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"version": "3.11.4" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/_downloads/535f32d48be9613371513d85cd515759/plot_sydney_tornado.ipynb b/_downloads/535f32d48be9613371513d85cd515759/plot_sydney_tornado.ipynb index 8f4fc364..72cca501 100644 --- a/_downloads/535f32d48be9613371513d85cd515759/plot_sydney_tornado.ipynb +++ b/_downloads/535f32d48be9613371513d85cd515759/plot_sydney_tornado.ipynb @@ -35,7 +35,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/_downloads/564db4ff92b5949f5865201b6023e7a2/plot_fun_with_constraints.py b/_downloads/564db4ff92b5949f5865201b6023e7a2/plot_fun_with_constraints.py index 26285410..4cb1c209 100644 --- a/_downloads/564db4ff92b5949f5865201b6023e7a2/plot_fun_with_constraints.py +++ b/_downloads/564db4ff92b5949f5865201b6023e7a2/plot_fun_with_constraints.py @@ -34,37 +34,36 @@ berr_grid = pydda.initialization.make_constant_wind_field( berr_grid, (0.0, 0.0, 0.0)) -# Let's make a plot on a map -fig = plt.figure(figsize=(7, 3)) - -pydda.vis.plot_xz_xsection_streamlines( - [cpol_grid, berr_grid], bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8]) -plt.show() - # Let's provide an initial state from the sounding u_back = sounding[1].u_wind v_back = sounding[1].v_wind z_back = sounding[1].height cpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1]) -new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], - +new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], u_back=u_back, v_back=v_back, z_back=z_back, - Co=10.0, Cm=4096.0, frz=5000.0, Cb=1e-6, - mask_outside_opt=False, wind_tol=0.2, + Co=1.0, Cm=64.0, frz=5000.0, Cb=1e-5, + Cx=1e2, Cy=1e2, Cz=1e2, + mask_outside_opt=False, wind_tol=0.1, engine="tensorflow") fig = plt.figure(figsize=(7, 7)) pydda.vis.plot_xz_xsection_streamlines( new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8]) plt.show() + # Let's see what happens when we use a zero initialization +# This causes there to be convergence in the cone of silence +# This is an artifact that we want to avoid! +# Prescribing winds inside the background through either a constraint +# Or through the initial state will help mitigate this issue. cpol_grid = pydda.initialization.make_constant_wind_field( - berr_grid, (0.0, 0.0, 0.0)) + cpol_grid, (0.0, 0.0, 0.0)) new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], u_back=u_back, v_back=v_back, z_back=z_back, - Co=1.0, Cm=128.0, frz=5000.0, Cb=1e-6, - mask_outside_opt=False, wind_tol=0.2, + Co=1.0, Cm=64.0, frz=5000.0, Cb=1e-5, + Cx=1e2, Cy=1e2, Cz=1e2, + mask_outside_opt=False, wind_tol=0.5, engine="tensorflow") fig = plt.figure(figsize=(7, 7)) @@ -74,9 +73,12 @@ plt.show() # Or, let's make the radar data more important! +cpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1]) new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], - Co=100.0, Cm=128.0, frz=5000.0, - mask_outside_opt=False, wind_tol=0.2, + Co=10.0, Cm=64.0, frz=5000.0, + u_back=u_back, v_back=v_back, z_back=z_back, Cb=1e-5, + Cx=1e2, Cy=1e2, Cz=1e2, + mask_outside_opt=False, wind_tol=0.1, engine="tensorflow") fig = plt.figure(figsize=(7, 7)) diff --git a/_downloads/5be7ec654a2b031eb942faf1f6463c7b/read_radar_data-2.png b/_downloads/5be7ec654a2b031eb942faf1f6463c7b/read_radar_data-2.png index 218d4095..ec56addd 100644 Binary files a/_downloads/5be7ec654a2b031eb942faf1f6463c7b/read_radar_data-2.png and b/_downloads/5be7ec654a2b031eb942faf1f6463c7b/read_radar_data-2.png differ diff --git a/_downloads/69088b28623951c8bc5bf24f02f5316d/plot_fun_with_constraints.ipynb b/_downloads/69088b28623951c8bc5bf24f02f5316d/plot_fun_with_constraints.ipynb index c20a0fe8..3989cf7f 100644 --- a/_downloads/69088b28623951c8bc5bf24f02f5316d/plot_fun_with_constraints.ipynb +++ b/_downloads/69088b28623951c8bc5bf24f02f5316d/plot_fun_with_constraints.ipynb @@ -15,7 +15,7 @@ }, "outputs": [], "source": [ - "import pydda\nimport pyart\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\n\n\nberr_grid = pyart.io.read_grid(pydda.tests.EXAMPLE_RADAR0)\ncpol_grid = pyart.io.read_grid(pydda.tests.EXAMPLE_RADAR1)\n\n# Load our radar data\nsounding = pyart.io.read_arm_sonde(\n pydda.tests.SOUNDING_PATH)\nberr_grid = pydda.initialization.make_constant_wind_field(\n berr_grid, (0.0, 0.0, 0.0))\n\n# Let's make a plot on a map\nfig = plt.figure(figsize=(7, 3))\n\npydda.vis.plot_xz_xsection_streamlines(\n [cpol_grid, berr_grid], bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()\n\n# Let's provide an initial state from the sounding\nu_back = sounding[1].u_wind\nv_back = sounding[1].v_wind\nz_back = sounding[1].height\ncpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1])\n\nnew_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],\n \n u_back=u_back, v_back=v_back, z_back=z_back,\n Co=10.0, Cm=4096.0, frz=5000.0, Cb=1e-6,\n mask_outside_opt=False, wind_tol=0.2,\n engine=\"tensorflow\")\nfig = plt.figure(figsize=(7, 7))\n\npydda.vis.plot_xz_xsection_streamlines(\n new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()\n# Let's see what happens when we use a zero initialization\ncpol_grid = pydda.initialization.make_constant_wind_field(\n berr_grid, (0.0, 0.0, 0.0)) \nnew_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],\n u_back=u_back, v_back=v_back, z_back=z_back,\n Co=1.0, Cm=128.0, frz=5000.0, Cb=1e-6,\n mask_outside_opt=False, wind_tol=0.2,\n engine=\"tensorflow\")\n\nfig = plt.figure(figsize=(7, 7))\n\npydda.vis.plot_xz_xsection_streamlines(\n new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()\n\n# Or, let's make the radar data more important!\nnew_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],\n Co=100.0, Cm=128.0, frz=5000.0,\n mask_outside_opt=False, wind_tol=0.2,\n engine=\"tensorflow\")\nfig = plt.figure(figsize=(7, 7))\n\npydda.vis.plot_xz_xsection_streamlines(\n new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()" + "import pydda\nimport pyart\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\n\n\nberr_grid = pyart.io.read_grid(pydda.tests.EXAMPLE_RADAR0)\ncpol_grid = pyart.io.read_grid(pydda.tests.EXAMPLE_RADAR1)\n\n# Load our radar data\nsounding = pyart.io.read_arm_sonde(\n pydda.tests.SOUNDING_PATH)\nberr_grid = pydda.initialization.make_constant_wind_field(\n berr_grid, (0.0, 0.0, 0.0))\n\n# Let's provide an initial state from the sounding\nu_back = sounding[1].u_wind\nv_back = sounding[1].v_wind\nz_back = sounding[1].height\ncpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1])\n\nnew_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], \n u_back=u_back, v_back=v_back, z_back=z_back,\n Co=1.0, Cm=64.0, frz=5000.0, Cb=1e-5,\n Cx=1e2, Cy=1e2, Cz=1e2,\n mask_outside_opt=False, wind_tol=0.1,\n engine=\"tensorflow\")\nfig = plt.figure(figsize=(7, 7))\n\npydda.vis.plot_xz_xsection_streamlines(\n new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()\n\n# Let's see what happens when we use a zero initialization\n# This causes there to be convergence in the cone of silence\n# This is an artifact that we want to avoid!\n# Prescribing winds inside the background through either a constraint\n# Or through the initial state will help mitigate this issue.\ncpol_grid = pydda.initialization.make_constant_wind_field(\n cpol_grid, (0.0, 0.0, 0.0)) \nnew_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],\n u_back=u_back, v_back=v_back, z_back=z_back,\n Co=1.0, Cm=64.0, frz=5000.0, Cb=1e-5,\n Cx=1e2, Cy=1e2, Cz=1e2,\n mask_outside_opt=False, wind_tol=0.5,\n engine=\"tensorflow\")\n\nfig = plt.figure(figsize=(7, 7))\n\npydda.vis.plot_xz_xsection_streamlines(\n new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()\n\n# Or, let's make the radar data more important!\ncpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1])\nnew_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid],\n Co=10.0, Cm=64.0, frz=5000.0,\n u_back=u_back, v_back=v_back, z_back=z_back, Cb=1e-5,\n Cx=1e2, Cy=1e2, Cz=1e2,\n mask_outside_opt=False, wind_tol=0.1,\n engine=\"tensorflow\")\nfig = plt.figure(figsize=(7, 7))\n\npydda.vis.plot_xz_xsection_streamlines(\n new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8])\nplt.show()" ] } ], @@ -35,7 +35,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/_downloads/7417f3bcb6ced8c409e5ef6b289cb6fd/auto_examples_python.zip b/_downloads/7417f3bcb6ced8c409e5ef6b289cb6fd/auto_examples_python.zip index 253f3c47..5c6dec80 100644 Binary files a/_downloads/7417f3bcb6ced8c409e5ef6b289cb6fd/auto_examples_python.zip and b/_downloads/7417f3bcb6ced8c409e5ef6b289cb6fd/auto_examples_python.zip differ diff --git a/_downloads/8d55ead0068800d7c0cc35db6486ea87/dealiasing_velocities-3.png b/_downloads/8d55ead0068800d7c0cc35db6486ea87/dealiasing_velocities-3.png index 47b88fdc..05a729b8 100644 Binary files a/_downloads/8d55ead0068800d7c0cc35db6486ea87/dealiasing_velocities-3.png and b/_downloads/8d55ead0068800d7c0cc35db6486ea87/dealiasing_velocities-3.png differ diff --git a/_downloads/99ef7a02342d24946876f1d3bc3ceef5/read_radar_data-1.hires.png b/_downloads/99ef7a02342d24946876f1d3bc3ceef5/read_radar_data-1.hires.png index 07e9bed0..c7b0450d 100644 Binary files a/_downloads/99ef7a02342d24946876f1d3bc3ceef5/read_radar_data-1.hires.png and b/_downloads/99ef7a02342d24946876f1d3bc3ceef5/read_radar_data-1.hires.png differ diff --git a/_downloads/9b31cb7fac98fb9abfed4664411cf2dd/dealiasing_velocities-2_00.pdf b/_downloads/9b31cb7fac98fb9abfed4664411cf2dd/dealiasing_velocities-2_00.pdf index 01c492b8..b3f22fd8 100644 Binary files a/_downloads/9b31cb7fac98fb9abfed4664411cf2dd/dealiasing_velocities-2_00.pdf and b/_downloads/9b31cb7fac98fb9abfed4664411cf2dd/dealiasing_velocities-2_00.pdf differ diff --git a/_downloads/ab3f1d02845a1733d70fa1920be9f038/gridding-1.pdf b/_downloads/ab3f1d02845a1733d70fa1920be9f038/gridding-1.pdf index 5ca7e0e8..44abf773 100644 Binary files a/_downloads/ab3f1d02845a1733d70fa1920be9f038/gridding-1.pdf and b/_downloads/ab3f1d02845a1733d70fa1920be9f038/gridding-1.pdf differ diff --git a/_downloads/b799f6942d408f2022a569a92a9e9293/dealiasing_velocities-1.png b/_downloads/b799f6942d408f2022a569a92a9e9293/dealiasing_velocities-1.png index 218d4095..ec56addd 100644 Binary files a/_downloads/b799f6942d408f2022a569a92a9e9293/dealiasing_velocities-1.png and b/_downloads/b799f6942d408f2022a569a92a9e9293/dealiasing_velocities-1.png differ diff --git a/_downloads/c002c4e3b5a34046abf989fddabc88ca/dealiasing_velocities-1.hires.png b/_downloads/c002c4e3b5a34046abf989fddabc88ca/dealiasing_velocities-1.hires.png index 62e8ca8f..c9ad8b99 100644 Binary files a/_downloads/c002c4e3b5a34046abf989fddabc88ca/dealiasing_velocities-1.hires.png and b/_downloads/c002c4e3b5a34046abf989fddabc88ca/dealiasing_velocities-1.hires.png differ diff --git a/_downloads/c653c7e4e1032b8be12e5549d43bd274/plot_examples.ipynb b/_downloads/c653c7e4e1032b8be12e5549d43bd274/plot_examples.ipynb index 66108110..d9a92edb 100644 --- a/_downloads/c653c7e4e1032b8be12e5549d43bd274/plot_examples.ipynb +++ b/_downloads/c653c7e4e1032b8be12e5549d43bd274/plot_examples.ipynb @@ -35,7 +35,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - 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Overview: module code — PyDDA 1.3.1 documentation + Overview: module code — PyDDA 1.4.0 documentation diff --git a/_modules/pydda/constraints/model_data.html b/_modules/pydda/constraints/model_data.html index b9fb9190..6cf45318 100644 --- a/_modules/pydda/constraints/model_data.html +++ b/_modules/pydda/constraints/model_data.html @@ -5,7 +5,7 @@ - pydda.constraints.model_data — PyDDA 1.3.1 documentation + pydda.constraints.model_data — PyDDA 1.4.0 documentation diff --git a/_modules/pydda/constraints/station_data.html b/_modules/pydda/constraints/station_data.html index 494b17cc..1b1ea094 100644 --- a/_modules/pydda/constraints/station_data.html +++ b/_modules/pydda/constraints/station_data.html @@ -5,7 +5,7 @@ - pydda.constraints.station_data — PyDDA 1.3.1 documentation + pydda.constraints.station_data — PyDDA 1.4.0 documentation diff --git a/_modules/pydda/cost_functions/_cost_functions_numpy.html b/_modules/pydda/cost_functions/_cost_functions_numpy.html index 3b64ffc8..1081ef99 100644 --- a/_modules/pydda/cost_functions/_cost_functions_numpy.html +++ b/_modules/pydda/cost_functions/_cost_functions_numpy.html @@ -5,7 +5,7 @@ - pydda.cost_functions._cost_functions_numpy — PyDDA 1.3.1 documentation + pydda.cost_functions._cost_functions_numpy — PyDDA 1.4.0 documentation @@ -364,15 +364,15 @@

