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How to use value_ranges method in pandera

Best Python code snippet using pandera_python

sheet.py

Source:sheet.py

1from google_documents.entities.from_itemable import FromItemable2class Color(FromItemable):3    red: float4    green: float5    blue: float6    def __init__(self, red=0, green=0, blue=0):7        self.red = red8        self.blue = blue9        self.green = green10    def to_item(self):11        return {12            "green": self.green,13            "blue": self.blue,14            "red": self.red15        }16class GridProperties(FromItemable):17    def __init__(self, row_count, column_count):18        self.row_count = row_count19        self.column_count = column_count20    def to_item(self):21        return {22            'rowCount': self.row_count,23            'columnCount': self.column_count24        }25    @classmethod26    def from_item(cls, item):27        return cls(item['rowCount'], item['columnCount'])28class Sheet(FromItemable):29    index: int30    title: str31    tab_color: Color = None32    grid_properties: GridProperties = None33    # Optional - if assigned, makes available sheet read/write methods34    spreadsheet = None35    def assign_spreadsheet(self, spreadsheet):36        self.spreadsheet = spreadsheet37    def _get_spreadsheet_range_name(self, sheet_range_name):38        """39        Returns range name in the sheet40        """41        return f"{self.title}!{sheet_range_name}"42    def read(self, range_name):43        """44        Reads data from the sheet45        :param range_name: Range to read46        """47        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."48        return self.spreadsheet.read(49            self._get_spreadsheet_range_name(range_name)50        )51    def write(self, range_name, data, value_input_option="RAW"):52        """53        Writes data into the sheet54        :param range_name: Range to write in55        :param data: Data to write56        :param value_input_option: How to recognize input data57        :return:58        """59        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."60        return self.spreadsheet.write(61            range_name=self._get_spreadsheet_range_name(range_name),62            data=data,63            value_input_option=value_input_option,64        )65    def clear(self, range_name):66        """67        Clears range in the sheet68        :param range_name: Range to clear69        :return:70        """71        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."72        return self.spreadsheet.clear(73            self._get_spreadsheet_range_name(range_name)74        )75    def batch_read(self, ranges_names):76        """77        Reads multiple ranges from the spreasheet78        :param ranges_names:79        :return:80        """81        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."82        # Convert everything to range names with the sheet name83        ranges_names = list(map(84            self._get_spreadsheet_range_name, ranges_names85        ))86        return self.spreadsheet.batch_read(ranges_names)87    def batch_write(self, value_ranges, value_input_option="RAW"):88        """89        Writes data tu the multiple ranges in the Google Sheet90        :param value_ranges: List of dicts like91        {"range": "A1:B2", data: [[data to write]]}92        :param value_input_option: How to recognize input data93        :return:94        """95        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."96        # Convert ranges in value_ranges to the ranges with the sheet name97        value_ranges = list(map(98            lambda r: {99                **r,100                "range": self._get_spreadsheet_range_name(r["range"])101            },102            value_ranges103        ))104        return self.spreadsheet.batch_write(value_ranges, value_input_option)105    def