API Description

Study class

class tmtk.Study(study_params_path=None, minimal=False)

Bases: object

Describes an entire TranSMART study. This is the main object used in tmtk. Studies can be initialized by pointing to a study.params file. This study has to be structured according to specification for transmart-batch.

>>> import tmtk
>>> study = tmtk.Study('./studies/valid_study/study.params')

This will create the study object which can be used as a starting point for custom curation or directly in The Arborist.

add_metadata()

Create the Tags object for this study. Does nothing if it is already present.

all_files

All file objects in this study.

annotation_files

All annotation file objects in this study.

call_boris(height=650)

Launch The Arborist GUI editor for the concept tree. This starts a Flask webserver in an IFrame when running in a Jupyter Notebook.

While The Arborist is opened, the GIL prevents any other actions. :param height: set the height of the output cell

clinical_files

All clinical file objects in this study.

concept_tree

ConceptTree object for this study.

concept_tree_json

Stringified JSON that is used by JSTree in The Arborist.

concept_tree_to_clipboard()

Send stringified JSON that is used by JSTree in The Arborist to clipboard.

create_clinical()

Add clinical data to a study object by creating empty params.

files_with_changes()

Find dataframes that have changed since they have been loaded.

find_annotation(platform=None)

Search for annotation data with this study and return it.

Parameters:platform – platform id to look for in this study. Returns:an Annotations object or nothing.

find_params_for_datatype(datatypes=None)

Search for parameter files within this study object and return them as list.

Parameters:datatypes – single string datatype or list of strings Returns:a list of parameter objects for specific datatype in this study

get_object_from_params_path(path)

Returns object that belongs to the params path given

get_objects_with_prop(prop: <built-in function all>)

Search for objects with a certain property.

Parameters:prop – string equal to the property name. Returns:generator for the found objects.

high_dim_files

All high dimensional file objects in this study.

load_to

publish_to_baas(url, study_name=None, username=None)

Publishes a tree on a Boris as a Service instance.

Parameters:

• url – url to a instance (e.g. http://transmart-arborist.thehyve.nl/).
• study_name – a nice name.
• username – if no username is given, you will be prompted for one.

Returns:

the url that points to the study you’ve just uploaded.

sample_mapping_files

All subject sample mapping file objects in this study.

study_id

The study ID as it is set in study params.

study_name

The study name, extracted from study param TOP_NODE.

tag_files

update_from_baas(url, username=None)

Give url to a tree in BaaS.

Parameters:

• url – url that has both the study and version of a tree in BaaS (e.g. http://transmart-arborist.thehyve.nl/trees/study-name/1/~edit/).
• username – if no username is given, you will be prompted for one.

update_from_treefile(treefile)

Give path to a treefile (from Boris as a Service or otherwise) and update the current study to match made changes.

Parameters:treefile – path to a treefile (stringified JSON).

validate_all(verbosity: int = 2)

Validate all items in this study.

Parameters:verbosity – set the verbosity of output, pick 0, 1, 2, 3 or 4. Returns:True if everything is okay, else return False.

write_to(root_dir, overwrite=False, return_new=True)

Write this study to a new directory on file system.

Parameters:

• root_dir – the base directory to write the study to.
• overwrite – set this to True to overwrite existing files.
• return_new – if True load the study object from the new location and return it.

Returns:

new study object if return_new == True.

---

Params classes

Params Container

class tmtk.params.Params.Params(study_folder=None)

Bases: object

Container class for all params files, called by Study to locate all params files.

add_params(path, parameters=None)

Add a new parameter file to the Params object.

Parameters:

• path – a path to a parameter file.
• new – if new, create parameter object.
• parameters – add dict here with parameters if you want to create a new parameter file.

static create_params(path, parameters=None, subdir=None)

Create a new parameter file object.

Parameters:

• path – a path to a parameter file.
• parameters – add dict here with parameters if you want to create a new parameter file.
• subdir – subdir is used as string representation.

Returns:

parameter file object.

validate_all(verbosity=3)

Base class: ParamsBase

class tmtk.params.ParamsBase.ParamsBase(path=None, parameters=None, subdir=None, parent=None)

Bases: object

Base class for parameter files.

get(parameter, default=None)

Return value for parameter.

Parameters:

• parameter – string will be converted to uppercase.
• default – return default if value is not found.