Source code for pydda.cost_functions._cost_functions_numpy

grad_u = np.zeros(w.shape) grad_v = np.zeros(w.shape) grad_w = np.zeros(w.shape) - scipy.ndimage.filters.laplace(u, du, mode='wrap') - scipy.ndimage.filters.laplace(v, dv, mode='wrap') - scipy.ndimage.filters.laplace(w, dw, mode='wrap') + scipy.ndimage.laplace(u, du, mode='wrap') + scipy.ndimage.laplace(v, dv, mode='wrap') + scipy.ndimage.laplace(w, dw, mode='wrap') du = du / dx dv = dv / dy dw = dw / dz - scipy.ndimage.filters.laplace(du, grad_u, mode='wrap') - scipy.ndimage.filters.laplace(dv, grad_v, mode='wrap') - scipy.ndimage.filters.laplace(dw, grad_w, mode='wrap') + scipy.ndimage.laplace(du, grad_u, mode='wrap') + scipy.ndimage.laplace(dv, grad_v, mode='wrap') + scipy.ndimage.laplace(dw, grad_w, mode='wrap') grad_u = grad_u / dx grad_v = grad_v / dy grad_w = grad_w / dz @@ -761,7 +761,7 @@

Source code for pydda.cost_functions._cost_functions_numpy

[docs]def calculate_vertical_vorticity_gradient(u, v, w, dx, dy, dz, Ut, Vt, - coeff=1e-5): + coeff=1e-5, upper_bc=True): """ Calculates the gradient of the cost function due to deviance from vertical vorticity equation. This is done by taking the functional derivative of @@ -852,7 +852,11 @@

Source code for pydda.cost_functions._cost_functions_numpy

u_grad = u_grad * 2 * dzeta_dt * coeff v_grad = v_grad * 2 * dzeta_dt * coeff w_grad = w_grad * 2 * dzeta_dt * coeff - + + # Impermeability condition + w_grad[0, :, :] = 0 + if(upper_bc is True): + w_grad[-1, :, :] = 0 y = np.stack([u_grad, v_grad, w_grad], axis=0) return y.flatten()
diff --git a/_modules/pydda/cost_functions/cost_functions.html b/_modules/pydda/cost_functions/cost_functions.html index 3d0a741a..9f71b142 100644 --- a/_modules/pydda/cost_functions/cost_functions.html +++ b/_modules/pydda/cost_functions/cost_functions.html @@ -5,7 +5,7 @@ - pydda.cost_functions.cost_functions — PyDDA 1.3.1 documentation + pydda.cost_functions.cost_functions — PyDDA 1.4.0 documentation @@ -486,7 +486,7 @@

Source code for pydda.cost_functions.cost_functions

grad += _cost_functions_numpy.calculate_vertical_vorticity_gradient( winds[0], winds[1], winds[2], parameters.dx, parameters.dy, parameters.dz, parameters.Ut, - parameters.Vt, coeff=parameters.Cv, upper_bc=parameters.upper_bc).numpy() + parameters.Vt, coeff=parameters.Cv, upper_bc=parameters.upper_bc) if (parameters.Cmod > 0): grad += _cost_functions_numpy.calculate_model_gradient( diff --git a/_modules/pydda/initialization/wind_fields.html b/_modules/pydda/initialization/wind_fields.html index 3345a272..1a3c8506 100644 --- a/_modules/pydda/initialization/wind_fields.html +++ b/_modules/pydda/initialization/wind_fields.html @@ -5,7 +5,7 @@ - pydda.initialization.wind_fields — PyDDA 1.3.1 documentation + pydda.initialization.wind_fields — PyDDA 1.4.0 documentation diff --git a/_modules/pydda/retrieval/wind_retrieve.html b/_modules/pydda/retrieval/wind_retrieve.html index 6cd5b4f0..2ad98b2b 100644 --- a/_modules/pydda/retrieval/wind_retrieve.html +++ b/_modules/pydda/retrieval/wind_retrieve.html @@ -5,7 +5,7 @@ - pydda.retrieval.wind_retrieve — PyDDA 1.3.1 documentation + pydda.retrieval.wind_retrieve — PyDDA 1.4.0 documentation @@ -517,11 +517,11 @@

Source code for pydda.retrieval.wind_retrieve

~parameters.azs[j][k].mask, ~parameters.els[j][k].mask)) cur_array = parameters.bg_weights[k] - cur_array[np.logical_or( + cur_array[np.logical_or.reduce((~valid, bca[i, j] < math.radians(min_bca), - bca[i, j] > math.radians(max_bca))] = 1 + bca[i, j] > math.radians(max_bca)))] = 1 cur_array[~valid] = 1 - parameters.bg_weights[i] = cur_array + parameters.bg_weights[i] += cur_array else: parameters.bg_weights[i] = weights_bg[i] @@ -751,7 +751,7 @@

Source code for pydda.retrieval.wind_retrieve

Ut=None, Vt=None, low_pass_filter=True, mask_outside_opt=False, weights_obs=None, weights_model=None, weights_bg=None, - max_iterations=1000, mask_w_outside_opt=True, + max_iterations=200, mask_w_outside_opt=True, filter_window=5, filter_order=3, min_bca=30.0, max_bca=150.0, upper_bc=True, model_fields=None, output_cost_functions=True, roi=1000.0, lower_bc=True, @@ -947,11 +947,11 @@

Source code for pydda.retrieval.wind_retrieve

~parameters.wts[j][k].mask, ~parameters.azs[j][k].mask, ~parameters.els[j][k].mask)) - cur_array[np.logical_or( + cur_array[np.logical_or.reduce((~valid, bca[i, j] < math.radians(min_bca), - bca[i, j] > math.radians(max_bca))] = 1 + bca[i, j] > math.radians(max_bca)))] = 1 cur_array[~valid] = 1 - parameters.bg_weights[i] = cur_array + parameters.bg_weights[i] += cur_array else: parameters.bg_weights[i] = weights_bg[i] @@ -1052,10 +1052,11 @@

