In [37]:
%matplotlib inline
import numpy as np
import pandas as pd
from hagelslag.evaluation import DistributedROC, DistributedReliability
from hagelslag.evaluation.MetricPlotter import roc_curve, performance_diagram, attributes_diagram
from hagelslag.data import ModelOutput
from IPython.display import Image
import os
from datetime import datetime, timedelta
import seaborn as sns
import matplotlib.pyplot as plt
from glob import glob
from netCDF4 import Dataset
In [2]:
merged_path = "/glade/scratch/dgagne/track_forecasts_ncar_2016_merged/"
track_files = sorted(os.listdir(merged_path))
merged_data = {}
members = []
for track_file in track_files:
print(track_file)
member = track_file.split("_")[4]
members.append(member)
merged_data[member] = pd.read_csv(merged_path + track_file)
track_forecast_data_NCAR_mem10_combined.csv
track_forecast_data_NCAR_mem1_combined.csv
track_forecast_data_NCAR_mem2_combined.csv
track_forecast_data_NCAR_mem3_combined.csv
track_forecast_data_NCAR_mem4_combined.csv
track_forecast_data_NCAR_mem5_combined.csv
track_forecast_data_NCAR_mem6_combined.csv
track_forecast_data_NCAR_mem7_combined.csv
track_forecast_data_NCAR_mem8_combined.csv
track_forecast_data_NCAR_mem9_combined.csv
In [5]:
merged_data["mem1"]["Date"]
AttributeErrorTraceback (most recent call last)
<ipython-input-5-a0c328bfdcc4> in <module>()
----> 1 merged_data["mem1"]["Date"].month
/glade/u/home/dgagne/miniconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __getattr__(self, name)
2670 if name in self._info_axis:
2671 return self[name]
-> 2672 return object.__getattribute__(self, name)
2673
2674 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'month'
In [11]:
day1_data = {}
for mem in merged_data.keys():
fh = merged_data[mem]["Forecast_Hour"]
date = pd.DatetimeIndex(merged_data[mem]["Date"])
day1_data[mem] = merged_data[mem].loc[(fh >= 13) & (fh <=36) & (date.month > 4) & (date.month < 7)]
In [18]:
day1_data["mem2"]["Forecast_Hour"].hist()
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x2b5f5854d790>
In [9]:
month = pd.DatetimeIndex(day1_data["mem1"]["Date"]).month
g = sns.jointplot(x="Scale", y="Random-Forest_Scale",
data=day1_data["mem1"].loc[(day1_data["mem1"]["Scale"] > 0) & (month==6)],
kind="hex", xlim=(3,10), ylim=(3,10), bins="log")
g.ax_joint.plot(np.arange(3, 10), np.arange(3,10), 'k--')
Out[9]:
[<matplotlib.lines.Line2D at 0x2b5f4d766b10>]
In [7]:
rocs = []
for mem in members:
rocs.append(DistributedROC(thresholds=np.arange(0, 1.1, 0.1)))
rocs[-1].update(day1_data[mem]["Random-Forest_Condition"].values,
np.where(day1_data[mem]["Hail_Size"] > 0, 1, 0))
In [8]:
colors = ["r", "b", "g", "orange", "purple", "cyan", "brown", "yellowgreen", "pink", "navy"]
roc_curve(rocs, [mem + " ({0:0.2f})".format(rocs[m].auc()) for m, mem in enumerate(members)], colors, ["o"] * 10,
"roc.png", title="Hail Yes-No NCAR Ensemble 2016")
In [9]:
Image("roc.png")
Out[9]:
In [10]:
rel = DistributedReliability(np.arange(0, 1.05, 0.05))
rel.update(day1_data["mem1"]["Random-Forest_Condition"].values, np.where(day1_data["mem1"]["Hail_Size"] > 0, 1, 0))
In [11]:
attributes_diagram([rel], ["Random Forest {0:0.2f}".format(rel.brier_skill_score())], ["r"], ["o"], "rel.png")
In [12]:
Image("rel.png")
Out[12]:
In [4]:
sns.jointplot(merged_data["mem1"]["Centroid_Lon"], merged_data["mem1"]["Centroid_Lat"], kind="kde")
Out[4]:
<seaborn.axisgrid.JointGrid at 0x2b86a8021dd0>
In [6]:
for col in merged_data["mem1"].columns:
print(col)
Step_ID
Track_ID
Date
Forecast_Hour
Valid_Hour_UTC
Duration
Centroid_Lon
Centroid_Lat
Storm_Motion_U
Storm_Motion_V
UP_HELI_MAX_mean
UP_HELI_MAX_max
UP_HELI_MAX_min
UP_HELI_MAX_std
UP_HELI_MAX_skew
UP_HELI_MAX_percentile_10
UP_HELI_MAX_percentile_50
UP_HELI_MAX_percentile_90
GRPL_MAX_mean
GRPL_MAX_max
GRPL_MAX_min
GRPL_MAX_std
GRPL_MAX_skew
GRPL_MAX_percentile_10
GRPL_MAX_percentile_50
GRPL_MAX_percentile_90
W_UP_MAX_mean
W_UP_MAX_max
W_UP_MAX_min
W_UP_MAX_std
W_UP_MAX_skew
W_UP_MAX_percentile_10
W_UP_MAX_percentile_50
W_UP_MAX_percentile_90
W_DN_MAX_mean
W_DN_MAX_max
W_DN_MAX_min
W_DN_MAX_std
W_DN_MAX_skew
W_DN_MAX_percentile_10
W_DN_MAX_percentile_50
W_DN_MAX_percentile_90
HAIL_MAX2D_mean
HAIL_MAX2D_max
HAIL_MAX2D_min
HAIL_MAX2D_std
HAIL_MAX2D_skew
HAIL_MAX2D_percentile_10
HAIL_MAX2D_percentile_50
HAIL_MAX2D_percentile_90
HAIL_MAXK1_mean
HAIL_MAXK1_max
HAIL_MAXK1_min
HAIL_MAXK1_std
HAIL_MAXK1_skew
HAIL_MAXK1_percentile_10
HAIL_MAXK1_percentile_50
HAIL_MAXK1_percentile_90
LTG3_MAX_mean
LTG3_MAX_max
LTG3_MAX_min
LTG3_MAX_std
LTG3_MAX_skew
LTG3_MAX_percentile_10
LTG3_MAX_percentile_50
LTG3_MAX_percentile_90
RVORT1_MAX_mean
RVORT1_MAX_max
RVORT1_MAX_min
RVORT1_MAX_std
RVORT1_MAX_skew
RVORT1_MAX_percentile_10
RVORT1_MAX_percentile_50
RVORT1_MAX_percentile_90
UP_HELI_MAX03_mean
UP_HELI_MAX03_max
UP_HELI_MAX03_min
UP_HELI_MAX03_std
UP_HELI_MAX03_skew
UP_HELI_MAX03_percentile_10
UP_HELI_MAX03_percentile_50
UP_HELI_MAX03_percentile_90
UP_HELI_MIN_mean
UP_HELI_MIN_max
UP_HELI_MIN_min
UP_HELI_MIN_std
UP_HELI_MIN_skew
UP_HELI_MIN_percentile_10
UP_HELI_MIN_percentile_50
UP_HELI_MIN_percentile_90
WSPD10MAX_mean
WSPD10MAX_max
WSPD10MAX_min
WSPD10MAX_std
WSPD10MAX_skew
WSPD10MAX_percentile_10
WSPD10MAX_percentile_50
WSPD10MAX_percentile_90
REFD_MAX_mean
REFD_MAX_max
REFD_MAX_min
REFD_MAX_std
REFD_MAX_skew
REFD_MAX_percentile_10
REFD_MAX_percentile_50
REFD_MAX_percentile_90
UBSHR1-potential_mean
UBSHR1-potential_max
UBSHR1-potential_min
UBSHR1-potential_std
UBSHR1-potential_skew
UBSHR1-potential_percentile_10
UBSHR1-potential_percentile_50
UBSHR1-potential_percentile_90
VBSHR1-potential_mean
VBSHR1-potential_max
VBSHR1-potential_min
VBSHR1-potential_std
VBSHR1-potential_skew
VBSHR1-potential_percentile_10
VBSHR1-potential_percentile_50
VBSHR1-potential_percentile_90
UBSHR6-potential_mean
UBSHR6-potential_max
UBSHR6-potential_min
UBSHR6-potential_std
UBSHR6-potential_skew
UBSHR6-potential_percentile_10
UBSHR6-potential_percentile_50
UBSHR6-potential_percentile_90
VBSHR6-potential_mean
VBSHR6-potential_max
VBSHR6-potential_min
VBSHR6-potential_std
VBSHR6-potential_skew
VBSHR6-potential_percentile_10
VBSHR6-potential_percentile_50
VBSHR6-potential_percentile_90
PWAT-potential_mean
PWAT-potential_max
PWAT-potential_min
PWAT-potential_std
PWAT-potential_skew
PWAT-potential_percentile_10
PWAT-potential_percentile_50
PWAT-potential_percentile_90
SRH3-potential_mean
SRH3-potential_max
SRH3-potential_min
SRH3-potential_std
SRH3-potential_skew
SRH3-potential_percentile_10
SRH3-potential_percentile_50
SRH3-potential_percentile_90
LCL_HEIGHT-potential_mean
LCL_HEIGHT-potential_max
LCL_HEIGHT-potential_min
LCL_HEIGHT-potential_std
LCL_HEIGHT-potential_skew
LCL_HEIGHT-potential_percentile_10
LCL_HEIGHT-potential_percentile_50
LCL_HEIGHT-potential_percentile_90
CAPE_SFC-potential_mean
CAPE_SFC-potential_max
CAPE_SFC-potential_min
CAPE_SFC-potential_std
CAPE_SFC-potential_skew
CAPE_SFC-potential_percentile_10
CAPE_SFC-potential_percentile_50
CAPE_SFC-potential_percentile_90
CIN_SFC-potential_mean
CIN_SFC-potential_max
CIN_SFC-potential_min
CIN_SFC-potential_std
CIN_SFC-potential_skew
CIN_SFC-potential_percentile_10
CIN_SFC-potential_percentile_50
CIN_SFC-potential_percentile_90
MUCAPE-potential_mean
MUCAPE-potential_max
MUCAPE-potential_min
MUCAPE-potential_std
MUCAPE-potential_skew
MUCAPE-potential_percentile_10
MUCAPE-potential_percentile_50
MUCAPE-potential_percentile_90