Returns:

value for this parameter if set, else None.

save()

Overwrite the original file with the current parameters.

update()

Iterate over parameters to change them interactively.

validate(verbosity=3)

Validate this parameter file. Return True if no errors were found in this parameter file.

Parameters:verbosity – set the verbosity of output, pick 0, 1, 2, 3 or 4. Returns:True or False if errors are found.

write_to(path, overwrite=False)

Writes parameters in object to file in path. Does not overwrite existing files unless specifically told.

Parameters:

• path – path to store parameters to.
• overwrite – allow overwriting existing files.

AnnotationParams

class tmtk.params.AnnotationParams.AnnotationParams(path=None, parameters=None, subdir=None, parent=None)

Bases: tmtk.params.ParamsBase.ParamsBase

is_viable()

Returns:True if both the platform is set and the annotations file is located, else returns False.

mandatory

optional

ClinicalParams

class tmtk.params.ClinicalParams.ClinicalParams(path=None, parameters=None, subdir=None, parent=None)

Bases: tmtk.params.ParamsBase.ParamsBase

docslink = 'https://github.com/thehyve/transmart-batch/blob/master/docs/clinical.md'

is_viable()

Returns:True if both the column mapping file is located, else returns False.

mandatory

optional

HighDimParams

class tmtk.params.HighDimParams.HighDimParams(path=None, parameters=None, subdir=None, parent=None)

Bases: tmtk.params.ParamsBase.ParamsBase

docslink = 'https://github.com/thehyve/transmart-batch/blob/master/docs/hd-params.md'

is_viable()

Returns:True if both the datafile and map file are located, else returns False.

mandatory

optional

StudyParams

class tmtk.params.StudyParams.StudyParams(path=None, parameters=None, subdir=None, parent=None)

Bases: tmtk.params.ParamsBase.ParamsBase

docslink = 'https://github.com/thehyve/transmart-batch/blob/master/docs/study-params.md'

is_viable()

Returns:True if STUDY_ID has been set.

mandatory

optional

TagsParams

class tmtk.params.TagsParams.TagsParams(path=None, parameters=None, subdir=None, parent=None)

Bases: tmtk.params.ParamsBase.ParamsBase

is_viable()

Returns:True if both the column mapping file is located, else returns False.

mandatory

optional

---

Clinical classes

Clinical Container

class tmtk.clinical.Clinical(clinical_params=None)

Bases: object

Container class for all clinical data related objects, i.e. the column mapping, word mapping, and clinical data files.

This object has methods that add data files, and for lookups of clinical files and variables.

ColumnMapping

WordMapping

add_datafile(filename, dataframe=None)

Add a clinical data file to study.

Parameters:

• filename – path to file or filename of file in clinical directory.
• dataframe – if given, add pd.DataFrame to study.

all_variables

Dictionary where {tmtk.VarID: tmtk.Variable} for all variables in the column mapping file.

apply_column_mapping_template(template)

Update the column mapping by applying a template.

Parameters:template –

expected input is a dictionary where keys are column names as found in clinical datafiles. Each column header name has a dictionary describing the path and data label. For example:

{‘GENDER’: {‘path’: ‘CharacteristicsDemographics’,

’label’: ‘Gender’},

’BPBASE’: {‘path’: ‘Lab resultsBlood’,

’label’: ‘Blood pressure (baseline)’}

}

call_boris(height=650)

Use The Arborist to modify only information in the column and word mapping files. :param height: set the height of the output cell

clinical_files

get_datafile(name: str)

Find datafile object by filename.

Parameters:name – name of file. Returns:tmtk.DataFile object.

get_variable(var_id: tuple)

Return a Variable object based on variable id.

Parameters:var_id – tuple of filename and column number. Returns:tmtk.Variable.

load_to

params

show_changes()

Print changes made to the column mapping and word mapping file.

validate_all(verbosity=3)

ColumnMapping

class tmtk.clinical.ColumnMapping(params=None)

Bases: tmtk.utils.filebase.FileBase

Class with utilities for the column mapping file for clinical data. Can be initiated with by giving a clinical params file object.

append_from_datafile(datafile)

Appends the column mapping file with rows based on datafile column names.

Parameters:datafile – tmtk.DataFile object.

build_index(df=None)

Build index for the column mapping dataframe. If pd.DataFrame (optional) is given, modify and return that.

