def update_rosters(gsheet_id, sheet_name='nhl_rosters', savefile=False): rosters = util.get_rosters() if savefile: util.save_csv(sheet_name + '.csv', rosters) else: push_update_to_sheet(rosters, gsheet_id, sheet_name) return
def update_stats(endYearOfSeason, regularSeason, gsheet_id, sheet_name='nhl_leaders', savefile=False): # Get skater and goalie stats, combine them in a dataframe. skaters = get_skater_stats(endYearOfSeason, regularSeason) goalies = get_goalie_stats(endYearOfSeason, regularSeason) all_stats = pd.concat([skaters, goalies], axis=0) all_stats = all_stats.sort_values('playerName') all_stats.fillna(0, inplace=True) if savefile: util.save_csv(sheet_name + '.csv', all_stats) else: push_update_to_sheet(all_stats, gsheet_id, sheet_name)
def main(): start_season = 1995 end_season = 2020 regular_season = True # Only need to update drafts once a year if False: drafts = util.get_drafts(start_season, end_season) # Takes a loonnnggg time to run util.save_csv("drafts.csv", drafts) else: drafts = util.load_csv("drafts.csv") if True: players = get_career_stats(start_season, end_season, regular_season) util.save_csv("players.csv", players) rosters = util.get_rosters() util.save_csv("rosters.csv", rosters) else: players = util.load_csv("players.csv") rosters = util.load_csv("rosters.csv") drafts = update_team_names(drafts) drafts = drafts.sort_values(["team.name", "year", "round"], ascending=[1, 0, 1]) # Merge all data into one dataframe drafts['name_lower'] = drafts['prospect.fullName'].str.lower() players['name_lower'] = players['playerName'].str.lower() rosters['name_lower'] = rosters['fullName'].str.lower() draft_data = pd.merge(drafts, players, how="left", on="name_lower", sort=False, suffixes=("", "_x")) draft_data = pd.merge(draft_data, rosters, how="left", on="name_lower", sort=False, suffixes=("", "_y")) # Update positions and set statuses for each filter in the visuzalization. Then get rid of unneeded columns. draft_data = set_statuses(draft_data) draft_data = clean_data(draft_data) draft_data = reduce_columns(draft_data) util.save_csv("draft_data.csv", draft_data)
beta = [] #Sound velocities (m/s) c= {'Water':1480, 'Stainless Steel':5800, 'Air':330, 'Polystyrene':2400} if continue_from_last==False: # Get path to save simulation results paths = utilities.get_paths("./results/") print("#"*40) print('Saving initial conditions') # Saving initial conditions utilities.save_csv(paths[2],iteration,{key: particles[key] for key in fluid_array}) utilities.save_moving_vtk(paths[0],iteration,{key: particles[key] for key in fluid_array}) try: utilities.save_boundary_vtk(paths[0],{key: particles[key] for key in boundary_array}) except: pass utilities.add_to_group(paths[0],iteration,time,paths[1]) else: paths = [pull_last[2] + "/vtk", pull_last[3],pull_last[2] + "/csv"] print("#"*40) # Stop when simulation time reaches final time while time < final_time: # Cleaning Force fields
def do_linear_search(test=False, test_dim=32): """ Linear search function... Returns ------- None. """ logger = ut.get_logger() device = "cuda" model_name = "EDSR" config = toml.load("../config.toml") run = config["run"] scale = int(config["scale"]) if config["scale"] else 4 # device information _, device_name = ut.get_device_details() total, _, _ = ut.get_gpu_details( device, "\nDevice info:", logger, print_details=False ) log_message = ( "\nDevice: " + device + "\tDevice name: " + device_name + "\tTotal memory: " + str(total) ) logger.info(log_message) ut.clear_cuda(None, None) state = "Before loading model: " total, used, _ = ut.get_gpu_details(device, state, logger, print_details=True) model = md.load_edsr(device=device) state = "After loading model: " total, used, _ = ut.get_gpu_details(device, state, logger, print_details=True) # ============================================================================= # file = open("temp_max_dim.txt", "r") # line = file.read() # max_dim = int(line.split(":")[1]) # ============================================================================= config = toml.load("../config.toml") max_dim = int(config["max_dim"]) if test == False: detailed_result, memory_used, memory_free = result_from_dimension_range( device, logger, config, model, 1, max_dim ) else: detailed_result, memory_used, memory_free = result_from_dimension_range( device, logger, config, model, test_dim, test_dim ) if test == False: # get mean # get std mean_time, std_time = ut.get_mean_std(detailed_result) mean_memory_used, std_memory_used = ut.get_mean_std(memory_used) mean_memory_free, std_memory_free = ut.get_mean_std(memory_free) # make folder for saving results plt_title = "Model: {} | GPU: {} | Memory: {} MB".format( model_name, device_name, total ) date = "_".join(str(time.ctime()).split()) date = "_".join(date.split(":")) foldername = date os.mkdir("results/" + foldername) # plot data ut.plot_data( foldername, "dimension_vs_meantime", mean_time, "Dimensionn of Patch(nxn)", "Mean Processing Time: LR -> SR, Scale: {} ( {} runs )".format(scale, run), mode="mean time", title=plt_title, ) ut.plot_data( foldername, "dimension_vs_stdtime", std_time, "Dimension n of Patch(nxn)", "Std of Processing Time: LR -> SR, Scale: {} ( {} runs )".format( scale, run ), mode="std time", title=plt_title, ) ut.plot_data( foldername, "dimension_vs_meanmemoryused", mean_memory_used, "Dimension n of Patch(nxn)", "Mean Memory used: LR -> SR, Scale: {} ( {} runs )".format(scale, run), mode="mean memory used", title=plt_title, ) ut.plot_data( foldername, "dimension_vs_stdmemoryused", std_memory_used, "Dimension n of Patch(nxn)", "Std Memory Used: LR -> SR, Scale: {} ( {} runs )".format(scale, run), mode="std memory used", title=plt_title, ) ut.plot_data( foldername, "dimension_vs_meanmemoryfree", mean_memory_free, "Dimension n of Patch(nxn)", "Mean Memory Free: LR -> SR, Scale: {} ( {} runs )".format(scale, run), mode="mean memory free", title=plt_title, ) ut.plot_data( foldername, "dimension_vs_stdmemoryfree", std_memory_free, "Dimension n of Patch(nxn)", "Std Memory Free: LR -> SR, Scale: {} ( {} runs )".format(scale, run), mode="std memory free", title=plt_title, ) # save data ut.save_csv( foldername, "total_stat", device, device_name, total, mean_time, std_time, mean_memory_used, std_memory_used, mean_memory_free, std_memory_free, )