OT: bidets
- Thread starter NebChicago
- Start date
You don't wanna see them while I'm at work.
First thing I do after coming home is finish the job the TP failed to do.
sorry, it was my attempt at a joke, but bidets are nothing to joke about.. 100% legit and way more sanitary.
It's a well known fact you'll get 96.438% of your money back on one of these when you sell your home.
TOTO SW3036R#01 WASHLET K300. Don’t skimp on your butt. Buy the best. If you shop around and are patient price is often around $500. (It does everything but auto open/close and turn on a light, which I didn’t want. Has almost “unlimited” warm water, warm seat, warm air dry, deodorizer etc.. Electrician must extend power to wall next to toilet.) As for any posts here that say the top end models don’t work well, that’s B.S. as the entire very clean population of Japan can attest.
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The crap falls on the bidet and gets all over it. It HAS to, right? Then theres someone elses crap resuidue on it that youre blowing up into your butthole?It's kinda like taking a shower.
Wouldn't taking a shower just wash all the dirt onto your feet?
When you blast your ***, the water goes down from betwixt the *** cheeks. Nature's pocket contains the dookie butter and the water washes into the toilet while toilet paper just spreads it all over the place like stinky peanut butter.
And again is it a wand? How is it accurate? Does water get on the toilet?
Im not against the bidet, I just cant comprehend it. Mind blown.
Correct and yes. A bunch of bidet heathens in this thread. Streaming is for digital, not arses.
Only if you sit way way back and intentionally aim for it, and even then, it would be very difficult. The nozzle retracts when no water is used.
Any self-respecting man has a crapper in the basement that no one else uses! I know I do.
But cold water will not remove peanut butter from a plate or butter from a knife.
Shoulder surgeries are in my wife and I future. Just bought the Toto 450 all in one toilet- bidet combo. I’ve never used one before but it seems Husker Land has spoken.
No, cold water just bonds the paste more tightly to the arse.
For the guy who said high-end models work, as "clean" Japanese can attest, he obviously has never done the wet wipe test afterwards. I have tried all the high end units, including those in Japan, and my wet wipes did not lie.
There's a reason why Japanese men wear Sumo belts -- the Mark Mangino thing -- and the women wear loose-fitting kimonos. Gross.
The internet of things.Just got this model during prime days…
.
Or is it the thing of the internet?
I cure my ear wax, shape it into loaded dice and sell them to crooks. It's how I rage against the machine.
Bud, I'm going to share this with you and only you - no one else look!Is that the @scopeandtime model?
import timeit
start = timeit.default_timer()
from fredapi import Fred
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time, scipy.stats
import datetime as dt
from scipy.interpolate import interp1d
from sklearn.metrics import mean_squared_error
import statsmodels.formula.api as smf
import statsmodels.api as sm
from statsmodels.tsa.seasonal import seasonal_decompose
from log_progress import *
from sklearn.model_selection import train_test_split
import inspect
import matplotlib.pyplot as plt
import statsmodels.api as sm
from scipy import stats
from pmdarima import auto_arima
from pmdarima.arima import auto_arima
import warnings
from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter('ignore', ConvergenceWarning)
fred = Fred(api_key=<FRED api key>)
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 999)
import matplotlib.style as style
style.use('fivethirtyeight')
print('(_)_)IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIID')
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
def FredDataPull(series_id_list, ob_start, ob_end, units, frequency, aggregation_method):
all_data = {'SeriesDate':[], 'SeriesID':[],'Measure':[]}
for series_id in log_progress(series_id_list, every = 1):
df = pd.DataFrame(fred.get_series(series_id
,observation_start = ob_start
,observation_end = ob_end
,units = units
,frequency = frequency
,aggregation_method = aggregation_method
)
,columns=['Measure'])
time.sleep(1)
[idx.strftime('%Y-%m-%d') for idx in df.index]
df = df.reset_index()
df.columns = ['SeriesDate', 'Measure']
for items in df.values:
all_data['SeriesDate'].append(items[0])
all_data['Measure'].append(items[1])
all_data['SeriesID'].append(series_id)
df = pd.DataFrame(all_data)
df_pivot = df.pivot(columns = 'SeriesID', index='SeriesDate', values='Measure')
return df_pivot
def auto_arimathon(series_id, startdate, enddate, units, freq, agg_method, column_name, arima_freq):
data = FredDataPull(series_id_list = [series_id]
,ob_start = startdate
,ob_end = enddate
,units = units
,frequency = freq
