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 execute_job(jid):
update_job_status(jid, 'in progress')
start, end = job.hmget(generate_job_key(jid), 'start', 'end')
start = int(start)
end = int(end)
years = list(range(start, end + 1))
numpoints = (end + 1 - start)
total = [0] * numpoints
count = [0] * numpoints
for key in rd.keys():
year = int(rd.hget(key, 'year'))
if year >= start and year <= end:
index = year - start
lettertotal = len(str(rd.hget(key, 'entity')))
total[index] = total[index] + lettertotal
count[index] = count[index] + 1
avg = [i / j for i, j in zip(total, count)]
title = "Average length of names in Oscar films between " + str(
start) + " and " + str(end)
plt.plot(years, avg, 'g-o', label="Nominees")
plt.xlabel("Years")
plt.ylabel("Average Num. Letters")
plt.title(title)
plt.savefig('/oscarlength.png')
with open('/oscarlength.png', 'rb') as f:
img = f.read()
job.hset(generate_job_key(jid), 'image', img)
update_job_status(jid, 'complete')
def execute_job(jid):
''' Define function that generates a plot of time-series data for a particular
, and randomly-generated, temperature field: '''
def generate_plot():
temp_num = rd_temp.dbsize(
) # Get the number of temperature dictionaries in database
''' Use the "random" module to randomly-generate a temp field to
develop a time-series line plot of from the list: "GALT", "GMAXLT",
and "GMINLT": '''
temp_field_list = [
"GALT (Celsius)", "GMAXLT (Celsius)", "GMINLT(Celsius)"
]
temp_field = temp_field_list[random.randint(0, 2)]
# Use matplolib to generate plot of time-series temperature data:
time_array = numpy.array([]) # Initialize array to store time data
temperature_array = numpy.array(
[]) # Initialize array to store temperature data
# Use for loop to append values to array storing time and temperature field values:
for i in range(0, temp_num):
temp_key = i # Assign keyname to temperature
time_array = numpy.append(time_array, i)
temperature_array = numpy.append(
temperature_array, float(rd_temp.hget(temp_key, temp_field)))
# Use "Matplotlib" to generate a line plot of the time-series dataset:
plt.plot(time_array, temperature_array)
plt.title("Plot of " + temp_field +
" vs. Time (Months) Starting from 01/01/1930 ")
plt.xlabel("Time (Months)")
plt.ylabel(temp_field)
plt.savefig('/Time-Series-Line-Plot.png')
# Open image and add to redis database containing job information:
with open('/Time-Series-Line-Plot.png', 'rb') as f:
img = f.read()
rd_jobs.hset(f'job.{jid}', "image",
img) # Set the image in redis database
return_status = "Plot Generated Successfully"
return return_status
# Invoke "generate_plot()" method to generate and store time-series line plot:
generate_plot()
# Invoke "update_job_status()" function to designate job as "complete":
jobs.update_job_status(jid, "complete")
def execute_job(jid):
# update job status
update_job_status(jid, 'in_progress')
# generate a graph from the data
execute(jid)
update_job_status(jid, 'completed')
def execute_job(jid):
update_job_status(jid, 'in progress')
# Create figure
count = int(rd.get('count'))
adoptions_dict = get_data()
age_x = []
adoptions_y = []
for i in range(0, count):
if (adoptions_dict[i]["Outcome Type"] == "Adoption"):
adoption_age = adoptions_dict[i]["Age upon Outcome"]
if adoption_age not in age_x:
age_x.append(adoption_age)
adoptions_y.append(1)
else:
for index, item in enumerate(age_x):
if item == adoption_age:
adoptions_y[index] = adoption_y[index] + 1
def execute_job(jid):
data = jobs.get_job_data(jid)
