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In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

Unprocessed parquet files

In [2]:
selection = [("date", ">=", "2019-05-09")]
hk_files = pd.read_parquet('data/hk_files_parquet', filters=selection).sort_index()
pbp_files = pd.read_parquet('data/pbp_files_parquet', filters=selection).sort_index()
In [3]:
hk_files.head()
Out[3]:
<style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>
Time Stamp Time (sec) Time Stamp (UTC sec) Elapsed Time Error Code Packet Time Stamp Laser TEC Temp (C) Crystal TEC Temp (C) Inlet Air Temp (C) Computer Heatsink Temp (C) ... t0 file folder_name delta_sec file_datetime date_floored floor_time Secs_2GB date hour
calculated_time
2019-05-09 00:00:00.992400 2019-05-09 00:00:01.090 2.419201e+06 3.640205e+09 2394625.789 0 2394657.25 29.230175 30352 34.956111 28.543153 ... 2019-05-08 17:22:18.400 20190508172218_x0002 20190508172218 23862.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 00:00:00 2419201 2019-05-09 0
2019-05-09 00:00:01.992400 2019-05-09 00:00:02.090 2.419202e+06 3.640205e+09 2394626.789 0 2394658.25 29.203979 30352 34.956111 28.543153 ... 2019-05-08 17:22:18.400 20190508172218_x0002 20190508172218 23863.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 00:00:01 2419202 2019-05-09 0
2019-05-09 00:00:02.992400 2019-05-09 00:00:03.020 2.419203e+06 3.640205e+09 2394627.716 0 2394659.25 29.230175 30352 34.956111 28.543153 ... 2019-05-08 17:22:18.400 20190508172218_x0002 20190508172218 23864.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 00:00:02 2419203 2019-05-09 0
2019-05-09 00:00:03.992400 2019-05-09 00:00:04.050 2.419204e+06 3.640205e+09 2394628.747 0 2394660.25 29.230175 30352 34.956111 28.543153 ... 2019-05-08 17:22:18.400 20190508172218_x0002 20190508172218 23865.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 00:00:03 2419204 2019-05-09 0
2019-05-09 00:00:04.992400 2019-05-09 00:00:05.070 2.419205e+06 3.640205e+09 2394629.760 0 2394661.25 29.230175 30352 34.956111 28.543153 ... 2019-05-08 17:22:18.400 20190508172218_x0002 20190508172218 23866.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 00:00:04 2419205 2019-05-09 0

5 rows × 151 columns

In [4]:
pbp_files.head()
Out[4]:
<style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>
Time (sec) Packet Time Stamp Flag Dropped Records Record Count Record Size Particle Time Stamp Particle Flags Scatter relPeak Scatter Transit Time ... t0 file folder_name delta_sec file_datetime date_floored floor_time Secs_2GB date hour
calculated_time
2019-05-09 00:00:00.992400 2.419202e+06 2419233.25 0 0 76 32 2419232.25 1 2015056.0 904.0 ... 2019-05-08 17:22:18.400 20190508172218_x0004 20190508172218 23862.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 2419201 2019-05-09 0
2019-05-09 00:00:00.992400 2.419202e+06 2419233.25 0 0 76 32 2419233.00 1 2107910.0 920.0 ... 2019-05-08 17:22:18.400 20190508172218_x0004 20190508172218 23862.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 2419201 2019-05-09 0
2019-05-09 00:00:00.992400 2.419202e+06 2419233.25 0 0 76 32 2419233.00 1 10092470.0 1282.0 ... 2019-05-08 17:22:18.400 20190508172218_x0004 20190508172218 23862.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 2419201 2019-05-09 0
2019-05-09 00:00:00.992400 2.419202e+06 2419233.25 0 0 76 32 2419233.00 1 873502.0 822.0 ... 2019-05-08 17:22:18.400 20190508172218_x0004 20190508172218 23862.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 2419201 2019-05-09 0
2019-05-09 00:00:00.992400 2.419202e+06 2419233.25 0 0 76 32 2419233.00 1 36306660.0 1582.0 ... 2019-05-08 17:22:18.400 20190508172218_x0004 20190508172218 23862.5924 2019-05-08 17:22:18 2019-05-09 2019-05-09 2419201 2019-05-09 0

