram/Distribution/Java/MeteoInfo/MeteoInfo'

meteoinfo-lab/pylib/mipylib/numeric/optimize/__init__.py

@@ -1,4 +1,4 @@
 from .minpack import *
-#from ._lsq import *
+from ._lsq import *
 
 __all__ = [s for s in dir() if not s.startswith('_')]

meteoinfo-lab/pylib/mipylib/numeric/optimize/_lsq/__init__.py

@@ -1,3 +1,3 @@
 from .least_squares import least_squares
 
-__all__ = ['least_squares', 'lsq_linear']
+__all__ = ['least_squares']

meteoinfo-lab/pylib/mipylib/numeric/optimize/minpack.py

@@ -132,19 +132,62 @@ def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False,
         n = p0.size
 
     func = UniFunc(f)
-    jac_func = OptimizeUtil.getJacobianFunction(func, xdata.asarray(), func.order, 5, 0.1)
+    best = OptimizeUtil.curveFit(func, xdata.asarray(), ydata.asarray(), 5, 0.1, p0.tojarray('double'))
-    problem = LeastSquaresBuilder().start(p0.tojarray('double')). \
+    r = tuple(best)
-        model(jac_func). \
-        target(ydata.tojarray('double')). \
-        lazyEvaluation(False). \
-        maxEvaluations(1000). \
-        maxIterations(1000). \
-        build()
-    optimum = LevenbergMarquardtOptimizer().optimize(problem)
-    r = tuple(optimum.getPoint().toArray())
 
     return r
 
+# def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False,
+#               check_finite=True, bounds=(-np.inf, np.inf), method=None,
+#               jac=None, **kwargs):
+#     """
+#     Use non-linear least squares to fit a function, f, to data.
+#
+#     Assumes ``ydata = f(xdata, *params) + eps``
+#
+#     :param f: callable
+#         The model function, f(x, ...).  It must take the independent
+#         variable as the first argument and the parameters to fit as
+#         separate remaining arguments.
+#     :param xdata: array_like or object
+#         The independent variable where the data is measured.
+#         Should usually be an M-length sequence or an (k,M)-shaped array for
+#         functions with k predictors, but can actually be any object.
+#     :param ydata: array_like
+#         The dependent data, a length M array - nominally ``f(xdata, ...)``.
+#     :param p0: array_like, optional
+#         Initial guess for the parameters (length N).  If None, then the
+#         initial values will all be 1 (if the number of parameters for the
+#         function can be determined using introspection, otherwise a
+#         ValueError is raised).
+#     :return:
+#     """
+#     if p0 is None:
+#         # determine number of parameters by inspecting the function
+#         from ..lib._util import getargspec_no_self as _getargspec
+#         args, varargs, varkw, defaults = _getargspec(f)
+#         if len(args) < 2:
+#             raise ValueError("Unable to determine number of fit parameters.")
+#         n = len(args) - 1
+#         p0 = np.ones(n)
+#     else:
+#         p0 = np.atleast_1d(p0)
+#         n = p0.size
+#
+#     func = UniFunc(f)
+#     jac_func = OptimizeUtil.getJacobianFunction(func, xdata.asarray(), func.order, 5, 0.1)
+#     problem = LeastSquaresBuilder().start(p0.tojarray('double')). \
+#         model(jac_func). \
+#         target(ydata.tojarray('double')). \
+#         lazyEvaluation(False). \
+#         maxEvaluations(1000). \
+#         maxIterations(1000). \
+#         build()
+#     optimum = LevenbergMarquardtOptimizer().optimize(problem)
+#     r = tuple(optimum.getPoint().toArray())
+#
+#     return r
+
 def _del2(p0, p1, d):
     return p0 - np.square(p1 - p0) / d
 