# call matplotlib to make a plot of something
# plot stuff here...
start = data['start']
end = data['end']
# pplot the counts of each type of animal adopted in between a date range
animal_types = ['Bird', 'Cat', 'Dog', 'Livestock', 'Other']
animal_counts = [0, 0, 0, 0, 0]
for key in rd1.keys():
if (start <= key[DateTime].decode('utf-8') <= end):
this_animal_type = key['Animal_Type']
if this_animal_type == 'Bird':
animal_counts[0] += 1
else if this_animal_type == 'Cat':
animal_counts[1] += 1
else if this_animal_type == 'Dog':
animal_counts[2] += 1
else if this_animal_type == 'Livestock':
animal_counts[3] += 1
else if this_animal_type == 'Other':
animal_counts[4] += 1
plt.clf()
plt.bar(animal_types, animal_counts, color='green')
plt.xlabel('Animal Type')
plt.ylabel('Frequency')
#plt.title('Amino Acid Frequency')
#plt.xticks(aas_pos, aas)
plt.savefig('/output_image.png')
with open('/output_image.png', 'rb') as f:
img = f.read()
rd.hset(f'job.{jid}', 'result', img)
jobs.update_job_status(jid, "completed")
def execute_job(jid):
update_job_status(jid, 'in progress')
restaurant = rd_plot.hget(f'job.{jid}', 'restaurant').decode('utf-8')
dates = []
scores = []
for key in rd.keys():
if rd.hget(key, 'Restaurant Name').decode('utf-8') == restaurant:
date = rd.hget(key, 'Date of Inspection').decode('utf-8')
score = rd.hget(key, 'Inspection Score').decode('utf-8')
dates.append(datetime.datetime.strptime(date, "%m/%d/%Y"))
scores.append(int(score))
x = dates
y = scores
plt.figure(figsize=(10, 10))
plt.plot_date(x, y)
restaurant = restaurant.replace(' ', '-')
plt.savefig(str(restaurant) + '.png')
file_bytes = open(str(restaurant) + '.png', 'rb').read()
rd_plot.set(str(restaurant) + '_plot', file_bytes)
print('Plot Key: ' + str(restaurant) + '_plot' + "\n")
update_job_status(jid, 'complete')
def execute_job(jobid):
update_job_status(jobid, 'in progress')
time.sleep(15)
df = pd.read_csv("./Austin_COVID-19_Complaint_Cases.csv")
def df_query(x):
return lambda dfx: dfx['TYPEOFCOMPLAINT'] == x
# complaints related to face masks
df1 = valid_df[df_query('Face Covering Non-Compliance - Business')]
df2 = valid_df[df_query('Face Covering')]
df3 = valid_df[df_query('Face-Covering')]
# complaints related to social distancing
df4 = valid_df[df_query('Social Distancing')]
df5 = valid_df[df_query('Over Occupancy')]
# complaints related to eviction
df6 = valid_df[df_query('Eviction')]
# complaints of other categories
df7 = valid_df[df_query('Other')]
plt.figure(figsize=(10, 10))
plt.scatter(df1.LATITUDE, df1.LONGITUDE, 1.0, 'b')
plt.scatter(df2.LATITUDE, df2.LONGITUDE, 1.0, 'b')
plt.scatter(df3.LATITUDE, df3.LONGITUDE, 1.0, 'b')
plt.scatter(df4.LATITUDE, df4.LONGITUDE, 1.0, 'r')
plt.scatter(df5.LATITUDE, df5.LONGITUDE, 1.0, 'r')
plt.scatter(df6.LATITUDE, df6.LONGITUDE, 1.0, 'g')
plt.scatter(df7.LATITUDE, df7.LONGITUDE, 1.0, 'y')
plt.savefig('Austin_COVID_Complaints_Mapped.png')
file_bytes = open('/tmp/Austin_COVID_Complaints_Mapped.png', 'rb').read()
# set the file bytes as a key in Redis
rd.set('key', file_bytes)
update_job_status(jobid, 'complete')
def execute_job(jid):
update_job_status(jid, 'in progress')
animals = json.loads(rd.get('key'))
outputType = animals['output_type']
job_id = 'job.{}'.format(jid)
euthanasia = 0
returnedToOwner = 0
adoption = 0
transfer = 0
for x in outputType:
if x == 'Euthanasia':
euthanasia = euthanasia + 1
if x == 'Return to Owner':
returnedToOwner = returnedToOwner + 1
if x == 'Adoption':
adoption = adoption + 1
if x == 'Transfer':
transfer = transfer + 1
x = ['Euthanasia', 'Return to Owner', 'Adoption', 'Transfer']
y = [euthanasia, returnedToOwner, adoption, transfer]
plt.figure()
plt.bar(y)
plt.set(gca, 'XTickLabel', {'EU', 'RTO', 'ADP', 'TR'})
plt.savefig('/outputtype.png')
with open('/outputtype.png', 'rb') as f:
img = f.read()
rd_imgs.hset(jid, 'image', img)
update_job_status(jid, 'complete')
from matplotlib import pyplot as plt
@q.worker
def execute_job(jid):
update_job_status(jid, 'in progress')
# Create figure
count = int(rd.get('count'))
adoptions_dict = get_data()
age_x = []
adoptions_y = []
for i in range(0, count):
