def test_create_training_pipeline_custom_training_managed_dataset_sample(
capsys, shared_state, pipeline_client):
create_training_pipeline_custom_training_managed_dataset_sample.create_training_pipeline_custom_training_managed_dataset_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
model_display_name=MODEL_DISPLAY_NAME,
dataset_id=DATASET_ID,
annotation_schema_uri=ANNOTATION_SCHEMA_URI,
training_container_spec_image_uri=TRAINING_CONTAINER_SPEC_IMAGE_URI,
model_container_spec_image_uri=MODEL_CONTAINER_SPEC_IMAGE_URI,
base_output_uri_prefix=BASE_OUTPUT_URI_PREFIX,
)
out, _ = capsys.readouterr()
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
# Poll until the pipeline succeeds because we want to test the model_upload step as well.
helpers.wait_for_job_state(
get_job_method=pipeline_client.get_training_pipeline,
name=shared_state["training_pipeline_name"],
expected_state="SUCCEEDED",
timeout=1800,
freq=20,
)
training_pipeline = pipeline_client.get_training_pipeline(
name=shared_state["training_pipeline_name"])
# Check that the model indeed has been uploaded.
assert training_pipeline.model_to_upload.name != ""
shared_state["model_name"] = training_pipeline.model_to_upload.name
def test_ucaip_generated_create_dataset_video_sample_vision(
capsys, shared_state):
create_dataset_video_sample.create_dataset_video_sample(
display_name=f"temp_create_dataset_test_{uuid4()}", project=PROJECT_ID)
out, _ = capsys.readouterr()
assert "create_dataset_response" in out
shared_state["dataset_name"] = helpers.get_name(out)
def test_ucaip_generated_create_endpoint_sample(capsys, shared_state):
create_endpoint_sample.create_endpoint_sample(display_name=DISPLAY_NAME,
project=PROJECT)
out, _ = capsys.readouterr()
assert "create_endpoint_response" in out
shared_state["endpoint_name"] = helpers.get_name(out)
def test_ucaip_generated_create_custom_job(capsys, shared_state):
create_custom_job_sample.create_custom_job_sample(
display_name=f"temp_create_custom_job_test_{uuid.uuid4()}",
container_image_uri=CONTAINER_IMAGE_URI,
project=PROJECT_ID,
)
out, _ = capsys.readouterr()
assert "response" in out
shared_state["custom_job_name"] = helpers.get_name(out)
def test_ucaip_generated_create_dataset_sample_vision(capsys, shared_state):
create_dataset_sample.create_dataset_sample(
display_name=f"temp_create_dataset_test_{uuid4()}",
metadata_schema_uri=IMAGE_METADATA_SCHEMA_URI,
project=PROJECT_ID,
)
out, _ = capsys.readouterr()
assert "create_dataset_response" in out
shared_state["dataset_name"] = helpers.get_name(out)
def test_ucaip_generated_create_dataset_tabular_bigquery(capsys, shared_state):
create_dataset_tabular_bigquery_sample.create_dataset_tabular_bigquery_sample(
display_name=f"temp_create_dataset_test_{uuid4()}",
bigquery_uri=BIGQUERY_URI,
project=PROJECT_ID,
)
out, _ = capsys.readouterr()
assert "create_dataset_response" in out
shared_state["dataset_name"] = helpers.get_name(out)
def test_ucaip_generated_upload_model_sample(capsys, shared_state):
upload_model_sample.upload_model_sample(
display_name=DISPLAY_NAME,
metadata_schema_uri="",
image_uri=IMAGE_URI,
artifact_uri=ARTIFACT_URI,
project=PROJECT_ID,
)
out, _ = capsys.readouterr()
shared_state["model_name"] = helpers.get_name(out, key="model")
def upload_handler():
"""
End point to upload files. Upload files asynchronously.
"""
path = request.form.get('path') or ''
if request.method == 'POST':
files = request.files.getlist('files')
filenames = [_ for _ in os.listdir(os.path.join(CONTENT_FOLDER, path ))]
for file in files:
if file and helpers.is_allowed_file(file.filename):
filename = helpers.get_name(file.filename, filenames)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], path, filename))
return helpers.generate_error('0', 'Upload successful.')
def test_ucaip_generated_upload_model_explain_image_managed_container_sample(
capsys, shared_state):
upload_model_explain_image_managed_container_sample.upload_model_explain_image_managed_container_sample(
display_name=DISPLAY_NAME,
artifact_uri=ARTIFACT_URI,
container_spec_image_uri=IMAGE_URI,
project=PROJECT_ID,
input_tensor_name=INPUT_TENSOR_NAME,
output_tensor_name=OUTPUT_TENSOR_NAME)
out, _ = capsys.readouterr()
shared_state["model_name"] = helpers.get_name(out, key="model")
def upload_handler():
"""
End point to upload files. Upload files asynchronously.