UBSHR6-tendency_mean
UBSHR6-tendency_max
UBSHR6-tendency_min
UBSHR6-tendency_std
UBSHR6-tendency_skew
UBSHR6-tendency_percentile_10
UBSHR6-tendency_percentile_50
UBSHR6-tendency_percentile_90
VBSHR6-tendency_mean
VBSHR6-tendency_max
VBSHR6-tendency_min
VBSHR6-tendency_std
VBSHR6-tendency_skew
VBSHR6-tendency_percentile_10
VBSHR6-tendency_percentile_50
VBSHR6-tendency_percentile_90
REFD_MAX-tendency_mean
REFD_MAX-tendency_max
REFD_MAX-tendency_min
REFD_MAX-tendency_std
REFD_MAX-tendency_skew
REFD_MAX-tendency_percentile_10
REFD_MAX-tendency_percentile_50
REFD_MAX-tendency_percentile_90
area
eccentricity
major_axis_length
minor_axis_length
orientation
extent
Hail_Size
Shape
Location
Scale
Random-Forest_Condition
Random-Forest_Shape
Random-Forest_Location
Random-Forest_Scale
Random-Forest-CV_Shape
Random-Forest-CV_Location
Random-Forest-CV_Scale
Elastic-Net_Shape
Elastic-Net_Location
Elastic-Net_Scale
In [18]:
plt.scatter(merged_data["mem1"]["HAIL_MAXK1_max"][merged_data["mem1"]["Hail_Size"] > 0] * 1000,
merged_data["mem1"]["Hail_Size"][merged_data["mem1"]["Hail_Size"] > 0])
Out[18]:
<matplotlib.collections.PathCollection at 0x2b86b8761490>
In [26]:
np.count_nonzero(merged_data["mem1"]["Hail_Size"].values == 0)
Out[26]:
31897
In [32]:
total_path = "/glade/scratch/dgagne/track_data_ncar_2016_csv/"
track_total_files = sorted(glob(total_path + "track_total_NCAR_mem1*.csv"))
all_tracks = pd.concat([pd.read_csv(track_total_file) for track_total_file in track_total_files], ignore_index=True)
In [50]:
np.where(~np.isnan(all_tracks["Start_Time_Error"]))
Out[50]:
(array([ 2032, 2035, 2037, ..., 80217, 80218, 80219]),)
In [52]:
all_tracks.loc[2035]
Out[52]:
Track_ID mem10_GRPL_MAX_20160501-0000_02_04_004
Start_Date 2016-05-01 02:00:00
End_Date 2016-05-01 04:00:00
Duration 3
Ensemble_Name NCAR
Ensemble_Member mem10
Object_Variable GRPL_MAX
Obs_Track_ID obs_mem10_20160501-0000_02_03_019
Translation_Error_X 10123.7
Translation_Error_Y -8003.2
Start_Time_Error 0
End_Time_Error 1
Name: 2035, dtype: object
In [57]:
all_tracks["Duration"].hist(bins=np.arange(1, 20))
ax = plt.gca()
ax.set_yscale("log")
In [63]:
all_tracks["Translation_Error_X"][~np.isnan(all_tracks["Translation_Error_X"])].hist(bins=30)
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x2b86b8849110>
In [65]:
all_tracks["Start_Time_Error"].dropna().hist()
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x2b86b9d3f1d0>
In [71]:
obs_tracks = pd.read_csv("/glade/scratch/dgagne/track_data_ncar_2016_csv/track_step_obs_mem1_20160527.csv")
fore_tracks = pd.read_csv("/glade/scratch/dgagne/track_data_ncar_2016_csv/track_step_NCAR_mem1_20160527.csv")
fore_total_tracks = pd.read_csv("/glade/scratch/dgagne/track_data_ncar_2016_csv/track_total_NCAR_mem1_20160527.csv")
In [68]:
plt.scatter(obs_tracks["Centroid_Lon"], obs_tracks["Centroid_Lat"])
Out[68]:
<matplotlib.collections.PathCollection at 0x2b86ba06d0d0>
In [72]:
fore_total_tracks
Out[72]:
Track_ID
Start_Date
End_Date
Duration
Ensemble_Name
Ensemble_Member
Object_Variable
Obs_Track_ID
Translation_Error_X
Translation_Error_Y
Start_Time_Error
End_Time_Error
0
mem1_GRPL_MAX_20160527-0000_01_05_000
2016-05-27 01:00:00
2016-05-27 05:00:00
5.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_005
-66576.96176
-63627.38781
-2.0
2.0
1
mem1_GRPL_MAX_20160527-0000_01_02_001
2016-05-27 01:00:00
2016-05-27 02:00:00
2.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_04_019
75894.39846
95580.41612
-2.0
-2.0
2
mem1_GRPL_MAX_20160527-0000_01_03_002
2016-05-27 01:00:00
2016-05-27 03:00:00
3.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
3
mem1_GRPL_MAX_20160527-0000_01_04_003
2016-05-27 01:00:00
2016-05-27 04:00:00
4.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_022
101105.17741
45964.39057
-2.0
1.0
4
mem1_GRPL_MAX_20160527-0000_01_03_004
2016-05-27 01:00:00
2016-05-27 03:00:00
3.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_025
34528.81023
35472.64677
-2.0
0.0
5
mem1_GRPL_MAX_20160527-0000_01_01_005
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
6
mem1_GRPL_MAX_20160527-0000_01_01_006
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_021
86733.57572
133844.57716
-2.0
-2.0
7
mem1_GRPL_MAX_20160527-0000_01_01_007
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
8
mem1_GRPL_MAX_20160527-0000_01_01_008
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
9
mem1_GRPL_MAX_20160527-0000_01_02_009
2016-05-27 01:00:00
2016-05-27 02:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
10
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 01:00:00
2016-05-27 07:00:00
7.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
11
mem1_GRPL_MAX_20160527-0000_01_06_011
2016-05-27 01:00:00
2016-05-27 06:00:00
6.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
12
mem1_GRPL_MAX_20160527-0000_01_01_012
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_04_031
-93698.79813
67369.61507
-2.0
-3.0
13
mem1_GRPL_MAX_20160527-0000_01_05_013
2016-05-27 01:00:00
2016-05-27 05:00:00
5.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
14
mem1_GRPL_MAX_20160527-0000_01_01_014
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
15
mem1_GRPL_MAX_20160527-0000_01_02_015
2016-05-27 01:00:00
2016-05-27 02:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
16
mem1_GRPL_MAX_20160527-0000_01_02_016
2016-05-27 01:00:00
2016-05-27 02:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
17
mem1_GRPL_MAX_20160527-0000_01_01_017
2016-05-27 01:00:00
2016-05-27 01:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
18
mem1_GRPL_MAX_20160527-0000_01_02_018
2016-05-27 01:00:00
2016-05-27 02:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
19
mem1_GRPL_MAX_20160527-0000_02_03_019
2016-05-27 02:00:00
2016-05-27 03:00:00
2.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_04_04_055
-49921.40032
-21172.09177
-2.0
-1.0
20
mem1_GRPL_MAX_20160527-0000_02_04_020
2016-05-27 02:00:00
2016-05-27 04:00:00
3.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_04_006
-22318.47868
9855.05467
-1.0
0.0
21
mem1_GRPL_MAX_20160527-0000_02_02_021
2016-05-27 02:00:00
2016-05-27 02:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_009
15077.38542
-69390.22999
-1.0
-1.0
22
mem1_GRPL_MAX_20160527-0000_02_02_022
2016-05-27 02:00:00
2016-05-27 02:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_024
7956.64897
16194.39984
-1.0
-1.0
23
mem1_GRPL_MAX_20160527-0000_02_03_023
2016-05-27 02:00:00
2016-05-27 03:00:00
2.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_04_027
22911.58540
23833.72220
-1.0
-1.0
24
mem1_GRPL_MAX_20160527-0000_02_03_024
2016-05-27 02:00:00
2016-05-27 03:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
25
mem1_GRPL_MAX_20160527-0000_02_02_025
2016-05-27 02:00:00
2016-05-27 02:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_03_03_032
-17987.74600
22632.23470
-1.0
-1.0
26
mem1_GRPL_MAX_20160527-0000_02_02_026
2016-05-27 02:00:00
2016-05-27 02:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
27
mem1_GRPL_MAX_20160527-0000_02_02_027
2016-05-27 02:00:00
2016-05-27 02:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
28
mem1_GRPL_MAX_20160527-0000_02_04_028
2016-05-27 02:00:00
2016-05-27 04:00:00
3.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
29
mem1_GRPL_MAX_20160527-0000_02_02_029
2016-05-27 02:00:00
2016-05-27 02:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
...
...
...
...
...
...
...
...
...
...
...
...
...
