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_low_level.py

Source:_low_level.py

1# Copyright (C) 2015 Simon Biggs2# This program is free software: you can redistribute it and/or modify3# it under the terms of the GNU Affero General Public License as published4# by the Free Software Foundation, either version 3 of the License, or5# (at your option) any later version (the "AGPL-3.0+").6# This program is distributed in the hope that it will be useful,7# but WITHOUT ANY WARRANTY; without even the implied warranty of8# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the9# GNU Affero General Public License and the additional terms for more10# details.11# You should have received a copy of the GNU Affero General Public License12# along with this program. If not, see <http://www.gnu.org/licenses/>.13# ADDITIONAL TERMS are also included as allowed by Section 7 of the GNU14# Affrero General Public License. These aditional terms are Sections 1, 5,15# 6, 7, 8, and 9 from the Apache License, Version 2.0 (the "Apache-2.0")16# where all references to the definition "License" are instead defined to17# mean the AGPL-3.0+.18# You should have received a copy of the Apache-2.0 along with this19# program. If not, see <http://www.apache.org/licenses/LICENSE-2.0>.20"""Compare two dose grids with the gamma index.21This module is a python implementation of the gamma index.22It computes 1, 2, or 3 dimensional gamma with arbitrary gird sizes while23interpolating on the fly.24This module makes use of some of the ideas presented within25<http://dx.doi.org/10.1118/1.2721657>.26It needs to be noted that this code base has not yet undergone sufficient27independent validation.28"""29import numpy as np30from scipy.interpolate import RegularGridInterpolator31from multiprocessing import Process, Queue32def _run_input_checks(33        coords_reference, dose_reference,34        coords_evaluation, dose_evaluation):35    """Check user inputs."""36    if (37            not isinstance(coords_evaluation, tuple) or38            not isinstance(coords_reference, tuple)):39        if (40                isinstance(coords_evaluation, np.ndarray) and41                isinstance(coords_reference, np.ndarray)):42            if (43                    len(np.shape(coords_evaluation)) == 1 and44                    len(np.shape(coords_reference)) == 1):45                coords_evaluation = (coords_evaluation,)46                coords_reference = (coords_reference,)47            else:48                raise Exception(49                    "Can only use numpy arrays as input for one dimensional "50                    "gamma."51                    )52        else:53            raise Exception(54                "Input coordinates must be inputted as a tuple, for "55                "one dimension input is (x,), for two dimensions, (x, y),  "56                "for three dimensions input is (x, y, z).")57    reference_coords_shape = tuple([len(item) for item in coords_reference])58    if reference_coords_shape != np.shape(dose_reference):59        raise Exception(60            "Length of items in coords_reference does not match the shape of "61            "dose_reference")62    evaluation_coords_shape = tuple([len(item) for item in coords_evaluation])63    if evaluation_coords_shape != np.shape(dose_evaluation):64        raise Exception(65            "Length of items in coords_evaluation does not match the shape of "66            "dose_evaluation")67    if not (len(np.shape(dose_evaluation)) ==68            len(np.shape(dose_reference)) ==69            len(coords_evaluation) ==70            len(coords_reference)):71        raise Exception(72            "The dimensions of the input data do not match")73    return coords_reference, coords_evaluation74def _calculate_coordinates_kernel(75        distance, num_dimensions, distance_step_size):76    """Determine the coodinate shifts required.77    Coordinate shifts are determined to check the reference dose for a78    given distance, dimension, and step size79    """80    if num_dimensions == 1:81        if distance == 0:82            x_coords = np.array([0])83        else:84            x_coords = np.array([distance, -distance])85        return (x_coords,)86    elif num_dimensions == 2:87        amount_to_check = np.floor(88            2 * np.pi * distance / distance_step_size) + 289        theta = np.linspace(0, 2*np.pi, amount_to_check + 1)[:-1:]90        x_coords = distance * np.cos(theta)91        y_coords = distance * np.sin(theta)92        return (x_coords, y_coords)93    elif num_dimensions == 3:94        number_of_rows = np.floor(95            np.pi * distance / distance_step_size) + 296        elevation = np.linspace(0, np.pi, number_of_rows)97        row_radii = distance * np.sin(elevation)98        row_circumference = 2 * np.pi * row_radii99        amount_in_row = np.floor(100            row_circumference / distance_step_size) + 2101        x_coords = []102        y_coords = []103        z_coords = []104        for i, phi in enumerate(elevation):105            azimuth = np.linspace(0, 2*np.pi, amount_in_row[i] + 1)[:-1:]106            x_coords.append(distance * np.sin(phi) * np.cos(azimuth))107            y_coords.append(distance * np.sin(phi) * np.sin(azimuth))108            z_coords.append(distance * np.cos(phi) * np.ones_like(azimuth))109        return (110            np.hstack(x_coords), np.hstack(y_coords), np.hstack(z_coords))111    else:112        raise Exception("No valid dimension")113def _calculate_min_dose_difference(114        num_dimensions, mesh_coords_evaluation, to_be_checked,115        reference_interpolation, dose_evaluation,116        