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How to use _ensure_list method in prospector
Best Python code snippet using prospector_python
xcrixml2json.py
Source:xcrixml2json.py 
...82def _remove_attributes(element):83 for key in element.keys():84 if key.startswith("@"):85 del element[key]86def _ensure_list(parent, listable):87 if not parent.has_key(listable):88 return89 if type(parent[listable]) != list:90 parent[listable] = [parent[listable]]91def _ensure_obj_to_text(parent, key, idx=None):92 if not parent.has_key(key):93 return94 95 if idx is not None:96 text = _extract_text(parent[key][idx])97 parent[key][idx] = text98 else:99 text = _extract_text(parent[key])100 parent[key] = text101def _ensure_text(parent, key):102 if not parent.has_key(key):103 return104 if type(parent[key]) == list:105 for i in range(len(parent[key])):106 parent[key] = _extract_text(parent[key][i])107 elif _is_dict(parent[key]):108 parent[key] = _extract_text(parent[key])109 110def _extract_text(element):111 if _is_dict(element):112 return _do_strip_cdata(element.get("#text", ""))113 else:114 return _do_strip_cdata(element)115def _remove_dud_text(element):116 if _is_dict(element):117 if element.has_key("#text"):118 del element["#text"]119def _prepend_namespace(parent, element_name, namespace, default=None):120 # look for reasons not to do this (the namespace prefix is already121 # applied, or the element doesn't exist to have it applied)122 if element_name.startswith(namespace + ":"):123 return124 if not parent.has_key(element_name):125 return126 127 # construct the name of the new key, with the namespace prefix128 new_key = namespace + ":" + element_name129 130 # initialise the new key, either using the default value or131 # around an existing value132 if not parent.has_key(new_key):133 parent[new_key] = default134 else:135 if type(default) == list:136 _ensure_list(parent, new_key)137 138 # now add or overwrite the value as necessary139 if type(parent[new_key]) == list:140 if type(parent[element_name]) == list:141 for e in parent[element_name]:142 parent[new_key].append(e)143 else:144 parent[new_key].append(parent[element_name])145 else:146 parent[new_key] = parent[element_name]147 148 # finally, remove the old dictionary entry149 del parent[element_name]150def _text_to_value(parent, element_name, idx=None):151 if not parent.has_key(element_name):152 return153 154 if idx is not None:155 element = parent[element_name][idx]156 if not _is_dict(element):157 nd = {"value" : _do_strip_cdata(element)}158 parent[element_name][idx] = nd159 else:160 element["value"] = _do_strip_cdata(element.get("#text", ""))161 del element["#text"]162 else:163 element = parent[element_name]164 if not _is_dict(element):165 nd = {"value" : _do_strip_cdata(element)}166 parent[element_name] = nd167 else:168 element["value"] = _do_strip_cdata(element.get("#text", ""))169 del element["#text"]170def _descriptive_text_element(parent, element, idx):171 if not parent.has_key(element):172 return173 e = parent[element][idx]174 if _is_dict(e):175 _dte_format(e)176 else:177 _text_to_value(parent, element, idx)178 _strip_cdata(parent[element][idx], "value")179def _temporal_element(parent, element):180 if not parent.has_key(element):181 return182 if _is_dict(parent[element]):183 _te_format(parent[element])184 else:185 _text_to_value(parent, element)186def _te_format(element):187 _rename_key(element, "@dtf", "dtf")188 _strip_cdata(element, "dtf")189 _rename_key(element, "#text", "value")190 _strip_cdata(element, "value")191 _remove_attributes(element)192def _dte_format(element):193 _rename_key(element, "@xml:lang", "lang")194 _strip_cdata(element, "lang")195 _rename_key(element, "@href", "href")196 _strip_cdata(element, "href")197 _rename_key(element, "#text", "value")198 _remove_attributes(element)199 _strip_cdata(element, "value")200def _strip_cdata(element, key):201 v = element.get(key, "")202 if v is None:203 return204 if v.startswith("<![CDATA["):205 v = v[9:]206 if v.endswith("]]>"):207 v = v[:-3]208 element[key] = v209def _do_strip_cdata(v):210 if v is None:211 return212 if v.startswith("<![CDATA["):213 v = v[9:]214 if v.endswith("]]>"):215 v = v[:-3]216 return v217def _rename_key(parent, original, new_key, format=None):218 if not parent.has_key(original):219 return220 if not parent.has_key(new_key):221 parent[new_key] = format222 223 # now add or overwrite the value as necessary224 if type(parent[new_key]) == list:225 if type(parent[original]) == list:226 for e in parent[original]:227 parent[new_key].append(e)228 else:229 parent[new_key].append(parent[original])230 else:231 parent[new_key] = parent[original] 232 233 del parent[original]234def _migrate_down(parent, target, to_move, format=None):235 if not parent.has_key(target):236 parent[target] = {}237 if parent.has_key(to_move):238 parent[target][to_move] = format239 if type(parent[to_move]) == list and type(format) == list:240 for a in parent[to_move]:241 parent[target][to_move].append(a)242 elif type(parent[to_move]) != list and type(format) == list:243 parent[target][to_move].append(parent[to_move])244 else:245 parent[target][to_move] = parent[to_move]246 del parent[to_move]247def upgrade_catalog(catalog):248 _ensure_list(catalog, "provider")249 for prov in catalog.get('provider', []):250 upgrade_provider(prov)251def upgrade_provider(prov):252 _migrate_down(prov, "mlo:location", "address", [])253 _migrate_down(prov, "mlo:location", "street")254 _migrate_down(prov, "mlo:location", "town")255 _migrate_down(prov, "mlo:location", "postcode")256 _migrate_down(prov, "mlo:location", "phone")257 _migrate_down(prov, "mlo:location", "fax")258 _migrate_down(prov, "mlo:location", "email")259 260 _ensure_list(provider, "course")261 for course in provider.get('course', []):262 cleanup_course(course)263def cleanup_catalog(catalog):264 """265 "catalog" : {266 "provider" : [ ]267 }268 """269 _remove_dud_text(catalog)270 _remove_attributes(catalog)271 _ensure_list(catalog, "provider")272 for prov in catalog.get('provider', []):273 cleanup_provider(prov)274def cleanup_provider(provider):275 """276 {277 "dc:contributor" : ["contributor"],278 "dc:description" : [{"lang" : "lang", "href" : "href", "value" : "value" }],279 "dc:identifier" : [{ "type" : "type", "value" : "value"}],280 "image" : {"src" : "src","title" : "title","alt" : "alt" },281 "dc:subject" : [{"type" : "type","identifier" : "identifier","lang" : "lang","value" : "value"}],282 "dc:title" : [{"lang" : "lang","value" : "value"}],283 "dc:type" : "type",284 "mlo:url" : "url",285 "mlo:location" : {286 "mlo:street" : "street",287 "mlo:town" : "town",288 "mlo:postcode" : "postcode",289 "mlo:phone" : "phone",290 "mlo:fax" : "fax",291 "mlo:email" : "email",292 "mlo:url" : "url",293 "mlo:address" : [{"type" : "type", "value" : "value"}]294 }295 "course" : [ ]296 }297 """298 _remove_dud_text(provider)299 300 _prepend_namespace(provider, "contributor", "dc", [])301 _ensure_list(provider, "dc:contributor")302 for i in range(len(provider.get("dc:contributor", []))):303 _ensure_obj_to_text(provider, "dc:contributor", i)304 305 _prepend_namespace(provider, "description", "dc", [])306 _ensure_list(provider, "dc:description")307 for i in range(len(provider.get('dc:description', []))):308 _descriptive_text_element(provider, "dc:description", i)309 310 _prepend_namespace(provider, "identifier", "dc", [])311 _ensure_list(provider, "dc:identifier")312 for i in range(len(provider.get('dc:identifier', []))):313 cleanup_identifier(provider, "dc:identifier", i)314 315 if provider.has_key("image"):316 cleanup_image(provider['image'])317 318 _prepend_namespace(provider, "subject", "dc", [])319 _ensure_list(provider, "dc:subject")320 for i in range(len(provider.get("dc:subject", []))):321 cleanup_subject(provider, "dc:subject", i)322 323 _prepend_namespace(provider, "title", "dc", [])324 _ensure_list(provider, "dc:title")325 for i in range(len(provider.get("dc:title", []))):326 cleanup_title(provider, "dc:title", i)327 328 _prepend_namespace(provider, "type", "dc")329 _ensure_obj_to_text(provider, "dc:type")330 331 _prepend_namespace(provider, "url", "mlo")332 _ensure_obj_to_text(provider, "mlo:url")333 334 _prepend_namespace(provider, "location", "mlo")335 if provider.has_key("mlo:location"):336 cleanup_location(provider, "mlo:location")337 338 _ensure_list(provider, "course")339 for course in provider.get('course', []):340 cleanup_course(course)341def cleanup_description(parent, desc, idx):342 # first see if we need to crosswalk to another element based on the field value343 # description.type=aim -> mlo:objective344 # description.type=applicationProcedure -> applicationProcedure345 # description.type=assessmentStrategy -> mlo:assessment346 # description.type=learningOutcome -> learningOutcome347 # description.type=prerequisites -> mlo:prerequisite348 # description.type=regulations -> regulations349 # some of these 1.1 to 1.2 crosswalks are encoded in the dc:description field350 # like dc:description = "aim: to do something"351 352 # all the target formats are descriptive text elements, so do that conversion first353 _descriptive_text_element(parent, desc, idx)354 355 if parent[desc][idx]['value'].strip().startswith("aim:"):356 _desc_copy(parent, "mlo:objective", desc, idx, "aim:")357 return True358 elif parent[desc][idx]['value'].strip().startswith("applicationProcedure:"):359 _desc_desc_copy(parent, "applicationProcedure", desc, idx, "applicationProcedure:")360 return True361 elif parent[desc][idx]['value'].strip().startswith("assessmentStrategy:"):362 _desc_copy(parent, "mlo:assessment", desc, idx, "assessmentStrategy:")363 return True364 elif parent[desc][idx]['value'].strip().startswith("learningOutcome:"):365 _desc_copy(parent, "learningOutcome", desc, idx, "learningOutcome:")366 return True367 elif parent[desc][idx]['value'].strip().startswith("prerequisites:"):368 _desc_copy(parent, "mlo:prerequisite", desc, idx, "prerequisites:")369 return True370 elif parent[desc][idx]['value'].strip().startswith("regulations:"):371 _desc_copy(parent, "regulations", desc, idx, "regulations:")372 return True373 return False374def _desc_copy(parent, target, source, idx, sub):375 parent[source][idx]['value'] = parent[source][idx]['value'][len(sub):].strip()376 if not parent.has_key(target):377 parent[target] = [parent[source][idx]]378 else:379 _ensure_list(parent, target)380 parent[target].append(parent[source][idx])381 382def cleanup_course(course):383 """384 {385 "mlo:level" : "level",386 "mlo:qualification" : [ ],387 "presentation" : [ ],388 "mlo:credit" : [{"credit:scheme" : "scheme", "credit:level" : "level", "credit:value" : "value"}],389 "dc:contributor" : ["contributor"],390 "dc:description" : [{"lang" : "lang", "href" : "href", "value" : "value"}],391 "dc:identifier" : [{"type" : "type","value" : "value"}],392 "image" : {"src" : "src","title" : "title","alt" : "alt"},393 "dc:subject" : [{"type" : "type","identifier" : "identifier","lang" : "lang","value" : "value"}],394 "dc:title" : [{"lang" : "lang","value" : "value"}],395 "dc:type" : "type",396 "mlo:url" : "url",397 "abstract" : [{"lang" : "lang", "href" : "href", "value" : "value"}], 398 "applicationProcedure" : [{"lang" : "lang", "href" : "href", "value" : "value"}],399 "mlo:assessment" : [{"lang" : "lang", "href" : "href", "value" : "value"}],400 "learningOutcome" : [{"lang" : "lang", "href" : "href", "value" : "value"}],401 "mlo:objective" : [{"lang" : "lang", "href" : "href", "value" : "value"}],402 "mlo:prerequisite" : [{"lang" : "lang", "href" : "href", "value" : "value"}],403 "regulations" : [{"lang" : "lang", "href" : "href", "value" : "value"}]404 }405 """406 _remove_dud_text(course)407 408 _prepend_namespace(course, "level", "mlo")409 _ensure_text(course, "mlo:level")410 411 _prepend_namespace(course, "qualification", "mlo", [])412 _ensure_list(course, "mlo:qualification")413 for q in course.get('mlo:qualification', []):414 