Source code for pydda.retrieval.wind_retrieve

parameters.points = points parameters.point_list = points loss_and_gradient = lambda x: (J_function(x, parameters), grad_J(x, parameters)) - tolerance = 1e-6 * (Co + Cm + Cx + Cy + Cz + Cb + Cv) + winds = tfp.optimizer.lbfgs_minimize( loss_and_gradient, initial_position=winds, - tolerance=tolerance, x_tolerance=wind_tol, + f_relative_tolerance=1e-3, + tolerance=1e-3, x_tolerance=wind_tol, max_iterations=max_iterations, parallel_iterations=parallel_iterations) winds = np.reshape( winds.position.numpy(), (3, parameters.grid_shape[0], parameters.grid_shape[1], parameters.grid_shape[2])) diff --git a/_modules/pydda/vis/barb_plot.html b/_modules/pydda/vis/barb_plot.html index 87528d54..b7c1c9c5 100644 --- a/_modules/pydda/vis/barb_plot.html +++ b/_modules/pydda/vis/barb_plot.html @@ -5,7 +5,7 @@ - pydda.vis.barb_plot — PyDDA 1.3.1 documentation + pydda.vis.barb_plot — PyDDA 1.4.0 documentation diff --git a/_modules/pydda/vis/quiver_plot.html b/_modules/pydda/vis/quiver_plot.html index 216d2cbd..891cdbcc 100644 --- a/_modules/pydda/vis/quiver_plot.html +++ b/_modules/pydda/vis/quiver_plot.html @@ -5,7 +5,7 @@ - pydda.vis.quiver_plot — PyDDA 1.3.1 documentation + pydda.vis.quiver_plot — PyDDA 1.4.0 documentation diff --git a/_modules/pydda/vis/streamline_plot.html b/_modules/pydda/vis/streamline_plot.html index 0c1e7ffa..f0f324df 100644 --- a/_modules/pydda/vis/streamline_plot.html +++ b/_modules/pydda/vis/streamline_plot.html @@ -5,7 +5,7 @@ - pydda.vis.streamline_plot — PyDDA 1.3.1 documentation + pydda.vis.streamline_plot — PyDDA 1.4.0 documentation diff --git a/_sources/source/auto_examples/hurricane_florence.rst.txt b/_sources/source/auto_examples/hurricane_florence.rst.txt index 7467329b..e434264b 100644 --- a/_sources/source/auto_examples/hurricane_florence.rst.txt +++ b/_sources/source/auto_examples/hurricane_florence.rst.txt @@ -86,7 +86,7 @@ Author: Robert C. Jackson .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.000 seconds) + **Total running time of the script:** (0 minutes 0.000 seconds) .. _sphx_glr_download_source_auto_examples_hurricane_florence.py: diff --git a/_sources/source/auto_examples/plot_examples.rst.txt b/_sources/source/auto_examples/plot_examples.rst.txt index 40a6201e..750c807b 100644 --- a/_sources/source/auto_examples/plot_examples.rst.txt +++ b/_sources/source/auto_examples/plot_examples.rst.txt @@ -78,12 +78,12 @@ Author: Robert C. Jackson The max of w_init is 18.516070425591412 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 0| 194.0315| 22.8134| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 18.5161 - The gradient of the cost functions is 3.237636 + The gradient of the cost functions is 3.2376356 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 10| 7.4545| 24.7059| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 18.5157 - The gradient of the cost functions is 0.15156192 + The gradient of the cost functions is 0.15156189 Applying low pass filter to wind field... - Done! Time = 7.1 + Done! Time = 9.5 /home/runner/work/PyDDA/PyDDA/pydda/vis/barb_plot.py:213: UserWarning: The following kwargs were not used by contour: 'color' ax.contour( /home/runner/work/PyDDA/PyDDA/pydda/vis/barb_plot.py:213: UserWarning: The following kwargs were not used by contour: 'color' @@ -150,7 +150,7 @@ Author: Robert C. Jackson .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 9.125 seconds) + **Total running time of the script:** (0 minutes 11.917 seconds) .. _sphx_glr_download_source_auto_examples_plot_examples.py: diff --git a/_sources/source/auto_examples/plot_fun_with_constraints.rst.txt b/_sources/source/auto_examples/plot_fun_with_constraints.rst.txt index badf4fc4..61c4c0d5 100644 --- a/_sources/source/auto_examples/plot_fun_with_constraints.rst.txt +++ b/_sources/source/auto_examples/plot_fun_with_constraints.rst.txt @@ -36,7 +36,7 @@ background will likely result in false regions of convergence and divergence that will generate artificial updrafts and downdrafts at the edges of data coverage. -.. GENERATED FROM PYTHON SOURCE LINES 21-86 +.. GENERATED FROM PYTHON SOURCE LINES 21-88 @@ -64,13 +64,6 @@ at the edges of data coverage. :srcset: /source/auto_examples/images/sphx_glr_plot_fun_with_constraints_003.png :class: sphx-glr-multi-img - * - - .. image-sg:: /source/auto_examples/images/sphx_glr_plot_fun_with_constraints_004.png - :alt: PyDDA retreived winds @10.0 km south of origin. - :srcset: /source/auto_examples/images/sphx_glr_plot_fun_with_constraints_004.png - :class: sphx-glr-multi-img - .. rst-class:: sphx-glr-script-out @@ -119,882 +112,140 @@ at the edges of data coverage. Total points: 81194 The max of w_init is 0.0 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 0|559642.2500| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000 - The gradient of the cost functions is 158.62973 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 10|26130.5117| 382.9626| 0.0000|4071.4351| 0.0000| 0.0000| 0.0000| 15.5264 - The gradient of the cost functions is 36.597107 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 20|18971.3613| 490.3067| 0.0000|3393.5815| 0.0000| 0.0000| 0.0000| 14.6217 - The gradient of the cost functions is 17.10828 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 30|18032.9668| 489.6299| 0.0000|3430.0486| 0.0000| 0.0000| 0.0000| 14.4372 - The gradient of the cost functions is 59.34763 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 40|17750.8301| 466.1873| 0.0000|3332.8171| 0.0000| 0.0000| 0.0000| 18.8427 - The gradient of the cost functions is 18.293718 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 50|16674.0078| 374.4199| 0.0000|3073.3652| 0.0000| 0.0000| 0.0000| 64.4673 - The gradient of the cost functions is 33.5473 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 60|15543.4268| 306.3219| 0.0000|2838.9236| 0.0000| 0.0000| 0.0000| 122.3628 - The gradient of the cost functions is 17.475016 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 70|14607.2051| 286.1732| 0.0000|2732.7402| 0.0000| 0.0000| 0.0000| 163.7126 - The gradient of the cost functions is 73.82053 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 80|14214.8330| 282.7234| 0.0000|2659.4236| 0.0000| 0.0000| 0.0000| 182.8911 - The gradient of the cost functions is 11.078694 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 90|13688.3906| 276.4021| 0.0000|2531.6599| 0.0000| 0.0000| 0.0000| 209.6049 - The gradient of the cost functions is 16.031052 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 100|13409.4785| 272.0520| 0.0000|2486.3435| 0.0000| 0.0000| 0.0000| 223.4294 - The gradient of the cost functions is 60.612865 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 110|13153.8242| 264.4493| 0.0000|2446.9255| 0.0000| 0.0000| 0.0000| 234.6842 - The gradient of the cost functions is 9.253555 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 120|12844.2100| 249.1004| 0.0000|2376.6252| 0.0000| 0.0000| 0.0000| 247.2666 - The gradient of the cost functions is 7.925502 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 130|12558.0078| 234.9919| 0.0000|2339.0354| 0.0000| 0.0000| 0.0000| 261.2097 - The gradient of the cost functions is 8.595217 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 140|12395.8438| 227.3731| 0.0000|2312.0710| 0.0000| 0.0000| 0.0000| 266.9670 - The gradient of the cost functions is 8.846944 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 150|12205.1846| 213.5754| 0.0000|2271.5303| 0.0000| 0.0000| 0.0000| 281.1895 - The gradient of the cost functions is 7.5795045 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 160|12030.6328| 202.7401| 0.0000|2245.4678| 0.0000| 0.0000| 0.0000| 293.4454 - The gradient of the cost functions is 44.360565 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 170|11914.6406| 195.1001| 0.0000|2227.8098| 0.0000| 0.0000| 0.0000| 301.5674 - The gradient of the cost functions is 6.0733347 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 180|11780.9883| 187.9669| 0.0000|2200.1680| 0.0000| 0.0000| 0.0000| 310.4844 - The gradient of the cost functions is 5.0861745 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 190|11702.7188| 183.5135| 0.0000|2190.3638| 0.0000| 0.0000| 0.0000| 323.7044 - The gradient of the cost functions is 24.117205 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 200|11618.0820| 181.2371| 0.0000|2176.6062| 0.0000| 0.0000| 0.0000| 328.0698 - The gradient of the cost functions is 5.057768 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 210|11527.6621| 176.3502| 0.0000|2157.7363| 0.0000| 0.0000| 0.0000| 337.9010 - The gradient of the cost functions is 4.367203 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 220|11466.2441| 171.4325| 0.0000|2152.9272| 0.0000| 0.0000| 0.0000| 353.7270 - The gradient of the cost functions is 23.97642 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 230|11408.8564| 169.0426| 0.0000|2142.2344| 0.0000| 0.0000| 0.0000| 359.7861 - The gradient of the cost functions is 4.1765084 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 240|11344.6182| 164.6061| 0.0000|2128.6104| 0.0000| 0.0000| 0.0000| 373.1705 - The gradient of the cost functions is 3.579382 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 250|11311.6113| 159.5042| 0.0000|2133.1208| 0.0000| 0.0000| 0.0000| 389.1096 - The gradient of the cost functions is 12.58042 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 260|11253.5547| 157.3535| 0.0000|2121.1335| 0.0000| 0.0000| 0.0000| 392.9562 - The gradient of the cost functions is 3.5444481 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 270|11212.0059| 153.3006| 0.0000|2109.1160| 0.0000| 0.0000| 0.0000| 403.4438 - The gradient of the cost functions is 2.7914727 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 280|11206.2637| 148.4919| 0.0000|2141.8188| 0.0000| 0.0000| 0.0000| 420.2546 - The gradient of the cost functions is 8.83588 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 290|11139.7305| 147.3608| 0.0000|2102.7249| 0.0000| 0.0000| 0.0000| 421.0657 - The gradient of the cost functions is 3.052596 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 300|11107.9668| 144.4033| 0.0000|2094.8997| 0.0000| 0.0000| 0.0000| 429.3805 - The gradient of the cost functions is 2.53795 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 310|11101.9746| 140.4870| 0.0000|2104.9795| 0.0000| 0.0000| 0.0000| 441.7443 - The gradient of the cost functions is 7.285406 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 320|11053.8779| 139.4210| 0.0000|2089.3899| 0.0000| 0.0000| 0.0000| 443.3749 - The gradient of the cost functions is 2.6200957 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 330|11030.0352| 137.0582| 0.0000|2084.2024| 0.0000| 0.0000| 0.0000| 449.5375 - The gradient of the cost functions is 2.2863402 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 340|11040.7451| 132.7930| 0.0000|2128.9448| 0.0000| 0.0000| 0.0000| 461.0066 - The gradient of the cost functions is 4.433825 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 350|10983.3838| 131.7672| 0.0000|2079.5496| 0.0000| 0.0000| 0.0000| 461.3635 - The gradient of the cost functions is 2.0171044 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 360|10958.8496| 129.0043| 0.0000|2076.9944| 0.0000| 0.0000| 0.0000| 467.4312 - The gradient of the cost functions is 14.222207 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 370|10946.9619| 127.7839| 0.0000|2074.1357| 0.0000| 0.0000| 0.0000| 470.0288 - The gradient of the cost functions is 3.4488196 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 380|10929.8887| 125.5376| 0.0000|2071.2871| 0.0000| 0.0000| 0.0000| 473.5926 - The gradient of the cost functions is 2.0516195 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 390|10910.0254| 122.9260| 0.0000|2071.0906| 0.0000| 0.0000| 0.0000| 477.3062 - The gradient of the cost functions is 1.6888528 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 400|10892.6465| 120.9352| 0.0000|2068.0271| 0.0000| 0.0000| 0.0000| 480.2476 - The gradient of the cost functions is 1.9005245 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 410|10883.0908| 119.7815| 0.0000|2065.7810| 0.0000| 0.0000| 0.0000| 481.6053 - The gradient of the cost functions is 2.905435 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 420|10867.9629| 117.7534| 0.0000|2065.3037| 0.0000| 0.0000| 0.0000| 483.3615 - The gradient of the cost functions is 2.4602866 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 430|10919.5449| 114.4655| 0.0000|2090.1213| 0.0000| 0.0000| 0.0000| 485.8299 - The gradient of the cost functions is 1.8027492 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 440|10845.7627| 114.7618| 0.0000|2061.2048| 0.0000| 0.0000| 0.0000| 484.8903 - The gradient of the cost functions is 2.4616132 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 450|10835.4004| 113.0963| 0.0000|2060.6550| 0.0000| 0.0000| 0.0000| 485.0492 - The gradient of the cost functions is 2.1645174 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 460|10873.3711| 110.0305| 0.0000|2075.6191| 0.0000| 0.0000| 0.0000| 484.2643 - The gradient of the cost functions is 1.6988244 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 470|10817.5645| 110.2706| 0.0000|2058.2441| 0.0000| 0.0000| 0.0000| 484.3195 - The gradient of the cost functions is 2.082442 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 480|10809.7061| 108.5818| 0.0000|2056.8577| 0.0000| 0.0000| 0.0000| 483.2464 - The gradient of the cost functions is 1.8734012 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 490|10847.9697| 105.5392| 0.0000|2073.2705| 0.0000| 0.0000| 0.0000| 519.9066 - The gradient of the cost functions is 1.7822801 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 500|10791.7588| 105.8412| 0.0000|2055.7520| 0.0000| 0.0000| 0.0000| 508.5755 - The gradient of the cost functions is 2.142732 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 510|10785.5029| 104.5250| 0.0000|2054.2161| 0.0000| 0.0000| 0.0000| 522.4568 - The gradient of the cost functions is 11.019128 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 520|10776.9775| 103.3231| 0.0000|2054.9133| 0.0000| 0.0000| 0.0000| 536.1901 - The gradient of the cost functions is 1.3536065 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 530|10769.2969| 101.9834| 0.0000|2053.3628| 0.0000| 0.0000| 0.0000| 552.9108 - The gradient of the cost functions is 2.1077852 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 540|10764.5488| 101.2346| 0.0000|2053.2439| 0.0000| 0.0000| 0.0000| 562.6788 - The gradient of the cost functions is 9.516924 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 550|10757.6582| 100.1041| 0.0000|2053.6755| 0.0000| 0.0000| 0.0000| 576.1837 - The gradient of the cost functions is 1.7056437 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 560|10751.8145| 98.6677| 0.0000|2051.2102| 0.0000| 0.0000| 0.0000| 594.4660 - The gradient of the cost functions is 1.6324582 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 570|10745.4531| 97.6584| 0.0000|2051.0825| 0.0000| 0.0000| 0.0000| 609.3251 - The gradient of the cost functions is 1.5362489 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 580|10741.3320| 96.9189| 0.0000|2050.7104| 0.0000| 0.0000| 0.0000| 619.3764 - The gradient of the cost functions is 1.7649353 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 590|10735.8760| 95.6771| 0.0000|2050.5342| 0.0000| 0.0000| 0.0000| 635.5566 - The gradient of the cost functions is 1.6610092 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 600|10730.2051| 94.3897| 0.0000|2049.4216| 0.0000| 0.0000| 0.0000| 654.1328 - The gradient of the cost functions is 1.3871174 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 610|10722.4570| 92.9304| 0.0000|2064.3171| 0.0000| 0.0000| 0.0000| 678.1376 - The gradient of the cost functions is 1.8017836 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 620|10720.9463| 92.5482| 0.0000|2048.7144| 0.0000| 0.0000| 0.0000| 683.3580 - The gradient of the cost functions is 1.4873265 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 630|10717.4121| 91.8133| 0.0000|2048.0203| 0.0000| 0.0000| 0.0000| 695.4266 - The gradient of the cost functions is 1.1926334 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 640|10709.7031| 90.5993| 0.0000|2060.5996| 0.0000| 0.0000| 0.0000| 716.6245 - The gradient of the cost functions is 1.745252 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 650|10709.7891| 90.1270| 0.0000|2047.5027| 0.0000| 0.0000| 0.0000| 724.1767 - The gradient of the cost functions is 1.4849939 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 660|10705.4414| 89.3377| 0.0000|2047.4451| 0.0000| 0.0000| 0.0000| 738.3028 - The gradient of the cost functions is 1.0738133 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 670|10704.5010| 88.2152| 0.0000|2051.5027| 0.0000| 0.0000| 0.0000| 759.6812 - The gradient of the cost functions is 1.8913196 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 680|10699.2188| 87.9206| 0.0000|2046.9883| 0.0000| 0.0000| 0.0000| 764.6730 - The gradient of the cost functions is 1.4995098 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 690|10695.9053| 87.2385| 0.0000|2046.6597| 0.0000| 0.0000| 0.0000| 777.6731 - The gradient of the cost functions is 0.92461616 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 700|10689.7773| 86.1343| 0.0000|2051.8064| 0.0000| 0.0000| 0.0000| 798.1397 - The gradient of the cost functions is 2.9122999 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 710|10690.0176| 85.6590| 0.0000|2045.8872| 0.0000| 0.0000| 0.0000| 807.2756 - The gradient of the cost functions is 1.3347957 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 720|10686.4980| 85.0519| 0.0000|2046.1754| 0.0000| 0.0000| 0.0000| 819.9306 - The gradient of the cost functions is 0.8317763 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 730|10679.3789| 84.2320| 0.0000|2053.1536| 0.0000| 0.0000| 0.0000| 837.5147 - The gradient of the cost functions is 2.5424666 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 740|10682.4189| 83.8359| 0.0000|2045.1057| 0.0000| 0.0000| 0.0000| 844.9264 - The gradient of the cost functions is 1.3744177 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 750|10679.6074| 83.1743| 0.0000|2044.9076| 0.0000| 0.0000| 0.0000| 858.2534 - The gradient of the cost functions is 0.7647624 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 760|10675.8154| 82.4411| 0.0000|2046.5983| 0.0000| 0.0000| 0.0000| 874.2782 - The gradient of the cost functions is 5.2374773 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 770|10674.1406| 81.8981| 0.0000|2045.1064| 0.0000| 0.0000| 0.0000| 885.9450 - The gradient of the cost functions is 1.3845519 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 780|10671.3477| 81.1545| 0.0000|2044.5247| 0.0000| 0.0000| 0.0000| 902.4760 - The gradient of the cost functions is 0.63905597 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 790|10670.6533| 80.6369| 0.0000|2043.7048| 0.0000| 0.0000| 0.0000| 914.8401 - The gradient of the cost functions is 3.331802 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 800|10667.9229| 80.3048| 0.0000|2044.4139| 0.0000| 0.0000| 0.0000| 922.2248 - The gradient of the cost functions is 1.33868 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 810|10665.6816| 79.6507| 0.0000|2044.0406| 0.0000| 0.0000| 0.0000| 936.0526 - The gradient of the cost functions is 0.6999946 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 