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How to use _merge_variables method in lisa

Best Python code snippet using lisa_python

workflow.py

Source:workflow.py

...184        file_output = [_nest_variable(x) for x in file_output]185        std_output = [_nest_variable(x) for x in std_output]186    # For combined output, ensure at the same level (not nested) as main samples187    if step.parallel in ["multi-combined"]:188        file_vs = _merge_variables([_clean_output(_flatten_nested_input(v) if not is_cwl_record(v) else v)189                                    for v in file_output], file_vs)190    else:191        file_vs = _merge_variables([_clean_output(v) for v in file_output], file_vs)192    std_vs = _merge_variables([_clean_output(v) for v in std_output], std_vs)193    return file_output + std_output, file_vs, std_vs194def _flatten_nested_input(v):195    """Flatten a parallel scatter input -- we only get one of them to tools.196    """197    v = copy.deepcopy(v)198    assert v["type"]["type"] == "array"199    v["type"] = v["type"]["items"]200    return v201def _nest_variable(v):202    """Nest a variable when moving from scattered back to consolidated.203    """204    v = copy.deepcopy(v)205    v["type"] = {"type": "array", "items": v["type"]}206    return v207def _clean_output(v):208    """Remove output specific variables to allow variables to be inputs to next steps.209    """210    out = copy.deepcopy(v)211    outb = out.pop("outputBinding", {})212    if "secondaryFiles" in outb:213        out["secondaryFiles"] = outb["secondaryFiles"]214    return out215def _get_string_vid(vid):216    if isinstance(vid, basestring):217        return vid218    assert isinstance(vid, (list, tuple)), vid219    return "__".join(vid)220def _get_variable(vid, variables):221    """Retrieve an input variable from our existing pool of options.222    """223    if isinstance(vid, basestring):224        vid = get_base_id(vid)225    else:226        vid = _get_string_vid(vid)227    for v in variables:228        if vid == get_base_id(v["id"]):229            return copy.deepcopy(v)230    raise ValueError("Did not find variable %s in \n%s" % (vid, pprint.pformat(variables)))231def _handle_special_inputs(inputs, variables):232    """Adjust input variables based on special cases.233    This case handles inputs where we are optional or can have flexible choices.234    XXX Need to better expose this at a top level definition.235    """236    optional = [["config", "algorithm", "coverage"],237                ["config", "algorithm", "variant_regions"],238                ["config", "algorithm", "sv_regions"],239                ["config", "algorithm", "validate"],240                ["config", "algorithm", "validate_regions"]]241    all_vs = set([get_base_id(v["id"]) for v in variables])242    out = []243    for input in inputs:244        if input == ["reference", "aligner", "indexes"]:245            found_indexes = False246            for v in variables:247                vid = get_base_id(v["id"]).split("__")248                if vid[0] == "reference" and vid[1] in alignment.TOOLS:249                    out.append(vid)250                    found_indexes = True251            assert found_indexes, "Found no aligner indexes in %s" % [v["id"] for v in variables]252        elif input == ["reference", "snpeff", "genome_build"]:253            found_indexes = False254            for v in variables:255                vid = get_base_id(v["id"]).split("__")256                if vid[0] == "reference" and vid[1] == "snpeff":257                    out.append(vid)258                    found_indexes = True259            assert found_indexes, "Found no snpEff indexes in %s" % [v["id"] for v in variables]260        elif input in optional:261            if _get_string_vid(input) in all_vs:262                out.append(input)263        else:264            out.append(input)265    return out266def _get_upload_output(vid, variables):267    if isinstance(vid, dict) and "id" in vid:268        parent_v = _get_variable(vid["id"], variables)269        v = copy.deepcopy(vid)270        v["id"] = _get_string_vid(vid["id"])271        v["outputSource"] = parent_v["id"]272    else:273        v = _nest_variable(_get_variable(vid, variables))274        v["outputSource"] = v["id"]275        v["id"] = get_base_id(v["id"])276    v.pop("secondaryFiles", None)277    v["type"].pop("secondaryFiles", None)278    return v279def _create_record(name, field_defs, step_name, inputs, unlist, file_vs, std_vs, parallel):280    """Create an output record by rearranging inputs.281    Batching processes create records that reformat the inputs for282    parallelization.283    """284    if field_defs:285        fields = []286        inherit = []287        inherit_all = False288        for fdef in field_defs:289            if not fdef.get("type"):290                if fdef["id"] == "inherit":291                    inherit_all = True292                else:293                    inherit.append(fdef["id"])294            else:295                cur = {"name": _get_string_vid(fdef["id"]),296                       "type": fdef["type"]}297                fields.append(_add_secondary_to_rec_field(fdef, cur))298        if inherit_all:299            fields.extend(_infer_record_outputs(inputs, unlist, file_vs, std_vs, parallel))300        elif inherit:301            fields.extend(_infer_record_outputs(inputs, unlist + inherit, file_vs, std_vs, parallel, inherit))302    else:303        fields = _infer_record_outputs(inputs, unlist, file_vs, std_vs, parallel)304    out = {"id": "%s/%s" % (step_name, name),305           "type": {"name": name,306                    "type": "record",307                    "fields": fields}}308    if parallel in ["batch-single", "multi-batch"]:309        out = _nest_variable(out)310    return out311def _add_secondary_to_rec_field(orig, cur):312    # CWL does not currently support secondaryFiles in fields313    if orig.get("secondaryFiles"):314        cur["secondaryFiles"] = orig.get("secondaryFiles")315    return cur316def _infer_record_outputs(inputs, unlist, file_vs, std_vs, parallel, to_include=None):317    """Infer the outputs of a record from the original inputs318    """319    fields = []320    unlist = set([_get_string_vid(x) for x in unlist])321    input_vids = set([_get_string_vid(v) for v in _handle_special_inputs(inputs, file_vs)])322    to_include = set([_get_string_vid(x) for x in to_include]) if to_include else None323    added = set([])324    for raw_v in std_vs + [v for v in file_vs if get_base_id(v["id"]) in input_vids]:325        # unpack record inside this record and un-nested inputs to avoid double nested326        cur_record = is_cwl_record(raw_v)327        if cur_record:328            #unlist = unlist | set([field["name"] for field in cur_record["fields"]])329            nested_vs = [{"id": field["name"], "type": field["type"]} for field in cur_record["fields"]]330        else:331            nested_vs = [raw_v]332        for orig_v in nested_vs:333            if orig_v["id"] not in added and (not to_include or get_base_id(orig_v["id"]) in to_include):334                cur_v = {}335                cur_v["name"] = get_base_id(orig_v["id"])336                cur_v["type"] = orig_v["type"]337                if cur_v["name"] in unlist:338                    cur_v = _flatten_nested_input(cur_v)339                if to_include:340                    cur_v = _nest_variable(cur_v)341                fields.append(_add_secondary_to_rec_field(orig_v, cur_v))342                added.add(orig_v["id"])343    return fields344def _create_variable(orig_v, step, variables):345    """Create a new output variable, potentially over-writing existing or creating new.346    """347    # get current variable, and convert to be the output of our process step348    try:349        v = _get_variable(orig_v["id"], variables)350    except ValueError:351        v = copy.deepcopy(orig_v)352        if not isinstance(v["id"], basestring):353            v["id"] = _get_string_vid(v["id"])354    for key, val in orig_v.items():355        if key not in ["id", "type"]:356            v[key] = val357    if orig_v.get("type") != "null":358        v["type"] = orig_v["type"]359    v["id"] = "%s/%s" % (step.name, get_base_id(v["id"]))360    return v361def _merge_variables(new, cur):362    """Add any new variables to the world representation in cur.363    Replaces any variables adjusted by previous steps.364    """365    new_added = set([])366    out = []367    for cur_var in cur:368        updated = False369        for new_var in new:370            if get_base_id(new_var["id"]) == get_base_id(cur_var["id"]):371                out.append(new_var)372                new_added.add(new_var["id"])373                updated = True374                break375        if not updated:...