if (adoptions_dict[i]["Outcome Type"] == "Adoption"):
adoption_age = adoptions_dict[i]["Age upon Outcome"]
if adoption_age not in age_x:
age_x.append(adoption_age)
adoptions_y.append(1)
else:
for index, item in enumerate(age_x):
if item == adoption_age:
adoptions_y[index] = adoption_y[index] + 1
plt.bar(age_x, adoptions_y)
plt.xlabel('Adoption Age')
plt.ylabel('# of Adoptions')
plt.savefig('adoption_ages.png')
update_job_status(jid, 'complete')
if __name__ == '__main__':
execute_job()
def do_work(jid):
update_job_status(jid, 'in progress')
time.sleep(15)
update_job_status(jid, 'complete')
def execute_job(jid):
data = jobs.get_job_data(jid)
job_type = jobs.get_job_type(jid)
#analysis: plot total number of outcomes for each day in date range
if (job_type == 'dates'):
start = jobs.get_job_start(jid)
end = jobs.get_job_end(jid)
start_date = datetime.datetime.strptime(start, "%m-%d-%Y")
end_date = datetime.datetime.strptime(end, '%m-%d-%Y')
consecutive_dates = [
start_date + datetime.timedelta(days=x)
for x in range((end_date - start_date).days + 1)
]
list_of_all_dates = []
for key in rd1.keys():
key_time_temp = rd1.hget(
key, 'Date_of_Entry').decode('utf-8').split()[0]
key_time = datetime.datetime.strptime(key_time_temp, '%m-%d-%Y')
list_of_all_dates.append(key_time)
list_of_outcomes_of_the_day = [0] * len(consecutive_dates)
x_labeler = []
for i in range(len(consecutive_dates)):
list_of_outcomes_of_the_day[i] = list_of_all_dates.count(
consecutive_dates[i])
x_labeler.append(i)
#consecutive_date_strings = []
#for item in consecutive_dates:
# consecutive_date_strings.append(item.strftime("%m-%d-%Y"))
plt.clf()
plt.bar(x_labeler, list_of_outcomes_of_the_day, color='green')
plt.xlabel('Days since Start')
plt.ylabel('# Outcomes per Day')
plt.title('Outcome Occurences by Day \n' + start + ' - ' + end)
plt.savefig('/outcomes_by_animal_type.png')
with open('/outcomes_by_animal_type.png', 'rb') as f:
img = f.read()
rd.hset(f'job.{jid}', 'result', img)
jobs.update_job_status(jid, 'complete')
#analysis: plot total # of outcomes by type of animal in date range
if (job_type == 'animal_type'):
#jobs.update_job_status(jid, 'it has entered the for loop')
animal_types = ['Bird', 'Cat', 'Dog', 'Livestock', 'Other']
animal_counts = [0, 0, 0, 0, 0]
for key in rd1.keys():
#jobs.update_job_status(jid, str(key))#'it has entered the for loop')
this_animal_type = str(rd1.hget(key, 'Animal_Type'))[1:]
#jobs.update_job_status(jid, str(this_animal_type))
if this_animal_type == "'Bird'":
animal_counts[0] += 1
elif this_animal_type == "'Cat'":
animal_counts[1] += 1
elif this_animal_type == "'Dog'":
animal_counts[2] += 1
elif this_animal_type == "'Livestock'":
animal_counts[3] += 1
elif this_animal_type == "'Other'":
animal_counts[4] += 1
#jobs.update_job_status(jid, str(animal_counts))
plt.clf()
plt.bar(animal_types, animal_counts, color='green')
plt.xlabel('Animal Type')
plt.ylabel('Frequency')
plt.title('Outcomes by Animal Type')
plt.savefig('/outcomes_by_animal_type.png')
with open('/outcomes_by_animal_type.png', 'rb') as f:
img = f.read()
rd.hset(f'job.{jid}', 'result', img)
jobs.update_job_status(jid, 'complete')
def execute_job(jid):
update_job_status(jid, 'in progress')
add_worker_ip(jid, worker_ip)
time.sleep(15)
update_job_status(jid, 'complete')
def execute_job(jid):
workerIP = os.environ.get('WORKER_IP')
update_job_status(jid, 'in progress', workerIP)
time.sleep(15)
update_job_status(jid, 'complete', workerIP)
def execute_job(jid):
data = jobs.get_job_data(jid)
job_type = jobs.get_job_type(jid)
#jobs.update_job_status(jid, job_type)#'in progress')
#analysis: plot total number of outcomes for each day in date range
#first job type: dates
if (job_type == 'dates'):
#jobs.update_job_status(jid, 'entered first if statement')
start = jobs.get_job_start(jid)
end = jobs.get_job_end(jid)
#set inital outcomes count
animal_outcomes_of_day = 0
#x values: list of dates
x_values_to_plot = []
#y values: list of integer numbers of outcomes per date
y_values_to_plot = []
start_date = datetime.datetime.strptime(start, "%m-%d-%Y")