5 rows × 32 columns

Processed parquet files

PbP calibrated

In [6]:
pbp_calib = pd.read_parquet('data/SP2XR_processed_1min/pbp_calibrated', filters=selection).sort_index()
In [7]:
pbp_calib.columns
Out[7]:
Index(['Time (sec)', 'Packet Time Stamp', 'Flag', 'Dropped Records',
       'Record Count', 'Record Size', 'Particle Time Stamp', 'Particle Flags',
       'Scatter relPeak', 'Scatter Transit Time', 'Scatter Peak Time',
       'Scatter FWHM', 'Scatter Size (nm)', 'Incand relPeak',
       'Incand Transit Time', 'Incand Peak Time', 'Incand FWHM',
       'Incand Delay', 'Incand Mass (fg)', 'Reserved', 'path', 'first_val',
       't0', 'file', 'folder_name', 'delta_sec', 'file_datetime',
       'date_floored', 'floor_time', 'Secs_2GB', 'BC mass', 'Opt diam',
       'time_lag', 'ratio_inc_scatt', 'flag_valid_inc_signal',
       'flag_inc_not_sat', 'flag_valid_inc_signal_in_range',
       'flag_valid_scatt_signal', 'flag_scatt_not_sat',
       'flag_valid_scatt_signal_in_range', 'flag_negative_timelag',
       'flag_extreme_positive_timelag', 'flag_valid_timelag_thin',
       'flag_valid_timelag_thick', 'flag_low_ratio_inc_scatt',
       'Opt diam scatt only', 'cnts_thin', 'cnts_thin_noScatt', 'cnts_thick',
       'cnts_thick_sat', 'cnts_thin_sat', 'cnts_ntl_sat', 'cnts_ntl',
       'cnts_extreme_positive_timelag', 'cnts_thin_low_inc_scatt_ratio',
       'cnts_particles_for_tl_dist', 'cnts_thin_total', 'cnts_thick_total',
       'cnts_unclassified', 'BC mass within range',
       'Opt diam scatt only within range', 'temporary_col',
       'Sample Flow Controller Read (vccm)', 'BC mass bin', 'date', 'hour'],
      dtype='object')
In [8]:
cnts_cols = ['cnts_thin', 'cnts_thin_noScatt', 'cnts_thick',
       'cnts_thick_sat', 'cnts_thin_sat', 'cnts_ntl_sat', 'cnts_ntl',
       'cnts_extreme_positive_timelag', 'cnts_thin_low_inc_scatt_ratio']
colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'k', 'grey']

fig, ax = plt.subplots(figsize=(8, 6), layout='constrained')


for i, cnts_col in enumerate(cnts_cols):
    df_filtered = pbp_calib[pbp_calib[cnts_col] == 1]

    x = df_filtered['Opt diam'].fillna(90)
    y = df_filtered['BC mass']

    ax.scatter(x, y, alpha=0.4, s=5, color=colors[i], label=cnts_col.replace('cnts_', ''))

ax.set_xlabel('Opt diam [nm]')
ax.set_ylabel('BC mass [fg]')
ax.set_xscale('log')
ax.set_yscale('log')
ax.grid(alpha=0.3)
ax.axvline(90, c='k', ls='--', alpha=0.5, lw=1, label='No scatter placeholder')
ax.legend(fontsize=8, markerscale=2)
Out[8]:
<matplotlib.legend.Legend at 0x7f2ba7f34be0>
No description has been provided for this image