meteoinfo-lab/pylib/mipylib/plotlib/_axes3dgl.py

@@ -810,59 +810,7 @@ class Axes3DGL(Axes3D):
         :param cmap: (*string*) Color map string.
         :return:
         """
-        warnings.warn("plot_slice is deprecated", DeprecationWarning)
+        return self.slice(*args, **kwargs)
-        if len(args) <= 3:
-            x = args[0].dimvalue(2)
-            y = args[0].dimvalue(1)
-            z = args[0].dimvalue(0)
-            data = args[0]
-            args = args[1:]
-        else:
-            x = args[0]
-            y = args[1]
-            z = args[2]
-            data = args[3]
-            args = args[4:]
-        if x.ndim == 3:
-            x = x[0,0]
-        if y.ndim == 3:
-            y = y[0,:,0]
-        if z.ndim == 3:
-            z = z[:,0,0]
-
-        cmap = plotutil.getcolormap(**kwargs)
-        if len(args) > 0:
-            level_arg = args[0]
-            if isinstance(level_arg, int):
-                cn = level_arg
-                ls = LegendManage.createLegendScheme(data.min(), data.max(), cn, cmap)
-            else:
-                if isinstance(level_arg, NDArray):
-                    level_arg = level_arg.aslist()
-                ls = LegendManage.createLegendScheme(data.min(), data.max(), level_arg, cmap)
-        else:
-            ls = LegendManage.createLegendScheme(data.min(), data.max(), cmap)
-        ls = ls.convertTo(ShapeTypes.POLYGON)
-        edge = kwargs.pop('edge', True)
-        kwargs['edge'] = edge
-        plotutil.setlegendscheme(ls, **kwargs)
-
-        xslice = kwargs.pop('xslice', [])
-        if isinstance(xslice, numbers.Number):
-            xslice = [xslice]
-        yslice = kwargs.pop('yslice', [])
-        if isinstance(yslice, numbers.Number):
-            yslice = [yslice]
-        zslice = kwargs.pop('zslice', [])
-        if isinstance(zslice, numbers.Number):
-            zslice = [zslice]
-        graphics = JOGLUtil.slice(data.asarray(), x.asarray(), y.asarray(), z.asarray(), xslice, \
-                                  yslice, zslice, ls)
-        visible = kwargs.pop('visible', True)
-        if visible:
-            for gg in graphics:
-                self.add_graphic(gg)
-        return graphics
 
     def contourslice(self, *args, **kwargs):
         """
@@ -1218,54 +1166,7 @@ class Axes3DGL(Axes3D):
         
         :returns: Legend
         """
-        warnings.warn("plot_surface is deprecated", DeprecationWarning)
+        return self.surf(*args, **kwargs)
-        if len(args) <= 2:
-            x = args[0].dimvalue(1)
-            y = args[0].dimvalue(0)
-            x, y = np.meshgrid(x, y)
-            z = args[0]    
-            args = args[1:]
-        else:
-            x = args[0]
-            y = args[1]
-            z = args[2]
-            args = args[3:]
-
-        if kwargs.has_key('colors'):
-            cn = len(kwargs['colors'])
-        else:
-            cn = None
-        cmap = plotutil.getcolormap(**kwargs)
-        if len(args) > 0:
-            level_arg = args[0]
-            if isinstance(level_arg, int):
-                cn = level_arg
-                ls = LegendManage.createLegendScheme(z.min(), z.max(), cn, cmap)
-            else:
-                if isinstance(level_arg, NDArray):
-                    level_arg = level_arg.aslist()
-                ls = LegendManage.createLegendScheme(z.min(), z.max(), level_arg, cmap)
-        else:  
-            if cn is None:
-                ls = LegendManage.createLegendScheme(z.min(), z.max(), cmap)
-            else:
-                ls = LegendManage.createLegendScheme(z.min(), z.max(), cn, cmap)
-        ls = ls.convertTo(ShapeTypes.POLYGON)
-        facecolor = kwargs.pop('facecolor', None)
-        face_interp = None
-        if not facecolor is None:
-            face_interp = (facecolor == 'interp')
-            if not face_interp:
-                if not facecolor in ['flat','texturemap','none']:
-                    kwargs['facecolor'] = facecolor
-        plotutil.setlegendscheme(ls, **kwargs)
-        graphics = JOGLUtil.surface(x.asarray(), y.asarray(), z.asarray(), ls)
-        if face_interp:
-            graphics.setFaceInterp(face_interp)
-        visible = kwargs.pop('visible', True)
-        if visible:
-            self.add_graphic(graphics)
-        return graphics
 
     def isosurface(self, *args, **kwargs):
         """
@@ -1343,54 +1244,7 @@ class Axes3DGL(Axes3D):
 
