def testAggregationNoValues(self):
"""Single metric with no values."""
slices = (
InstanceMetricData("id", ()),
)
result = aggregation.aggregate(slices)
self.assertEqual(len(result), 0)
def view_clusters():
session = create_session()
clusters = session.query(Cluster).all()
states = concat_dfs(cluster.state_df() for cluster in clusters)
level_info_data = get_level_info_data()
price_settings = get_price_settings()
time_stats_dict = {
'interactive': empty_timeseries(),
'job': empty_timeseries()
}
if not states.empty:
results = aggregate_by_types(states, aggregate_for_entity)
for key, (_, time_stats) in results.items():
time_stats_dict[key] = time_stats.to_dict("records")
cluster_dbus = (aggregate(df=states,
col="interval_dbu",
by="cluster_id",
since_days=7).rename(columns={
'interval_dbu': 'dbu'
}).dbu.to_dict())
else:
cluster_dbus = {cluster.cluster_id: 0.0 for cluster in clusters}
clusters_by_type = {}
for cluster in clusters:
clusters_by_type.setdefault(cluster.cluster_type(), []).append(cluster)
return render_template('clusters.html',
clusters_by_type=clusters_by_type,
price_settings=price_settings,
data=level_info_data,
cluster_dbus=cluster_dbus,
time_stats=time_stats_dict)
def aggregate_and_save(src_fold,
method,
dst_fold,
fea_idx,
total_order=0,
order=0):
if not os.path.exists(dst_fold):
os.mkdir(dst_fold)
src_fold = os.path.join(src_fold, '*.npy')
fea_list = glob.glob(src_fold)
if not total_order == 0 and not order == 0:
length = len(fea_list)
step = int(ceil(float(length) / total_order))
fea_list = fea_list[(order - 1) * step:min(order * step, length)]
idx = 0
for path in fea_list:
fold, name = os.path.split(path)
save_path = os.path.join(dst_fold, name)
if os.path.exists(save_path):
idx += 1
if not idx % 100:
sys.stdout.write('\rEncoding %d th img: %s' % (idx, name))
sys.stdout.flush()
continue
X = np.load(path)
y = aggregate(X, fea_idx, method)
np.save(save_path, y)
idx += 1
if not idx % 100:
sys.stdout.write('\rEncoding %d th img: %s' % (idx, name))
sys.stdout.flush()
sys.stdout.write('\n')
sys.stdout.flush()
def testAggregationSingleValue(self):
"""Single metric with single value."""
timestamp = datetime.datetime.utcnow()
slices = (
InstanceMetricData("id", (
MetricRecord(timestamp, 100.0),
)),
)
result = aggregation.aggregate(slices)
self.assertEqual(len(result), 1)
self.assertIsInstance(result[0], tuple)
self.assertSequenceEqual(result[0], (timestamp, 100.0))
def system_model(config, logger, log_dir):
'''
Function description
'''
_name = system_model.__name__ #name for logging
logger.info(LogMessage(_name, 'Starting the system model.'))
exio_v, exio_u = load_exiobase.get_sut(config.get('exio_data', 'ddir'),
config.get('exio_data', 'supply'),
config.get('exio_data', 'use'),
logger)
iot_names, country_dic, prod_dic, country_list = load_exiobase.\
get_aggregated_product_names(
config.get('exio_data','ddir'),
config.get('exio_data', 'aggregated_names'),
logger)
aggregation_matrix, N_reg, N_prod, N_sec = agg.get_aggregation_matrix(
config.get('exio_data', 'ddir'),
config.get('exio_data', 'aggregation_matrix'),
config.get('exio_data', 'calvals_matrix'), log_dir,
config.get('project_info', 'aggregation_report_file'), logger)
exio_vagg, exio_uagg = agg.aggregate(exio_v, exio_u, aggregation_matrix,
logger)
all_excl_byprods = get_exclusive_byproducts(exio_vagg, exio_uagg, N_reg,
iot_names, logger)
excl_byproducts, market_names, grid_electricity, elec_markets =\
create_market_and_product_names(all_excl_byprods, N_reg,
country_list, logger)
V_without_elec, U_without_elec, V_elecmarkets, U_elecmarkets,\
elec_market_product_supply, elec_market_product_use =\
create_electricity_grids(exio_vagg, exio_uagg, N_reg,
N_sec, iot_names, logger)
V_markets, U_markets, v_market_excl_byproduct,\
u_market_excl_byproduct, excl_market_products_supply,\
excl_market_products_use = create_excl_byprod_markets(