,aggregation_method = agg_method)
data.dropna(inplace = True)
data.reset_index(inplace = True)
data.columns = ['SeriesDate', column_name]
df = data.dropna().set_index('SeriesDate')
y = np.array(df[column_name])
full_model = auto_arima(y, d=1, D=1, max_p=7, max_q=7,
max_P=7, max_Q=7, max_d=7, max_D=7,
max_order=7, m=arima_freq, seasonal=True,
error_action='ignore',suppress_warnings=True
#, exogenous = exog
#,n_jobs = -1
)
print(full_model.summary())
periods = 12
preds = full_model.predict(n_periods=12)
return preds
print('\n',preds)
def control_chart(series_id, startdate, enddate, series_units,series_freq, agg_method, sigma):
df = FredDataPull(series_id_list = [series_id]
,ob_start = startdate
,ob_end = enddate
,units = series_units
,frequency = series_freq
,aggregation_method = agg_method)
df.dropna(inplace = True)
mean = df[series_id].mean()
std = df[series_id].std()
upper_bound = mean + (sigma * std)
lower_bound = mean - (sigma * std)
fig, ax = plt.subplots(figsize=(16, 9))
df[series_id].plot(ax=ax, color='blue', linewidth=2)
ax.axhline(df[series_id].mean(), color='black', linewidth=1)
ax.fill_between(df.index, upper_bound, lower_bound, color='white')
ax.set_xlabel('Date')
ax.set_ylabel(series_id)
ax.grid(False)
plt.title('Control Chart - '+str(series_id), fontsize = 12)
plt.show();
chart_start_date = '2022-01-01'
next_date = '2023-07-01'
PPI = auto_arimathon(series_id = 'PPIFIS'
,startdate = None
,enddate = None
,units = 'lin'
,freq = 'm'
,agg_method = 'eop'
,column_name = 'PPI'
,arima_freq = 12)
FF = auto_arimathon(series_id = 'FEDFUNDS'
,startdate = None
,enddate = None
,units = 'lin'
,freq = 'm'
,agg_method = 'eop'
,column_name = 'PPI'
,arima_freq = 12)
WTI = auto_arimathon(series_id = 'DCOILWTICO'
,startdate = None
,enddate = None
,units = 'lin'
,freq = 'm'
,agg_method = 'eop'
,column_name = 'PPI'
,arima_freq = 12)
data = FredDataPull(series_id_list = ['CPIAUCSL', 'PPIFIS', 'FEDFUNDS', 'DCOILWTICO']
,ob_start = None
,ob_end = None
,units = 'lin'
,frequency = 'm'
,aggregation_method = 'eop')
data.dropna(inplace = True)
data.reset_index(inplace = True)
df = data.dropna().set_index('SeriesDate')
df['year'] = df.index.year
df['month'] = df.index.month
y = np.array(df['CPIAUCSL'])
exog = np.array(df[['PPIFIS', 'FEDFUNDS', 'DCOILWTICO']])
full_model = auto_arima(y, d=1, D=1, max_p=12, max_q=12,
max_P=12, max_Q=12, max_d=12, max_D=12,
max_order=12, m=12, seasonal=True,
error_action='ignore',suppress_warnings=True
, exogenous = exog
#,n_jobs = -1
)
print(full_model.summary());
exog_data = {'PPIFIS': PPI,
'FEDFUNDS': FF,
'DCOILWTICO': WTI
}
exog_df = pd.DataFrame(exog_data)
dt_index = pd.date_range(start=latest_date, periods=12, freq='MS')
exog_df['SeriesDate'] = dt_index
exog_df.set_index('SeriesDate', inplace = True)
exog = np.array(exog_df[['PPIFIS', 'FEDFUNDS', 'DCOILWTICO']])
periods = 12
preds = full_model.predict(n_periods=12, exogenous = exog)
exog_df['PredCPI'] = preds
exog_df = exog_df[['PPIFIS', 'FEDFUNDS', 'DCOILWTICO', 'PredCPI']]
exog_df.columns = ['PPIFIS', 'FEDFUNDS', 'DCOILWTICO', 'CPIAUCSL']
exog_df
df = df[['PPIFIS', 'FEDFUNDS', 'DCOILWTICO', 'CPIAUCSL']]
full_df = pd.concat([df, exog_df], axis=1)
df_grouped = full_df.groupby(full_df.columns, axis= 1)
df_merged = df_grouped.first()
df_merged.tail(13);
df_merged['CPI_12moPrev'] = df_merged['CPIAUCSL'].shift(12)
df_merged['Pred_CPI_Cng'] = df_merged['CPIAUCSL'] - df_merged['CPI_12moPrev']
df_merged['PredYoYInflation'] = (df_merged['Pred_CPI_Cng'] / df_merged['CPI_12moPrev'])*100
final_df = df_merged.tail(12)[['CPIAUCSL', 'PredYoYInflation']]
final_df.columns = ['Predicted CPI', 'Predicted YoY %']
final_df;
mask = df_merged.index < pd.to_datetime(latest_date)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=[24, 16])
df_merged.PredYoYInflation[mask].plot(linestyle='-', linewidth=3, color='#E24A33', ax=ax1)
df_merged.PredYoYInflation[~mask].plot(linestyle='--', linewidth=3, color='#348ABD', ax=ax1)
ax1.set_title("Inflation Prediction\n" , fontsize=14)
ax1.set_xlabel('')
ax1.set_ylabel('\nCPI YoY Change\n', fontsize=14)
ax1.tick_params(axis='both', which='major', labelsize=12)
ax1.legend(['Actual YoY Inflation', 'Predicted YoY Inflation'], fontsize=10)
ax1.grid(axis='x')
df_merged_recent = df_merged[df_merged.index >= chart_start_date]
mask = df_merged_recent.index < pd.to_datetime(latest_date)
df_merged_recent.PredYoYInflation[mask].plot(linestyle='-', linewidth=3, color='#E24A33', ax=ax2)
df_merged_recent.PredYoYInflation[~mask].plot(linestyle='--', linewidth=3, color='#348ABD', ax=ax2)
ax2.set_xlabel('Date\n', fontsize=14)
ax2.set_ylabel('CPI YoY Change\n', fontsize=12)
ax2.tick_params(axis='both', which='major', labelsize=12)
ax2.grid(axis='x')
plt.savefig('CPI.jpg')
end = timeit.default_timer()
print('Time elapsed (min): ', (end - start)/60)
Run the code!Is that the secret location coordinates to all your truck stop glory holes?