coordinates_at_distance_kernel):117    """Determine the minimum dose difference.118    Calculated for a given distance from each evaluation point.119    """120    coordinates_at_distance = []121    for i in range(num_dimensions):122        coordinates_at_distance.append(np.array(123            mesh_coords_evaluation[i][to_be_checked][None, :] +124            coordinates_at_distance_kernel[i][:, None])[:, :, None])125    all_points = np.concatenate(coordinates_at_distance, axis=2)126    dose_difference = np.array([127        reference_interpolation(points) -128        dose_evaluation[to_be_checked] for129        points in all_points130    ])131    min_dose_difference = np.min(np.abs(dose_difference), axis=0)132    return min_dose_difference133def _calculate_min_dose_difference_by_slice(134        max_concurrent_calc_points,135        num_dimensions, mesh_coords_evaluation, to_be_checked,136        reference_interpolation, dose_evaluation,137        coordinates_at_distance_kernel, **kwargs):138    """Determine minimum dose differences.139    Calculation is made with the evaluation set divided into slices. This140    enables less RAM usage.141    """142    all_checks = np.where(to_be_checked)143    min_dose_difference = (np.nan * np.ones_like(all_checks[0]))144    num_slices = np.floor(145        len(coordinates_at_distance_kernel[0]) *146        len(all_checks[0]) / max_concurrent_calc_points) + 1147    index = np.arange(len(all_checks[0]))148    np.random.shuffle(index)149    sliced = np.array_split(index, num_slices)150    for current_slice in sliced:151        current_to_be_checked = np.zeros_like(to_be_checked).astype(bool)152        current_to_be_checked[tuple([153            item[current_slice] for154            item in all_checks])] = True155        assert np.all(to_be_checked[current_to_be_checked])156        min_dose_difference[np.sort(current_slice)] = (157            _calculate_min_dose_difference(158                num_dimensions, mesh_coords_evaluation, current_to_be_checked,159                reference_interpolation, dose_evaluation,160                coordinates_at_distance_kernel))161    assert np.all(np.invert(np.isnan(min_dose_difference)))162    return min_dose_difference163def _calculation_loop(**kwargs):164    """Iteratively calculates gamma at increasing distances."""165    dose_valid = kwargs['dose_evaluation'] >= kwargs['lower_dose_cutoff']166    gamma_valid = np.ones_like(kwargs['dose_evaluation']).astype(bool)167    running_gamma = np.inf * np.ones_like(kwargs['dose_evaluation'])168    if kwargs.get('output_components', False):169        output_components = True170        running_dd = np.inf * np.ones_like(kwargs['dose_evaluation'])171        running_dta = np.inf * np.ones_like(kwargs['dose_evaluation'])172    else:173        output_components = False174    distance = 0175    while True:176        to_be_checked = (177            dose_valid & gamma_valid)178        coordinates_at_distance_kernel = _calculate_coordinates_kernel(179            distance, kwargs['num_dimensions'], kwargs['distance_step_size'])180        min_dose_difference = _calculate_min_dose_difference_by_slice(181            to_be_checked=to_be_checked,182            coordinates_at_distance_kernel=coordinates_at_distance_kernel,183            **kwargs)184        dd_term = min_dose_difference / kwargs['dose_threshold']185        dta_term = distance / kwargs['distance_threshold']186        gamma_at_distance = np.sqrt( dd_term**2 + dta_term**2)187        update_mask = (gamma_at_distance < running_gamma[to_be_checked])188        running_gamma[to_be_checked] = np.where(update_mask, gamma_at_distance, running_gamma[to_be_checked])189        #  running_gamma[to_be_checked] = np.min(190        #      np.vstack((191        #          gamma_at_distance, running_gamma[to_be_checked]192        #      )), axis=0)193        if output_components:194            running_dd[to_be_checked] = np.where(update_mask, dd_term, running_dd[to_be_checked])195            running_dta[to_be_checked] = np.where(update_mask, dta_term, running_dta[to_be_checked])196        gamma_valid = running_gamma > distance / kwargs['distance_threshold']197        distance += kwargs['distance_step_size']198        if (199                (np.sum(to_be_checked) == 0) |200                (distance > kwargs['maximum_test_distance'])):201            break202    if output_components:203        return (running_gamma, running_dd, running_dta)204    else:205        return running_gamma206def _new_thread(kwargs, output, thread_index, gamma_store):207    if kwargs.get('output_components', False):208        dd_store = np.nan*np.ones_like(gamma_store)209        dta_store = np.nan*np.ones_like(gamma_store)210        gamma_store[thread_index], dd_store[thread_index], dta_store[thread_index] = _calculation_loop(**kwargs)211        if output is not None:212            output.put((gamma_store, dd_store, dta_store))213        else:214            return (gamma_store, dd_store, dta_store)215    else:216        gamma_store[thread_index] = _calculation_loop(**kwargs)217        if output is not None:218            output.put(gamma_store)219        else:220            return gamma_store221def calc_gamma(coords_reference, dose_reference,222               coords_evaluation, dose_evaluation,223               distance_threshold, dose_threshold,224               lower_dose_cutoff=0, distance_step_size=None,225               maximum_test_distance=np.inf,226               max_concurrent_calc_points=np.inf,227               num_threads=1, output_components=False):228    """Compare two dose grids with the gamma index.229    Args:230        coords_reference (tuple): The