<jupyter_start><jupyter_text>Demand Response Intervention<jupyter_code>import os
import pandas as pd
import numpy as np
from datetime import datetime
LABELS = ['so2_kg', 'nox_kg', 'pm25_kg', 'co2_kg', 'so2_dam_ap2', 'nox_dam_ap2',
       'pm25_dam_ap2', 'so2_dam_eas', 'nox_dam_eas', 'pm25_dam_eas',
       'co2_dam', 'dam_ap2', 'dam_eas']
LABELS.sort()
SE_LABELS = ['{}-se'.format(x) for x in LABELS]
dam_cols_ap2 = ['co2_dam', 'so2_dam_ap2', 'nox_dam_ap2', 'pm25_dam_ap2']
dam_cols_eas = ['co2_dam', 'so2_dam_eas', 'nox_dam_eas', 'pm25_dam_eas']
GROUPING_NAMES = ['SeasonalTOD', 'MonthTOD', 'TOD', 'YearOnly', 'Month']
GROUPING_COLS = [['year', 'season', 'hour'], ['year', 'month', 'hour'], 
        ['year', 'hour'], ['year'], ['year', 'month']]
GROUPING_NAMES_COLS = dict(zip(GROUPING_NAMES, GROUPING_COLS))<jupyter_output><empty_output><jupyter_text>Read in data<jupyter_code>def get_factor_df(kind='MEF', time='Month', region='PJM', fuel_type='FossilOnly'):
    kind_folder = 'mefs' if kind=='MEF' else 'aefs'
    
    # Read in file
    if fuel_type == 'FossilOnly':
        region_breakdown = 'isorto' if region == 'PJM' else 'nerc'
        df = pd.read_csv(os.path.join(os.pardir, os.pardir, 'factor_estimates', 'calculated_factors', 
                                      kind_folder, time, 
                                      '{}_{}.csv'.format(region_breakdown, kind_folder)),
                         index_col=GROUPING_NAMES_COLS[time])
        df = df[df[region_breakdown] == region].drop(region_breakdown, axis=1)
    else:
        if region != 'PJM':
            raise NotImplementedError('fossil-plus factors are only available for PJM')
        df = pd.read_csv(os.path.join(os.pardir, os.pardir, 'factor_estimates', 'calculated_factors', 
                                      kind_folder, time, 
                                      'pjm_fplus_{}.csv'.format(kind_folder)),
                         index_col=GROUPING_NAMES_COLS[time])
        
    # Filter MEF columns
    if kind == 'MEF':
        df = df.drop([x for x in df.columns if '-r' in x or '-int' in x], axis=1)
        df.columns = [x.replace('-est', '') for x in df.columns]
        
    # Ensure columns have numeric type
    df = df.apply(pd.to_numeric, axis=1)
    
    return df
all_dfs = {}
for kind in ['MEF', 'AEF']:
    for region in ['PJM', 'RFC']:
        for fuel_type in ['FossilOnly', 'FossilPlus']:
            for time in ['YearOnly', 'Month']:
                if region == 'RFC' and fuel_type == 'FossilPlus': 
                    continue
#                 print(kind, region, fuel_type, time)
                df = get_factor_df(kind=kind, time=time, region=region, fuel_type=fuel_type)
                all_dfs[(kind, region, fuel_type, time)] = df<jupyter_output><empty_output><jupyter_text>Get DR reductions<jupyter_code>import calendar
import matplotlib.pyplot as plt
%matplotlib inline
intervention_df = pd.DataFrame(pd.read_csv(os.path.join(os.pardir, 'demand_response', 'monthly_dr.csv'), index_col=0).stack())
intervention_df.index.names = ['year', 'month']
intervention_df.columns = ['MWh']

months2017 = pd.date_range(start='2017-01-01', end='2017-12-31', freq='MS')
intervention_df = intervention_df[intervention_df.index.get_level_values('year') == 2017]
intervention_df.index = months2017
fontsize=13

fig, ax = plt.subplots(figsize=(8.5, 2))
# # df = dr_reductions[dr_reductions.index.get_level_values('year') == 2017]/1e3
# rev_month_dict = dict((k,v) for k,v in enumerate(calendar.month_abbr))
# xticks = pd.date_range(start='2017-01-01', end='2017-12-31', freq='M')
df = intervention_df/1e3

