def __init__(self, num_notes=20, starting_note="0"): """ __init__ """ self.res_path = str(Path("components/res/")) self.data_table = Table.read_table(self.res_path + "/probability_table.csv") self.notes = self.data_table.column("octave") self.num_notes = num_notes self.starting_note = starting_note
def from_file(self, filepath_or_buffer, *args, **vargs): try: table = Table.read_table(filepath_or_buffer, *args, **vargs) df_name = find_name() return self.create_with_table_wrap(table, df_name) except FileNotFoundError: red_print(f"File {filepath_or_buffer} does not exist!") except UserError as err: red_print(err)
#set the table name here table_name = "ahlxgcalc"+str(i) try: create_table = '''CREATE TABLE %s ( XLocation INT, YLocation INT, xG FLOAT )''' cursor.execute(create_table % table_name) connection.commit() except (Exception, psycopg2.DatabaseError) as error : print ("Error while creating PostgreSQL table", error) # events is a csv file containing all X Location and Y Location values # based on the database it will calculate an xG value events = Table.read_table("input_simple.csv") #populate each ahlxgCalc table w/ datapoints from CSV for i in range(0,5): #set the table name here table_name = "ahlxgcalc"+str(i) print("Populating %s with all x,y points" % table_name) for event_row in events.rows: values = """INSERT INTO %s (XLocation, YLocation) VALUES (%%s,%%s)""" cursor.execute(values % table_name, [int(event_row[0]),int(event_row[1])]) #variable sized smoothing swaths for different strengths smoothing_swath = [5,30,39,28,8] for i in range(0,5): #set the table name here
def read_table(self, *args, **kwargs): return self._fix_(Table.read_table(*args, **kwargs))
# Importing Data Science Libraries import pandas as pd import numpy as np import scipy as sp import matplotlib.pyplot as plt plt.style.use('fivethirtyeight') get_ipython().run_line_magic('matplotlib', 'inline') from datascience import Table from datascience.predicates import are from datascience.util import * # Importing Data as Table general_tbl = Table.read_table("DemogFinalProject.csv") general_tbl = general_tbl.drop("BPL", "BPLD", "YRIMMIG") general_tbl # Creating Tables Filtered by Year and Origin, Grouped By Metropolitan Area grouped2000 = general_tbl.where("YEAR", are.equal_to(2000)).group("MET2013") grouped2000 = grouped2000.where("MET2013", are.not_equal_to(0)) num_hispanic2000 = make_array() af_wage2000 = make_array() for i in np.arange(grouped2000.num_rows): tbl_specific = general_tbl.where( "MET2013", are.equal_to(grouped2000.column("MET2013").item(i))) num_hispanic2000 = np.append( num_hispanic2000, (tbl_specific.where("HISPAN", are.not_equal_to(0)).num_rows) /
from flask import Flask, jsonify,request from datascience import Table from intervaltree import Interval, IntervalTree prefixcode = '' t = Table.read_table('CourseWhere.csv') trees = {day:IntervalTree() for day in ['M','T','W','R','F','S']} for row in t.rows: for day in row[5]: trees[day][row[6]:row[7]] = row room_table = t.group('Building',collect=set).select(['Building','Facility set']) room_list = {building.lower():rooms for building,rooms in zip(room_table['Building'],room_table['Facility set'])} app = Flask(__name__) def class_to_dict(clas): convert = lambda x: x if isinstance(x,str) else int(x) return {label:convert(v) for label,v in zip(t.column_labels,clas)} @app.route(prefixcode+'/rooms/<building>/<room>') def get_room(building,room): weekday = request.args.get('day', 'M') if weekday not in "MTWRFS": weekday = 'M' values = [class_to_dict(v) for v in t.where('Building',building).where('Room',room).rows] values = [v for v in values if weekday in v['Days']] values = sorted(values,key=lambda x:x['Start'])