"""
path = request.form.get('path') or ''
if request.method == 'POST':
files = request.files.getlist('files')
filenames = [_ for _ in os.listdir(os.path.join(CONTENT_FOLDER, path))]
for file in files:
if file and helpers.is_allowed_file(file.filename):
filename = helpers.get_name(file.filename, filenames)
file.save(
os.path.join(app.config['UPLOAD_FOLDER'], path, filename))
return helpers.generate_error('0', 'Upload successful.')
def test_create_hyperparameter_tuning_job_python_package_sample(capsys, shared_state):
create_hyperparameter_tuning_job_python_package_sample.create_hyperparameter_tuning_job_python_package_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
executor_image_uri=EXECUTOR_IMAGE_URI,
package_uri=PACKAGE_URI,
python_module=PYTHON_MODULE,
)
out, _ = capsys.readouterr()
assert "response" in out
shared_state["hyperparameter_tuning_job_name"] = helpers.get_name(out)
def test_ucaip_generated_create_training_pipeline_video_object_tracking_sample(
capsys, shared_state):
create_training_pipeline_video_object_tracking_sample.create_training_pipeline_video_object_tracking_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name=f"Temp Model for {DISPLAY_NAME}",
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_create_training_pipeline_video_action_recognition_sample(
capsys, shared_state):
create_training_pipeline_video_action_recognition_sample.create_training_pipeline_video_action_recognition_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name=MODEL_DISPLAY_NAME,
model_type=MODEL_TYPE,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):
create_training_pipeline_tabular_classification_sample.create_training_pipeline_tabular_classification_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name=f"Temp Model for {DISPLAY_NAME}",
target_column=TARGET_COLUMN,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def compute_covariance_matrix(model_name, to_json=True):
model = gensim.models.Doc2Vec.load(model_name)
doctags = list(model.docvecs.doctags)
N = len(doctags)
X = []
for x in doctags:
X.append(model.docvecs[x])
X = np.array(X)
# R[i, j] = R[j, i] = dot(vi, vj) / (norm(vi) * norm(vj))
R = np.corrcoef(X)
if to_json:
RR = {}
for x, dx in enumerate(doctags):
for y, dy in enumerate(doctags):
RR[get_name(dx), get_name(dy)] = R[x, y]
return doctags, RR
else:
return doctags, R
def test_ucaip_generated_create_batch_prediction_tcn_sample(capsys, shared_state):
model_name = f"projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}"
create_batch_prediction_job_text_classification_sample.create_batch_prediction_job_text_classification_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
model_name=model_name,
gcs_source_uri=GCS_SOURCE_URI,
gcs_destination_output_uri_prefix=GCS_OUTPUT_URI,
)
out, _ = capsys.readouterr()
# Save resource name of the newly created batch prediction job
shared_state["batch_prediction_job_name"] = helpers.get_name(out)
def test_ucaip_generated_create_training_pipeline_custom_job_sample(
capsys, shared_state):
create_training_pipeline_custom_job_sample.create_training_pipeline_custom_job_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
model_display_name=f"Temp Model for {DISPLAY_NAME}",
container_image_uri='gcr.io/ucaip-sample-tests/mnist-custom-job:latest',
base_output_directory_prefix=
'gs://ucaip-samples-us-central1/training_pipeline_output')
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_create_data_labeling_job_active_learning_sample(capsys, shared_state):
create_data_labeling_job_active_learning_sample.create_data_labeling_job_active_learning_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset=f"projects/{PROJECT_ID}/locations/{LOCATION}/datasets/{DATASET_ID}",
instruction_uri=INSTRUCTIONS_GCS_URI,
inputs_schema_uri=INPUTS_SCHEMA_URI,
annotation_spec=ANNOTATION_SPEC,
api_endpoint=API_ENDPOINT,
)
out, _ = capsys.readouterr()
# Save resource name of the newly created data labeing job
shared_state["data_labeling_job_name"] = helpers.get_name(out)
def test_ucaip_generated_deploy_model_sample(capsys, shared_state):
assert shared_state["endpoint_name"] is not None
# Deploy existing image classification model to endpoint
deploy_model_sample.deploy_model_sample(
project=PROJECT_ID,
model_name=MODEL_NAME,
deployed_model_display_name=f"temp_deploy_model_test_{uuid4()}",
endpoint_id=shared_state["endpoint_name"].split("/")[-1],
)
# Store deployed model ID for undeploying
out, _ = capsys.readouterr()
assert "deploy_model_response" in out
shared_state["deployed_model_id"] = helpers.get_name(out=out, key="id")
def test_ucaip_generated_create_data_labeling_job_sample(capsys, shared_state):
dataset_name = f"projects/{PROJECT_ID}/locations/{LOCATION}/datasets/{DATASET_ID}"
create_data_labeling_job_video_sample.create_data_labeling_job_video_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
instruction_uri=INSTRUCTIONS_GCS_URI,
dataset=dataset_name,
annotation_spec=ANNOTATION_SPEC,
api_endpoint=API_ENDPOINT,
)
out, _ = capsys.readouterr()
# Save resource name of the newly created data labeing job
shared_state["data_labeling_job_name"] = helpers.get_name(out)
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):
shared_state["cancel_batch_prediction_job_timeout"] = 300
create_training_pipeline_sample.create_training_pipeline_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
training_task_definition=TRAINING_DEFINITION_GCS_PATH,
dataset_id=DATASET_ID,
model_display_name=f"Temp Model for {DISPLAY_NAME}",
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):