reference coordinates.231        dose_reference (np.array): The reference dose grid.232        coords_evaluation (tuple): The evaluation coordinates.233        dose_evaluation (np.array): The evaluation dose grid.234        distance_threshold (float): The gamma distance threshold. Units must235            match of the coordinates given.236        dose_threshold (float): An absolute dose threshold.237            If you wish to use 3% of maximum reference dose input238            np.max(dose_reference) * 0.03 here.239        lower_dose_cutoff (:obj:`float`, optional): The lower dose cutoff below240            which gamma will not be calculated.241        distance_step_size (:obj:`float`, optional): The step size to use in242            within the reference grid interpolation. Defaults to a tenth of the243            distance threshold as recommended within244            <http://dx.doi.org/10.1118/1.2721657>.245        maximum_test_distance (:obj:`float`, optional): The distance beyond246            which searching will stop. Defaults to np.inf. To speed up247            calculation it is recommended that this parameter is set to248            something reasonable such as 2*distance_threshold249    Returns:250        gamma (np.array): The array of gamma values the same shape as that251            given by the evaluation coordinates and dose.252    """253    #  num_threads = 1 # force single-threading to avoid memory leaks254    coords_reference, coords_evaluation = _run_input_checks(255        coords_reference, dose_reference,256        coords_evaluation, dose_evaluation)257    if distance_step_size is None:258        distance_step_size = distance_threshold / 10259    reference_interpolation = RegularGridInterpolator(260        coords_reference, np.array(dose_reference),261        bounds_error=False, fill_value=np.inf262    )263    dose_evaluation = np.array(dose_evaluation)264    dose_evaluation_flat = np.ravel(dose_evaluation)265    mesh_coords_evaluation = np.meshgrid(*coords_evaluation, indexing='ij')266    coords_evaluation_flat = [267        np.ravel(item)268        for item in mesh_coords_evaluation]269    kwargs = {270        "coords_reference": coords_reference,271        "num_dimensions": len(coords_evaluation),272        "reference_interpolation": reference_interpolation,273        "lower_dose_cutoff": lower_dose_cutoff,274        "distance_threshold": distance_threshold,275        "dose_threshold": dose_threshold,276        "distance_step_size": distance_step_size,277        "max_concurrent_calc_points": max_concurrent_calc_points / num_threads,278        "maximum_test_distance": maximum_test_distance,279        "output_components": output_components}280    gamma_flat = np.nan * np.ones_like(dose_evaluation_flat)281    if output_components:282        dd_flat = np.nan * np.ones_like(dose_evaluation_flat)283        dta_flat = np.nan * np.ones_like(dose_evaluation_flat)284    evaluation_index = np.arange(len(dose_evaluation_flat))285    if num_threads > 1:286        np.random.shuffle(evaluation_index)287        thread_indicies = np.array_split(evaluation_index, num_threads)288        output = Queue()289        processes = []290        for thread_index in thread_indicies:291            thread_index.sort()292            thread_dose_evaluation = dose_evaluation_flat[thread_index]293            thread_coords_evaluation = [294                coords[thread_index]295                for coords in coords_evaluation_flat]296            kwargs['dose_evaluation'] = thread_dose_evaluation297            kwargs['mesh_coords_evaluation'] = thread_coords_evaluation298            p = Process(299                target=_new_thread,300                args=(301                    kwargs, output, thread_index,302                    np.nan * np.ones_like(dose_evaluation_flat)))303            p.start()304            processes.append(p)305        for i in range(num_threads):306            if output_components:307                res_gamma, res_dd, res_dta = output.get()308                thread_reference = np.invert(np.isnan(res_gamma))309                gamma_flat[thread_reference] = res_gamma[thread_reference]310                dd_flat[thread_reference] = res_dd[thread_reference]311                dta_flat[thread_reference] = res_dta[thread_reference]312            else:313                result = output.get()314                thread_reference = np.invert(np.isnan(result))315                gamma_flat[thread_reference] = result[thread_reference]316        for p in processes:317            p.join()318    else:319        kwargs['dose_evaluation'] = dose_evaluation_flat[evaluation_index]320        kwargs['mesh_coords_evaluation'] = [coords[evaluation_index] for coords in coords_evaluation_flat]321        result = _new_thread(kwargs, None, evaluation_index, np.nan*np.ones_like(dose_evaluation_flat))322        if output_components:323            res_gamma, res_dd, res_dta = result324            reference = np.invert(np.isnan(res_gamma))325            gamma_flat[reference] = res_gamma[reference]326            dd_flat[reference] = res_dd[reference]327            dta_flat[reference] = res_dta[reference]328        else:329            reference = np.invert(np.isnan(result))330            gamma_flat[reference] = result[reference]331    assert np.all(np.invert(np.isnan(gamma_flat)))332    #  gamma_flat[np.isinf(gamma_flat)] = np.nan333    gamma = np.reshape(gamma_flat, np.shape(dose_evaluation))334    if output_components:335        dd = np.reshape(dd_flat, np.shape(dose_evaluation))336        dta = np.reshape(dta_flat, np.shape(dose_evaluation))337        return (gamma, dd, dta)338    else:...