# df = df.set_index(df.index.get_level_values('month').map(lambda x: rev_month_dict[x]))
# df.index = df.index.map(lambda x: pd.datetime(x[0], x[1], 1))
df.plot(color='blue', ax=ax, legend=False, fontsize=fontsize);

ax.set_ylim(0,10)
ax.set_ylabel('DR Reduction (GWh)');
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
         ax.get_xticklabels() + ax.get_yticklabels()):
    item.set_fontsize(13)
# if not os.path.exists(os.path.join('figures', 'dr')):
#     os.makedirs(os.path.join('figures', 'dr'))
# fig.savefig(os.path.join('figures', 'dr', 'dr_profile.pdf'), bbox_inches='tight')<jupyter_output><empty_output><jupyter_text>Get intervention effects<jupyter_code>def get_month_factors(df, time, months, prev_year=False):
    year_series = months.map(lambda x: x.year-1) if prev_year else months.map(lambda x: x.year)
    month_series = months.map(lambda x: x.month)
#     hour_series = hours2017.map(lambda x: x.hour)
    if time == 'YearOnly':
        df2 = df.loc[year_series]
    elif time == 'Month':
        df2 = df.loc[list(zip(year_series, month_series))]
    df2 = df2.set_index(months)
    return df2
# Get factor for each point in time series
months2017_factors = {}
for key in all_dfs.keys():
#     print(key)
    df = all_dfs[key]
    
    # 2017
    df2 = get_month_factors(df, key[-1], months2017)
    months2017_factors[key + (2017,)] = df2
    
    # 2016 for PJM fossil-only
    if key[1] == 'PJM' and key[2] == 'FossilPlus':
        df3 = get_month_factors(df, key[-1], months2017, True)
        months2017_factors[key + (2016,)] = df3
# Calculate effects
intervention_effects = {}
for key in months2017_factors.keys():
    reds = months2017_factors[key].multiply(intervention_df['MWh'], axis='index')
    effects = reds[LABELS].sum()  # total effect
    
    # For MEFs, propagate error. 
    #   When same factor is applied to multiple reductions 
    #      (i.e. multiplied by their total reduction amount), this factor's SE should be summed across
    #      these reductions (i.e. also multiplied by the total reduction amount).
    #   Independent errors (from different factors) should then be combined by the sqrt of their
    #      sum of squares.
    if key[0] == 'MEF':
        
        # Per-factor (non-independent) errors
        groupby_list = dict([('YearOnly', reds.index.year), 
                             ('Month', [reds.index.year, reds.index.month]),
                             ('MonthTOD', [reds.index.year, reds.index.month, reds.index.hour])])
        per_factor_errors = reds[['{}-se'.format(x) for x in LABELS]].groupby(
            groupby_list[key[-2]]).sum()
        
        # Combine per-factor errors to get independent errors
        ses = np.sqrt((per_factor_errors ** 2).sum())
#         ses = np.sqrt((reds[['{}-se'.format(x) for x in LABELS]] ** 2).sum())

        effects = pd.concat([effects, ses])
    intervention_effects[key] = effects
intervention_effects_df = pd.DataFrame(intervention_effects).T
intervention_effects_df.index.names = ['kind', 'region', 'fuel_type', 'time', 'year']<jupyter_output><empty_output><jupyter_text>Some analysis<jupyter_code>def get_summary(intervention_effects_df, pollutant):
    poll_effect = intervention_effects_df[pollutant]
    print('Min: {}, {}'.format(poll_effect.min(), poll_effect.idxmin()))
    print('Max: {}, {}'.format(poll_effect.max(), poll_effect.idxmax()))
    print(poll_effect.T)
    return poll_effect.T
df = get_summary(intervention_effects_df, 'dam_eas')
baseline = df.loc[('MEF', 'PJM', 'FossilPlus', 'Month', 2017)]
baseline
((df/baseline) * 100).round(0).astype(int)
((df / df.loc[('MEF', 'PJM', 'FossilOnly', 'Month', 2017)])*100).round(0).astype(int)
df = get_summary(intervention_effects_df, 'dam_ap2')
baseline = df.loc[('MEF', 'PJM', 'FossilPlus', 'Month', 2017)]
baseline
((df/baseline) * 100).round(0).astype(int)
((df / df.loc[('MEF', 'PJM', 'FossilOnly', 'Month', 2017)])*100).round(0).astype(int)<jupyter_output><empty_output><jupyter_text>Bar plots for SI<jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
def get_dam_name(dam_abbr):
    return 'AP2' if dam_abbr == 'ap2' else 'EASIUR'
    
