def alter_native_filters(dashboard: Dashboard) -> None:
json_metadata = json.loads(dashboard.json_metadata)
native_filter_configuration = json_metadata.get("native_filter_configuration")
if not native_filter_configuration:
return
for native_filter in native_filter_configuration:
for target in native_filter.get("targets", []):
old_dataset_id = target.get("datasetId")
if dataset_id_mapping and old_dataset_id is not None:
target["datasetId"] = dataset_id_mapping.get(
old_dataset_id,
old_dataset_id,
)
dashboard.json_metadata = json.dumps(json_metadata)
def load_birth_names(only_metadata: bool = False,
force: bool = False,
sample: bool = False) -> None:
"""Loading birth name dataset from a zip file in the repo"""
# pylint: disable=too-many-locals
tbl_name = "birth_names"
database = get_example_database()
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
load_data(tbl_name, database, sample=sample)
obj = db.session.query(TBL).filter_by(table_name=tbl_name).first()
if not obj:
print(f"Creating table [{tbl_name}] reference")
obj = TBL(table_name=tbl_name)
db.session.add(obj)
obj.main_dttm_col = "ds"
obj.database = database
obj.filter_select_enabled = True
if not any(col.column_name == "num_california" for col in obj.columns):
col_state = str(column("state").compile(db.engine))
col_num = str(column("num").compile(db.engine))
obj.columns.append(
TableColumn(
column_name="num_california",
expression=
f"CASE WHEN {col_state} = 'CA' THEN {col_num} ELSE 0 END",
))
if not any(col.metric_name == "sum__num" for col in obj.metrics):
col = str(column("num").compile(db.engine))
obj.metrics.append(
SqlMetric(metric_name="sum__num", expression=f"SUM({col})"))
db.session.commit()
obj.fetch_metadata()
tbl = obj
metrics = [{
"expressionType": "SIMPLE",
"column": {
"column_name": "num",
"type": "BIGINT"
},
"aggregate": "SUM",
"label": "Births",
"optionName": "metric_11",
}]
metric = "sum__num"
defaults = {
"compare_lag": "10",
"compare_suffix": "o10Y",
"limit": "25",
"granularity_sqla": "ds",
"groupby": [],
"row_limit": config["ROW_LIMIT"],
"since": "100 years ago",
"until": "now",
"viz_type": "table",
"markup_type": "markdown",
}
admin = security_manager.find_user("admin")
print("Creating some slices")
slices = [
Slice(
slice_name="Participants",
viz_type="big_number",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="big_number",
granularity_sqla="ds",
compare_lag="5",
compare_suffix="over 5Y",
metric=metric,
),
),
Slice(
slice_name="Genders",
viz_type="pie",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(defaults,
viz_type="pie",
groupby=["gender"],
metric=metric),
),
Slice(
slice_name="Trends",
viz_type="line",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="line",
groupby=["name"],
granularity_sqla="ds",
rich_tooltip=True,
show_legend=True,
metrics=metrics,
),
),
Slice(
slice_name="Genders by State",
viz_type="dist_bar",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
adhoc_filters=[{
"clause": "WHERE",
"expressionType": "SIMPLE",
"filterOptionName": "2745eae5",
"comparator": ["other"],
"operator": "NOT IN",
"subject": "state",
}],
viz_type="dist_bar",
metrics=[
{
"expressionType": "SIMPLE",
"column": {
"column_name": "sum_boys",
"type": "BIGINT(20)"
},
"aggregate": "SUM",
"label": "Boys",
"optionName": "metric_11",
},
{
"expressionType": "SIMPLE",
"column": {
"column_name": "sum_girls",
"type": "BIGINT(20)"
},
"aggregate": "SUM",
"label": "Girls",
"optionName": "metric_12",
},
],
groupby=["state"],
),
),
Slice(
slice_name="Girls",
