Data Table

A class to sync a data object and list_ctrl

class iqdma.data_table.DataTable(list_ctrl: wx.ListCtrl, data: Optional[dict] = None, columns: Optional[list] = None, widths: Optional[list] = None, formats: Optional[list] = None)

Bases: object

Helper class for wx.ListCtrl

Init DataTable class

Parameters

• list_ctrl (wx.ListCtrl) – the list_ctrl in the GUI to be updated with data in this class

• data (dict) – data should be formatted in a dictionary with keys being the column names and values being lists

• columns (list) – the keys of the data object to be visible in the list_ctrl

• widths (list) – optionally specify the widths of the columns

• formats (list) – optionally specify wx Format values (e.g., wx.LIST_FORMAT_LEFT)

append_row(row: list, layout_only: bool = False)

Add a row of data

Parameters

• row (list) – data ordered by self.columns

• layout_only (bool) – If true, only add row to the GUI

append_row_to_data(row: list)

Add a row of data to self.data

Parameters

row (list) – data ordered by self.columns

clear()

Delete all data in self.data and clear the table view

property column_count: int

Number of columns

Returns

Length of columns

Return type

int

property data_for_csv: list

Iterate through data to get a list of csv rows

Returns

list of rows. Each row is a list of column data

Return type

list of lists

data_to_list_of_rows() → list

Convert data into a list of rows as needed for list_ctrl

Returns

data in the format of list of rows

Return type

list

delete_all_rows(layout_only: bool = False, force_delete_data: bool = False)

Clear all data from data and the layout view

Parameters

• layout_only (bool) – If True, do not remove the row from self.data

• force_delete_data (bool) – If true, force deletion even if layout is not set

get_csv_rows() → list

Convert data to a list of strings for CSV writing

Returns

Each item is a str for a CSV file

Return type

list of str

get_data_in_original_order() → dict

Get data in the order it was original set

Returns

keys are column names with voalues of row data

Return type

dict

get_row(row_index: int) → list

Get a row of data from self.data with the given row index

Parameters

row_index (int) – retrieve all values from row with this index

Returns

values for the specified row

Return type

list

get_value(row_index: int, column_index: int)

Get a specific table value with a column name and row index

Parameters

• row_index (int) – retrieve value from row with this index

• column_index (int) – retrieve value from column with this index

Returns

value corresponding to provided indices

Return type

any

property has_data: bool

Check if there are any rows of data

Returns

True if row_count > 0

Return type

bool

increment_index(evt: Optional[wx.Event] = None, increment: Optional[int] = None)

Increment the ListCtrl selection with an event or fixed increment

Parameters

• evt (wx.Event) – An event with a GetKeyCode method

• increment (int) – If no event is passed, use a fixed index increment

property keys: list

Column names

Returns

A copy of columns

Return type

list

property row_count: int

Number of rows

Returns

Length of first column in data

Return type

int

property selected_row_data: list

Row data from the current selection in wx.ListCtrl

Returns

row data of the currently selected row in the GUI

Return type

list

property selected_row_index: list

Get the indices of selected rows in wx.ListCtrl

Returns

List of indices

Return type

list

set_column_width(index: int, width: int)

Change the column width in the view

Parameters

• index (int) – index of column

• width (int) – the specified width

set_column_widths(auto: bool = False)

Set all widths in layout based on widths

Parameters

auto (bool) – Use wx.LIST_AUTOSIZE_USEHEADER rather than widths

set_data(data: dict, columns: list, formats: Optional[list] = None, ignore_layout: bool = False)

Set data and update layout

Parameters

• data (dict) – data should be formatted in a dictionary with keys being the column names and values being lists

• columns (list) – the keys of the data object to be visible in the list_ctrl

• formats (list) – optionally specify wx Format values (e.g., wx.LIST_FORMAT_LEFT)

• ignore_layout (bool) – If true, do not update layout

set_data_in_layout()

Set data in layout from data

set_layout_columns()

Clear layout and re-add columns

sort_table(evt: wx.EVT_LIST_COL_CLICK)

Sort the data based on the clicked column header

Parameters

evt (wx.EVT_LIST_COL_CLICK) – Event from a ListCtrl column header click

Importer

Import output from IQDM-PDF

class iqdma.importer.CSVParser(json_file_path: str)

Bases: object

Import CSV Template from JSON

Initialization of CSVParser

Parameters

json_file_path (str) – file path to JSON file containing CSV template info

get_index(value: str) → int

If value is a string, return its index of columns

Parameters

value (str, int) – any value

Returns

If value from analysis_columns is a string, return its index

Return type

int

set_values_to_index()

If values are str, set to column index

class iqdma.importer.ReportImporter(report_file_path: str, parser: str, duplicate_detection: bool)

Bases: object

Class to import IQDM-PDF CSV output

Initialize ReportImporter

Parameters

• report_file_path (str) – File path to CSV output from IQDM-PDF

• parser (str) – The parser used to generate the report. Either ‘SNCPatient2020’, ‘SNCPatientCustom’, ‘Delta4’, ‘Verisoft’, ‘VarianPortalDosimetry’