FULL_DAMS = ['dam_ap2', 'dam_eas']
def format_title(label):
    l = label.split('_')
    if label in FULL_DAMS:
        t = 'Total damages ({})'.format('AP2' if l[1] == 'ap2' else 'EASIUR')
    else:
        t = '{0}$_{{{1}}}$ {2}'.format(l[0][:2].upper(), l[0][2:], 'emissions' if l[1] == 'kg' else 'damages')
        if len(l) > 2: t += ' ({})'.format('AP2' if l[2] == 'ap2' else 'EASIUR')
    return t

def format_axis(label):
    l = label.split('_')
    if label in FULL_DAMS:
        t = 'Total damages\navoided ($)'
    elif len(l) > 2 or l[1] == 'dam':
        t = 'Damages\navoided ($)'
    else:
        t = 'Emissions\navoided (kg)'
    return t
# Get formatted df with intervention effects for given label
def get_onelabel_formatted(label):
    kind_map = dict([('MEF', 'Marginal'), ('AEF', 'Average')])
    time_map = dict([('YearOnly', 'Annual'), ('Month', 'Monthly')])
    
    df = intervention_effects_df[label].reset_index()
    df['spat'] = df.apply(
        lambda x: '{} ({}-{}{})'.format(
            x['region'], x['fuel_type'][:-4].lower(), x['fuel_type'][-4:].lower(), 
            ' 2016' if x['year'] == 2016 else ''), axis=1)
    df['spat'] = df['spat'].str.replace('fossil-plus', 'fossil+non-emit')
    df = df.drop(['region', 'fuel_type', 'year'], axis=1)
    df['kind'] = df['kind'].map(lambda x: kind_map[x]).astype(
        pd.CategoricalDtype(categories=['Marginal', 'Average'], ordered=True))
    df['time'] = df['time'].map(lambda x: time_map[x]).astype(
        pd.CategoricalDtype(categories=['Annual', 'Monthly'], ordered=True))
    df['spat'] = df['spat'].astype(pd.CategoricalDtype(
        categories=['PJM (fossil-only)', 'PJM (fossil+non-emit 2016)', 'PJM (fossil+non-emit)', 'RFC (fossil-only)'], 
        ordered=True))
    df = df.sort_values(['spat', 'kind', 'time'])
    
    return df
def get_stacked_plot(label):
    df = get_onelabel_formatted(label)
    df_se = get_onelabel_formatted('{}-se'.format(label))
    
    # Get bar plot
    sns.set(style="whitegrid")
    g = sns.catplot(x='kind', y=label, hue='time', col='spat', data=df,
                       kind='bar', palette=sns.color_palette('muted')[::2], legend=False, ci=None,
                       height=3, aspect=1).set_titles('{col_name}')
    g.despine(left=True);
    
    # Adjust font size and add legend
    fontsize=18
    for i, ax in enumerate(g.axes.flatten()):
        ax.set_xlabel('')
        for item in ([#ax.title, 
                      ax.xaxis.label, ax.yaxis.label] +
                 ax.get_xticklabels() + ax.get_yticklabels()):
            item.set_fontsize(fontsize)
        ax.title.set_fontsize(fontsize-2)

        if i == 0:
            lgd = ax.legend(loc='center left', bbox_to_anchor=(1.2, -0.35), ncol=2, frameon=True, fontsize=fontsize)
            ax.set_ylabel(format_axis(label))
            
        # Annotate baseline -- PJM fossil-plus marginal monthly
        if i == 2:
            baseline_x = 0.2
            patch_width = [p.get_width() for p in ax.patches][0]
            baseline_y = max([p.get_height() \
                          for p in ax.patches if abs(p.get_xy()[0]+patch_width/2-baseline_x)<=patch_width/4])
            ax.text(s='*', x=baseline_x, y=1.05 * baseline_y,
                   horizontalalignment='center', verticalalignment='center', 
                    fontsize=fontsize*2, fontweight='bold')
    