viz_type="table",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
groupby=["name"],
adhoc_filters=[gen_filter("gender", "girl")],
row_limit=50,
timeseries_limit_metric="sum__num",
metrics=metrics,
),
),
Slice(
slice_name="Girl Name Cloud",
viz_type="word_cloud",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="word_cloud",
size_from="10",
series="name",
size_to="70",
rotation="square",
limit="100",
adhoc_filters=[gen_filter("gender", "girl")],
metric=metric,
),
),
Slice(
slice_name="Boys",
viz_type="table",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
groupby=["name"],
adhoc_filters=[gen_filter("gender", "boy")],
row_limit=50,
metrics=metrics,
),
),
Slice(
slice_name="Boy Name Cloud",
viz_type="word_cloud",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="word_cloud",
size_from="10",
series="name",
size_to="70",
rotation="square",
limit="100",
adhoc_filters=[gen_filter("gender", "boy")],
metric=metric,
),
),
Slice(
slice_name="Top 10 Girl Name Share",
viz_type="area",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
adhoc_filters=[gen_filter("gender", "girl")],
comparison_type="values",
groupby=["name"],
limit=10,
stacked_style="expand",
time_grain_sqla="P1D",
viz_type="area",
x_axis_forma="smart_date",
metrics=metrics,
),
),
Slice(
slice_name="Top 10 Boy Name Share",
viz_type="area",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
adhoc_filters=[gen_filter("gender", "boy")],
comparison_type="values",
groupby=["name"],
limit=10,
stacked_style="expand",
time_grain_sqla="P1D",
viz_type="area",
x_axis_forma="smart_date",
metrics=metrics,
),
),
]
misc_slices = [
Slice(
slice_name="Average and Sum Trends",
viz_type="dual_line",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="dual_line",
metric={
"expressionType": "SIMPLE",
"column": {
"column_name": "num",
"type": "BIGINT(20)"
},
"aggregate": "AVG",
"label": "AVG(num)",
"optionName": "metric_vgops097wej_g8uff99zhk7",
},
metric_2="sum__num",
granularity_sqla="ds",
metrics=metrics,
),
),
Slice(
slice_name="Num Births Trend",
viz_type="line",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(defaults, viz_type="line", metrics=metrics),
),
Slice(
slice_name="Daily Totals",
viz_type="table",
datasource_type="table",
datasource_id=tbl.id,
created_by=admin,
params=get_slice_json(
defaults,
groupby=["ds"],
since="40 years ago",
until="now",
viz_type="table",
metrics=metrics,
),
),
Slice(
slice_name="Number of California Births",
viz_type="big_number_total",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
metric={
"expressionType": "SIMPLE",
"column": {
"column_name": "num_california",
"expression":
"CASE WHEN state = 'CA' THEN num ELSE 0 END",
},
"aggregate": "SUM",
"label": "SUM(num_california)",
},
viz_type="big_number_total",
granularity_sqla="ds",
),
),
Slice(
slice_name="Top 10 California Names Timeseries",
viz_type="line",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
metrics=[{
"expressionType": "SIMPLE",
"column": {
"column_name": "num_california",
"expression":
"CASE WHEN state = 'CA' THEN num ELSE 0 END",
},
"aggregate": "SUM",
"label": "SUM(num_california)",
}],
viz_type="line",
granularity_sqla="ds",
groupby=["name"],
timeseries_limit_metric={
"expressionType": "SIMPLE",
"column": {
"column_name": "num_california",
"expression":
"CASE WHEN state = 'CA' THEN num ELSE 0 END",
},
"aggregate": "SUM",
"label": "SUM(num_california)",
},
limit="10",
),
),
Slice(
slice_name="Names Sorted by Num in California",
