How to use the parsl.monitoring.web_app.app.close_db function in parsl
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Parsl / parsl / parsl / monitoring / web_app / plots / default / total_tasks.py View on Github 
def setup(self, run_id, columns=20):
sql_conn = get_db()
df_status = pd.read_sql_query('SELECT run_id, task_id, task_status_name, timestamp FROM task_status WHERE run_id=(?)',
sql_conn, params=(run_id, ))
close_db()
min_time = timestamp_to_int(min(df_status['timestamp']))
max_time = timestamp_to_int(max(df_status['timestamp']))
time_step = int((max_time - min_time) / columns)
minutes = time_step // 60
seconds = time_step % 60
return [html.H4('Bin width'),
html.Div(children=[html.Label(htmlFor='bin_width_minutes', children='Minutes'),
dcc.Input(id='bin_width_minutes', type='number', min=0, value=minutes)]),
html.Div(children=[html.Label(htmlFor='bin_width_seconds', children='Seconds'),
dcc.Input(id='bin_width_seconds', type='number', min=0, value=seconds)])]def setup(self, run_id, columns=20):
sql_conn = get_db()
df_status = pd.read_sql_query('SELECT run_id, task_id, task_status_name, timestamp FROM task_status WHERE run_id=(?)',
sql_conn, params=(run_id, ))
close_db()
min_time = timestamp_to_int(min(df_status['timestamp']))
max_time = timestamp_to_int(max(df_status['timestamp']))
time_step = int((max_time - min_time) / columns)
minutes = time_step // 60
seconds = time_step % 60
return [html.P('Bin width'),
html.Label(htmlFor='bin_width_minutes', children='Minutes'),
dcc.Input(id='bin_width_minutes', type='number', min=0, value=minutes),
html.Label(htmlFor='bin_width_seconds', children='Seconds'),
dcc.Input(id='bin_width_seconds', type='number', min=0, value=seconds)]def tasks_details(run_id, pathname):
sql_conn = get_db()
df_task = pd.read_sql_query('SELECT task_id, task_func_name FROM task WHERE run_id=(?)',
sql_conn, params=(run_id,))
close_db()
tasks = []
for task_id in df_task['task_id']:
tasks.append({'label': task_id, 'value': task_id})
return [html.P(className='view_title', children='Tasks view'),
html.Div(id='select_task_id',
children=[
html.H4('Select Task ID'),
dcc.Dropdown(
id='tasks_dropdown',
options=tasks,
value=pathname.split('/').pop())
])
] + [plot.html(run_id) for plot in plots]df_status = pd.read_sql_query('SELECT run_id, task_id, task_status_name, timestamp FROM task_status WHERE run_id=(?)',
sql_conn, params=(run_id, ))
if apps:
if type(apps) is dict:
apps = ['', apps['label']]
elif len(apps) == 1:
apps.append('')
df_task = pd.read_sql_query('SELECT task_id, task_func_name FROM task WHERE run_id=(?) AND task_func_name IN {apps}'.format(apps=tuple(apps)),
sql_conn, params=(run_id, ))
else:
df_task = pd.read_sql_query('SELECT task_id, task_func_name FROM task WHERE run_id=(?)',
sql_conn, params=(run_id, ))
close_db()
min_time = timestamp_to_int(min(df_status['timestamp']))
max_time = timestamp_to_int(max(df_status['timestamp']))
time_step = 60 * minutes + seconds
x_axis = []
for i in range(min_time, max_time, time_step):
x_axis.append(num_to_timestamp(i).strftime(DB_DATE_FORMAT))
# Fill up dict "apps" like: {app1: [#task1, #task2], app2: [#task4], app3: [#task3]}
apps_dict = dict()
for i in range(len(df_task)):
row = df_task.iloc[i]
if row['task_func_name'] in apps_dict:
apps_dict[row['task_func_name']].append(row['task_id'])
else:def plot(self, option, columns, run_id):
sql_conn = get_db()
df_resources = pd.read_sql_query('SELECT psutil_process_memory_virtual, timestamp, task_id FROM task_resources WHERE run_id=(?)',
sql_conn, params=(run_id, ))
df_task = pd.read_sql_query('SELECT task_id, task_time_returned FROM task WHERE run_id=(?)',
sql_conn, params=(run_id, ))
close_db()
min_range = min(df_resources['psutil_process_memory_virtual'].astype('float'))