Bulk Concentrations

In [9]:
conc_60s = pd.read_parquet("data/SP2XR_processed_1min/conc_60s", filters=selection).sort_index()
In [10]:
conc_60s.head()
Out[10]:
<style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>
Dropped Records Incand Mass (fg) BC mass BC mass within range cnts_thin cnts_thin_noScatt cnts_thick cnts_thick_sat cnts_thin_sat cnts_ntl_sat ... BC_massConc_within_range_std BC_massConc_within_range_vol BC_numConc_within_range_std BC_numConc_within_range_vol S_numConc_std S_numConc_vol S_numConc_within_range_std S_numConc_within_range_vol date hour
calculated_time
2019-05-09 00:01:00 0.0 16.855231 174.442428 159.740085 22 6 52 0 0 0 ... 0.005323 0.004794 2.732726 2.460738 125.238836 112.773832 124.872251 112.443733 2019-05-09 0
2019-05-09 00:02:00 0.0 30.337629 192.119622 177.837469 29 1 34 0 0 0 ... 0.005929 0.005339 2.200244 1.981288 120.646739 108.640617 120.613402 108.610597 2019-05-09 0
2019-05-09 00:03:00 0.0 30.950136 198.343122 188.532185 24 5 55 0 0 0 ... 0.006284 0.00566 2.799689 2.522018 117.786913 106.104884 117.586935 105.92474 2019-05-09 0
2019-05-09 00:04:00 0.0 44.184496 220.411625 206.865198 17 7 65 0 0 1 ... 0.006898 0.006212 3.034345 2.732733 118.772924 106.966968 118.506169 106.726727 2019-05-09 0
2019-05-09 00:05:00 0.0 50.056468 230.294057 217.496214 27 2 58 0 0 0 ... 0.007251 0.00653 2.900645 2.61209 119.993332 108.056445 119.793287 107.876301 2019-05-09 0

5 rows × 39 columns

In [11]:
conc_60s.columns
Out[11]:
Index(['Dropped Records', 'Incand Mass (fg)', 'BC mass',
       'BC mass within range', 'cnts_thin', 'cnts_thin_noScatt', 'cnts_thick',
       'cnts_thick_sat', 'cnts_thin_sat', 'cnts_ntl_sat', 'cnts_ntl',
       'cnts_extreme_positive_timelag', 'cnts_thin_low_inc_scatt_ratio',
       'cnts_thin_total', 'cnts_thick_total', 'cnts_unclassified',
       'BC numb from file', 'BC numb', 'BC numb within range',
       'scatter numb from file', 'Scatt numb', 'Scatt numb within range',
       'Secs_2GB_mean', 'Sample Flow Controller Read (sccm)',
       'Sample Flow Controller Read (vccm)', 'BC_massConc_std',
       'BC_massConc_vol', 'BC_numConc_std', 'BC_numConc_vol',
       'BC_massConc_within_range_std', 'BC_massConc_within_range_vol',
       'BC_numConc_within_range_std', 'BC_numConc_within_range_vol',
       'S_numConc_std', 'S_numConc_vol', 'S_numConc_within_range_std',
       'S_numConc_within_range_vol', 'date', 'hour'],
      dtype='object')
In [13]:
fig, axs = plt.subplots(nrows=3, figsize=(5, 6), sharex=True, layout='constrained')

axs[0].plot(conc_60s['BC_massConc_std'], label='BC_massConc_std')
axs[0].plot(conc_60s['BC_massConc_vol'], label='BC_massConc_vol')
axs[0].plot(conc_60s['BC_massConc_within_range_std'], label='BC_massConc_within_range_std')
axs[0].plot(conc_60s['BC_massConc_within_range_vol'], label='BC_massConc_within_range_vol')
axs[0].set_ylabel('[ug m-3]')

axs[1].plot(conc_60s['BC_numConc_std'], label='BC_numConc_std')
axs[1].plot(conc_60s['BC_numConc_vol'], label='BC_numConc_vol')
axs[1].plot(conc_60s['BC_numConc_within_range_std'], label='BC_numConc_within_range_std')
axs[1].plot(conc_60s['BC_numConc_within_range_vol'], label='BC_numConc_within_range_vol')
axs[1].set_ylabel('[# cm-3]')