         :returns: Legend
         """
-        warnings.warn("plot_isosurface is deprecated", DeprecationWarning)
+        return self.isosurface(*args, **kwargs)
-        if len(args) <= 3:
-            x = args[0].dimvalue(2)
-            y = args[0].dimvalue(1)
-            z = args[0].dimvalue(0)
-            data = args[0]
-            isovalue = args[1]
-            args = args[2:]
-        else:
-            x = args[0]
-            y = args[1]
-            z = args[2]
-            data = args[3]
-            isovalue = args[4]
-            args = args[5:]
-        cmap = plotutil.getcolormap(**kwargs)
-        cvalue = kwargs.pop('cvalue', None)
-        if not cvalue is None:
-            if len(args) > 0:
-                level_arg = args[0]
-                if isinstance(level_arg, int):
-                    cn = level_arg
-                    ls = LegendManage.createLegendScheme(data.min(), data.max(), cn, cmap)
-                else:
-                    if isinstance(level_arg, NDArray):
-                        level_arg = level_arg.aslist()
-                    ls = LegendManage.createLegendScheme(data.min(), data.max(), level_arg, cmap)
-            else:
-                ls = LegendManage.createLegendScheme(data.min(), data.max(), cmap)
-            ls = ls.convertTo(ShapeTypes.POLYGON)
-            edge = kwargs.pop('edge', True)
-            kwargs['edge'] = edge
-            plotutil.setlegendscheme(ls, **kwargs)
-        else:
-            ls = plotutil.getlegendbreak('polygon', **kwargs)[0]
-        nthread = kwargs.pop('nthread', None)
-        if nthread is None:
-            graphics = JOGLUtil.isosurface(data.asarray(), x.asarray(), y.asarray(), z.asarray(), isovalue, ls)
-        else:
-            data = data.asarray().copyIfView()
-            x = x.asarray().copyIfView()
-            y = y.asarray().copyIfView()
-            z = z.asarray().copyIfView()
-            graphics = JOGLUtil.isosurface(data, x, y, z, isovalue, ls, nthread)
-        visible = kwargs.pop('visible', True)
-        if visible:
-            self.add_graphic(graphics)
-        return graphics
 
     def particles(self, *args, **kwargs):
         """
@@ -1473,51 +1327,7 @@ class Axes3DGL(Axes3D):
 
         :returns: Legend
         """
-        warnings.warn("plot_particles is deprecated", DeprecationWarning)
+        return self.particles(*args, **kwargs)
-        if len(args) <= 3:
-            x = args[0].dimvalue(2)
-            y = args[0].dimvalue(1)
-            z = args[0].dimvalue(0)
-            data = args[0]
-            args = args[1:]
-        else:
-            x = args[0]
-            y = args[1]
-            z = args[2]
-            data = args[3]
-            args = args[4:]
-        cmap = plotutil.getcolormap(**kwargs)
-        vmin = kwargs.pop('vmin', data.min())
-        vmax = kwargs.pop('vmax', data.max())
-        if vmin >= vmax:
-            raise ValueError("Minimum value larger than maximum value")
-
-        if len(args) > 0:
-            level_arg = args[0]
-            if isinstance(level_arg, int):
-                cn = level_arg
-                ls = LegendManage.createLegendScheme(vmin, vmax, cn, cmap)
-            else:
-                if isinstance(level_arg, NDArray):
-                    level_arg = level_arg.aslist()
-                ls = LegendManage.createLegendScheme(vmin, vmax, level_arg, cmap)
-        else:
-            ls = LegendManage.createLegendScheme(vmin, vmax, cmap)
-        plotutil.setlegendscheme(ls, **kwargs)
-        alpha_min = kwargs.pop('alpha_min', 0.1)
-        alpha_max = kwargs.pop('alpha_max', 0.6)
-        density = kwargs.pop('density', 2)
-        graphics = JOGLUtil.particles(data.asarray(), x.asarray(), y.asarray(), z.asarray(), ls, \
-                                      alpha_min, alpha_max, density)
-        s = kwargs.pop('s', None)
-        if s is None:
-            s = kwargs.pop('size', None)
-        if not s is None:
-            graphics.setPointSize(s)
-        visible = kwargs.pop('visible', True)
-        if visible:
-            self.add_graphic(graphics)
-        return graphics
 
     def volumeplot(self, *args, **kwargs):
         """

meteoinfo-math/src/main/java/org/meteoinfo/math/optimize/MyParametricUnivariateFunction.java