viz_type="table",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
metrics=metrics,
groupby=["name"],
row_limit=50,
timeseries_limit_metric={
"expressionType": "SIMPLE",
"column": {
"column_name": "num_california",
"expression":
"CASE WHEN state = 'CA' THEN num ELSE 0 END",
},
"aggregate": "SUM",
"label": "SUM(num_california)",
},
),
),
Slice(
slice_name="Number of Girls",
viz_type="big_number_total",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
metric=metric,
viz_type="big_number_total",
granularity_sqla="ds",
adhoc_filters=[gen_filter("gender", "girl")],
subheader="total female participants",
),
),
Slice(
slice_name="Pivot Table",
viz_type="pivot_table",
datasource_type="table",
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="pivot_table",
groupby=["name"],
columns=["state"],
metrics=metrics,
),
),
]
for slc in slices:
merge_slice(slc)
for slc in misc_slices:
merge_slice(slc)
misc_dash_slices.add(slc.slice_name)
print("Creating a dashboard")
dash = db.session.query(Dashboard).filter_by(slug="births").first()
if not dash:
dash = Dashboard()
db.session.add(dash)
dash.published = True
dash.json_metadata = textwrap.dedent("""\
{
"label_colors": {
"Girls": "#FF69B4",
"Boys": "#ADD8E6",
"girl": "#FF69B4",
"boy": "#ADD8E6"
}
}""")
js = textwrap.dedent(
# pylint: disable=line-too-long
"""\
{
"CHART-6GdlekVise": {
"children": [],
"id": "CHART-6GdlekVise",
"meta": {
"chartId": 5547,
"height": 50,
"sliceName": "Top 10 Girl Name Share",
"width": 5
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-eh0w37bWbR"
],
"type": "CHART"
},
"CHART-6n9jxb30JG": {
"children": [],
"id": "CHART-6n9jxb30JG",
"meta": {
"chartId": 5540,
"height": 36,
"sliceName": "Genders by State",
"width": 5
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW--EyBZQlDi"
],
"type": "CHART"
},
"CHART-Jj9qh1ol-N": {
"children": [],
"id": "CHART-Jj9qh1ol-N",
"meta": {
"chartId": 5545,
"height": 50,
"sliceName": "Boy Name Cloud",
"width": 4
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-kzWtcvo8R1"
],
"type": "CHART"
},
"CHART-ODvantb_bF": {
"children": [],
"id": "CHART-ODvantb_bF",
"meta": {
"chartId": 5548,
"height": 50,
"sliceName": "Top 10 Boy Name Share",
"width": 5
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-kzWtcvo8R1"
],
"type": "CHART"
},
"CHART-PAXUUqwmX9": {
"children": [],
"id": "CHART-PAXUUqwmX9",
"meta": {
"chartId": 5538,
"height": 34,
"sliceName": "Genders",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-2n0XgiHDgs"
],
"type": "CHART"
},
"CHART-_T6n_K9iQN": {
"children": [],
"id": "CHART-_T6n_K9iQN",
"meta": {
"chartId": 5539,
"height": 36,
"sliceName": "Trends",
"width": 7
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW--EyBZQlDi"
],
"type": "CHART"
},
"CHART-eNY0tcE_ic": {
"children": [],
"id": "CHART-eNY0tcE_ic",
"meta": {
"chartId": 5537,
"height": 34,
"sliceName": "Participants",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-2n0XgiHDgs"
],
"type": "CHART"
},
"CHART-g075mMgyYb": {
"children": [],
"id": "CHART-g075mMgyYb",
"meta": {
"chartId": 5541,
"height": 50,
"sliceName": "Girls",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-eh0w37bWbR"
],
"type": "CHART"
},
"CHART-n-zGGE6S1y": {
"children": [],
"id": "CHART-n-zGGE6S1y",
"meta": {
"chartId": 5542,
"height": 50,
"sliceName": "Girl Name Cloud",
"width": 4
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-eh0w37bWbR"
],
"type": "CHART"
},
"CHART-vJIPjmcbD3": {
"children": [],
"id": "CHART-vJIPjmcbD3",
"meta": {
"chartId": 5543,
"height": 50,