820|10663.2051| 78.9984| 0.0000|2044.4921| 0.0000| 0.0000| 0.0000| 950.9426 - The gradient of the cost functions is 5.55527 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 830|10661.9775| 78.5999| 0.0000|2043.6938| 0.0000| 0.0000| 0.0000| 960.4744 - The gradient of the cost functions is 1.1979549 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 840|10659.3027| 78.0049| 0.0000|2043.9314| 0.0000| 0.0000| 0.0000| 975.0164 - The gradient of the cost functions is 1.179098 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 850|10657.7012| 77.5594| 0.0000|2043.5354| 0.0000| 0.0000| 0.0000| 986.6296 - The gradient of the cost functions is 1.1366893 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 860|10656.1680| 77.0610| 0.0000|2043.0460| 0.0000| 0.0000| 0.0000| 998.9828 - The gradient of the cost functions is 0.7009634 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 870|10654.5938| 76.6607| 0.0000|2043.0972| 0.0000| 0.0000| 0.0000|1007.9814 - The gradient of the cost functions is 0.7200936 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 880|10652.6660| 76.1213| 0.0000|2043.3247| 0.0000| 0.0000| 0.0000|1019.9363 - The gradient of the cost functions is 1.0226402 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 890|10651.0820| 75.5495| 0.0000|2043.0909| 0.0000| 0.0000| 0.0000|1032.6171 - The gradient of the cost functions is 0.87161237 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 900|10656.8203| 74.6436| 0.0000|2045.0024| 0.0000| 0.0000| 0.0000|1055.9175 - The gradient of the cost functions is 0.7668628 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 910|10648.3799| 74.6978| 0.0000|2042.7023| 0.0000| 0.0000| 0.0000|1052.2004 - The gradient of the cost functions is 0.95730007 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 920|10646.9561| 74.1494| 0.0000|2042.5734| 0.0000| 0.0000| 0.0000|1064.6730 - The gradient of the cost functions is 0.83189625 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 930|10649.8594| 73.3394| 0.0000|2047.0575| 0.0000| 0.0000| 0.0000|1083.7124 - The gradient of the cost functions is 0.6575666 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 940|10644.9492| 73.4518| 0.0000|2042.2782| 0.0000| 0.0000| 0.0000|1079.6584 - The gradient of the cost functions is 0.94137937 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 950|10643.6230| 72.9147| 0.0000|2042.2258| 0.0000| 0.0000| 0.0000|1091.5248 - The gradient of the cost functions is 0.714766 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 960|10646.7910| 72.0780| 0.0000|2045.7407| 0.0000| 0.0000| 0.0000|1111.0836 - The gradient of the cost functions is 0.58593243 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 970|10641.6543| 72.2505| 0.0000|2042.0903| 0.0000| 0.0000| 0.0000|1105.4153 - The gradient of the cost functions is 0.80744654 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 980|10640.7178| 71.8421| 0.0000|2041.9919| 0.0000| 0.0000| 0.0000|1114.2916 - The gradient of the cost functions is 0.79510343 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 990|10645.6436| 70.9568| 0.0000|2044.7820| 0.0000| 0.0000| 0.0000|1135.5911 - The gradient of the cost functions is 0.6097142 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1000|10638.6338| 71.0943| 0.0000|2041.8427| 0.0000| 0.0000| 0.0000|1129.7869 - The gradient of the cost functions is 0.7340976 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1010|10637.9658| 70.6705| 0.0000|2041.6321| 0.0000| 0.0000| 0.0000|1138.0632 - The gradient of the cost functions is 0.61940575 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1020|10639.1562| 69.8994| 0.0000|2045.7405| 0.0000| 0.0000| 0.0000|1154.3678 - The gradient of the cost functions is 0.6288717 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1030|10636.0693| 70.0033| 0.0000|2041.7260| 0.0000| 0.0000| 0.0000|1150.6057 - The gradient of the cost functions is 0.6279401 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1040|10635.4668| 69.5915| 0.0000|2041.5093| 0.0000| 0.0000| 0.0000|1158.2736 - The gradient of the cost functions is 0.571083 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1050|10634.8867| 69.2433| 0.0000|2041.2283| 0.0000| 0.0000| 0.0000|1164.6986 - The gradient of the cost functions is 3.490645 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1060|10633.5938| 68.9650| 0.0000|2041.8972| 0.0000| 0.0000| 0.0000|1169.2979 - The gradient of the cost functions is 0.5035049 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1070|10633.2129| 68.5565| 0.0000|2041.4248| 0.0000| 0.0000| 0.0000|1175.7424 - The gradient of the cost functions is 0.924977 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1080|10632.4688| 68.1865| 0.0000|2041.4889| 0.0000| 0.0000| 0.0000|1181.6189 - The gradient of the cost functions is 0.56707925 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1090|10631.9395| 67.7113| 0.0000|2041.1318| 0.0000| 0.0000| 0.0000|1189.1063 - The gradient of the cost functions is 0.47594053 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1100|10631.1689| 67.3689| 0.0000|2041.2860| 0.0000| 0.0000| 0.0000|1193.8195 - The gradient of the cost functions is 0.49741808 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1110|10630.6328| 67.0295| 0.0000|2041.2100| 0.0000| 0.0000| 0.0000|1198.5792 - The gradient of the cost functions is 0.5101925 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1120|10630.1270| 66.6607| 0.0000|2041.0813| 0.0000| 0.0000| 0.0000|1203.5787 - The gradient of the cost functions is 0.5081533 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1130|10629.3496| 66.2471| 0.0000|2041.2825| 0.0000| 0.0000| 0.0000|1208.4283 - The gradient of the cost functions is 0.46946263 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1140|10628.6562| 65.8749| 0.0000|2041.4176| 0.0000| 0.0000| 0.0000|1212.7236 - The gradient of the cost functions is 0.59045756 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1150|10628.5742| 65.6032| 0.0000|2041.0533| 0.0000| 0.0000| 0.0000|1215.9670 - The gradient of the cost functions is 0.5714786 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1160|10627.9619| 65.3349| 0.0000|2041.2335| 0.0000| 0.0000| 0.0000|1218.7728 - The gradient of the cost functions is 0.44572777 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1170|10629.4502| 64.8495| 0.0000|2040.6622| 0.0000| 0.0000| 0.0000|1223.5768 - The gradient of the cost functions is 0.6922104 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1180|10627.6328| 64.7019| 0.0000|2040.7825| 0.0000| 0.0000| 0.0000|1224.9270 - The gradient of the cost functions is 0.56742215 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1190|10626.9150| 64.4253| 0.0000|2041.0424| 0.0000| 0.0000| 0.0000|1227.5894 - The gradient of the cost functions is 0.4061697 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1200|10625.0010| 63.9926| 0.0000|2043.7045| 0.0000| 0.0000| 0.0000|1231.3690 - The gradient of the cost functions is 0.6763766 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1210|10626.4473| 63.8968| 0.0000|2040.9115| 0.0000| 0.0000| 0.0000|1231.9764 - The gradient of the cost functions is 0.5921245 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1220|10626.1670| 63.6246| 0.0000|2040.8320| 0.0000| 0.0000| 0.0000|1233.8647 - The gradient of the cost functions is 0.35100925 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1230|10624.5801| 63.1773| 0.0000|2042.5957| 0.0000| 0.0000| 0.0000|1236.8035 - The gradient of the cost functions is 0.9322826 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1240|10625.5420| 63.0669| 0.0000|2040.9187| 0.0000| 0.0000| 0.0000|1237.4135 - The gradient of the cost functions is 0.4824558 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1250|10625.3945| 62.8712| 0.0000|2040.8368| 0.0000| 0.0000| 0.0000|1238.4135 - The gradient of the cost functions is 0.41266543 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1260|10624.5879| 62.3781| 0.0000|2042.1624| 0.0000| 0.0000| 0.0000|1240.7235 - The gradient of the cost functions is 1.0202751 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1270|10624.8057| 62.2190| 0.0000|2040.8079| 0.0000| 0.0000| 0.0000|1241.3856 - The gradient of the cost functions is 0.439119 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1280|10624.6055| 62.0214| 0.0000|2040.7866| 0.0000| 0.0000| 0.0000|1242.1270 - The gradient of the cost functions is 0.28079566 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1290|10623.9160| 61.7604| 0.0000|2041.4047| 0.0000| 0.0000| 0.0000|1242.8746 - The gradient of the cost functions is 1.7850564 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1300|10624.1338| 61.5451| 0.0000|2040.8348| 0.0000| 0.0000| 0.0000|1243.3900 - The gradient of the cost functions is 0.47505078 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1310|10623.9668| 61.2932| 0.0000|2040.7394| 0.0000| 0.0000| 0.0000|1243.8705 - The gradient of the cost functions is 0.2510506 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1320|10623.4219| 61.0686| 0.0000|2041.2545| 0.0000| 0.0000| 0.0000|1244.1113 - The gradient of the cost functions is 1.1773826 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1330|10623.7090| 60.8907| 0.0000|2040.6932| 0.0000| 0.0000| 0.0000|1244.1866 - The gradient of the cost functions is 0.48085508 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1340|10623.4238| 60.6403| 0.0000|2040.7552| 0.0000| 0.0000| 0.0000|1244.1694 - The gradient of the cost functions is 0.23485608 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1350|10623.0713| 60.4177| 0.0000|2041.0356| 0.0000| 0.0000| 0.0000|1244.0037 - The gradient of the cost functions is 1.8009061 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1360|10623.2334| 60.2553| 0.0000|2040.7089| 0.0000| 0.0000| 0.0000|1243.7792 - The gradient of the cost functions is 0.45986655 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1370|10622.9121| 59.9950| 0.0000|2040.8459| 0.0000| 0.0000| 0.0000|1243.2653 - The gradient of the cost functions is 0.24884264 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1380|10622.8271| 59.7808| 0.0000|2040.8236| 0.0000| 0.0000| 0.0000|1242.6995 - The gradient of the cost functions is 1.826371 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1390|10622.7871| 59.5825| 0.0000|2040.7306| 0.0000| 0.0000| 0.0000|1242.1155 - The gradient of the cost functions is 0.46007875 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1400|10622.5859| 59.3108| 0.0000|2040.7618| 0.0000| 0.0000| 0.0000|1241.1532 - The gradient of the cost functions is 0.22977662 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1410|10621.9775| 59.1208| 0.0000|2041.2657| 0.0000| 0.0000| 0.0000|1240.2955 - The gradient of the cost functions is 0.26911643 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1420|10622.3887| 58.9599| 0.0000|2040.7208| 0.0000| 0.0000| 0.0000|1239.4883 - The gradient of the cost functions is 0.2558977 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1430|10622.2812| 58.7323| 0.0000|2040.7273| 0.0000| 0.0000| 0.0000|1238.3177 - The gradient of the cost functions is 0.26401874 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1440|10621.8730| 58.3409| 0.0000|2042.2867| 0.0000| 0.0000| 0.0000|1235.9679 - The gradient of the cost functions is 0.4664472 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1450|10622.2607| 58.3108| 0.0000|2040.5842| 0.0000| 0.0000| 0.0000|1235.7241 - The gradient of the cost functions is 0.23689163 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1460|10622.0918| 58.0255| 0.0000|2040.6467| 0.0000| 0.0000| 0.0000|1233.7310 - The gradient of the cost functions is 0.26766935 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1470|10621.9678| 57.8359| 0.0000|2040.6770| 0.0000| 0.0000| 0.0000|1232.3409 - The gradient of the cost functions is 0.36634654 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1480|10621.8818| 57.5983| 0.0000|2040.7151| 0.0000| 0.0000| 0.0000|1230.4600 - The gradient of the cost functions is 0.29295486 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1490|10622.6973| 57.2077| 0.0000|2041.1290| 0.0000| 0.0000| 0.0000|1226.9547 - The gradient of the cost functions is 0.28275537 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1500|10621.6250| 57.2633| 0.0000|2040.8136| 0.0000| 0.0000| 0.0000|1227.5011 - The gradient of the cost functions is 0.40270168 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1510|10621.6484| 56.9722| 0.0000|2040.7372| 0.0000| 0.0000| 0.0000|1224.7820 - The gradient of the cost functions is 0.31878883 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1520|10621.5840| 56.7308| 0.0000|2040.7499| 0.0000| 0.0000| 0.0000|1222.6053 - The gradient of the cost functions is 0.25599217 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1530|10623.5557| 56.4054| 0.0000|2039.6481| 0.0000| 0.0000| 0.0000|1219.2273 - The gradient of the cost functions is 0.29195625 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1540|10621.4775| 56.4074| 0.0000|2040.7524| 0.0000| 0.0000| 0.0000|1219.2393 - The gradient of the cost functions is 0.36912447 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1550|10621.4062| 56.0716| 0.0000|2040.8123| 0.0000| 0.0000| 0.0000|1215.5607 - The gradient of the cost functions is 0.2693449 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1560|10622.4844| 55.6025| 0.0000|2041.1565| 0.0000| 0.0000| 0.0000|1210.3810 - The gradient of the cost functions is 0.26139072 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1570|10621.3828| 55.7189| 0.0000|2040.7322| 0.0000| 0.0000| 0.0000|1211.7528 - The gradient of the cost functions is 0.33462748 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1580|10621.2383| 55.4596| 0.0000|2040.8735| 0.0000| 0.0000| 0.0000|1208.7125 - The gradient of the cost functions is 0.29132107 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1590|10622.7393| 54.9496| 0.0000|2040.9156| 0.0000| 0.0000| 0.0000|1202.2738 - The gradient of the cost functions is 0.25965938 + 0|55964.2227| 0.0000| 0.0091| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000 + The gradient of the cost functions is 28.290936 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1600|10621.2344| 55.0714| 0.0000|2040.7881| 0.0000| 0.0000| 0.0000|1204.0112 - The gradient of the cost functions is 0.3035212 + 10| 433.0938| 7.0422| 0.0092| 3.0986| 0.0000| 0.0000| 0.0000| 14.7014 + The gradient of the cost functions is 0.93045557 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1610|10621.2588| 54.8117| 0.0000|2040.7767| 0.0000| 0.0000| 0.0000|1200.8207 - The gradient of the cost functions is 0.26736605 + 20| 7.5543| 8.1688| 0.0093| 3.1913| 0.0000| 0.0000| 0.0000| 14.4342 + The gradient of the cost functions is 0.09527958 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1620|10622.1465| 54.3191| 0.0000|2041.4502| 0.0000| 0.0000| 0.0000|1194.2948 - The gradient of the cost functions is 0.27125648 + 30| 4.1321| 8.0791| 0.0093| 3.1982| 0.0000| 0.0000| 0.0000| 14.4571 + The gradient of the cost functions is 0.1433602 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1630|10621.2227| 54.4467| 0.0000|2040.7406| 0.0000| 0.0000| 0.0000|1196.1508 - The gradient of the cost functions is 0.30330306 + 40| 0.0596| 8.0676| 0.0093| 3.2001| 0.0000| 0.0000| 0.0000| 14.5702 + The gradient of the cost functions is 0.082182944 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1640|10621.2959| 54.1843| 0.0000|2040.6827| 0.0000| 0.0000| 0.0000|1192.7461 - The gradient of the cost functions is 0.28104252 + 50| 0.1758| 7.5954| 0.0093| 3.2102| 0.0000| 0.0000| 0.0000| 14.6147 + The gradient of the cost functions is 0.098248966 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1650|10621.3984| 53.6229| 0.0000|2042.2721| 0.0000| 0.0000| 0.0000|1185.1567 - The gradient of the cost functions is 0.27503034 + 60| 3.0544| 4.3691| 0.0093| 4.1731| 0.0000| 0.0000| 0.0000| 18.6557 + The gradient of the cost functions is 0.19085652 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1660|10621.1680| 53.7675| 0.0000|2040.7649| 0.0000| 0.0000| 0.0000|1187.2688 - The gradient of the cost functions is 0.28742248 + 70| 0.2120| 4.6897| 0.0093| 3.8213| 0.0000| 0.0000| 0.0000| 17.6196 + The gradient of the cost functions is 0.037094783 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1670|10621.0117| 53.4958| 0.0000|2040.9437| 0.0000| 0.0000| 0.0000|1183.5223 - The gradient of the cost functions is 0.2518867 + 80| 0.1678| 4.5853| 0.0093| 3.8627| 0.0000| 0.0000| 0.0000| 17.8189 + The gradient of the cost functions is 0.0451551 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1680|10621.2529| 53.2547| 0.0000|2040.6868| 0.0000| 0.0000| 0.0000|1180.1694 - The gradient of the cost functions is 0.25607947 + 90| 0.5275| 3.9846| 0.0093| 4.1231| 0.0000| 0.0000| 0.0000| 19.5749 + The gradient of the cost functions is 0.116284646 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1690|10621.0000| 53.0237| 0.0000|2040.9406| 0.0000| 0.0000| 0.0000|1177.0356 - The gradient of the cost functions is 0.30640352 + 100| 0.0519| 4.0239| 0.0093| 4.0928| 0.0000| 0.0000| 0.0000| 19.4148 + The gradient of the cost functions is 0.019677764 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1700|10621.2158| 52.7160| 0.0000|2040.7393| 0.0000| 0.0000| 0.0000|1172.7590 - The gradient of the cost functions is 0.28634554 + 110| 0.0498| 3.9728| 0.0093| 4.1092| 0.0000| 0.0000| 0.0000| 19.6031 + The gradient of the cost functions is 0.040121827 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1710|10620.8633| 52.4133| 0.0000|2041.0869| 0.0000| 0.0000| 0.0000|1168.3160 - The gradient of the cost functions is 0.24921805 + 120| 0.2664| 3.5551| 0.0093| 4.2000| 0.0000| 0.0000| 0.0000| 21.3789 + The gradient of the cost functions is 0.21858713 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1720|10620.9053| 52.1680| 0.0000|2041.0439| 0.0000| 0.0000| 0.0000|1164.8248 - The gradient of the cost functions is 0.27010044 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1730|10620.9102| 51.8955| 0.0000|2041.0425| 0.0000| 0.0000| 0.0000|1160.9496 - The gradient of the cost functions is 0.24402037 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1740|10621.0127| 51.6225| 0.0000|2040.9421| 0.0000| 0.0000| 0.0000|1156.9319 - The gradient of the cost functions is 0.29498106 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1750|10621.1396| 51.4474| 0.0000|2040.8206| 0.0000| 0.0000| 0.0000|1154.3508 - The gradient of the cost functions is 0.29712418 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1760|10620.8018| 51.2696| 0.0000|2041.1514| 0.0000| 0.0000| 0.0000|1151.7993 - The gradient of the cost functions is 0.34179816 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1770|10621.0000| 50.9104| 0.0000|2040.9630| 0.0000| 0.0000| 0.0000|1146.5433 - The gradient of the cost functions is 0.26381412 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1780|10620.9473| 50.6379| 0.0000|2041.0220| 0.0000| 0.0000| 0.0000|1142.2932 - The gradient of the cost functions is 0.24708496 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1790|10621.0303| 50.3521| 0.0000|2041.3964| 0.0000| 0.0000| 0.0000|1137.7842 - The gradient of the cost functions is 0.40109497 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1800|10620.8086| 50.2170| 0.0000|2041.1686| 0.0000| 0.0000| 0.0000|1135.7438 - The gradient of the cost functions is 0.3117913 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1810|10620.9736| 