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How to use tabular_output method in avocado

Best Python code snippet using avocado_python

app.py

Source:app.py

...4from tabulate import tabulate5app = Flask(__name__)6number_stations = pd.read_csv("./data/number_stations.csv")7# Print tabular data8def tabular_output(i):9    print(tabulate(i, tablefmt='simple', headers=["ID", "Name", "Country", "Active Counterparts", "Inactive Counterparts", "Nickname", "Status", "Frequency", "Voice", "Emission Mode", "Location"], showindex='never'))  10# List all stations11@app.route('/')12def index():13    f = number_stations.to_json()14    return json.loads(f)15# List all stations under the column Name16@app.route('/station_names')17def station_names():18    # Filter the DataFrame columns to retrieve all stations under 'Name' column19    st_name = number_stations['Name']20    # If the DataFrame returns a response, convert the Series to a JSON string21    f = st_name.to_json()22    # Then convert the JSON string to a dict to properly format it when sending the response to the client23    return json.loads(f)24# Check for active stations25@app.route('/is_active_station')26def is_active_station():27    # Pass the parameter from the URL and check if it matches against an active station28    active_df = number_stations[number_stations['Status'] == 'Active']29    # Use tabulate to print the response to the terminal30    tabular_output(active_df)31    r = active_df.to_json()32    return json.loads(r)33# Check for inactive stations34@app.route('/is_inactive_station')35def is_inactive_station():36    # Pass the parameter from the URL and check if it matches against an inactive station37    inactive_df = number_stations[number_stations['Status'] == 'Inactive']38    # Use tabulate to print the response to the terminal39    tabular_output(inactive_df)40    r = inactive_df.to_json()41    return json.loads(r)42# Filter by station names43@app.route('/filter_station_names/<name>')44def filer_station_names(name):45    if name is None:46        return jsonify({ 'error': 'Station name cannot be blank' })47    # Pass the parameter from the URL and check if it matches against a known station name48    f_name = number_stations[number_stations['Name'] == name]49    # Use tabulate to print the response to the terminal50    tabular_output(f_name)51    # If the DataFrame returns empty, send an error back to the client52    if f_name.empty:53        return jsonify({ 'error': 'That station does not exist'})54    # If the DataFrame returns a response, convert the Series to a JSON string55    r = f_name.to_json()56    # Then convert the JSON string to a dict to properly format it when sending the response to the client57    return json.loads(r)58# Filter by station nicknames59@app.route('/filter_station_nickname/<nickname>')60def filer_station_nickname(nickname):61    if nickname is None:62        return jsonify({ 'error': 'Station nickname cannot be blank' })63    # Pass the parameter from the URL and check if it matches against a known station nickname64    f_nickname = number_stations[number_stations['Nickname'].str.contains(nickname)]65    # Use tabulate to print the response to the terminal66    tabular_output(f_nickname)67    # If the DataFrame returns empty, send an error back to the client68    if f_nickname.empty:69        return jsonify({ 'error': 'That station does not exist'})70    # If the DataFrame returns a response, convert the Series to a JSON string71    r = f_nickname.to_json()72    # Then convert the JSON string to a dict to properly format it when sending the response to the client73    return json.loads(r)74# Filter by station id's75@app.route('/filter_by_id/<id>')76def filter_by_id(id):77    if id is None:78        return jsonify({ 'error': 'ID cannot be empty' })79    # Pass the parameter from the URL and check if it matches against a known station id80    f_id = number_stations[number_stations['ID'] == id]81    # Use tabulate to print the response to the terminal82    tabular_output(f_id)83    # If the DataFrame returns empty, send an error back to the client84    if f_id.empty:85        return jsonify({ 'error': 'That ID does not exist'})86    # If the DataFrame returns a response, convert the Series to a JSON string87    r = f_id.to_json()88    # Then convert the JSON string to a dict to properly format it when sending the response to the client89    return json.loads(r)90# Filter by station locations91@app.route('/filter_by_location/<loc>')92def filter_by_location(loc):93    if loc is None:94        return jsonify({ 'error': 'Location cannot be empty' })95    # Pass the parameter from the URL and check if it matches against a known location - 'na=False' ignores any possibly empty values in the column to avoid throwing an error96    f_loc = number_stations[number_stations['Location'].str.contains(loc, na=False)]97    # Use tabulate to print the response to the terminal98    tabular_output(f_loc)99    # If the DataFrame returns empty, send an error back to the client100    if f_loc.empty:101        return jsonify({ 'error': 'That location does not exist'})102    # If the DataFrame returns a response, convert the Series to a JSON string103    r = f_loc.to_json()104    # Then convert the JSON string to a dict to properly format it when sending the response to the client105    return json.loads(r)106# Catch all HTTP 404's and return the below JSON message107@app.errorhandler(404)108def route_not_found(e):...

