KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use from_results method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

farm_layout.py

Source:farm_layout.py

1import json2import numpy as np3import pandas as pd4import matplotlib.pyplot as plt5import foxes.constants as FC6import foxes.variables as FV7from foxes.output.output import Output8class FarmLayoutOutput(Output):9    """10    Plot the farm layout11    Parameters12    ----------13    farm : foxes.WindFarm14        The wind farm15    farm_results : xarray.Dataset, optional16        The wind farm calculation results17    from_results : bool, optional18        Flag for coordinates from results data19    results_state : int, optional20        The state index, for from_res21    D : float, optional22        The rotor diameter, if not from data23    Attributes24    ----------25    farm : foxes.WindFarm26        The wind farm27    fres : xarray.Dataset28        The wind farm calculation results29    from_res : bool30        Flag for coordinates from results data31    results_state : int32        The state index, for from_res33    D : float34        The rotor diameter, if not from data35    """36    def __init__(37        self, farm, farm_results=None, from_results=False, results_state=None, D=None38    ):39        self.farm = farm40        self.fres = farm_results41        self.from_res = from_results42        self.rstate = results_state43        self.D = D44        if from_results and farm_results is None:45            raise ValueError(f"Missing farm_results for switch from_results.")46        if from_results and results_state is None:47            raise ValueError(f"Please specify results_state for switch from_results.")48    def get_layout_data(self):49        """50        Returns wind farm layout.51        Returns52        -------53        numpy.ndarray :54            The wind farm layout, shape:55            (n_turbines, 3) where the 356            represents x, y, h57        """58        data = np.zeros([self.farm.n_turbines, 3], dtype=FC.DTYPE)59        if self.from_res:60            data[:, 0] = self.fres[FV.X][self.rstate]61            data[:, 1] = self.fres[FV.Y][self.rstate]62            data[:, 2] = self.fres[FV.H][self.rstate]63        else:64            for ti, t in enumerate(self.farm.turbines):65                data[ti, :2] = t.xy66                data[ti, 2] = t.H67        return data68    def get_layout_dict(self):69        """70        Returns wind farm layout.71        Returns72        -------73        dict :74            The wind farm layout in dict75            format, as in json output76        """77        data = self.get_layout_data()78        out = {self.farm.name: {}}79        for ti, p in enumerate(data):80            t = self.farm.turbines[ti]81            out[self.farm.name][t.name] = {82                "id": t.index,83                "name": t.name,84                "UTMX": p[0],85                "UTMY": p[1],86            }87        return out88    def get_figure(89        self,90        fontsize=8,91        figsize=None,92        annotate=1,93        title=None,94        fig=None,95        ax=None,96        normalize_D=False,97        ret_im=False,98        **kwargs,99    ):100        """101        Creates farm layout figure.102        Parameters103        ----------104        fontsize : int, optional105            Size of the turbine numbers106        figsize : tuple, optional107            The figsize for plt.Figure108        annotate : int, optional109            Turbine index printing, Choices:110            0 = No annotation111            1 = Turbine indices112            2 = Turbine names113        title : str, optional114            The plot title, or None for automatic115        fig : matplotlib.pyplot.Figure, optional116            The figure object to which to add117        ax : matplotlib.pyplot.Axis, optional118            The axis object, to which to add119        normalize_D : bool120            Normalize x, y wrt rotor diameter121        ret_im : bool122            Flag for returned image object123        **kwargs : dict, optional124            Parameters forwarded to `matplotlib.pyplot.scatter`125        Returns126        -------127        ax : matplotlib.pyplot.Axis128            The axis object129        im : matplotlib.pyplot.PathCollection, optional130            The image object131        """132        if fig is None:133            fig = plt.figure(figsize=figsize)134            ax = fig.add_subplot(111)135        else:136            ax = fig.axes[0] if ax is None else ax137        # for b in self.farm.boundary_geometry:138        #    b.add_to_figure(fig, ax)139        D = self.D140        if normalize_D and D is None:141            if self.from_res:142                if self.fres[FV.D].min() != self.fres[FV.D].max():143                    raise ValueError(f"Expecting uniform D, found {self.fres[FV.D]}")144                D = self.fres[FV.D][0]145            else:146                D = None147                for ti, t in enumerate(self.farm.turbines):148                    hD = t.D149                    if D is None:150                        D = hD151                    elif D != hD:152                        raise ValueError(153                            f"Turbine {ti} has wrong rotor diameter, expecting D = {D} m, found D = {hD} m"154                        )155                if D is None:156                    raise ValueError(157                        f"Variable '{FV.D}' not found in turbines. Maybe set explicitely, or try from_results?"158                    )159        data = self.get_layout_data()160        x = data[:, 0] / D if normalize_D else data[:, 0]161        y = data[:, 1] / D if normalize_D else data[:, 1]162        n = range(len(x))163        kw = {"c": "orange"}164        kw.update(**kwargs)165        im = ax.scatter(x, y, **kw)166        if annotate == 1:167            for i, txt in enumerate(n):168                ax.annotate(int(txt), (x[i], y[i]), size=fontsize)169        elif annotate == 2:170            for i, t in enumerate(self.farm.turbines):171                ax.annotate(t.name, (x[i], y[i]), size=fontsize)172        ti = (173            title174            if title is not None175            else self.farm.name176            if D is None or not normalize_D177            else f"{self.farm.name} (D = {D} m)"178        )179        ax.set_title(ti)180        ax.set_xlabel("x [m]" if not normalize_D else "x [D]")181        ax.set_ylabel("y [m]" if not normalize_D else "y [D]")182        ax.grid()183        # if len(self.farm.boundary_geometry) \184        #    or ( min(x) != max(x) and min(y) != max(y) ):185        if min(x) != max(x) and min(y) != max(y):186            ax.set_aspect("equal", adjustable="box")187        ax.autoscale_view(tight=True)188        if ret_im:189            return ax, im190        return ax191    def write_plot(self, file_path=None, fontsize=8, **kwargs):192        """193        Writes the layout plot to file.194        The kwargs are forwarded to self.get_figure195        Parameters196        ----------197        file_path : str198            The file into which to plot, or None199            for default200        fontsize : int201            Size of the turbine numbers202        """203        ax = self.get_figure(fontsize=fontsize, ret_im=False, **kwargs)204        fig = ax.get_figure()205        fname = file_path if file_path is not None else self.farm.name + ".png"206        fig.savefig(fname, bbox_inches="tight")207        plt.close(fig)208    def write_xyh(self, file_path=None):209        """210        Writes xyh layout file.211        Parameters212        ----------213        file_path : str214            The file into which to plot, or None215            for default216        """217        data = self.get_layout_data()218        fname = file_path if file_path is not None else self.farm.name + ".xyh"219        np.savetxt(fname, data, header="x y h")220    def write_csv(self, file_path=None):221        """222        Writes csv layout file.223        Parameters224        ----------225        file_path : str226            The file into which to plot, or None227            for default228        """229        data = self.get_layout_data()230        fname = file_path if file_path is not None else self.farm.name + ".csv"231        lyt = pd.DataFrame(232            index=range(len(data)), columns=["id", "name", "x", "y", "h", "D"]233        )234        lyt.index.name = "index"235        lyt["id"] = [t.info["id"] for t in self.farm.turbines]236        lyt["name"] = [t.info["name"] for t in self.farm.turbines]237        lyt["x"] = data[:, 0]238        lyt["y"] = data[:, 1]239        lyt["h"] = data[:, 2]240        lyt["D"] = [t.D for t in self.farm.turbines]241        lyt.to_csv(fname)242    def write_json(self, file_path=None):243        """244        Writes xyh layout file.245        Parameters246        ----------247        file_path : str248            The file into which to plot, or None249            for default250        """251        data = self.get_layout_dict()252        fname = file_path if file_path is not None else self.farm.name + ".json"253        with open(fname, "w") as outfile:...