@@ -0,0 +1,57 @@
+package org.meteoinfo.math.optimize;
+
+import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
+import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
+import org.apache.commons.math3.analysis.differentiation.FiniteDifferencesDifferentiator;
+import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction;
+
+public class MyParametricUnivariateFunction implements ParametricUnivariateFunction {
+
+    private ParamUnivariateFunction function;
+    private int nbPoints;
+    private double stepSize;
+
+    /**
+     * Constructor
+     * @param function ParamUnivariateFunction
+     * @param nbPoints Number of points for difference calculation
+     * @param stepSize Step size for difference calculation
+     */
+    public MyParametricUnivariateFunction(ParamUnivariateFunction function, int nbPoints, double stepSize) {
+        this.function = function;
+        this.nbPoints = nbPoints;
+        this.stepSize = stepSize;
+    }
+
+    @Override
+    public double value(double v, double... parameters) {
+        function.setParameters(parameters);
+        return function.value(v);
+    }
+
+    @Override
+    public double[] gradient(double v, double... parameters) {
+        function.setParameters(parameters);
+
+        // create a differentiator
+        FiniteDifferencesDifferentiator differentiator =
+                new FiniteDifferencesDifferentiator(nbPoints, stepSize);
+
+        // create a new function that computes both the value and the derivatives
+        // using DerivativeStructure
+        UnivariateDifferentiableFunction diffFunc = differentiator.differentiate(function);
+
+        double y = function.value(v);
+        int n = parameters.length;
+        double[] gradients = new double[n];
+        for (int i = 0; i < n; i++) {
+            DerivativeStructure xDS = new DerivativeStructure(n, 1, i, parameters[i]);
+            DerivativeStructure yDS = diffFunc.value(xDS);
+            int[] idx = new int[n];
+            idx[i] = 1;
+            gradients[i] = yDS.getPartialDerivative(idx);
+        }
+
+        return gradients;
+    }
+}

meteoinfo-math/src/main/java/org/meteoinfo/math/optimize/OptimizeUtil.java

@@ -1,9 +1,12 @@
 package org.meteoinfo.math.optimize;
 
+import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
 import org.apache.commons.math3.analysis.UnivariateFunction;
 import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
 import org.apache.commons.math3.analysis.differentiation.FiniteDifferencesDifferentiator;
 import org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction;
+import org.apache.commons.math3.fitting.SimpleCurveFitter;
+import org.apache.commons.math3.fitting.WeightedObservedPoints;
 import org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction;
 import org.apache.commons.math3.linear.Array2DRowRealMatrix;
 import org.apache.commons.math3.linear.ArrayRealVector;
@@ -104,4 +107,31 @@ public class OptimizeUtil {
 
         return jacobianFunc;
     }
+
+    /**
+     * Get curve fitting parameters.
+     * @param func The uni-variate function
+     * @param x X values
+     * @param y Y values
+     * @param nbPoints Number of points for difference calculation
+     * @param stepSize Step size for difference calculation
+     * @param guess Guess values
+     * @return Curve fitting parameters
+     */
+    public static double[] curveFit(ParamUnivariateFunction func, Array x, Array y,
+                                    int nbPoints, double stepSize, double[] guess) {
+        ParametricUnivariateFunction function = new MyParametricUnivariateFunction(func, nbPoints, stepSize);
+        SimpleCurveFitter curveFitter = SimpleCurveFitter.create(function, guess);
+
+        IndexIterator xIter = x.getIndexIterator();
+        IndexIterator yIter = y.getIndexIterator();
+        WeightedObservedPoints observedPoints = new WeightedObservedPoints();
+        while (xIter.hasNext()){
+            observedPoints.add(xIter.getDoubleNext(), yIter.getDoubleNext());
+        }
+
+        double[] best = curveFitter.fit(observedPoints.toList());
+
+        return best;
+    }
 }