414
mem1_GRPL_MAX_20160527-0000_45_45_414
2016-05-28 21:00:00
2016-05-28 21:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
415
mem1_GRPL_MAX_20160527-0000_46_48_415
2016-05-28 22:00:00
2016-05-29 00:00:00
3.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
416
mem1_GRPL_MAX_20160527-0000_46_46_416
2016-05-28 22:00:00
2016-05-28 22:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
417
mem1_GRPL_MAX_20160527-0000_46_46_417
2016-05-28 22:00:00
2016-05-28 22:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
418
mem1_GRPL_MAX_20160527-0000_46_47_418
2016-05-28 22:00:00
2016-05-28 23:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
419
mem1_GRPL_MAX_20160527-0000_46_48_419
2016-05-28 22:00:00
2016-05-29 00:00:00
3.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
420
mem1_GRPL_MAX_20160527-0000_46_46_420
2016-05-28 22:00:00
2016-05-28 22:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
421
mem1_GRPL_MAX_20160527-0000_46_48_421
2016-05-28 22:00:00
2016-05-29 00:00:00
3.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_47_48_423
6281.23177
7530.05386
-1.0
0.0
422
mem1_GRPL_MAX_20160527-0000_46_48_422
2016-05-28 22:00:00
2016-05-29 00:00:00
3.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_46_46_402
-61480.72155
-12989.85051
0.0
2.0
423
mem1_GRPL_MAX_20160527-0000_46_46_423
2016-05-28 22:00:00
2016-05-28 22:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_45_45_379
34087.30557
-117295.81489
1.0
1.0
424
mem1_GRPL_MAX_20160527-0000_46_48_424
2016-05-28 22:00:00
2016-05-29 00:00:00
3.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_47_47_428
-101503.82430
23400.71297
-1.0
1.0
425
mem1_GRPL_MAX_20160527-0000_46_46_425
2016-05-28 22:00:00
2016-05-28 22:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_45_45_387
-3811.95214
-45830.69875
1.0
1.0
426
mem1_GRPL_MAX_20160527-0000_46_46_426
2016-05-28 22:00:00
2016-05-28 22:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
427
mem1_GRPL_MAX_20160527-0000_47_47_427
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
428
mem1_GRPL_MAX_20160527-0000_47_48_428
2016-05-28 23:00:00
2016-05-29 00:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
429
mem1_GRPL_MAX_20160527-0000_47_47_429
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
430
mem1_GRPL_MAX_20160527-0000_47_48_430
2016-05-28 23:00:00
2016-05-29 00:00:00
2.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
431
mem1_GRPL_MAX_20160527-0000_47_47_431
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
432
mem1_GRPL_MAX_20160527-0000_47_47_432
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_47_48_425
-24520.09536
-15754.91501
0.0
-1.0
433
mem1_GRPL_MAX_20160527-0000_47_48_433
2016-05-28 23:00:00
2016-05-29 00:00:00
2.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_47_48_427
-105170.65060
4811.24793
0.0
0.0
434
mem1_GRPL_MAX_20160527-0000_47_48_434
2016-05-28 23:00:00
2016-05-29 00:00:00
2.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_46_46_404
24218.76973
-35497.12619
1.0
2.0
435
mem1_GRPL_MAX_20160527-0000_47_47_435
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_48_48_451
-43219.71509
-46665.60876
-1.0
-1.0
436
mem1_GRPL_MAX_20160527-0000_47_47_436
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
437
mem1_GRPL_MAX_20160527-0000_47_47_437
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
438
mem1_GRPL_MAX_20160527-0000_47_47_438
2016-05-28 23:00:00
2016-05-28 23:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_45_45_393
-92213.13665
81579.56728
2.0
2.0
439
mem1_GRPL_MAX_20160527-0000_48_48_439
2016-05-29 00:00:00
2016-05-29 00:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_47_48_414
132504.51821
76327.99538
1.0
0.0
440
mem1_GRPL_MAX_20160527-0000_48_48_440
2016-05-29 00:00:00
2016-05-29 00:00:00
1.0
NCAR
mem1
GRPL_MAX
None
NaN
NaN
NaN
NaN
441
mem1_GRPL_MAX_20160527-0000_48_48_441
2016-05-29 00:00:00
2016-05-29 00:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_48_48_446
-54683.63633
47303.71226
0.0
0.0
442
mem1_GRPL_MAX_20160527-0000_48_48_442
2016-05-29 00:00:00
2016-05-29 00:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_48_48_454
-115305.00714
-20325.56988
0.0
0.0
443
mem1_GRPL_MAX_20160527-0000_48_48_443
2016-05-29 00:00:00
2016-05-29 00:00:00
1.0
NCAR
mem1
GRPL_MAX
obs_mem1_20160527-0000_47_47_438
-53536.95471
-101643.40768
1.0
1.0
444 rows × 12 columns
In [85]:
start = lambda x: x.values[-1]
obs_track_mean = obs_tracks.groupby("Obs_Track_ID")["Centroid_Lon", "Centroid_Lat"].agg(np.mean)
In [89]:
obs_tracks["Obs_Track_ID"].unique()
Out[89]:
array(['obs_mem1_20160527-0000_03_04_000',
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In [100]:
fore_tracks_combo = pd.merge(fore_tracks, fore_total_tracks, on="Track_ID")
In [103]:
In [112]:
mean_fore_tracks = fore_tracks.groupby("Track_ID")["Centroid_Lon", "Centroid_Lat"].agg(np.mean)
mean_fore_tracks = pd.merge(mean_fore_tracks, fore_total_tracks.loc[:, ["Track_ID", "Obs_Track_ID"]], left_index=True, right_on="Track_ID")
In [119]:
matched_tracks = pd.merge(mean_fore_tracks, obs_track_mean, left_on="Obs_Track_ID", right_index=True)
In [121]:
all_xs = []
all_ys = []
for r in matched_tracks.index:
all_xs.extend([matched_tracks.loc[r, "Centroid_Lon_x"], matched_tracks.loc[r, "Centroid_Lon_y"], np.nan])
all_ys.extend([matched_tracks.loc[r, "Centroid_Lat_x"], matched_tracks.loc[r, "Centroid_Lat_y"], np.nan])
In [123]:
plt.figure(figsize=(10, 6))
plt.plot(all_xs, all_ys)
Out[123]:
[<matplotlib.lines.Line2D at 0x2b86ba296ed0>]
In [129]:
(np.sqrt(fore_tracks_combo["Translation_Error_X"] ** 2 + fore_tracks_combo["Translation_Error_Y"] ** 2) / 1000.0).hist()
Out[129]:
<matplotlib.axes._subplots.AxesSubplot at 0x2b86ba6cb310>
In [139]:
unique_ids = fore_total_tracks["Obs_Track_ID"][fore_total_tracks["Obs_Track_ID"] != "None"].unique()
counts = np.zeros(unique_ids.size)
for u, unique_id in enumerate(unique_ids):
counts[u] = np.count_nonzero(fore_total_tracks["Obs_Track_ID"] == unique_id)
In [141]:
unique_ids.size
Out[141]:
139
In [163]:
%matplotlib notebook
plt.figure(figsize=(10, 6))
rank = np.argsort(obs_tracks["MESH_max"])
plt.plot(all_xs, all_ys)
plt.scatter(obs_tracks["Centroid_Lon"][rank], obs_tracks["Centroid_Lat"][rank], 20, obs_tracks["MESH_max"][rank],
vmin=0, vmax=100, cmap="RdBu_r")
plt.scatter(fore_tracks["Centroid_Lon"], fore_tracks["Centroid_Lat"], 20, fore_tracks["GRPL_MAX_max"],
vmin=0, vmax=100, cmap="PuOr", marker="v")
plt.colorbar()
Out[163]:
<matplotlib.colorbar.Colorbar at 0x2b86bbc3f2d0>
In [161]:
fore_tracks["GRPL_MAX_max"].max()
Out[161]:
86.159480000000002
In [162]:
fore_tracks
Out[162]:
Step_ID
Track_ID
Date
Forecast_Hour
Valid_Hour_UTC
Duration
Centroid_Lon
Centroid_Lat
Storm_Motion_U
Storm_Motion_V
...
area
eccentricity
major_axis_length
minor_axis_length
orientation
extent
Hail_Size
Shape
Location
Scale
0
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0.00000
0.00000
0.00000
0.00000
23
mem1_GRPL_MAX_20160527-0000_01_07_010_00
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 01:00:00
1.0
1.0
1.0
-94.95842
39.27034
0.0
0.0
...
103.0
0.76616
14.60310
9.38469
-0.63749
0.71528
0.00000
0.00000
0.00000
0.00000
24
mem1_GRPL_MAX_20160527-0000_01_07_010_01
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 02:00:00
2.0
2.0
2.0
-94.95094
39.05666
9000.0
-21000.0
...
101.0
0.85633
15.94115
8.23252
-1.22544
0.72143
0.00000
0.00000
0.00000
0.00000
25
mem1_GRPL_MAX_20160527-0000_01_07_010_02
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 03:00:00
3.0
3.0
3.0
-94.61350
39.07666
42000.0
-9000.0
...
137.0
0.94459
26.36128
8.65281
-0.37784
0.44481
0.00000
0.00000
0.00000
0.00000
26
mem1_GRPL_MAX_20160527-0000_01_07_010_03
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 04:00:00
4.0
4.0
4.0
-94.62840
39.09164
12000.0
-15000.0
...
19.0
0.85362
6.76306
3.52287
-0.57440
0.63333
0.00000
0.00000
0.00000
0.00000
27
mem1_GRPL_MAX_20160527-0000_01_07_010_04
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 05:00:00
5.0
5.0
5.0
-94.77990
39.57457
-21000.0
45000.0
...
99.0
0.94368
20.63822
6.82835
-0.79479
0.41250
0.00000
0.00000
0.00000
0.00000
28
mem1_GRPL_MAX_20160527-0000_01_07_010_05
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 06:00:00
6.0
6.0
6.0
-94.55252
39.67907
24000.0
12000.0
...
133.0
0.97396
29.34177
6.65249
-0.64604
0.30435
0.00000
0.00000
0.00000
0.00000
29
mem1_GRPL_MAX_20160527-0000_01_07_010_06
mem1_GRPL_MAX_20160527-0000_01_07_010
2016-05-27 07:00:00
7.0
7.0
7.0
-94.23470
39.80790
15000.0
6000.0
...
65.0
0.68775
11.17137
8.10981
-0.73173
0.65000
0.00000
0.00000
0.00000
0.00000
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
738
mem1_GRPL_MAX_20160527-0000_46_48_422_00
mem1_GRPL_MAX_20160527-0000_46_48_422
2016-05-28 22:00:00
46.0
22.0
1.0
-92.68474
38.00041
30000.0
60000.0
...
69.0
0.94201
21.86272
7.33684
-1.22763
0.34500
28.09961
1.71358
6.40000
5.48600
739
mem1_GRPL_MAX_20160527-0000_46_48_422_01
mem1_GRPL_MAX_20160527-0000_46_48_422
2016-05-28 23:00:00
47.0
23.0
2.0
-92.68327
38.22593
-12000.0
30000.0
...
54.0
0.94312
19.62745
6.52508
-0.38196
0.31765
28.09961
1.71358
6.40000
5.48600
740
mem1_GRPL_MAX_20160527-0000_46_48_422_02
mem1_GRPL_MAX_20160527-0000_46_48_422
2016-05-29 00:00:00
48.0
0.0
3.0
-92.33738
38.08455
21000.0
-21000.0
...
82.0
0.72389
15.82884
10.92057
-0.09208
0.37104
28.09961
1.71358
6.40000
5.48600
741
mem1_GRPL_MAX_20160527-0000_46_46_423_00
mem1_GRPL_MAX_20160527-0000_46_46_423
2016-05-28 22:00:00
46.0
22.0
1.0
-87.00246
37.98042
15000.0
21000.0
...
27.0
0.82443
7.79578
4.41217
-1.09620
0.77143
23.90039
1.13475
6.30039
9.84053
742
mem1_GRPL_MAX_20160527-0000_46_48_424_00
mem1_GRPL_MAX_20160527-0000_46_48_424
2016-05-28 22:00:00
46.0
22.0
1.0
-92.07940
39.37115
21000.0
-3000.0
...
33.0
0.72099
7.79536
5.40176
-0.64350
0.78571
19.20020
1.07695
6.10020
5.72993
743
mem1_GRPL_MAX_20160527-0000_46_48_424_01
mem1_GRPL_MAX_20160527-0000_46_48_424
2016-05-28 23:00:00
47.0
23.0
2.0
-91.77966
39.34455
24000.0
-3000.0
...
