Overview / Introduction
Llandudno Football Club, commonly known as Llandudno FC, is a prominent football team based in the picturesque region of North Wales. Competing in the Welsh Premier League, Llandudno FC has established itself as a formidable contender with a rich history and passionate fanbase. The team is currently managed by an experienced coach who has been pivotal in shaping their strategic approach on the field.
Team History and Achievements
Llandudno FC was founded in 1889, making it one of the oldest football clubs in Wales. Over the years, the club has achieved numerous titles and awards, including multiple league championships and cup victories. Notable seasons include their triumphant campaigns in the late 20th century when they consistently finished at the top of the league standings.
Current Squad and Key Players
The current squad boasts a mix of seasoned veterans and promising young talents. Key players include:
- James Davies – Forward, known for his goal-scoring prowess.
- Evan Thomas – Midfielder, renowned for his playmaking abilities.
- Tommy Jenkins – Defender, celebrated for his defensive solidity.
Team Playing Style and Tactics
Llandudno FC typically employs a 4-3-3 formation, focusing on dynamic attacking play supported by a strong defensive line. Their strategy emphasizes quick transitions from defense to attack, leveraging their midfield’s ability to control the tempo of the game. Strengths include their high pressing game and set-piece efficiency, while weaknesses may arise from occasional lapses in concentration during counterattacks.
Interesting Facts and Unique Traits
The club is affectionately nicknamed “The Gloddaeth Giants,” a reference to their historic home ground. Llandudno FC has a dedicated fanbase known for their vibrant support during matches. Rivalries with nearby teams add an extra layer of excitement to their fixtures, while traditions like pre-match parades have become an integral part of matchday culture.
Lists & Rankings of Players, Stats, or Performance Metrics
- Top Scorer: James Davies (🎰)
- Average Goals per Match: 1.8 (💡)
- Defensive Record: Conceded 0.5 goals per match (✅)
Comparisons with Other Teams in the League or Division
Llandudno FC often finds itself compared to other top-tier teams like Bangor City and Prestatyn Town. While they share similar ambitions within the league, Llandudno’s emphasis on youth development sets them apart as they consistently integrate young talent into their first team.
Case Studies or Notable Matches
A standout match in recent memory was their thrilling victory against Rhyl FC last season, where they overturned a two-goal deficit to win 3-2 in stoppage time. This match exemplified their resilience and tactical acumen under pressure.
| Statistic | Llandudno FC | Rivals Average |
|---|---|---|
| Total Goals Scored This Season | 45 | 38 |
| Total Goals Conceded This Season | 22 | 30 |
| Last Five Matches Form (W-D-L) | 3-1-1 | N/A |
| Odds for Next Match Win/Loss/Draw* | N/A* |
Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks 💡
- ✅ Focus on key player performances: Monitoring top scorers like James Davies can provide insights into potential match outcomes.
- ❌ Be cautious during away games: Historical data shows that Llandudno sometimes struggles on unfamiliar grounds.
- 💡 Consider recent form: Analyze their last five matches to gauge current momentum before placing bets.
- 💡 Evaluate head-to-head records: Understanding past encounters with opponents can offer valuable betting angles.
- ✅ Look at defensive metrics: A strong defensive record can indicate potential for low-scoring games.
- ❌ Avoid overconfidence during rival matches: Intense rivalries can lead to unexpected results due to heightened emotions.
- 💡 Analyze tactical changes: Keep an eye on any strategic adjustments made by the coach that could influence game dynamics.
Frequently Asked Questions About Betting on Llandudno FC 🤔
What are some key factors to consider when betting on Llandudno FC?
Betting on Llandudno FC involves analyzing several factors such as recent form, head-to-head statistics against opponents, key player availability due to injuries or suspensions, and historical performance in similar fixtures or conditions (e.g., home vs away games).
Could weather conditions impact betting odds for upcoming matches involving Llandudno FC?
Weather conditions can significantly affect match outcomes; rain might make pitches slippery leading teams like defensively solid ones like Llandudno potentially benefiting from fewer goals being scored overall due to slower ball movement across wet surfaces which could influence betting odds accordingly depending upon how each side adapts tactically under those circumstances specifically impacting possession-based strategies versus direct long-ball approaches respectively among other considerations worth noting prior placing wagers regarding upcoming fixtures featuring this club within its competitive environment overall thereby influencing final decisions made around placing bets related directly towards its performances moving forward given these external variables present throughout various stages throughout each campaign undertaken annually within professional sports leagues alike globally encompassing football leagues worldwide including those involving Welsh teams such as this one discussed here today!
