def plot_top_names_count_proportion_derivative(timelines: TimelineCollection,
fname: str) -> None:
print('Generating top count plots')
matrix = []
first_year = timelines.first_year
last_year = timelines.last_year
smooth = 2
for sex in ('M', 'F'):
name_positions = timelines.get_top_count_list(first_year,
last_year + 1, 1, sex)
count_plots = []
proportion_plots = []
derivative_plots = []
for name_pos in name_positions:
years, counts = timelines.get_name_count_over_time(name_pos.name)
plot = plotter.Plot(
years,
counts,
title=
f'{name_pos.name} top from {name_pos.start_year} till {name_pos.end_year}'
)
count_plots.append(plot)
years, proportions = timelines.get_name_proportion_over_time(
name_pos.name)
plot = plotter.Plot(years,
proportions,
title=f'{name_pos.name} proportions')
proportion_plots.append(plot)
years, derivatives = timelines.get_name_proportion_derivative(
name_pos.name, smooth)
plot = plotter.Plot(years, derivatives,
f'{name_pos.name} proportion derivative')
derivative_plots.append(plot)
matrix.append(count_plots)
matrix.append(proportion_plots)
matrix.append(derivative_plots)
plotter.save_jagged_matrix(matrix, fname)
def plot_top_derivative_names(timelines: TimelineCollection,
fname: str) -> None:
matrix = []
first_year = timelines.first_year
last_year = timelines.last_year
plot_count = 10
smooth = 2
for sex in ('M', 'F'):
top_derivatives = timelines.get_n_top_derivative_names(
sex, plot_count, smooth)
count_plots = []
proportion_plots = []
derivative_plots = []
for name in top_derivatives:
years, counts = timelines.get_name_count_over_time(name)
plot = plotter.Plot(years, counts, title=f'{name.name} counts')
count_plots.append(plot)
years, proportions = timelines.get_name_proportion_over_time(name)
plot = plotter.Plot(years,
proportions,
title=f'{name.name} proportions')
proportion_plots.append(plot)
years, derivatives = timelines.get_name_proportion_derivative(
name, smooth)
plot = plotter.Plot(years,
derivatives,
title=f'{name.name} proportion derivative')
derivative_plots.append(plot)
matrix.append(count_plots)
matrix.append(proportion_plots)
matrix.append(derivative_plots)
plotter.save_jagged_matrix(matrix, fname)
def plot_commonality_quantiles_by_decade(timelines: TimelineCollection,
granularity: int, fname: str) -> None:
print('Generating name commonality preference histograms')
matrix = []
first_year = timelines.first_year
last_year = timelines.last_year
step = 10
for sex in ('M', 'F'):
row = []
for start_year in range(first_year, last_year + 1, step):
Y = timelines.get_commonality_quantiles_for_era(
start_year, start_year + step, granularity, sex)
X = list(range(1, granularity + 1))
sex_word = 'boy' if sex == 'M' else 'girl'
title = f'distribution of {sex_word} from {start_year} till {start_year+step}'
plot = plotter.Plot(X, Y, title=title, display='bar')
row.append(plot)
matrix.append(row)
plotter.save_jagged_matrix(matrix, fname)
def plot_name_frequency_histogram(timelines: TimelineCollection,
fname: str) -> None:
print('Generating top ten histograms')
matrix = []
step = 10
topN = 10
first_year = timelines.first_year
last_year = timelines.last_year
for sex in ('M', 'F'):
babes = []
for start_year in range(first_year, last_year + 1, step):
end_year = start_year + step
top_count_names = timelines.get_top_counts_names_for_era(
start_year, end_year, sex, topN)
total = timelines.get_total_for_era(start_year, end_year, sex)
top_total = 0
X = []
Y = []
for count, name in top_count_names:
X.append(name.name)
Y.append(count / total)
top_total += count
percent_in_top = 100 * top_total / total
sex_word = 'boys' if sex == 'M' else 'girls'
title = f'{percent_in_top:.2g}% {sex_word} had top {topN} names from {start_year} till {end_year}'
plot = plotter.Plot(X, Y, title=title, display='bar')
babes.append(plot)
matrix.append(babes)
plotter.save_jagged_matrix(matrix, fname)
import plotter
from timelines import get_timelines, Name
from expected_age import get_life_table
timelines = get_timelines()
lifetable = get_life_table()
while True:
name = input('Name: ')
name = name[0].upper() + name[1:].lower()
matrix = [[], []]
for sex in ('M', 'F'):
name_gender = Name(name, sex)
years, counts = timelines.get_name_count_over_time(name_gender)
if not counts:
print(f'Name: {name}, Sex: {sex} not found.')
continue
pronoun = 'boys' if sex == 'M' else 'girls'
expected_age = lifetable.get_expected_age(name_gender)
print(f'Expected age for {pronoun} named {name} is {expected_age}')
count_plot = plotter.Plot(years, counts, title=f'Counts of {pronoun} named {name}')
matrix[0].append(count_plot)
years, proportions = timelines.get_name_proportion_over_time(name_gender)
proportion_plot = plotter.Plot(years, proportions, title=f'Proportion of {pronoun} named {name}')
matrix[1].append(proportion_plot)
plotter.display_jagged_matrix(matrix)
os.makedirs(writer_path)
yes = ['y', 'yes']
choice = 'y'
if test_model and refresh:
choice =input('Are you sure you want to refresh the data for a test run?')
if not choice.lower() in yes:
refresh = False
gd = getData(tickers, config_path, data_path, meta_data_path, weather_data, refresh_data=refresh)
dat, meta_data, titles = gd.read_data()
data = dat.values
if plot_inputs:
dplot = plotter.Plot(dat)
dplot.plot_shape()
#display_output(data, titles, print_data=False)
data_prepper = prepareData(data, time_steps, batch_size, shift, test_percent)
data_prepper.prep_train_data()
x, y, n_features = data_prepper.output_train_data()
plotter.Plot(x)
#display_output(x[0], titles)
assert_error = f'Input length {len(x)} not equal to target {len(y)}'
assert len(x) == len(y), assert_error
tf.reset_default_graph()
lstm_graph = tf.Graph()
train_cost = []