axs[2].plot(conc_60s['S_numConc_std'], label='S_numConc_std')
axs[2].plot(conc_60s['S_numConc_vol'], label='S_numConc_vol')
axs[2].plot(conc_60s['S_numConc_within_range_std'], label='S_numConc_within_range_std')
axs[2].plot(conc_60s['S_numConc_within_range_vol'], label='S_numConc_within_range_vol')
axs[2].set_ylabel('[# cm-3]')

locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
formatter = mdates.ConciseDateFormatter(locator)
axs[2].xaxis.set_major_locator(locator)
axs[2].xaxis.set_major_formatter(formatter)

for ax in axs:
    ax.legend(fontsize=6)
    ax.grid(alpha=0.4)
No description has been provided for this image
In [14]:
fig, axs = plt.subplots(nrows=2, figsize=(5, 4), sharex=True, layout='constrained')

axs[0].plot(conc_60s['Sample Flow Controller Read (sccm)'], label='Sample Flow Controller Read (sccm)')
axs[1].plot(conc_60s['Sample Flow Controller Read (vccm)'], label='Sample Flow Controller Read (vccm)')


locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
formatter = mdates.ConciseDateFormatter(locator)
axs[1].xaxis.set_major_locator(locator)
axs[1].xaxis.set_major_formatter(formatter)

for ax in axs:
    ax.legend(fontsize=6)
    ax.grid(alpha=0.4)
No description has been provided for this image

Distributions

In [15]:
hists_60s = pd.read_parquet('data/SP2XR_processed_1min/hists_60s', filters=selection).sort_index()
hists_60s.drop(columns=['date', 'hour'], inplace=True)
In [16]:
hists_60s.head()
Out[16]:
<style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>
dNdlogDmev_all_70.03 dNdlogDmev_all_73.55 dNdlogDmev_all_77.24 dNdlogDmev_all_81.11 dNdlogDmev_all_85.18 dNdlogDmev_all_89.45 dNdlogDmev_all_93.94 dNdlogDmev_all_98.65 dNdlogDmev_all_103.60 dNdlogDmev_all_108.80 ... dNdlogDsc_all_223.81 dNdlogDsc_all_243.59 dNdlogDsc_all_265.12 dNdlogDsc_all_288.56 dNdlogDsc_all_314.07 dNdlogDsc_all_341.83 dNdlogDsc_all_372.05 dNdlogDsc_all_404.94 dNdlogDsc_all_440.74 dNdlogDsc_all_479.70
calculated_time
2019-05-09 00:00:00 5.509758 7.060021 7.060021 7.060021 4.236013 11.296034 5.648017 9.88403 12.708039 4.236013 ... 247.228126 213.774815 179.50557 142.788522 77.513769 64.458818 31.005508 20.39836 10.607147 1.701024
2019-05-09 00:01:00 11.01676 8.469907 7.058256 5.646605 4.234953 14.116512 4.234953 5.646605 7.058256 4.234953 ... 252.876411 235.746073 200.669668 132.964048 75.862923 53.838204 28.550562 16.314607 9.788764 0.850299
2019-05-09 00:02:00 8.265464 4.236437 4.236437 1.412146 1.412146 2.824291 5.648582 4.236437 5.648582 2.824291 ... 238.276687 172.995403 167.283291 137.090697 80.785589 41.616819 28.560562 16.320321 13.056257 4.252986
2019-05-09 00:03:00 2.755568 5.64943 11.29886 7.061788 7.061788 11.29886 5.64943 7.061788 1.412358 9.886503 ... 215.460578 194.240976 171.389096 115.075536 87.326825 53.865144 37.542373 17.13891 6.529108 6.805799
2019-05-09 00:04:00 12.402539 8.475842 8.475842 12.713763 7.063202 8.475842 5.650561 11.301123 1.41264 2.825281 ... 235.09496 191.830957 184.48424 131.424613 93.874724 53.059626 29.38687 12.244529 5.714114 2.552685