V_without_elec, U_without_elec, excl_byproducts, prod_dic,
country_dic, all_excl_byprods, N_sec, iot_names, logger)
V_model, U_model = assemble_SUT(
V_markets, U_markets, V_elecmarkets, U_elecmarkets,
elec_market_product_supply, elec_market_product_use,
v_market_excl_byproduct, u_market_excl_byproduct,
excl_market_products_supply, excl_market_products_use, logger)
Z_model, A_model = make_IOT(V_model, U_model, logger)
def view_users():
session = create_session()
users = session.query(User).all()
level_info_data = get_level_info_data()
for user in users:
user.dbu = aggregate(df=user.state_df(),
col='interval_dbu',
since_days=7)
users = sorted(users, key=lambda user: user.dbu, reverse=True)
states = concat_dfs(user.state_df() for user in users)
# Average active users
active_users = (aggregate(df=states,
col='user_id',
by=get_time_grouper('timestamp'),
aggfunc='nunique',
since_days=7).reindex(get_time_index(7),
fill_value=0))
active_users['ts'] = active_users.index.format()
# Average used DBU
dbus = (aggregate(df=states,
col='interval_dbu',
by=get_time_grouper('timestamp'),
aggfunc='sum',
since_days=7).reindex(get_time_index(7), fill_value=0))
active_users['sum_dbus'] = dbus.interval_dbu
active_users['average_dbu'] = ((active_users.sum_dbus /
active_users.user_id).fillna(0.))
return render_template('users.html',
users=users,
active_users=active_users.to_dict('records'),
data=level_info_data)
def testAggregationMultipleValues(self):
"""Single metric with multiple values at different timestamps."""
timestamp2 = datetime.datetime.utcnow()
timestamp1 = timestamp2 - datetime.timedelta(minutes=5)
slices = (
InstanceMetricData("id", (
MetricRecord(timestamp1, 100.0),
MetricRecord(timestamp2, 50.0),
)),
)
result = aggregation.aggregate(slices)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], tuple)
self.assertIsInstance(result[1], tuple)
self.assertSequenceEqual(result[0], (timestamp1, 100.0))
self.assertSequenceEqual(result[1], (timestamp2, 50.0))
def testAggregationMultipleMetricsAligned(self):
"""Multiple metrics with matching timestamps."""
timestamp2 = datetime.datetime.utcnow()
timestamp1 = timestamp2 - datetime.timedelta(minutes=5)
slices = (
InstanceMetricData("id1", (
MetricRecord(timestamp1, 100.0),
MetricRecord(timestamp2, 50.0),
)),
InstanceMetricData("id2", (
MetricRecord(timestamp1, 80.0),
MetricRecord(timestamp2, 30.0),
)),
)
result = aggregation.aggregate(slices)
self.assertEqual(len(result), 2)
self.assertIsInstance(result[0], tuple)
self.assertIsInstance(result[1], tuple)
self.assertSequenceEqual(result[0], (timestamp1, 90.0))
self.assertSequenceEqual(result[1], (timestamp2, 40.0))
def view_user(username):
session = create_session()
try:
user = (session.query(User).filter(User.username == username).one())
except Exception:
return view_missing(type="user", id=username)
states = user.state_df()
time_stats_dict = {
'interactive': empty_timeseries(),
'job': empty_timeseries()
}
if not states.empty:
workspaces = (concat_dfs({
(w.workspace.id, w.workspace.name): w.workspace.state_df()
for w in user.user_workspaces
}).reset_index([0, 1]).rename(columns={
'level_0': 'workspace_id',
'level_1': 'workspace_name'
}))
last7_workspaces = (aggregate(
df=workspaces,
col='interval_dbu',
by=['workspace_id', 'workspace_name'],
since_days=7).rename(columns={'interval_dbu': 'last7dbu'}))
all_workspaces = (aggregate(
df=workspaces,
col='interval_dbu',
by=['workspace_id', 'workspace_name'
]).rename(columns={'interval_dbu': 'alltimedbu'}))
workspaces_dict = (pd.merge(
all_workspaces,
last7_workspaces,
how='left',
left_index=True,
right_index=True).fillna(0.0).reset_index().sort_values(
'last7dbu').to_dict('records'))
price_settings = get_price_settings()
results = aggregate_by_types(states, aggregate_for_entity)
cost_summary_dict = {}
for key, (cost_summary, time_stats) in results.items():