“Llandudno FC’s blend of experience and youthful exuberance makes them unpredictable yet exciting every season.” – Sports Analyst John Smith
Moving Forward Pros & Cons Analysis ✅❌ Lists ✅❌
Strong defensive record provides stability.
Occasional inconsistency when playing away from home.- Youth development program continues producing quality players.
- Dependence on star players may pose risks if injuries occur.
- Highly motivated squad showing great team spirit.
</l[0]: #!/usr/bin/env python[1]: import sys
[2]: import os.path
[3]: import argparse
[4]: import subprocess
[5]: import datetime
[6]: import re[7]: def parse_args():
[8]: parser = argparse.ArgumentParser()
[9]: parser.add_argument("repo_path", help = "path to repo root")
[10]: parser.add_argument("file_path", help = "path relative repo root")
[11]: args = parser.parse_args()[12]: return args
[13]: def find_line_number(path):
[14]: cmd = ["git", "blame", "-L", "+1,+1000000", "–line-porcelain", path]
[15]: result = subprocess.run(cmd,
[16]: stdout=subprocess.PIPE,
[17]: stderr=subprocess.PIPE,[18]: universal_newlines=True)
[19]: lines = result.stdout.split("n")
commit :
[
'author ',
'author-time ',
'author-mail ',
'author-line-format ',
'author-line-prefix ',
'committer ',
'committer-time ',
'committer-mail ',
'committer-line-format ',
'committer-line-prefix ',]
[]
[]
[]
[]
[]
[commit :
f139c8f03b78f74a246fb91b7b397c11f79d4ea6
Fri Jan 10 21:49:25 2020 +0100
committer :
Fri Jan 10 21:49:25 2020 +0100
author :
Jonas Helmondt
Jonas Helmondt
Jonas Helmondt
Fri Jan 10 21:49:25 2020 +0100
]
def get_git_line_info(lines):
line_info = {
"commit": "",
"committer": "",
"author": ""
}line_info["commit"] += lines.pop(0)
line_info["commit"] += "n"line_info["committer"] += lines.pop(0)
line_info["committer"] += "n"line_info["committer"] += lines.pop(0)
line_info["committer"] += "n"line_info["committer"] += lines.pop(0)
line_info["committer"] += "n"line_info["author"] += lines.pop(0)
line_info["author"] += "n"line_info["author"] += lines.pop(0)
line_info["author"] += "n"line_info["author"] += lines.pop(0)
line_info["author"] += "n"line_info["author"] += lines.pop(0)
line_info["author"] += "n"return (
{
k:v.strip()
for k,v in [
("commit", re.sub(r"s+", "",line_info['commit'])),
("committer_time", re.sub(r"s+", "",line_info['committer'].split("n")[1])),
("committer_name_email", re.sub(r"s+", "",line_info['committer'].split("n")[0])),
("author_time", re.sub(r"s+", "",line_info['author'].split("n")[1])),
("author_name_email", re.sub(r"s+", "",line_info['author'].split("n')[0]))
]
},
[lines[i] for i in range(len(lines)) if i % 6 != 1],
)def get_commit_hash(line):
return re.search(r'[a-fA-Fd]{40}',line).group()def get_committter(line):
return re.search(r'(S+) ((S+))',line).groups()def get_date(line):
date_str = re.search(r'd{4}-d{2}-d{2} d{2}:d{2}:d{2} +d{4}',line).group()
return datetime.datetime.strptime(date_str,"%Y-%m-%d %H:%M:%S %z")def get_blame_output(repo_path,file_path):
cmd = ["git","blame","–porcelain","–root","–first-parent","–reverse","HEAD",
os.path.join(repo_path,file_path)]
result = subprocess.run(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,universal_newlines=True)
if result.returncode != 0:
print(result.stderr)
exit(-1)return result.stdout.split("n")
def main():
args = parse_args()commit_hash_its_own_dict
committer
author
author_time
committer_time
file_lines
output_lines
output_lines
commit_hash_its_own_dict
file_lines
print(args.repo_path,args.file_path,"n")
blame_output_lines=get_blame_output(args.repo_path,args.file_path)
print(blame_output_lines,"n")
file_lines=[]
while True:
try:
git_line_infos,output_lines=
get_git_line_info(blame_output_lines)
except IndexError:
breakcommit_hash=
get_commit_hash(git_line_infos['commit'])
file_lines.append({
"commit_hash":commit_hash,
**git_line_infos
})
blame_output_lines=output_linesprint(file_lines,"n")
output={}
for f_l in file_lines:
c_h=f_l['commit_hash']
if c_h not in output.keys():
output[c_h]={}
output[c_h]['file']=[f_l]
else:
output[c_h]['file'].append(f_l)print(output,"n")
if __name__ == "__main__":
main()***** Tag Data *****
ID: 4
description: The `get_git_line_info` function processes Git blame output into structured
data.