5 rows × 1293 columns

In [17]:
prefixes = hists_60s.columns.str.rsplit('_', n=1).str[0]
groups = prefixes.unique()

groups
Out[17]:
Index(['dNdlogDmev_all', 'dMdlogDmev_all', 'dNdlogDmev_thin',
       'dMdlogDmev_thin', 'dNdlogDmev_thin_noScatt', 'dMdlogDmev_thin_noScatt',
       'dNdlogDmev_thick', 'dMdlogDmev_thick', 'dNdlogDmev_thick_sat',
       'dMdlogDmev_thick_sat', 'dNdlogDmev_thin_sat', 'dMdlogDmev_thin_sat',
       'dNdlogDmev_ntl_sat', 'dMdlogDmev_ntl_sat', 'dNdlogDmev_ntl',
       'dMdlogDmev_ntl', 'dNdlogDmev_extreme_positive_timelag',
       'dMdlogDmev_extreme_positive_timelag',
       'dNdlogDmev_thin_low_inc_scatt_ratio',
       'dMdlogDmev_thin_low_inc_scatt_ratio', 'dNdlogDmev_thin_total',
       'dMdlogDmev_thin_total', 'dNdlogDmev_thick_total',
       'dMdlogDmev_thick_total', 'dNdlogDmev_unclassified',
       'dMdlogDmev_unclassified', 'dNdlogDsc_all'],
      dtype='object')
In [18]:
t0 = '2019-05-09 15:00:00'
t1 = '2019-05-09 16:00:00'
resample = '15min'
use = 2
hists_60s_plot = hists_60s.loc[t0:t1].resample(resample).mean().iloc[use]

dNdlogDmev_all_col = [col for col in hists_60s.columns if 'dNdlogDmev_all' in col]
dMdlogDmev_all_col = [col for col in hists_60s.columns if 'dMdlogDmev_all' in col]
dNdlogDsc_all_col = [col for col in hists_60s.columns if 'dNdlogDsc_all' in col]
Dmev = [float(d.rsplit('_')[-1]) for d in dNdlogDmev_all_col]
Dsc = [float(d.rsplit('_')[-1]) for d in dNdlogDsc_all_col]

fig, axs = plt.subplots(ncols=3, figsize=(6, 2), layout='constrained')

axs[0].set_title('dN/dlogDmev [# cm-3]', fontsize=8)
axs[1].set_title('dM/dlogDmev [ug m-3]', fontsize=8)
axs[2].set_title('dN/dlogDsc [# cm-3]', fontsize=8)

axs[0].plot(Dmev, hists_60s_plot[dNdlogDmev_all_col])
axs[1].plot(Dmev, hists_60s_plot[dMdlogDmev_all_col])
axs[2].plot(Dsc, hists_60s_plot[dNdlogDsc_all_col])

axs[0].set_xlabel('Dmev [nm]')
axs[1].set_xlabel('Dmev [nm]')
axs[2].set_xlabel('Dscatt [nm]')
for ax in axs:
    ax.set_xscale('log')
No description has been provided for this image
In [21]:
t0 = '2019-05-09 15:00:00'
t1 = '2019-05-09 16:00:00'
resample = '1h'
use = 0
hists_60s_plot = hists_60s.loc[t0:t1].resample(resample).mean().iloc[use]

dMdlogDmev_all_col = [col for col in hists_60s.columns if 'dMdlogDmev_all' in col]
dMdlogDmev_thin_tot_col = [col for col in hists_60s.columns if 'dMdlogDmev_thin_total' in col]
dMdlogDmev_thick_tot_col = [col for col in hists_60s.columns if 'dMdlogDmev_thick_total' in col]
Dmev = [float(d.rsplit('_')[-1]) for d in dMdlogDmev_all_col]

fig, axs = plt.subplots(figsize=(4, 4), layout='constrained')