"sliceName": "Boys",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-kzWtcvo8R1"
],
"type": "CHART"
},
"DASHBOARD_VERSION_KEY": "v2",
"GRID_ID": {
"children": [
"ROW-2n0XgiHDgs",
"ROW--EyBZQlDi",
"ROW-eh0w37bWbR",
"ROW-kzWtcvo8R1"
],
"id": "GRID_ID",
"parents": [
"ROOT_ID"
],
"type": "GRID"
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {
"text": "Births"
},
"type": "HEADER"
},
"MARKDOWN-zaflB60tbC": {
"children": [],
"id": "MARKDOWN-zaflB60tbC",
"meta": {
"code": "<div style=\\"text-align:center\\"> <h1>Birth Names Dashboard</h1> <img src=\\"/static/assets/images/babies.png\\" style=\\"width:50%;\\"></div>",
"height": 34,
"width": 6
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-2n0XgiHDgs"
],
"type": "MARKDOWN"
},
"ROOT_ID": {
"children": [
"GRID_ID"
],
"id": "ROOT_ID",
"type": "ROOT"
},
"ROW--EyBZQlDi": {
"children": [
"CHART-_T6n_K9iQN",
"CHART-6n9jxb30JG"
],
"id": "ROW--EyBZQlDi",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
},
"ROW-2n0XgiHDgs": {
"children": [
"CHART-eNY0tcE_ic",
"MARKDOWN-zaflB60tbC",
"CHART-PAXUUqwmX9"
],
"id": "ROW-2n0XgiHDgs",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
},
"ROW-eh0w37bWbR": {
"children": [
"CHART-g075mMgyYb",
"CHART-n-zGGE6S1y",
"CHART-6GdlekVise"
],
"id": "ROW-eh0w37bWbR",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
},
"ROW-kzWtcvo8R1": {
"children": [
"CHART-vJIPjmcbD3",
"CHART-Jj9qh1ol-N",
"CHART-ODvantb_bF"
],
"id": "ROW-kzWtcvo8R1",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
}
}
"""
# pylint: enable=line-too-long
)
pos = json.loads(js)
# dashboard v2 doesn't allow add markup slice
dash.slices = [slc for slc in slices if slc.viz_type != "markup"]
update_slice_ids(pos, dash.slices)
dash.dashboard_title = "USA Births Names"
dash.position_json = json.dumps(pos, indent=4)
dash.slug = "births"
db.session.commit()
def create_dashboard(slices: List[Slice]) -> Dashboard:
print("Creating a dashboard")
dash = db.session.query(Dashboard).filter_by(slug="births").first()
if not dash:
dash = Dashboard()
dash.owners = [admin]
dash.created_by = admin
db.session.add(dash)
dash.published = True
dash.json_metadata = textwrap.dedent("""\
{
"label_colors": {
"Girls": "#FF69B4",
"Boys": "#ADD8E6",
"girl": "#FF69B4",
"boy": "#ADD8E6"
}
}""")
pos = json.loads(
textwrap.dedent(
# pylint: disable=line-too-long
"""\
{
"CHART-6GdlekVise": {
"children": [],
"id": "CHART-6GdlekVise",
"meta": {
"chartId": 5547,
"height": 50,
"sliceName": "Top 10 Girl Name Share",
"width": 5
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-eh0w37bWbR"
],
"type": "CHART"
},
"CHART-6n9jxb30JG": {
"children": [],
"id": "CHART-6n9jxb30JG",
"meta": {
"chartId": 5540,
"height": 36,
"sliceName": "Genders by State",
"width": 5
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW--EyBZQlDi"
],
"type": "CHART"
},
"CHART-Jj9qh1ol-N": {
"children": [],
"id": "CHART-Jj9qh1ol-N",
"meta": {
"chartId": 5545,
"height": 50,
"sliceName": "Boy Name Cloud",
"width": 4
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-kzWtcvo8R1"
],
"type": "CHART"
},
"CHART-ODvantb_bF": {
"children": [],
"id": "CHART-ODvantb_bF",
"meta": {
"chartId": 5548,
"height": 50,
"sliceName": "Top 10 Boy Name Share",
"width": 5
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-kzWtcvo8R1"
],
"type": "CHART"
},
"CHART-PAXUUqwmX9": {
"children": [],
"id": "CHART-PAXUUqwmX9",
"meta": {
"chartId": 5538,
"height": 34,
"sliceName": "Genders",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-2n0XgiHDgs"
],
"type": "CHART"
},
"CHART-_T6n_K9iQN": {