time_stats_dict[key] = time_stats.to_dict("records")
cost_summary = cost_summary.to_dict()
cost = cost_summary['interval_dbu'] * price_settings[key]
weekly_cost = (cost_summary['weekly_interval_dbu_sum'] *
price_settings[key])
cost_summary['cost'] = cost
cost_summary['weekly_cost'] = weekly_cost
cost_summary_dict[key] = cost_summary
# We aren't sure if we have both interactive and job
present_key = list(cost_summary_dict.keys())[0]
cost_summary_dict = {
key: sum([cost_summary_dict[type][key] for type in results.keys()])
for key in cost_summary_dict[present_key]
}
else:
workspaces_dict = [{
'workspace_id': w.workspace.id,
'workspace_name': w.workspace.name,
'last7dbu': 0.0,
'alltimedbu': 0.0
} for w in user.user_workspaces]
cost_summary_dict = {
"interval": 0.0,
"interval_dbu": 0.0,
"weekly_interval_sum": 0.0,
"weekly_interval_dbu_sum": 0.0,
"cost": 0.0,
"weekly_cost": 0.0
}
return render_template('user.html',
user=user,
workspaces=workspaces_dict,
cost=cost_summary_dict,
time_stats=time_stats_dict)
def view_workspace(workspace_id):
session = create_session()
try:
workspace = (session.query(Workspace).filter(
Workspace.id == workspace_id).one())
except Exception:
return view_missing(type="workspace", id=workspace_id)
states = workspace.state_df()
numbjobs_dict = get_running_jobs(workspace.jobruns)
price_settings = get_price_settings()
time_stats_dict = {
'interactive': empty_timeseries(),
'job': empty_timeseries()
}
if not states.empty:
results = aggregate_by_types(states, aggregate_for_entity)
cost_summary_dict = {}
for key, (cost_summary, time_stats) in results.items():
time_stats_dict[key] = time_stats.to_dict("records")
cost_summary = cost_summary.to_dict()
cost = cost_summary['interval_dbu'] * price_settings[key]
weekly_cost = (cost_summary['weekly_interval_dbu_sum'] *
price_settings[key])
cost_summary['cost'] = cost
cost_summary['weekly_cost'] = weekly_cost
cost_summary_dict[key] = cost_summary
# We aren't sure if we have both interactive and job
present_key = list(cost_summary_dict.keys())[0]
cost_summary_dict = {
key: sum([cost_summary_dict[type][key] for type in results.keys()])
for key in cost_summary_dict[present_key]
}
top_users = (aggregate(
df=states, col='interval_dbu', by='user_id',
since_days=7).reset_index().rename(columns={
'interval_dbu': 'dbu'
}).sort_values('dbu', ascending=False))
top_users_dict = (top_users.loc[~top_users.user_id.isin(['UNKONWN'])].
to_dict("records")[:3])
else:
cost_summary_dict = {
"interval": 0.0,
"interval_dbu": 0.0,
"weekly_interval_sum": 0.0,
"weekly_interval_dbu_sum": 0.0,
"cost": 0.0,
"weekly_cost": 0.0
}
top_users_dict = {}
clusters_by_type = {}
for cluster in workspace.clusters:
clusters_by_type.setdefault(cluster.cluster_type(), []).append(cluster)
return render_template('workspace.html',
workspace=workspace,
clusters_by_type=clusters_by_type,
cost=cost_summary_dict,
time_stats=time_stats_dict,
top_users=top_users_dict,
numjobs=numbjobs_dict,
empty=states.empty)
def testAggregationEmptyTuple(self): """No values in input tuple.""" result = aggregation.aggregate(()) self.assertSequenceEqual(result, ())
def testAggregationEmptyList(self): """No values in input list.""" result = aggregation.aggregate([]) self.assertSequenceEqual(result, ())
def main():
filename = "./Dataframes/" + "The big one" + ".csv"
patterns = ["World", "Champ", "Candidates", "Interzonal", "PCA"]
# patterns = ["2000"]
# patterns = ["World"]
# patterns = ["Dae"]
directory = r'/home/jake/Downloads/pgn/'
# directory = r'./pgn/'
# directory = r'./pgn/FISC'
dfs = []
list_of_games = []
for entry in os.scandir(directory):
if (entry.path.endswith(".pgn") and entry.is_file()) and any(True for pattern in patterns \
if pattern in entry.name[:-4]):
print(entry.path)
df, games = aggregate(entry.path)
rows = []
for game in games:
lost_pieces = process_game(game)
rows.append(lost_pieces)