axs.set_title('dM/dlogDmev [ug m-3]', fontsize=8)

axs.plot(Dmev, hists_60s_plot[dMdlogDmev_all_col], c='k', alpha=0.2, lw=3, label='Total')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thin_tot_col], label='Thin (total)')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thick_tot_col], label='Thick (total)')

axs.set_xlabel('Dmev [nm]')
axs.legend()
axs.grid(alpha=0.4, which='both')
axs.set_xscale('log')
No description has been provided for this image
In [22]:
t0 = '2019-05-09 15:00:00'
t1 = '2019-05-09 16:00:00'
resample = '1h'
use = 0
hists_60s_plot = hists_60s.loc[t0:t1].resample(resample).mean().iloc[use]

dMdlogDmev_thin_col = [col for col in hists_60s.columns if 'dMdlogDmev_thin' in col 
                       and not any(x in col for x in ['total', 'low', 'sat', 'noScatt'])]
dMdlogDmev_thin_noScatt_col = [col for col in hists_60s.columns if 'dMdlogDmev_thin_noScatt' in col]
dMdlogDmev_thin_tot_col = [col for col in hists_60s.columns 
                       if 'dMdlogDmev_thin_total' in col]

Dmev = [float(d.rsplit('_')[-1]) for d in dMdlogDmev_thin_col]

fig, axs = plt.subplots(figsize=(4, 4), layout='constrained')

axs.set_title('dM/dlogDmev [ug m-3]', fontsize=8)

axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thin_tot_col], c='k', alpha=0.4, lw=4, label='Thin total')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thin_col], label='-10 < Dt < 50')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thin_noScatt_col], label='no scatt peak')
axs.legend()

axs.set_xlabel('Dmev [nm]')
axs.grid(alpha=0.4, which='both')
axs.set_xscale('log')
No description has been provided for this image
In [23]:
t0 = '2019-05-09 15:00:00'
t1 = '2019-05-09 16:00:00'
resample = '1h'
use = 0
hists_60s_plot = hists_60s.loc[t0:t1].fillna(0).resample(resample).mean().iloc[use]

dMdlogDmev_thick_col = [col for col in hists_60s.columns 
                       if 'dMdlogDmev_thick' in col 
                       and not any(x in col for x in ['total', 'low', 'sat', 'noScatt'])]
dMdlogDmev_thick_sat_col = [col for col in hists_60s.columns 
                            if 'dMdlogDmev_thick_sat' in col]
dMdlogDmev_thin_sat_col = [col for col in hists_60s.columns 
                            if 'dMdlogDmev_thin_sat' in col]
dMdlogDmev_thick_tot_col = [col for col in hists_60s.columns 
                            if 'dMdlogDmev_thick_total' in col]
dMdlogDmev_ntl_col = [col for col in hists_60s.columns 
                      if 'dMdlogDmev_ntl' in col 
                      and not any(x in col for x in ['sat'])]
dMdlogDmev_ntl_sat_col = [col for col in hists_60s.columns 
                          if 'dMdlogDmev_ntl_sat' in col]

Dmev = [float(d.rsplit('_')[-1]) for d in dMdlogDmev_all_col]

fig, axs = plt.subplots(figsize=(4, 4), layout='constrained')

axs.set_title('dM/dlogDmev [ug m-3]', fontsize=8)

axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thick_tot_col], c='k', alpha=0.4, lw=4, label='Thick total')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thick_col], label='Dt > 50')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thick_sat_col], label='Dt > 50, scatt saturated')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_thin_sat_col], label='-10 < Dt < 50, scatt saturated')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_ntl_col], label='Dt < -10')
axs.plot(Dmev, hists_60s_plot[dMdlogDmev_ntl_sat_col], label='Dt < -10, scatt saturated')

axs.legend(fontsize=8)

axs.set_xlabel('Dmev [nm]')
axs.grid(alpha=0.4, which='both')
axs.set_xscale('log')
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