test_performance_curves.py

Source:test_performance_curves.py

...47        np.testing.assert_almost_equal(curves.tpr(), tp / 9)48        np.testing.assert_almost_equal(curves.fpr(), (10 - tn) / 10)49    @patch("Orange.evaluation.performance_curves.Curves.__init__",50           return_value=None)51    def test_curves_from_results(self, init):52        res = Results()53        ytrue, probs = self.data.T54        res.actual = ytrue.astype(float)55        res.probabilities = np.vstack((1 - probs, probs)).T.reshape(1, -1, 2)56        Curves.from_results(res)57        cytrue, cprobs = init.call_args[0]58        np.testing.assert_equal(cytrue, ytrue)59        np.testing.assert_equal(cprobs, probs)60        Curves.from_results(res, target_class=0)61        cytrue, cprobs = init.call_args[0]62        np.testing.assert_equal(cytrue, 1 - ytrue)63        np.testing.assert_equal(cprobs, 1 - probs)64        res.actual = ytrue.astype(float)65        res.probabilities = np.random.random((2, 19, 2))66        res.probabilities[1] = np.vstack((1 - probs, probs)).T67        Curves.from_results(res, model_index=1)68        cytrue, cprobs = init.call_args[0]69        np.testing.assert_equal(cytrue, ytrue)70        np.testing.assert_equal(cprobs, probs)71        self.assertRaises(ValueError, Curves.from_results, res)72        ytrue[ytrue == 0] = 2 * (np.arange(10) % 2)73        res.actual = ytrue.astype(float)74        res.probabilities = np.random.random((2, 19, 3))75        res.probabilities[1] = np.vstack(76            ((1 - probs) / 3, probs, (1 - probs) * 2 / 3)).T77        Curves.from_results(res, model_index=1, target_class=1)78        cytrue, cprobs = init.call_args[0]79        np.testing.assert_equal(cytrue, ytrue == 1)80        np.testing.assert_equal(cprobs, probs)81        Curves.from_results(res, model_index=1, target_class=0)82        cytrue, cprobs = init.call_args[0]83        np.testing.assert_equal(cytrue, ytrue == 0)84        np.testing.assert_equal(cprobs, (1 - probs) / 3)85        Curves.from_results(res, model_index=1, target_class=2)86        cytrue, cprobs = init.call_args[0]87        np.testing.assert_equal(cytrue, ytrue == 2)88        np.testing.assert_equal(cprobs, (1 - probs) * 2 / 3)89        self.assertRaises(ValueError, Curves.from_results, res, model_index=1)90    @patch("Orange.evaluation.performance_curves.Curves.__init__",91           return_value=None)92    def test_curves_from_results_nans(self, init):93        res = Results()94        ytrue, probs = self.data.T95        ytrue[0] = np.nan96        probs[-1] = np.nan97        res.actual = ytrue.astype(float)98        res.probabilities = np.vstack((1 - probs, probs)).T.reshape(1, -1, 2)99        Curves.from_results(res)100        cytrue, cprobs = init.call_args[0]101        np.testing.assert_equal(cytrue, ytrue[1:-1])...

generate_truncated_json

Source:generate_truncated_json

1#!/usr/bin/python2.72"""3Given a full set of results, generate new JSON files with truncated iterations.4MUST be run before plot_truncated_iterations.5"""6import argparse7import os.path8import sys9sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "warmup_stats"))10from warmup.krun_results import read_krun_results_file, write_krun_results_file11MAX_ITERS = 199012MIN_ITERS = 1013ITER_STEP = -1014WINDOW_PROPORTION = 0.1  # Needed for outlier script options.15STEADY_PROPORTION = 0.25  # Needed for changepoint script options.16_BLANK_BENCHMARK = { 'wallclock_times': dict(), # Measurement data.17                    'core_cycle_counts': dict(), 'aperf_counts': dict(),18                    'mperf_counts': dict(), 'audit': dict(), 'config': '',19                    'reboots': 0, 'starting_temperatures': list(),20                    'eta_estimates': list(), 'error_flag': list(),21                    'all_outliers': dict(), 'unique_outliers': dict(),22                    'common_outliers': dict(), }23def parse_krun_file_with_changepoints(json_files):24    """Simplified version of the same function from krun_results."""25    data_dictionary = dict()26    window_size = None27    for filename in json_files:28        assert os.path.exists(filename), 'File %s does not exist.' % filename29        data = read_krun_results_file(filename)30        machine_name = data['audit']['uname'].split(' ')[1]31        if '.' in machine_name:  # Remove domain, if there is one.32            machine_name = machine_name.split('.')[0]33        if machine_name not in data_dictionary:34            data_dictionary[machine_name] = data35        else:36            assert 'Found two datasets from machine %s (expected one)' % machine_name37        if window_size is None:38            window_size = data['window_size']39        else:40            assert window_size == data['window_size'], \41                   ('Cannot summarise categories generated with different window-size '42                    'options. Please re-run the mark_outliers_in_json script.')43    return data['audit'], window_size, data_dictionary44def copy_results(last_iter, audit, window_size, from_results, to_results):45    """Copy results into a new dict. ASSUME only one machine per results file."""46    to_results['audit'] = audit47    to_results['window_size'] = window_size48    for mc in from_results:49        for key in from_results[mc]['wallclock_times']:50            to_results['wallclock_times'][key] = list()51            for p_exec in xrange(len(from_results[mc]['wallclock_times'][key])):52                to_results['wallclock_times'][key].append(from_results[mc]['wallclock_times'][key][p_exec][:last_iter])53def create_outlier_filename(in_file_name, window):54    directory = os.path.dirname(in_file_name)55    basename = os.path.basename(in_file_name)56    if basename.endswith('.json.bz2'):57        root_name = basename[:-9]58    else:59        root_name = os.path.splitext(basename)[0]60    base_out = ('%s_outliers_w%d.json.bz2' % (root_name, window))61    return os.path.join(directory, base_out)62def generate_truncated_json(json_file):63    """Generate an output file for each truncated data set."""64    warmup_stats_bin_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "warmup_stats", "bin")65    outlier_script = os.path.join(warmup_stats_bin_dir, 'mark_outliers_in_json')66    cpt_script = os.path.join(warmup_stats_bin_dir, 'mark_changepoints_in_json')67    directory = os.path.dirname(json_file)68    audit, window_size, original_json = parse_krun_file_with_changepoints([json_file])69    for last_iter in range(MAX_ITERS, MIN_ITERS, ITER_STEP):70        print 'Truncated to iteration:', last_iter71        new_json_data = _BLANK_BENCHMARK72        copy_results(last_iter, audit, window_size, original_json, new_json_data)73        outfile = os.path.join(directory, ('truncated_%g.json.bz2') % last_iter)74        write_krun_results_file(new_json_data, outfile)75        window = int(last_iter * WINDOW_PROPORTION)76        os.system('%s %s --window %d' % (outlier_script, outfile, window))77        steady = int(last_iter * STEADY_PROPORTION)78        os.system('%s %s --steady %d' % (cpt_script, create_outlier_filename(outfile, window), steady))79def create_cli_parser():80    """Create a parser to deal with command line switches."""81    script = os.path.basename(__file__)82    description = (('Generate results files by repeatedly truncating a Krun data file.\n' +83                    '\n\nExample usage:\n\n' +84                    '\t$ python %s results.json.bz2') % script)85    parser = argparse.ArgumentParser(description=description,86                                     formatter_class=argparse.RawTextHelpFormatter)87    parser.add_argument('results_file', action='store', default='.', type=str,88                        help='Results file to be truncated.')89    return parser90if __name__ == '__main__':91    parser = create_cli_parser()92    options = parser.parse_args()...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.