Instruction:
Explain what the following Python 3 code does
def create_data_and_metadata(self, data: numpy.ndarray, intensity_calibration: CalibrationModule.Calibration = None,
                                 dimensional_calibrations: typing.List[CalibrationModule.Calibration] = None, metadata: dict = None,
                                 timestamp: str = None, data_descriptor: DataAndMetadata.DataDescriptor = None) -> DataAndMetadata.DataAndMetadata:
        """Create a data_and_metadata object from data.

        :param data: an ndarray of data.
        :param intensity_calibration: An optional calibration object.
        :param dimensional_calibrations: An optional list of calibration objects.
        :param metadata: A dict of metadata.
        :param timestamp: A datetime object.
        :param data_descriptor: A data descriptor describing the dimensions.

        .. versionadded:: 1.0

        Scriptable: Yes
        """
        return DataAndMetadata.new_data_and_metadata(data, intensity_calibration, dimensional_calibrations, metadata, timestamp, data_descriptor)

Response: Create a data_and_metadata object from data.


Instruction:
Implement a function in Python 3 to
create a data_and_metadata object from data.

Response:
def create_data_and_metadata_from_data(self, data: numpy.ndarray, intensity_calibration: CalibrationModule.Calibration=None, dimensional_calibrations: typing.List[CalibrationModule.Calibration]=None, metadata: dict=None, timestamp: str=None) -> DataAndMetadata.DataAndMetadata:
        """Create a data_and_metadata object from data.

        .. versionadded:: 1.0
        .. deprecated:: 1.1
           Use :py:meth:`~nion.swift.Facade.DataItem.create_data_and_metadata` instead.

        Scriptable: No
        """
        return self.create_data_and_metadata(numpy.copy(data), intensity_calibration, dimensional_calibrations, metadata, timestamp)


Instruction:
Can you implement a function in Python 3 that
creates an I/O handler that reads and writes a single data_and_metadata.

Response:
def create_data_and_metadata_io_handler(self, io_handler_delegate):
        """Create an I/O handler that reads and writes a single data_and_metadata.

        :param io_handler_delegate: A delegate object :py:class:`DataAndMetadataIOHandlerInterface`

        .. versionadded:: 1.0

        Scriptable: No
        """
        class DelegateIOHandler(ImportExportManager.ImportExportHandler):
            def __init__(self):
                super().__init__(io_handler_delegate.io_handler_id, io_handler_delegate.io_handler_name, io_handler_delegate.io_handler_extensions)

            def read_data_elements(self, ui, extension, file_path):
                data_and_metadata = io_handler_delegate.read_data_and_metadata(extension, file_path)
                data_element = ImportExportManager.create_data_element_from_extended_data(data_and_metadata)
                return [data_element]

            def can_write(self, data_and_metadata, extension):
                return io_handler_delegate.can_write_data_and_metadata(data_and_metadata, extension)

            def write_display_item(self, ui, display_item: DisplayItemModule.DisplayItem, file_path: str, extension: str) -> None:
                data_item = display_item.data_item
                if data_item:
                    self.write_data_item(ui, data_item, file_path, extension)

            def write_data_item(self, ui, data_item, file_path, extension):
                data_and_metadata = data_item.xdata
                data = data_and_metadata.data
                if data is not None:
                    if hasattr(io_handler_delegate, "write_data_item"):
                        io_handler_delegate.write_data_item(DataItem(data_item), file_path, extension)
                    else:
                        assert hasattr(io_handler_delegate, "write_data_and_metadata")
                        io_handler_delegate.write_data_and_metadata(data_and_metadata, file_path, extension)

        class IOHandlerReference:

            def __init__(self):
                self.__io_handler_delegate = io_handler_delegate
                self.__io_handler = DelegateIOHandler()
                ImportExportManager.ImportExportManager().register_io_handler(self.__io_handler)

            def __del__(self):
                self.close()

            def close(self):
                if self.__io_handler_delegate:
                    io_handler_delegate_close_fn = getattr(self.__io_handler_delegate, "close", None)
                    if io_handler_delegate_close_fn:
                       io_handler_delegate_close_fn()
                    ImportExportManager.ImportExportManager().unregister_io_handler(self.__io_handler)
                    self.__io_handler_delegate = None

        return IOHandlerReference()


Instruction:
Can you write a function in Python 3 where it
creates a new panel that can be attached to a window.

Response:
def create_panel(self, panel_delegate):
        """Create a utility panel that can be attached to a window.

        .. versionadded:: 1.0

        Scriptable: No

         The panel_delegate should respond to the following:
            (property, read-only) panel_id
            (property, read-only) panel_name
            (property, read-only) panel_positions (a list from "top", "bottom", "left", "right", "all")
            (property, read-only) panel_position (from "top", "bottom", "left", "right", "none")
            (method, required) create_panel_widget(ui), returns a widget
            (method, optional) close()
        """

        panel_id = panel_delegate.panel_id
        panel_name = panel_delegate.panel_name
        panel_positions = getattr(panel_delegate, "panel_positions", ["left", "right"])
        panel_position = getattr(panel_delegate, "panel_position", "none")
        properties = getattr(panel_delegate, "panel_properties", None)

        workspace_manager = Workspace.WorkspaceManager()

        def create_facade_panel(document_controller, panel_id, properties):
            panel = Panel(document_controller, panel_id, properties)
            ui = UserInterface(self.__ui_version, document_controller.ui)
            document_controller = DocumentWindow(document_controller)
            panel.widget = panel_delegate.create_panel_widget(ui, document_controller)._widget
            return panel

        class PanelReference:

            def __init__(self):
                self.__panel_delegate = panel_delegate
                workspace_manager.register_panel(create_facade_panel, panel_id, panel_name, panel_positions, panel_position, properties)

            def __del__(self):
                self.close()

            def close(self):
                if self.__panel_delegate:
                    panel_delegate_close_fn = getattr(self.__panel_delegate, "close", None)
                    if panel_delegate_close_fn:
                       panel_delegate_close_fn()
                    workspace_manager.unregister_panel(panel_id)
                    self.__panel_delegate = None

        return PanelReference()


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_hardware_source_by_id(self, hardware_source_id: str, version: str):
        actual_version = "1.0.0"
        if Utility.compare_versions(version, actual_version) > 0:
            raise NotImplementedError("Hardware API requested version %s is greater than %s." % (version, actual_version))
        hardware_source = HardwareSourceModule.HardwareSourceManager().get_hardware_source_for_hardware_source_id(hardware_source_id)
        return HardwareSource(hardware_source) if hardware_source else None

Response: Return the hardware source API matching the hardware_source_id and version.


Instruction:
Write a Python 3 script for
returning the library object.

Response:
def library(self) -> Library:
        """Return the library object.

        .. versionadded:: 1.0

        Scriptable: Yes
        """
        assert self.__app.document_model
        return Library(self.__app.document_model)


Instruction:
How would you implement a function in Python 3 that
creates a cost matrix from a profit matrix by calling inversion_function to invert each value in the profit matrix.

Response:
def make_cost_matrix(profit_matrix, inversion_function):
    """
    Create a cost matrix from a profit matrix by calling
    'inversion_function' to invert each value. The inversion
    function must take one numeric argument (of any type) and return
    another numeric argument which is presumed to be the cost inverse
    of the original profit.

    This is a static method. Call it like this:

    .. python::

        cost_matrix = Munkres.make_cost_matrix(matrix, inversion_func)

    For example:

    .. python::

        cost_matrix = Munkres.make_cost_matrix(matrix, lambda x : sys.maxsize - x)

    :Parameters:
        profit_matrix : list of lists
            The matrix to convert from a profit to a cost matrix

        inversion_function : function
            The function to use to invert each entry in the profit matrix

    :rtype: list of lists
    :return: The converted matrix
    """
    cost_matrix = []
    for row in profit_matrix:
        cost_matrix.append([inversion_function(value) for value in row])
    return cost_matrix


Instruction:
Can you generate a brief explanation for the following Python 3 code
def pad_matrix(self, matrix, pad_value=0):
        max_columns = 0
        total_rows = len(matrix)

        for row in matrix:
            max_columns = max(max_columns, len(row))

        total_rows = max(max_columns, total_rows)

        new_matrix = []
        for row in matrix:
            row_len = len(row)
            new_row = row[:]
            if total_rows > row_len:
                # Row too short. Pad it.
                new_row += [pad_value] * (total_rows - row_len)
            new_matrix += [new_row]

        while len(new_matrix) < total_rows:
            new_matrix += [[pad_value] * total_rows]

        return new_matrix

Response: Pads a possibly non - square matrix to make it square.


Instruction:
How would you code a function in Python 3 to
compute the indexes for the lowest - cost pairings between rows and columns in the database.

Response:
def compute(self, cost_matrix):
        """
        Compute the indexes for the lowest-cost pairings between rows and
        columns in the database. Returns a list of (row, column) tuples
        that can be used to traverse the matrix.

        :Parameters:
            cost_matrix : list of lists
                The cost matrix. If this cost matrix is not square, it
                will be padded with zeros, via a call to ``pad_matrix()``.
                (This method does *not* modify the caller's matrix. It
                operates on a copy of the matrix.)

                **WARNING**: This code handles square and rectangular
                matrices. It does *not* handle irregular matrices.

        :rtype: list
        :return: A list of ``(row, column)`` tuples that describe the lowest
                 cost path through the matrix

        """
        self.C = self.pad_matrix(cost_matrix)
        self.n = len(self.C)
        self.original_length = len(cost_matrix)
        self.original_width = len(cost_matrix[0])
        self.row_covered = [False for i in range(self.n)]
        self.col_covered = [False for i in range(self.n)]
        self.Z0_r = 0
        self.Z0_c = 0
        self.path = self.__make_matrix(self.n * 2, 0)
        self.marked = self.__make_matrix(self.n, 0)

        done = False
        step = 1

        steps = { 1 : self.__step1,
                  2 : self.__step2,
                  3 : self.__step3,
                  4 : self.__step4,
                  5 : self.__step5,
                  6 : self.__step6 }

        while not done:
            try:
                func = steps[step]
                step = func()
            except KeyError:
                done = True

        # Look for the starred columns
        results = []
        for i in range(self.original_length):
            for j in range(self.original_width):
                if self.marked[i][j] == 1:
                    results += [(i, j)]

        return results


Instruction:
Can you generate a brief explanation for the following Python 3 code
def __make_matrix(self, n, val):
        matrix = []
        for i in range(n):
            matrix += [[val for j in range(n)]]
        return matrix

Response: Create an n x n matrix populating it with the specific value.