end_date = datetime.datetime.strptime(end, '%m-%d-%Y')
#format to check for full day
for key in rd1.keys():
#jobs.update_job_status(jid, 'you have entered first for loop')
#decode time to a string without hours and minutes
key_time_temp = rd1.hget(
key, 'Date_of_Entry').decode('utf-8').split()[0]
#string to datetime
key_time = datetime.datetime.strptime(key_time_temp, '%m-%d-%Y')
#jobs.update_job_status(jid, str(key_time))
#check for keys in date range
if (start_date <= key_time and end_date >= key_time):
#jobs.update_job_status(jid, 'this is entering 2nd if statement')
#set specific date
x = key_time_temp
#jobs.update_job_status(jid, x)
#check if date is alread in x_values_to_plot
if x not in x_values_to_plot:
#jobs.update_job_status(jid, 'this is entering 3rd if statement')
#if new date: add to list of x_values_to_plot
x_values_to_plot.append(x)
#check through db for each animal with matching Date_of_Entry
for i in range(rd1.dbsize()):
#jobs.update_job_status(jid, 'this is entering 2nd for loop')
#issue with formatting?
if (x == rd1.hget(
i,
'Date_of_Entry').decode('utf-8').split()[0]):
#jobs.update_job_status(jid, 'this is entering 4th if statement')
#increment animal_outcomes_of_day count
animal_outcomes_of_day = animal_outcomes_of_day + 1
#finalize count for the day
y = animal_outcomes_of_day
#add total count to list of y_values_to_plot
y_values_to_plot.append(y)
#reset animal_outcomes_of_day count before moving to next day
animal_outcomes_of_day = 0
jobs.update_job_status(jid, 'we have exited first loop')
#plot scatter plot
plt.scatter(x_values_to_plot, y_values_to_plot)
plt.savefig('/outcomes_by_date.png')
with open('/outcomes_by_date.png', 'rb') as f:
img = f.read()
rd.hset(f'job.{jid}', 'result', img)
jobs.update_job_status(jid, 'complete')
#analysis: plot total # of outcomes by type of animal in date range
if (job_type == 'animal_type'):
#jobs.update_job_status(jid, 'it has entered the for loop')
animal_types = ['Bird', 'Cat', 'Dog', 'Livestock', 'Other']
animal_counts = [0, 0, 0, 0, 0]
for key in rd1.keys():
#jobs.update_job_status(jid, str(key))#'it has entered the for loop')
this_animal_type = str(rd1.hget(key, 'Animal_Type'))[1:]
#jobs.update_job_status(jid, str(this_animal_type))
if this_animal_type == "'Bird'":
animal_counts[0] += 1
elif this_animal_type == "'Cat'":
animal_counts[1] += 1
elif this_animal_type == "'Dog'":
animal_counts[2] += 1
elif this_animal_type == "'Livestock'":
animal_counts[3] += 1
elif this_animal_type == "'Other'":
animal_counts[4] += 1
#jobs.update_job_status(jid, str(animal_counts))
plt.clf()
plt.bar(animal_types, animal_counts, color='green')
plt.xlabel('Animal Type')
plt.ylabel('Frequency')
plt.title('Outcomes by Animal Type')
plt.savefig('/outcomes_by_animal_type.png')
with open('/outcomes_by_animal_type.png', 'rb') as f:
img = f.read()
rd.hset(f'job.{jid}', 'result', img)
jobs.update_job_status(jid, 'complete')
def execute_job(jid):
update_job_status(jid, "in progress")
## Added for section B
add_worker_IP(jid, worker_ip)
time.sleep(15)
update_job_status(jid, "complete")
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')
def execute_job(jid):
update_job_status(jid, 'in progress')
time.sleep(15)
update_job_status(jid, 'complete')
def execute_job(jid):
data = jobs.get_job_data(jid)
job_type = jobs.get_job_type(jid)
#jobs.update_job_status(jid, job_type)#'in progress')
#analysis: plot total number of outcomes for each day in date range
#first job type: dates
if (job_type == 'dates'):
#jobs.update_job_status(jid, str(data))
#jobs.update_job_status(jid, 'You have entered the if statement')
#format of how dates come in??????????? should be a string
start = jobs.get_job_start(jid)
end = jobs.get_job_end(jid)
#jobs.update_job_status(jid, str(start))
#set inital outcomes count
animal_outcomes_of_day = 0
#x values: list of dates
x_values_to_plot = []
#y values: list of integer numbers of outcomes per date
y_values_to_plot = []
start_date = datetime.datetime.strptime(start, "%m-%d-%Y")
end_date = datetime.datetime.strptime(end, '%m-%d-%Y')
#format to check for full day, rather than specific time??????????