batch_clear(self, ranges_names):106        """107        Clears multiple ranges in the Google Sheets108        :param ranges_names: Ranges to clear109        :return:110        """111        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."112        ranges_names = list(map(113            self._get_spreadsheet_range_name, ranges_names114        ))115        return self.spreadsheet.batch_clear(ranges_names)116    def delete(self):117        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."118        response = self.spreadsheet._sheets_api_service. \119            spreadsheets().batchUpdate(120                spreadsheetId=self.spreadsheet.id,121                body={"requests": [{"deleteSheet": {"sheetId": self.id}}]}122            ).execute()123        # Delete spreadhseet from sheet124        self.spreadsheet = None125        return response126    @classmethod127    def from_item(cls, item):128        properties = item["properties"]129        grid_properties = None130        if properties.get("gridProperties"):131            grid_properties = GridProperties.from_item(132                properties["gridProperties"])133        tab_color = None134        if properties.get("tabColor"):135            tab_color = Color.from_item(properties["tabColor"])136        return cls(137            id=properties["sheetId"],138            index=properties["index"],139            title=properties["title"],140            tab_color=tab_color,141            grid_properties=grid_properties,142        )143    def to_item(self):144        return {145            "sheetId": self.id,146            "index": self.index,147            "title": self.title,148            "gridProperties": self.grid_properties.to_item()149            if self.grid_properties else None,150            "tabColor": self.tab_color and self.tab_color.to_item(),151        }152    def __getitem__(self, item):153        """154        Allows get data from the spreadsheet using sheet["A1:B2"]155        or batch get using sheet[["A1:B2", "A5:B6"]]156        """157        if isinstance(item, str):158            return self.read(item)159        else:160            return self.batch_read(item)161    set_item_value_input_option = "RAW"162    def __setitem__(self, item, value):163        """164        Allows writing data into the spreadsheet using165        sheet["A1:B2"] = [["l", "o"], ["l", "!"]]166        or using batch writing167        sheet[["A1:B2", "A5:B6]] = \168        ([["l", "o"], ["l", "!"]], [["f", "o"], ["o", "!"]])169        Value input option should be set here (if needed)170        via set_item_value_input_option171        """172        if isinstance(item, str):173            self.write(item, value, self.set_item_value_input_option)174        else:175            # There should be equal count of ranges and values to set176            if not len(item) == len(value):177                raise ValueError("Length of ranges and vales is not equal")178            value_ranges = [179                {"range": range_name,180                 "values": values}181                for range_name, values182                in zip(item, value)183            ]184            self.batch_write(value_ranges, self.set_item_value_input_option)185    def __init__(186            self,187            id=None,188            index=None,189            title=None,190            tab_color=None,191            grid_properties=None192    ):193        self.id = id194        self.index = index195        self.title = title196        self.tab_color = tab_color197        self.grid_properties = grid_properties198    def __eq__(self, other):199        assert self.spreadsheet, "Spreadsheet for the sheet is unknown."200        assert other.spreadsheet, "Spreadsheet for the sheet is unknown."201        return self.spreadsheet == other.spreadsheet and self.id == other.id202    def __repr__(self):203        return f'<Sheet title="{self.title}" index="{self.index}">'...

gridsearch.py

Source:gridsearch.py

1# coding=utf-82import nevo.eval.fitness as fitness3import nevo.chromgen as chromgen4import math5import operator6from itertools import product7logger = None8QUEUESIZE = 1009#------------------------------------------------10def start(pconf, **args):11    mode = pconf.get("mode", "Simulation")12    global logger13    logger = pconf.get_logger("gridsearch")14    proj_name = pconf.parse_project_data()["proj_name"]15    num_currents = int(pconf.get_list("currents", "Simulation")[0])16    fitness_args = { "pconf"  : pconf,17                     "mode"       : mode,18                     "proj_name"  : proj_name,19                     "numCurrents": num_currents,20                   }21    for item in pconf.cfg.items("fitness.calc_fitness_candidates"):22        fitness_args[item[0]] = eval(item[1])23    24    (l_bounds, u_bounds, deltas) = ([], [], [])25    for channel in ["ar", "cal", "cat", "k2", "ka", "kahp", "hc", "alpha", "km", "naf", "nap", "pas"]:26        _range = map(eval, pconf.get(channel, "gridsearch").split(','))27        if len(_range) == 1:28            l_bounds.append(_range[0])29            u_bounds.append(_range[0])30            deltas.append(1)31        elif len(_range) == 3:32            l_bounds.append(_range[0])33            u_bounds.append(_range[1])34            deltas.append(_range[2])35        else:36            raise RuntimeError("Invalid parameters for channel " +repr(channel))37    grid = []38    gridmode = pconf.get("gridmode", "gridsearch")39    logger.info("Generating grid...")40    if(gridmode == "linear"):41        grid = generate_linear_grid(l_bounds, u_bounds, deltas)42    elif(gridmode == "exponential"):43        grid = generate_exponential_grid(l_bounds, u_bounds, deltas)44    else:45        raise RuntimeError("Invalid gridmode: " + repr(gridmode))46    logger.info("Generated " + gridmode + " grid:")47    logger.info("Lower bounds: " + repr(l_bounds))48    logger.info("Upper bounds: " + repr(u_bounds))49    logger.info("Steps       : " + repr(deltas))50    gs = GridSearch(pconf, fitness_args)51    best = gs.evaluate(grid)52    logger.info("Grid search complete.")53    logger.info("Best result:" +repr(best))54    55def generate_linear_grid(l_bounds, u_bounds, deltas):56    value_ranges = map(list, map(generate_steps, l_bounds, u_bounds, deltas))57    58    logger.info("Value ranges:")59    for x in value_ranges:60        logger.info(repr(x))61    logger.info("Grid size: " + repr(reduce(operator.mul, map(len, value_ranges), 1)))62    # return cartesian product, i.e. all combinations of values63    return product(*value_ranges)64#------------------------------------------------65def generate_exponential_grid(l_bounds, u_bounds, deltas):66    exp_l_bounds = map(math.log10, l_bounds)67    exp_u_bounds = map(math.log10, u_bounds)68    exp_value_ranges = map(generate_steps, exp_l_bounds, exp_u_bounds, deltas)69    value_ranges     = [map(lambda x:10**x, list(l)) for l in exp_value_ranges]70    # Special case: scalar in position 7:71    value_ranges[7] = list(generate_steps(l_bounds[7], u_bounds[7], deltas[7]))72    logger.info("Value ranges:")73    for x in value_ranges:74        logger.info(repr(x))75    logger.info("Grid size: " + repr(reduce(operator.mul, map(len, value_ranges), 1)))76    # return cartesian product, i.e. all combinations of values77    return product(*value_ranges)78#------------------------------------------------79def generate_steps(l_bound, u_bound, delta):80    yield l_bound81    l_bound += delta82    while l_bound <= u_bound:83        yield l_bound84        l_bound += delta85#------------------------------------------------86class GridSearch(object):87    #------------------------------------------------88    def __init__(self, pconf, fitness_args):89        self.pconf = pconf90        self.fitness_args = fitness_args91        self.outfile = pconf.local_path("grid.csv")92        self.queue = []93        self.best  = ([], -20000)94    #------------------------------------------------95    def evaluate(self, grid):96        for point in grid:97            self.add(point)98        self.update()99        return self.best100    #------------------------------------------------101    def add(self, point):102        self.queue.append(list(point))103        if len(self.queue) > QUEUESIZE:104            self.update()105   #------------------------------------------------106    def update(self):107        results = fitness.calc_fitness_candidates(self.queue, self.fitness_args)108        with open(self.outfile, "a") as file:109            for i in range(len(results)):110                s = repr(results[i]) + ", " + repr(self.queue[i])111                logger.info(s)112                file.write(s + "\n")113                if results[i] > self.best[1]:114                    self.best = (self.queue[i], results[i])...

histogram_peak.py

Source:histogram_peak.py

1class HistogramPeak:2    def __init__(self, peaks=None, range_length=100, parameter_theta=0.1):3        self.histogram = None4        self.peaks = peaks5        self.parameter_theta = parameter_theta6        self.valid_peak_counts = [0] * range_length7        self.value_ranges = self._init_value_range(range_length)8    def _init_value_range(self, range_length):9        value_ranges = list()10        interval_value = 1.0 / range_length11        for i in range(0, range_length):12            value_ranges.append(round(interval_value * (i + 1), 2))13        return value_ranges14    def extract_peak_count(self):15        for peak in self.peaks:16            value = peak.x.value17            if value < self.parameter_theta:18                continue19            index = -120            for point in self.value_ranges:21                index += 122                if value < point:23                    break24            self.valid_peak_counts[index] += 125    def extract_feature(self):26        for value in self.histogram:27            if value < self.parameter_theta:28                continue29            index = -130            for point in self.value_ranges:31                index += 132                if value < point:33                    break34            self.valid_peak_counts[index] += 135    def to_string(self, label):36        feature = '{0}'.format(label)37        index = 038        for count in self.valid_peak_counts:39            feature += ' {0}:{1}'.format(index, count)40            index += 1...

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