Instruction:
Implement a Python 3 function for
going to Step 1.

Response:
def __step1(self):
        """
        For each row of the matrix, find the smallest element and
        subtract it from every element in its row. Go to Step 2.
        """
        C = self.C
        n = self.n
        for i in range(n):
            minval = min(self.C[i])
            # Find the minimum value for this row and subtract that minimum
            # from every element in the row.
            for j in range(n):
                self.C[i][j] -= minval

        return 2


Instruction:
Implement a function in Python 3 to
find a zero (Z) in the resulting matrix. If there is no starred zero in its row or column, star Z. Repeat for each element in the matrix. Go to Step 3.

Response:
def __step2(self):
        """
        Find a zero (Z) in the resulting matrix. If there is no starred
        zero in its row or column, star Z. Repeat for each element in the
        matrix. Go to Step 3.
        """
        n = self.n
        for i in range(n):
            for j in range(n):
                if (self.C[i][j] == 0) and \
                        (not self.col_covered[j]) and \
                        (not self.row_covered[i]):
                    self.marked[i][j] = 1
                    self.col_covered[j] = True
                    self.row_covered[i] = True

        self.__clear_covers()
        return 3


Instruction:
Write a Python 3 script for
covering each column containing a starred zero. If K columns are covered, the starred zeros describe a complete set of unique assignments. In this case, Go to DONE, otherwise, Go to Step 4.

Response:
def __step3(self):
        """
        Cover each column containing a starred zero. If K columns are
        covered, the starred zeros describe a complete set of unique
        assignments. In this case, Go to DONE, otherwise, Go to Step 4.
        """
        n = self.n
        count = 0
        for i in range(n):
            for j in range(n):
                if self.marked[i][j] == 1:
                    self.col_covered[j] = True
                    count += 1

        if count >= n:
            step = 7 # done
        else:
            step = 4

        return step


Instruction:
How would you implement a function in Python 3 that
goes to Step 4.

Response:
def __step4(self):
        """
        Find a noncovered zero and prime it. If there is no starred zero
        in the row containing this primed zero, Go to Step 5. Otherwise,
        cover this row and uncover the column containing the starred
        zero. Continue in this manner until there are no uncovered zeros
        left. Save the smallest uncovered value and Go to Step 6.
        """
        step = 0
        done = False
        row = -1
        col = -1
        star_col = -1
        while not done:
            (row, col) = self.__find_a_zero()
            if row < 0:
                done = True
                step = 6
            else:
                self.marked[row][col] = 2
                star_col = self.__find_star_in_row(row)
                if star_col >= 0:
                    col = star_col
                    self.row_covered[row] = True
                    self.col_covered[col] = False
                else:
                    done = True
                    self.Z0_r = row
                    self.Z0_c = col
                    step = 5

        return step


Instruction:
Write a Python 3 function that can
construct a series of alternating primed and starred zeros as follows. Let Z0 represent the uncovered primed zero found in Step 4. Let Z1 denote the starred zero in the column of Z0 (if any). Let Z2 denote the primed zero in the row of Z1 (there will always be one). Continue until the series terminates at a primed zero that has no starred zero in its column. Unstar each starred zero of the series, star each primed zero of the series, erase all primes and uncover every line in the matrix. Return to Step 3

Response:
def __step5(self):
        """
        Construct a series of alternating primed and starred zeros as
        follows. Let Z0 represent the uncovered primed zero found in Step 4.
        Let Z1 denote the starred zero in the column of Z0 (if any).
        Let Z2 denote the primed zero in the row of Z1 (there will always
        be one). Continue until the series terminates at a primed zero
        that has no starred zero in its column. Unstar each starred zero
        of the series, star each primed zero of the series, erase all
        primes and uncover every line in the matrix. Return to Step 3
        """
        count = 0
        path = self.path
        path[count][0] = self.Z0_r
        path[count][1] = self.Z0_c
        done = False
        while not done:
            row = self.__find_star_in_col(path[count][1])
            if row >= 0:
                count += 1
                path[count][0] = row
                path[count][1] = path[count-1][1]
            else:
                done = True

            if not done:
                col = self.__find_prime_in_row(path[count][0])
                count += 1
                path[count][0] = path[count-1][0]
                path[count][1] = col

        self.__convert_path(path, count)
        self.__clear_covers()
        self.__erase_primes()
        return 3


Instruction:
Can you generate a brief explanation for the following Python 3 code
def __step6(self):
        minval = self.__find_smallest()
        for i in range(self.n):
            for j in range(self.n):
                if self.row_covered[i]:
                    self.C[i][j] += minval
                if not self.col_covered[j]:
                    self.C[i][j] -= minval
        return 4

Response:
Add the value found in Step 4 to every element of each covered
        row, and subtract it from every element of each uncovered column.
        Return to Step 4 without altering any stars, primes, or covered
        lines.


Instruction:
Implement a function in Python 3 to
find the smallest uncovered value in the matrix.

Response:
def __find_smallest(self):
        """Find the smallest uncovered value in the matrix."""
        minval = sys.maxsize
        for i in range(self.n):
            for j in range(self.n):
                if (not self.row_covered[i]) and (not self.col_covered[j]):
                    if minval > self.C[i][j]:
                        minval = self.C[i][j]
        return minval


Instruction:
Can you generate the documentation for the following Python 3 function
def __find_a_zero(self):
        row = -1
        col = -1
        i = 0
        n = self.n
        done = False

        while not done:
            j = 0
            while True:
                if (self.C[i][j] == 0) and \
                        (not self.row_covered[i]) and \
                        (not self.col_covered[j]):
                    row = i
                    col = j
                    done = True
                j += 1
                if j >= n:
                    break
            i += 1
            if i >= n:
                done = True

        return (row, col)

Response: Find the first uncovered element with value 0


Instruction:
Create a Python 3 function to
find the first starred element in the specified row. Returns - 1 if no starred element was found.

Response:
def __find_star_in_row(self, row):
        """
        Find the first starred element in the specified row. Returns
        the column index, or -1 if no starred element was found.
        """
        col = -1
        for j in range(self.n):
            if self.marked[row][j] == 1:
                col = j
                break

        return col


Instruction:
How would you code a function in Python 3 to
find the first starred element in the specified column. Returns - 1 if no starred element was found.

Response:
def __find_star_in_col(self, col):
        """
        Find the first starred element in the specified row. Returns
        the row index, or -1 if no starred element was found.
        """
        row = -1
        for i in range(self.n):
            if self.marked[i][col] == 1:
                row = i
                break

        return row


Instruction:
Can you generate a brief explanation for the following Python 3 code
def __find_prime_in_row(self, row):
        col = -1
        for j in range(self.n):
            if self.marked[row][j] == 2:
                col = j
                break

        return col

Response: Find the first prime element in the specified row. Returns - 1 if no prime element was found.


Instruction:
Make a summary of the following Python 3 code
def __clear_covers(self):
        for i in range(self.n):
            self.row_covered[i] = False
            self.col_covered[i] = False

Response: Clear all covered matrix cells


Instruction:
Write a Python 3 script for
erasing all prime markings

Response:
def __erase_primes(self):
        """Erase all prime markings"""
        for i in range(self.n):
            for j in range(self.n):
                if self.marked[i][j] == 2:
                    self.marked[i][j] = 0


Instruction:
Write a Python 3 function for
updating the contingency table with new values.

Response:
def update(self, a, b, c, d):
        """
        Update contingency table with new values without creating a new object.
        """
        self.table.ravel()[:] = [a, b, c, d]
        self.N = self.table.sum()


Instruction:
Create a Python 3 function for
computing the frequency bias of the current set of entries.

Response:
def bias(self):
        """
        Frequency Bias.
        Formula:  (a+b)/(a+c)"""
        return (self.table[0, 0] + self.table[0, 1]) / (self.table[0, 0] + self.table[1, 0])


Instruction:
How would you explain what the following Python 3 function does
def hss(self):
        return 2 * (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / (
            (self.table[0, 0] + self.table[0, 1]) * (self.table[0, 1] + self.table[1, 1]) +
            (self.table[0, 0] + self.table[1, 0]) * (self.table[1, 0] + self.table[1, 1]))

Response: Doolittle ( Heidke ) Skill Score. 2 ad - bc


Instruction:
Can you generate a brief explanation for the following Python 3 code
def pss(self):
        return (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / \
               ((self.table[0, 0] + self.table[1, 0]) * (self.table[0, 1] + self.table[1, 1]))

Response: Return the PSS of the current Skill Score.


Instruction:
Write a Python 3 script to
return the CSS value of the clayton skill score.

Response:
def css(self):
        """Clayton Skill Score (ad - bc)/((a+b)(c+d))"""
        return (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / \
               ((self.table[0, 0] + self.table[0, 1]) * (self.table[1, 0] + self.table[1, 1]))


Instruction:
How would you implement a function in Python 3 that
loads the scikit - learn decision tree ensemble object from a pickle file.

Response:
def load_tree_object(filename):
    """
    Load scikit-learn decision tree ensemble object from file.

    Parameters
    ----------
    filename : str
        Name of the pickle file containing the tree object.

    Returns
    -------
    tree ensemble object
    """
    with open(filename) as file_obj:
        tree_ensemble_obj = pickle.load(file_obj)
    return tree_ensemble_obj


Instruction:
Can you tell what is the following Python 3 function doing
def output_tree_ensemble(tree_ensemble_obj, output_filename, attribute_names=None):
    for t, tree in enumerate(tree_ensemble_obj.estimators_):
        print("Writing Tree {0:d}".format(t))
        out_file = open(output_filename + ".{0:d}.tree", "w")
        #out_file.write("Tree {0:d}\n".format(t))
        tree_str = print_tree_recursive(tree.tree_, 0, attribute_names)
        out_file.write(tree_str)
        #out_file.write("\n")
        out_file.close()
    return

Response: Writes each decision tree in an ensemble to a file.