for key in rd1.keys():
#jobs.update_job_status(jid, 'you have entered the for loop')
key_time_temp = str(rd1.hget(key, 'Date_of_Entry'))[1:]
#jobs.update_job_status(jid, str(key_time_temp))
#key_time_temp = key_time_temp.replace("/","-")
#jobs.update_job_status(jid, str(key_time_temp))
key_time = datetime.datetime.strptime(key_time_temp, "'%m-%d-%Y'")
#jobs.update_job_status(jid, str(key_time_temp)) # -------------------
#check for keys in date range
if (start_date <= key_time and end_date >= key_time):
#jobs.update_job_status(jid, 'this is entering 2nd if statement')
#set specific date
x = str(key_time)
jobs.update_job_status(jid, str(x)) # -----------
#check if date is alread in x_values_to_plot
if x not in x_values_to_plot:
jobs.update_job_status(
jid, 'this is entering 3rd if statement')
#if new date: add to list of x_values_to_plot
x_values_to_plot.append(x)
#check through db for each animal with matching Date_of_Entry
for i in range(rd1.dbsize()):
#will there be an issue with b formatting? should we use [1:]?
if (x == rd1.hget(i, 'Date_of_Entry')):
#increment animal_outcomes_of_day count
animal_outcomes_of_day = animal_outcomes_of_day + 1
#finalize count for the day
y = animal_outcomes_of_day
#add total count to list of y_values_to_plot
y_values_to_plot.append(y)
#reset animal_outcomes_of_day count before moving to next day
animal_outcomes_of_day = 0
#plot line graph
plt.scatter(x_values_to_plot, y_values_to_plot)
#plt.show()
plt.savefig('/outcomes_by_date.png')
with open('/outcomes_by_date.png', 'rb') as f:
img = f.read()
rd.hset(f'job.{jid}', 'result', img)
#rd.hset(jobid, 'image', img)
#jobs.update_job_status(jid, 'complete')
#analysis: plot total # of outcomes by type of animal in date range
if (job_type == 'animal_type'):
#jobs.update_job_status(jid, 'it has entered the for loop')
animal_types = ['Bird', 'Cat', 'Dog', 'Livestock', 'Other']
animal_counts = [0, 0, 0, 0, 0]
for key in rd1.keys():
#jobs.update_job_status(jid, str(key))#'it has entered the for loop')
this_animal_type = str(rd1.hget(key, 'Animal_Type'))[1:]
#jobs.update_job_status(jid, str(this_animal_type))
if this_animal_type == "'Bird'":
animal_counts[0] += 1
elif this_animal_type == "'Cat'":
animal_counts[1] += 1
elif this_animal_type == "'Dog'":
animal_counts[2] += 1
elif this_animal_type == "'Livestock'":
animal_counts[3] += 1
elif this_animal_type == "'Other'":
animal_counts[4] += 1
#jobs.update_job_status(jid, str(animal_counts))
plt.clf()
plt.bar(animal_types, animal_counts, color='green')
plt.xlabel('Animal Type')
plt.ylabel('Frequency')
plt.title('Outcomes by Animal Type')
#plt.show()
plt.savefig('/outcomes_by_animal_type.png')
with open('/outcomes_by_animal_type.png', 'rb') as f:
img = f.read()
#rd.hset("job.{}".format(jid), 'image' img)
rd.hset(f'job.{jid}', 'result', img)
jobs.update_job_status(jid, 'complete')