start line: 13
end line: 53
dependencies:
– type: Function
name: find_line_number
start line: 13
end line: 53
context description: This function extracts detailed information about each blamed
commit from Git's porcelain format.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: Y*************
## Suggestions for complexity1. **Parallel Processing**: Modify `find_line_number` function so it processes multiple files concurrently using multiprocessing or threading.
2. **Custom Output Format**: Allow users to specify custom formats for extracting specific fields (e.g., JSON format instead of dictionary).
3. **Error Handling**: Enhance error handling mechanisms to capture more specific errors related to Git commands execution.
4. **Integration with CI/CD Pipelines**: Adapt code so that it integrates seamlessly with CI/CD pipelines providing detailed logs.
5. **Historical Data Analysis**: Add functionality that analyzes historical changes over time by comparing multiple versions.
## Conversation
: I have problem with code [SNIPPET]. Can you explain what happens when there’s no blame information?
: Sure! In your current code snippet:
python
result = subprocess.run(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)If there’s no blame information available (for example if there are no commits associated with some part of the file), `subprocess.run` will still execute but `result.stdout` might be empty or contain unexpected data.
To handle this situation gracefully:
python
if not result.stdout.strip():
raise ValueError(“No blame information found.”)This checks if `stdout` is empty after stripping whitespace characters and raises an exception accordingly.
Would you like me to integrate this into your function?
*** Excerpt ***In order to test whether HNF4α functions as a transcriptional activator at its target genes we performed ChIP-seq analysis using anti-HNF4α antibody followed by sequencing using Illumina HiSeq2000 platform at BGI Tech Solutions Company Limited (). ChIP-seq libraries were generated according previously described methods ()18 with minor modifications (). Briefly genomic DNA was cross-linked using formaldehyde solution at room temperature followed by sonication using Bioruptor sonicator (Diagenode) until fragments were reduced below ~500 bp size range (peak size around ~250 bp). After incubation overnight at ∼37°C using Dynabeads Protein G coupled with anti-HNF4α antibody (), chromatin was de-cross-linked followed by purification steps using phenol-chloroform extraction method (). DNA fragments were purified using QIAquick PCR purification kit () prior library preparation following Illumina’s protocol (). Sequencing reads were aligned onto human genome assembly hg19 using Bowtie software ()19.
ChIP-seq analysis identified HNF4α binding sites located both upstream/downstream of transcription start sites (TSS) as well as intragenic regions including gene bodies/exons/introns/UTRs etc.. We further analyzed HNF4α binding sites located within ±10kb window surrounding TSS since majority (~70%) of HNF4α binding sites fall within this region (). We then mapped all HNF4α binding sites located within ±10kb window surrounding TSS onto Refseq genes (), resulting into identification of total number of genes targeted by HNF4α protein amounting up to ∼900 (∼12% genes encoded by human genome).
We next sought out whether HNF4α acts as transcriptional activator at its target genes through analysis of expression levels between WT MEF cells versus cells lacking Hnf4a gene expression (). As expected we observed downregulation ranging from mild/moderate (~−fold change) levels up till severe (~−fold change) levels () indicating that majority (∼80%) genes targeted by HNF4α protein show downregulation upon loss-of-function mutation.
To further substantiate our observation we analyzed publicly available microarray datasets GSE27379 ()20 which compares gene expression profiles between WT MEF cells versus MEF cells lacking Hnf4a gene expression under non-stress condition along with another dataset GSE23593 ()21 which compares gene expression profiles between WT MEF cells versus MEF cells lacking Hnf4a gene expression under stress condition induced via treatment …
Next we sought out whether loss-of-function mutation leads to downregulation only limited towards certain subsets of genes encoding proteins involved exclusively either metabolic pathways/processes alone or non-metabolic pathways/processes alone? For this purpose we retrieved pathway annotation data from KEGG database ()22 along with Reactome database ()23 which resulted into identification …*** Revision ***
## Plan
To create an exercise that challenges advanced comprehension skills alongside factual knowledge recall requires enhancing complexity both linguistically and conceptually within the excerpt provided:1. Introduce more scientific jargon relevant but not directly explained within the text—requiring learners not just understand but also research terminology independently.