Instruction:
Explain what the following Python 3 code does
def print_tree_recursive(tree_obj, node_index, attribute_names=None):
    tree_str = ""
    if node_index == 0:
        tree_str += "{0:d}\n".format(tree_obj.node_count)
    if tree_obj.feature[node_index] >= 0:
        if attribute_names is None:
            attr_val = "{0:d}".format(tree_obj.feature[node_index])
        else:
            attr_val = attribute_names[tree_obj.feature[node_index]]
        tree_str += "b {0:d} {1} {2:0.4f} {3:d} {4:1.5e}\n".format(node_index,
                                                                   attr_val,
                                                                   tree_obj.weighted_n_node_samples[node_index],
                                                                   tree_obj.n_node_samples[node_index],
                                                                   tree_obj.threshold[node_index])
    else:
        if tree_obj.max_n_classes > 1:
            leaf_value = "{0:d}".format(tree_obj.value[node_index].argmax())
        else:
            leaf_value = "{0}".format(tree_obj.value[node_index][0][0])
        tree_str += "l {0:d} {1} {2:0.4f} {3:d}\n".format(node_index,
                                                          leaf_value,
                                                          tree_obj.weighted_n_node_samples[node_index],
                                                          tree_obj.n_node_samples[node_index])
    if tree_obj.children_left[node_index] > 0:
        tree_str += print_tree_recursive(tree_obj, tree_obj.children_left[node_index], attribute_names)
    if tree_obj.children_right[node_index] > 0:
        tree_str += print_tree_recursive(tree_obj, tree_obj.children_right[node_index], attribute_names)
    return tree_str

Response: This function writes a string representation of a base decision tree object.


Instruction:
How would you explain what the following Python 3 function does
def set_classifier_mask(self, v, base_mask=True):
        base = self._base
        v = tonparray(v)
        a = np.unique(v)
        if a[0] != -1 or a[1] != 1:
            raise RuntimeError("The labels must be -1 and 1 (%s)" % a)
        mask = np.zeros_like(v)
        cnt = min([(v == x).sum() for x in a]) * base._tr_fraction
        cnt = int(round(cnt))
        for i in a:
            index = np.where((v == i) & base_mask)[0]
            np.random.shuffle(index)
            mask[index[:cnt]] = True
        base._mask = SparseArray.fromlist(mask)
        return SparseArray.fromlist(v)

Response: Computes the mask used to create the training and validation set


Instruction:
Create a Python 3 function to
compute the mask used to create the training and validation set

Response:
def set_regression_mask(self, v):
        """Computes the mask used to create the training and validation set"""
        base = self._base
        index = np.arange(v.size())
        np.random.shuffle(index)
        ones = np.ones(v.size())
        ones[index[int(base._tr_fraction * v.size()):]] = 0
        base._mask = SparseArray.fromlist(ones)


Instruction:
Can you generate the documentation for the following Python 3 function
def fitness_vs(self, v):
        base = self._base
        if base._classifier:
            if base._multiple_outputs:
                v.fitness_vs = v._error
                # if base._fitness_function == 'macro-F1':
                #     v.fitness_vs = v._error
                # elif base._fitness_function == 'BER':
                #     v.fitness_vs = v._error
                # elif base._fitness_function == 'macro-Precision':
                #     v.fitness_vs = v._error
                # elif base._fitness_function == 'accDotMacroF1':
                #     v.fitness_vs = v._error
                # elif base._fitness_function == 'macro-RecallF1':
                #     v.fitness_vs = v._error
                # elif base._fitness_function == 'F1':
                #     v.fitness_vs = v._error
                # else:
                #     v.fitness_vs = - v._error.dot(base._mask_vs) / base._mask_vs.sum()
            else:
                v.fitness_vs = -((base.y - v.hy.sign()).sign().fabs() *
                                 base._mask_vs).sum()
        else:
            mask = base._mask
            y = base.y
            hy = v.hy
            if not isinstance(mask, list):
                mask = [mask]
                y = [y]
                hy = [hy]
            fit = []
            for _mask, _y, _hy in zip(mask, y, hy):
                m = (_mask + -1).fabs()
                x = _y * m
                y = _hy * m
                a = (x - y).sq().sum()
                b = (x + -x.sum() / x.size()).sq().sum()
                fit.append(-a / b)
            v.fitness_vs = np.mean(fit)

Response: Fitness function in the validation set. In classification it uses BER and RSE in regression


Instruction:
Given the following Python 3 function, write the documentation
def set_fitness(self, v):
        base = self._base
        self.fitness(v)
        if not np.isfinite(v.fitness):
            self.del_error(v)
            return False
        if base._tr_fraction < 1:
            self.fitness_vs(v)
            if not np.isfinite(v.fitness_vs):
                self.del_error(v)
                return False
        self.del_error(v)
        return True

Response: Set the fitness to a new node. Returns false in case fitness is not finite


Instruction:
Make a summary of the following Python 3 code
def analisar(retorno):
        resposta = analisar_retorno(forcar_unicode(retorno),
                funcao='EnviarDadosVenda',
                classe_resposta=RespostaCancelarUltimaVenda,
                campos=(
                        ('numeroSessao', int),
                        ('EEEEE', unicode),
                        ('CCCC', unicode),
                        ('mensagem', unicode),
                        ('cod', unicode),
                        ('mensagemSEFAZ', unicode),
                        ('arquivoCFeBase64', unicode),
                        ('timeStamp', as_datetime),
                        ('chaveConsulta', unicode),
                        ('valorTotalCFe', Decimal),
                        ('CPFCNPJValue', unicode),
                        ('assinaturaQRCODE', unicode),
                    ),
                campos_alternativos=[
                        # se a venda falhar apenas os primeiros seis campos
                        # especificados na ER deverão ser retornados...
                        (
                                ('numeroSessao', int),
                                ('EEEEE', unicode),
                                ('CCCC', unicode),
                                ('mensagem', unicode),
                                ('cod', unicode),
                                ('mensagemSEFAZ', unicode),
                        ),
                        # por via das dúvidas, considera o padrão de campos,
                        # caso não haja nenhuma coincidência...
                        RespostaSAT.CAMPOS,
                    ]
            )
        if resposta.EEEEE not in ('07000',):
            raise ExcecaoRespostaSAT(resposta)
        return resposta

Response:
Analisar uma resposta a partir do
       .


Instruction:
Given the following Python 3 function, write the documentation
def convert_data_element_to_data_and_metadata_1(data_element) -> DataAndMetadata.DataAndMetadata:
    # data. takes ownership.
    data = data_element["data"]
    dimensional_shape = Image.dimensional_shape_from_data(data)
    is_sequence = data_element.get("is_sequence", False)
    dimension_count = len(Image.dimensional_shape_from_data(data))
    adjusted_dimension_count = dimension_count - (1 if is_sequence else 0)
    collection_dimension_count = data_element.get("collection_dimension_count", 2 if adjusted_dimension_count in (3, 4) else 0)
    datum_dimension_count = data_element.get("datum_dimension_count", adjusted_dimension_count - collection_dimension_count)
    data_descriptor = DataAndMetadata.DataDescriptor(is_sequence, collection_dimension_count, datum_dimension_count)

    # dimensional calibrations
    dimensional_calibrations = None
    if "spatial_calibrations" in data_element:
        dimensional_calibrations_list = data_element.get("spatial_calibrations")
        if len(dimensional_calibrations_list) == len(dimensional_shape):
            dimensional_calibrations = list()
            for dimension_calibration in dimensional_calibrations_list:
                offset = float(dimension_calibration.get("offset", 0.0))
                scale = float(dimension_calibration.get("scale", 1.0))
                units = dimension_calibration.get("units", "")
                units = str(units) if units is not None else str()
                if scale != 0.0:
                    dimensional_calibrations.append(Calibration.Calibration(offset, scale, units))
                else:
                    dimensional_calibrations.append(Calibration.Calibration())

    # intensity calibration
    intensity_calibration = None
    if "intensity_calibration" in data_element:
        intensity_calibration_dict = data_element.get("intensity_calibration")
        offset = float(intensity_calibration_dict.get("offset", 0.0))
        scale = float(intensity_calibration_dict.get("scale", 1.0))
        units = intensity_calibration_dict.get("units", "")
        units = str(units) if units is not None else str()
        if scale != 0.0:
            intensity_calibration = Calibration.Calibration(offset, scale, units)

    # properties (general tags)
    metadata = dict()
    if "metadata" in data_element:
        metadata.update(Utility.clean_dict(data_element.get("metadata")))
    if "properties" in data_element and data_element["properties"]:
        hardware_source_metadata = metadata.setdefault("hardware_source", dict())
        hardware_source_metadata.update(Utility.clean_dict(data_element.get("properties")))

    # dates are _local_ time and must use this specific ISO 8601 format. 2013-11-17T08:43:21.389391
    # time zones are offsets (east of UTC) in the following format "+HHMM" or "-HHMM"
    # daylight savings times are time offset (east of UTC) in format "+MM" or "-MM"
    # timezone is for conversion and is the Olson timezone string.
    # datetime.datetime.strptime(datetime.datetime.isoformat(datetime.datetime.now()), "%Y-%m-%dT%H:%M:%S.%f" )
    # datetime_modified, datetime_modified_tz, datetime_modified_dst, datetime_modified_tzname is the time at which this image was modified.
    # datetime_original, datetime_original_tz, datetime_original_dst, datetime_original_tzname is the time at which this image was created.
    timestamp = data_element.get("timestamp", datetime.datetime.utcnow())
    datetime_item = data_element.get("datetime_modified", Utility.get_datetime_item_from_utc_datetime(timestamp))

    local_datetime = Utility.get_datetime_from_datetime_item(datetime_item)
    dst_value = datetime_item.get("dst", "+00")
    tz_value = datetime_item.get("tz", "+0000")
    timezone = datetime_item.get("timezone")
    time_zone = { "dst": dst_value, "tz": tz_value}
    if timezone is not None:
        time_zone["timezone"] = timezone
    # note: dst is informational only; tz already include dst
    tz_adjust = (int(tz_value[1:3]) * 60 + int(tz_value[3:5])) * (-1 if tz_value[0] == '-' else 1)
    utc_datetime = local_datetime - datetime.timedelta(minutes=tz_adjust)  # tz_adjust already contains dst_adjust
    timestamp = utc_datetime

    return DataAndMetadata.new_data_and_metadata(data,
                                                 intensity_calibration=intensity_calibration,
                                                 dimensional_calibrations=dimensional_calibrations,
                                                 metadata=metadata,
                                                 timestamp=timestamp,
                                                 data_descriptor=data_descriptor,
                                                 timezone=timezone,
                                                 timezone_offset=tz_value)

Response: Convert a data element to xdata.


Instruction:
Implement a Python 3 function for
segmenting forecast tracks to only output data contined within a region in the CONUS, as defined by the mapfile. Args: csv_path(str): Path to the full CONUS csv file. file_dict_key(str): Dictionary key for the csv files, currently either 'track_step' or 'track_total' out_path (str): Path to output new segmented csv files. Returns: Segmented forecast tracks in a csv file.