agevalues.py

Source:agevalues.py

...6def _roll(a):  # rolls a single die of "a" sides7    return random.randrange(1, a + 1)8910def _merge_variables(race, ch_class):               # returns three-item lists [base_age, #ofrolls, #ofsides]11    temp, final, number_of_classes = [], [0, 0, 0], len(ch_class)12    for a in range(number_of_classes):13        temp.append(datalocus.age_variables(race, ch_class[a]))14    for a in range(number_of_classes):15        if final[0] < temp[a][0]:16            final[0] = temp[a][0]17    for a in range(number_of_classes):18        if final[1] * final[2] < temp[a][1] * temp[a][2]:19            final[1], final[2] = temp[a][1], temp[a][2]20    return final212223def _natural_death(race):                                                                   # accepts ('Human')24    temp = random.choice([0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4])      # returns (96)25    var_value, age_thresholds = [8, 4, 6, 10, 20], datalocus.age_thresholds(race)26    # this calculates the age-span for the relevant category to determine which age_modifier to apply27    term = age_thresholds[4+round(temp/3)] - age_thresholds[3+round(temp/3)]28    age_modifier = 1 * int(term < 100) + 10 * int(100 <= term <= 250) + 20 * int(term > 250)29    # this formula computes the base age corresponding with the temp value30    temp_base = int(age_thresholds[round((temp+6.5)/2)]) + int(temp == 0 or temp == 2)31    # this formula computes the die roll, span modifier and operator corresponding with the temp value32    temp_roll = (_roll(var_value[temp]) * age_modifier + _roll(age_modifier) - 1) * (temp % 2 * -2 + 1)33    return temp_base + temp_roll343536# for i in range(100):37#     temp_death = _natural_death("Human")38#     print(temp_death)394041def generate_age(race, ch_class, level):42    temp, final, attr_names = _merge_variables(race, ch_class), [], ['Str', 'Int', 'Wis', 'Dex', 'Con', 'Cha', 'Com']43    age = temp[0]44    for a in range(temp[1]):45        age += _roll(temp[2])46    final.append(age - 1 + level)47    final.append(datalocus.age_cat(race, age)[0])48    final.append(datalocus.age_cat(race, age)[1])49    final.append(dict(zip(attr_names, datalocus.age_adj(final[1]))))50    final[3]['Exc'] = 051    final.append(_natural_death(race))52    return final535455# start = time.time()56# testrace = "Korobokuru"
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