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OT: bidets

Bidets reduce anal itching and abrasions by 95%+.
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Do they make these for dogs? It is literally my pet peeve when he drags his ass across the carpet like it’s a giant piece of toilet paper.
 
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For the unheated versions, how does that cold water feel on the old arsehole in January? Feel good after a hot one?
 
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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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It's kinda like taking a shower.

Wouldn't taking a shower just wash all the dirt onto your feet?

When you blast your ass, the water goes down from betwixt the ass 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.

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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?

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.
 
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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?

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.
 
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When I was in Argentina a few years ago our hotel bathroom had a bidet, and my asshole felt amazing the hole trip. Then on prime day saw one of those bidets built into a toilet seat and thought about buying it

Anyone own a bidet and have an opinion?
 
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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?

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.
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.

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What is wrong with you-all. Do you not trim your nethers with a weed wacker?

Buy a cheap one that requires no batteries. Cold water. Takes 20 minutes to install and join the dark side. Either you will be turned our you lost like $30.
 
What is wrong with you-all. Do you not trim your nethers with a weed wacker?

Buy a cheap one that requires no batteries. Cold water. Takes 20 minutes to install and join the dark side. Either you will be turned our you lost like $30.
But cold water will not remove peanut butter from a plate or butter from a knife.
 
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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.
 
We are not melting the waste.
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.
 
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Is that the @scopeandtime model?😂
Bud, I'm going to share this with you and only you - no one else look!

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)
 
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Bud, I'm going to share this with you and only you - no one else look!

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)
Is that the secret location coordinates to all your truck stop glory holes? 😂
 
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