Response:
def output_sector_csv(self,csv_path,file_dict_key,out_path):
        """
        Segment forecast tracks to only output data contined within a
        region in the CONUS, as defined by the mapfile.

        Args:
            csv_path(str): Path to the full CONUS csv file.
            file_dict_key(str): Dictionary key for the csv files,
                currently either 'track_step' or 'track_total'
            out_path (str): Path to output new segmented csv files.
        Returns:
            Segmented forecast tracks in a csv file.
        """
        csv_file = csv_path + "{0}_{1}_{2}_{3}.csv".format(
                                                        file_dict_key,
                                                        self.ensemble_name,
                                                        self.member,
                                                        self.run_date.strftime(self.date_format))
        if exists(csv_file):
            csv_data = pd.read_csv(csv_file)

            if self.inds is None:
                lon_obj = csv_data.loc[:,"Centroid_Lon"]
                lat_obj = csv_data.loc[:,"Centroid_Lat"]

                self.inds = np.where((self.ne_lat>=lat_obj)&(self.sw_lat<=lat_obj)\
                        &(self.ne_lon>=lon_obj)&(self.sw_lon<=lon_obj))[0]

            if np.shape(self.inds)[0] > 0:
                csv_data = csv_data.reindex(np.array(self.inds))
                sector_csv_filename = out_path + "{0}_{1}_{2}_{3}.csv".format(
                                                        file_dict_key,
                                                        self.ensemble_name,
                                                        self.member,
                                                        self.run_date.strftime(self.date_format))
                print("Output sector csv file " + sector_csv_filename)
                csv_data.to_csv(sector_csv_filename,
                        na_rep="nan",
                        float_format="%0.5f",
                        index=False)
                os.chmod(sector_csv_filename, 0o666)
            else:
                print('No {0} {1} sector data found'.format(self.member,
                                self.run_date.strftime("%Y%m%d")))

        else:
            print('No {0} {1} csv file found'.format(self.member,
                                self.run_date.strftime("%Y%m%d")))
        return


Instruction:
Given the following Python 3 function, write the documentation
def output_sector_netcdf(self,netcdf_path,out_path,patch_radius,config):

        nc_data = self.load_netcdf_data(netcdf_path,patch_radius)

        if nc_data is not None:
            out_filename = out_path + "{0}_{1}_{2}_model_patches.nc".format(
                                                            self.ensemble_name,
                                                            self.run_date.strftime(self.date_format),
                                                            self.member)
            out_file = Dataset(out_filename, "w")
            out_file.createDimension("p", np.shape(nc_data.variables['p'])[0])
            out_file.createDimension("row", np.shape(nc_data.variables['row'])[0])
            out_file.createDimension("col", np.shape(nc_data.variables['col'])[0])
            out_file.createVariable("p", "i4", ("p",))
            out_file.createVariable("row", "i4", ("row",))
            out_file.createVariable("col", "i4", ("col",))
            out_file.variables["p"][:] = nc_data.variables['p'][:]
            out_file.variables["row"][:] =  nc_data.variables['row'][:]
            out_file.variables["col"][:] =  nc_data.variables['col'][:]
            out_file.Conventions = "CF-1.6"
            out_file.title = "{0} Storm Patches for run {1} member {2}".format(self.ensemble_name,
                                                                       self.run_date.strftime(self.date_format),
                                                                       self.member)
            out_file.object_variable = config.watershed_variable
            meta_variables = ["lon", "lat", "i", "j", "x", "y", "masks"]
            meta_units = ["degrees_east", "degrees_north", "", "", "m", "m", ""]
            center_vars = ["time", "centroid_lon", "centroid_lat", "centroid_i", "centroid_j", "track_id", "track_step"]
            center_units = ["hours since {0}".format(self.run_date.strftime("%Y-%m-%d %H:%M:%S")),
                    "degrees_east",
                    "degrees_north",
                    "",
                    "",
                    "",
                    ""]

            label_columns = ["Matched", "Max_Hail_Size", "Num_Matches", "Shape", "Location", "Scale"]

            for m, meta_variable in enumerate(meta_variables):
                if meta_variable in ["i", "j", "masks"]:
                    dtype = "i4"
                else:
                    dtype = "f4"
                m_var = out_file.createVariable(meta_variable, dtype, ("p", "row", "col"), complevel=1, zlib=True)
                m_var.long_name = meta_variable
                m_var.units = meta_units[m]

            for c, center_var in enumerate(center_vars):
                if center_var in ["time", "track_id", "track_step"]:
                    dtype = "i4"
                else:
                    dtype = "f4"
                c_var = out_file.createVariable(center_var, dtype, ("p",), zlib=True, complevel=1)
                c_var.long_name = center_var
                c_var.units =center_units[c]

            for storm_variable in config.storm_variables:
                s_var = out_file.createVariable(storm_variable + "_curr", "f4", ("p", "row", "col"), complevel=1, zlib=True)
                s_var.long_name = storm_variable
                s_var.units = ""

            for potential_variable in config.potential_variables:
                p_var = out_file.createVariable(potential_variable + "_prev", "f4", ("p", "row", "col"),
                                        complevel=1, zlib=True)
                p_var.long_name = potential_variable
                p_var.units = ""

            if config.train:
                for label_column in label_columns:
                    if label_column in ["Matched", "Num_Matches"]:
                        dtype = "i4"
                    else:
                        dtype = "f4"
                    l_var = out_file.createVariable(label_column, dtype, ("p",), zlib=True, complevel=1)
                    l_var.long_name = label_column
                    l_var.units = ""

            out_file.variables["time"][:] = nc_data.variables['time'][:]

            for c_var in ["lon", "lat"]:
                out_file.variables["centroid_" + c_var][:] =  nc_data.variables['centroid_' + c_var][:]

            for c_var in ["i", "j"]:
                out_file.variables["centroid_" + c_var][:] =  nc_data.variables["centroid_" + c_var][:]

            out_file.variables["track_id"][:] =  nc_data.variables['track_id'][:]
            out_file.variables["track_step"][:] =  nc_data.variables['track_step'][:]

            for meta_var in meta_variables:
                if meta_var in ["lon", "lat"]:
                    out_file.variables[meta_var][:] = nc_data.variables[meta_var][:]
                else:
                    out_file.variables[meta_var][:] = nc_data.variables[meta_var][:]

            for storm_variable in config.storm_variables:
                out_file.variables[storm_variable + "_curr"][:] = nc_data.variables[storm_variable + '_curr'][:]

            for p_variable in config.potential_variables:
                out_file.variables[p_variable + "_prev"][:] = nc_data.variables[p_variable + '_prev'][:]

            if config.train:
                for label_column in label_columns:
                    try:
                        out_file.variables[label_column][:] = nc_data.variables[label_column][:]
                    except:
                        out_file.variables[label_column][:] = 0

            out_file.close()

            print("Output sector nc file " + out_filename)
        else:
            print('No {0} {1} netcdf file/sector data found'.format(self.member,
                                self.run_date.strftime("%Y%m%d")))

        return

Response: This function will output a segmented netcdf file for the specified sector.


Instruction:
Can you generate the documentation for the following Python 3 function
def clean_dict(d0, clean_item_fn=None):
    clean_item_fn = clean_item_fn if clean_item_fn else clean_item
    d = dict()
    for key in d0:
        cleaned_item = clean_item_fn(d0[key])
        if cleaned_item is not None:
            d[key] = cleaned_item
    return d

Response: Returns a json - clean dict. Will log info message for failures.


Instruction:
Write a Python 3 function that can
return a json - clean list. Will log info message for failures.

Response:
def clean_list(l0, clean_item_fn=None):
    """
        Return a json-clean list. Will log info message for failures.
    """
    clean_item_fn = clean_item_fn if clean_item_fn else clean_item
    l = list()
    for index, item in enumerate(l0):
        cleaned_item = clean_item_fn(item)
        l.append(cleaned_item)
    return l


Instruction:
Can you implement a function in Python 3 that
returns a json - clean tuple. Will log info message for failures.

Response:
def clean_tuple(t0, clean_item_fn=None):
    """
        Return a json-clean tuple. Will log info message for failures.
    """
    clean_item_fn = clean_item_fn if clean_item_fn else clean_item
    l = list()
    for index, item in enumerate(t0):
        cleaned_item = clean_item_fn(item)
        l.append(cleaned_item)
    return tuple(l)


Instruction:
Write a Python 3 script for
returning a json - clean item or None. Will log info message for failure.

Response:
def clean_item(i):
    """
        Return a json-clean item or None. Will log info message for failure.
    """
    itype = type(i)
    if itype == dict:
        return clean_dict(i)
    elif itype == list:
        return clean_list(i)
    elif itype == tuple:
        return clean_tuple(i)
    elif itype == numpy.float32:
        return float(i)
    elif itype == numpy.float64:
        return float(i)
    elif itype == numpy.int16:
        return int(i)
    elif itype == numpy.uint16:
        return int(i)
    elif itype == numpy.int32:
        return int(i)
    elif itype == numpy.uint32:
        return int(i)
    elif itype == numpy.int64:
        return int(i)
    elif itype == numpy.uint64:
        return int(i)
    elif itype == float:
        return i
    elif itype == str:
        return i
    elif itype == int:
        return i
    elif itype == bool:
        return i
    elif itype == type(None):
        return i
    logging.info("[1] Unable to handle type %s", itype)
    import traceback
    traceback.print_stack()
    return None


Instruction:
How would you explain what the following Python 3 function does
def clean_item_no_list(i):
    itype = type(i)
    if itype == dict:
        return clean_dict(i, clean_item_no_list)
    elif itype == list:
        return clean_tuple(i, clean_item_no_list)
    elif itype == tuple:
        return clean_tuple(i, clean_item_no_list)
    elif itype == numpy.float32:
        return float(i)
    elif itype == numpy.float64:
        return float(i)
    elif itype == numpy.int16:
        return int(i)
    elif itype == numpy.uint16:
        return int(i)
    elif itype == numpy.int32:
        return int(i)
    elif itype == numpy.uint32:
        return int(i)
    elif itype == float:
        return i
    elif itype == str:
        return i
    elif itype == int:
        return i
    elif itype == bool:
        return i
    elif itype == type(None):
        return i
    logging.info("[2] Unable to handle type %s", itype)
    return None

Response: Clean a json - clean item.


Instruction:
Implement a function in Python 3 to
sample the stack in a thread and print it at regular intervals.