cleanup_qualification(q)415 416 _ensure_list(course, "presentation")417 for p in course.get("presentation", []):418 cleanup_presentation(p)419 420 _prepend_namespace(course, "credit", "mlo", [])421 _ensure_list(course, "mlo:credit")422 for c in course.get("mlo:credit", []):423 cleanup_credit(c)424 425 _prepend_namespace(course, "contributor", "dc", [])426 _ensure_list(course, "dc:contributor")427 for i in range(len(course.get("dc:contributor", []))):428 _ensure_obj_to_text(course, "dc:contributor", i)429 430 _prepend_namespace(course, "description", "dc", [])431 _ensure_list(course, "dc:description")432 removable_descs = []433 for i in range(len(course.get('dc:description', []))):434 remove = cleanup_description(course, "dc:description", i)435 if remove:436 removable_descs.append(i)437 438 removable_descs.sort(reverse=True)439 for i in removable_descs:440 del course['dc:description'][i]441 442 _prepend_namespace(course, "identifier", "dc", [])443 _ensure_list(course, "dc:identifier")444 for i in range(len(course.get('dc:identifier', []))):445 cleanup_identifier(course, "dc:identifier", i)446 447 if course.has_key("image"):448 cleanup_image(course[image])449 450 _prepend_namespace(course, "subject", "dc", [])451 _ensure_list(course, "dc:subject")452 for i in range(len(course.get("dc:subject", []))):453 cleanup_subject(course, "dc:subject", i)454 455 _prepend_namespace(course, "title", "dc", [])456 _ensure_list(course, "dc:title")457 for i in range(len(course.get("dc:title", []))):458 cleanup_title(course, "dc:title", i)459 460 _prepend_namespace(course, "type", "dc")461 _ensure_obj_to_text(course, "dc:type")462 463 _prepend_namespace(course, "url", "mlo")464 _ensure_obj_to_text(course, "mlo:url")465 466 _ensure_list(course, "abstract")467 for i in range(len(course.get('abstract', []))):468 _descriptive_text_element(course, "abstract", i)469 470 _ensure_list(course, "applicationProcedure")471 for i in range(len(course.get('applicationProcedure', []))):472 _descriptive_text_element(course, "applicationProcedure", i)473 474 _prepend_namespace(course, "assessment", "mlo", [])475 _ensure_list(course, "mlo:assessment")476 for i in range(len(course.get('mlo:assessment', []))):477 _descriptive_text_element(course, "mlo:assessment", i)478 479 _ensure_list(course, "learningOutcome")480 for i in range(len(course.get('learningOutcome', []))):481 _descriptive_text_element(course, "learningOutcome", i)482 483 _prepend_namespace(course, "objective", "mlo", [])484 _ensure_list(course, "mlo:objective")485 for i in range(len(course.get('mlo:objective', []))):486 _descriptive_text_element(course, "mlo:objective", i)487 488 _prepend_namespace(course, "prerequisite", "mlo", [])489 _ensure_list(course, "mlo:prerequisite")490 for i in range(len(course.get('mlo:prerequisite', []))):491 _descriptive_text_element(course, "mlo:prerequisite", i)492 493 _ensure_list(course, "regulations")494 for i in range(len(course.get('regulations', []))):495 _descriptive_text_element(course, "regulations", i)496def cleanup_location(parent, location):497 """498 {499 "mlo:street" : "street",500 "mlo:town" : "town",501 "mlo:postcode" : "postcode",502 "mlo:phone" : "phone",503 "mlo:fax" : "fax",504 "mlo:email" : "email",505 "mlo:url" : "url",506 "mlo:address" : [{"type" : "type", "value" : "value"}]507 }508 """509 loc = parent[location]510 _remove_dud_text(loc)511 512 _prepend_namespace(loc, "street", "mlo")513 _ensure_text(loc, "mlo:street")514 515 _prepend_namespace(loc, "town", "mlo")516 _ensure_text(loc, "mlo:town")517 518 _prepend_namespace(loc, "postcode", "mlo")519 _ensure_text(loc, "mlo:postcode")520 521 _prepend_namespace(loc, "phone", "mlo")522 _ensure_text(loc, "mlo:phone")523 524 _prepend_namespace(loc, "fax", "mlo")525 _ensure_text(loc, "mlo:fax")526 527 _prepend_namespace(loc, "email", "mlo")528 _ensure_text(loc, "mlo:email")529 530 _prepend_namespace(loc, "url", "mlo")531 _ensure_text(loc, "mlo:url")532 533 _prepend_namespace(loc, "address", "mlo")534 _ensure_list(loc, "mlo:address")535 for i in range(len(loc.get("mlo:address", []))):536 cleanup_address(loc, "mlo:address", i)537 538def cleanup_address(parent, address, idx):539 """540 {"type" : "type", "value" : "value"}541 """542 _text_to_value(parent, address, idx)543 _rename_key(parent[address][idx], "@xsi:type", "type")544 _remove_attributes(parent[address][idx])545def cleanup_identifier(parent, identifier_key, idx):546 # convert to object and set the #text field correctly547 _text_to_value(parent, identifier_key, idx)548 _rename_key(parent[identifier_key][idx], "@xsi:type", "type")549 _remove_attributes(parent[identifier_key][idx])550def cleanup_title(parent, title, idx):551 """552 {"lang" : "lang","value" : "value"}553 """554 _text_to_value(parent, title, idx)555 _rename_key(parent[title][idx], "@xml:lang", "lang")556 _remove_attributes(parent[title][idx])557def cleanup_subject(parent, subject, idx):558 """559 {"type" : "type","identifier" : "identifier","lang" : "lang","value" : "value"}560 """561 _text_to_value(parent, subject, idx)562 _rename_key(parent[subject][idx], "@xsi:type", "type")563 _rename_key(parent[subject][idx], "@identifier", "identifier")564 _rename_key(parent[subject][idx], "@xml:lang", "lang")565 _remove_attributes(parent[subject][idx])566 if parent[subject][idx]['value'].startswith("LDCS class:"):567 parent[subject][idx]['value'] = parent[subject][idx]['value'][11:].strip()568 parent[subject][idx]['type'] = "LDCS"569 570def cleanup_image(image):571 """572 {"src" : "src","title" : "title","alt" : "alt" }573 """574 _remove_dud_text(image)575 _rename_key(image, "@src", "src")576 _rename_key(image, "@title", "title")577 _rename_key(image, "@alt", "alt")578 _remove_attributes(image)579 580def cleanup_credit(credit):581 """582 {"credit:scheme" : "scheme", "credit:level" : "level", "credit:value" : "value"}583 """584 _remove_dud_text(credit)585 _prepend_namespace(credit, "scheme", "credit")586 _ensure_text(credit, "credit:scheme")587 _prepend_namespace(credit, "level", "credit")588 _ensure_text(credit, "credit:level")589 _prepend_namespace(credit, "value", "credit")590 _ensure_text(credit, "credit:value")591 _remove_attributes(credit)592def cleanup_presentation(pres):593 """594 {595 "dc:description" : [{"lang" : "lang", "href" : "href", "value" : "value"}],596 "dc:identifier" : [{"type" : "type","value" : "value"}],597 "image" : {"src" : "src","title" : "title","alt" : "alt"},598 "dc:subject" : [{"type" : "type","identifier" : "identifier","lang" : "lang","value" : "value"}],599 "dc:title" : [{"lang" : "lang","value" : "value"}],600 "dc:type" : "type",601 "mlo:url" : "url",602 "abstract" : [{"lang" : "lang", "href" : "href", "value" : "value"}], 603 "applicationProcedure" : [{"lang" : "lang", "href" : "href", "value" : "value"}],604 "mlo:assessment" : [{"lang" : "lang", "href" : "href", "value" : "value"}],605 "learningOutcome" : [{"lang" : "lang", "href" : "href", "value" : "value"}],606 "mlo:objective" : [{"lang" : "lang", "href" : "href", "value" : "value"}],607 "mlo:prerequisite" : [{"lang" : "lang", "href" : "href", "value" : "value"}],608 "regulations" : [{"lang" : "lang", "href" : "href", "value" : "value"}]609 "mlo:start" : {"dtf" : "datetime", "value" : "value"},610 "mlo:end" : {"dtf" : "datetime", "value" : "value"},611 "mlo:duration" : {"interval" : "interval", "value" : "value"},612 "applyFrom" : {"dtf" : "datetime", "value" : "value"},613 "applyUntil" : {"dtf" : "datetime", "value" : "value"},614 "applyTo" : "apply to",615 "mlo:engagement" : [{}],616 "studyMode" : {"identifier" : "identifier", "value" : "value"},617 "attendanceMode" : {"identifier" : "identifier", "value" : "value"},618 "attendancePattern" : {"identifier" : "identifier", "value" : "value"},619 "mlo:languageOfInstruction" : ["lang"],620 "languageOfAssessment" : ["lang"],621 "mlo:places" : "places",622 "mlo:cost" : "cost",623 "age" : "age",624 "venue" : [ ]625 }626 """627 _remove_dud_text(pres)628 629 _prepend_namespace(pres, "description", "dc", [])630 _ensure_list(pres, "dc:description")631 for i in range(len(pres.get('dc:description', []))):632 _descriptive_text_element(pres, "dc:description", i)633 634 _prepend_namespace(pres, "identifier", "dc", [])635 _ensure_list(pres, "dc:identifier")636 for i in range(len(pres.get('dc:identifier', []))):637 cleanup_identifier(pres, "dc:identifier", i)638 639 if pres.has_key("image"):640 cleanup_image(pres[image])641 642 _prepend_namespace(pres, "subject", "dc", [])643 _ensure_list(pres, "dc:subject")644 for i in range(len(pres.get("dc:subject", []))):645 cleanup_subject(pres, "dc:subject", i)646 647 _prepend_namespace(pres, "title", "dc", [])648 _ensure_list(pres, "dc:title")649 for i in range(len(pres.get("dc:title", []))):650 cleanup_title(pres, "dc:title", i)651 652 _prepend_namespace(pres, "type", "dc")653 _ensure_obj_to_text(pres, "dc:type")654 655 _prepend_namespace(pres, "url", "mlo")656 _ensure_obj_to_text(pres, "mlo:url")657 658 _ensure_list(pres, "abstract")659 for i in range(len(pres.get('abstract', []))):660 _descriptive_text_element(pres, "abstract", i)661 662 _ensure_list(pres, "applicationProcedure")663 for i in range(len(pres.get('applicationProcedure', []))):664 _descriptive_text_element(pres, "applicationProcedure", i)665 666 _prepend_namespace(pres, "assessment", "mlo", [])667 _ensure_list(pres, "mlo:assessment")668 for i in range(len(pres.get('mlo:assessment', []))):669 _descriptive_text_element(pres, "mlo:assessment", i)670 671 _ensure_list(pres, "learningOutcome")672 for i in range(len(pres.get('learningOutcome', []))):673 _descriptive_text_element(pres, "learningOutcome", i)674 675 _prepend_namespace(pres, "objective", "mlo", [])676 _ensure_list(pres, "mlo:objective")677 for i in range(len(pres.get('mlo:objective', []))):678 _descriptive_text_element(pres, "mlo:objective", i)679 680 _prepend_namespace(pres, "prerequisite", "mlo", [])681 _ensure_list(pres, "mlo:prerequisite")682 for i in range(len(pres.get('mlo:prerequisite', []))):683 _descriptive_text_element(pres, "mlo:prerequisite", i)684 685 _ensure_list(pres, "regulations")686 for i in range(len(pres.get('regulations', []))):687 _descriptive_text_element(pres, "regulations", i)688 689 _prepend_namespace(pres, "start", "mlo")690 _temporal_element(pres, "mlo:start")691 692 _prepend_namespace(pres, "end", "mlo")693 _temporal_element(pres, "mlo:end")694 695 _prepend_namespace(pres, "duration", "mlo")696 _temporal_element(pres, "mlo:duration")697 698 _temporal_element(pres, "applyFrom")699 700 _temporal_element(pres, "applyUntil")701 702 if pres.has_key("applyTo"):703 _ensure_text(pres, "applyTo")704 705 # not enough information about mlo:engagement to do any serious cleanup on it706 _prepend_namespace(pres, "engagement", "mlo", [])707 708 if pres.has_key("studyMode"):709 cleanup_mode(pres, "studyMode")710 711 if pres.has_key("attendanceMode"):712 cleanup_mode(pres, "attendanceMode")713 714 if pres.has_key("attendancePattern"):715 cleanup_mode(pres, "attendancePattern")716 717 _prepend_namespace(pres, "languageOfInstruction", "mlo", [])718 _ensure_list(pres, "mlo:languageOfInstruction")719 for i in range(len(pres.get("mlo:languageOfInstruction", []))):720 _ensure_obj_to_text(pres, "mlo:languageOfInstruction", i)721 722 _ensure_list(pres, "languageOfAssessment")723 for i in range(len(pres.get("languageOfAssessment", []))):724 _ensure_obj_to_text(pres, "languageOfAssessment", i)725 726 _prepend_namespace(pres, "places", "mlo")727 _ensure_text(pres, "mlo:places")728 729 _prepend_namespace(pres, "cost", "mlo")730 _ensure_text(pres, "mlo:cost")731 732 _prepend_namespace(pres, "age", "mlo")733 _ensure_text(pres, "mlo:age")734 735 _ensure_list(pres, "venue")736 for i in range(len(pres.get("venue", []))):737 cleanup_venue(pres, "venue", i)738def cleanup_venue(parent, venue, idx):739 element = parent[venue][idx]740 _remove_dud_text(element)741 if not _is_dict(element):742 # not spec conformant, so delete743 del parent[venue][idx]744 return745 if element.has_key("provider"):746 cleanup_provider(element["provider"])747def cleanup_mode(parent, mode):748 """749 {"identifier" : "identifier", "value" : "value"}750 """751 _text_to_value(parent, mode)752 _rename_key(parent[mode], "@identifier", "identifier")753 _remove_attributes(parent[mode])754def cleanup_qualification(qual):755 """756 {757 "dc:identifier" : [{"type" : "type","value" : "value"}],758 "dc:title" : [{"lang" : "lang","value" : "value"}],759 "abbr" : "abbr",760 "dc:description" : [{"lang" : "lang","href" : "href","value" : "value"}],761 "dcterms:educationLevel" : ["education level"],762 "dc:type" : "type",763 "mlo:url" : "url",764 "awardedBy" : "awarded by",765 "accreditedBy" : "accredited by"766 }767 """768 _remove_dud_text(qual)769 770 _prepend_namespace(qual, "identifier", "dc", [])771 _ensure_list(qual, "dc:identifier")772 for i in range(len(qual.get('dc:identifier', []))):773 cleanup_identifier(qual, "dc:identifier", i)774 775 _prepend_namespace(qual, "title", "dc", [])776 _ensure_list(qual, "dc:title")777 for i in range(len(qual.get("dc:title", []))):778 cleanup_title(qual, "dc:title", i)779 780 if qual.has_key("abbr"):781 _ensure_text(qual, "abbr")782 783 _prepend_namespace(qual, "description", "dc", [])784 _ensure_list(qual, "dc:description")785 for i in range(len(qual.get('dc:description', []))):786 _descriptive_text_element(qual, "dc:description", i)787 788 _prepend_namespace(qual, "educationLevel", "dcterms", [])789 _ensure_list(qual, "dcterms:educationLevel")790 for i in range(len(qual.get("dcterms:educationLevel", []))):791 _ensure_obj_to_text(qual, "dcterms:educationLevel", i)792 793 _prepend_namespace(qual, "type", "dc")794 _ensure_obj_to_text(qual, "dc:type")795 796 _prepend_namespace(qual, "url", "mlo")797 _ensure_obj_to_text(qual, "mlo:url")798 799 # we don't know enough about awardedBy and accreditedBy, so 800 # just leave them alone801if __name__ == "__main__":802 parser = argparse.ArgumentParser(description='Parse a directory of XCRI data files')803 parser.add_argument('-d', help='Source directory of XCRI XML files', required=True)...
data.py
Source:data.py
2import numpy as np3import pandas as pd4import scipy as sp5import scipy.signal6def _ensure_list(data):7 if type(data) is not list:8 data = [data]9 return data10'''11PEAKS12'''13def find_peaks(data, distance, inv=False):14 if inv:15 peaks, _ = sp.signal.find_peaks(1./data, distance=distance)16 else:17 peaks, _ = sp.signal.find_peaks(data, distance=distance)18 return peaks19def find_peaks_savgol(data, distance, inv=False, polyorder=3):20 # distance needs to be an odd int21 if distance % 2 == 0:22 distance = distance + 123 # smooth data24 if inv:25 s = scipy.signal.savgol_filter(1/data, distance, polyorder)26 else:27 s = scipy.signal.savgol_filter(data, distance, polyorder)28 # get peaks29 peaks = scipy.signal.argrelmax(s)[0]30 return peaks31'''32SUPPORT RESISTANCE33'''34def support_resistance(ltp, n):35 '''36 This function takes a numpy array of last traded price37 and returns a list of support and resistance levels38 respectively. n is the number of entries to be scanned.39 Params:40 - ltp: list with data (close)41 - n: smooth period (distance)42 Returns:43 - support: Support levels44 - resistance: Resistance levels45 '''46 from scipy.signal import savgol_filter as smooth47 # converting n to a nearest even number48 if n % 2 != 0:49 n += 150 n_ltp = ltp.shape[0]51 # smoothening the curve52 ltp_s = smooth(ltp, int(n+1), 3)53 # taking a simple derivative54 ltp_d = np.zeros(n_ltp)55 ltp_d[1:] = np.subtract(ltp_s[1:], ltp_s[:-1])56 resistance = []57 support = []58 for i in range(n_ltp - n):59 arr_sl = ltp_d[i:int(i+n)]60 first = arr_sl[:int(n/2)] # first half61 last = arr_sl[int(n/2):] # second half62 r_1 = np.sum(first > 0)63 r_2 = np.sum(last < 0)64 s_1 = np.sum(first < 0)65 s_2 = np.sum(last > 0)66 # local maxima detection67 if (r_1 == (n/2)) and (r_2 == int(n/2)):68 resistance.append(ltp[i+(int(n/2)-1)])69 # local minima detection70 if (s_1 == int(n/2)) and (s_2 == int(n/2)):71 support.append(ltp[i+(int(n/2)-1)])72 return support, resistance73'''74TRENDY TRENDS75'''76def segtrends(x, segments=2):77 '''78 Turn minitrends to iterative process more easily adaptable to79 implementation in simple trading systems; allows backtesting functionality.80 Arguments:81 x -- One-dimensional data set82 segments -- (Default 2)83 Example:84 import matplotlib.pyplot as plt85 x_maxima, maxima, x_minima, minima = segtrends(x, segments=segments)86 plt.plot(x)87 plt.grid(True)88 for i in range(0, segments - 1):89 maxslope = ((maxima[i + 1] - maxima[i])90 / (x_maxima[i + 1] - x_maxima[i]))91 a_max = maxima[i] - (maxslope * x_maxima[i])92 b_max = maxima[i] + (maxslope * (len(x) - x_maxima[i]))93 maxline = np.linspace(a_max, b_max, len(x))94 minslope = ((minima[i + 1] - minima[i])95 / (x_minima[i+1] - x_minima[i]))96 a_min = minima[i] - (minslope * x_minima[i])97 b_min = minima[i] + (minslope * (len(x) - x_minima[i]))98 minline = np.linspace(a_min, b_min, len(x))99 plt.plot(maxline, "g")100 plt.plot(minline, "r")101 plt.show()102 '''103 x = np.array(x)104 # Implement trendlines105 segments = int(segments)106 segsize = int(len(x) / segments)107 maxima = np.ones(segments)108 minima = np.ones(segments)109 x_maxima = np.ones(segments)110 x_minima = np.ones(segments)111 for i in range(1, segments + 1):112 ind2 = i * segsize113 ind1 = ind2 - segsize114 maxima[i-1] = max(x[ind1:ind2])115 minima[i-1] = min(x[ind1:ind2])116 x_maxima[i-1] = np.where(x[ind1:ind2] == maxima[i-1])[0][0] + ind1117 x_minima[i-1] = np.where(x[ind1:ind2] == minima[i-1])[0][0] + ind1118 for i in range(1, segments+1):119 ind2 = i * segsize120 ind1 = ind2 - segsize121 return x_maxima, maxima, x_minima, minima122def gentrends(x, window=1/3.0):123 '''124 Returns a Pandas dataframe with support and resistance lines.125 Arguments:126 x -- One-dimensional data set127 window -- How long the trendlines should be. If window < 1, then128 it will be taken as a percentage of the size of the129 data (Default 1/3.)130 Example:131 import matplotlib.pyplot as plt132 trends, maxslope, minslope = gentrends(x, window=window)133 plt.plot(trends)134 plt.grid()135 plt.show()136 '''137 x = np.array(x)138 if window < 1:139 window = int(window * len(x))140 max1 = np.where(x == max(x))[0][0] # find the index of the abs max141 min1 = np.where(x == min(x))[0][0] # find the index of the abs min142 # First the max143 if max1 + window >= len(x):144 max2 = max(x[0:max(1, (max1 - window))])145 else:146 max2 = max(x[(max1 + window):])147 # Now the min148 if min1 - window < 0:149 min2 = min(x[(min1 + window):])150 else:151 min2 = min(x[0:max(1, (min1 - window))])152 # Now find the indices of the secondary extrema153 max2 = np.where(x == max2)[0][0] # find the index of the 2nd max154 min2 = np.where(x == min2)[0][0] # find the index of the 2nd min155 # Create & extend the lines156 # slope between max points157 if max1 - max2 != 0:158 maxslope = (x[max1] - x[max2]) / (max1 - max2)159 else:160 maxslope = x[max1] - x[max2]161 # slope between min points162 if min1 - min2 != 0:163 minslope = (x[min1] - x[min2]) / (min1 - min2)164 else:165 minslope = x[min1] - x[min2]166 aMax = x[max1] - (maxslope * max1) # y-intercept for max trendline167 aMin = x[min1] - (minslope * min1) # y-intercept for min trendline168 bMax = x[max1] + (maxslope * (len(x) - max1)) # extend to last data pt169 bMin = x[min1] + (minslope * (len(x) - min1)) # extend to last data point170 maxline = np.linspace(171 aMax,172 bMax,173 len(x),174 endpoint=False) # Y values between max's175 minline = np.linspace(176 aMin,177 bMin,178 len(x),179 endpoint=False) # Y values between min's180 trends = np.transpose(np.array((x, maxline, minline)))181 trends = pd.DataFrame(trends, index=np.arange(0, len(x)),182 columns=["Data", "Max Line", "Min Line"])183 return trends, maxslope, minslope184def minitrends(x, window=20):185 '''186 Turn minitrends to iterative process more easily adaptable to187 implementation in simple trading systems; allows backtesting188 functionality.189 Arguments:190 x -- One-dimensional data set191 window -- How long the trendlines should be. If window < 1, then192 it will be taken as a percentage of the size of the193 data (Default 20)194 Example:195 import matplotlib.pyplot as plt196 trends, maxslope, minslope = gentrends(x, window=window)197 plt.plot(trends)198 plt.grid()199 plt.show()200 '''201 y = np.array(x)202 if window < 1: # if window is given as fraction of data length203 window = float(window)204 window = int(window * len(y))205 x = np.arange(0, len(y))206 dy = y[window:] - y[:-window]207 crit = dy[:-1] * dy[1:] < 0208 xmax = np.array([])209 xmin = np.array([])210 # Find whether max's or min's211 for i, val in enumerate(crit):212 if val is True:213 if (y[i] - y[i + window] > 0) and (y[i] - y[i - window] > 0):214 xmax = np.append(xmax, i)215 if (y[i] - y[i + window] < 0) and (y[i] - y[i - window] < 0):216 xmin = np.append(xmin, i)217 xmax = xmax.astype(int)218 xmin = xmin.astype(int)219 # See if better max or min in region220 yMax = np.array([])221 xMax = np.array([])222 for i in xmax:223 indx = np.where(xmax == i)[0][0] + 1224 try:225 Y = y[i:xmax[indx]]226 yMax = np.append(yMax, Y.max())227 xMax = np.append(xMax, np.where(y == yMax[-1])[0][0])228 except Exception:229 pass230 yMin = np.array([])231 xMin = np.array([])232 for i in xmin:233 indx = np.where(xmin == i)[0][0] + 1234 try:235 Y = y[i:xmin[indx]]236 yMin = np.append(yMin, Y.min())237 xMin = np.append(xMin, np.where(y == yMin[-1])[0][0])238 except Exception:239 pass240 if y[-1] > yMax[-1]:241 yMax = np.append(yMax, y[-1])242 xMax = np.append(xMax, x[-1])243 if y[0] not in yMax:244 yMax = np.insert(yMax, 0, y[0])245 xMax = np.insert(xMax, 0, x[0])246 if y[-1] < yMin[-1]:247 yMin = np.append(yMin, y[-1])248 xMin = np.append(xMin, x[-1])249 if y[0] not in yMin:250 yMin = np.insert(yMin, 0, y[0])251 xMin = np.insert(xMin, 0, x[0])252 # Return arrays of critical points253 return xMin, yMin, xMax, yMax254def iterlines(x, window=20):255 '''256 Turn minitrends to iterative process more easily adaptable to257 implementation in simple trading systems; allows backtesting functionality.258 Arguments:259 x -- One-dimensional data set260 window -- How long the trendlines should be. If window < 1, then261 it will be taken as a percentage of the size of the262 data (Default 20)263 Example:264 import matplotlib.pyplot as plt265 sigs, xMin, yMin, xMax, yMax = iterlines(x, window=window)266 plt.plot(x)267 plt.plot(xMin, yMin, "ro")268 plt.plot(xMax, yMax, "go")269 plt.grid(True)270 plt.show()271 '''272 x = np.array(x)273 n = len(x)274 if window < 1:275 window = int(window * n)276 sigs = np.zeros(n, dtype=float)277 i = window278 while i != n:279 if x[i] > max(x[i-window:i]):280 sigs[i] = 1281 elif x[i] < min(x[i-window:i]):282 sigs[i] = -1283 i += 1284 xMin = np.where(sigs == -1.0)[0]285 xMax = np.where(sigs == 1.0)[0]286 yMin = x[xMin]287 yMax = x[xMax]288 return sigs, xMin, yMin, xMax, yMax289'''290PIVOT POINTS291'''292def pivot_points(highList, lowList, closeList):293 '''294 Returns the standard pivot points, three support levels (s1,s2 and s3)295 and three resistance levels (r1, r2 and r3) of the296 given data series.297 These values for a given day are calculated based on the day before298 so expect n values as output for a given list of n days.299 Standard Pivot Points begin with a base Pivot Point. This is a simple300 average of the high, low and close. The middle Pivot Point is shown as301 a solid line between the support and resistance pivots. Keep in mind that302 the high, low and close are all from the prior period.303 Params:304 - highList: list of high values305 - lowList: list of low values306 - closeList: list of closing values307 Returns:308 - p: pivot point309 - s1: support first point310 - s2: support second point311 - s3: support third point312 - r1: resistance first point313 - r2: resistance second point314 - r3: resistence third point315 '''316 # ensure np array is being used317 highList = np.array(_ensure_list(highList))318 lowList = np.array(_ensure_list(lowList))319 closeList = np.array(_ensure_list(closeList))320 # calculation321 p = (highList + lowList + closeList) / 3322 s1 = (2 * p) - highList323 s2 = p - highList + lowList324 s3 = s1 - highList + lowList325 r1 = (2 * p) - lowList326 r2 = p + highList - lowList327 r3 = r1 + highList - lowList328 # return lists with results329 return p, s1, s2, s3, r1, r2, r3330def tom_demark_points(openList, highList, lowList, closeList):331 '''332 Returns the Tom Demark points, the predicted low and highs333 of the period.334 These values for a given day are calculated based on the day before335 so expect n values as output for a given list of n days.336 Demark Pivot Points start with a different base and use different337 formulas for support and resistance. These Pivot Points are conditional338 on the relationship between the close and the open.339 If Close < Open, then X = High + (2 x Low) + Close340 If Close > Open, then X = (2 x High) + Low + Close341 If Close = Open, then X = High + Low + (2 x Close)342 Pivot Point (P) = X/4343 Support 1 (S1) = X/2 - High344 Resistance 1 (R1) = X/2 - Low345 Params:346 - openList: list of open values347 - highList: list of high values348 - lowList: list of low values349 - closeList: list of closing values350 Returns:351 - p: Pivot point352 - r1: Resistance 1353 - s1: Support 1354 '''355 # ensure np array is being used356 openList = np.array(_ensure_list(openList))357 highList = np.array(_ensure_list(highList))358 lowList = np.array(_ensure_list(lowList))359 closeList = np.array(_ensure_list(closeList))360 # calculation361 p = []362 s1 = []363 r1 = []364 for o, h, l, c in np.nditer([openList, highList, lowList, closeList]):365 if c < o:366 x = h + (2 * l) + c367 elif c > o:368 x = (2 * h) + l + c369 elif c == o:370 x = h + l + (2 * c)371 p.append(x / 4)372 s1.append((x / 2) - h)373 r1.append((x / 2) - l)374 # return lists with results375 return p, s1, r1376def woodies_points(highList, lowList, closeList):377 '''378 Returns the Woodies points: pivot, supports (s1 and s2) and379 resistance values (r1 and r2).380 These values for a given day are calculated based on the day before381 so expect n values as output for a given list of n days.382 * - p: pivot value.383 *384 Params:385 - highList: list of high values386 - lowList: list of low values387 - closeList: list of closing values388 Returns:389 - pl: pivot level390 - s1: support (s1)391 - s2: secondary support (s2)392 - r1: resistance (r1)393 - r2: secondary resistance (r2)394 '''395 # ensure np array is being used396 highList = np.array(_ensure_list(highList))397 lowList = np.array(_ensure_list(lowList))398 closeList = np.array(_ensure_list(closeList))399 # calculation400 p = (highList + lowList + 2 * closeList) / 4401 s1 = (2 * p) - highList402 s2 = p - highList + lowList403 r1 = (2 * p) - lowList404 r2 = p + highList - lowList405 # return lists with results406 return p, s1, s2, r1, r2407def camarilla_points(highList, lowList, closeList):408 '''409 Returns the Camarilla points: supports (s1,s2,3 and s4)) and410 resistance values (r1, r2, r3 and r4).411 Params:412 - highList: list of high values413 - lowList: list of low values414 - closeList: list of closing values415 Returns:416 - s1: s1 support417 - s2: s2 support418 - s3: s3 support419 - s4: s4 support420 - r1: r1 resistance421 - r2: r2 resistance422 - r3: r3 resistance423 - r4: r4 resistance424 '''425 # ensure np array is being used426 highList = np.array(_ensure_list(highList))427 lowList = np.array(_ensure_list(lowList))428 closeList = np.array(_ensure_list(closeList))429 # calculation430 diff = highList - lowList431 s1 = closeList - (diff * 1.1 / 12)432 s2 = closeList - (diff * 1.1 / 6)433 s3 = closeList - (diff * 1.1 / 4)434 s4 = closeList - (diff * 1.1 / 2)435 r1 = ((diff * 1.1) / 12) + closeList436 r2 = ((diff * 1.1) / 6) + closeList437 r3 = ((diff * 1.1) / 4) + closeList438 r4 = ((diff * 1.1) / 2) + closeList439 # return lists with results440 return s1, s2, s3, s4, r1, r2, r3, r4441def fibanocci_points(highList, lowList, closeList):442 '''443 Returns the fibanocci points: supports (s1,s2,3)) and444 resistance values (r1, r2, r3).445 Params:446 - highList: list of high values447 - lowList: list of low values448 - closeList: list of closing values449 Returns:450 - p: pivot point451 - s1: s1 support452 - s2: s2 support453 - s3: s3 support454 - r1: r1 resistance455 - r2: r2 resistance456 - r3: r3 resistance457 '''458 # ensure np array is being used459 highList = np.array(_ensure_list(highList))460 lowList = np.array(_ensure_list(lowList))461 closeList = np.array(_ensure_list(closeList))462 # calculation463 p = (highList + lowList + closeList) / 3464 s1 = p - (0.382 * (highList - lowList))465 s2 = p - (0.618 * (highList - lowList))466 s3 = p - (1.0 * (highList - lowList))467 r1 = p + (0.382 * (highList - lowList))468 r2 = p + (0.618 * (highList - lowList))469 r3 = p + (1.0 * (highList - lowList))470 # return lists with results471 return p, s1, s2, s3, r1, r2, r3472def fibonacci_retracements(highList, lowList):473 '''474 Returns the fibanocci retracements.475 Params:476 - highList: list of high values477 - lowList: list of low values478 Returns:479 - upTrend480 - downTrend481 '''482 # ensure np array is being used483 highList = np.array(_ensure_list(highList))484 lowList = np.array(_ensure_list(lowList))485 # calculation486 retracements = [1, 0.618, 0.5, 0.382, 0.236, 0]487 diff = highList - lowList488 upTrend = []489 downTrend = []490 for d, h, l in np.nditer([diff, highList, lowList]):491 up = h - d * retracements492 do = l + d * retracements493 upTrend.append(up)494 downTrend.append(do)495 # return lists with results...
cmd_runner.py
Source:cmd_runner.py
...5__metaclass__ = type6from functools import wraps7from ansible.module_utils.common.collections import is_sequence8from ansible.module_utils.six import iteritems9def _ensure_list(value):10 return list(value) if is_sequence(value) else [value]11def _process_as_is(rc, out, err):12 return rc, out, err13class CmdRunnerException(Exception):14 pass15class MissingArgumentFormat(CmdRunnerException):16 def __init__(self, arg, args_order, args_formats):17 self.args_order = args_order18 self.arg = arg19 self.args_formats = args_formats20 def __repr__(self):21 return "MissingArgumentFormat({0!r}, {1!r}, {2!r})".format(22 self.arg,23 self.args_order,24 self.args_formats,25 )26 def __str__(self):27 return "Cannot find format for parameter {0} {1} in: {2}".format(28 self.arg,29 self.args_order,30 self.args_formats,31 )32class MissingArgumentValue(CmdRunnerException):33 def __init__(self, args_order, arg):34 self.args_order = args_order35 self.arg = arg36 def __repr__(self):37 return "MissingArgumentValue({0!r}, {1!r})".format(38 self.args_order,39 self.arg,40 )41 def __str__(self):42 return "Cannot find value for parameter {0} in {1}".format(43 self.arg,44 self.args_order,45 )46class FormatError(CmdRunnerException):47 def __init__(self, name, value, args_formats, exc):48 self.name = name49 self.value = value50 self.args_formats = args_formats51 self.exc = exc52 super(FormatError, self).__init__()53 def __repr__(self):54 return "FormatError({0!r}, {1!r}, {2!r}, {3!r})".format(55 self.name,56 self.value,57 self.args_formats,58 self.exc,59 )60 def __str__(self):61 return "Failed to format parameter {0} with value {1}: {2}".format(62 self.name,63 self.value,64 self.exc,65 )66class _ArgFormat(object):67 def __init__(self, func, ignore_none=None):68 self.func = func69 self.ignore_none = ignore_none70 def __call__(self, value, ctx_ignore_none):71 ignore_none = self.ignore_none if self.ignore_none is not None else ctx_ignore_none72 if value is None and ignore_none:73 return []74 f = self.func75 return [str(x) for x in f(value)]76class _Format(object):77 @staticmethod78 def as_bool(args):79 return _ArgFormat(lambda value: _ensure_list(args) if value else [])80 @staticmethod81 def as_bool_not(args):82 return _ArgFormat(lambda value: [] if value else _ensure_list(args), ignore_none=False)83 @staticmethod84 def as_optval(arg, ignore_none=None):85 return _ArgFormat(lambda value: ["{0}{1}".format(arg, value)], ignore_none=ignore_none)86 @staticmethod87 def as_opt_val(arg, ignore_none=None):88 return _ArgFormat(lambda value: [arg, value], ignore_none=ignore_none)89 @staticmethod90 def as_opt_eq_val(arg, ignore_none=None):91 return _ArgFormat(lambda value: ["{0}={1}".format(arg, value)], ignore_none=ignore_none)92 @staticmethod93 def as_list(ignore_none=None):94 return _ArgFormat(_ensure_list, ignore_none=ignore_none)95 @staticmethod96 def as_fixed(args):97 return _ArgFormat(lambda value: _ensure_list(args), ignore_none=False)98 @staticmethod99 def as_func(func, ignore_none=None):100 return _ArgFormat(func, ignore_none=ignore_none)101 @staticmethod102 def as_map(_map, default=None, ignore_none=None):103 return _ArgFormat(lambda value: _ensure_list(_map.get(value, default)), ignore_none=ignore_none)104 @staticmethod105 def as_default_type(_type, arg="", ignore_none=None):106 fmt = _Format107 if _type == "dict":108 return fmt.as_func(lambda d: ["--{0}={1}".format(*a) for a in iteritems(d)],109 ignore_none=ignore_none)110 if _type == "list":111 return fmt.as_func(lambda value: ["--{0}".format(x) for x in value], ignore_none=ignore_none)112 if _type == "bool":113 return fmt.as_bool("--{0}".format(arg))114 return fmt.as_opt_val("--{0}".format(arg), ignore_none=ignore_none)115 @staticmethod116 def unpack_args(func):117 @wraps(func)118 def wrapper(v):119 return func(*v)120 return wrapper121 @staticmethod122 def unpack_kwargs(func):123 @wraps(func)124 def wrapper(v):125 return func(**v)126 return wrapper127class CmdRunner(object):128 """129 Wrapper for ``AnsibleModule.run_command()``.130 It aims to provide a reusable runner with consistent argument formatting131 and sensible defaults.132 """133 @staticmethod134 def _prepare_args_order(order):135 return tuple(order) if is_sequence(order) else tuple(order.split())136 def __init__(self, module, command, arg_formats=None, default_args_order=(),137 check_rc=False, force_lang="C", path_prefix=None, environ_update=None):138 self.module = module139 self.command = _ensure_list(command)140 self.default_args_order = self._prepare_args_order(default_args_order)141 if arg_formats is None:142 arg_formats = {}143 self.arg_formats = dict(arg_formats)144 self.check_rc = check_rc145 self.force_lang = force_lang146 self.path_prefix = path_prefix147 if environ_update is None:148 environ_update = {}149 self.environ_update = environ_update150 self.command[0] = module.get_bin_path(command[0], opt_dirs=path_prefix, required=True)151 for mod_param_name, spec in iteritems(module.argument_spec):152 if mod_param_name not in self.arg_formats:153 self.arg_formats[mod_param_name] = _Format.as_default_type(spec['type'], mod_param_name)...
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