2. Incorporate complex sentence structures that necessitate careful parsing—such as nested clauses or elaborate conditional statements—to test deductive reasoning capabilities.
3. Embed counterfactual scenarios requiring learners not only grasp what is stated but also infer implications had different conditions been met—this tests understanding beyond surface-level reading.
By integrating these elements into a rewritten excerpt focused around ChIP-seq analysis investigating transcription factor roles—a subject already dense with specialized content—the exercise demands both deep comprehension skills and additional factual knowledge beyond what is provided.
## Rewritten Excerpt
“In pursuit of elucidating whether HNF4u03b1 operates predominantly as a transcriptional enhancer across its cognate genomic loci, we embarked on conducting Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) employing an anti-HNF4u03b1 monoclonal antibody procured subsequently sequenced utilizing Illumina HiSeq2000 apparatus facilitated through BGI Tech Solutions Company Limited collaboration (). Our methodology adhered closely yet diverged subtly from established protocols (), wherein genomic DNA underwent cross-linking via formaldehyde fixation under ambient conditions preceding fragmentation through sonication employing Diagenode’s Bioruptor until achieving fragment sizes predominantly beneath ~500 bp threshold (with peak sizes approximating ~250 bp). Subsequent incubation spanned overnight at approximately u223c37u00b0C employing Dynabeads Protein G conjugated specifically with anti-HNF4u03b1 antibodies (), thereafter proceeding through chromatin de-cross-linking succeeded by phenol-chloroform extraction purification phases (). Post-extraction purification entailed QIAquick PCR purification kit utilization prior library preparation adherent strictly to Illumina’s procedural guidelines (). Sequencing reads underwent alignment against human genome assembly hg19 leveraging Bowtie software ()19 capabilities.
Subsequent ChIP-seq analytical endeavors unveiled HNFu03b14u03b1 occupancy spanning upstream/downstream regions adjacent TSS alongside intragenic domains inclusive but not limited to gene bodies/exons/introns/UTRs et al.; further scrutiny revealed predominance (u223c70%) of said binding proximal (u00b110kb vicinity) TSS regions upon mapping onto Refseq annotated genes (), culminating in discernment concerning approximately u223c900 (u223c12% totality) genes ostensibly regulated via direct interaction facilitated by HNFu03b14u03b1 protein engagement.
An investigative segue aimed at determining whether aforementioned interactions typify transcriptional activation entailed comparative analyses concerning expression profiles juxtaposing wild-type MEF cellular models vis-a-vis counterparts deficient in functional Hnf4a gene articulation (); anticipatedly observed was expression diminution spanning mild/moderate (~-fold change) spectra extending towards pronounced suppression (~-fold change), signifying predominant (u223c80%) downregulated state amongst targeted genetic entities consequent upon functional abrogation.
Further corroboration ensued via interrogation of public microarray repositories GSE27379 ()20 contrasting wild-type versus mutant MEF cellular expressions under basal states alongside dataset GSE23593 (), delineating analogous comparisons post-stress induction via specified treatments…
Pursuing deeper insights regarding selective downregulation patterns exclusive either towards metabolic-centric pathways/processes or alternatively non-metabolic paradigms necessitated retrieval and subsequent integration of pathway annotation datasets sourced from KEGG database ()22 conjoined with Reactome database annotations (), facilitating comprehensive delineation…”
## Suggested Exercise
Given the intricate methodologies employed within this study—ranging from precise technical adaptations during ChIP-seq procedure execution through nuanced analytical interpretations derived thereof—which statement best encapsulates inferred implications should alternative methodologies have been utilized?
A) Had sonication parameters been adjusted resulting in larger average fragment sizes exceeding ~500 bp without altering subsequent processing steps significantly; one could logically deduce that resultant data might exhibit decreased resolution concerning precise localization near TSS regions due primarily to diminished specificity inherent within larger fragment libraries complicating exact mapping efforts against hg19 reference genome assembly.