Response:
def sample_stack_all(count=10, interval=0.1):
    """Sample the stack in a thread and print it at regular intervals."""

    def print_stack_all(l, ll):
        l1 = list()
        l1.append("*** STACKTRACE - START ***")
        code = []
        for threadId, stack in sys._current_frames().items():
            sub_code = []
            sub_code.append("# ThreadID: %s" % threadId)
            for filename, lineno, name, line in traceback.extract_stack(stack):
                sub_code.append('File: "%s", line %d, in %s' % (filename, lineno, name))
                if line:
                    sub_code.append("  %s" % (line.strip()))
            if not "in select" in sub_code[-2] and \
               not "in wait" in sub_code[-2] and \
               not "in print_stack_all" in sub_code[-2] and \
               not "in sample_stack_all" in sub_code[-2] and \
               not "in checkcache" in sub_code[-2] and \
               not "do_sleep" in sub_code[-2] and \
               not "sleep" in sub_code[-1] and \
               not any(["in do_sample" in s for s in sub_code]):
                code.extend(sub_code)
        for line in code:
            l1.append(line)
        l1.append("*** STACKTRACE - END ***")
        with l:
            ll.extend(l1)

    def do_sample():
        l = threading.RLock()
        ll = list()
        for i in range(count):
            print_stack_all(l, ll)
            time.sleep(interval)
        with l:
            print("\n".join(ll))

    threading.Thread(target=do_sample).start()


Instruction:
Given the following Python 3 function, write the documentation
def decision_function(self, X):
        "Decision function i.e. the raw data of the prediction"
        self._X = Model.convert_features(X)
        self._eval()
        return self._ind[0].hy

Response: Decision function i. e. the raw data of the prediction


Instruction:
Create a Python 3 function to
evaluate a individual using recursion and self. _pos as pointer

Response:
def _eval(self):
        "Evaluates a individual using recursion and self._pos as pointer"
        pos = self._pos
        self._pos += 1
        node = self._ind[pos]
        if isinstance(node, Function):
            args = [self._eval() for x in range(node.nargs)]
            node.eval(args)
            for x in args:
                x.hy = None
                x.hy_test = None
        else:
            node.eval(self._X)
        return node


Instruction:
Make a summary of the following Python 3 code
def grow_use_function(self, depth=0):
        "Select either function or terminal in grow method"
        if depth == 0:
            return False
        if depth == self._depth:
            return True
        return np.random.random() < 0.5

Response: Select either function or terminal in grow method


Instruction:
Create a Python 3 function to
create random population using ramped half - and - half method

Response:
def create_population(self, popsize=1000, min_depth=2,
                          max_depth=4,
                          X=None):
        "Creates random population using ramped half-and-half method"
        import itertools
        args = [x for x in itertools.product(range(min_depth,
                                                   max_depth+1),
                                             [True, False])]
        index = 0
        output = []
        while len(output) < popsize:
            depth, full = args[index]
            index += 1
            if index >= len(args):
                index = 0
            if full:
                ind = self.create_random_ind_full(depth=depth)
            else:
                ind = self.create_random_ind_grow(depth=depth)
            flag = True
            if X is not None:
                x = Individual(ind)
                x.decision_function(X)
                flag = x.individual[0].isfinite()
            l_vars = (flag, len(output), full, depth, len(ind))
            l_str = " flag: %s len(output): %s full: %s depth: %s len(ind): %s"
            self._logger.debug(l_str % l_vars)
            if flag:
                output.append(ind)
        return output


Instruction:
Explain what the following Python 3 code does
def decision_function(self, X, **kwargs):
        "Decision function i.e. the raw data of the prediction"
        if X is None:
            return self._hy_test
        X = self.convert_features(X)
        if len(X) < self.nvar:
            _ = 'Number of variables differ, trained with %s given %s' % (self.nvar, len(X))
            raise RuntimeError(_)
        hist = self._hist
        for node in hist:
            if node.height:
                node.eval(hist)
            else:
                node.eval(X)
        node.normalize()
        r = node.hy
        for i in hist[:-1]:
            i.hy = None
            i.hy_test = None
        gc.collect()
        return r

Response: Decision function i. e. the raw data of the prediction


Instruction:
How would you explain what the following Python 3 function does
def fitness_vs(self):
        "Median Fitness in the validation set"
        l = [x.fitness_vs for x in self.models]
        return np.median(l)

Response: Median Fitness in the validation set


Instruction:
Can you write a function in Python 3 where it
loads the data for the current instance of the class.

Response:
def load_data(self, num_samples=1000, percentiles=None):
        """
        Args:
            num_samples: Number of random samples at each grid point
            percentiles: Which percentiles to extract from the random samples

        Returns:
        """
        self.percentiles = percentiles
        self.num_samples = num_samples
        if self.model_name.lower() in ["wrf"]:
            mo = ModelOutput(self.ensemble_name, self.member, self.run_date, self.variable,
                             self.start_date, self.end_date, self.path, self.map_file, self.single_step)
            mo.load_data()
            self.data = mo.data[:]
            if mo.units == "m":
                self.data *= 1000
                self.units = "mm"
            else:
                self.units = mo.units
        else:
            if self.track_forecasts is None:
                self.load_track_data()
            self.units = "mm"
            self.data = np.zeros((self.forecast_hours.size,
                                  self.mapping_data["lon"].shape[0],
                                  self.mapping_data["lon"].shape[1]), dtype=np.float32)

            if self.percentiles is not None:
                self.percentile_data = np.zeros([len(self.percentiles)] + list(self.data.shape))
            full_condition_name = "condition_" + self.condition_model_name.replace(" ", "-")
            dist_model_name = "dist" + "_" + self.model_name.replace(" ", "-")
            for track_forecast in self.track_forecasts:
                times = track_forecast["properties"]["times"]
                for s, step in enumerate(track_forecast["features"]):
                    forecast_params = step["properties"][dist_model_name]
                    if self.condition_model_name is not None:
                        condition = step["properties"][full_condition_name]
                    else:
                        condition = None
                    forecast_time = self.run_date + timedelta(hours=times[s])
                    if forecast_time in self.times:
                        t = np.where(self.times == forecast_time)[0][0]
                        mask = np.array(step["properties"]["masks"], dtype=int).ravel()
                        rankings = np.argsort(np.array(step["properties"]["timesteps"]).ravel()[mask==1])
                        i = np.array(step["properties"]["i"], dtype=int).ravel()[mask == 1][rankings]
                        j = np.array(step["properties"]["j"], dtype=int).ravel()[mask == 1][rankings]
                        if rankings.size > 0 and forecast_params[0] > 0.1 and 1 < forecast_params[2] < 100:
                            raw_samples = np.sort(gamma.rvs(forecast_params[0], loc=forecast_params[1],
                                                            scale=forecast_params[2],
                                                            size=(num_samples, rankings.size)),
                                                  axis=1)
                            if self.percentiles is None:
                                samples = raw_samples.mean(axis=0)
                                if condition >= self.condition_threshold:
                                    self.data[t, i, j] = samples
                            else:
                                for p, percentile in enumerate(self.percentiles):
                                    if percentile != "mean":
                                        if condition >= self.condition_threshold:
                                            self.percentile_data[p, t, i, j] = np.percentile(raw_samples, percentile,
                                                                                axis=0)
                                    else:
                                        if condition >= self.condition_threshold:
                                            self.percentile_data[p, t, i, j] = np.mean(raw_samples, axis=0)
                                samples = raw_samples.mean(axis=0)
                                if condition >= self.condition_threshold:
                                    self.data[t, i, j] = samples


Instruction:
Create a Python 3 function for
calculating a probability based on the number of grid points that exceed a threshold and a radius.

Response:
def neighborhood_probability(self, threshold, radius):
        """
        Calculate a probability based on the number of grid points in an area that exceed a threshold.

        Args:
            threshold:
            radius:

        Returns:

        """
        weights = disk(radius, dtype=np.uint8)
        thresh_data = np.zeros(self.data.shape[1:], dtype=np.uint8)
        neighbor_prob = np.zeros(self.data.shape, dtype=np.float32)
        for t in np.arange(self.data.shape[0]):
            thresh_data[self.data[t] >= threshold] = 1
            maximized = fftconvolve(thresh_data, weights, mode="same")
            maximized[maximized > 1] = 1
            maximized[maximized < 1] = 0
            neighbor_prob[t] = fftconvolve(maximized, weights, mode="same")
            thresh_data[:] = 0
        neighbor_prob[neighbor_prob < 1] = 0
        neighbor_prob /= weights.sum()
        return neighbor_prob


Instruction:
Create a Python 3 function to
encode member percentile data to GRIB2 format.

Response:
def encode_grib2_percentile(self):
        """
        Encodes member percentile data to GRIB2 format.

        Returns:
            Series of GRIB2 messages
        """
        lscale = 1e6
        grib_id_start = [7, 0, 14, 14, 2]
        gdsinfo = np.array([0, np.product(self.data.shape[-2:]), 0, 0, 30], dtype=np.int32)
        lon_0 = self.proj_dict["lon_0"]
        sw_lon = self.grid_dict["sw_lon"]
        if lon_0 < 0:
            lon_0 += 360
        if sw_lon < 0:
            sw_lon += 360
        gdtmp1 = [1, 0, self.proj_dict['a'], 0, float(self.proj_dict['a']), 0, float(self.proj_dict['b']),
                  self.data.shape[-1], self.data.shape[-2], self.grid_dict["sw_lat"] * lscale,
                  sw_lon * lscale, 0, self.proj_dict["lat_0"] * lscale,
                  lon_0 * lscale,
                  self.grid_dict["dx"] * 1e3, self.grid_dict["dy"] * 1e3, 0b00000000, 0b01000000,
                  self.proj_dict["lat_1"] * lscale,
                  self.proj_dict["lat_2"] * lscale, -90 * lscale, 0]
        pdtmp1 = np.array([1,                # parameter category Moisture
                           31,               # parameter number Hail
                           4,                # Type of generating process Ensemble Forecast
                           0,                # Background generating process identifier
                           31,               # Generating process or model from NCEP
                           0,                # Hours after reference time data cutoff
                           0,                # Minutes after reference time data cutoff
                           1,                # Forecast time units Hours
                           0,                # Forecast time
                           1,                # Type of first fixed surface Ground
                           1,                # Scale value of first fixed surface
                           0,                # Value of first fixed surface
                           1,                # Type of second fixed surface
                           1,                # Scale value of 2nd fixed surface
                           0,                # Value of 2nd fixed surface
                           0,                # Derived forecast type
                           self.num_samples  # Number of ensemble members
                           ], dtype=np.int32)
        grib_objects = pd.Series(index=self.times, data=[None] * self.times.size, dtype=object)
        drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)
        for t, time in enumerate(self.times):
            time_list = list(self.run_date.utctimetuple()[0:6])
            if grib_objects[time] is None:
                grib_objects[time] = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))
                grib_objects[time].addgrid(gdsinfo, gdtmp1)
            pdtmp1[8] = (time.to_pydatetime() - self.run_date).total_seconds() / 3600.0
            data = self.percentile_data[:, t] / 1000.0
            masked_data = np.ma.array(data, mask=data <= 0)
            for p, percentile in enumerate(self.percentiles):
                print("GRIB {3} Percentile {0}. Max: {1} Min: {2}".format(percentile,
                                                                          masked_data[p].max(),
                                                                          masked_data[p].min(),
                                                                          time))
                if percentile in range(1, 100):
                    pdtmp1[-2] = percentile
                    grib_objects[time].addfield(6, pdtmp1[:-1], 0, drtmp1, masked_data[p])
                else:
                    pdtmp1[-2] = 0
                    grib_objects[time].addfield(2, pdtmp1, 0, drtmp1, masked_data[p])
        return grib_objects