# The return of the cancellation can be flaky; max of 20 runs was 215 sec.
shared_state["cancel_batch_prediction_job_timeout"] = 300
create_training_pipeline_tabular_regression_sample.create_training_pipeline_tabular_regression_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name=f"Temp Model for {DISPLAY_NAME}",
target_column=TARGET_COLUMN,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_ucaip_generated_upload_model_explain_tabular_managed_constainer_sample(
capsys, shared_state):
upload_model_explain_tabular_managed_container_sample.upload_model_explain_tabular_managed_container_sample(
display_name=DISPLAY_NAME,
artifact_uri=ARTIFACT_URI,
container_spec_image_uri=IMAGE_URI,
project=PROJECT_ID,
input_tensor_name=INPUT_TENSOR_NAME,
output_tensor_name=OUTPUT_TENSOR_NAME,
feature_names=[
"crim", "zn", "indus", "chas", "nox", "rm", "age", "dis", "rad",
"tax", "ptratio", "b", "lstat"
])
out, _ = capsys.readouterr()
shared_state["model_name"] = helpers.get_name(out, key="model")
def test_create_training_pipeline_custom_training_managed_dataset_sample(
capsys, shared_state, pipeline_client):
create_training_pipeline_custom_training_managed_dataset_sample.create_training_pipeline_custom_training_managed_dataset_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
model_display_name=MODEL_DISPLAY_NAME,
dataset_id=DATASET_ID,
annotation_schema_uri=ANNOTATION_SCHEMA_URI,
training_container_spec_image_uri=TRAINING_CONTAINER_SPEC_IMAGE_URI,
model_container_spec_image_uri=MODEL_CONTAINER_SPEC_IMAGE_URI,
base_output_uri_prefix=BASE_OUTPUT_URI_PREFIX,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_ucaip_generated_create_batch_prediction_job_bigquery_sample(
capsys, shared_state):
model_name = f"projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}"
create_batch_prediction_job_bigquery_sample.create_batch_prediction_job_bigquery_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
model_name=model_name,
bigquery_source_input_uri=BIGQUERY_SOURCE_INPUT_URI,
bigquery_destination_output_uri=BIGQUERY_DESTINATION_OUTPUT_URI,
instances_format=INSTANCES_FORMAT,
predictions_format=PREDICTIONS_FORMAT,
)
out, _ = capsys.readouterr()
# Save resource name of the newly created batch prediction job
shared_state["batch_prediction_job_name"] = helpers.get_name(out)
def training_pipeline_id(capsys):
create_training_pipeline_sample.create_training_pipeline_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
training_task_definition=TRAINING_DEFINITION_GCS_PATH,
dataset_id=DATASET_ID,
model_display_name=f"Temp Model for {DISPLAY_NAME}",
)
out, _ = capsys.readouterr()
training_pipeline_name = helpers.get_name(out)
assert "/" in training_pipeline_name
training_pipeline_id = training_pipeline_name.split("/")[-1]
yield training_pipeline_id
delete_training_pipeline_sample.delete_training_pipeline_sample(
project=PROJECT_ID, training_pipeline_id=training_pipeline_id)
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):
create_training_pipeline_tabular_forecasting_sample.create_training_pipeline_tabular_forecasting_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name="permanent_tabular_forecasting_model",
target_column=TARGET_COLUMN,
time_series_identifier_column="county",
time_column="date",
static_columns=["state_name"],
time_variant_past_only_columns=["deaths"],
time_variant_past_and_future_columns=["date"],
forecast_window_end=10,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):
shared_state["cancel_batch_prediction_job_timeout"] = 300
create_training_pipeline_tabular_forecasting_sample.create_training_pipeline_tabular_forecasting_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
dataset_id=DATASET_ID,
model_display_name="permanent_tabular_forecasting_model",
target_column=TARGET_COLUMN,
time_series_identifier_column="county",
time_column="date",
time_series_attribute_columns=["state_name"],
unavailable_at_forecast=["deaths"],
available_at_forecast=["date"],
forecast_horizon=10,
)
out, _ = capsys.readouterr()
assert "response:" in out
# Save resource name of the newly created training pipeline
shared_state["training_pipeline_name"] = helpers.get_name(out)
import random
import sqlite3
import string
from PyQt5.QtWidgets import *
from PyQt5 import QtCore
from PyQt5.QtGui import *
import sys
# create a Window class
from helpers import get_name
NAME = get_name().replace('-', '_')
letters = string.ascii_lowercase
GAME_NAME = ''.join(random.choice(letters) for i in range(10))
from trainingWin import TrainingWin
from connect_game import ConnectGame
from create_game import CreateGame
training_window = None
class Window(QMainWindow):
# constructor
def __init__(self):
super().__init__()