Parameters:df – pd.DataFrame. Returns:pd.DataFrame.

create_df()

Create pd.DataFrame with a correct header.

Returns:pd.DataFrame.

get_concept_path(var_id: tuple)

Return concept path for given variable identifier tuple.

Parameters:var_id – tuple of filename and column number. Return str:concept path for this variable.

ids

A list of variable identifier tuples.

included_datafiles

List of datafiles included in column mapping file.

path_changes(silent=False)

Determine changes made to column mapping file.

Parameters:silent – if True, only print output. Returns:if silent=False return dictionary with changes since load.

path_id_dict

Dictionary with all variable ids as keys and paths as value.

select_row(var_id: tuple)

Select row based on variable identifier tuple. Raises exception if variable is not in this column mapping.

Parameters:var_id – tuple of filename and column number. Returns:list of items in selected row.

set_concept_path(var_id: tuple, path, label)

Return concept path for given variable identifier tuple.

Parameters:

• var_id – tuple of filename and column number.
• path – new value for path.
• label – new value for data label.

validate(verbosity=2)

DataFile

class tmtk.clinical.DataFile(path=None)

Bases: tmtk.utils.filebase.FileBase

Class for clinical data files, does not do much more than tmkt.FileBase.

validate(verbosity=2)

Variable

class tmtk.clinical.Variable(datafile, column: int = None, clinical_parent=None)

Bases: object

Base class for clinical variables

column_map_data

Column mapping row as dictionary where keys are short descriptors.

Returns:dict.

concept_path

Concept path after conversions by transmart-batch.

Returns:str.

data_label

Variable data label.

Returns:str.

forced_categorical

Check if forced categorical by entering ‘CATEGORICAL’ in 7th column.

Returns:bool.

is_empty

Check if variable is fully empty.

Returns:bool.

is_in_wordmap

Check if variable is represented in word mapping file.

Returns:bool.

is_numeric

True if transmart-batch will load this concept as numerical. This includes information from word mapping and column mapping.

Returns:bool.

is_numeric_in_datafile

True if the datafile contains only numerical items.

Returns:bool.

mapped_values

Data items after word mapping.

Returns:list.

unique_values

Returns:Unique set of values in the datafile.

validate(verbosity=2)

values

Returns:All values as found in the datafile.

var_id

Returns:Variable identifier tuple (datafile.name, column).

word_map_dict

A dictionary with word mapped categoricals. Keys are items in the datafile, values are what they will be mapped to through the word mapping file. Unmapped items are also added as key, value pair.

Returns:dict.

WordMapping

class tmtk.clinical.WordMapping(params=None)

Bases: tmtk.utils.filebase.FileBase

Class representing the word mapping file.

build_index(df=None)

Build and sort multi-index for dataframe based on filename and column number columns. If no df parameter is not set, build index for self.df.

Parameters:df – pd.DataFrame. Returns:pd.DataFrame.

create_df()

Create pd.DataFrame with a correct header.

Returns:pd.DataFrame.

get_word_map(var_id)

Return dict with value in data file, and the mapped value as keyword-value pairs.

Parameters:var_id – tuple of filename and column number. Returns:dict.

validate(verbosity=2)

word_map_changes(silent=False)

Determine changes made to word mapping file.

Parameters:silent – if True, only print output. Returns:if silent=False return dictionary with changes since load.

word_map_dicts

Dictionary with all variable ids as keys and word map dicts as value.

Annotations

Annotations Container

class tmtk.annotation.Annotations.Annotations(params_list=None, parent=None)

Bases: object

Class containing all AnnotationFile objects.

annotation_files

validate_all(verbosity=3)

Base class: AnnotationBase

class tmtk.annotation.AnnotationBase.AnnotationBase(params=None, path=None)

Bases: tmtk.utils.filebase.FileBase

Base class for annotation files.

load_to

marker_type

validate(verbosity=2)

ChromosomalRegions

class tmtk.annotation.ChromosomalRegions.ChromosomalRegions(params=None, path=None)

Bases: tmtk.annotation.AnnotationBase.AnnotationBase

Subclass for CNV (aCGh, qDNAseq) annotation

biomarkers

MicroarrayAnnotation

class tmtk.annotation.MicroarrayAnnotation.MicroarrayAnnotation(params=None, path=None)