Instruction:
Can you generate a brief explanation for the following Python 3 code
def encode_grib2_data(self):
        lscale = 1e6
        grib_id_start = [7, 0, 14, 14, 2]
        gdsinfo = np.array([0, np.product(self.data.shape[-2:]), 0, 0, 30], dtype=np.int32)
        lon_0 = self.proj_dict["lon_0"]
        sw_lon = self.grid_dict["sw_lon"]
        if lon_0 < 0:
            lon_0 += 360
        if sw_lon < 0:
            sw_lon += 360
        gdtmp1 = [1, 0, self.proj_dict['a'], 0, float(self.proj_dict['a']), 0, float(self.proj_dict['b']),
                  self.data.shape[-1], self.data.shape[-2], self.grid_dict["sw_lat"] * lscale,
                  sw_lon * lscale, 0, self.proj_dict["lat_0"] * lscale,
                  lon_0 * lscale,
                  self.grid_dict["dx"] * 1e3, self.grid_dict["dy"] * 1e3, 0b00000000, 0b01000000,
                  self.proj_dict["lat_1"] * lscale,
                  self.proj_dict["lat_2"] * lscale, -90 * lscale, 0]
        pdtmp1 = np.array([1,                # parameter category Moisture
                           31,               # parameter number Hail
                           4,                # Type of generating process Ensemble Forecast
                           0,                # Background generating process identifier
                           31,               # Generating process or model from NCEP
                           0,                # Hours after reference time data cutoff
                           0,                # Minutes after reference time data cutoff
                           1,                # Forecast time units Hours
                           0,                # Forecast time
                           1,                # Type of first fixed surface Ground
                           1,                # Scale value of first fixed surface
                           0,                # Value of first fixed surface
                           1,                # Type of second fixed surface
                           1,                # Scale value of 2nd fixed surface
                           0,                # Value of 2nd fixed surface
                           0,                # Derived forecast type
                           1                 # Number of ensemble members
                           ], dtype=np.int32)
        grib_objects = pd.Series(index=self.times, data=[None] * self.times.size, dtype=object)
        drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)
        for t, time in enumerate(self.times):
            time_list = list(self.run_date.utctimetuple()[0:6])
            if grib_objects[time] is None:
                grib_objects[time] = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))
                grib_objects[time].addgrid(gdsinfo, gdtmp1)
            pdtmp1[8] = (time.to_pydatetime() - self.run_date).total_seconds() / 3600.0
            data = self.data[t] / 1000.0
            data[np.isnan(data)] = 0
            masked_data = np.ma.array(data, mask=data<=0)
            pdtmp1[-2] = 0
            grib_objects[time].addfield(1, pdtmp1, 0, drtmp1, masked_data)
        return grib_objects

Response: Encodes member percentile data to GRIB2 format.


Instruction:
Make a summary of the following Python 3 code
def load_data(self):
        for m, member in enumerate(self.members):
            mo = ModelOutput(self.ensemble_name, member, self.run_date, self.variable,
                             self.start_date, self.end_date, self.path, self.map_file, self.single_step)
            mo.load_data()
            if self.data is None:
                self.data = np.zeros((len(self.members), mo.data.shape[0], mo.data.shape[1], mo.data.shape[2]),
                                     dtype=np.float32)
            if mo.units == "m":
                self.data[m] = mo.data * 1000
                self.units = "mm"
            else:
                self.data[m] = mo.data
            if self.units == "":
                self.units = mo.units
            del mo.data
            del mo

Response: Loads data from each ensemble member.


Instruction:
Write a Python 3 function that can
calculate grid - point statistics across ensemble members.

Response:
def point_consensus(self, consensus_type):
        """
        Calculate grid-point statistics across ensemble members.

        Args:
            consensus_type: mean, std, median, max, or percentile_nn

        Returns:
            EnsembleConsensus containing point statistic
        """
        if "mean" in consensus_type:
            consensus_data = np.mean(self.data, axis=0)
        elif "std" in consensus_type:
            consensus_data = np.std(self.data, axis=0)
        elif "median" in consensus_type:
            consensus_data = np.median(self.data, axis=0)
        elif "max" in consensus_type:
            consensus_data = np.max(self.data, axis=0)
        elif "percentile" in consensus_type:
            percentile = int(consensus_type.split("_")[1])
            consensus_data = np.percentile(self.data, percentile, axis=0)
        else:
            consensus_data = np.zeros(self.data.shape[1:])
        consensus = EnsembleConsensus(consensus_data, consensus_type, self.ensemble_name,
                                      self.run_date, self.variable, self.start_date, self.end_date, self.units)
        return consensus


Instruction:
Can you generate the documentation for the following Python 3 function
def point_probability(self, threshold):
        point_prob = np.zeros(self.data.shape[1:])
        for t in range(self.data.shape[1]):
            point_prob[t] = np.where(self.data[:, t] >= threshold, 1.0, 0.0).mean(axis=0)
        return EnsembleConsensus(point_prob, "point_probability", self.ensemble_name,
                                 self.run_date, self.variable + "_{0:0.2f}_{1}".format(threshold,
                                                                                self.units.replace(" ", "_")),
                                 self.start_date, self.end_date, "")

Response: Determines the probability of exceeding a threshold at a grid point.


Instruction:
Can you tell what is the following Python 3 function doing
def neighborhood_probability(self, threshold, radius, sigmas=None):
        if sigmas is None:
            sigmas = [0]
        weights = disk(radius)
        filtered_prob = []
        for sigma in sigmas:
            filtered_prob.append(EnsembleConsensus(np.zeros(self.data.shape[1:], dtype=np.float32),
                                                   "neighbor_prob_r_{0:d}_s_{1:d}".format(radius, sigma),
                                                   self.ensemble_name,
                                                   self.run_date, self.variable + "_{0:0.2f}".format(threshold),
                                                   self.start_date, self.end_date, ""))
        thresh_data = np.zeros(self.data.shape[2:], dtype=np.uint8)
        neighbor_prob = np.zeros(self.data.shape[2:], dtype=np.float32)
        for t in range(self.data.shape[1]):
            for m in range(self.data.shape[0]):
                thresh_data[self.data[m, t] >= threshold] = 1
                maximized = fftconvolve(thresh_data, weights, mode="same")
                maximized[maximized > 1] = 1
                maximized[maximized < 1] = 0
                neighbor_prob += fftconvolve(maximized, weights, mode="same")
                neighbor_prob[neighbor_prob < 1] = 0
                thresh_data[:] = 0
            neighbor_prob /= (self.data.shape[0] * float(weights.sum()))
            for s, sigma in enumerate(sigmas):
                if sigma > 0:
                    filtered_prob[s].data[t] = gaussian_filter(neighbor_prob, sigma=sigma)
                else:
                    filtered_prob[s].data[t] = neighbor_prob
            neighbor_prob[:] = 0
        return filtered_prob

Response: Calculates the probability of exceeding a threshold based on model values within a specified radius of a point.


Instruction:
Create a Python 3 function to
calculate the maximum neighborhood probability at any time over the period loaded.

Response:
def period_max_neighborhood_probability(self, threshold, radius, sigmas=None):
        """
        Calculates the neighborhood probability of exceeding a threshold at any time over the period loaded.

        Args:
            threshold (float): splitting threshold for probability calculatations
            radius (int): distance from point in number of grid points to include in neighborhood calculation.
            sigmas (array of ints): Radii for Gaussian filter used to smooth neighborhood probabilities.

        Returns:
            list of EnsembleConsensus objects
        """
        if sigmas is None:
            sigmas = [0]
        weights = disk(radius)
        neighborhood_prob = np.zeros(self.data.shape[2:], dtype=np.float32)
        thresh_data = np.zeros(self.data.shape[2:], dtype=np.uint8)
        for m in range(self.data.shape[0]):
            thresh_data[self.data[m].max(axis=0) >= threshold] = 1
            maximized = fftconvolve(thresh_data, weights, mode="same")
            maximized[maximized > 1] = 1
            neighborhood_prob += fftconvolve(maximized, weights, mode="same")
        neighborhood_prob[neighborhood_prob < 1] = 0
        neighborhood_prob /= (self.data.shape[0] * float(weights.sum()))
        consensus_probs = []
        for sigma in sigmas:
            if sigma > 0:
                filtered_prob = gaussian_filter(neighborhood_prob, sigma=sigma)
            else:
                filtered_prob = neighborhood_prob
            ec = EnsembleConsensus(filtered_prob,
                                   "neighbor_prob_{0:02d}-hour_r_{1:d}_s_{2:d}".format(self.data.shape[1],
                                                                                radius, sigma),
                                   self.ensemble_name,
                                   self.run_date, self.variable + "_{0:0.2f}".format(float(threshold)),
                                   self.start_date, self.end_date, "")
            consensus_probs.append(ec)
        return consensus_probs


Instruction:
How would you explain what the following Python 3 function does
def load_data(self, grid_method="gamma", num_samples=1000, condition_threshold=0.5, zero_inflate=False,
                  percentile=None):
        """
        Reads the track forecasts and converts them to grid point values based on random sampling.