preprocess.py

Source:preprocess.py

1import os2from pathlib import Path3import numpy as np4import pandas as pd5import torch6class TabularData(object):7    r"""Object to store train + testing data for ML runs8    """9    def __init__(10            self,11            X_train: np.array,12            X_test: np.array,13            y_train: np.array,14            y_test: np.array15    ):16        self.X_train = pd.DataFrame(X_train)17        self.X_test = pd.DataFrame(X_test)18        self.y_train = pd.DataFrame(y_train)19        self.y_test = pd.DataFrame(y_test)20        self.save()21    def save(self):22        os.makedirs('output/tabular_output', exist_ok=True)23        self.X_train.to_csv('output/tabular_output/X_train.csv')24        self.y_train.to_csv('output/tabular_output/y_train.csv')25        self.X_test.to_csv('output/tabular_output/X_test.csv')26        self.y_test.to_csv('output/tabular_output/y_test.csv')27class GraphsToTabularConverter(object):28    def __init__(self, data_dir):29        self.data_dir = data_dir30        self.train_files = Path(data_dir).joinpath('train_files.csv')31        self.test_files = Path(data_dir).joinpath('test_files.csv')32        self.tabular_data: TabularData = TabularData([], [], [], [])33        self.execute()34    def execute(self):35        train_df = pd.read_csv(self.train_files)36        train_df['x,y'] = train_df['filename'].apply(self.graph_to_df)37        test_df = pd.read_csv(self.test_files)38        test_df['x,y'] = test_df['filename'].apply(self.graph_to_df)39        X_train = self.x_list_to_numpy(train_df)40        y_train = self.y_list_to_numpy(train_df)41        X_test = self.x_list_to_numpy(test_df)42        y_test = self.y_list_to_numpy(test_df)43        self.tabular_data = TabularData(X_train, X_test, y_train, y_test)44    @staticmethod45    def graph_to_df(file):46        graph = torch.load(file)47        x = graph.x48        y = graph.y49        return [x, y]50    @staticmethod51    def x_list_to_numpy(data_df):52        data = data_df['x,y'].apply(lambda x: x[0]).tolist()53        data = np.array([[__.numpy() for __ in _] for _ in np.array(data).T])54        n_pts = data.shape[0]55        n_nodes = data.shape[1]56        data = data.reshape(n_pts, n_nodes)57        return data58    @staticmethod59    def y_list_to_numpy(data_df):60        y_list = data_df['x,y'].apply(lambda x: x[1]).tolist()61        y_np = np.array([y[0].numpy() for y in np.array(y_list).T])...

test_utils_astring.py

Source:test_utils_astring.py

1import unittest2from avocado.utils import astring3class AstringUtilsTest(unittest.TestCase):4    def test_tabular_output(self):5        self.assertEqual(astring.tabular_output([]), "")6        self.assertEqual(astring.tabular_output([],7                                                header=('C1', 'C2', 'C3')),8                         "C1 C2 C3")9        self.assertEqual(astring.tabular_output([['v11', 'v12', 'v13']]),10                         "v11 v12 v13")11        self.assertEqual(astring.tabular_output([['v11', 'v12', 'v13'],12                                                 ['v21', 'v22', 'v23']],13                                                header=('C1', 'C2', 'C3')),14                         "C1  C2  C3" + "\n" +15                         "v11 v12 v13" + "\n" +16                         "v21 v22 v23")17        self.assertEqual(astring.tabular_output([['v11', 'v12', ''],18                                                 ['v21', 'v22', 'v23']],19                                                header=('C1', 'C2', 'C3')),20                         "C1  C2  C3" + "\n" +21                         "v11 v12 " + "\n" +22                         "v21 v22 v23")23        self.assertEqual(astring.tabular_output([['v11', 'v12', ''],24                                                 ['v21', 'v22', 'v23']],25                                                header=('C1', 'C2', 'C3'),26                                                strip=True),27                         "C1  C2  C3" + "\n" +28                         "v11 v12" + "\n" +29                         "v21 v22 v23")30        self.assertEqual(astring.tabular_output([['v11', 'v12', ''],31                                                 ['v2100', 'v22', 'v23'],32                                                 ['v31', 'v320', 'v33']],33                                                header=('C1', 'C02', 'COL3')),34                         "C1    C02  COL3" + "\n" +35                         "v11   v12  " + "\n" +36                         "v2100 v22  v23" + "\n" +37                         "v31   v320 v33")38if __name__ == '__main__':...

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