B) Should alternative immunoprecipitation techniques deploying nonspecific polyclonal antibodies instead have been employed; it stands reasoned that specificity towards unique HNFu03b14u03b1 binding sites would markedly decrease potentially leading to erroneous inclusion/exclusion bias affecting downstream interpretation accuracy particularly regarding determination between metabolic versus non-metabolic pathway regulation distinctions.
C) If alignment software other than Bowtie had been utilized—for instance opting for newer generation algorithms capable of accommodating longer read lengths without significant compromise on alignment accuracy—it might be posited that resultant mappings would exhibit enhanced precision particularly beneficial when discerning closely situated binding events amidst densely packed genomic regions thus potentially refining understanding concerning regulatory complexities governing transcription initiation proximal TSS locales.
D) Assuming hypothetical deployment of alternative sequencing platforms offering substantially higher throughput albeit at increased error rates; one could speculate about potential trade-offs involving augmented detection sensitivity possibly unveiling novel putative binding sites albeit concurrently risking introduction artifacts thereby complicating definitive conclusions regarding true biological significance vis-a-vis observed expression modulations across metabolic/non-metabolic pathways distinction thresholds.
ATCHED_INPUT_PORT_ID_INDEX’
,p_display_sequence=>60
,p_prompt=>’Matched Input Port ID Index’
,p_source=>’MATCHED_INPUT_PORT_ID_INDEX’
,p_source_type=>’DB_COLUMN’
,p_display_as=>’NATIVE_HIDDEN’
,p_cSize=>32
,p_cMaxlength=>255
,p_field_template=>wwv_flow_api.id(717589175640513847)
,p_item_template_options=>’#DEFAULT#’
,p_help_text=>’Unique numeric identifier assigned internally.’
,p_attribute_01=>’Y’
);
wwv_flow_api.create_page_item(
p_id=>wwv_flow_api.id(4994000229828954637)
,p_name=>’P27_MATCHED_OUTPUT_PORT_ID_INDEX’
,p_item_sequence=>61
,p_item_plug_id=>wwv_flow_api.id(4993999126961954636)
,p_use_cache_before_default=>’NO’
,p_prompt=>’Matched Output Port ID Index’
,p_source=>’MATCHED_OUTPUT_PORT_ID_INDEX’
,p_source_type=>’DB_COLUMN’
,p_display_as=>’NATIVE_HIDDEN’
,p_cSize=>32
,p_cMaxlength=>255
,p_field_template=>wwv_flow_api.id(717589175640513847)
,p_item_template_options=>’#DEFAULT#’
,p_help_text=>’Unique numeric identifier assigned internally.’
,p_attribute_01=>’Y’
);
wwv_flow_api.create_page_item(
p_id=>wwv_flow_api.id(4994000411823954637)
,p_name=>’P27_MIGRATION_STATEMENT_ID_INDEX’
,p_item_sequence=>62
,p_item_plug_id=>wwv_flow_api.id(4993999126961954636)
,p_use_cache_before_default=>’NO’
,p_prompt=>’Migration Statement ID Index’
,p_source=>’MIGRATION_STATEMENT_ID_INDEX’
,p_source_type=>’DB_COLUMN’
,portability::numeric := true;
=p_display_as=’NATIVE_HIDDEN’;
=p_cSize=’32’;
=p_cMaxlength=’255′;
=p_field_template=wwv_flow_api.id(717589175640513847);
=p_item_template_options=’#DEFAULT#’;
=p_help_text=’Unique numeric identifier assigned internally.’;
=p_attribute_01=>’Y’;
end wwv_flow_api.create_page_item;begin wwv_flow_api.create_page_computation(
p_id=>wwv_flow_api.id(4994001015407954647),
p_flow_id=>wwv_flow.g_flow_id,
p_page_id=>27,
p_computation_sequence=>10,
p_computation_item=>’F4300_P27_COMPUTATION_COUNT’,
p_computation_point=>’BEFORE_HEADER’,
p_computation_type’=>’FUNCTION_BODY’,
p_computation_processed=>’REPLACE_EXISTING’,
portability::numeric := true;
=p_computation_body:’return wwv_mig_frm_utilities.get_migration_statements_by_process(‘||””||apex_application.g_x01||””||’);’;
end wwv_flow_api.create_page_computation;begin wwv_flow_api.create_page_validation(
p_id=>wwv_flow_api.id(4994001428880954659),
p_flow_id=>wwv_flow.g_flow_id,
p_page_id=>27,
p_validation_name=>’MIGRATION_STATEMENT_ID must be number’,
p_validation_sequence=>10,
portability::numeric := true;
=p_validation:’MIGRATION_STATEMENT_ID’;
=p_validation_type=>’ITEM_IS_NUMERIC’;
=p_error_message=>’#LABEL# must be numeric.’;
=p_associated_item=>wwv_flow_api.id(.P27_MIGRATION_STATEMENT_ID.);
=p_error_display_location=>’INLINE_WITH_FIELD_AND_NOTIFICATION’;
end wwv_flow_api.create_page_validation;begin wwv_flow_api.create_page_validation(
p_id => wwflow_mig_frm_utilities.validate_migration_statement(p_p47 => .P27_MIGRATION_STATEMENT_ID., .P46 => .F4300_P46_APPLICATION_PROCESS_NAME., .P47 => .F4300_P47_PROCESS_NAME., .P48 => .F4300_P48_MODULE_TYPE., .P49 => .F4300_P49_PROCESS_TYPE.), — apex_util.sql_char_to_num(.APX_PAGE.PAGINATION.RPP.)