49.9959| 0.0000|2040.9868| 0.0000| 0.0000| 0.0000|1132.3538 - The gradient of the cost functions is 0.20708281 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1820|10621.4258| 49.7414| 0.0000|2040.8732| 0.0000| 0.0000| 0.0000|1128.4535 - The gradient of the cost functions is 0.33665 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1830|10620.8457| 49.6318| 0.0000|2041.1364| 0.0000| 0.0000| 0.0000|1126.7822 - The gradient of the cost functions is 0.34193406 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1840|10620.8809| 49.3876| 0.0000|2041.0859| 0.0000| 0.0000| 0.0000|1123.1229 - The gradient of the cost functions is 0.23209961 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1850|10620.9229| 49.0374| 0.0000|2041.4896| 0.0000| 0.0000| 0.0000|1117.9258 - The gradient of the cost functions is 0.38809198 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1860|10620.8486| 48.9630| 0.0000|2041.1577| 0.0000| 0.0000| 0.0000|1116.9058 - The gradient of the cost functions is 0.33151615 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1870|10620.9844| 48.7575| 0.0000|2041.0154| 0.0000| 0.0000| 0.0000|1113.9596 - The gradient of the cost functions is 0.19549856 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1880|10620.2139| 48.4767| 0.0000|2042.0244| 0.0000| 0.0000| 0.0000|1109.8604 - The gradient of the cost functions is 0.43995622 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1890|10620.8145| 48.3796| 0.0000|2041.2118| 0.0000| 0.0000| 0.0000|1108.4480 - The gradient of the cost functions is 0.32106537 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1900|10620.7588| 48.1842| 0.0000|2041.2510| 0.0000| 0.0000| 0.0000|1105.6528 - The gradient of the cost functions is 0.20583107 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1910|10621.3008| 47.9046| 0.0000|2041.0177| 0.0000| 0.0000| 0.0000|1101.5792 - The gradient of the cost functions is 0.4279917 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1920|10620.8438| 47.8396| 0.0000|2041.1812| 0.0000| 0.0000| 0.0000|1100.6157 - The gradient of the cost functions is 0.36555344 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1930|10620.8125| 47.6369| 0.0000|2041.1815| 0.0000| 0.0000| 0.0000|1097.6798 - The gradient of the cost functions is 0.20260368 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1940|10620.2354| 47.3211| 0.0000|2041.9655| 0.0000| 0.0000| 0.0000|1093.0607 - The gradient of the cost functions is 0.48997313 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1950|10620.7930| 47.2559| 0.0000|2041.2076| 0.0000| 0.0000| 0.0000|1092.1946 - The gradient of the cost functions is 0.27508995 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1960|10620.7168| 47.1313| 0.0000|2041.2708| 0.0000| 0.0000| 0.0000|1090.5684 - The gradient of the cost functions is 0.23357163 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1970|10619.8643| 46.8071| 0.0000|2042.4291| 0.0000| 0.0000| 0.0000|1086.3562 - The gradient of the cost functions is 0.59233797 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1980|10620.7305| 46.7079| 0.0000|2041.2621| 0.0000| 0.0000| 0.0000|1085.0529 - The gradient of the cost functions is 0.26356968 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 1990|10620.7363| 46.5795| 0.0000|2041.2407| 0.0000| 0.0000| 0.0000|1083.4154 - The gradient of the cost functions is 0.17730932 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2000|10620.5303| 46.3858| 0.0000|2041.5439| 0.0000| 0.0000| 0.0000|1081.1040 - The gradient of the cost functions is 0.6972646 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2010|10620.6406| 46.2424| 0.0000|2041.3408| 0.0000| 0.0000| 0.0000|1079.4446 - The gradient of the cost functions is 0.2841888 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2020|10620.7539| 46.0796| 0.0000|2041.2090| 0.0000| 0.0000| 0.0000|1077.5962 - The gradient of the cost functions is 0.16840227 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2030|10621.1543| 45.8941| 0.0000|2040.9269| 0.0000| 0.0000| 0.0000|1075.5466 - The gradient of the cost functions is 0.64041764 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2040|10620.5469| 45.7920| 0.0000|2041.4155| 0.0000| 0.0000| 0.0000|1074.4790 - The gradient of the cost functions is 0.29717904 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2050|10620.7324| 45.6350| 0.0000|2041.2079| 0.0000| 0.0000| 0.0000|1072.8618 - The gradient of the cost functions is 0.15582973 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2060|10620.1621| 45.4699| 0.0000|2041.8263| 0.0000| 0.0000| 0.0000|1071.3217 - The gradient of the cost functions is 0.86074144 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2070|10620.4648| 45.3590| 0.0000|2041.4811| 0.0000| 0.0000| 0.0000|1070.3497 - The gradient of the cost functions is 0.31567252 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2080|10620.5029| 45.1862| 0.0000|2041.4261| 0.0000| 0.0000| 0.0000|1068.9163 - The gradient of the cost functions is 0.1700356 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2090|10620.4775| 44.9891| 0.0000|2041.5433| 0.0000| 0.0000| 0.0000|1067.3071 - The gradient of the cost functions is 0.72796804 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2100|10620.5596| 44.9018| 0.0000|2041.3669| 0.0000| 0.0000| 0.0000|1066.6738 - The gradient of the cost functions is 0.26393315 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2110|10620.4316| 44.7747| 0.0000|2041.4857| 0.0000| 0.0000| 0.0000|1065.8386 - The gradient of the cost functions is 0.15650746 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2120|10620.2021| 44.6309| 0.0000|2041.7386| 0.0000| 0.0000| 0.0000|1064.9823 - The gradient of the cost functions is 1.2139463 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2130|10620.4824| 44.5038| 0.0000|2041.4272| 0.0000| 0.0000| 0.0000|1064.2628 - The gradient of the cost functions is 0.2969899 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2140|10620.4238| 44.3261| 0.0000|2041.4756| 0.0000| 0.0000| 0.0000|1063.3992 - The gradient of the cost functions is 0.1465103 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2150|10620.6689| 44.1806| 0.0000|2041.2748| 0.0000| 0.0000| 0.0000|1062.7987 - The gradient of the cost functions is 0.82853645 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2160|10620.3867| 44.0942| 0.0000|2041.5201| 0.0000| 0.0000| 0.0000|1062.4983 - The gradient of the cost functions is 0.29374045 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2170|10620.3965| 43.9499| 0.0000|2041.4958| 0.0000| 0.0000| 0.0000|1062.0837 - The gradient of the cost functions is 0.16144647 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2180|10620.1172| 43.7966| 0.0000|2041.7869| 0.0000| 0.0000| 0.0000|1061.7308 - The gradient of the cost functions is 1.3444794 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2190|10620.3271| 43.6928| 0.0000|2041.5536| 0.0000| 0.0000| 0.0000|1061.5529 - The gradient of the cost functions is 0.24857613 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2200|10620.3301| 43.5647| 0.0000|2041.5435| 0.0000| 0.0000| 0.0000|1061.4329 - The gradient of the cost functions is 0.14502576 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2210|10620.3164| 43.4522| 0.0000|2041.5676| 0.0000| 0.0000| 0.0000|1061.4327 - The gradient of the cost functions is 1.1191498 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2220|10620.2969| 43.3334| 0.0000|2041.5779| 0.0000| 0.0000| 0.0000|1061.5039 - The gradient of the cost functions is 0.2929772 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2230|10620.2637| 43.1540| 0.0000|2041.6121| 0.0000| 0.0000| 0.0000|1061.7079 - The gradient of the cost functions is 0.14312014 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2240|10620.2930| 43.0261| 0.0000|2041.5941| 0.0000| 0.0000| 0.0000|1061.9454 - The gradient of the cost functions is 0.16945213 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2250|10620.2666| 42.9309| 0.0000|2041.6049| 0.0000| 0.0000| 0.0000|1062.1998 - The gradient of the cost functions is 0.18863127 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2260|10620.2539| 42.7895| 0.0000|2041.6216| 0.0000| 0.0000| 0.0000|1062.6765 - The gradient of the cost functions is 0.16406153 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2270|10620.2207| 42.5255| 0.0000|2042.2153| 0.0000| 0.0000| 0.0000|1063.8118 - The gradient of the cost functions is 0.28852305 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2280|10620.1240| 42.5363| 0.0000|2041.7520| 0.0000| 0.0000| 0.0000|1063.7886 - The gradient of the cost functions is 0.15932795 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2290|10620.1973| 42.4452| 0.0000|2041.6692| 0.0000| 0.0000| 0.0000|1064.3143 - The gradient of the cost functions is 0.18448862 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2300|10620.2246| 42.3130| 0.0000|2041.6456| 0.0000| 0.0000| 0.0000|1065.1876 - The gradient of the cost functions is 0.2686258 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2310|10620.0781| 42.1483| 0.0000|2041.7950| 0.0000| 0.0000| 0.0000|1066.3694 - The gradient of the cost functions is 0.24015921 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2320|10620.1836| 42.0202| 0.0000|2041.6852| 0.0000| 0.0000| 0.0000|1067.4128 - The gradient of the cost functions is 0.26908478 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2330|10620.0918| 41.9315| 0.0000|2041.7683| 0.0000| 0.0000| 0.0000|1068.2598 - The gradient of the cost functions is 0.18402296 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2340|10620.1348| 41.8269| 0.0000|2041.7048| 0.0000| 0.0000| 0.0000|1069.3513 - The gradient of the cost functions is 0.20349228 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2350|10620.0459| 41.6735| 0.0000|2041.7930| 0.0000| 0.0000| 0.0000|1070.9932 - The gradient of the cost functions is 0.19359994 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2360|10620.1602| 41.5405| 0.0000|2041.6761| 0.0000| 0.0000| 0.0000|1072.4347 - The gradient of the cost functions is 1.0736165 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2370|10620.0352| 41.4619| 0.0000|2041.8026| 0.0000| 0.0000| 0.0000|1073.3529 - The gradient of the cost functions is 0.13540551 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2380|10619.8643| 41.3553| 0.0000|2041.9701| 0.0000| 0.0000| 0.0000|1074.6777 - The gradient of the cost functions is 0.17307703 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2390|10620.0674| 41.2461| 0.0000|2041.7659| 0.0000| 0.0000| 0.0000|1076.0869 - The gradient of the cost functions is 0.19415039 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2400|10619.9600| 41.1062| 0.0000|2041.8862| 0.0000| 0.0000| 0.0000|1077.9496 - The gradient of the cost functions is 1.2558079 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2410|10620.1436| 40.9915| 0.0000|2041.7014| 0.0000| 0.0000| 0.0000|1079.6182 - The gradient of the cost functions is 0.13974603 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2420|10620.0449| 40.8739| 0.0000|2041.7922| 0.0000| 0.0000| 0.0000|1081.4734 - The gradient of the cost functions is 0.23047888 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2430|10619.8887| 40.8023| 0.0000|2041.9464| 0.0000| 0.0000| 0.0000|1082.6963 - The gradient of the cost functions is 1.089708 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2440|10620.0762| 40.7090| 0.0000|2041.7582| 0.0000| 0.0000| 0.0000|1084.2203 - The gradient of the cost functions is 0.13899536 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2450|10619.9570| 40.5950| 0.0000|2041.8741| 0.0000| 0.0000| 0.0000|1086.1316 - The gradient of the cost functions is 0.2156136 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2460|10619.9170| 40.5108| 0.0000|2041.9182| 0.0000| 0.0000| 0.0000|1087.6230 - The gradient of the cost functions is 1.1581929 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2470|10619.9863| 40.3990| 0.0000|2041.8473| 0.0000| 0.0000| 0.0000|1089.6552 - The gradient of the cost functions is 0.13144083 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2480|10619.9082| 40.2905| 0.0000|2041.9186| 0.0000| 0.0000| 0.0000|1091.7173 - The gradient of the cost functions is 0.20624782 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2490|10619.9160| 40.2322| 0.0000|2041.9125| 0.0000| 0.0000| 0.0000|1092.8417 - The gradient of the cost functions is 1.0488719 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2500|10620.0352| 40.1397| 0.0000|2041.7947| 0.0000| 0.0000| 0.0000|1094.6226 - The gradient of the cost functions is 0.15885513 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2510|10619.7900| 39.9902| 0.0000|2042.0419| 0.0000| 0.0000| 0.0000|1097.5316 - The gradient of the cost functions is 0.14429945 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2520|10619.8135| 39.9078| 0.0000|2042.0171| 0.0000| 0.0000| 0.0000|1099.1970 - The gradient of the cost functions is 0.17827532 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2530|10619.7988| 39.8221| 0.0000|2042.0415| 0.0000| 0.0000| 0.0000|1100.9625 - The gradient of the cost functions is 0.16951546 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2540|10621.1016| 39.6133| 0.0000|2041.2682| 0.0000| 0.0000| 0.0000|1105.7484 - The gradient of the cost functions is 0.16936913 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2550|10619.7754| 39.6533| 0.0000|2042.0499| 0.0000| 0.0000| 0.0000|1104.7559 - The gradient of the cost functions is 0.17777918 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2560|10619.7627| 39.5545| 0.0000|2042.0725| 0.0000| 0.0000| 0.0000|1107.0284 - The gradient of the cost functions is 0.1731965 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2570|10621.0068| 39.3424| 0.0000|2041.4591| 0.0000| 0.0000| 0.0000|1112.0719 - The gradient of the cost functions is 0.14922969 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2580|10619.7412| 39.4031| 0.0000|2042.0858| 0.0000| 0.0000| 0.0000|1110.5002 - The gradient of the cost functions is 0.17127313 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2590|10619.8242| 39.3141| 0.0000|2042.0143| 0.0000| 0.0000| 0.0000|1112.5573 - The gradient of the cost functions is 0.17030345 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2600|10620.8936| 39.0728| 0.0000|2041.5868| 0.0000| 0.0000| 0.0000|1118.3654 - The gradient of the cost functions is 0.17106143 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2610|10619.7783| 39.1270| 0.0000|2042.0601| 0.0000| 0.0000| 0.0000|1116.8279 - The gradient of the cost functions is 0.14788374 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2620|10619.7559| 39.0350| 0.0000|2042.0901| 0.0000| 0.0000| 0.0000|1118.9596 - The gradient of the cost functions is 0.17527255 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2630|10619.8975| 38.9473| 0.0000|2041.9601| 0.0000| 0.0000| 0.0000|1121.1139 - The gradient of the cost functions is 0.16864996 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2640|10620.4023| 38.7336| 0.0000|2042.0750| 0.0000| 0.0000| 0.0000|1126.6232 - The gradient of the cost functions is 0.14974102 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2650|10619.7451| 38.7945| 0.0000|2042.1088| 0.0000| 0.0000| 0.0000|1124.8591 - The gradient of the cost functions is 0.1676738 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2660|10619.6846| 38.6991| 0.0000|2042.1864| 0.0000| 0.0000| 0.0000|1127.1509 - The gradient of the cost functions is 0.17210469 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2670|10619.7451| 38.6132| 0.0000|2042.1252| 0.0000| 0.0000| 0.0000|1129.2703 - The gradient of the cost functions is 0.16164221 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2680|10620.4727| 38.4208| 0.0000|2041.9280| 0.0000| 0.0000| 0.0000|1134.3459 - The gradient of the cost functions is 0.14738868 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2690|10619.6523| 38.4635| 0.0000|2042.2181| 0.0000| 0.0000| 0.0000|1133.0870 - The gradient of the cost functions is 0.17195798 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2700|10619.4531| 38.3707| 0.0000|2042.4326| 0.0000| 0.0000| 0.0000|1135.4835 - The gradient of the cost functions is 0.13469164 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2710|10619.5898| 38.2207| 0.0000|2042.7067| 0.0000| 0.0000| 0.0000|1139.4425 - The gradient of the cost functions is 0.14918035 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2720|10619.5859| 38.2530| 0.0000|2042.2902| 0.0000| 0.0000| 0.0000|1138.4410 - The gradient of the cost functions is 0.17298818 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2730|10619.7139| 38.1580| 0.0000|2042.1754| 0.0000| 0.0000| 0.0000|1140.7605 - The gradient of the cost functions is 0.9858409 + 130| 0.0598| 3.2439| 0.0094| 4.3475| 0.0000| 0.0000| 0.0000| 23.3502 + The gradient of the cost functions is 0.0137825 + Applying low pass filter to wind field... + Done! Time = 278.8 + Interpolating sounding to radar grid + Interpolated U field: + tf.Tensor( + [ 0.60573435 1.0229144 6.434673 11.977132 12.919597 + 9.719978 14.212554 17.673002 9.373551 4.932108 + 7.0470777 4.581866 3.8447456 1.9784147 -2.7835784 + -4.8090096 -8.388001 -8.310871 -6.199968 -8.8743305 + -7.5828705 -6.553336 -7.493186 -8.902025 -11.6616335 + -13.175965 -15.263804 -16.580751 -17.48236 -23.17236 + -20.712376 -18.823587 -16.356308 -25.138748 -29.280111 + -28.940443 -19.437548 -10.714591 -9.689518 ], shape=(39,), dtype=float32) + Interpolated V field: + tf.Tensor( + [-6.9235525 -6.326603 -6.434671 -6.9149985 -6.5828586 -3.927123 + -6.6274214 -8.241055 -9.051932 -3.2364333 0.8652741 -1.430016 + 0.54577833 -1.4374017 -0.30149823 3.5418017 4.45998 5.19321 + 5.667021 7.446448 10.062807 10.487528 11.109102 10.993081 + 9.54446 11.055948 9.912431 11.183857 9.295537 2.4355142 + -0.723292 -1.9784379 1.1915643 5.803736 3.5951443 -2.0237138 + 2.7317686 6.437976 2.7784247 ], shape=(39,), dtype=float32) + Grid levels: + [ 1000. 1500. 2000. 2500. 3000. 3500. 4000. 4500. 5000. 5500. + 6000. 6500. 7000. 7500. 8000. 8500. 9000. 9500. 10000. 10500. + 11000. 11500. 12000. 12500. 13000. 13500. 14000. 14500. 15000. 15500. + 16000. 16500. 17000. 17500. 18000. 18500. 19000. 19500. 