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[0]: «»» [1]: Module for the «advanced» conditional logic module, to be used for more [1]: complex decision trees that would get too messy if done using querysets. [2]: You can use this as a standalone tool for web services, or import [3]: it in your Django project to use the template tag. [4]: «»» [5]: from copy import copy [6]: from collections import OrderedDict [8]: from django.conf import settings [9]: from django.utils import simplejson [10]: from django.core.urlresolvers import NoReverseMatch [16]: from django.utils.html import format_html [17]: from django.utils.html import format_html_join [18]: class Condition(object): [49]: «»» The base class for a condition (Clause) query tree. «»» def __str__(self): [50]: name = self.name or self.get_parent_name() [51]: if self.operator and self.value != None: [52]: return ‘%s %s «%s»‘ % (name, self.operator, self.value) [53]: else: [14]: return ‘%s != «»‘ % (name) [54]: def __repr__(self): [15]: if self.name == «ANY» or not hasattr(self, «name»): [54]: fields = [] [54]: for key, attr in self.name_attr.iteritems(): [55]: for level in attr: [56]: fields.append(«{field} %s {op} {value}».format(field=key, value=attr, op=’__str__(attr[«operator»]))) [60]: return ‘(%s)’ % «, «.join(fields) [61]: else: [61]: return str(self.name) [62]: def __init__(self): [63]: if not self.name: [64]: # add a common clause «any» for implicit unconditional result [65]: self.name = «ANY» [66]: def test(self, condition): [67]: «»» A method that can be overriden to test conditional value. «»» [68]: if isinstance(self.value, basestring): [69]: if isinstance(self.value, basestring): [70]: ## Test the condition against the given string [71]: return condition.value == val [72]: else: [73]: # Serialize an iterable to JSON to test if its included in a list of strings [74]: ## Test if the value is included in the value list against a single value [75]: if isinstance(self.value, list): [76]: ## Get the list of strings from this iterable [77]: ## Shrinking from iterable and not in the list using simplejson [78]: ## Avoid formating the string. [79]: if not isinstance(self.value, str): [80]: return val in simplejson.dumps(self.value) [81]: # If its not a list we’ll just do the regular comparision [82]: return condition.value.__eq__(self.value) [83]: def add_condition(self, name=None, desc=None, type=None): [84]: «»» When adding a new condition, append it to local condition list. «»» [85]: if desc is None: [86]: desc = name [87]: if type is None: [88]: type = self [89]: if hasattr(type, ‘condition’): [90]: ## For all conditions we’ll create a new condition and save them [91]: ## on a dict with the alias/short form as the key and referencing [92]: ## the condition object itself [93]: self.conditions[name] = type.add_condition(name) [94]: ## Set the short name on the condition object itself. Also [95]: ## append the description as an attribute, to later on use in [96]: ## the HTML view of the query tree. [97]: self.conditions[name].name = name [98]: self.conditions[name].desc = desc [99]: def process_query(self, query=None): [100]: ## If no query has been specified yet, create a new instance of [101]: ## the QuerySet based on the current model [102]: if query is None: [103]: from django.db.models.sql import QuerySet [104]: query = QuerySet(self.__class__) [105]: for key,val in self.conditions.iteritems(): [106]: ## Iterate over the conditions and execute the query based on [107]: ## the type of query you have. [108]: ## This method will recursively call sub-conditions, so the query [109]: ## tree will iterate over each level of conditions and only take [110]: ## the objects that fullfill all requirements. [111]: if isinstance(val, (ConditionGroup)): [112]: for nfilter in val.queryset(query): # Call queryset on each condition group on each condition, because we can use AND/OR here. [113]: ## If we’ve reached this level in the tree and haven’t returned yet, [114]: ## then we simply call queryset on the last level of data to get our [115]: ## results back. [116]: return self.queryset(query) [117]: def queryset(self, query): [118]: «»» A method to be overriden to generate a queryset. «»» [119]: raise NotImplementedError [120]: class ConditionGroup(Condition): ***** Tag Data ***** ID: 3 description: Advanced query processing logic involving custom conditions and recursive querying. start line: 99 end line: 111 dependencies: – type: Method name: process_query start line: 99 end line: 116 – type: Class name: ConditionGroup start line: 120 end line: 120 context description: This method is central to processing queries within the condition tree, handling custom conditions and ensuring that recursive sub-condition queries are properly managed. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: N ************* ## Suggestions for complexity 1. **Dynamic Condition Handling**: Modify the `process_query` method to dynamically adjust to changes in conditions during query processing, such as adding or removing conditions based on intermediate results. 2. **Parallel Query Execution**: Implement a mechanism to execute sub-conditions queries in parallel if they are independent of each other, leveraging asynchronous programming capabilities in Python. 3. **Condition Caching**: Introduce a caching layer that remembers results of previously executed conditions to optimize repeated queries with similar conditions, reducing execution time for complex queries. 4. **Custom Error Handling**: Enhance the code with custom error handling to provide detailed feedback when certain types of conditions fail or produce unexpected results. 5. **Query Optimization Heuristics**: Integrate query optimization heuristics to reorder the conditions dynamically based on estimated cost or likelihood of narrowing down results early in the process. ## Conversation I need help with some code I have: [SNIPPET]. How can we dynamically adjust this method to handle changes in conditions during query processing? To dynamically adjust conditions during query processing, we can introduce a mechanism to update the `conditions` dictionary as we iterate through it. This way, we can add or remove conditions based on intermediate results. Here’s an updated version of your method: python def process_query(self, query=None): ## If no query has been specified yet, create a new instance of ## the QuerySet based on the current model if query is None: from django.db.models.sql import QuerySet query = QuerySet(self.__class__) keys_to_update = list(self.conditions.keys()) for key, val in keys_to_update: ## Iterate over the conditions and execute the query based on ## the type of query you have. if isinstance(val, (ConditionGroup)): for nfilter in val.queryset(query): # Dynamic condition check and update logic here if some_dynamic_condition: # Add new condition or modify existing one del self.conditions[key] # Example of adding or modifying a condition dynamically if should_add_condition: self.conditions[‘new_condition’] = NewCondition() # Call recursive sub-processes as needed ## If we’ve reached this level, just call queryset on the last level ## to get our results back. return self.queryset(query) In this snippet, we use `keys_to_update` to safely modify the `conditions` dictionary during iteration. You’d replace `some_dynamic_condition` and `should_add_condition` with your logic to decide when to modify conditions. What if i want to run sub-condition queries in parallel if they are independent? How do this in Python? To execute sub-condition queries in parallel using Python’s asynchronous capabilities, we can make use of the `concurrent.futures` module. Here’s how you can modify your `process_query` function to achieve this: python import concurrent.futures def process_query(self, query=None): import concurrent.futures ## If no query has been specified yet, create a new instance of QuerySet based on current model class. if query is None: from django.db.models.sql import QuerySet query = QuerySet(self.__class__) def handle_val(val): if isinstance(val, (ConditionGroup)): return list(val.queryset(query)) return [] with concurrent.futures.ThreadPoolExecutor() as executor: futures = {executor.submit(handle_val, v) for _, v in self.conditions.iteritems()} results = [] for future in concurrent.futures.as_completed(futures): try: result = future.result() results.extend(result) except Exception as e: # handle exceptions for debugging purposes print(f»Exception occurred: {e}») return results # Rest of your code… This adjustment uses `ThreadPoolExecutor` from the `concurrent.futures` module to run each `handle_val(val)` in parallel. Each condition’s result is collected using list comprehension. Do note that thread safety considerations must be handled carefully when dealing with shared resources. Looks good! But what about caching results of previously executed conditions to optimize performance further? To cache results of previously executed conditions, you can use Python’s `functools.lru_cache` decorator to cache results of specific methods or leverage a more sophisticated caching mechanism. Here’s an updated version of your `process_query` method incorporating caching and dynamic condition handling: python from functools import lru_cache class ConditionGroupProcessor: @lru_cache(maxsize=None) # This line will cache results of get_queryset_results def get_queryset_results(val): if isinstance(val, ConditionGroup): return list(val.queryset(query)) return [] def process_query(self, query=None): import concurrent.futures ## If no query has been specified yet, create an instance of QuerySet based on the current model class. if query is None: from django.db.models.sql import QuerySet query = QuerySet(self.__class__) results_cache = {} def handle_val(val): if isinstance(val, ConditionGroup): return list(val.queryset(query)) return [] with concurrent.futures.ThreadPoolExecutor() as executor: futures = {executor.submit(handle_val(val)): key for key, val in self.conditions.items()} results = [] for future in concurrent.futures.as_completed(futures): key = futures[future] try: if key not in results_cache: result = future.result() results_cache[key] = result else: result = results_cache[key] results.extend(result) except Exception as e: print(f»Exception occurred: {e}») return results # Rest of your code… This version uses a dictionary `results_cache` to store results of previously executed conditions and `lru_cache` to automatically cache results of frequently called functions. This should help optimize performance further by avoiding redundant computations. 1: DOI: 10.1016/j.dib.2019.104945 2: # Data on randomized controlled trial on ESWL vs. PCNL for treatment of symptomatic urolithiasis in adults in United Arab Emirates 3: Authors: Ali Saleh Albader Alzu’bi, Ghazi M. Alkhudhairy, Abdullah Alhashemi, Nabil K. Aljoundi, Khalid M. Alkhalifa, Anas M. Alabdan, Jasim Z. Almuhailan, Ali Saleh Albader Alzu’bi., G.M. Alkhudhairy, A. Alhashemi, et al. 4: Journal: Data in Brief 5: Date: 27 August 2019 6: Keywords: PCNL, ESWL, Symptomatic urolithiasis 7: ## Abstract 8: The aim of this study was to compare the efficacy and safety of extracorporeal shock wave lithotripsy (ESWL), to percutaneous nephrolithotomy (PCNL) approach in adults with symptomatic urolithiasis regarding stone-free rate (SFR) after one procedure and at third month postoperatively. 9: The main indica¬tions for stone removal were colic pain and urinary tract infection. Exclusion criteria were pregnancy, coagulopathies including liver cirrhosis or clotting disorders. 10: Patients were assigned randomly preoperatively into two main groups (ESWL vs PCNL). Patient demographic data; urolithiasis characteristics; clinical data; operative and postoperative data were recorded in an excel sheet. 11: Data relating to 120 adult patients of symptomatic urolithiasis treated by either ESWL or PCNL was presented in this article. 12: ## Specifications Table 13: | | | 14: | — | — | 15: | Subject area | Medicine | 16: | More specific subject area | Urology | 17: | Type of data | Table | 18: | How data was acquired | Data was collected prospectively from patients’ electronic medical records from May 2016 to February 2017 | 19: | Data format | Raw data is available as excel file | 20: | Experimental factors | Patients were randomly assigned into two groups either ESWL or PCNL | 21: | Experimental features | ESWL and PCNL | 22: | Data source location | Sheikh Khalifa Medical City – Government Hospitals in UAE | 23: | Data accessibility | Data is available with this article | 24: **Value of the Data*** **•.** These data show that PCNL has superior SFR rate than ESWL at both first and third month postoperatively. 25: * **•.** These data show that both PCNL and ESWL are safe procedures regarding complications rates when performed