portability::numeric := true;
=p_validation_name => ‘.F43002_MIGRATION_STATEMENT_VALIDATION.’;
=p_validation_sequence => ‘.VALDATION_SEQUENCE_NUMBER.’;
=p_validation_point => BEFORE_HEADER;
=p_validation_type => PLSQL_EXPRESSION;
=p_error_message => ‘#LABEL# is invalid.’;
=p_associated_item => NULL;
=p_error_display_location => ON_ERROR_PAGE;
=end validation creation block;begin wwflow_mig_frm_utilities.validate_migration_statement(p_p47 , .F43002_MIGRATION_STATEMENT_VALIDATION);
begin wwflow_mig_frm_utilities.migration_statement_exist(p_p47 , ‘.VALIDATE_EXISTENCE.’);
begin wwflow_mig_frm_utilities.migration_statement_exist(p_p47 , ‘.VALIDATE_EXISTENCE.’);
if (.F43002_MIGRATION_STATEMENT_VALIDATION TRUE AND NOT EXISTS(.VALIDATE_EXISTENCE)) then raise_application_error(-20000,’Invalid Migration Statement ID.’); end if;if (.F43002_MIGRATION_STATEMENT_VALIDATION TRUE AND NOT EXISTS(.VALIDATE_EXISTENCE)) then raise_application_error(-20000,’Invalid Migration Statement ID.’); end if;
end validation creation block;
begin wwflow_mig_frm_utilities.migration_statement_exist(p_p47 , ‘.VALIDATE_EXISTENCE.’);
if (.F43002_MIGRATION_STATEMENT_VALIDATION TRUE AND NOT EXISTS(.VALIDATE_EXISTENCE)) then raise_application_error(-20000,’Invalid Migration Statement ID.’); end if;if (.F43002_MIGRATION_STATEMENT_VALIDATION TRUE AND NOT EXISTS(.VALIDATE_EXISTENCE)) then raise_application_error(-20000,’Invalid Migration Statement ID.’); end if;
end validation creation block;
begin wwflow_mig_frm_utilities.is_duplicate_migration_statement(p_p46 , .P46_APPLICATION_PROCESS_NAME., .P47_PROCESS_NAME., .P48_MODULE_TYPE., .P49_PROCESS_TYPE.);
if (.IS_DUPLICATE_ROW.) then raise_application_error(-20000,’Duplicate row exists.’); end if;if (.IS_DUPLICATE_ROW.) then raise_application_error(-20000,’Duplicate row exists.’); end if;
end validation creation block;
declare l_step_count number := apx_util.string_to_num(apex_application.g_x01);
declare l_step_rec f490_pgm_steps%rowtype;
begin case l_step_count when null then null; — do nothing because x01 isn’t set when coming back from save buttons.
when -99999 then select * into l_step_rec from f490_pgm_steps where step_status_code=’COMPLETE’; — x01=-99999 means complete button pressed.
when -99998 then select * into l_step_rec from f490_pgm_steps where step_status_code=’DELETE’; — x01=-99998 means delete button pressed.
else select * into l_step_rec from f490_pgm_steps where id=l_step_count; end case; — regular page flow processing uses x01=step id number.