20000.] + /home/runner/work/PyDDA/PyDDA/pydda/retrieval/angles.py:24: RuntimeWarning: invalid value encountered in arccos + elev = np.arccos((Re**2 + slantrsq - rh**2)/(2 * Re * slantr)) + Calculating weights for radars 0 and 1 + Calculating weights for radars 1 and 0 + Calculating weights for models... + Points from Radar 0: 40597 + Points from Radar 1: 40597 + Starting solver + rmsVR = 6.827304 + Total points: 81194 + The max of w_init is 0.0 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2740|10619.6064| 38.0822| 0.0000|2042.2852| 0.0000| 0.0000| 0.0000|1142.5941 - The gradient of the cost functions is 0.10978998 + 0|83859.8125| 0.0000| 0.0000|1274.4282| 0.0000| 0.0000| 0.0000| 0.0000 + The gradient of the cost functions is 31.80345 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2750|10619.5273| 38.0093| 0.0000|2042.3669| 0.0000| 0.0000| 0.0000|1144.3412 - The gradient of the cost functions is 0.19596298 + 10| 704.4634| 7.6629| 0.0003|1271.5490| 0.0000| 0.0000| 0.0000| 11.8140 + The gradient of the cost functions is 1.190283 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2760|10619.6123| 37.9565| 0.0000|2042.2885| 0.0000| 0.0000| 0.0000|1145.5875 - The gradient of the cost functions is 0.94182396 + 20| 11.1175| 8.6839| 0.0003|1262.6156| 0.0000| 0.0000| 0.0000| 11.5597 + The gradient of the cost functions is 0.330789 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2770|10619.4941| 37.8773| 0.0000|2042.4132| 0.0000| 0.0000| 0.0000|1147.4806 - The gradient of the cost functions is 0.13110481 + 30| 19.4754| 8.5079| 0.0003|1241.5200| 0.0000| 0.0000| 0.0000| 11.3770 + The gradient of the cost functions is 2.2754292 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2780|10619.6221| 37.7777| 0.0000|2042.2908| 0.0000| 0.0000| 0.0000|1149.8529 - The gradient of the cost functions is 0.18878038 + 40| 62.3501| 7.4340| 0.0004|1120.5829| 0.0000| 0.0000| 0.0000| 11.9248 + The gradient of the cost functions is 3.5188248 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2790|10619.5957| 37.7214| 0.0000|2042.3197| 0.0000| 0.0000| 0.0000|1151.2087 - The gradient of the cost functions is 0.8176819 + 50| 175.4700| 13.4436| 0.0051| 111.8275| 0.0000| 0.0000| 0.0000| 15.0880 + The gradient of the cost functions is 0.58877474 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2800|10619.7832| 37.6578| 0.0000|2042.1357| 0.0000| 0.0000| 0.0000|1152.6906 - The gradient of the cost functions is 0.11895635 + 60| 169.9729| 27.4714| 0.0096| 11.7312| 0.0000| 0.0000| 0.0000| 19.1073 + The gradient of the cost functions is 0.91222954 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2810|10619.6230| 37.5783| 0.0000|2042.3003| 0.0000| 0.0000| 0.0000|1154.4772 - The gradient of the cost functions is 0.19855744 + 70| 6.8313| 24.8837| 0.0089| 12.7703| 0.0000| 0.0000| 0.0000| 17.9690 + The gradient of the cost functions is 0.2905506 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2820|10619.5322| 37.5181| 0.0000|2042.4020| 0.0000| 0.0000| 0.0000|1155.7650 - The gradient of the cost functions is 0.83499336 + 80| 5.0923| 22.3917| 0.0090| 11.5872| 0.0000| 0.0000| 0.0000| 17.5588 + The gradient of the cost functions is 0.25399297 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2830|10619.5947| 37.4569| 0.0000|2042.3451| 0.0000| 0.0000| 0.0000|1157.0924 - The gradient of the cost functions is 0.11247532 + 90| 21.6780| 11.7222| 0.0092| 7.3413| 0.0000| 0.0000| 0.0000| 16.4682 + The gradient of the cost functions is 0.6049808 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2840|10619.5684| 37.3855| 0.0000|2042.3745| 0.0000| 0.0000| 0.0000|1158.6157 - The gradient of the cost functions is 0.19544175 + 100| 2.0664| 12.5581| 0.0092| 7.6458| 0.0000| 0.0000| 0.0000| 16.4887 + The gradient of the cost functions is 0.14174253 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2850|10619.5586| 37.3228| 0.0000|2042.3928| 0.0000| 0.0000| 0.0000|1159.9458 - The gradient of the cost functions is 0.85355383 + 110| 2.1252| 11.3306| 0.0092| 7.2155| 0.0000| 0.0000| 0.0000| 16.4568 + The gradient of the cost functions is 0.21625641 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2860|10619.5469| 37.2545| 0.0000|2042.4071| 0.0000| 0.0000| 0.0000|1161.3544 - The gradient of the cost functions is 0.10901082 + 120| 8.8208| 5.2879| 0.0093| 4.9891| 0.0000| 0.0000| 0.0000| 16.2498 + The gradient of the cost functions is 0.70314753 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2870|10619.6191| 37.1862| 0.0000|2042.3368| 0.0000| 0.0000| 0.0000|1162.6929 - The gradient of the cost functions is 0.19930123 + 130| 0.5751| 5.0909| 0.0093| 4.8852| 0.0000| 0.0000| 0.0000| 16.2025 + The gradient of the cost functions is 0.048374817 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2880|10619.4453| 37.1302| 0.0000|2042.5184| 0.0000| 0.0000| 0.0000|1163.7809 - The gradient of the cost functions is 0.92937195 + 140| 0.0371| 5.0346| 0.0093| 4.8496| 0.0000| 0.0000| 0.0000| 16.1679 + The gradient of the cost functions is 0.026955975 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2890|10619.4600| 37.0627| 0.0000|2042.5081| 0.0000| 0.0000| 0.0000|1165.0845 - The gradient of the cost functions is 0.10292884 + 150| 0.1442| 4.9044| 0.0093| 4.8104| 0.0000| 0.0000| 0.0000| 16.1535 + The gradient of the cost functions is 0.19354095 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2900|10619.6104| 36.9957| 0.0000|2042.3636| 0.0000| 0.0000| 0.0000|1166.3315 - The gradient of the cost functions is 0.16233526 + 160| 0.3021| 4.4439| 0.0093| 4.6783| 0.0000| 0.0000| 0.0000| 16.1147 + The gradient of the cost functions is 0.083998986 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 2910|10619.5137| 36.9437| 0.0000|2042.4685| 0.0000| 0.0000| 0.0000|1167.2386 + 170| 0.0857| 3.7469| 0.0094| 4.5192| 0.0000| 0.0000| 0.0000| 17.1091 Applying low pass filter to wind field... - Done! Time = 2195.8 + Done! Time = 363.3 Interpolating sounding to radar grid Interpolated U field: tf.Tensor( @@ -1025,47 +276,37 @@ at the edges of data coverage. Calculating weights for radars 0 and 1 Calculating weights for radars 1 and 0 Calculating weights for models... - /home/runner/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:810: RuntimeWarning: divide by zero encountered in divide - coverage_grade = coverage_grade / coverage_grade.max() - /home/runner/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:810: RuntimeWarning: invalid value encountered in divide - coverage_grade = coverage_grade / coverage_grade.max() - Points from Radar 0: 0 - Points from Radar 1: 0 + Points from Radar 0: 40597 + Points from Radar 1: 40597 Starting solver - rmsVR = nan - Total points: 0 + rmsVR = 6.827304 + Total points: 81194 The max of w_init is 0.0 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 0| nan| 0.0000| 0.0000|24745026.0000| 0.0000| 0.0000| 0.0000| 0.0000 - Applying low pass filter to wind field... - Done! Time = 1.7 - /usr/share/miniconda3/envs/pydda-docs/lib/python3.11/site-packages/matplotlib/contour.py:1454: UserWarning: Warning: converting a masked element to nan. - self.zmax = float(z.max()) - /usr/share/miniconda3/envs/pydda-docs/lib/python3.11/site-packages/matplotlib/contour.py:1455: UserWarning: Warning: converting a masked element to nan. - self.zmin = float(z.min()) - /home/runner/work/PyDDA/PyDDA/pydda/retrieval/angles.py:24: RuntimeWarning: invalid value encountered in arccos - elev = np.arccos((Re**2 + slantrsq - rh**2)/(2 * Re * slantr)) - Calculating weights for radars 0 and 1 - Calculating weights for radars 1 and 0 - Calculating weights for models... - /home/runner/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:810: RuntimeWarning: divide by zero encountered in divide - coverage_grade = coverage_grade / coverage_grade.max() - /home/runner/work/PyDDA/PyDDA/pydda/retrieval/wind_retrieve.py:810: RuntimeWarning: invalid value encountered in divide - coverage_grade = coverage_grade / coverage_grade.max() - Points from Radar 0: 0 - Points from Radar 1: 0 - Starting solver - rmsVR = nan - Total points: 0 - The max of w_init is 0.0 + 0|559642.2500| 0.0000| 0.0091| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000 + The gradient of the cost functions is 43.086624 + Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w + 10|1749.9241| 7.5786| 0.0093| 3.1306| 0.0000| 0.0000| 0.0000| 14.5110 + The gradient of the cost functions is 4.6896725 + Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w + 20|1137.4941| 8.5454| 0.0093| 3.2925| 0.0000| 0.0000| 0.0000| 14.2475 + The gradient of the cost functions is 6.302576 + Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w + 30| 2.9904| 8.2880| 0.0093| 3.2073| 0.0000| 0.0000| 0.0000| 14.5463 + The gradient of the cost functions is 0.22074592 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 0| nan| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000 + 40| 0.2788| 8.2843| 0.0093| 3.2055| 0.0000| 0.0000| 0.0000| 14.5582 + The gradient of the cost functions is 0.060598515 + Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w + 50| 0.2472| 8.2683| 0.0093| 3.2056| 0.0000| 0.0000| 0.0000| 14.5609 + The gradient of the cost functions is 0.38104638 + Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w + 60| 0.1309| 8.2121| 0.0093| 3.2062| 0.0000| 0.0000| 0.0000| 14.5681 + The gradient of the cost functions is 0.09837987 + Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w + 70| 0.0240| 8.1106| 0.0093| 3.2093| 0.0000| 0.0000| 0.0000| 14.5778 Applying low pass filter to wind field... - Done! Time = 1.3 - /usr/share/miniconda3/envs/pydda-docs/lib/python3.11/site-packages/matplotlib/contour.py:1454: UserWarning: Warning: converting a masked element to nan. - self.zmax = float(z.max()) - /usr/share/miniconda3/envs/pydda-docs/lib/python3.11/site-packages/matplotlib/contour.py:1455: UserWarning: Warning: converting a masked element to nan. - self.zmin = float(z.min()) + Done! Time = 160.4 @@ -1092,37 +333,36 @@ at the edges of data coverage. berr_grid = pydda.initialization.make_constant_wind_field( berr_grid, (0.0, 0.0, 0.0)) - # Let's make a plot on a map - fig = plt.figure(figsize=(7, 3)) - - pydda.vis.plot_xz_xsection_streamlines( - [cpol_grid, berr_grid], bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8]) - plt.show() - # Let's provide an initial state from the sounding u_back = sounding[1].u_wind v_back = sounding[1].v_wind z_back = sounding[1].height cpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1]) - new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], - + new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], u_back=u_back, v_back=v_back, z_back=z_back, - Co=10.0, Cm=4096.0, frz=5000.0, Cb=1e-6, - mask_outside_opt=False, wind_tol=0.2, + Co=1.0, Cm=64.0, frz=5000.0, Cb=1e-5, + Cx=1e2, Cy=1e2, Cz=1e2, + mask_outside_opt=False, wind_tol=0.1, engine="tensorflow") fig = plt.figure(figsize=(7, 7)) pydda.vis.plot_xz_xsection_streamlines( new_grids, bg_grid_no=-1, level=50, w_vel_contours=[1, 3, 5, 8]) plt.show() + # Let's see what happens when we use a zero initialization + # This causes there to be convergence in the cone of silence + # This is an artifact that we want to avoid! + # Prescribing winds inside the background through either a constraint + # Or through the initial state will help mitigate this issue. cpol_grid = pydda.initialization.make_constant_wind_field( - berr_grid, (0.0, 0.0, 0.0)) + cpol_grid, (0.0, 0.0, 0.0)) new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], u_back=u_back, v_back=v_back, z_back=z_back, - Co=1.0, Cm=128.0, frz=5000.0, Cb=1e-6, - mask_outside_opt=False, wind_tol=0.2, + Co=1.0, Cm=64.0, frz=5000.0, Cb=1e-5, + Cx=1e2, Cy=1e2, Cz=1e2, + mask_outside_opt=False, wind_tol=0.5, engine="tensorflow") fig = plt.figure(figsize=(7, 7)) @@ -1132,9 +372,12 @@ at the edges of data coverage. plt.show() # Or, let's make the radar data more important! + cpol_grid = pydda.initialization.make_wind_field_from_profile(cpol_grid, sounding[1]) new_grids, _ = pydda.retrieval.get_dd_wind_field([cpol_grid, berr_grid], - Co=100.0, Cm=128.0, frz=5000.0, - mask_outside_opt=False, wind_tol=0.2, + Co=10.0, Cm=64.0, frz=5000.0, + u_back=u_back, v_back=v_back, z_back=z_back, Cb=1e-5, + Cx=1e2, Cy=1e2, Cz=1e2, + mask_outside_opt=False, wind_tol=0.1, engine="tensorflow") fig = plt.figure(figsize=(7, 7)) @@ -1145,7 +388,7 @@ at the edges of data coverage. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 36 minutes 44.053 seconds) + **Total running time of the script:** (13 minutes 27.311 seconds) .. _sphx_glr_download_source_auto_examples_plot_fun_with_constraints.py: diff --git a/_sources/source/auto_examples/plot_sydney_tornado.rst.txt b/_sources/source/auto_examples/plot_sydney_tornado.rst.txt index 813b61e6..baa703d3 100644 --- a/_sources/source/auto_examples/plot_sydney_tornado.rst.txt +++ b/_sources/source/auto_examples/plot_sydney_tornado.rst.txt @@ -81,185 +81,83 @@ This example uses pooch to download the data files. The gradient of the cost functions is 1.5443492 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 10| 160.7619| 84.6624| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 19.8056 - The gradient of the cost functions is 0.11083611 + The gradient of the cost functions is 0.11083612 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 20| 67.2361| 40.4497| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 43.9986 The gradient of the cost functions is 0.046650108 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 30| 74.9021| 31.8725| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 87.2355 - The gradient of the cost functions is 0.16772418 + 30| 74.9020| 31.8725| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 87.2355 + The gradient of the cost functions is 0.16772412 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 40| 53.7402| 21.6132| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 88.5672 - The gradient of the cost functions is 0.039173342 + The gradient of the cost functions is 0.039173346 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 50| 49.7931| 17.5485| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 106.1469 - The gradient of the cost functions is 0.023509864 + 50| 49.7931| 17.5485| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 106.1468 + The gradient of the cost functions is 0.023509862 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 60| 50.2131| 16.8178| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 145.9839 The gradient of the cost functions is 0.09374788 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 70| 45.3509| 13.8503| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 153.9241 - The gradient of the cost functions is 0.027020399 + 70| 45.3509| 13.8503| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 153.9240 + The gradient of the cost functions is 0.0270204 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w 80| 43.1789| 12.4852| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 176.4540 - The gradient of the cost functions is 0.017706629 + The gradient of the cost functions is 0.01770663 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 90| 42.9505| 12.1104| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 211.0336 - The gradient of the cost functions is 0.073807366 + 90| 42.9505| 12.1104| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 211.0335 + The gradient of the cost functions is 0.07380735 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 100| 40.3303| 10.7333| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 221.5479 + 100| 40.3303| 10.7333| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 221.5478 The gradient of the cost functions is 0.022351442 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 110| 38.8250| 9.8692| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 247.0874 - The gradient of the cost functions is 0.015094837 + 110| 38.8249| 9.8692| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 247.0872 + The gradient of the cost functions is 0.015094841 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 120| 38.3343| 9.8641| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 308.7441 - The gradient of the cost functions is 0.06109772 + 120| 38.3343| 9.8641| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 308.7439 + The gradient of the cost functions is 0.06109773 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 130| 36.4593| 8.8712| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 331.2395 - The gradient of the cost functions is 0.019810997 + 130| 36.4593| 8.8712| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 331.2393 + The gradient of the cost functions is 0.019810995 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 140| 35.1107| 8.4133| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 387.6579 - The gradient of the cost functions is 0.013441316 + 140| 35.1107| 8.4133| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 387.6576 + The gradient of the cost functions is 0.013441319 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 150| 34.4118| 8.5971| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 470.7531 - The gradient of the cost functions is 0.054734387 + 150| 34.4118| 8.5971| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 470.7526 + The gradient of the cost functions is 0.054734394 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 160| 32.9937| 7.8767| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 497.6726 - The gradient of the cost functions is 0.017656278 + 160| 32.9937| 7.8767| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 497.6720 + The gradient of the cost functions is 0.017656276 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 170| 31.8791| 7.5525| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 560.2026 - The gradient of the cost functions is 0.012031976 + 170| 31.8791| 7.5525| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 560.2021 + The gradient of the cost functions is 0.01203198 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 180| 31.3087| 7.7279| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 649.6065 - The gradient of the cost functions is 0.047821913 + 180| 31.3087| 7.7279| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 649.6060 + The gradient of the cost functions is 0.047821872 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 190| 30.1715| 7.1526| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 676.6850 - The gradient of the cost functions is 0.015947402 + 190| 30.1715| 7.1526| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 676.6842 + The gradient of the cost functions is 0.015947394 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 200| 29.2386| 6.9574| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 739.9429 + 200| 29.2386| 6.9574| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 739.9419 The gradient of the cost functions is 0.010787779 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 210| 28.7032| 7.2065| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 831.8099 - The gradient of the cost functions is 0.043327026 + 210| 28.7032| 7.2065| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 831.8090 + The gradient of the cost functions is 0.043327007 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 220| 27.7870| 6.7180| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 857.8863 - The gradient of the cost functions is 0.014088896 + 220| 27.7870| 6.7180| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 857.8856 + The gradient of the cost functions is 0.014088898 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 230| 27.0351| 6.5782| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 919.1457 - The gradient of the cost functions is 0.009548959 + 230| 27.0351| 6.5782| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000| 919.1454 + The gradient of the cost functions is 0.009548963 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 240| 26.6050| 6.8166| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1005.9415 - The gradient of the cost functions is 0.038505632 + 240| 26.6050| 6.8166| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1005.9420 + The gradient of the cost functions is 0.03850561 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 250| 25.8553| 6.4350| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1029.0482 - The gradient of the cost functions is 0.012485673 + 250| 25.8553| 6.4350| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1029.0486 + The gradient of the cost functions is 0.012485672 Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 260| 25.2462| 6.3516| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1083.9883 - The gradient of the cost functions is 0.008461924 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 270| 24.8755| 