        Args:
            grid_method: "gamma" by default
            num_samples: Number of samples drawn from predicted pdf
            condition_threshold: Objects are not written to the grid if condition model probability is below this
                threshold.
            zero_inflate: Whether to sample zeros from a Bernoulli sampler based on the condition model probability
            percentile: If None, outputs the mean of the samples at each grid point, otherwise outputs the specified
                percentile from 0 to 100.

        Returns:
            0 if tracks are successfully sampled on to grid. If no tracks are found, returns -1.
        """
        self.percentile = percentile
        if self.track_forecasts == {}:
            self.load_track_forecasts()
        if self.track_forecasts == {}:
            return -1
        if self.data is None:
            self.data = np.zeros((len(self.members), self.times.size, self.grid_shape[0], self.grid_shape[1]),
                                 dtype=np.float32)
        else:
            self.data[:] = 0
        if grid_method in ["mean", "median", "samples"]:
            for m, member in enumerate(self.members):
                print("Sampling " + member)
                for track_forecast in self.track_forecasts[member]:
                    times = track_forecast["properties"]["times"]
                    for s, step in enumerate(track_forecast["features"]):
                        forecast_pdf = np.array(step['properties'][self.variable + "_" +
                                                                   self.ensemble_name.replace(" ", "-")])
                        forecast_time = self.run_date + timedelta(hours=times[s])
                        t = np.where(self.times == forecast_time)[0][0]
                        mask = np.array(step['properties']["masks"], dtype=int)
                        i = np.array(step['properties']["i"], dtype=int)
                        i = i[mask == 1]
                        j = np.array(step['properties']["j"], dtype=int)
                        j = j[mask == 1]
                        if grid_method == "samples":
                            intensities = np.array(step["properties"]["timesteps"], dtype=float)[mask == 1]
                            rankings = np.argsort(intensities)
                            samples = np.random.choice(self.forecast_bins, size=intensities.size, replace=True,
                                                       p=forecast_pdf)
                            self.data[m, t, i[rankings], j[rankings]] = samples
                        else:
                            if grid_method == "mean":
                                forecast_value = np.sum(forecast_pdf * self.forecast_bins)
                            elif grid_method == "median":
                                forecast_cdf = np.cumsum(forecast_pdf)
                                forecast_value = self.forecast_bins[np.argmin(np.abs(forecast_cdf - 0.5))]
                            else:
                                forecast_value = 0
                            self.data[m, t, i, j] = forecast_value
        if grid_method in ["gamma"]:
            full_condition_name = "condition_" + self.condition_model_name.replace(" ", "-")
            dist_model_name = self.variable + "_" + self.ensemble_name.replace(" ", "-")
            for m, member in enumerate(self.members):
                for track_forecast in self.track_forecasts[member]:
                    times = track_forecast["properties"]["times"]
                    for s, step in enumerate(track_forecast["features"]):
                        forecast_params = step["properties"][dist_model_name]
                        if self.condition_model_name is not None:
                            condition = step["properties"][full_condition_name]
                        else:
                            condition = None
                        forecast_time = self.run_date + timedelta(hours=times[s])
                        if forecast_time in self.times:
                            t = np.where(self.times == forecast_time)[0][0]
                            mask = np.array(step["properties"]["masks"], dtype=int)
                            rankings = np.argsort(step["properties"]["timesteps"])[mask == 1]
                            i = np.array(step["properties"]["i"], dtype=int)[mask == 1][rankings]
                            j = np.array(step["properties"]["j"], dtype=int)[mask == 1][rankings]
                            if rankings.size > 0:
                                raw_samples = np.sort(gamma.rvs(forecast_params[0], loc=forecast_params[1],
                                                                scale=forecast_params[2],
                                                                size=(num_samples, rankings.size)),
                                                      axis=1)
                                if zero_inflate:
                                    raw_samples *= bernoulli.rvs(condition,
                                                                 size=(num_samples, rankings.size))
                                if percentile is None:
                                    samples = raw_samples.mean(axis=0)
                                else:
                                    samples = np.percentile(raw_samples, percentile, axis=0)
                                if condition is None or condition >= condition_threshold:
                                    self.data[m, t, i, j] = samples
        return 0

Response: Reads the track forecasts and converts them to grid points based on the condition model probability.


Instruction:
How would you code a function in Python 3 to
write data to grib2 file. Currently only works for GRIB2.

Response:
def write_grib2(self, path):
        """
        Writes data to grib2 file. Currently, grib codes are set by hand to hail.

        Args:
            path: Path to directory containing grib2 files.

        Returns:

        """
        if self.percentile is None:
            var_type = "mean"
        else:
            var_type = "p{0:02d}".format(self.percentile)
        lscale = 1e6
        grib_id_start = [7, 0, 14, 14, 2]
        gdsinfo = np.array([0, np.product(self.data.shape[-2:]), 0, 0, 30], dtype=np.int32)
        lon_0 = self.proj_dict["lon_0"]
        sw_lon = self.grid_dict["sw_lon"]
        if lon_0 < 0:
            lon_0 += 360
        if sw_lon < 0:
            sw_lon += 360
        gdtmp1 = np.array([7, 1, self.proj_dict['a'], 1, self.proj_dict['a'], 1, self.proj_dict['b'],
                           self.data.shape[-2], self.data.shape[-1], self.grid_dict["sw_lat"] * lscale,
                           sw_lon * lscale, 0, self.proj_dict["lat_0"] * lscale,
                           lon_0 * lscale,
                           self.grid_dict["dx"] * 1e3, self.grid_dict["dy"] * 1e3, 0,
                           self.proj_dict["lat_1"] * lscale,
                           self.proj_dict["lat_2"] * lscale, 0, 0], dtype=np.int32)
        pdtmp1 = np.array([1, 31, 2, 0, 116, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 192, 0, self.data.shape[0]], dtype=np.int32)
        for m, member in enumerate(self.members):
            pdtmp1[-2] = m
            for t, time in enumerate(self.times):
                time_list = list(time.utctimetuple()[0:6])
                grbe = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))
                grbe.addgrid(gdsinfo, gdtmp1)
                pdtmp1[8] = (time.to_pydatetime() - self.run_date).total_seconds() / 3600.0
                drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)
                data = self.data[m, t].astype(np.float32) / 1000.0
                masked_data = np.ma.array(data, mask=data <= 0)
                grbe.addfield(1, pdtmp1, 0, drtmp1, masked_data)
                grbe.end()
                filename = path + "{0}_{1}_mlhail_{2}_{3}.grib2".format(self.ensemble_name.replace(" ", "-"), member,
                                                                        var_type,
                                                                        time.to_datetime().strftime("%Y%m%d%H%M"))
                print("Writing to " + filename)
                grib_file = open(filename, "wb")
                grib_file.write(grbe.msg)
                grib_file.close()
        return


Instruction:
Create a Python 3 function for
initializing the netCDF file for writing.

Response:
def init_file(self, filename, time_units="seconds since 1970-01-01T00:00"):
        """
        Initializes netCDF file for writing

        Args:
            filename: Name of the netCDF file
            time_units: Units for the time variable in format "<time> since <date string>"
        Returns:
            Dataset object
        """
        if os.access(filename, os.R_OK):
            out_data = Dataset(filename, "r+")
        else:
            out_data = Dataset(filename, "w")
            if len(self.data.shape) == 2:
                for d, dim in enumerate(["y", "x"]):
                    out_data.createDimension(dim, self.data.shape[d])
            else:
                for d, dim in enumerate(["y", "x"]):
                    out_data.createDimension(dim, self.data.shape[d+1])
            out_data.createDimension("time", len(self.times))
            time_var = out_data.createVariable("time", "i8", ("time",))
            time_var[:] = date2num(self.times.to_pydatetime(), time_units)
            time_var.units = time_units
            out_data.Conventions = "CF-1.6"
        return out_data


Instruction:
Create a Python 3 function to
output data to a netCDF file.

Response:
def write_to_file(self, out_data):
        """
        Outputs data to a netCDF file. If the file does not exist, it will be created. Otherwise, additional variables
        are appended to the current file

        Args:
            out_data: Full-path and name of output netCDF file
        """
        full_var_name = self.consensus_type + "_" + self.variable
        if "-hour" in self.consensus_type:
            if full_var_name not in out_data.variables.keys():
                var = out_data.createVariable(full_var_name, "f4", ("y", "x"), zlib=True,
                                              least_significant_digit=3, shuffle=True)
            else:
                var = out_data.variables[full_var_name]
            var.coordinates = "y x"
        else:
            if full_var_name not in out_data.variables.keys():
                var = out_data.createVariable(full_var_name, "f4", ("time", "y", "x"), zlib=True,
                                              least_significant_digit=3, shuffle=True)
            else:
                var = out_data.variables[full_var_name]
            var.coordinates = "time y x"
        var[:] = self.data
        var.units = self.units
        var.long_name = self.consensus_type + "_" + self.variable
        return


Instruction:
Can you generate the documentation for the following Python 3 function
def restore(self, workspace_uuid):
        workspace = next((workspace for workspace in self.document_model.workspaces if workspace.uuid == workspace_uuid), None)
        if workspace is None:
            workspace = self.new_workspace()
        self._change_workspace(workspace)

Response: Restore the workspace to the given workspace_uuid.


Instruction:
How would you code a function in Python 3 to
create a new workspace and insert it into document_model and return it.

Response:
def new_workspace(self, name=None, layout=None, workspace_id=None, index=None) -> WorkspaceLayout.WorkspaceLayout:
        """ Create a new workspace, insert into document_model, and return it. """
        workspace = WorkspaceLayout.WorkspaceLayout()
        self.document_model.insert_workspace(index if index is not None else len(self.document_model.workspaces), workspace)
        d = create_image_desc()
        d["selected"] = True
        workspace.layout = layout if layout is not None else d
        workspace.name = name if name is not None else _("Workspace")
        if workspace_id:
            workspace.workspace_id = workspace_id
        return workspace


Instruction:
Make a summary of the following Python 3 code
def ensure_workspace(self, name, layout, workspace_id):
        workspace = next((workspace for workspace in self.document_model.workspaces if workspace.workspace_id == workspace_id), None)
        if not workspace:
            workspace = self.new_workspace(name=name, layout=layout, workspace_id=workspace_id)
        self._change_workspace(workspace)

Response: Ensures that a workspace with the given name and layout exists.


Instruction:
Create a Python 3 function to
pose a dialog to name and create a workspace.