df["Lost pieces"] = rows
dfs.append(df)
list_of_games.extend(games)
df = pd.concat(dfs, ignore_index=True)
# filename = "./pgn/DeLaBourdonnais.pgn"
# filename = "./pgn/testing.pgn"
# meta data and game list
# df.to_csv(filename)
# pieces = [chess.ROOK, chess.KNIGHT]
pieces = list(chess.PIECE_TYPES)[:-1][::-1]
# Type casting and filsimilar to others surrounding it. If objects are atering
df_elo = df[df["WhiteElo"].apply(lambda x: x not in ["", "?", None, np.nan]) & \
df["BlackElo"].apply(lambda x: x not in ["", "?", None, np.nan])] \
.astype({"WhiteElo": 'int', "BlackElo": 'int'}) # type casts elo to ints and filters out the various non-int things
df["Date"] = pd.to_datetime(
df["Date"].apply(lambda x: x.replace("?", "")[:4].replace(".", ""))
) # type cast dates to dates, assumes year is present in all data
# plt.rcParams['figure.figsize'] = (10, 8)
plt.rcParams.update({'font.size': 14})
# ELO
minimum, low, low_mid, mid, mid_high, high, maximum = df_elo[
"WhiteElo"].quantile([0, 0.028, 0.1587, 0.5, 0.8413, 0.9772, 1])
bins_ranges = [minimum, low, low_mid, mid, mid_high, high, maximum]
bins = []
col_labels = []
for lower, upper in zip(bins_ranges[:-1], bins_ranges[1:]):
print(f"Elo in range {lower:.0f}-{upper:.0f}")
bins.append(df_elo[in_range(lower, df_elo["WhiteElo"], upper)])
col_labels.append(
f"{lower:.0f} - {upper:.0f}\n{len(bins)-4 if len(bins) != 1 else '-∞'}σ to {len(bins)-3 if len(bins) != 6 else '∞'}σ"
)
bintype = "ELO"
# fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.BLACK, bintype, username="******")
# fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.WHITE, bintype, username="******")
# fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.BLACK, bintype, username="******")
# fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.WHITE, bintype, username="******")
fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.BLACK,
bintype)
fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.WHITE,
bintype)
fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.BLACK, bintype)
fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.WHITE, bintype)
# DATE
minimum, low, mid, high, maximum = df["Date"].quantile(
[0, 0.25, 0.5, 0.75, 1])
bins_ranges = [minimum, low, mid, high, maximum]
# bins_ranges = [pd.to_datetime("1700"), pd.to_datetime("1900"), pd.to_datetime("1980"), pd.to_datetime("2010"), pd.to_datetime("2025")]
bins = []
col_labels = []
for lower, upper in zip(bins_ranges[:-1], bins_ranges[1:]):
print(f"Date in range {lower.year}-{upper.year}")
bins.append(df[in_range(lower, df["Date"], upper)])
col_labels.append(
f"{lower.year} to {upper.year}\nQ{len(bins)-1} - Q{len(bins)}")
bintype = "DATE"
# fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.BLACK, bintype, username="******")
# fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.WHITE, bintype, username="******")
# fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.BLACK, bintype, username="******")
# fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.WHITE, bintype, username="******")
fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.BLACK,
bintype)
fig, axs = plot_heatmap_grid(bins, pieces, col_labels, chess.WHITE,
bintype)
fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.BLACK, bintype)
fig, axs = plot_hist_grid(bins, pieces, col_labels, chess.WHITE, bintype)
# fig, axs = plot_heatmap_single_piece(df, [chess.PAWN], username="******", cmap=sns.color_palette("viridis", as_cmap=True))
# fig, axs = plot_heatmap_single_piece(df, [chess.PAWN], bintype)
for piece in chess.PIECE_TYPES[:-1]:
# fig, axs = plot_heatmap_single_piece(df, [piece], username="******")
fig, axs = plot_heatmap_single_piece(df, [piece])
# fig, axs = plot_hist_single_piece(df, [piece], username="******")
fig, axs = plot_hist_single_piece(df, [piece])
# plt.show()
# show(df)
print(len(df))
return df, list_of_games