• duplicate_detection (bool) – If true, apply a multi_value policy from options

property charting_options: list

Column names of y-axis options

Returns

Column names from analysis_columns['y']

Return type

list

property criteria_col: list

Column names of analysis criteria options

Returns

Column names from analysis_columns['criteria']

Return type

list

property lcl

Lower Control Limit minimums

Returns

keys are column names, values are minimum LCL values (or None)

Return type

dict

remove_non_numeric(val: str) → float

Remove all non-numeric characters, convert to float, use to highjack dtype in widen_data

Parameters

val (str) – Any string

Returns

val converted into a float

Return type

float

property ucl: dict

Upper Control Limit caps

Returns

keys are column names, values are maximum UCL values (or None)

Return type

dict

property uid_col: list

Column names, when combined create a UID

Returns

Column names from analysis_columns['uid']

Return type

list

iqdma.importer.copy_default_csv_templates()

Copy default JSON file form resources/csv_templates

iqdma.importer.create_csv_template(parser: IQDMPDF.parsers.generic.ParserBase)

Write a CSV_TEMPLATE to JSON

Parameters

parser (ParserBase) – a parser from IQDMPDF

iqdma.importer.create_default_parsers()

Generate CSV_TEMPLATE JSON files from IQDMPDF, if it doesn’t exist

iqdma.importer.import_csv_templates() → dict

Import CSV Templates

Returns

keys are parser names and values are CSVParser objects. If a default parser is missing from CSV_TEMPLATES_DIR, load directly from IQDMPDF

Return type

dict

Stats

Modified DVHA-Stats for IQDM-PDF output

class iqdma.stats.ControlChart(y, std=3, ucl_limit=None, lcl_limit=None, x=None, range=None)

Bases: object

Calculate control limits for a standard univariate Control Chart”

Parameters

• y (list, np.ndarray) – Input data (1-D)

• std (int, float, optional) – Number of standard deviations used to calculate if a y-value is out-of-control.

• ucl_limit (float, optional) – Limit the upper control limit to this value

• lcl_limit (float, optional) – Limit the lower control limit to this value

• range (tuple, list, ndarray) – 2-item object containing start and end index of y

Initialization of a ControlChart

property avg_moving_range

Avg moving range based on 2 consecutive points

Returns

Average moving range. Returns NaN if arr is empty.

Return type

np.ndarray, np.nan

property center_line

Center line of charting data (i.e., mean value)

Returns

Mean value of y with np.mean() or np.nan if y is empty

Return type

np.ndarray, np.nan

property chart_data

JSON compatible dict for chart generation

Returns

Data used for Histogram visuals. Keys include ‘x’, ‘y’, ‘out_of_control’, ‘center_line’, ‘lcl’, ‘ucl’

Return type

dict

property control_limits

Calculate the lower and upper control limits

Returns

• lcl (float) – Lower Control Limit (LCL)

• ucl (float) – Upper Control Limit (UCL)

property out_of_control

Get the indices of out-of-control observations

Returns

An array of indices that are not between the lower and upper control limits

Return type

np.ndarray

property out_of_control_high

Get the indices of observations > ucl

Returns

An array of indices that are greater than the upper control limit

Return type

np.ndarray

property out_of_control_low

Get the indices of observations < lcl

Returns

An array of indices that are less than the lower control limit

Return type

np.ndarray

property sigma

UCL/LCL = center_line +/- sigma * std

Returns

sigma or np.nan if arr is empty

Return type

np.ndarray, np.nan

property x_ranged: list

Return x within range

Returns

x data from range[0] to range[1]

Return type

list

property y_ranged

Return y within range

Returns

y data from range[0] to range[1]

Return type

list

class iqdma.stats.IQDMStats(report_file_path: str, charting_column: str, multi_val_policy: str, duplicate_detection: bool, parser: str)

Bases: object

Modified DVHAStats class for IQDM-PDF output

Initialize IQDMStats

Parameters

• report_file_path (str) – File path to CSV output from IQDM-PDF

• charting_column (str) – Column of y-axis data

• multi_val_policy (str) – Duplicate value policy from options

• duplicate_detection (bool) – If true, apply a multi_value policy from options

• parser (str) – CSV format

get_index_by_var_name(var_name: str)

Get the variable index by var_name

Parameters

var_name (int, str) – The name (str) or index (int) of the variable of interest

Returns

The column index for the given var_name

Return type

int

get_index_description() → tuple

Get a dict of data and columns for DataTable

Returns

• dict – Keys are column names with values being a list of values

• list – Column names in order to be displayed

univariate_control_chart(var_name: str, std: float = 3, ucl_limit: Optional[float] = None, lcl_limit: Optional[float] = None, range: Optional[tuple] = None)