6.5834| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1159.2755 - The gradient of the cost functions is 0.034072217 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 280| 24.2880| 6.2752| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1179.3236 - The gradient of the cost functions is 0.011349832 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 290| 23.7788| 6.2063| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1228.2101 - The gradient of the cost functions is 0.007855405 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 300| 23.4562| 6.3997| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1297.5356 - The gradient of the cost functions is 0.032471403 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 310| 22.9405| 6.1303| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1314.6139 - The gradient of the cost functions is 0.010743443 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 320| 22.5054| 6.0578| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1356.9799 - The gradient of the cost functions is 0.0073547303 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 330| 22.2549| 6.1879| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1413.1760 - The gradient of the cost functions is 0.029959224 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 340| 21.8069| 5.9618| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1427.7045 - The gradient of the cost functions is 0.009906556 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 350| 21.4343| 5.8864| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1461.0905 - The gradient of the cost functions is 0.0069369487 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 360| 21.2237| 5.9827| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1503.4631 - The gradient of the cost functions is 0.028667746 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 370| 20.8370| 5.7814| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1514.0259 - The gradient of the cost functions is 0.009357173 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 380| 20.5119| 5.7149| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1537.2170 - The gradient of the cost functions is 0.006426818 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 390| 20.3290| 5.8006| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1564.5397 - The gradient of the cost functions is 0.02592759 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 400| 19.9954| 5.6250| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1570.4498 - The gradient of the cost functions is 0.008685899 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 410| 19.7176| 5.5622| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1583.1708 - The gradient of the cost functions is 0.005967927 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 420| 19.5569| 5.6341| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1595.7274 - The gradient of the cost functions is 0.024018707 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 430| 19.2753| 5.4834| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1597.6135 - The gradient of the cost functions is 0.008230817 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 440| 19.0312| 5.4319| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1600.8894 - The gradient of the cost functions is 0.0056738057 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 450| 18.8791| 5.5030| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1600.8486 - The gradient of the cost functions is 0.02298888 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 460| 18.6180| 5.3679| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1598.9517 - The gradient of the cost functions is 0.007739951 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 470| 18.3886| 5.3224| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1593.4803 - The gradient of the cost functions is 0.0054327347 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 480| 18.2515| 5.3871| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1581.7062 - The gradient of the cost functions is 0.021888722 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 490| 18.0285| 5.2617| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1576.7397 - The gradient of the cost functions is 0.0072399112 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 500| 17.8319| 5.2227| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1564.3191 - The gradient of the cost functions is 0.0050076796 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 510| 17.7196| 5.2767| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1543.0151 - The gradient of the cost functions is 0.020155214 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 520| 17.5154| 5.1719| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1535.5204 - The gradient of the cost functions is 0.0067139613 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 530| 17.3468| 5.1364| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1517.5198 - The gradient of the cost functions is 0.0047206064 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 540| 17.2475| 5.1898| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1489.2612 - The gradient of the cost functions is 0.018979384 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 550| 17.0698| 5.0946| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1480.1777 - The gradient of the cost functions is 0.006368702 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 560| 16.9203| 5.0603| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1457.8083 - The gradient of the cost functions is 0.004448607 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 570| 16.8384| 5.0961| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1424.1523 - The gradient of the cost functions is 0.018531704 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 580| 16.6769| 5.0091| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1413.4814 - The gradient of the cost functions is 0.0061640223 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 590| 16.5429| 4.9750| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1388.7900 - The gradient of the cost functions is 0.0043050284 - Nfeval | Jvel | Jmass | Jsmooth | Jbg | Jvort | Jmodel | Jpoint | Max w - 600| 16.4637| 5.0097| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1352.7836 + 260| 25.2462| 6.3516| 0.0000| 0.0000| 0.0000| 0.0000| 0.0000|1083.9890 Applying low pass filter to wind field... - Done! Time = 1950.9 + Done! Time = 760.0 @@ -321,7 +219,7 @@ This example uses pooch to download the data files. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 32 minutes 42.199 seconds) + **Total running time of the script:** (13 minutes 4.751 seconds) .. _sphx_glr_download_source_auto_examples_plot_sydney_tornado.py: diff --git a/_sources/source/auto_examples/sg_execution_times.rst.txt b/_sources/source/auto_examples/sg_execution_times.rst.txt index d6692298..da932954 100644 --- a/_sources/source/auto_examples/sg_execution_times.rst.txt +++ b/_sources/source/auto_examples/sg_execution_times.rst.txt @@ -6,14 +6,14 @@ Computation times ================= -**69:35.378** total execution time for **source_auto_examples** files: +**26:43.979** total execution time for **source_auto_examples** files: +------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_source_auto_examples_plot_fun_with_constraints.py` (``plot_fun_with_constraints.py``) | 36:44.053 | 0.0 MB | +| :ref:`sphx_glr_source_auto_examples_plot_fun_with_constraints.py` (``plot_fun_with_constraints.py``) | 13:27.311 | 0.0 MB | +------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_source_auto_examples_plot_sydney_tornado.py` (``plot_sydney_tornado.py``) | 32:42.199 | 0.0 MB | +| :ref:`sphx_glr_source_auto_examples_plot_sydney_tornado.py` (``plot_sydney_tornado.py``) | 13:04.751 | 0.0 MB | +------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_source_auto_examples_plot_examples.py` (``plot_examples.py``) | 00:09.125 | 0.0 MB | +| :ref:`sphx_glr_source_auto_examples_plot_examples.py` (``plot_examples.py``) | 00:11.917 | 0.0 MB | +------------------------------------------------------------------------------------------------------+-----------+--------+ | :ref:`sphx_glr_source_auto_examples_hurricane_florence.py` (``hurricane_florence.py``) | 00:00.000 | 0.0 MB | +------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/_static/documentation_options.js b/_static/documentation_options.js index f86cf150..b067ff66 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '1.3.1', + VERSION: '1.4.0', LANGUAGE: 'None', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/_static/pygments.css b/_static/pygments.css index 691aeb82..0d49244e 100644 --- a/_static/pygments.css +++ b/_static/pygments.css @@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */ .highlight .gd { color: #A00000 } /* Generic.Deleted */ .highlight .ge { font-style: italic } /* Generic.Emph */ +.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */ .highlight .gr { color: #FF0000 } /* Generic.Error */ .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */ .highlight .gi { color: #00A000 } /* Generic.Inserted */ diff --git a/_static/sg_gallery-dataframe.css b/_static/sg_gallery-dataframe.css index 25be7309..fac74c43 100644 --- a/_static/sg_gallery-dataframe.css +++ b/_static/sg_gallery-dataframe.css @@ -19,6 +19,7 @@ table.dataframe { color: var(--sg-text-color); font-size: 12px; table-layout: fixed; + width: auto; } table.dataframe thead { border-bottom: 1px solid var(--sg-text-color); diff --git a/contributors_guide/index.html b/contributors_guide/index.html index 38b77a54..d6ecd9d9 100644 --- a/contributors_guide/index.html +++ b/contributors_guide/index.html @@ -6,7 +6,7 @@ - Contributor’s Guide — PyDDA 1.3.1 documentation + Contributor’s Guide — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.constraints.add_hrrr_constraint_to_grid.html b/dev_reference/generated/pydda.constraints.add_hrrr_constraint_to_grid.html index c5d55d65..5dd5558f 100644 --- a/dev_reference/generated/pydda.constraints.add_hrrr_constraint_to_grid.html +++ b/dev_reference/generated/pydda.constraints.add_hrrr_constraint_to_grid.html @@ -6,7 +6,7 @@ - pydda.constraints.add_hrrr_constraint_to_grid — PyDDA 1.3.1 documentation + pydda.constraints.add_hrrr_constraint_to_grid — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.constraints.download_needed_era_data.html b/dev_reference/generated/pydda.constraints.download_needed_era_data.html index 7f2152fe..92c4119b 100644 --- a/dev_reference/generated/pydda.constraints.download_needed_era_data.html +++ b/dev_reference/generated/pydda.constraints.download_needed_era_data.html @@ -6,7 +6,7 @@ - pydda.constraints.download_needed_era_data — PyDDA 1.3.1 documentation + pydda.constraints.download_needed_era_data — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.constraints.get_iem_obs.html b/dev_reference/generated/pydda.constraints.get_iem_obs.html index fa2aba71..b0f02824 100644 --- a/dev_reference/generated/pydda.constraints.get_iem_obs.html +++ b/dev_reference/generated/pydda.constraints.get_iem_obs.html @@ -6,7 +6,7 @@ - pydda.constraints.get_iem_obs — PyDDA 1.3.1 documentation + pydda.constraints.get_iem_obs — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.constraints.make_constraint_from_era_interim.html b/dev_reference/generated/pydda.constraints.make_constraint_from_era_interim.html index f3891260..dc219861 100644 --- a/dev_reference/generated/pydda.constraints.make_constraint_from_era_interim.html +++ b/dev_reference/generated/pydda.constraints.make_constraint_from_era_interim.html @@ -6,7 +6,7 @@ - pydda.constraints.make_constraint_from_era_interim — PyDDA 1.3.1 documentation + pydda.constraints.make_constraint_from_era_interim — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.constraints.make_constraint_from_wrf.html b/dev_reference/generated/pydda.constraints.make_constraint_from_wrf.html index fe460e8f..2bcd6977 100644 --- a/dev_reference/generated/pydda.constraints.make_constraint_from_wrf.html +++ b/dev_reference/generated/pydda.constraints.make_constraint_from_wrf.html @@ -6,7 +6,7 @@ - pydda.constraints.make_constraint_from_wrf — PyDDA 1.3.1 documentation + pydda.constraints.make_constraint_from_wrf — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.J_function.html b/dev_reference/generated/pydda.cost_functions.J_function.html index 9c30636c..44307a8a 100644 --- a/dev_reference/generated/pydda.cost_functions.J_function.html +++ b/dev_reference/generated/pydda.cost_functions.J_function.html @@ -6,7 +6,7 @@ - pydda.cost_functions.J_function — PyDDA 1.3.1 documentation + pydda.cost_functions.J_function — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_background_cost.html b/dev_reference/generated/pydda.cost_functions.calculate_background_cost.html index 7f73dc6e..44679c16 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_background_cost.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_background_cost.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_background_cost — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_background_cost — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_background_gradient.html b/dev_reference/generated/pydda.cost_functions.calculate_background_gradient.html index ab988718..c2c32582 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_background_gradient.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_background_gradient.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_background_gradient — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_background_gradient — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_fall_speed.html b/dev_reference/generated/pydda.cost_functions.calculate_fall_speed.html index 21d4f425..d18135d3 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_fall_speed.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_fall_speed.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_fall_speed — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_fall_speed — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_grad_radial_vel.html b/dev_reference/generated/pydda.cost_functions.calculate_grad_radial_vel.html index bb46f0a2..19ce2a9d 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_grad_radial_vel.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_grad_radial_vel.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_grad_radial_vel — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_grad_radial_vel — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity.html b/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity.html index f8512187..0db69662 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_mass_continuity — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_mass_continuity — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity_gradient.html b/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity_gradient.html index 26f3480b..66512a41 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity_gradient.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_mass_continuity_gradient.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_mass_continuity_gradient — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_mass_continuity_gradient — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_model_cost.html b/dev_reference/generated/pydda.cost_functions.calculate_model_cost.html index 6f079809..29ba0854 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_model_cost.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_model_cost.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_model_cost — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_model_cost — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_model_gradient.html b/dev_reference/generated/pydda.cost_functions.calculate_model_gradient.html index d3d1942f..ee1d4721 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_model_gradient.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_model_gradient.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_model_gradient — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_model_gradient — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_point_cost.html b/dev_reference/generated/pydda.cost_functions.calculate_point_cost.html index 4a66b726..2b327b33 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_point_cost.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_point_cost.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_point_cost — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_point_cost — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_point_gradient.html b/dev_reference/generated/pydda.cost_functions.calculate_point_gradient.html index 36dafe47..68f77294 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_point_gradient.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_point_gradient.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_point_gradient — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_point_gradient — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_radial_vel_cost_function.html b/dev_reference/generated/pydda.cost_functions.calculate_radial_vel_cost_function.html index 61d38121..a2f6fad4 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_radial_vel_cost_function.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_radial_vel_cost_function.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_radial_vel_cost_function — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_radial_vel_cost_function — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_smoothness_cost.html b/dev_reference/generated/pydda.cost_functions.calculate_smoothness_cost.html index 86e03fa3..b484eb0e 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_smoothness_cost.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_smoothness_cost.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_smoothness_cost — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_smoothness_cost — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_smoothness_gradient.html b/dev_reference/generated/pydda.cost_functions.calculate_smoothness_gradient.html index f5c62a31..4e545e8a 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_smoothness_gradient.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_smoothness_gradient.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_smoothness_gradient — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_smoothness_gradient — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_cost.html b/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_cost.html index 1ea2341a..f3c9f7b9 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_cost.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_cost.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_vertical_vorticity_cost — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_vertical_vorticity_cost — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_gradient.html b/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_gradient.html index 065ea682..204902e7 100644 --- a/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_gradient.html +++ b/dev_reference/generated/pydda.cost_functions.calculate_vertical_vorticity_gradient.html @@ -6,7 +6,7 @@ - pydda.cost_functions.calculate_vertical_vorticity_gradient — PyDDA 1.3.1 documentation + pydda.cost_functions.calculate_vertical_vorticity_gradient — PyDDA 1.4.0 documentation @@ -383,7 +383,7 @@