self.setGeometry(100, 100, 800, 600)
self.setWindowTitle("New Game")
self.main_menu()
def clean_roster_data(version):
if version == '11':
ext = '.xls'
else:
ext = '.xlsx'
teams = helpers.get_teams(version)
for team in teams:
print(team)
if version == '2002':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '2003':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df['First Name'], df['Last Name'] = df['Name'].str.split(' ',
1).str
df.rename(columns={'Overall Rating': 'Overall'}, inplace=True)
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '2004':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df['First Name'], df['Last Name'] = df['Name'].str.split(' ',
1).str
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '2005' or version == '06':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df.rename(columns={
'FIRSTNAME': 'First Name',
'LASTNAME': 'Last Name',
'OVERALLRATING': 'Overall'
},
inplace=True)
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '07':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df.rename(columns={
'PLYR_FIRSTNAME': 'First Name',
'PLYR_LASTNAME': 'Last Name',
'PLYR_OVERALLRATING': 'Overall'
},
inplace=True)
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '08':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df.rename(columns={
'First_Name': 'First Name',
'Last_Name': 'Last Name',
'Overall_Rating': 'Overall'
},
inplace=True)
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '09':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df.rename(columns={
'FIRSTNAME': 'First Name',
'LASTNAME': 'Last Name',
'OVERALL': 'Overall'
},
inplace=True)
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '10':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df.rename(columns={
'First': 'First Name',
'Last': 'Last Name',
'OVR': 'Overall',
'POS': 'Position'
},
inplace=True)
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '11':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df.rename(columns={
'FIRST NAME': 'First Name',
'LAST NAME': 'Last Name',
'OVERALL RATING': 'Overall',
'POSITION': 'Position'
},
inplace=True)
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '12':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df = df.drop(df[df.Name == 'Name'].index)
df.dropna(subset=['Name'], inplace=True)
df['First Name'], df['Last Name'] = df['Name'].str.split(' ',
1).str
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '13' or version == '17' or version == '18' or version == '25':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '15':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df.rename(columns={
'FIRST': 'First Name',
'LAST': 'Last Name',
'OVERALL RATING': 'Overall',
'POSITION': 'Position'
},
inplace=True)
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '16':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df.rename(columns={'OVR': 'Overall'}, inplace=True)
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
if version == '19':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=0)
df['First Name'], df['Last Name'] = df['Name'].str.split(' ',
1).str
df = df[['Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv')
if version == '20':
df = pd.read_excel('MaddenRosters/' + version + '/' + team + ext,
index_col=None)
df['Team'] = helpers.get_name(team)
df['First Name'], df['Last Name'] = df['Name'].str.split(' ',
1).str
df = df[['Team', 'Position', 'First Name', 'Last Name', 'Overall']]
df['Version'] = version
df.to_csv('MaddenRosters/' + version + '/' + team + '.csv',
index=False)
def get_name_and_pw():
print("You will need your login information for https://www.dmr.nd.gov/oilgas/")
global username
global password
username = get_name()
password = get_pass()
# session. if a session refered by i has a value of 5, it
# means that it has 5 correlated objects with a predecessor at
# exactly i times steps behind.
session_sequences = {}
# keeps track of the number of events taken into account for one
# session in order to normalize
session_norm = {}
for file_path in hp.get_csv():
# iterating over the files (sessions)
session_id = hp.get_id(file_path)
# The Id in an integer
session_id = int(session_id)
session_norm[session_id] = 0
# the name of the session is used as a table name in the
# database
session_name = hp.get_name(file_path)
# array of arrays, stores for each coordinated object the
# list of timespans between student switches
event_time_differences = []
con = lite.connect('mysteries.db')
with con:
cur = con.cursor()
# Gets the list of all distinct objects
cur.execute("\
SELECT distinct_objects FROM (\
SELECT DISTINCT object_nature || object_id\
AS distinct_objects\
FROM %s\
UNION\
SELECT DISTINCT 'group' || on_group\