"children": [],
"id": "CHART-_T6n_K9iQN",
"meta": {
"chartId": 5539,
"height": 36,
"sliceName": "Trends",
"width": 7
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW--EyBZQlDi"
],
"type": "CHART"
},
"CHART-eNY0tcE_ic": {
"children": [],
"id": "CHART-eNY0tcE_ic",
"meta": {
"chartId": 5537,
"height": 34,
"sliceName": "Participants",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-2n0XgiHDgs"
],
"type": "CHART"
},
"CHART-g075mMgyYb": {
"children": [],
"id": "CHART-g075mMgyYb",
"meta": {
"chartId": 5541,
"height": 50,
"sliceName": "Girls",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-eh0w37bWbR"
],
"type": "CHART"
},
"CHART-n-zGGE6S1y": {
"children": [],
"id": "CHART-n-zGGE6S1y",
"meta": {
"chartId": 5542,
"height": 50,
"sliceName": "Girl Name Cloud",
"width": 4
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-eh0w37bWbR"
],
"type": "CHART"
},
"CHART-vJIPjmcbD3": {
"children": [],
"id": "CHART-vJIPjmcbD3",
"meta": {
"chartId": 5543,
"height": 50,
"sliceName": "Boys",
"width": 3
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-kzWtcvo8R1"
],
"type": "CHART"
},
"DASHBOARD_VERSION_KEY": "v2",
"GRID_ID": {
"children": [
"ROW-2n0XgiHDgs",
"ROW--EyBZQlDi",
"ROW-eh0w37bWbR",
"ROW-kzWtcvo8R1"
],
"id": "GRID_ID",
"parents": [
"ROOT_ID"
],
"type": "GRID"
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {
"text": "Births"
},
"type": "HEADER"
},
"MARKDOWN-zaflB60tbC": {
"children": [],
"id": "MARKDOWN-zaflB60tbC",
"meta": {
"code": "<div style=\\"text-align:center\\"> <h1>Birth Names Dashboard</h1> <img src=\\"/static/assets/images/babies.png\\" style=\\"width:50%;\\"></div>",
"height": 34,
"width": 6
},
"parents": [
"ROOT_ID",
"GRID_ID",
"ROW-2n0XgiHDgs"
],
"type": "MARKDOWN"
},
"ROOT_ID": {
"children": [
"GRID_ID"
],
"id": "ROOT_ID",
"type": "ROOT"
},
"ROW--EyBZQlDi": {
"children": [
"CHART-_T6n_K9iQN",
"CHART-6n9jxb30JG"
],
"id": "ROW--EyBZQlDi",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
},
"ROW-2n0XgiHDgs": {
"children": [
"CHART-eNY0tcE_ic",
"MARKDOWN-zaflB60tbC",
"CHART-PAXUUqwmX9"
],
"id": "ROW-2n0XgiHDgs",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
},
"ROW-eh0w37bWbR": {
"children": [
"CHART-g075mMgyYb",
"CHART-n-zGGE6S1y",
"CHART-6GdlekVise"
],
"id": "ROW-eh0w37bWbR",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
},
"ROW-kzWtcvo8R1": {
"children": [
"CHART-vJIPjmcbD3",
"CHART-Jj9qh1ol-N",
"CHART-ODvantb_bF"
],
"id": "ROW-kzWtcvo8R1",
"meta": {
"background": "BACKGROUND_TRANSPARENT"
},
"parents": [
"ROOT_ID",
"GRID_ID"
],
"type": "ROW"
}
}
"""
# pylint: enable=line-too-long
))
# dashboard v2 doesn't allow add markup slice
dash.slices = [slc for slc in slices if slc.viz_type != "markup"]
update_slice_ids(pos, dash.slices)
dash.dashboard_title = "USA Births Names"
dash.position_json = json.dumps(pos, indent=4)
dash.slug = "births"
db.session.commit()
return dash
def set_dash_metadata(
dashboard: Dashboard,
data: Dict[Any, Any],
old_to_new_slice_ids: Optional[Dict[int, int]] = None,
) -> None:
positions = data["positions"]
# find slices in the position data
slice_ids = [
value.get("meta", {}).get("chartId")
for value in positions.values() if isinstance(value, dict)
]
session = db.session()
current_slices = session.query(Slice).filter(
Slice.id.in_(slice_ids)).all()
dashboard.slices = current_slices
# add UUID to positions
uuid_map = {slice.id: str(slice.uuid) for slice in current_slices}
for obj in positions.values():
if (isinstance(obj, dict) and obj["type"] == "CHART"
and obj["meta"]["chartId"]):
