def plot_queue_length_table(system_name, agreggate_by):
indate_table = get_psql_table.get_table('tasks.indate as time',
system_name + '.tasks')
indate_table = add_queue_column(indate_table, 1)
rundate_table = get_psql_table.get_table('runs.rundate as time',
system_name + '.runs')
rundate_table = add_queue_column(rundate_table, -1)
stopdate_table = get_psql_table.get_table(
'runs.stopdate as time, runs.exittype', system_name + '.runs')
stopdate_table = add_queue_column(stopdate_table, 0)
stopdate_table = alter_exittype(stopdate_table, 1)
stopdate_table = drop_exittype_column(stopdate_table)
print(stopdate_table)
queue_length_table = concatenate_tables(indate_table, rundate_table,
stopdate_table)
queue_length_table = queue_length_table.sort_values(by=['time'])
queue_length_table = queue_length_table.groupby(['time']).sum()
queue_length_table = queue_length_table.resample(agreggate_by).sum()
# queue_length_table.queue_length = queue_length_table.queue_length.cumsum()
queue_length_table.plot()
plt.show()
def errors_density():
errors_table = get_psql_table.get_table(
'runs.stopdate as time, runs.exittype', 'mvs10p.runs')
errors_table = errors_table.sort_values(by=['time'])
errors_table.plot()
plt.show()
def get_queue_length_table():
"""
Retrieve data from database and transform it to the form convenient for plotting.
Function uses Pandas and manipulates the dataframe's structure.
"""
# Compose a query for database and get a table
df = get_psql_table.get_table(
'tasks.indate, runs.rundate',
'mvs100k.tasks join mvs100k.runs on tasks.tid = runs.tid')
# Create new dataframe with new ``queue_length`` column with all 1 values.
indates = pd.DataFrame()
indates["time"] = df.indate
indates["queue_length"] = 1
# Create new dataframe with new ``queue_length`` column with all -1 values.
rundates = pd.DataFrame()
rundates["time"] = df.rundate
rundates["queue_length"] = -1
# Concatenate two dataframes
queue_length_table = pd.concat([indates, rundates])
queue_length_table = queue_length_table.sort_values(by=['time'])
# Exclude repetitive rows
queue_length_table = queue_length_table.groupby(['time']).sum()
# Summarize all 1 and -1 values and get the total number for every date in the table
queue_length_table.queue_length = queue_length_table.queue_length.cumsum()
return queue_length_table
def plot_computer_power_pie_chart(tuple):
table = get_psql_table.get_table(
'runs.tid,runs.rundate,runs.stopdate,tasks.nproc',
'mvs10p.runs left join'
' mvs10p.tasks on runs.tid = tasks.tid')
add_nodes_per_hour_column(table)
table = drop_columns(table)
table = table.groupby(['tid']).sum()
table = table.reset_index()
nproc_table = get_psql_table.get_table('tasks.tid,tasks.nproc',
'mvs10p.tasks')
table = table.merge(nproc_table, how='left')
seaborn.set()
fig1, ax1 = plt.subplots()
ax1.pie(get_percentage_list(table, tuple)[0],
autopct='%1.1f%%',
labels=get_percentage_list(table, tuple)[1],
shadow=False,
startangle=80,
pctdistance=0.75,
textprops={'fontsize': 14},
wedgeprops=dict(width=0.5))
ax1.set_title('Power distribution', fontsize=16)
ax1.axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
def get_error_codes_dict(system):
"""
Return a dictionary representing number of completion codes in the database.
:param system: name of supercomputer system in the database.
"""
# Compose an sql query to the database and return a dataframe
number_of_errors = [
get_psql_table.get_table(
'runs.exittype', '{0}.runs where runs.exittype = {1}'.format(
system, i))['exittype'].count() for i in range(-1, 6)
]
result_dict = dict(enumerate(number_of_errors, start=-1))
return result_dict