Calculate control limits for a standard univariate Control Chart

Parameters

• var_name (str, int) – The name (str) or index (int) of teh variable to plot

• std (int, float, optional) – Number of standard deviations used to calculate if a y-value is out-of-control

• ucl_limit (float, optional) – Limit the upper control limit to this value

• lcl_limit (float, optional) – Limit the lower control limit to this value

• range (tuple, list, ndarray) – 2-item object containing start and end index of data

Returns

stats.ControlChart class object

Return type

stats.ControlChart

univariate_control_charts(**kwargs)

Calculate Control charts for all variables

Parameters

kwargs (any) – See univariate_control_chart for keyword parameters

Returns

ControlChart class objects stored in a dictionary with var_names and indices as keys (can use var_name or index)

Return type

dict

property variable_count

Number of variables in data

Returns

Number of columns in data

Return type

int

iqdma.stats.avg_moving_range(arr, nan_policy='omit')

Calculate the average moving range (over 2-consecutive point1)

Parameters

• arr (array-like (1-D)) – Input array. Must be positive 1-dimensional.

• nan_policy (str, optional) – Value must be one of the following: {‘propagate’, ‘raise’, ‘omit’} Defines how to handle when input contains nan. The following options are available (default is ‘omit’): ‘propagate’: returns nan ‘raise’: throws an error ‘omit’: performs the calculations ignoring nan values

Returns

Average moving range. Returns NaN if arr is empty

Return type

np.ndarray, np.nan

iqdma.stats.process_nan_policy(arr, nan_policy)

Calculate the average moving range (over 2-consecutive point1)

Parameters

• arr (array-like (1-D)) – Input array. Must be positive 1-dimensional.

• nan_policy (str) – Value must be one of the following: {‘propagate’, ‘raise’, ‘omit’} Defines how to handle when input contains nan. The following options are available (default is ‘omit’): ‘propagate’: returns nan ‘raise’: throws an error ‘omit’: performs the calculations ignoring nan values

Returns

Input array evaluated per nan_policy

Return type

np.ndarray, np.nan

iqdma.stats.remove_nan(arr)

Remove indices from 1-D array with values of np.nan

Parameters

arr (np.ndarray (1-D)) – Input array. Must be positive 1-dimensional.

Returns

arr with NaN values deleted

Return type

np.ndarray

Utilities ported from DVHA-Stats

Common functions for DVHA-Stats. Copied to limit required libraries.

iqdma.utilities_dvha_stats.apply_dtype(value, dtype)

Convert value with the provided data type

Parameters

• value (any) – Value to be converted

• dtype (function, None) – python reserved types, e.g., int, float, str, etc. However, dtype could be any callable that raises a ValueError on failure.

Returns

The return of dtype(value) or numpy.nan on ValueError

Return type

any

iqdma.utilities_dvha_stats.csv_to_dict(csv_file_path, delimiter=',', dtype=None, header_row=True)

Read in a csv file, return data as a dictionary

Parameters

• csv_file_path (str) – File path to the CSV file to be processed.

• delimiter (str) – Specify the delimiter used in the csv file (default = ‘,’)

• dtype (callable, type, optional) – Optionally force values to a type (e.g., float, int, str, etc.).

• header_row (bool, optional) – If True, the first row is interpreted as column keys, otherwise row indices will be used

Returns

CSV data as a dict, using the first row values as keys

Return type

dict

iqdma.utilities_dvha_stats.dict_to_array(data, key_order=None)

Convert a dict of data to a numpy array

Parameters

• data (dict) – Dictionary of data to be converted to np.array.

• key_order (None, list of str) – Optionally the order of columns

Returns

A dictionary with keys of ‘data’ and ‘columns’, pointing to a numpy array and list of str, respectively

Return type

dict

iqdma.utilities_dvha_stats.get_sorted_indices(list_data)

Get original indices of a list after sorting

Parameters

list_data (list) – Any python sortable list

Returns

list_data indices of sorted(list_data)

Return type

list

iqdma.utilities_dvha_stats.import_data(data, var_names=None)

Generalized data importer for np.ndarray, dict, and csv file

Parameters

• data (numpy.array, dict, str) – Input data (2-D) with N rows of observations and p columns of variables. The CSV file must have a header row for column names.

• var_names (list of str, optional) – If data is a numpy array, optionally provide the column names.

Returns

A tuple: data as an array and variable names as a list

Return type

np.ndarray, list

iqdma.utilities_dvha_stats.is_numeric(val)

Check if value is numeric (float or int)

Parameters

val (any) – Any value

Returns

Returns true if float(val) doesn’t raise a ValueError

Return type

bool

iqdma.utilities_dvha_stats.sort_2d_array(arr, index, mode='col')

Sort a 2-D numpy array

Parameters

• arr (np.ndarray) – Input 2-D array to be sorted

• index (int, list) – Index of column or row to sort arr. If list, will sort by each index in the order provided.

• mode (str) – Either ‘col’ or ‘row’