chart_id = obj["meta"]["chartId"]
obj["meta"]["uuid"] = uuid_map.get(chart_id)
# remove leading and trailing white spaces in the dumped json
dashboard.position_json = json.dumps(positions,
indent=None,
separators=(",", ":"),
sort_keys=True)
md = dashboard.params_dict
dashboard.css = data.get("css")
dashboard.dashboard_title = data["dashboard_title"]
if "timed_refresh_immune_slices" not in md:
md["timed_refresh_immune_slices"] = []
new_filter_scopes = {}
if "filter_scopes" in data:
# replace filter_id and immune ids from old slice id to new slice id:
# and remove slice ids that are not in dash anymore
slc_id_dict: Dict[int, int] = {}
if old_to_new_slice_ids:
slc_id_dict = {
old: new
for old, new in old_to_new_slice_ids.items()
if new in slice_ids
}
else:
slc_id_dict = {sid: sid for sid in slice_ids}
new_filter_scopes = copy_filter_scopes(
old_to_new_slc_id_dict=slc_id_dict,
old_filter_scopes=json.loads(data["filter_scopes"] or "{}"),
)
if new_filter_scopes:
md["filter_scopes"] = new_filter_scopes
else:
md.pop("filter_scopes", None)
md["expanded_slices"] = data.get("expanded_slices", {})
md["refresh_frequency"] = data.get("refresh_frequency", 0)
default_filters_data = json.loads(data.get("default_filters", "{}"))
applicable_filters = {
key: v
for key, v in default_filters_data.items() if int(key) in slice_ids
}
md["default_filters"] = json.dumps(applicable_filters)
md["color_scheme"] = data.get("color_scheme")
if data.get("color_namespace"):
md["color_namespace"] = data.get("color_namespace")
if data.get("label_colors"):
md["label_colors"] = data.get("label_colors")
dashboard.json_metadata = json.dumps(md)
def set_dash_metadata( # pylint: disable=too-many-locals
dashboard: Dashboard,
data: Dict[Any, Any],
old_to_new_slice_ids: Optional[Dict[int, int]] = None,
commit: bool = False,
) -> Dashboard:
positions = data.get("positions")
new_filter_scopes = {}
md = dashboard.params_dict
if positions is not None:
# find slices in the position data
slice_ids = [
value.get("meta", {}).get("chartId")
for value in positions.values() if isinstance(value, dict)
]
session = db.session()
current_slices = session.query(Slice).filter(
Slice.id.in_(slice_ids)).all()
dashboard.slices = current_slices
# add UUID to positions
uuid_map = {slice.id: str(slice.uuid) for slice in current_slices}
for obj in positions.values():
if (isinstance(obj, dict) and obj["type"] == "CHART"
and obj["meta"]["chartId"]):
chart_id = obj["meta"]["chartId"]
obj["meta"]["uuid"] = uuid_map.get(chart_id)
# remove leading and trailing white spaces in the dumped json
dashboard.position_json = json.dumps(positions,
indent=None,
separators=(",", ":"),
sort_keys=True)
if "filter_scopes" in data:
# replace filter_id and immune ids from old slice id to new slice id:
# and remove slice ids that are not in dash anymore
slc_id_dict: Dict[int, int] = {}
if old_to_new_slice_ids:
slc_id_dict = {
old: new
for old, new in old_to_new_slice_ids.items()
if new in slice_ids
}
else:
slc_id_dict = {sid: sid for sid in slice_ids}
new_filter_scopes = copy_filter_scopes(
old_to_new_slc_id_dict=slc_id_dict,
old_filter_scopes=json.loads(data["filter_scopes"] or "{}")
if isinstance(data["filter_scopes"], str) else
data["filter_scopes"],
)
default_filters_data = json.loads(data.get("default_filters",
"{}"))
applicable_filters = {
key: v
for key, v in default_filters_data.items()
if int(key) in slice_ids
}
md["default_filters"] = json.dumps(applicable_filters)
# positions have its own column, no need to store it in metadata
md.pop("positions", None)