__init__.py

Source:__init__.py

1# Copyright (C) 2015 Simon Biggs2# This program is free software: you can redistribute it and/or modify3# it under the terms of the GNU Affero General Public License as published4# by the Free Software Foundation, either version 3 of the License, or5# (at your option) any later version (the "AGPL-3.0+").6# This program is distributed in the hope that it will be useful,7# but WITHOUT ANY WARRANTY; without even the implied warranty of8# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the9# GNU Affero General Public License and the additional terms for more10# details.11# You should have received a copy of the GNU Affero General Public License12# along with this program. If not, see <http://www.gnu.org/licenses/>.13# ADDITIONAL TERMS are also included as allowed by Section 7 of the GNU14# Affrero General Public License. These aditional terms are Sections 1, 5,15# 6, 7, 8, and 9 from the Apache License, Version 2.0 (the "Apache-2.0")16# where all references to the definition "License" are instead defined to17# mean the AGPL-3.0+.18# You should have received a copy of the Apache-2.0 along with this19# program. If not, see <http://www.apache.org/licenses/LICENSE-2.0>.20"""[DEPRECATED] Use `pymedphys.gamma` instead. See21https://pymedphys.com/en/latest/user/gamma.html22Compare two dose grids with the gamma index.23This module is a python implementation of the gamma index.24It computes 1, 2, or 3 dimensional gamma with arbitrary gird sizes while25interpolating on the fly.26This module makes use of some of the ideas presented within27<http://dx.doi.org/10.1118/1.2721657>.28It needs to be noted that this code base has not yet undergone sufficient29independent validation.30"""31import numpy as np32from scipy.interpolate import RegularGridInterpolator33from multiprocessing import Process, Queue34import warnings35WARNING_STRING = (36    'The `npgamma` module is deprecated. It contains known bugs. '37    'The bugfixes for these are within the `pymedphys.gamma` module. '38    'See https://pymedphys.com/en/latest/user/gamma.html for how to use '39    'the module that has superseded `npgamma`.'40)41warnings.warn(WARNING_STRING, UserWarning)42def _run_input_checks(43        coords_reference, dose_reference,44        coords_evaluation, dose_evaluation):45    """Check user inputs."""46    if (47            not isinstance(coords_evaluation, tuple) or48            not isinstance(coords_reference, tuple)):49        if (50                isinstance(coords_evaluation, np.ndarray) and51                isinstance(coords_reference, np.ndarray)):52            if (53                    len(np.shape(coords_evaluation)) == 1 and54                    len(np.shape(coords_reference)) == 1):55                coords_evaluation = (coords_evaluation,)56                coords_reference = (coords_reference,)57            else:58                raise Exception(59                    "Can only use numpy arrays as input for one dimensional "60                    "gamma."61                    )62        else:63            raise Exception(64                "Input coordinates must be inputted as a tuple, for "65                "one dimension input is (x,), for two dimensions, (x, y),  "66                "for three dimensions input is (x, y, z).")67    reference_coords_shape = tuple([len(item) for item in coords_reference])68    if reference_coords_shape != np.shape(dose_reference):69        raise Exception(70            "Length of items in coords_reference does not match the shape of "71            "dose_reference")72    evaluation_coords_shape = tuple([len(item) for item in coords_evaluation])73    if evaluation_coords_shape != np.shape(dose_evaluation):74        raise Exception(75            "Length of items in coords_evaluation does not match the shape of "76            "dose_evaluation")77    if not (len(np.shape(dose_evaluation)) ==78            len(np.shape(dose_reference)) ==79            len(coords_evaluation) ==80            len(coords_reference)):81        raise Exception(82            "The dimensions of the input data do not match")83    return coords_reference, coords_evaluation84def _calculate_coordinates_kernel(85        distance, num_dimensions, distance_step_size):86    """Determine the coodinate shifts required.87    Coordinate shifts are determined to check the reference dose for a88    given distance, dimension, and step size89    """90    if num_dimensions == 1:91        if distance == 0:92            x_coords = np.array([0])93        else:94            x_coords = np.array([distance, -distance])95        return (x_coords,)96    elif num_dimensions == 2:97        amount_to_check = np.floor(98            2 * np.pi * distance / distance_step_size) + 299        theta = np.linspace(0, 2*np.pi, amount_to_check + 1)[:-1:]100        x_coords = distance * np.cos(theta)101        y_coords = distance * np.sin(theta)102        return (x_coords, y_coords)103    elif num_dimensions == 3:104        number_of_rows = np.floor(105            np.pi * distance / distance_step_size) + 2106        elevation = np.linspace(0, np.pi, number_of_rows)107        row_radii = distance * np.sin(elevation)108        row_circumference = 2 * np.pi * row_radii109        amount_in_row = np.floor(110            row_circumference / distance_step_size) + 2111        x_coords = []112        y_coords = []113        z_coords = []114        for i, phi in enumerate(elevation):115            azimuth = np.linspace(0, 2*np.pi, amount_in_row[i] + 1)[:-1:]116            x_coords.append(distance * np.sin(phi) * np.cos(azimuth))117            y_coords.append(distance * np.sin(phi) * np.sin(azimuth))118            z_coords.append(distance * np.cos(phi) * np.ones_like(azimuth))119        return (120            np.hstack(x_coords), np.hstack(y_coords), np.hstack(z_coords))121    else:122        raise Exception("No valid dimension")123def _calculate_min_dose_difference(124        num_dimensions, mesh_coords_evaluation, to_be_checked,125        reference_interpolation, dose_evaluation,126        coordinates_at_distance_kernel):127    """Determine the minimum dose difference.128    Calculated for a given distance from each evaluation point.129    """130    coordinates_at_distance = []131    for i in range(num_dimensions):132        coordinates_at_distance.append(np.array(133            mesh_coords_evaluation[i][to_be_checked][None, :] +134            coordinates_at_distance_kernel[i][:, None])[:, :, None])135    all_points = np.concatenate(coordinates_at_distance, axis=2)136    dose_difference = np.array([137        reference_interpolation(points) -138        dose_evaluation[to_be_checked] for139        points in all_points140    ])141    min_dose_difference = np.min(np.abs(dose_difference), axis=0)142    return min_dose_difference143def _calculate_min_dose_difference_by_slice(144        max_concurrent_calc_points,145        num_dimensions, mesh_coords_evaluation, to_be_checked,146        reference_interpolation, dose_evaluation,147        coordinates_at_distance_kernel, **kwargs):148    """Determine minimum dose differences.149    Calculation is made with the evaluation set divided into slices. This150    enables less RAM usage.151    """152    all_checks = np.where(to_be_checked)153    min_dose_difference = (np.nan * np.ones_like(all_checks[0]))154    num_slices = np.floor(155        len(coordinates_at_distance_kernel[0]) *156        len(all_checks[0]) / max_concurrent_calc_points) + 1157    index = np.arange(len(all_checks[0]))158    np.random.shuffle(index)159    sliced = np.array_split(index, num_slices)160    for current_slice in sliced:161        current_to_be_checked = np.zeros_like(to_be_checked).astype(bool)162        current_to_be_checked[[163            item[current_slice] for164            item in all_checks]] = True165        assert np.all(to_be_checked[current_to_be_checked])166        min_dose_difference[np.sort(current_slice)] = (167            _calculate_min_dose_difference(168                num_dimensions, mesh_coords_evaluation, current_to_be_checked,169                reference_interpolation, dose_evaluation,170                coordinates_at_distance_kernel))171    assert np.all(np.invert(np.isnan(min_dose_difference)))172    return min_dose_difference173def _calculation_loop(**kwargs):174    """Iteratively calculates gamma at increasing distances."""175    dose_valid = kwargs['dose_evaluation'] >= kwargs['lower_dose_cutoff']176    gamma_valid = np.ones_like(kwargs['dose_evaluation']).astype(bool)177    running_gamma = np.inf * np.ones_like(kwargs['dose_evaluation'])178    distance = 0179    while True:180        to_be_checked = (181            dose_valid & gamma_valid)182        coordinates_at_distance_kernel = _calculate_coordinates_kernel(183            distance, kwargs['num_dimensions'], kwargs['distance_step_size'])184        min_dose_difference = _calculate_min_dose_difference_by_slice(185            to_be_checked=to_be_checked,186            coordinates_at_distance_kernel=coordinates_at_distance_kernel,187            **kwargs)188        gamma_at_distance = np.sqrt(189            min_dose_difference ** 2 / kwargs['dose_threshold'] ** 2 +190            distance ** 2 / kwargs['distance_threshold'] ** 2)191        running_gamma[to_be_checked] = np.min(192            np.vstack((193                gamma_at_distance, running_gamma[to_be_checked]194            )), axis=0)195        gamma_valid = running_gamma > distance / kwargs['distance_threshold']196        distance += kwargs['distance_step_size']197        if (198                (np.sum(to_be_checked) == 0) |199                (distance > kwargs['maximum_test_distance'])):200            break201    return running_gamma202def _new_thread(kwargs, output, thread_index, gamma_store):203    gamma_store[thread_index] = _calculation_loop(**kwargs)204    output.put(gamma_store)205def calc_gamma(coords_reference, dose_reference,206               coords_evaluation, dose_evaluation,207               distance_threshold, dose_threshold,208               lower_dose_cutoff=0, distance_step_size=None,209               maximum_test_distance=np.inf,210               max_concurrent_calc_points=np.inf,211               num_threads=1):212    """[DEPRECATED] Use `pymedphys.gamma` instead. See213    https://pymedphys.com/en/latest/user/gamma.html214    Compare two dose grids with the gamma index.215    Args:216        coords_reference (tuple): The reference coordinates.217        dose_reference (np.array): The reference dose grid.218        coords_evaluation (tuple): The evaluation coordinates.219        dose_evaluation (np.array): The evaluation dose grid.220        distance_threshold (float): The gamma distance threshold. Units must221            match of the coordinates given.222        dose_threshold (float): An absolute dose threshold.223            If you wish to use 3% of maximum reference dose input224            np.max(dose_reference) * 0.03 here.225        lower_dose_cutoff (:obj:`float`, optional): The lower dose cutoff below226            which gamma will not be calculated.227        distance_step_size (:obj:`float`, optional): The step size to use in228            within the reference grid interpolation. Defaults to a tenth of the229            distance threshold as recommended within230            <http://dx.doi.org/10.1118/1.2721657>.231        maximum_test_distance (:obj:`float`, optional): The distance beyond232            which searching will stop. Defaults to np.inf. To speed up233            calculation it is recommended that this parameter is set to234            something reasonable such as 2*distance_threshold235    Returns:236        gamma (np.array): The array of gamma values the same shape as that237            given by the evaluation coordinates and dose.238    """239    warnings.warn(WARNING_STRING, UserWarning)240    coords_reference, coords_evaluation = _run_input_checks(241        coords_reference, dose_reference,242        coords_evaluation, dose_evaluation)243    if distance_step_size is None:244        distance_step_size = distance_threshold / 10245    reference_interpolation = RegularGridInterpolator(246        coords_reference, np.array(dose_reference),247        bounds_error=False, fill_value=np.inf248    )249    dose_evaluation = np.array(dose_evaluation)250    dose_evaluation_flat = np.ravel(dose_evaluation)251    mesh_coords_evaluation = np.meshgrid(*coords_evaluation, indexing='ij')252    coords_evaluation_flat = [253        np.ravel(item)254        for item in mesh_coords_evaluation]255    evaluation_index = np.arange(len(dose_evaluation_flat))256    np.random.shuffle(evaluation_index)257    thread_indicies = np.array_split(evaluation_index, num_threads)258    output = Queue()259    kwargs = {260        "coords_reference": coords_reference,261        "num_dimensions": len(coords_evaluation),262        "reference_interpolation": reference_interpolation,263        "lower_dose_cutoff": lower_dose_cutoff,264        "distance_threshold": distance_threshold,265        "dose_threshold": dose_threshold,266        "distance_step_size": distance_step_size,267        "max_concurrent_calc_points": max_concurrent_calc_points / num_threads,268        "maximum_test_distance": maximum_test_distance}269    for thread_index in thread_indicies:270        thread_index.sort()271        thread_dose_evaluation = dose_evaluation_flat[thread_index]272        thread_coords_evaluation = [273            coords[thread_index]274            for coords in coords_evaluation_flat]275        kwargs['dose_evaluation'] = thread_dose_evaluation276        kwargs['mesh_coords_evaluation'] = thread_coords_evaluation277        Process(278            target=_new_thread,279            args=(280                kwargs, output, thread_index,281                np.nan * np.ones_like(dose_evaluation_flat))).start()282    gamma_flat = np.nan * np.ones_like(dose_evaluation_flat)283    for i in range(num_threads):284        result = output.get()285        thread_reference = np.invert(np.isnan(result))286        gamma_flat[thread_reference] = result[thread_reference]287    assert np.all(np.invert(np.isnan(gamma_flat)))288    gamma_flat[np.isinf(gamma_flat)] = np.nan289    gamma = np.reshape(gamma_flat, np.shape(dose_evaluation))...