max_range = max(df_resources['psutil_process_memory_virtual'].astype('float'))
time_step = (max_range - min_range) / columns
x_axis = []
for i in np.arange(min_range, max_range + time_step, time_step):
x_axis.append(i)
apps_dict = dict()
for i in range(len(df_task)):
row = df_task.iloc[i]
apps_dict[row['task_id']] = []
def y_axis_setup():
items = []def tasks_details(run_id):
sql_conn = get_db()
df_task = pd.read_sql_query('SELECT task_id, task_func_name FROM task WHERE run_id=(?)',
sql_conn, params=(run_id,))
close_db()
apps = []
for _app in df_task['task_func_name'].unique():
apps.append(dict(label=_app, value=_app))
return [html.P(className='view_title', children='Apps view'),
html.Div(id='select_apps',
children=[
html.H4('Select apps'),
dcc.Dropdown(
id='apps_dropdown',
options=apps,
value=apps[0],
multi=True)
])
] + [plot.html(run_id) for plot in plots]def plot(self, option, columns, run_id):
sql_conn = get_db()
df_resources = pd.read_sql_query('SELECT psutil_process_time_system, timestamp, task_id FROM task_resources WHERE run_id=(?)',
sql_conn, params=(run_id, ))
df_task = pd.read_sql_query('SELECT task_id, task_time_returned FROM task WHERE run_id=(?)',
sql_conn, params=(run_id, ))
close_db()
min_range = min(df_resources['psutil_process_time_system'].astype('float'))
max_range = max(df_resources['psutil_process_time_system'].astype('float'))
time_step = (max_range - min_range) / columns
x_axis = []
for i in np.arange(min_range, max_range + time_step, time_step):
x_axis.append(i)
apps_dict = dict()
for i in range(len(df_task)):
row = df_task.iloc[i]
apps_dict[row['task_id']] = []
def y_axis_setup():
items = []def tasks_per_app_plot_callback(apps, run_id):
if type(apps) is dict:
apps = ['', apps['label']]
elif len(apps) == 1:
apps.append('')
sql_conn = get_db()
df_status = pd.read_sql_query('SELECT run_id, task_id, task_status_name, timestamp FROM task_status WHERE run_id=(?)',
sql_conn, params=(run_id, ))
df_task = pd.read_sql_query('SELECT task_id, task_func_name FROM task WHERE run_id=(?) AND task_fn_hash IN {apps}'.format(apps=tuple(apps)),
sql_conn, params=(run_id, ))
close_db()
def y_axis_setup(array):
count = 0
items = []
for n in array:
if n:
count += 1
elif count > 0:
count -= 1
items.append(count)
return items
# Fill up dict "apps" like: {app1: [#task1, #task2], app2: [#task4], app3: [#task3]}
apps = dict()
for i in range(len(df_task)):def display_workflow(workflow_name):
sql_conn = get_db()
df_workflows = pd.read_sql_query('SELECT workflow_name, time_began, rundir, run_id FROM workflows WHERE workflow_name=(?)',
sql_conn, params=(workflow_name, ))
close_db()
return html.Div(children=[
html.H2(id='workflow_name', children=df_workflows['workflow_name'][0]),
dropdown(id='run_number_dropdown', dataframe=df_workflows.sort_values(by='time_began', ascending=False), field='rundir'),
dcc.Tabs(id="tabs", value='workflow', children=[
dcc.Tab(label='Workflow', value='workflow'),
dcc.Tab(label='Tasks', value='tasks'),
]),
html.Div(id='tabs-content')
])def tasks_details(run_id):
sql_conn = get_db()
df_task = pd.read_sql_query('SELECT task_id, task_func_name FROM task WHERE run_id=(?)',
sql_conn, params=(run_id,))
close_db()
apps = []
for _app in df_task['task_func_name'].unique():
apps.append(dict(label=_app, value=_app))
return [dcc.Dropdown(
id='apps_dropdown',
options=apps,
value=apps[0],
multi=True),
tasks_per_app_plot(run_id),
total_tasks_plot(run_id)]
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- parsl.config.Config
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- parsl.monitoring.web_app.app.close_db
- parsl.monitoring.web_app.app.get_db
- parsl.providers.LocalProvider
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- parsl.utils.RepresentationMixin