pydda.cost_functions.calculate_vertical_vorticity_gradient#

-pydda.cost_functions.calculate_vertical_vorticity_gradient(u, v, w, dx, dy, dz, Ut, Vt, coeff=1e-05)[source]#
+pydda.cost_functions.calculate_vertical_vorticity_gradient(u, v, w, dx, dy, dz, Ut, Vt, coeff=1e-05, upper_bc=True)[source]#

Calculates the gradient of the cost function due to deviance from vertical vorticity equation. This is done by taking the functional derivative of the vertical vorticity cost function. diff --git a/dev_reference/generated/pydda.cost_functions.grad_J.html b/dev_reference/generated/pydda.cost_functions.grad_J.html index 679e2872..3ff25740 100644 --- a/dev_reference/generated/pydda.cost_functions.grad_J.html +++ b/dev_reference/generated/pydda.cost_functions.grad_J.html @@ -6,7 +6,7 @@ - pydda.cost_functions.grad_J — PyDDA 1.3.1 documentation + pydda.cost_functions.grad_J — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.initialization.make_background_from_wrf.html b/dev_reference/generated/pydda.initialization.make_background_from_wrf.html index 8ff99fdf..3e416938 100644 --- a/dev_reference/generated/pydda.initialization.make_background_from_wrf.html +++ b/dev_reference/generated/pydda.initialization.make_background_from_wrf.html @@ -6,7 +6,7 @@ - pydda.initialization.make_background_from_wrf — PyDDA 1.3.1 documentation + pydda.initialization.make_background_from_wrf — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.initialization.make_constant_wind_field.html b/dev_reference/generated/pydda.initialization.make_constant_wind_field.html index 122508ca..889847d1 100644 --- a/dev_reference/generated/pydda.initialization.make_constant_wind_field.html +++ b/dev_reference/generated/pydda.initialization.make_constant_wind_field.html @@ -6,7 +6,7 @@ - pydda.initialization.make_constant_wind_field — PyDDA 1.3.1 documentation + pydda.initialization.make_constant_wind_field — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.initialization.make_initialization_from_era_interim.html b/dev_reference/generated/pydda.initialization.make_initialization_from_era_interim.html index e0ab01ae..b022f502 100644 --- a/dev_reference/generated/pydda.initialization.make_initialization_from_era_interim.html +++ b/dev_reference/generated/pydda.initialization.make_initialization_from_era_interim.html @@ -6,7 +6,7 @@ - pydda.initialization.make_initialization_from_era_interim — PyDDA 1.3.1 documentation + pydda.initialization.make_initialization_from_era_interim — PyDDA 1.4.0 documentation diff --git a/dev_reference/generated/pydda.initialization.make_wind_field_from_profile.html b/dev_reference/generated/pydda.initialization.make_wind_field_from_profile.html index b90ba559..3e77914a 100644 --- a/dev_reference/generated/pydda.initialization.make_wind_field_from_profile.html +++ b/dev_reference/generated/pydda.initialization.make_wind_field_from_profile.html @@ -6,7 +6,7 @@ - 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Developer Reference Manual#

Release
-

1.3.1

+

1.4.0

Date
-

Jul 27, 2023

+

Aug 30, 2023

This is the developer reference guide for PyDDA which covers most of the diff --git a/examples.html b/examples.html index 75881590..736d42f2 100644 --- a/examples.html +++ b/examples.html @@ -6,7 +6,7 @@ - <no title> — PyDDA 1.3.1 documentation + <no title> — PyDDA 1.4.0 documentation diff --git a/genindex.html b/genindex.html index dbec38f0..6bd1b1e2 100644 --- a/genindex.html +++ b/genindex.html @@ -5,7 +5,7 @@ - Index — PyDDA 1.3.1 documentation + Index — PyDDA 1.4.0 documentation diff --git a/index.html b/index.html index e14fa9a8..ccc0ac3c 100644 --- a/index.html +++ b/index.html @@ -6,7 +6,7 @@ - Welcome to the PyDDA documentation! — PyDDA 1.3.1 documentation + Welcome to the PyDDA documentation! — PyDDA 1.4.0 documentation diff --git a/objects.inv b/objects.inv index 7dcce3bf..ef59ac22 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html index d7f3d037..47573f9e 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -5,7 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