    # Hacky errorbars
    for i, ax in enumerate(g.axes[0]):
        spat = df['spat'].dtype.categories[i]
        df_slice = df.query('spat == @spat').query('kind == "Marginal"')
        df_se_slice = df_se.query('spat == @spat').query('kind == "Marginal"')
        ax.errorbar([-0.2,0.2], df_slice[label].values,
                        yerr=df_se_slice['{}-se'.format(label)].values, ms=20, color='black', 
                        linewidth=0, elinewidth=2, capsize=2, capthick=2)
        
    # Plot title
    fig = plt.gcf()
#     sup=fig.text(0.5, 1, format_title(label), 
#              fontsize=fontsize, fontweight='bold', fontstyle='italic',
#             transform=fig.transFigure, ha='center')
    
    plt.tight_layout()
    
    dirname = os.path.join('plots', 'demand_response')
    if not os.path.exists(dirname): os.makedirs(dirname)
    g.fig.savefig(os.path.join(dirname, 'si-{}.pdf'.format(label)),
                 bbox_extra_artists=(lgd,), #(lgd,sup), 
                  bbox_inches='tight')
for label in LABELS:
    get_stacked_plot(label)<jupyter_output><empty_output><jupyter_text>Stacked bar plots for total damages<jupyter_code>import matplotlib.patches as mpatches
def plot_total_damages(dam_type, title=False):
    
    fontsize=18
    
    plt.rcParams['hatch.linewidth'] = 0.5
    sns.set(style="whitegrid", color_codes=True)

    dam_cols = dam_cols_eas if dam_type == 'EASIUR' else dam_cols_ap2
    se_col = 'dam_{}-se'.format('eas' if dam_type == 'EASIUR' else 'ap2')

    df = get_onelabel_formatted(dam_cols)  
    df = (df.set_index(['spat', 'kind', 'time'])/1e6).reset_index() # millions of dollars
    
    df_cum = df.set_index(['spat', 'kind', 'time']).cumsum(axis=1).reset_index()

    # Stacked bar plot
    g = sns.FacetGrid(data=df_cum, col='spat', size=3, aspect=1)
    hatches = ['||', '///', '', '\\\\\\']

    g.map(sns.barplot, 'kind', dam_cols[-1], 'time', 
          hue_order=['Annual', 'Monthly'], order=['Marginal', 'Average'],
          palette=sns.color_palette('muted')[::2], edgecolor='black', hatch=hatches[0])  
    g.map(sns.barplot, 'kind', dam_cols[-2], 'time', 
          hue_order=['Annual', 'Monthly'], order=['Marginal', 'Average'],
          palette=sns.color_palette('muted')[::2], edgecolor='black', hatch=hatches[1])
    g.map(sns.barplot, 'kind', dam_cols[-3], 'time', 
          hue_order=['Annual', 'Monthly'], order=['Marginal', 'Average'],
          palette=sns.color_palette('muted')[::2], edgecolor='black', hatch=hatches[2])
    g.map(sns.barplot, 'kind', dam_cols[-4], 'time', 
          hue_order=['Annual', 'Monthly'], order=['Marginal', 'Average'],
          palette=sns.color_palette('muted')[::2], edgecolor='black', hatch=hatches[3]).set_titles('{col_name}')
    g.despine(left='true')

    # Legend, fontsize, and other formatting
    xoffset=0.035

    for i, ax in enumerate(g.axes.flatten()):

        ax.set_xlabel('') # No x-label

        if i == 0:
            # y label on left plot
            ax.set_ylabel('Total damages\navoided (\$ millions)'.format(dam_type))

            # pollutants legend
            leg_dict = dict(zip(dam_cols, ['CO$_2$', 'SO$_2$', 'NO$_x$', 'PM$_{{2.5}}$']))
            dam_patches = []
            for dam, hatch in zip(dam_cols, hatches[::-1]):
                patch = mpatches.Patch(facecolor='white', label=leg_dict[dam], edgecolor='black', hatch=hatch)
                dam_patches.append(patch)

            lgd = ax.legend(handles=dam_patches, loc='center left', 
                            bbox_to_anchor=(0.3+xoffset, -0.15), ncol=4, frameon=True, fontsize=fontsize,
                            bbox_transform=plt.gcf().transFigure)
            plt.text(0.18+xoffset, -0.17, 'Pollutants:', transform=plt.gcf().transFigure,
                     fontsize=fontsize, fontweight='bold')
            lgd.get_frame().set_edgecolor('white')


        if i == 1:

            # temporal scope legend (same length as pollutants legend for alignment)
            blank_patch = mpatches.Patch(color='white', label='')
            ann_patch = mpatches.Patch(color=sns.color_palette('muted')[0], label='Annual', edgecolor='black')
            tod_patch = mpatches.Patch(color=sns.color_palette('muted')[2], label='Monthly', edgecolor='black')
            time_patches = [blank_patch, ann_patch, tod_patch, blank_patch]
            lgd2 = ax.legend(handles=time_patches, loc='center left', 
                            bbox_to_anchor=(0.3+xoffset, -0.0115), ncol=4, frameon=True, fontsize=fontsize,
                            bbox_transform=plt.gcf().transFigure)
            plt.text(0.11+xoffset, -0.0425, 'Temporal scopes:', transform=plt.gcf().transFigure, 
                     fontsize=fontsize, fontweight='bold')
            lgd2.get_frame().set_edgecolor('white')
            
        # Annotate baseline -- PJM fossil-plus marginal monthly
        if i == 2:
            baseline_x = 0.2
            patch_width = [p.get_width() for p in ax.patches][0]
            baseline_y = max([p.get_height() \
                          for p in ax.patches if abs(p.get_xy()[0]+patch_width/2-baseline_x)<=patch_width/4])
            ax.text(s='*', x=baseline_x, y=1.05 * baseline_y,
                   horizontalalignment='center', verticalalignment='center', 
                    fontsize=fontsize*2, fontweight='bold')

        # Set font size
        for item in ([#ax.title, 
                      ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):
            item.set_fontsize(fontsize)
        ax.title.set_fontsize(fontsize-2)


    # Hacky errorbars
    #   TODO fix: divide by 1e6 to account for millions of dollars
    df_tot = get_onelabel_formatted(se_col[:-3])
    df_tot_se = get_onelabel_formatted(se_col)
    for i, ax in enumerate(g.axes[0]):
        spat = df_tot['spat'].dtype.categories[i]
        df_tot_slice = df_tot.query('spat == @spat').query('kind == "Marginal"')
        df_tot_se_slice = df_tot_se.query('spat == @spat').query('kind == "Marginal"')
        ax.errorbar([-0.2,0.2], df_tot_slice[se_col[:-3]].values/1e6,
                        yerr=df_tot_se_slice[se_col].values/1e6, ms=20, color='black', 
                        linewidth=0, elinewidth=2, capsize=2, capthick=2)   

    # Line around legend
    fig = plt.gcf()
    leg_line = \
        mpatches.Rectangle(
            (0.1+xoffset, -0.195), 0.77, 0.24, facecolor='none', edgecolor='lightgray', 
            transform=fig.transFigure, figure=fig)
    fig.patches.extend([leg_line])

    extra_artists = (lgd, lgd2)
    if title:
        sup=fig.text(0.5, 0.9, 'Total damages ({})\n'.format(dam_type), 
                         fontsize=fontsize, fontweight='bold', fontstyle='italic',
                        transform=fig.transFigure, ha='center')
        extra_artists = extra_artists + (sup,)