Bases: tmtk.annotation.AnnotationBase.AnnotationBase

Subclass for microarray (mRNA) expression annotation files.

biomarkers

MirnaAnnotation

class tmtk.annotation.MirnaAnnotation.MirnaAnnotation(params=None, path=None)

Bases: tmtk.annotation.AnnotationBase.AnnotationBase

Subclass for micro RNA (miRNA) expression annotation files.

biomarkers

ProteomicsAnnotation

class tmtk.annotation.ProteomicsAnnotation.ProteomicsAnnotation(params=None, path=None)

Bases: tmtk.annotation.AnnotationBase.AnnotationBase

Subclass for proteomics annotation

biomarkers

High Dimensional data

HighDim

class tmtk.highdim.HighDim.HighDim(params_list=None, parent=None)

Bases: object

Container class for all High Dimensional data types.

Parameters:params_list – contains a list with Params objects.

high_dim_files

sample_mapping_files

update_high_dim_paths(high_dim_paths)

Update sample mapping if path has been changed.

Parameters:high_dim_paths – dictionary with paths and old concept paths.

validate_all(verbosity=3)

HighDimBase

class tmtk.highdim.HighDimBase.HighDimBase(params=None, path=None, parent=None)

Bases: tmtk.utils.filebase.FileBase

Base class for high dimensional data structures.

load_to

validate(verbosity=3)

Validate high dimensional data object

Parameters:verbosity – set the verbosity of output, pick 0, 1, 2, 3 or 4. Returns:True if everything is okay, else return False.

CopyNumberVariation

class tmtk.highdim.CopyNumberVariation.CopyNumberVariation(params=None, path=None, parent=None)

Bases: tmtk.highdim.HighDimBase.HighDimBase

Base class for copy number variation datatypes (aCGH, qDNAseq)

allowed_header

remap_to(destination=None)

Parameters:destination – Returns:

samples

Expression

class tmtk.highdim.Expression.Expression(params=None, path=None, parent=None)

Bases: tmtk.highdim.HighDimBase.HighDimBase

Base class for microarray mRNA expression data.

samples

Mirna

class tmtk.highdim.Mirna.Mirna(params=None, path=None, parent=None)

Bases: tmtk.highdim.HighDimBase.HighDimBase

Base class for proteomics data.

samples

Proteomics

class tmtk.highdim.Proteomics.Proteomics(params=None, path=None, parent=None)

Bases: tmtk.highdim.HighDimBase.HighDimBase

Base class for proteomics data.

samples

ReadCounts

class tmtk.highdim.ReadCounts.ReadCounts(params=None, path=None, parent=None)

Bases: tmtk.highdim.HighDimBase.HighDimBase

Subclass for ReadCounts.

allowed_header

remap_to(destination=None)

Parameters:destination – Returns:

samples

SampleMapping

class tmtk.highdim.SampleMapping.SampleMapping(path=None)

Bases: tmtk.utils.filebase.FileBase

Base class for subject sample mapping

get_concept_paths

Get all concept paths from file, replaces ATTR1 and ATTR2.

Returns:dictionary with md5 hash values as key and paths as value

platform

Returns:the platform id in this sample mapping file.

samples

slice_path(path)

Give slice of the dataframe where the paths are equal to given path. :param path: path (will be converted using global logic). :return: slice of dataframe.

study_id

Returns:study_id in sample mapping file

update_concept_paths(path_dict)

validate(verbosity=2)

Checks whether transmart-batch likes this file.

Metadata Tags

Tags

class tmtk.tags.Tags.MetaDataTags(params=None, parent=None)

Bases: tmtk.utils.filebase.FileBase

static create_df()

get_tags()

generator that gets tags from tags file.

Returns:tuples (<path>, <title>, <description>)

invalid_paths

load_to

tag_paths

Return tag paths delimited by the path_converter.

validate(verbosity=2)

Utilities

FileBase

class tmtk.utils.filebase.FileBase

Bases: object

Super class with shared utilities for file objects.

df

The pd.DataFrame for this file object.

df_has_changed

header

name

save()

Overwrite the original file with the current dataframe.

tabs_in_first_line()

Check if file is tab delimited.

write_to(path, overwrite=False)

Wrapper for tmtk.utils.df2file().

