def select_output():
jid = request.args.get('jid')
output_json = (rd_jobs.hget(jobs._generate_job_key(jid), 'output json'))
output_df = pd.read_json(output_json)
return output_df.to_markdown()
def select_summary():
jid = request.args.get('jid')
summary_json = (rd_jobs.hget(jobs._generate_job_key(jid), 'summary json'))
summary_df = pd.read_json(summary_json)
return summary_df.to_markdown()
def do_work(jid):
update_job_status(jid, 'in progress')
locations = get_locations()
menus = get_menus()
pricing = get_price()
rd_jobs.hset(_generate_job_key(jid), 'location number', len(locations))
max_price = max(pricing)
columns1 = ['id', 'address', 'categories', 'menus', 'pricing']
output = pd.DataFrame(columns = columns1)
index = 0
counter = 0
subset1 = subset_.append(get_locations.loc[(get_locations['address'] == st\
atus) & (start_id <= get_locations['id']) & (end_id >= get_locations['id']), ['
id', 'address', 'category']])
for index in range(len(subset1)):
location = subset1.iloc[index]['address']
menu = subset1.iloc[index]['menu']
pricing = subset1.iloc[index]['pricing']
if (index ==0):
counter = 0
elif(pricing != subset1.iloc[index-1]['pricing']):
counter = 0)
new_table = formula(id, address, categories, menus_amountmax, menus_amountmin)
update_job_status(jid, 'complete')
def delete_job():
jobid = request.args.get('jobid')
for key in rd.keys():
if jobid == (rd.hget(key, 'JOBID')).decode('utf-8'):
casenumber = (rd.hget(key, 'CASENUMBER')).decode('utf-8')
rd.delete(jobs._generate_job_key(jobid))
return 'Deleted complaint! (CASENUMBER: ' + casenumber + ', JOBID: ' + jobid + ')'
def get_job_form():
job_dict = jobs.get_jobs()
jid = request.form.get('jsonData')
try:
strJob = json.dumps(job_dict[jobs._generate_job_key(jid)])
jsonData = json.loads(strJob)
return render_template('jobReturn.html',
jsonData=jsonData,
data=strJob)
except:
return abort(404)
def get_job(jid):
job_dict = jobs.get_jobs()
try:
return json.dumps(job_dict[jobs._generate_job_key(jid)], indent=4)
except:
return abort(404)
def delete_job():
jid = request.args.get('jid')
rd_jobs.delete(jobs._generate_job_key(jid))
return 'Job ' + jid + ' has been deleted'
def execute_job(jid):
update_job_status(jid, 'in progress')
stock_and_sales_df = get_stock_and_sales()
products_df = get_products()
rd_jobs.hset(_generate_job_key(jid), 'stock rows', len(stock_and_sales_df))
#finds the maximum shelf life a product can have
products_df = get_products()
shelf_life_column = products_df["shelf_life"]
max_shelf_life = shelf_life_column.max() - 2
output_columns = ['Date', 'Store', 'Product', 'Stock', 'Sales']
#create columns based on maximum shelf life
for index in range(max_shelf_life):
start_index = "start" + str(index + 1)
end_index = "end" + str(index + 1)
output_columns.extend([start_index, end_index])
output_columns.extend(['total start', 'total end', 'surplus', 'gapout'])
output_df = pd.DataFrame(columns=output_columns)
store_input = (rd_jobs.hget(_generate_job_key(jid),
'store_input')).decode("utf-8")
start_date = (rd_jobs.hget(_generate_job_key(jid),
'start_date')).decode("utf-8")
end_date = (rd_jobs.hget(_generate_job_key(jid),
'end_date')).decode("utf-8")
subset_df = stock_and_sales_df.loc[
(stock_and_sales_df['Store'] == store_input)
& (start_date <= stock_and_sales_df['Date'])
& (stock_and_sales_df['Date'] <= end_date),
['Date', 'Product', 'Store', 'Stock', 'Sales']]
rd_jobs.hset(_generate_job_key(jid), 'subset rows', len(subset_df))
#initializes index and counter variables to know where the method is at in the for loop
index = 0
counter = 0
#iterates through every row and runs surplus / gapout formulas
for index in range(len(subset_df)):
#creates local variables for values that carry over from subset dataframe to output dataframe
date = subset_df.iloc[index]['Date']
product = subset_df.iloc[index]['Product']
store = subset_df.iloc[index]['Store']
stock = subset_df.iloc[index]['Stock']
sales = subset_df.iloc[index]['Sales']
shelf_life = products_df.loc[product]['wf_shelf_life']
#counter resets to 0 if a new product is being started
if (index == 0):
counter = 0
elif (product != subset_df.iloc[index - 1]['Product']):
counter = 0
new_row = formulas_dynamic(index, counter, date, store, product, stock,
sales, shelf_life, output_df)
output_df = output_df.append(new_row, ignore_index=True)
counter += 1
index += 1
products_df = products_df.reset_index()
output_json = output_df.to_json()
summary_df = summary(output_df, products_df)
summary_json = summary_df.to_json()
rd_jobs.hset(_generate_job_key(jid), 'output json', output_json)
rd_jobs.hset(_generate_job_key(jid), 'summary json', summary_json)
update_job_status(jid, 'complete')