    plt.tight_layout()
    dirname = os.path.join('plots', 'demand_response')
    if not os.path.exists(dirname): os.makedirs(dirname)
    g.fig.savefig(os.path.join(dirname, 
                               '{}-stacked-with-error{}.pdf'.format(dam_type, '-titled' if title else '')),
                 bbox_extra_artists=extra_artists, bbox_inches='tight')
plot_total_damages('EASIUR', title=False)
plot_total_damages('AP2', title=False)<jupyter_output>/Users/priyadonti/anaconda2/envs/py35/lib/python3.5/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)
/Users/priyadonti/anaconda2/envs/py35/lib/python3.5/site-packages/matplotlib/patches.py:120: UserWarning: Setting the 'color' property will overridethe edgecolor or facecolor properties. 
  warnings.warn("Setting the 'color' property will override"<jupyter_text>Sensitivity analysis  What happens if DR reductions occur in highest marginal emissions hour of each month and get shifted to lowest marginal emissions hour of each month? Vice versa?<jupyter_code>df_pjm_monthtod_fplus = get_factor_df(kind='MEF', time='MonthTOD', region='PJM', fuel_type='FossilPlus').loc[2017]
means = df_pjm_monthtod_fplus[LABELS]
stds = df_pjm_monthtod_fplus[['{}-se'.format(x) for x in LABELS]]
highest_factors = means.groupby(means.index.get_level_values('month'))[LABELS].max()
highest_factors_std = means.groupby(means.index.get_level_values('month'))[LABELS].idxmax().apply(
    lambda x: x.map(lambda y: stds.loc[y,'{}-se'.format(x.name)]))
highest_factors_std.columns = ['{}-se'.format(x) for x in highest_factors_std.columns]
highest_factors = pd.concat([highest_factors, highest_factors_std], axis=1)
highest_factors.index = intervention_df.index
lowest_factors = means.groupby(means.index.get_level_values('month'))[LABELS].min()
lowest_factors_std = means.groupby(means.index.get_level_values('month'))[LABELS].idxmin().apply(
    lambda x: x.map(lambda y: stds.loc[y,'{}-se'.format(x.name)]))
lowest_factors_std.columns = ['{}-se'.format(x) for x in lowest_factors_std.columns]
lowest_factors = pd.concat([lowest_factors, lowest_factors_std], axis=1)
lowest_factors.index = intervention_df.index
# Calculate effects
sensitivity_effects = {}
for key, df in zip(['highest', 'lowest'], [highest_factors, lowest_factors]):
    reds = df.multiply(intervention_df['MWh'], axis='index')
    effects = reds[LABELS].sum()  # total effect
    
    # Per-factor (non-independent) errors
    groupby_list = [reds.index.year, reds.index.month]
    per_factor_errors = reds[['{}-se'.format(x) for x in LABELS]].groupby(
        groupby_list).sum()
        
    # Combine per-factor errors to get independent errors
    ses = np.sqrt((per_factor_errors ** 2).sum())
#         ses = np.sqrt((reds[['{}-se'.format(x) for x in LABELS]] ** 2).sum())

    effects = pd.concat([effects, ses])
    sensitivity_effects[key] = effects
# Savings when do DR reductions when factors are highest and load shifted to when factors are lowest
savings = sensitivity_effects['highest'][LABELS] - sensitivity_effects['lowest'][LABELS]
savings_se = np.sqrt((sensitivity_effects['highest'][SE_LABELS])**2 + (sensitivity_effects['lowest'][SE_LABELS] ** 2))
savings_se.index = savings.index
results_df = pd.DataFrame((savings/1e6).round(2).astype(str) + ' $\pm$ ' + (savings_se/1e6).round(2).astype(str)).T
results_df
emit_sens = results_df[[x for x in results_df.columns if 'dam' not in x]]
emit_sens
ap2_sens = results_df[[x for x in results_df.columns if 'ap2' in x or 'co2_dam' in x]]
ap2_sens
eas_sens = results_df[[x for x in results_df.columns if 'eas' in x or 'co2_dam' in x]]
eas_sens
colnames = ['\co', 'Total damage factor', '\\nox', '\partic', '\so']
ap2_sens.columns = colnames
eas_sens.columns = colnames
display_sens = pd.concat([ap2_sens, eas_sens])
display_sens = display_sens[['Total damage factor', '\co', '\so', '\\nox', '\partic']]
display_sens.index = ['AP2', 'EASIUR']
display_sens
print(display_sens.to_latex())<jupyter_output>\begin{tabular}{llllll}
\toprule
{} & Total damage factor &              \textbackslash co &              \textbackslash so &             \textbackslash nox &          \textbackslash partic \\
\midrule
AP2    &     7.09 \$\textbackslash pm\$ 1.14 &  0.59 \$\textbackslash pm\$ 0.06 &  6.67 \$\textbackslash pm\$ 1.14 &  0.53 \$\textbackslash pm\$ 0.08 &  0.32 \$\textbackslash pm\$ 0.04 \\
EASIUR &      4.4 \$\textbackslash pm\$ 0.58 &  0.59 \$\textbackslash pm\$ 0.06 &   3.9 \$\textbackslash pm\$ 0.61 &  0.34 \$\textbackslash pm\$ 0.04 &  0.32 \$\textbackslash pm\$ 0.03 \\
\bottomrule
\end{tabular}
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