Parameters:

• path – path to write file to.
• overwrite – write over existing files in the filesystem)

class tmtk.utils.filebase.cached_property(func, name=None, doc=None)

Bases: property

A decorator that converts a function into a lazy property. The function wrapped is called the first time to retrieve the result and then that calculated result is used the next time you access the value:

class Foo(object):
    @cached_property
    def foo(self):
        # calculate something important here
        return 42

The class has to have a __dict__ in order for this property to work.

Implementation ported from werkzeug: http://werkzeug.pocoo.org/

Generic module

tmtk.utils.Generic.clean_for_namespace(path) → str

Converts a path and returns a namespace safe variant. Converts characters that give errors to underscore.

Parameters:path – usually a descriptive subdirectory Returns:string

tmtk.utils.Generic.column_map_diff(a_column, b_column)

tmtk.utils.Generic.df2file(df=None, path=None, overwrite=False)

Write a dataframe to file safely. Does not overwrite existing files automatically. This function converts concept path delimiters.

Parameters:

• df – pd.DataFrame
• path – path to write to
• overwrite – False (default) or True

tmtk.utils.Generic.file2df(path=None)

Load a file specified by path into a Pandas dataframe. If hashed is True, return a a (dataframe, hash) value tuple.

Parameters:path – to file to load Returns:pd.DataFrame

tmtk.utils.Generic.find_fully_unique_columns(df)

Check if a dataframe contains a fully unique column (SUBJ_ID candidate).

Parameters:df – pd.DataFrame Returns:list of names of unique columns

tmtk.utils.Generic.fix_everything()

Scans over all the data and indicates which errors have been fixed. This function is great for stress relieve.

Returns:All your problems fixed by Rick

tmtk.utils.Generic.is_numeric(values)

Check if list of values are numeric.

Parameters:values – iterable

tmtk.utils.Generic.md5(s: str)

utf-8 encoded md5 hash string of input s.

Parameters:s – string Returns:md5 hash string

tmtk.utils.Generic.merge_two_dicts(x, y)

Given two dicts, merge them into a new dict as a shallow copy.

tmtk.utils.Generic.numeric(x)

tmtk.utils.Generic.path_converter(path, internal=False)

Convert paths by creating delimiters of backslash “” and “+” sign, additionally converting underscores “_” to a single space.

Parameters:

• path – concept path
• internal – if path is for internal use delimit with Mappings.PATH_DELIM

Returns:

delimited path

tmtk.utils.Generic.path_join(*args)

Join items with the used path delimiter.

Parameters:args – path items Returns:path as string

tmtk.utils.Generic.summarise(list_or_dict=None, max_items: int = 7) → str

Takes an iterable and returns a summarized string statement. Picks a random sample if number of items > max_items.

Parameters:

• list_or_dict – list or dict to summarise
• max_items – maximum number of items to keep.

Returns:

the items joined as string with end statement.

tmtk.utils.Generic.word_map_diff(a_word_map, b_word_map)

utils.CPrint module

class tmtk.utils.CPrint.CPrint

Bases: object

static error(message)

static head(message)

static info(message)

static okay(message)

static warn(message)

class tmtk.utils.CPrint.MessageCollector(verbosity=5)

Bases: object

error(message)

flush()

head(message)

info(message)

okay(message)

warn(message)

utils.Exceptions module

exception tmtk.utils.Exceptions.ClassError(found=None, expected=None)

Bases: BaseException

Error raised when unexpected class is found.

Parameters:

• found – is the Object class of found
• expected – is the required Object class

exception tmtk.utils.Exceptions.DatatypeError(found=None, expected=None)

Bases: BaseException

Error raised when incorrect datatype is found.

Parameters:

• found – is the datatype of object
• expected – is the required datatype

exception tmtk.utils.Exceptions.NotYetImplemented

Bases: BaseException

exception tmtk.utils.Exceptions.PathError(found=None)

Bases: BaseException

Error raised when an incorrect path is given.

exception tmtk.utils.Exceptions.TooManyValues(found=None, expected=None, id_=None)

Bases: BaseException

Error raised when too many values are found.

utils.HighDimUtils module

tmtk.utils.HighDimUtils.check_datafile_header_with_subjects(header_samples, mapping_samples)

Inputs two pandas series, returns all missing annotations.

Parameters:

• header_samples – samples from datafile header.
• mapping_samples – samples present in sample mapping file.