# The css and dashboard_title properties are not part of the metadata
# TODO (geido): remove by refactoring/deprecating save_dash endpoint
if data.get("css") is not None:
dashboard.css = data.get("css")
if data.get("dashboard_title") is not None:
dashboard.dashboard_title = data.get("dashboard_title")
if new_filter_scopes:
md["filter_scopes"] = new_filter_scopes
else:
md.pop("filter_scopes", None)
md.setdefault("timed_refresh_immune_slices", [])
if data.get("color_namespace") is None:
md.pop("color_namespace", None)
else:
md["color_namespace"] = data.get("color_namespace")
md["expanded_slices"] = data.get("expanded_slices", {})
md["refresh_frequency"] = data.get("refresh_frequency", 0)
md["color_scheme"] = data.get("color_scheme", "")
md["label_colors"] = data.get("label_colors", {})
md["shared_label_colors"] = data.get("shared_label_colors", {})
dashboard.json_metadata = json.dumps(md)
if commit:
db.session.commit()
return dashboard
def set_dash_metadata( # pylint: disable=too-many-locals,too-many-branches,too-many-statements
dashboard: Dashboard,
data: Dict[Any, Any],
old_to_new_slice_ids: Optional[Dict[int, int]] = None,
) -> None:
positions = data["positions"]
# find slices in the position data
slice_ids = []
slice_id_to_name = {}
for value in positions.values():
if isinstance(value, dict):
try:
slice_id = value["meta"]["chartId"]
slice_ids.append(slice_id)
slice_id_to_name[slice_id] = value["meta"]["sliceName"]
except KeyError:
pass
session = db.session()
current_slices = session.query(Slice).filter(
Slice.id.in_(slice_ids)).all()
dashboard.slices = current_slices
# update slice names. this assumes user has permissions to update the slice
# we allow user set slice name be empty string
for slc in dashboard.slices:
try:
new_name = slice_id_to_name[slc.id]
if slc.slice_name != new_name:
slc.slice_name = new_name
session.merge(slc)
session.flush()
except KeyError:
pass
# remove leading and trailing white spaces in the dumped json
dashboard.position_json = json.dumps(positions,
indent=None,
separators=(",", ":"),
sort_keys=True)
md = dashboard.params_dict
dashboard.css = data.get("css")
dashboard.dashboard_title = data["dashboard_title"]
if "timed_refresh_immune_slices" not in md:
md["timed_refresh_immune_slices"] = []
new_filter_scopes = {}
if "filter_scopes" in data:
# replace filter_id and immune ids from old slice id to new slice id:
# and remove slice ids that are not in dash anymore
slc_id_dict: Dict[int, int] = {}
if old_to_new_slice_ids:
slc_id_dict = {
old: new
for old, new in old_to_new_slice_ids.items()
if new in slice_ids
}
else:
slc_id_dict = {sid: sid for sid in slice_ids}
new_filter_scopes = copy_filter_scopes(
old_to_new_slc_id_dict=slc_id_dict,
old_filter_scopes=json.loads(data["filter_scopes"] or "{}"),
)
if new_filter_scopes:
md["filter_scopes"] = new_filter_scopes
else:
md.pop("filter_scopes", None)
md["expanded_slices"] = data.get("expanded_slices", {})
md["refresh_frequency"] = data.get("refresh_frequency", 0)
default_filters_data = json.loads(data.get("default_filters", "{}"))
applicable_filters = {
key: v
for key, v in default_filters_data.items() if int(key) in slice_ids
}
md["default_filters"] = json.dumps(applicable_filters)
md["color_scheme"] = data.get("color_scheme")
if data.get("color_namespace"):
md["color_namespace"] = data.get("color_namespace")
if data.get("label_colors"):
md["label_colors"] = data.get("label_colors")
dashboard.json_metadata = json.dumps(md)