Response:
def create_workspace(self) -> None:
        """ Pose a dialog to name and create a workspace. """

        def create_clicked(text):
            if text:
                command = Workspace.CreateWorkspaceCommand(self, text)
                command.perform()
                self.document_controller.push_undo_command(command)

        self.pose_get_string_message_box(caption=_("Enter a name for the workspace"), text=_("Workspace"),
                                         accepted_fn=create_clicked, accepted_text=_("Create"),
                                         message_box_id="create_workspace")


Instruction:
Explain what the following Python 3 code does
def rename_workspace(self) -> None:

        def rename_clicked(text):
            if len(text) > 0:
                command = Workspace.RenameWorkspaceCommand(self, text)
                command.perform()
                self.document_controller.push_undo_command(command)

        self.pose_get_string_message_box(caption=_("Enter new name for workspace"), text=self.__workspace.name,
                                         accepted_fn=rename_clicked, accepted_text=_("Rename"),
                                         message_box_id="rename_workspace")

Response: Pose a dialog to rename the workspace.


Instruction:
Can you tell what is the following Python 3 function doing
def remove_workspace(self):

        def confirm_clicked():
            if len(self.document_model.workspaces) > 1:
                command = Workspace.RemoveWorkspaceCommand(self)
                command.perform()
                self.document_controller.push_undo_command(command)

        caption = _("Remove workspace named '{0}'?").format(self.__workspace.name)
        self.pose_confirmation_message_box(caption, confirm_clicked, accepted_text=_("Remove Workspace"),
                                           message_box_id="remove_workspace")

Response: Pose a dialog to confirm removal then remove workspace.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def clone_workspace(self) -> None:

        def clone_clicked(text):
            if text:
                command = Workspace.CloneWorkspaceCommand(self, text)
                command.perform()
                self.document_controller.push_undo_command(command)

        self.pose_get_string_message_box(caption=_("Enter a name for the workspace"), text=self.__workspace.name,
                                         accepted_fn=clone_clicked, accepted_text=_("Clone"),
                                         message_box_id="clone_workspace")

Response: Pose a dialog to name and clone a workspace.


Instruction:
Can you write a function in Python 3 where it
replaces the display item with the given display item.

Response:
def __replace_displayed_display_item(self, display_panel, display_item, d=None) -> Undo.UndoableCommand:
        """ Used in drag/drop support. """
        self.document_controller.replaced_display_panel_content = display_panel.save_contents()
        command = DisplayPanel.ReplaceDisplayPanelCommand(self)
        if display_item:
            display_panel.set_display_panel_display_item(display_item, detect_controller=True)
        elif d is not None:
            display_panel.change_display_panel_content(d)
        display_panel.request_focus()
        self.__sync_layout()
        return command


Instruction:
Can you tell what is the following Python 3 function doing
def bootstrap(score_objs, n_boot=1000):
    all_samples = np.random.choice(score_objs, size=(n_boot, len(score_objs)), replace=True)
    return all_samples.sum(axis=1)

Response:
This function performs a
    bootstrap resampling of the objects and returns the n_boot aggregations of the objects.


Instruction:
Explain what the following Python 3 code does
def update(self, forecasts, observations):
        for t, threshold in enumerate(self.thresholds):
            tp = np.count_nonzero((forecasts >= threshold) & (observations >= self.obs_threshold))
            fp = np.count_nonzero((forecasts >= threshold) &
                                  (observations < self.obs_threshold))
            fn = np.count_nonzero((forecasts < threshold) &
                                  (observations >= self.obs_threshold))
            tn = np.count_nonzero((forecasts < threshold) &
                                  (observations < self.obs_threshold))
            self.contingency_tables.iloc[t] += [tp, fp, fn, tn]

Response: Update the ROC curve with a set of forecasts and observations.


Instruction:
How would you explain what the following Python 3 function does
def merge(self, other_roc):
        if other_roc.thresholds.size == self.thresholds.size and np.all(other_roc.thresholds == self.thresholds):
            self.contingency_tables += other_roc.contingency_tables
        else:
            print("Input table thresholds do not match.")

Response: Merge the values of another DistributedROC object into this one and update the statistics inplace.


Instruction:
Write a Python 3 script to
generate a ROC curve from the contingency table by calculating the probability of false detection and the probability of detection.

Response:
def roc_curve(self):
        """
        Generate a ROC curve from the contingency table by calculating the probability of detection (TP/(TP+FN)) and the
        probability of false detection (FP/(FP+TN)).

        Returns:
            A pandas.DataFrame containing the POD, POFD, and the corresponding probability thresholds.
        """
        pod = self.contingency_tables["TP"].astype(float) / (self.contingency_tables["TP"] +
                                                             self.contingency_tables["FN"])
        pofd = self.contingency_tables["FP"].astype(float) / (self.contingency_tables["FP"] +
                                                              self.contingency_tables["TN"])
        return pd.DataFrame({"POD": pod, "POFD": pofd, "Thresholds": self.thresholds},
                            columns=["POD", "POFD", "Thresholds"])


Instruction:
How would you explain what the following Python 3 function does
def performance_curve(self):
        pod = self.contingency_tables["TP"] / (self.contingency_tables["TP"] + self.contingency_tables["FN"])
        far = self.contingency_tables["FP"] / (self.contingency_tables["FP"] + self.contingency_tables["TP"])
        far[(self.contingency_tables["FP"] + self.contingency_tables["TP"]) == 0] = np.nan
        return pd.DataFrame({"POD": pod, "FAR": far, "Thresholds": self.thresholds},
                            columns=["POD", "FAR", "Thresholds"])

Response: Calculates the Probability of Detection and False Alarm Ratio in order to output a performance diagram.


Instruction:
How would you explain what the following Python 3 function does
def auc(self):
        roc_curve = self.roc_curve()
        return np.abs(np.trapz(roc_curve['POD'], x=roc_curve['POFD']))

Response: Calculate the Area Under the ROC Curve.


Instruction:
Create a Python 3 function to
calculate the maximum Critical Success Index across all probability thresholds Returns a float

Response:
def max_csi(self):
        """
        Calculate the maximum Critical Success Index across all probability thresholds

        Returns:
            The maximum CSI as a float
        """
        csi = self.contingency_tables["TP"] / (self.contingency_tables["TP"] + self.contingency_tables["FN"] +
                                               self.contingency_tables["FP"])
        return csi.max()


Instruction:
Write a Python 3 function for
creating an Array of ContingencyTable objects for each probability threshold.

Response:
def get_contingency_tables(self):
        """
        Create an Array of ContingencyTable objects for each probability threshold.

        Returns:
            Array of ContingencyTable objects
        """
        return np.array([ContingencyTable(*ct) for ct in self.contingency_tables.values])


Instruction:
How would you implement a function in Python 3 that
reads the DistributedROC string and parses the contingency tables from it.

Response:
def from_str(self, in_str):
        """
        Read the DistributedROC string and parse the contingency table values from it.

        Args:
            in_str (str): The string output from the __str__ method
        """
        parts = in_str.split(";")
        for part in parts:
            var_name, value = part.split(":")
            if var_name == "Obs_Threshold":
                self.obs_threshold = float(value)
            elif var_name == "Thresholds":
                self.thresholds = np.array(value.split(), dtype=float)
                self.contingency_tables = pd.DataFrame(columns=self.contingency_tables.columns,
                                                       data=np.zeros((self.thresholds.size,
                                                                     self.contingency_tables.columns.size)))
            elif var_name in self.contingency_tables.columns:
                self.contingency_tables[var_name] = np.array(value.split(), dtype=int)


Instruction:
Given the following Python 3 function, write the documentation
def update(self, forecasts, observations):
        for t, threshold in enumerate(self.thresholds[:-1]):
            self.frequencies.loc[t, "Positive_Freq"] += np.count_nonzero((threshold <= forecasts) &
                                                                         (forecasts < self.thresholds[t+1]) &
                                                                         (observations >= self.obs_threshold))
            self.frequencies.loc[t, "Total_Freq"] += np.count_nonzero((threshold <= forecasts) &
                                                                      (forecasts < self.thresholds[t+1]))

Response: Update the statistics with a set of forecasts and observations.


Instruction:
Can you create a Python 3 function that
ingests another DistributedReliability and add its contents to the current object.

Response:
def merge(self, other_rel):
        """
        Ingest another DistributedReliability and add its contents to the current object.

        Args:
            other_rel: a Distributed reliability object.
        """
        if other_rel.thresholds.size == self.thresholds.size and np.all(other_rel.thresholds == self.thresholds):
            self.frequencies += other_rel.frequencies
        else:
            print("Input table thresholds do not match.")


Instruction:
Can you create a Python 3 function that
calculates the reliability diagram statistics. The columns are Bin_Start Positive_Relative_Freq and Total_Relative_Freq.

Response:
def reliability_curve(self):
        """
        Calculates the reliability diagram statistics. The key columns are Bin_Start and Positive_Relative_Freq

        Returns:
            pandas.DataFrame
        """
        total = self.frequencies["Total_Freq"].sum()
        curve = pd.DataFrame(columns=["Bin_Start", "Bin_End", "Bin_Center",
                                      "Positive_Relative_Freq", "Total_Relative_Freq"])
        curve["Bin_Start"] = self.thresholds[:-1]
        curve["Bin_End"] = self.thresholds[1:]
        curve["Bin_Center"] = 0.5 * (self.thresholds[:-1] + self.thresholds[1:])
        curve["Positive_Relative_Freq"] = self.frequencies["Positive_Freq"] / self.frequencies["Total_Freq"]
        curve["Total_Relative_Freq"] = self.frequencies["Total_Freq"] / total
        return curve


Instruction:
Make a summary of the following Python 3 code
def brier_score_components(self):
        rel_curve = self.reliability_curve()
        total = self.frequencies["Total_Freq"].sum()
        climo_freq = float(self.frequencies["Positive_Freq"].sum()) / self.frequencies["Total_Freq"].sum()
        reliability = np.sum(self.frequencies["Total_Freq"] * (rel_curve["Bin_Start"] -
                                                               rel_curve["Positive_Relative_Freq"]) ** 2) / total
        resolution = np.sum(self.frequencies["Total_Freq"] * (rel_curve["Positive_Relative_Freq"] - climo_freq) ** 2) \
                     / total
        uncertainty = climo_freq * (1 - climo_freq)
        return reliability, resolution, uncertainty

Response: Calculate the components of the Brier score decomposition.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def brier_score(self):
        reliability, resolution, uncertainty = self.brier_score_components()
        return reliability - resolution + uncertainty

Response: Calculate the Brier Score of the current set of attributes.