Returns:

a dict with mismapped and excluded samples.

tmtk.utils.HighDimUtils.find_missing_annotations(annotation_series, data_series)

Inputs two pandas series, returns all missing annotations.

Parameters:

• annotation_series – the annotation that will be uploaded.
• data_series – series of genes that is in the datafile.

Returns:

a new series with all missing annotations.

utils.mappings module

class tmtk.utils.mappings.Mappings

Bases: object

Collection of statics used in various parts of the code.

EXT_PATH_DELIM = '\\'

PATH_DELIM = '∕'

annotation_data_types = {'mirna': 'micro RNA data (PCR)', 'cnv': 'ACGH data', 'vcf': 'Genomic variant data', 'expression': 'Messenger RNA data (microarray)', 'rnaseq': 'Messenger RNA data (sequencing)', 'proteomics': 'Proteomics data (mass spec)'}

annotation_marker_types = {'mrna_annotation': 'Gene expression', 'rnaseq_annotation': 'RNASEQ_RCNT', 'vcf_annotation': 'VCF', 'mirna_annotation': 'MIRNA_QPCR', 'cnv_annotation': 'Chromosomal', 'proteomics_annotation': 'PROTEOMICS'}

cat_cd = 'Category Code'

cat_cd_s = 'ccd'

col_num = 'Column Number'

col_num_s = 'col'

column_mapping_header = ['Filename', 'Category Code', 'Column Number', 'Data Label', 'Magic 5th', 'Magic 6th', 'Concept Type']

column_mapping_s = ['fn', 'ccd', 'col', 'dl', 'm5', 'm6', 'cty']

concept_type = 'Concept Type'

concept_type_s = 'cty'

data_label = 'Data Label'

data_label_s = 'dl'

df_value = 'Datafile Value'

df_value_s = 'dfv'

filename = 'Filename'

filename_s = 'fn'

static get_annotations(dtype=None)

Return mapping for annotations classes. Return only for datatype if dtype is set. Else return full map.

Parameters:dtype – optional datatype (e.g. cnv_annotation) Returns:dict with mapping, or class.

static get_highdim(dtype=None)

Return mapping for high dimensional classes. Return only for datatype if dtype is set. Else return full map.

Parameters:dtype – optional datatype (e.g. cnv) Returns:dict with mapping, or class.

static get_params(dtype=None)

Return mapping for params classes. Return only for datatype if dtype is set. Else return full map.

Parameters:dtype – optional datatype (e.g. cnv) Returns:dict with mapping, or class.

magic_5 = 'Magic 5th'

magic_5_s = 'm5'

magic_6 = 'Magic 6th'

magic_6_s = 'm6'

map_value = 'Mapping Value'

map_value_s = 'map'

tags_description = 'Description'

tags_header = ['Concept Path', 'Title', 'Description', 'Weight']

tags_node_name = 'Tags'

tags_path = 'Concept Path'

tags_title = 'Title'

tags_weight = 'Weight'

word_mapping_header = ['Filename', 'Column Number', 'Datafile Value', 'Mapping Value']

Toolbox package

Generate chromosomal regions file

tmtk.toolbox.generate_chromosomal_regions_file.generate_chromosomal_regions_file(platform_id=None, reference_build='hg19', **kwargs)

This creates a new chromosomal regions annotation file.

Parameters:

• platform_id – Give the new platform a name to fill first column
• reference_build – choose either hg18, hg19 or hg38

Returns:

a pandas dataframe with the new platform

Remap chromosomal regions data

tmtk.toolbox.remap_chromosomal_regions.map_index_to_region_ids(gene, origin_platform, region_origin)

tmtk.toolbox.remap_chromosomal_regions.remap_chromosomal_regions(origin_platform=None, destination_platform=None, datafile=None, flag_indicator='.flag', to_dest=2, start_dest=3, end_dest=4, region_dest=1, chr_origin=2, start_origin=3, end_origin=4, region_origin=1, region_data=0)

tmtk.toolbox.remap_chromosomal_regions.return_mean(datafile, mapping, flag_columns=None)

Study Wizard

tmtk.toolbox.wizard.create_study(path)

Start a study object by pointing by giving a folder that contains clinical data files only.

Parameters:path – path to folder with files. Returns:study object.

Create study from templates

tmtk.toolbox.create_study_from_templates(ID, source_dir, output_dir=None, sec_req='Y')

Create tranSMART files in designated output_dir for all data provided in templates in the source_dir.