infint.py

Source:infint.py

1# imports2import sys3# Takes number string and creates list with each node of a predetermined length.4def infinite_num_build(digits, node_length):5    number_list = []6    i = 07    node = ''8    for index in digits:9        if i % node_length == 0:10            if node != '':11                number_list.append(node)12            node = ''13        node += index14        i += 115    number_list.append(node)16    return number_list17# Checks for correct inputs based on assignment18def limit_check(to_be_checked, function):19    node = 420    input_length_limit = 4021    output_length_limit = 10022    length_limit = 023    if function == 'node':24        if int(to_be_checked) > node:25            return 'invalid expression'26    else:27        if function == 'input':28            length_limit = input_length_limit29        elif function == 'output':30            length_limit = output_length_limit31        # Length check32        i = 033        while i < len(to_be_checked):34            digit_length = 035            while i < len(to_be_checked) and to_be_checked[i].isdigit():36                digit_length += 137                i += 138                if digit_length > length_limit:39                    return 'invalid expression'40            i += 141        return to_be_checked42# Finds digit at specific index43def infinite_int_digit_index(number, index):44    count = 045    for sublist in number:46        for digit in sublist:47            if count == index:48                return digit49            count += 150# Counts number of digits in infinite int format number51def infinite_int_digit_count(number):52    count = 053    for sublist in number:54        count += len(sublist)55    return count56# Iteratively adds two numbers built from lists.57def add(num1, num2, node_length):58    # Error Checking59    if num1 == 'invalid expression' or num2 == 'invalid expression':60        return 'invalid expression'61    answer = ''62    overflow = 063    num1_length = infinite_int_digit_count(num1)64    num2_length = infinite_int_digit_count(num2)65    add_number_length = max(num1_length, num2_length)66    for i in range(add_number_length):67        if i >= num1_length:68            digit1 = 069        else:70            digit1 = infinite_int_digit_index(num1, num1_length-1-i)71        if i >= num2_length:72            digit2 = 073        else:74            digit2 = infinite_int_digit_index(num2, num2_length-1-i)75        added = int(digit1) + int(digit2) + overflow76        overflow = 077        if added > 9:78            overflow = 179            added -= 1080        answer = str(added) + answer81    if overflow > 0:82        answer = str(overflow) + answer83    answer_infinite_format = infinite_num_build(answer, node_length)84    return answer_infinite_format85# Function to multiply two numbers86def multiply(num1, num2, node_length):87    num1_length = infinite_int_digit_count(num1)88    num2_length = infinite_int_digit_count(num2)89    # Error Checking90    if num1 == 'invalid expression' or num2 == 'invalid expression':91        return 'invalid expression'92    if num1 == ['0'] or num2 == ['0']:93        return ['0']94    answer = infinite_num_build('0', node_length)95    for i in range(num1_length):96        for j in range(num2_length):97            digit1 = infinite_int_digit_index(num1, num1_length-i-1)98            # print('Digit 1: ' + str(digit1))99            digit2 = infinite_int_digit_index(num2, num2_length-j-1)100            # print('To be multiplied: {} * {}'.format(digit1, digit2))101            multiplied = str(int(digit1) * int(digit2)) + (i * '0') + (j * '0')102            # print(multiplied)103            multiplied_infinite_num = infinite_num_build(multiplied, node_length)104            answer = add(answer, multiplied_infinite_num, node_length)105            # print(answer)106    return answer107# Converts nested number list to string for printing108def list_print_to_string(list_to_print):109    answer_string = ''110    for sublist in list_to_print:111        for digit in sublist:112            answer_string += digit113    answer_string = limit_check(answer_string, 'output')114    return answer_string115# To solve nested operations, loosely based on evaluation expression examples on GeekForGeeks116def nested_operations(string_line, node_length):117    try:118        operation_stack = []119        digit_stack = []120        i = 0121        while i < len(string_line):122            operator = ''123            if string_line[i].isalpha():124                while i < len(string_line) and string_line[i].isalpha():125                    operator += string_line[i]126                    i += 1127                if string_line[i] == '(':128                    operation_stack.append(operator)129                    i += 1130            elif string_line[i].isdigit():131                digit = ''132                while i < len(string_line) and string_line[i].isdigit():133                    digit += string_line[i]134                    i += 1135                digit_stack.append(infinite_num_build(digit, node_length))136            elif string_line[i] == ')':137                operation = operation_stack.pop()138                if operation == 'multiply':139                    digit_stack.append(multiply(digit_stack.pop(), digit_stack.pop(), node_length))140                elif operation == 'add':141                    digit_stack.append(add(digit_stack.pop(), digit_stack.pop(), node_length))142                else:143                    return 'invalid expression'144                i += 1145            else:146                i += 1147        if len(digit_stack) != 1:148            return 'invalid expression'149        return limit_check(list_print_to_string(digit_stack.pop()), 'output')150    except:151        return 'invalid expression'152# Main function153# Check argument length154if len(sys.argv) < 2:155    print('Error: Insufficient filename arguments found')156    sys.exit()157arg1 = sys.argv[1]158fileName = ''159nodeLength = ''160# Separate file name from input argument161for ind in range(6, arg1.find(';')):162    fileName += arg1[ind]163# Separate node length from input argument164try:165    for ind in range(arg1.find(';') + 15, len(arg1)):166        nodeLength += arg1[ind]167    nodeLengthInt = int(nodeLength)168    if limit_check(nodeLengthInt, 'node') == 'invalid expression':169        print('Invalid node length')170        sys.exit()171except Exception:172    nodeLengthInt = 3173with open(fileName) as fp:174    for line in fp:175        line = line.rstrip('\n')176        if line != '':...

problem_3.py

Source:problem_3.py

1import argparse2import fileinput3import functools4import logging5from typing import List6# Redefine the print function with flush=True. Needed for interactive problems.7print = functools.partial(print, flush=True)8# Configure logging9logging.basicConfig(10    format='[%(lineno)03d]: %(message)s', level=logging.WARNING)11LOG = logging.getLogger(__name__)12def get_int() -> int:13    """Read an int from file or stdin."""14    string = FILE.readline().strip()15    return int(string)16def get_float() -> float:17    """Read a float from file or stdin."""18    string = FILE.readline().strip()19    return float(string)20def get_ints() -> List[int]:21    """Read multiple ints from file or stdin."""22    string = FILE.readline().strip()23    return [int(s) for s in string.split()]24def get_string() -> str:25    """Read a string from file or stdin."""26    string = FILE.readline().strip()27    return string28def interest_level(floor: dict):29    """Calculate the interest level of the current floor."""30    level = sum(value for value in floor.values())31    return level32def initial_neighbours(floor: dict, rows: int, cols: int) -> dict:33    """Find all the inital neighbours."""34    all_neighbours = dict()35    for row in range(rows):36        for col in range(cols):37            cell_neigbours = dict()38            if col > 0:39                cell_neigbours["W"] = (row, col - 1)40            if col + 1 < cols:41                cell_neigbours["E"] = (row, col + 1)42            if row > 0:43                cell_neigbours["N"] = (row - 1, col)44            if row + 1 < rows:45                cell_neigbours["S"] = (row + 1, col)46            all_neighbours[(row, col)] = cell_neigbours47    return all_neighbours48def eliminate_and_update(to_eliminated: List, floor: dict, neighbours: dict) -> set:49    """Eliminate dancers and create list of dancers to check in next round."""50    LOG.info("Eliminating %d dancers", len(to_eliminated))51    to_be_checked = set()52    for pos in to_eliminated:53        LOG.debug("Eliminating pos: {}".format(pos))54        # Remove the person from the floor55        del floor[pos]56        # Update the neighbours of our neigbours57        to_west = neighbours[pos].get("W", None)58        to_east = neighbours[pos].get("E", None)59        to_north = neighbours[pos].get("N", None)60        to_south = neighbours[pos].get("S", None)61        if to_west:62            if to_east:63                neighbours[to_west]["E"] = to_east64            else:65                del neighbours[to_west]["E"]66        if to_east:67            if to_west:68                neighbours[to_east]["W"] = to_west69            else:70                del neighbours[to_east]["W"]71        if to_north:72            if to_south:73                neighbours[to_north]["S"] = to_south74            else:75                del neighbours[to_north]["S"]76        if to_south:77            if to_north:78                neighbours[to_south]["N"] = to_north79            else:80                del neighbours[to_south]["N"]81        # Add our neighbours to the check_list82        for neig in neighbours[pos].values():83            to_be_checked.add(neig)84        # Remove person from neighbours list85        del neighbours[pos]86    # No need to check persons that have been eliminated87    to_be_checked -= set(to_eliminated)88    return to_be_checked89def main():90    """This is where you write your solution."""91    cases = get_int()92    for case in range(1, cases + 1):93        # Read input data94        rows, cols = get_ints()95        # Store skill levels in a dictionary96        floor = dict()97        to_be_checked = []98        for row in range(rows):99            for col, value in enumerate(get_ints()):100                floor[(row, col)] = value101                to_be_checked.append((row, col))102        # Find initial neighbours, store in a dictionary103        neighbours = initial_neighbours(floor, rows, cols)104        # Interest level before any eliminations105        competition_interest = interest_level(floor)106        # Loop until competition is over107        n_rounds = 0108        while True:109            n_rounds += 1110            to_be_eliminated = []111            # Loop over the positions we need to check112            LOG.info("Need to check %s positions", len(to_be_checked))113            for pos in to_be_checked:114                if len(neighbours[pos]) == 0:115                    continue  # This dancer doesn't have any neighbours116                neig_values = [floor[neig] for neig in neighbours[pos].values()]117                if floor[pos] < (sum(neig_values) / len(neig_values)):118                    to_be_eliminated.append(pos)119            # Finished or not?120            if len(to_be_eliminated) == 0:121                break122            # Eliminate dancers and create new list to be checked123            to_be_checked = eliminate_and_update(to_be_eliminated, floor, neighbours)124            # Increase competition interest125            competition_interest += interest_level(floor)126            LOG.info("End of round %d: %d dancers left", n_rounds, len(floor))127        LOG.info("End of competition after %d rounds: %d dancers left",128                 n_rounds, len(floor))129        print('Case #{}: {}'.format(case, competition_interest))130if __name__ == '__main__':131    # Parse command line arguments132    parser = argparse.ArgumentParser(description='Code Jam solution')133    parser.add_argument('-v', '--verbose', action='count', default=0,134                        help="Increase verbosity. "135                        "Can be repeated up to two times.")136    parser.add_argument('infile', default="-", nargs="?",137                        help="Read from file instead of stdin")138    arguments = parser.parse_args()139    # Possibly change logging level of the top-level logger140    if arguments.verbose == 1:141        logging.getLogger().setLevel(logging.INFO)142    if arguments.verbose >= 2:143        logging.getLogger().setLevel(logging.DEBUG)144    # Print debugging information145    LOG.debug("Finished parsing arguments: %s", arguments)146    # Define a global FILE variable (sys.stdin or infile) and run main()147    FILE = fileinput.input(files=arguments.infile)...

Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.