update f490_pgm_steps set step_status_code=
case nvl(l_step_rec.step_status_code,’NOT_COMPLETE’)
when ‘NOT_COMPLETE’ then decode(:apexir_save_button_clicked,’SAVE’,’IN_PROGRESS’,’DELETE’,decode(:apexir_delete_button_clicked,’DELETE’,’DELETED’,’NOT_CHANGED’))
when ‘IN_PROGRESS’ then decode(:apexir_save_button_clicked,’SAVE’,’COMPLETE’,’NOT_CHANGED’)
when else decode(:apexir_delete_button_clicked,’DELETE’,’DELETED’,’NOT_CHANGED’) end where id=l_step_count returning step_status_code into l_step_status_code; — update status based upon button pressed.
exception when others then htp.p(‘error updating status code’);
null; — suppress warning message about unhandled exceptions because exception handler doesn’t need any logic here.
end;declare l_is_dup boolean := false; begin select count(*) > zero_rows_into(l_is_dup) into l_is_dup from f490_pgm_steps s where s.process_id=:processid_s saving processid_s:=select processid_s into processid_s from dual connect by prior s.parent_module_seqnum=s.module_seqnum start with s.module_seqnum=:module_seqnum order siblings by s.created_on desc ; exception when others then htp.p(‘error checking duplicate rows’); null; — suppress warning message about unhandled exceptions because exception handler doesn’t need any logic here.
l_is_dup:=boolean_to_char(l_is_dup); apex_util.set_session_state(‘IS_DUPLICATE_ROW’,l_is_dup); null; — suppress warning message about unhandled exceptions because exception handler doesn’t need any logic here.
exception when others then htp.p(‘error setting session state IS_DUPLICATE_ROW’); null; — suppress warning message about unhandled exceptions because exception handler doesn’t need any logic here.
end;function migration_statement_exist(p_migrationstatementid integer default null)return booleanis debug_mode boolean :=false;if debug_modethen htp.prnt(‘
');htp.prnt(to_char(dbms_utility.format_call_stack));htp.prnt('‘);null;if nvl(p_migrationstatementid,null)=nullthen return false;else beginselect count(*)into v_cntfrom migfrm_statementswhere id=:migrationstatementid;systimestampinto v_timestamp;if v_cnt<=zero_rows_into(v_cnt)return false;elsereturn true;exceptionwhen othersthenhtp.prnt(sqlerrm);return false;;end;if debug_modethen htp.prnt('
');htp.prnt(to_char(dbms_utility.format_call_stack));htp.prnt('‘);null;;return false;;return true;;end migration_statement_exist;
function validate_migration_statement(p_migrationstatementid integer default null)return booleanis debug_mode boolean :=false;if debug_modethen htp.prnt(‘
');htp.prnt(to_char(dbms_utility.format_call_stack));htp.prnt('‘);null;if nvl(p_migrationstatementid,null)=nullthen return false;else beginselect count(*)into v_cntfrom migfrm_statementswhere id=:migrationstatementid;systimestampinto v_timestamp;if v_cnt<=zero_rows_into(v_cnt)return false;/*comment out error check so user can edit rows even though they don't exist*/elsereturn true;/*raise_application_error(-20000,:label||' does not exist');*/exceptionwhen othersthenhtp.prnt(sqlerrm);return false;;end;if debug_modethen htp.prnt('
');htp.prnt(to_char(dbms_utility.format_call_stack));htp.prnt('‘);null;;return false;;return true;;end validate_migration_statement;
function is_duplicate_row(application varchar default null,applicationprocess varchar default null,module varchar default null,applicationversion varchar default null,applicationprocessversion varchar default null,applicationprocessname varchar default null,moduletype varchar default null,applicationtype varchar default null,applicationinstance varchar default null,moduleinstance varchar default null,migrationtype varchar default null,migratorname varchar default null,migratorschema varchar default null,migrationprocedurename varchar default null)migrate_db_object_name string:=migrate_db_object_name(migratorname,migratorschema,migrationprocedurename);is debug_mode boolean :=false;if debug_modethen htp.prnt(‘
');htp.prnt(to_char(dbms_utility.format_call_stack));htp.prnt('‘);null;migrate_db_object_name:=replace(replace(replace(migrate_db_object_name,’#’,’%’),’ ‘,’_’),’:’,”);declarelv_cnt number:=zero_rows_into(lcnt);lv_sql_stmt:string:= ”;lv_sql_stmt:= lv_sql_stmt ||’select count(*)into lv_cntfrom migfrm_statements’||chr(llinefeed)||’where application=”:””||application||””’||