Parameters:

• ID – study ID.
• source_dir – directory containing all the templates.
• output_dir – directory where the output should be written.
• sec_req – security required? “Y” or “N”, default=”Y”.

Returns:

None

The Arborist

tmtk.arborist.common module

tmtk.arborist.common.call_boris(to_be_shuffled=None, **kwargs)

This function loads the Arborist if it has been properly installed in your environment.

Parameters:to_be_shuffled – has to be either a tmtk.Study object, a Pandas column mapping dataframe, or a path to column mapping file.

tmtk.arborist.common.launch_arborist_gui(json_data: str, height=650)

Parameters:

• json_data –
• height –

Returns:

tmtk.arborist.common.update_clinical_from_json(clinical, json_data)

Parameters:

• clinical –
• json_data –

Returns:

tmtk.arborist.common.update_study_from_json(study, json_data)

Parameters:

• study –
• json_data –

Returns:

tmtk.arborist.common.valid_arborist_or_exception(**kwargs)

tmtk.arborist.connect_to_baas module

tmtk.arborist.connect_to_baas.get_instance_url(url)

tmtk.arborist.connect_to_baas.get_json_from_baas(url, username=None)

Get a json file from a Boris as a Service instance.

Parameters:

• url – url should study name and version. (e.g. http://transmart-arborist.thehyve.nl/trees/study-name/1/~edit/).
• username – if no username is given, you will be prompted for one.

Returns:

the JSON string from BaaS.

tmtk.arborist.connect_to_baas.json_url(url)

tmtk.arborist.connect_to_baas.login_url(url)

tmtk.arborist.connect_to_baas.publish_to_baas(url, json, study_name, username=None)

Publishes a tree on a Boris as a Service instance.

Parameters:

• url – url to a BaaS instance.
• json – the stringified json you want to publish.
• study_name – a nice name.
• username – if no username is given, you will be prompted for one.

Returns:

the url that points to the study you’ve just uploaded.

tmtk.arborist.connect_to_baas.start_session(url, username)

tmtk.arborist.jstreecontrol module

class tmtk.arborist.jstreecontrol.ConceptNode(path, var_id=None, node_type='numeric', data_args=None)

Bases: object

class tmtk.arborist.jstreecontrol.ConceptTree(json_data=None)

Bases: object

Build a ConceptTree to be used in the graphical tree editor.

add_node(path, var_id=None, node_type=None, data_args=None)

Add ConceptNode object nodes list.

Parameters:

• path – Concept path for this node.
• var_id – Unique ID that allows to keep track of a node.
• node_type – Explicitly set node type (highdim, numerical, categorical)
• data_args – Any additional parameters are put a ‘data’ dictionary.

column_mapping_file

Returns:Column Mapping file based on ConceptTree object.

high_dim_paths

All high dimensional nodes in concept tree as dict

jstree

tags_file

word_mapping

class tmtk.arborist.jstreecontrol.JSNode(path, oid=None, **kwargs)

Bases: object

This class exists as a helper to the JSTree. Its “json_data” method can generate sub-tree JSON without putting the logic directly into the JSTree.

get_child(var_id, text)

json_data()

class tmtk.arborist.jstreecontrol.JSTree(concept_nodes)

Bases: object

An json like object that converts a list of nodes into something that jQuery jstree can use.

json_data

Convert this object to json ready to be consumed by jstree.

json_data_string

Returns:Returns the json_data properly formatted as string.

pretty(root=None, depth=0, spacing=2)

Create a pretty representation of tree.

to_clipboard()

class tmtk.arborist.jstreecontrol.MyEncoder(skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: json.encoder.JSONEncoder

Overwriting the standard JSON Encoder to treat numpy ints as native ints.

default(obj)

tmtk.arborist.jstreecontrol.create_concept_tree(column_object)

Parameters:column_object – tmtk.Study object, tmtk.Clinical object, or ColumnMapping dataframe Returns:json string to be interpreted by the JSTree

tmtk.arborist.jstreecontrol.create_tree_from_clinical(clinical_object, concept_tree=None)

Parameters:

• clinical_object –
• concept_tree –

Returns:

tmtk.arborist.jstreecontrol.create_tree_from_study(study, concept_tree=None)

Parameters:

• study –
• concept_tree –

Returns: