def test_api3_plot_from_pandas(self):
g = graphistry.edges(pd.DataFrame({
's': [0],
'd': [0]
})).bind(source='s', destination='d')
ds = g.plot(skip_upload=True)
assert isinstance(ds.edges, pa.Table)
def plotter_thread(df2):
g = graphistry.edges(df2).bind(edge_color='my_color',
source='Source',
destination='Destination')
g = g.settings(url_params={'bg': '%23FFFFFF'})
graph = g.plot(render=False, name="Zargonovski")
print("Thread Plotted {}".format(graph))
def test_addStyle_good(self):
g = graphistry.bind()
bg = {'color': 'red'}
fg = {'blendMode': 1}
logo = {'url': 'zzz'}
page = {'title': 'zzz'}
assert g.addStyle()._style == {}
g.addStyle(fg={'blendMode': 'screen'})
assert g.addStyle()._style == {}
assert g.addStyle(bg=copy.deepcopy(bg))._style == {'bg': bg}
assert g.addStyle(bg={
'color': 'blue'
}).addStyle(bg=copy.deepcopy(bg))._style == {
'bg': bg
}
assert g.addStyle(bg={
'image': {
'url': 'http://asdf.com/b.png'
}
}).addStyle(bg=copy.deepcopy(bg))._style == {
'bg': {
**bg, 'image': {
'url': 'http://asdf.com/b.png'
}
}
}
assert g.addStyle(bg=copy.deepcopy(bg),
fg=copy.deepcopy(fg),
logo=copy.deepcopy(logo),
page=copy.deepcopy(page))._style == {
'bg': bg,
'fg': fg,
'logo': logo,
'page': page
}
assert g.addStyle(bg=copy.deepcopy(bg), fg=copy.deepcopy(fg), logo=copy.deepcopy(logo), page=copy.deepcopy(page))\
.addStyle(bg={'color': 'green'})._style == {'bg': {'color': 'green'}, 'fg': fg, 'logo': logo, 'page': page}
g2 = graphistry.edges(pd.DataFrame({
's': [0],
'd': [0]
})).bind(source='s', destination='d')
ds = g2.addStyle(bg=copy.deepcopy(bg),
fg=copy.deepcopy(fg),
page=copy.deepcopy(page),
logo=copy.deepcopy(logo)).plot(skip_upload=True)
assert ds.metadata['bg'] == bg
assert ds.metadata['fg'] == fg
assert ds.metadata['logo'] == logo
assert ds.metadata['page'] == page
def test_styleApi_reject(self):
bg = {'color': 'red'}
fg = {'blendMode': 1}
logo = {'url': 'zzz'}
page = {'title': 'zzz'}
g2 = graphistry.edges(pd.DataFrame({
's': [0],
'd': [0]
})).bind(source='s', destination='d')
g3 = g2.addStyle(bg=copy.deepcopy(bg),
fg=copy.deepcopy(fg),
page=copy.deepcopy(page),
logo=copy.deepcopy(logo))
with pytest.raises(ValueError):
g3.plot(skip_upload=True)
import graphistry as graphistry
from dash.dependencies import Input, Output, State
import pandas as pd
import urllib.request
from pandas.tseries.offsets import *
graphistry.register(api=3,
protocol="https",
server="hub.graphistry.com",
username="******",
password="******")
url = "https://raw.githubusercontent.com/uber-web/kepler.gl-data/master/earthquakes/data.csv"
download_file = urllib.request.urlretrieve(url, "data.csv")
df = pd.read_csv("data.csv")
my_src_col = 'DateTime'
my_dest_col = 'EventID'
g = None
g = graphistry.edges(df).bind(source=my_src_col, destination=my_dest_col)
graph = g.plot()
graph_viz = html.Iframe(src=graph, height='800', width='100%')
graphistry_page = html.Div([html.H2("Graphistry Visualization"), graph_viz])
def get_page():
return graphistry_page
def data_overview():
result = conn.runInstalledQuery('data_overview', timeout = 16000)
data = pd.DataFrame(result[0]['@@edgeList'])
g = graphistry.edges(data, 'from_id', 'to_id')
graph = g.plot(render=False)
return graph
def main_area(fdnm, conn):
file_path = os.path.dirname(os.path.realpath(__file__))
parent_path = os.path.abspath(os.path.join(file_path, os.path.pardir))
logo_its = Image.open(os.path.join(parent_path, 'logo.jpeg'))
st.image(logo_its,
caption=None,
width=250,
use_column_width='True',
clamp=False,
channels='RGB',
output_format='auto')
st.write(""" # LODHalal: Search Product """)
st.write('Search product that contains the input word.')
tic = time.perf_counter()
if conn is None:
logger.error('Cannot run tg demo without creds')
st.write(
RuntimeError(
'Demo requires a TigerGraph connection. Put creds into left sidebar, or fill in envs/tigergraph.env & restart'
))
return None
# Search Product by Name
query = "GetProductByName"
resultTG = conn.runInstalledQuery(query, params={'foodname': fdnm})
results = resultTG[0]['@@tupleRecords']
data = pd.DataFrame(results)
data = pd.DataFrame({
'Food ID': data['id'],
'Name': data['foodname'],
'Halal Certifate': data['cert'],
'Issuing Authority': data['org']
})
try:
res = data.values.tolist()
toc = time.perf_counter()
logger.info(f'Query Execution: {toc - tic:0.02f} seconds')
logger.debug('Query Result Count: %s', len(res))
metrics['tigergraph_time'] = toc - tic
# Calculate the metrics
metrics['node_cnt'] = data.size
metrics['prop_cnt'] = (data.size * data.columns.size)
if data.size > 0:
# url = plot_url(nodes_df, edges_df)
g = graphistry.edges(data).bind(source='Name', destination='Name') \
.nodes(data).bind(node='Name')
url = g.plot(render=False, as_files=True)
else:
url = ""
except Exception as e:
logger.error('oops in TigerGraph', exc_info=True)
raise e
logger.info("Finished compute phase")
try:
pass
except RuntimeError as e:
if str(
e
) == "There is no current event loop in thread 'ScriptRunner.scriptThread'.":
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
else:
raise e
except Exception as e:
logger.error('oops in TigerGraph', exc_info=True)
raise e
logger.info('rendering main area, with url: %s', url)
GraphistrySt().render_url(url)
def run_filters(num_nodes, num_edges, bank, bank_ids):
transactions_map_df = fetch_csv()
#### GLOBAL STATS #####
sample_df = transactions_map_df #.sample(10000)
originator_stats_df = sample_df.groupby('originator_bank_id').agg({
'begin_date':
'min',
'end_date':
'max',
'number_transactions': ['sum', 'max'],
'amount_transactions': ['sum', 'max']
})
originator_stats_df.columns = [
'originator_' + '_'.join(tup).rstrip('_')
for tup in originator_stats_df.columns
]
originator_stats_df = originator_stats_df.reset_index().rename(
columns={'originator_bank_id': 'bank_id'})
beneficiary_stats_df = sample_df.groupby('beneficiary_bank_id').agg({
'begin_date':
'min',
'end_date':
'max',
'number_transactions': ['sum', 'max'],
'amount_transactions': ['sum', 'max']
})
beneficiary_stats_df.columns = [
'beneficiary_' + '_'.join(tup).rstrip('_')
for tup in beneficiary_stats_df.columns
]
beneficiary_stats_df = beneficiary_stats_df.reset_index().rename(
columns={'beneficiary_bank_id': 'bank_id'})
originator_cols = [c for c in sample_df.columns if 'originator_' in c]
beneficiary_cols = [c for c in sample_df.columns if 'beneficiary_' in c]
nodes_df = pd.concat(
[
sample_df[originator_cols]\
.rename(columns={c: c.replace("originator_", "") for c in originator_cols})\
.drop_duplicates(subset=['bank_id'])\
.assign(type='bank', entity_id=sample_df['originator_bank_id']),
sample_df[beneficiary_cols]\
.rename(columns={c: c.replace("beneficiary_", "") for c in beneficiary_cols})\
.drop_duplicates(subset=['bank_id'])\
.assign(type='bank')\
.assign(type='bank', entity_id=sample_df['beneficiary_bank_id'])
],
ignore_index=True, sort=False)
nodes_df = nodes_df\
.merge(beneficiary_stats_df, on='bank_id', how='left')\
.merge(originator_stats_df, on='bank_id', how='left')
nodes_df['abbreviation'] = nodes_df['bank'].apply(
lambda s: ''.join([x[:1] for x in s.split(" ")[:3]]))
nodes_df['sum_transactions'] = nodes_df[
'originator_amount_transactions_sum'].fillna(
0) + nodes_df['beneficiary_amount_transactions_sum'].fillna(0)
nodes_df['number_transactions'] = nodes_df[
'originator_number_transactions_sum'].fillna(
0) + nodes_df['beneficiary_number_transactions_sum'].fillna(0)
iso3611_df = fetch_iso3611()
iso3_to_iso2 = {
row['ISO3166-1-Alpha-3'].lower(): row['ISO3166-1-Alpha-2'].lower()
for row in iso3611_df[['ISO3166-1-Alpha-2', 'ISO3166-1-Alpha-3']].to_dict(orient='records')
if ('ISO3166-1-Alpha-3' in row and type(row['ISO3166-1-Alpha-3']) == str) \
and ('ISO3166-1-Alpha-2' in row and type(row['ISO3166-1-Alpha-2']) == str)
}
nodes_df['iso2'] = nodes_df['iso'].apply(
lambda v: iso3_to_iso2[v.lower()] if v.lower() in iso3_to_iso2 else '')
iso2_to_flags = {
iso2: 'flag-icon-' + iso2
for iso2 in nodes_df['iso2'].unique()
}
##### PROJECTION ######
ids = bank_name_to_ids(nodes_df, bank, bank_ids)
ego_banks_df = nodes_df.merge(pd.DataFrame({'entity_id': ids}),
how='inner')
nodes_df, sample_df = ego_edges(nodes_df,
sample_df,
ids,
src='originator_bank_id',
dst='beneficiary_bank_id',
node='entity_id')
##### VIZ #####
#abbrv_to_abbrv = {x: x for x in nodes_df['abbreviation'].unique().tolist()}
bank_to_bank = {x: x for x in nodes_df['bank'].unique().tolist()}
g = graphistry.edges(sample_df).nodes(nodes_df)\
.bind(source='originator_bank_id', destination='beneficiary_bank_id', node='entity_id')\
.bind(point_title='bank', point_size='sum_transactions')\
.encode_point_icon('bank', as_text=True, categorical_mapping=bank_to_bank)\
.encode_point_badge('iso2', 'TopRight', categorical_mapping=iso2_to_flags)\
.encode_point_color('beneficiary_amount_transactions_sum', palette=['white', 'pink', 'purple'], as_continuous=True, for_current=True)\
.encode_edge_color('amount_transactions', ["maroon", "red", "yellow", "white", "cyan"], as_continuous=True, for_current=True)\
.addStyle(bg={'color': '#EEE'})\
.settings(
height=800,
url_params={
'pointOpacity': 0.3 if len(sample_df) > 1500 else 1.0,
'edgeOpacity': 0.2 if len(sample_df) > 1500 else 1.0,
'strongGravity': True,
'showPointsOfInterestLabel': False,
'play': 5000})
graph_url = g.plot(render=False)
return {
'nodes_df': g._nodes,
'edges_df': g._edges,
'graph_url': graph_url,
'ego_banks_df': ego_banks_df
}