33.0
0.95272
11.76348
3.57431
-0.66203
0.45833
19.20020
1.07695
6.10020
5.72993
744
mem1_GRPL_MAX_20160527-0000_46_48_424_02
mem1_GRPL_MAX_20160527-0000_46_48_424
2016-05-29 00:00:00
48.0
0.0
3.0
-91.68529
39.35567
6000.0
0.0
...
18.0
0.74910
5.86133
3.88290
-0.58682
0.72000
19.20020
1.07695
6.10020
5.72993
745
mem1_GRPL_MAX_20160527-0000_46_46_425_00
mem1_GRPL_MAX_20160527-0000_46_46_425
2016-05-28 22:00:00
46.0
22.0
1.0
-75.60593
40.81259
-6000.0
-6000.0
...
16.0
0.85862
6.25701
3.20738
0.46105
0.66667
15.09961
1.15812
6.99961
4.39727
746
mem1_GRPL_MAX_20160527-0000_46_46_426_00
mem1_GRPL_MAX_20160527-0000_46_46_426
2016-05-28 22:00:00
46.0
22.0
1.0
-91.81593
47.70733
9000.0
24000.0
...
19.0
0.91840
7.73686
3.06119
1.46556
0.79167
0.00000
0.00000
0.00000
0.00000
747
mem1_GRPL_MAX_20160527-0000_47_47_427_00
mem1_GRPL_MAX_20160527-0000_47_47_427
2016-05-28 23:00:00
47.0
23.0
1.0
-80.68186
25.55475
0.0
0.0
...
22.0
0.68015
6.14652
4.50587
1.38089
0.73333
0.00000
0.00000
0.00000
0.00000
748
mem1_GRPL_MAX_20160527-0000_47_48_428_00
mem1_GRPL_MAX_20160527-0000_47_48_428
2016-05-28 23:00:00
47.0
23.0
1.0
-95.97983
29.55550
-27000.0
-18000.0
...
39.0
0.73590
8.93477
6.04967
0.10466
0.72222
0.00000
0.00000
0.00000
0.00000
749
mem1_GRPL_MAX_20160527-0000_47_48_428_01
mem1_GRPL_MAX_20160527-0000_47_48_428
2016-05-29 00:00:00
48.0
0.0
2.0
-96.15748
29.62991
-21000.0
12000.0
...
82.0
0.89141
17.93939
8.13017
0.70178
0.42051
0.00000
0.00000
0.00000
0.00000
750
mem1_GRPL_MAX_20160527-0000_47_47_429_00
mem1_GRPL_MAX_20160527-0000_47_47_429
2016-05-28 23:00:00
47.0
23.0
1.0
-95.94254
30.08742
9000.0
-15000.0
...
26.0
0.87507
9.40231
4.55071
1.19664
0.54167
0.00000
0.00000
0.00000
0.00000
751
mem1_GRPL_MAX_20160527-0000_47_48_430_00
mem1_GRPL_MAX_20160527-0000_47_48_430
2016-05-28 23:00:00
47.0
23.0
1.0
-97.67246
32.54804
6000.0
-3000.0
...
22.0
0.60971
6.68628
5.29972
-0.54557
0.61111
0.00000
0.00000
0.00000
0.00000
752
mem1_GRPL_MAX_20160527-0000_47_48_430_01
mem1_GRPL_MAX_20160527-0000_47_48_430
2016-05-29 00:00:00
48.0
0.0
2.0
-97.22455
32.82507
33000.0
18000.0
...
43.0
0.92972
12.34192
4.54501
-0.32079
0.59722
0.00000
0.00000
0.00000
0.00000
753
mem1_GRPL_MAX_20160527-0000_47_47_431_00
mem1_GRPL_MAX_20160527-0000_47_47_431
2016-05-28 23:00:00
47.0
23.0
1.0
-91.60449
35.62336
-6000.0
21000.0
...
29.0
0.55648
7.00517
5.82035
-0.89392
0.69048
0.00000
0.00000
0.00000
0.00000
754
mem1_GRPL_MAX_20160527-0000_47_47_432_00
mem1_GRPL_MAX_20160527-0000_47_47_432
2016-05-28 23:00:00
47.0
23.0
1.0
-92.35716
37.98266
15000.0
0.0
...
25.0
0.65150
6.51962
4.94612
-0.37463
0.71429
38.50000
1.13504
6.80039
11.26954
755
mem1_GRPL_MAX_20160527-0000_47_48_433_00
mem1_GRPL_MAX_20160527-0000_47_48_433
2016-05-28 23:00:00
47.0
23.0
1.0
-92.56770
38.81923
12000.0
3000.0
...
27.0
0.85970
8.26243
4.22040
-1.10245
0.67500
23.00000
1.15928
6.10020
8.39205
756
mem1_GRPL_MAX_20160527-0000_47_48_433_01
mem1_GRPL_MAX_20160527-0000_47_48_433
2016-05-29 00:00:00
48.0
0.0
2.0
-92.55438
38.77011
0.0
-6000.0
...
41.0
0.70512
8.68666
6.15964
-0.94194
0.73214
15.90039
1.47765
6.60020
3.54370
757
mem1_GRPL_MAX_20160527-0000_47_48_434_00
mem1_GRPL_MAX_20160527-0000_47_48_434
2016-05-28 23:00:00
47.0
23.0
1.0
-85.09612
38.72612
-6000.0
-15000.0
...
34.0
0.94178
11.80347
3.96872
-1.08940
0.48571
16.50000
1.67415
6.40000
4.24098
758
mem1_GRPL_MAX_20160527-0000_47_48_434_01
mem1_GRPL_MAX_20160527-0000_47_48_434
2016-05-29 00:00:00
48.0
0.0
2.0
-84.79608
38.82804
18000.0
6000.0
...
35.0
0.90152
10.28845
4.45229
-1.13607
0.64815
16.50000
1.67415
6.40000
4.24098
759
mem1_GRPL_MAX_20160527-0000_47_47_435_00
mem1_GRPL_MAX_20160527-0000_47_47_435
2016-05-28 23:00:00
47.0
23.0
1.0
-81.83842
40.82559
-3000.0
0.0
...
23.0
0.88634
9.12061
4.22317
0.31416
0.51111
15.09961
0.84705
8.19980
4.63555
760
mem1_GRPL_MAX_20160527-0000_47_47_436_00
mem1_GRPL_MAX_20160527-0000_47_47_436
2016-05-28 23:00:00
47.0
23.0
1.0
-76.22705
40.71091
-21000.0
0.0
...
28.0
0.69177
7.17971
5.18462
-0.06972
0.80000
0.00000
0.00000
0.00000
0.00000
761
mem1_GRPL_MAX_20160527-0000_47_47_437_00
mem1_GRPL_MAX_20160527-0000_47_47_437
2016-05-28 23:00:00
47.0
23.0
1.0
-94.73226
44.41585
3000.0
24000.0
...
17.0
0.94067
9.94969
3.37611
-1.33812
0.53125
0.00000
0.00000
0.00000
0.00000
762
mem1_GRPL_MAX_20160527-0000_47_47_438_00
mem1_GRPL_MAX_20160527-0000_47_47_438
2016-05-28 23:00:00
47.0
23.0
1.0
-83.52107
44.56459
24000.0
21000.0
...
32.0
0.51821
8.79056
7.51817
0.25658
0.57143
9.70020
3.26076
6.30039
0.63743
763
mem1_GRPL_MAX_20160527-0000_48_48_439_00
mem1_GRPL_MAX_20160527-0000_48_48_439
2016-05-29 00:00:00
48.0
0.0
1.0
-97.74541
29.88109
-6000.0
-12000.0
...
17.0
0.62594
5.18190
4.04120
-0.26479
0.85000
44.79980
0.95442
7.10020
17.74188
764
mem1_GRPL_MAX_20160527-0000_48_48_440_00
mem1_GRPL_MAX_20160527-0000_48_48_440
2016-05-29 00:00:00
48.0
0.0
1.0
-93.88905
34.13011
-39000.0
9000.0
...
48.0
0.64067
12.16585
9.34109
0.12227
0.30769
0.00000
0.00000
0.00000
0.00000
765
mem1_GRPL_MAX_20160527-0000_48_48_441_00
mem1_GRPL_MAX_20160527-0000_48_48_441
2016-05-29 00:00:00
48.0
0.0
1.0
-92.83457
37.76584
18000.0
-24000.0
...
33.0
0.61549
8.61641
6.79099
0.68014
0.51562
19.20020
2.07800
5.99961
3.97778
766
mem1_GRPL_MAX_20160527-0000_48_48_442_00
mem1_GRPL_MAX_20160527-0000_48_48_442
2016-05-29 00:00:00
48.0
0.0
1.0
-81.11217
41.45095
9000.0
9000.0
...
30.0
0.94633
11.34915
3.66804
1.43201
0.68182
23.70020
1.24641
6.19980
5.03632
767
mem1_GRPL_MAX_20160527-0000_48_48_443_00
mem1_GRPL_MAX_20160527-0000_48_48_443
2016-05-29 00:00:00
48.0
0.0
1.0
-83.41288
43.01787
18000.0
54000.0
...
79.0
0.96661
20.08248
5.14652
-1.09283
0.43889
17.90039
1.63597
6.30039
4.65762
768 rows × 220 columns
In [4]:
ls /glade/p/work/dgagne/ncar_coarse_neighbor_eval_2016/
coarse_neighbor_eval_NCAR_20160502.csv coarse_neighbor_eval_NCAR_20160701.csv
coarse_neighbor_eval_NCAR_20160503.csv coarse_neighbor_eval_NCAR_20160702.csv
coarse_neighbor_eval_NCAR_20160504.csv coarse_neighbor_eval_NCAR_20160703.csv
coarse_neighbor_eval_NCAR_20160505.csv coarse_neighbor_eval_NCAR_20160704.csv
coarse_neighbor_eval_NCAR_20160506.csv coarse_neighbor_eval_NCAR_20160705.csv
coarse_neighbor_eval_NCAR_20160507.csv coarse_neighbor_eval_NCAR_20160706.csv
coarse_neighbor_eval_NCAR_20160508.csv coarse_neighbor_eval_NCAR_20160707.csv
coarse_neighbor_eval_NCAR_20160509.csv coarse_neighbor_eval_NCAR_20160708.csv
coarse_neighbor_eval_NCAR_20160510.csv coarse_neighbor_eval_NCAR_20160709.csv
coarse_neighbor_eval_NCAR_20160511.csv coarse_neighbor_eval_NCAR_20160710.csv
coarse_neighbor_eval_NCAR_20160512.csv coarse_neighbor_eval_NCAR_20160711.csv
coarse_neighbor_eval_NCAR_20160513.csv coarse_neighbor_eval_NCAR_20160712.csv
coarse_neighbor_eval_NCAR_20160514.csv coarse_neighbor_eval_NCAR_20160713.csv
coarse_neighbor_eval_NCAR_20160515.csv coarse_neighbor_eval_NCAR_20160714.csv
coarse_neighbor_eval_NCAR_20160516.csv coarse_neighbor_eval_NCAR_20160715.csv
coarse_neighbor_eval_NCAR_20160517.csv coarse_neighbor_eval_NCAR_20160716.csv
coarse_neighbor_eval_NCAR_20160518.csv coarse_neighbor_eval_NCAR_20160717.csv
coarse_neighbor_eval_NCAR_20160519.csv coarse_neighbor_eval_NCAR_20160718.csv
coarse_neighbor_eval_NCAR_20160520.csv coarse_neighbor_eval_NCAR_20160719.csv
coarse_neighbor_eval_NCAR_20160521.csv coarse_neighbor_eval_NCAR_20160720.csv
coarse_neighbor_eval_NCAR_20160522.csv coarse_neighbor_eval_NCAR_20160721.csv
coarse_neighbor_eval_NCAR_20160523.csv coarse_neighbor_eval_NCAR_20160722.csv
coarse_neighbor_eval_NCAR_20160524.csv coarse_neighbor_eval_NCAR_20160723.csv
coarse_neighbor_eval_NCAR_20160525.csv coarse_neighbor_eval_NCAR_20160724.csv
coarse_neighbor_eval_NCAR_20160526.csv coarse_neighbor_eval_NCAR_20160725.csv
coarse_neighbor_eval_NCAR_20160527.csv coarse_neighbor_eval_NCAR_20160726.csv
coarse_neighbor_eval_NCAR_20160528.csv coarse_neighbor_eval_NCAR_20160727.csv
coarse_neighbor_eval_NCAR_20160529.csv coarse_neighbor_eval_NCAR_20160729.csv
coarse_neighbor_eval_NCAR_20160530.csv coarse_neighbor_eval_NCAR_20160730.csv
coarse_neighbor_eval_NCAR_20160531.csv coarse_neighbor_eval_NCAR_20160731.csv
coarse_neighbor_eval_NCAR_20160601.csv coarse_neighbor_eval_NCAR_20160801.csv
coarse_neighbor_eval_NCAR_20160602.csv coarse_neighbor_eval_NCAR_20160802.csv
coarse_neighbor_eval_NCAR_20160603.csv coarse_neighbor_eval_NCAR_20160803.csv
coarse_neighbor_eval_NCAR_20160604.csv coarse_neighbor_eval_NCAR_20160804.csv
coarse_neighbor_eval_NCAR_20160605.csv coarse_neighbor_eval_NCAR_20160808.csv
coarse_neighbor_eval_NCAR_20160606.csv coarse_neighbor_eval_NCAR_20160809.csv
coarse_neighbor_eval_NCAR_20160607.csv coarse_neighbor_eval_NCAR_20160810.csv
coarse_neighbor_eval_NCAR_20160608.csv coarse_neighbor_eval_NCAR_20160811.csv
coarse_neighbor_eval_NCAR_20160609.csv coarse_neighbor_eval_NCAR_20160812.csv
coarse_neighbor_eval_NCAR_20160610.csv coarse_neighbor_eval_NCAR_20160813.csv
coarse_neighbor_eval_NCAR_20160611.csv coarse_neighbor_eval_NCAR_20160814.csv
coarse_neighbor_eval_NCAR_20160612.csv coarse_neighbor_eval_NCAR_20160815.csv
coarse_neighbor_eval_NCAR_20160613.csv coarse_neighbor_eval_NCAR_20160816.csv
coarse_neighbor_eval_NCAR_20160614.csv coarse_neighbor_eval_NCAR_20160817.csv
coarse_neighbor_eval_NCAR_20160615.csv coarse_neighbor_eval_NCAR_20160818.csv
coarse_neighbor_eval_NCAR_20160616.csv coarse_neighbor_eval_NCAR_20160819.csv
coarse_neighbor_eval_NCAR_20160617.csv coarse_neighbor_eval_NCAR_20160820.csv
coarse_neighbor_eval_NCAR_20160618.csv coarse_neighbor_eval_NCAR_20160821.csv
coarse_neighbor_eval_NCAR_20160619.csv coarse_neighbor_eval_NCAR_20160826.csv
coarse_neighbor_eval_NCAR_20160620.csv coarse_neighbor_eval_NCAR_20160827.csv
coarse_neighbor_eval_NCAR_20160621.csv coarse_neighbor_eval_NCAR_20160829.csv
coarse_neighbor_eval_NCAR_20160622.csv coarse_neighbor_eval_NCAR_20160830.csv
coarse_neighbor_eval_NCAR_20160623.csv coarse_neighbor_eval_NCAR_20160831.csv
coarse_neighbor_eval_NCAR_20160630.csv
In [6]:
eval_data = pd.read_csv("/glade/p/work/dgagne/ncar_coarse_neighbor_eval_2016/coarse_neighbor_eval_NCAR_20160526.csv")
In [10]:
for c in eval_data.columns:
print(c)
i
i_small
j
j_small
lat
lon
us_mask
x
y
Run_Date
Start_Date
End_Date
MESH_Max_60min_00.50_25
MESH_Max_60min_00.50_50
MESH_Max_60min_00.50_75
NCAR_HAIL_MAX2D_mem1_5
NCAR_HAIL_MAX2D_mem1_20
NCAR_HAIL_MAX2D_mem1_25
NCAR_HAIL_MAX2D_mem1_30
NCAR_HAIL_MAX2D_mem1_35
NCAR_HAIL_MAX2D_mem1_40
NCAR_HAIL_MAX2D_mem1_45
NCAR_HAIL_MAX2D_mem1_50
NCAR_HAIL_MAX2D_mem1_75
NCAR_HAIL_MAX2D_mem2_5
NCAR_HAIL_MAX2D_mem2_20
NCAR_HAIL_MAX2D_mem2_25
NCAR_HAIL_MAX2D_mem2_30
NCAR_HAIL_MAX2D_mem2_35
NCAR_HAIL_MAX2D_mem2_40
NCAR_HAIL_MAX2D_mem2_45
NCAR_HAIL_MAX2D_mem2_50
NCAR_HAIL_MAX2D_mem2_75
NCAR_HAIL_MAX2D_mem3_5
NCAR_HAIL_MAX2D_mem3_20
NCAR_HAIL_MAX2D_mem3_25
NCAR_HAIL_MAX2D_mem3_30
NCAR_HAIL_MAX2D_mem3_35
NCAR_HAIL_MAX2D_mem3_40
NCAR_HAIL_MAX2D_mem3_45
NCAR_HAIL_MAX2D_mem3_50
NCAR_HAIL_MAX2D_mem3_75
NCAR_HAIL_MAX2D_mem4_5
NCAR_HAIL_MAX2D_mem4_20
NCAR_HAIL_MAX2D_mem4_25
NCAR_HAIL_MAX2D_mem4_30
NCAR_HAIL_MAX2D_mem4_35
NCAR_HAIL_MAX2D_mem4_40
NCAR_HAIL_MAX2D_mem4_45
NCAR_HAIL_MAX2D_mem4_50
NCAR_HAIL_MAX2D_mem4_75
NCAR_HAIL_MAX2D_mem5_5
NCAR_HAIL_MAX2D_mem5_20
NCAR_HAIL_MAX2D_mem5_25
NCAR_HAIL_MAX2D_mem5_30
NCAR_HAIL_MAX2D_mem5_35
NCAR_HAIL_MAX2D_mem5_40
NCAR_HAIL_MAX2D_mem5_45
NCAR_HAIL_MAX2D_mem5_50
NCAR_HAIL_MAX2D_mem5_75
NCAR_HAIL_MAX2D_mem6_5
NCAR_HAIL_MAX2D_mem6_20
NCAR_HAIL_MAX2D_mem6_25
NCAR_HAIL_MAX2D_mem6_30
NCAR_HAIL_MAX2D_mem6_35
NCAR_HAIL_MAX2D_mem6_40
NCAR_HAIL_MAX2D_mem6_45
NCAR_HAIL_MAX2D_mem6_50
NCAR_HAIL_MAX2D_mem6_75
NCAR_HAIL_MAX2D_mem7_5
NCAR_HAIL_MAX2D_mem7_20
NCAR_HAIL_MAX2D_mem7_25
NCAR_HAIL_MAX2D_mem7_30
NCAR_HAIL_MAX2D_mem7_35
NCAR_HAIL_MAX2D_mem7_40
NCAR_HAIL_MAX2D_mem7_45
NCAR_HAIL_MAX2D_mem7_50
NCAR_HAIL_MAX2D_mem7_75
NCAR_HAIL_MAX2D_mem8_5
NCAR_HAIL_MAX2D_mem8_20
NCAR_HAIL_MAX2D_mem8_25
NCAR_HAIL_MAX2D_mem8_30
NCAR_HAIL_MAX2D_mem8_35
NCAR_HAIL_MAX2D_mem8_40
NCAR_HAIL_MAX2D_mem8_45
NCAR_HAIL_MAX2D_mem8_50
NCAR_HAIL_MAX2D_mem8_75
NCAR_HAIL_MAX2D_mem9_5
NCAR_HAIL_MAX2D_mem9_20
NCAR_HAIL_MAX2D_mem9_25
NCAR_HAIL_MAX2D_mem9_30
NCAR_HAIL_MAX2D_mem9_35
NCAR_HAIL_MAX2D_mem9_40
NCAR_HAIL_MAX2D_mem9_45
NCAR_HAIL_MAX2D_mem9_50
NCAR_HAIL_MAX2D_mem9_75
NCAR_HAIL_MAX2D_mem10_5
NCAR_HAIL_MAX2D_mem10_20
NCAR_HAIL_MAX2D_mem10_25
NCAR_HAIL_MAX2D_mem10_30
NCAR_HAIL_MAX2D_mem10_35
NCAR_HAIL_MAX2D_mem10_40
NCAR_HAIL_MAX2D_mem10_45
NCAR_HAIL_MAX2D_mem10_50
NCAR_HAIL_MAX2D_mem10_75
NCAR_HAIL_MAX2D_mean_5
NCAR_HAIL_MAX2D_mean_20
NCAR_HAIL_MAX2D_mean_25
NCAR_HAIL_MAX2D_mean_30
NCAR_HAIL_MAX2D_mean_35
NCAR_HAIL_MAX2D_mean_40
NCAR_HAIL_MAX2D_mean_45
NCAR_HAIL_MAX2D_mean_50
NCAR_HAIL_MAX2D_mean_75
NCAR_HAIL_MAXK1_mem1_5
NCAR_HAIL_MAXK1_mem1_20
NCAR_HAIL_MAXK1_mem1_25
NCAR_HAIL_MAXK1_mem1_30
NCAR_HAIL_MAXK1_mem1_35
NCAR_HAIL_MAXK1_mem1_40
NCAR_HAIL_MAXK1_mem1_45
NCAR_HAIL_MAXK1_mem1_50
NCAR_HAIL_MAXK1_mem1_75
NCAR_HAIL_MAXK1_mem2_5
NCAR_HAIL_MAXK1_mem2_20
NCAR_HAIL_MAXK1_mem2_25
NCAR_HAIL_MAXK1_mem2_30
NCAR_HAIL_MAXK1_mem2_35
NCAR_HAIL_MAXK1_mem2_40
NCAR_HAIL_MAXK1_mem2_45
NCAR_HAIL_MAXK1_mem2_50
NCAR_HAIL_MAXK1_mem2_75
NCAR_HAIL_MAXK1_mem3_5
NCAR_HAIL_MAXK1_mem3_20
NCAR_HAIL_MAXK1_mem3_25
NCAR_HAIL_MAXK1_mem3_30
NCAR_HAIL_MAXK1_mem3_35
NCAR_HAIL_MAXK1_mem3_40
NCAR_HAIL_MAXK1_mem3_45
NCAR_HAIL_MAXK1_mem3_50
NCAR_HAIL_MAXK1_mem3_75
NCAR_HAIL_MAXK1_mem4_5
NCAR_HAIL_MAXK1_mem4_20
NCAR_HAIL_MAXK1_mem4_25
NCAR_HAIL_MAXK1_mem4_30
NCAR_HAIL_MAXK1_mem4_35
NCAR_HAIL_MAXK1_mem4_40
NCAR_HAIL_MAXK1_mem4_45
NCAR_HAIL_MAXK1_mem4_50
NCAR_HAIL_MAXK1_mem4_75
NCAR_HAIL_MAXK1_mem5_5
NCAR_HAIL_MAXK1_mem5_20
NCAR_HAIL_MAXK1_mem5_25
NCAR_HAIL_MAXK1_mem5_30
NCAR_HAIL_MAXK1_mem5_35
NCAR_HAIL_MAXK1_mem5_40
NCAR_HAIL_MAXK1_mem5_45
NCAR_HAIL_MAXK1_mem5_50
NCAR_HAIL_MAXK1_mem5_75
NCAR_HAIL_MAXK1_mem6_5
NCAR_HAIL_MAXK1_mem6_20
NCAR_HAIL_MAXK1_mem6_25
NCAR_HAIL_MAXK1_mem6_30
NCAR_HAIL_MAXK1_mem6_35
NCAR_HAIL_MAXK1_mem6_40
NCAR_HAIL_MAXK1_mem6_45
NCAR_HAIL_MAXK1_mem6_50
NCAR_HAIL_MAXK1_mem6_75
NCAR_HAIL_MAXK1_mem7_5
NCAR_HAIL_MAXK1_mem7_20
NCAR_HAIL_MAXK1_mem7_25
NCAR_HAIL_MAXK1_mem7_30
NCAR_HAIL_MAXK1_mem7_35
NCAR_HAIL_MAXK1_mem7_40
NCAR_HAIL_MAXK1_mem7_45
NCAR_HAIL_MAXK1_mem7_50
NCAR_HAIL_MAXK1_mem7_75
NCAR_HAIL_MAXK1_mem8_5
NCAR_HAIL_MAXK1_mem8_20
NCAR_HAIL_MAXK1_mem8_25
NCAR_HAIL_MAXK1_mem8_30
NCAR_HAIL_MAXK1_mem8_35
NCAR_HAIL_MAXK1_mem8_40
NCAR_HAIL_MAXK1_mem8_45
NCAR_HAIL_MAXK1_mem8_50
NCAR_HAIL_MAXK1_mem8_75
NCAR_HAIL_MAXK1_mem9_5
NCAR_HAIL_MAXK1_mem9_20
NCAR_HAIL_MAXK1_mem9_25
NCAR_HAIL_MAXK1_mem9_30
NCAR_HAIL_MAXK1_mem9_35
NCAR_HAIL_MAXK1_mem9_40
NCAR_HAIL_MAXK1_mem9_45
NCAR_HAIL_MAXK1_mem9_50
NCAR_HAIL_MAXK1_mem9_75
NCAR_HAIL_MAXK1_mem10_5
NCAR_HAIL_MAXK1_mem10_20
NCAR_HAIL_MAXK1_mem10_25
NCAR_HAIL_MAXK1_mem10_30
NCAR_HAIL_MAXK1_mem10_35
NCAR_HAIL_MAXK1_mem10_40
NCAR_HAIL_MAXK1_mem10_45
NCAR_HAIL_MAXK1_mem10_50
NCAR_HAIL_MAXK1_mem10_75
NCAR_HAIL_MAXK1_mean_5
NCAR_HAIL_MAXK1_mean_20
NCAR_HAIL_MAXK1_mean_25
NCAR_HAIL_MAXK1_mean_30
NCAR_HAIL_MAXK1_mean_35
NCAR_HAIL_MAXK1_mean_40
NCAR_HAIL_MAXK1_mean_45
NCAR_HAIL_MAXK1_mean_50
NCAR_HAIL_MAXK1_mean_75
NCAR_UP_HELI_MAX_mem1_5
NCAR_UP_HELI_MAX_mem1_25
NCAR_UP_HELI_MAX_mem1_50
NCAR_UP_HELI_MAX_mem1_75
NCAR_UP_HELI_MAX_mem1_100
NCAR_UP_HELI_MAX_mem1_125
NCAR_UP_HELI_MAX_mem1_150
NCAR_UP_HELI_MAX_mem1_175
NCAR_UP_HELI_MAX_mem1_200
NCAR_UP_HELI_MAX_mem2_5
NCAR_UP_HELI_MAX_mem2_25
NCAR_UP_HELI_MAX_mem2_50
NCAR_UP_HELI_MAX_mem2_75
NCAR_UP_HELI_MAX_mem2_100
NCAR_UP_HELI_MAX_mem2_125
NCAR_UP_HELI_MAX_mem2_150
NCAR_UP_HELI_MAX_mem2_175
NCAR_UP_HELI_MAX_mem2_200
NCAR_UP_HELI_MAX_mem3_5
NCAR_UP_HELI_MAX_mem3_25
NCAR_UP_HELI_MAX_mem3_50
NCAR_UP_HELI_MAX_mem3_75
NCAR_UP_HELI_MAX_mem3_100
NCAR_UP_HELI_MAX_mem3_125
NCAR_UP_HELI_MAX_mem3_150
NCAR_UP_HELI_MAX_mem3_175
NCAR_UP_HELI_MAX_mem3_200
NCAR_UP_HELI_MAX_mem4_5
NCAR_UP_HELI_MAX_mem4_25
NCAR_UP_HELI_MAX_mem4_50
NCAR_UP_HELI_MAX_mem4_75
NCAR_UP_HELI_MAX_mem4_100
NCAR_UP_HELI_MAX_mem4_125
NCAR_UP_HELI_MAX_mem4_150
NCAR_UP_HELI_MAX_mem4_175
NCAR_UP_HELI_MAX_mem4_200
NCAR_UP_HELI_MAX_mem5_5
NCAR_UP_HELI_MAX_mem5_25
NCAR_UP_HELI_MAX_mem5_50
NCAR_UP_HELI_MAX_mem5_75
NCAR_UP_HELI_MAX_mem5_100
NCAR_UP_HELI_MAX_mem5_125
NCAR_UP_HELI_MAX_mem5_150
NCAR_UP_HELI_MAX_mem5_175
NCAR_UP_HELI_MAX_mem5_200
NCAR_UP_HELI_MAX_mem6_5
NCAR_UP_HELI_MAX_mem6_25
NCAR_UP_HELI_MAX_mem6_50
NCAR_UP_HELI_MAX_mem6_75
NCAR_UP_HELI_MAX_mem6_100
NCAR_UP_HELI_MAX_mem6_125
NCAR_UP_HELI_MAX_mem6_150
NCAR_UP_HELI_MAX_mem6_175
NCAR_UP_HELI_MAX_mem6_200
NCAR_UP_HELI_MAX_mem7_5
NCAR_UP_HELI_MAX_mem7_25
NCAR_UP_HELI_MAX_mem7_50
NCAR_UP_HELI_MAX_mem7_75
NCAR_UP_HELI_MAX_mem7_100
NCAR_UP_HELI_MAX_mem7_125
NCAR_UP_HELI_MAX_mem7_150
NCAR_UP_HELI_MAX_mem7_175
NCAR_UP_HELI_MAX_mem7_200
NCAR_UP_HELI_MAX_mem8_5
NCAR_UP_HELI_MAX_mem8_25
NCAR_UP_HELI_MAX_mem8_50
NCAR_UP_HELI_MAX_mem8_75
NCAR_UP_HELI_MAX_mem8_100
NCAR_UP_HELI_MAX_mem8_125
NCAR_UP_HELI_MAX_mem8_150
NCAR_UP_HELI_MAX_mem8_175
NCAR_UP_HELI_MAX_mem8_200
NCAR_UP_HELI_MAX_mem9_5
NCAR_UP_HELI_MAX_mem9_25
NCAR_UP_HELI_MAX_mem9_50
NCAR_UP_HELI_MAX_mem9_75
NCAR_UP_HELI_MAX_mem9_100
NCAR_UP_HELI_MAX_mem9_125
NCAR_UP_HELI_MAX_mem9_150
NCAR_UP_HELI_MAX_mem9_175
NCAR_UP_HELI_MAX_mem9_200
NCAR_UP_HELI_MAX_mem10_5
NCAR_UP_HELI_MAX_mem10_25
NCAR_UP_HELI_MAX_mem10_50
NCAR_UP_HELI_MAX_mem10_75
NCAR_UP_HELI_MAX_mem10_100
NCAR_UP_HELI_MAX_mem10_125
NCAR_UP_HELI_MAX_mem10_150
NCAR_UP_HELI_MAX_mem10_175
NCAR_UP_HELI_MAX_mem10_200
NCAR_UP_HELI_MAX_mean_5
NCAR_UP_HELI_MAX_mean_25
NCAR_UP_HELI_MAX_mean_50
NCAR_UP_HELI_MAX_mean_75
NCAR_UP_HELI_MAX_mean_100
NCAR_UP_HELI_MAX_mean_125
NCAR_UP_HELI_MAX_mean_150
NCAR_UP_HELI_MAX_mean_175
NCAR_UP_HELI_MAX_mean_200
NCAR_GRPL_MAX_mem1_5
NCAR_GRPL_MAX_mem1_10
NCAR_GRPL_MAX_mem1_15
NCAR_GRPL_MAX_mem1_20
NCAR_GRPL_MAX_mem1_25
NCAR_GRPL_MAX_mem1_30
NCAR_GRPL_MAX_mem1_35
NCAR_GRPL_MAX_mem1_40
NCAR_GRPL_MAX_mem1_45
NCAR_GRPL_MAX_mem1_50
NCAR_GRPL_MAX_mem2_5
NCAR_GRPL_MAX_mem2_10
NCAR_GRPL_MAX_mem2_15
NCAR_GRPL_MAX_mem2_20
NCAR_GRPL_MAX_mem2_25
NCAR_GRPL_MAX_mem2_30
NCAR_GRPL_MAX_mem2_35
NCAR_GRPL_MAX_mem2_40
NCAR_GRPL_MAX_mem2_45
NCAR_GRPL_MAX_mem2_50
NCAR_GRPL_MAX_mem3_5
NCAR_GRPL_MAX_mem3_10
NCAR_GRPL_MAX_mem3_15
NCAR_GRPL_MAX_mem3_20
NCAR_GRPL_MAX_mem3_25
NCAR_GRPL_MAX_mem3_30
NCAR_GRPL_MAX_mem3_35
NCAR_GRPL_MAX_mem3_40
NCAR_GRPL_MAX_mem3_45
NCAR_GRPL_MAX_mem3_50
NCAR_GRPL_MAX_mem4_5
NCAR_GRPL_MAX_mem4_10
NCAR_GRPL_MAX_mem4_15
NCAR_GRPL_MAX_mem4_20
NCAR_GRPL_MAX_mem4_25
NCAR_GRPL_MAX_mem4_30
NCAR_GRPL_MAX_mem4_35
NCAR_GRPL_MAX_mem4_40
NCAR_GRPL_MAX_mem4_45
NCAR_GRPL_MAX_mem4_50
NCAR_GRPL_MAX_mem5_5
NCAR_GRPL_MAX_mem5_10
NCAR_GRPL_MAX_mem5_15
NCAR_GRPL_MAX_mem5_20
NCAR_GRPL_MAX_mem5_25
NCAR_GRPL_MAX_mem5_30
NCAR_GRPL_MAX_mem5_35
NCAR_GRPL_MAX_mem5_40
NCAR_GRPL_MAX_mem5_45
NCAR_GRPL_MAX_mem5_50
NCAR_GRPL_MAX_mem6_5
NCAR_GRPL_MAX_mem6_10
NCAR_GRPL_MAX_mem6_15
NCAR_GRPL_MAX_mem6_20
NCAR_GRPL_MAX_mem6_25
NCAR_GRPL_MAX_mem6_30
NCAR_GRPL_MAX_mem6_35
NCAR_GRPL_MAX_mem6_40
NCAR_GRPL_MAX_mem6_45
NCAR_GRPL_MAX_mem6_50
NCAR_GRPL_MAX_mem7_5
NCAR_GRPL_MAX_mem7_10
NCAR_GRPL_MAX_mem7_15
NCAR_GRPL_MAX_mem7_20
NCAR_GRPL_MAX_mem7_25
NCAR_GRPL_MAX_mem7_30
NCAR_GRPL_MAX_mem7_35
NCAR_GRPL_MAX_mem7_40
NCAR_GRPL_MAX_mem7_45
NCAR_GRPL_MAX_mem7_50
NCAR_GRPL_MAX_mem8_5
NCAR_GRPL_MAX_mem8_10
NCAR_GRPL_MAX_mem8_15
NCAR_GRPL_MAX_mem8_20
NCAR_GRPL_MAX_mem8_25
NCAR_GRPL_MAX_mem8_30
NCAR_GRPL_MAX_mem8_35
NCAR_GRPL_MAX_mem8_40
NCAR_GRPL_MAX_mem8_45
NCAR_GRPL_MAX_mem8_50
NCAR_GRPL_MAX_mem9_5
NCAR_GRPL_MAX_mem9_10
NCAR_GRPL_MAX_mem9_15
NCAR_GRPL_MAX_mem9_20
NCAR_GRPL_MAX_mem9_25
NCAR_GRPL_MAX_mem9_30
NCAR_GRPL_MAX_mem9_35
NCAR_GRPL_MAX_mem9_40
NCAR_GRPL_MAX_mem9_45
NCAR_GRPL_MAX_mem9_50
NCAR_GRPL_MAX_mem10_5
NCAR_GRPL_MAX_mem10_10
NCAR_GRPL_MAX_mem10_15
NCAR_GRPL_MAX_mem10_20
NCAR_GRPL_MAX_mem10_25
NCAR_GRPL_MAX_mem10_30
NCAR_GRPL_MAX_mem10_35
NCAR_GRPL_MAX_mem10_40
NCAR_GRPL_MAX_mem10_45
NCAR_GRPL_MAX_mem10_50
NCAR_GRPL_MAX_mean_5
NCAR_GRPL_MAX_mean_10
NCAR_GRPL_MAX_mean_15
NCAR_GRPL_MAX_mean_20
NCAR_GRPL_MAX_mean_25
NCAR_GRPL_MAX_mean_30
NCAR_GRPL_MAX_mean_35
NCAR_GRPL_MAX_mean_40
NCAR_GRPL_MAX_mean_45
NCAR_GRPL_MAX_mean_50
NCAR_Random-Forest_mem1_5
NCAR_Random-Forest_mem2_5
NCAR_Random-Forest_mem3_5
NCAR_Random-Forest_mem4_5
NCAR_Random-Forest_mem5_5
NCAR_Random-Forest_mem6_5
NCAR_Random-Forest_mem7_5
NCAR_Random-Forest_mem8_5
NCAR_Random-Forest_mem9_5
NCAR_Random-Forest_mem10_5
NCAR_Random-Forest_mean_5
NCAR_Random-Forest_mem1_20
NCAR_Random-Forest_mem2_20
NCAR_Random-Forest_mem3_20
NCAR_Random-Forest_mem4_20
NCAR_Random-Forest_mem5_20
NCAR_Random-Forest_mem6_20
NCAR_Random-Forest_mem7_20
NCAR_Random-Forest_mem8_20
NCAR_Random-Forest_mem9_20
NCAR_Random-Forest_mem10_20
NCAR_Random-Forest_mean_20
NCAR_Random-Forest_mem1_25
NCAR_Random-Forest_mem2_25
NCAR_Random-Forest_mem3_25
NCAR_Random-Forest_mem4_25
NCAR_Random-Forest_mem5_25
NCAR_Random-Forest_mem6_25
NCAR_Random-Forest_mem7_25
NCAR_Random-Forest_mem8_25
NCAR_Random-Forest_mem9_25
NCAR_Random-Forest_mem10_25
NCAR_Random-Forest_mean_25
NCAR_Random-Forest_mem1_30
NCAR_Random-Forest_mem2_30
NCAR_Random-Forest_mem3_30
NCAR_Random-Forest_mem4_30
NCAR_Random-Forest_mem5_30
NCAR_Random-Forest_mem6_30
NCAR_Random-Forest_mem7_30
NCAR_Random-Forest_mem8_30
NCAR_Random-Forest_mem9_30
NCAR_Random-Forest_mem10_30
NCAR_Random-Forest_mean_30
NCAR_Random-Forest_mem1_35
NCAR_Random-Forest_mem2_35
NCAR_Random-Forest_mem3_35
NCAR_Random-Forest_mem4_35
NCAR_Random-Forest_mem5_35
NCAR_Random-Forest_mem6_35
NCAR_Random-Forest_mem7_35
NCAR_Random-Forest_mem8_35
NCAR_Random-Forest_mem9_35
NCAR_Random-Forest_mem10_35
NCAR_Random-Forest_mean_35
NCAR_Random-Forest_mem1_40
NCAR_Random-Forest_mem2_40
NCAR_Random-Forest_mem3_40
NCAR_Random-Forest_mem4_40
NCAR_Random-Forest_mem5_40
NCAR_Random-Forest_mem6_40
NCAR_Random-Forest_mem7_40
NCAR_Random-Forest_mem8_40
NCAR_Random-Forest_mem9_40
NCAR_Random-Forest_mem10_40
NCAR_Random-Forest_mean_40
NCAR_Random-Forest_mem1_45
NCAR_Random-Forest_mem2_45
NCAR_Random-Forest_mem3_45
NCAR_Random-Forest_mem4_45
NCAR_Random-Forest_mem5_45
NCAR_Random-Forest_mem6_45
NCAR_Random-Forest_mem7_45
NCAR_Random-Forest_mem8_45
NCAR_Random-Forest_mem9_45
NCAR_Random-Forest_mem10_45
NCAR_Random-Forest_mean_45
NCAR_Random-Forest_mem1_50
NCAR_Random-Forest_mem2_50
NCAR_Random-Forest_mem3_50
NCAR_Random-Forest_mem4_50
NCAR_Random-Forest_mem5_50
NCAR_Random-Forest_mem6_50
NCAR_Random-Forest_mem7_50
NCAR_Random-Forest_mem8_50
NCAR_Random-Forest_mem9_50
NCAR_Random-Forest_mem10_50
NCAR_Random-Forest_mean_50
NCAR_Random-Forest_mem1_75
NCAR_Random-Forest_mem2_75
NCAR_Random-Forest_mem3_75
NCAR_Random-Forest_mem4_75
NCAR_Random-Forest_mem5_75
NCAR_Random-Forest_mem6_75
NCAR_Random-Forest_mem7_75
NCAR_Random-Forest_mem8_75
NCAR_Random-Forest_mem9_75
NCAR_Random-Forest_mem10_75
NCAR_Random-Forest_mean_75
NCAR_Elastic-Net_mem1_5
NCAR_Elastic-Net_mem2_5
NCAR_Elastic-Net_mem3_5
NCAR_Elastic-Net_mem4_5
NCAR_Elastic-Net_mem5_5
NCAR_Elastic-Net_mem6_5
NCAR_Elastic-Net_mem7_5
NCAR_Elastic-Net_mem8_5
NCAR_Elastic-Net_mem9_5
NCAR_Elastic-Net_mem10_5
NCAR_Elastic-Net_mean_5
NCAR_Elastic-Net_mem1_20
NCAR_Elastic-Net_mem2_20
NCAR_Elastic-Net_mem3_20
NCAR_Elastic-Net_mem4_20
NCAR_Elastic-Net_mem5_20
NCAR_Elastic-Net_mem6_20
NCAR_Elastic-Net_mem7_20
NCAR_Elastic-Net_mem8_20
NCAR_Elastic-Net_mem9_20
NCAR_Elastic-Net_mem10_20
NCAR_Elastic-Net_mean_20
NCAR_Elastic-Net_mem1_25
NCAR_Elastic-Net_mem2_25
NCAR_Elastic-Net_mem3_25
NCAR_Elastic-Net_mem4_25
NCAR_Elastic-Net_mem5_25
NCAR_Elastic-Net_mem6_25
NCAR_Elastic-Net_mem7_25
NCAR_Elastic-Net_mem8_25
NCAR_Elastic-Net_mem9_25
NCAR_Elastic-Net_mem10_25
NCAR_Elastic-Net_mean_25
NCAR_Elastic-Net_mem1_30
NCAR_Elastic-Net_mem2_30
NCAR_Elastic-Net_mem3_30
NCAR_Elastic-Net_mem4_30
NCAR_Elastic-Net_mem5_30
NCAR_Elastic-Net_mem6_30
NCAR_Elastic-Net_mem7_30
NCAR_Elastic-Net_mem8_30
NCAR_Elastic-Net_mem9_30
NCAR_Elastic-Net_mem10_30
NCAR_Elastic-Net_mean_30
NCAR_Elastic-Net_mem1_35
NCAR_Elastic-Net_mem2_35
NCAR_Elastic-Net_mem3_35
NCAR_Elastic-Net_mem4_35
NCAR_Elastic-Net_mem5_35
NCAR_Elastic-Net_mem6_35
NCAR_Elastic-Net_mem7_35
NCAR_Elastic-Net_mem8_35
NCAR_Elastic-Net_mem9_35
NCAR_Elastic-Net_mem10_35
NCAR_Elastic-Net_mean_35
NCAR_Elastic-Net_mem1_40
NCAR_Elastic-Net_mem2_40
NCAR_Elastic-Net_mem3_40
NCAR_Elastic-Net_mem4_40
NCAR_Elastic-Net_mem5_40
NCAR_Elastic-Net_mem6_40
NCAR_Elastic-Net_mem7_40
NCAR_Elastic-Net_mem8_40
NCAR_Elastic-Net_mem9_40
NCAR_Elastic-Net_mem10_40
NCAR_Elastic-Net_mean_40
NCAR_Elastic-Net_mem1_45
NCAR_Elastic-Net_mem2_45
NCAR_Elastic-Net_mem3_45
NCAR_Elastic-Net_mem4_45
NCAR_Elastic-Net_mem5_45
NCAR_Elastic-Net_mem6_45
NCAR_Elastic-Net_mem7_45
NCAR_Elastic-Net_mem8_45
NCAR_Elastic-Net_mem9_45
NCAR_Elastic-Net_mem10_45
NCAR_Elastic-Net_mean_45
NCAR_Elastic-Net_mem1_50
NCAR_Elastic-Net_mem2_50
NCAR_Elastic-Net_mem3_50
NCAR_Elastic-Net_mem4_50
NCAR_Elastic-Net_mem5_50
NCAR_Elastic-Net_mem6_50
NCAR_Elastic-Net_mem7_50
NCAR_Elastic-Net_mem8_50
NCAR_Elastic-Net_mem9_50
NCAR_Elastic-Net_mem10_50
NCAR_Elastic-Net_mean_50
NCAR_Elastic-Net_mem1_75
NCAR_Elastic-Net_mem2_75
NCAR_Elastic-Net_mem3_75
NCAR_Elastic-Net_mem4_75
NCAR_Elastic-Net_mem5_75
NCAR_Elastic-Net_mem6_75
NCAR_Elastic-Net_mem7_75
NCAR_Elastic-Net_mem8_75
NCAR_Elastic-Net_mem9_75
NCAR_Elastic-Net_mem10_75
NCAR_Elastic-Net_mean_75
NCAR_Random-Forest-CV_mem1_5
NCAR_Random-Forest-CV_mem2_5
NCAR_Random-Forest-CV_mem3_5
NCAR_Random-Forest-CV_mem4_5
NCAR_Random-Forest-CV_mem5_5
NCAR_Random-Forest-CV_mem6_5
NCAR_Random-Forest-CV_mem7_5
NCAR_Random-Forest-CV_mem8_5
NCAR_Random-Forest-CV_mem9_5
NCAR_Random-Forest-CV_mem10_5
NCAR_Random-Forest-CV_mean_5
NCAR_Random-Forest-CV_mem1_20
NCAR_Random-Forest-CV_mem2_20
NCAR_Random-Forest-CV_mem3_20
NCAR_Random-Forest-CV_mem4_20
NCAR_Random-Forest-CV_mem5_20
NCAR_Random-Forest-CV_mem6_20
NCAR_Random-Forest-CV_mem7_20
NCAR_Random-Forest-CV_mem8_20
NCAR_Random-Forest-CV_mem9_20
NCAR_Random-Forest-CV_mem10_20
NCAR_Random-Forest-CV_mean_20
NCAR_Random-Forest-CV_mem1_25
NCAR_Random-Forest-CV_mem2_25
NCAR_Random-Forest-CV_mem3_25
NCAR_Random-Forest-CV_mem4_25
NCAR_Random-Forest-CV_mem5_25
NCAR_Random-Forest-CV_mem6_25
NCAR_Random-Forest-CV_mem7_25
NCAR_Random-Forest-CV_mem8_25
NCAR_Random-Forest-CV_mem9_25
NCAR_Random-Forest-CV_mem10_25
NCAR_Random-Forest-CV_mean_25
NCAR_Random-Forest-CV_mem1_30
NCAR_Random-Forest-CV_mem2_30
NCAR_Random-Forest-CV_mem3_30
NCAR_Random-Forest-CV_mem4_30
NCAR_Random-Forest-CV_mem5_30
NCAR_Random-Forest-CV_mem6_30
NCAR_Random-Forest-CV_mem7_30
NCAR_Random-Forest-CV_mem8_30
NCAR_Random-Forest-CV_mem9_30
NCAR_Random-Forest-CV_mem10_30
NCAR_Random-Forest-CV_mean_30
NCAR_Random-Forest-CV_mem1_35
NCAR_Random-Forest-CV_mem2_35
NCAR_Random-Forest-CV_mem3_35
NCAR_Random-Forest-CV_mem4_35
NCAR_Random-Forest-CV_mem5_35
NCAR_Random-Forest-CV_mem6_35
NCAR_Random-Forest-CV_mem7_35
NCAR_Random-Forest-CV_mem8_35
NCAR_Random-Forest-CV_mem9_35
NCAR_Random-Forest-CV_mem10_35
NCAR_Random-Forest-CV_mean_35
NCAR_Random-Forest-CV_mem1_40
NCAR_Random-Forest-CV_mem2_40
NCAR_Random-Forest-CV_mem3_40
NCAR_Random-Forest-CV_mem4_40
NCAR_Random-Forest-CV_mem5_40
NCAR_Random-Forest-CV_mem6_40
NCAR_Random-Forest-CV_mem7_40
NCAR_Random-Forest-CV_mem8_40
NCAR_Random-Forest-CV_mem9_40
NCAR_Random-Forest-CV_mem10_40
NCAR_Random-Forest-CV_mean_40
NCAR_Random-Forest-CV_mem1_45
NCAR_Random-Forest-CV_mem2_45
NCAR_Random-Forest-CV_mem3_45
NCAR_Random-Forest-CV_mem4_45
NCAR_Random-Forest-CV_mem5_45
NCAR_Random-Forest-CV_mem6_45
NCAR_Random-Forest-CV_mem7_45
NCAR_Random-Forest-CV_mem8_45
NCAR_Random-Forest-CV_mem9_45
NCAR_Random-Forest-CV_mem10_45
NCAR_Random-Forest-CV_mean_45
NCAR_Random-Forest-CV_mem1_50
NCAR_Random-Forest-CV_mem2_50
NCAR_Random-Forest-CV_mem3_50
NCAR_Random-Forest-CV_mem4_50
NCAR_Random-Forest-CV_mem5_50
NCAR_Random-Forest-CV_mem6_50
NCAR_Random-Forest-CV_mem7_50
NCAR_Random-Forest-CV_mem8_50
NCAR_Random-Forest-CV_mem9_50
NCAR_Random-Forest-CV_mem10_50
NCAR_Random-Forest-CV_mean_50
NCAR_Random-Forest-CV_mem1_75
NCAR_Random-Forest-CV_mem2_75
NCAR_Random-Forest-CV_mem3_75
NCAR_Random-Forest-CV_mem4_75
NCAR_Random-Forest-CV_mem5_75
NCAR_Random-Forest-CV_mem6_75
NCAR_Random-Forest-CV_mem7_75
NCAR_Random-Forest-CV_mem8_75
NCAR_Random-Forest-CV_mem9_75
NCAR_Random-Forest-CV_mem10_75
NCAR_Random-Forest-CV_mean_75
In [26]:
plt.contourf(eval_data["NCAR_UP_HELI_MAX_mean_150"].values.reshape((71, 113)))
Out[26]:
<matplotlib.contour.QuadContourSet at 0x2b0b83cc78d0>
In [20]:
eval_data.j_small.max()
Out[20]:
112
In [32]:
d = Dataset("/glade/scratch/dgagne/RT2016/2016052600/mem1_surrogate_2016052600.nc")
hail_max = d.variables["HAIL_MAXK1"][:]
In [34]:
hail_max.max() * 1000
Out[34]:
86.87271922826767
In [38]:
model_output = ModelOutput("NCAR", "mem1", datetime(2016, 5, 26), "HAIL_MAXK1",
datetime(2016, 5, 26) + timedelta(hours=13), datetime(2016,5,26) + timedelta(hours=36),
"/glade/scratch/dgagne/RT2016/", single_step=False)
model_output.load_data()
In [42]:
model_output.units
Out[42]:
u'm'
In [ ]:
Content source: djgagne/hagelslag
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