diff --git a/Chapter2-DataManipulation/2.6_resampling.html b/Chapter2-DataManipulation/2.6_resampling.html index 2c3e8bd2..296ab9b5 100644 --- a/Chapter2-DataManipulation/2.6_resampling.html +++ b/Chapter2-DataManipulation/2.6_resampling.html @@ -801,12 +801,32 @@

2.6 Resampling Methods
+
# fix the random seed for reproducibility. Place it on the top to reproduce the entire notebook exactly once. But use it in every cell to re-run each cells independently.
+np.random.seed(42)
+
+
+
+
+
+
# We define a random generator
 rng = np.random.default_rng()
 
+
+
+
rng
+
+
+
+
+
Generator(PCG64) at 0x318353900
+
+
+
+

1. Examples of resampling techniques (Level 1)#

@@ -816,6 +836,8 @@

1.1 Randomization

@@ -933,7 +955,7 @@

1.2 Bootstrapping -../_images/2.6_resampling_14_0.png +../_images/2.6_resampling_16_0.png

We can verify that the Pearson correlation coefficient is close to our target using the numpy function corrcoef.

@@ -941,14 +963,32 @@

1.2 Bootstrapping
# The correlation matrix of the first and second columns of correlated_data
 correlation_matrix = np.corrcoef(correlated_data[:, 0], correlated_data[:, 1])
-
-# Check the correlation coefficient
+
+
+ + +
+
+
correlation_matrix
+
+
+
+
+
array([[ 1.        , -0.72540304],
+       [-0.72540304,  1.        ]])
+
+
+
+
+
+ +
+
+
subset 
+
+
+
+
+
array([[ 1.08149552, -0.75473902],
+       [-0.15064708, -0.88421426],
+       [ 0.03331803,  0.51951422],
+       [ 0.58669726,  0.51967288],
+       [ 0.42511267,  0.47888931],
+       [-0.05371048,  0.15998988],
+       [ 0.13428352,  0.24026582],
+       [-0.13976786,  0.57151718],
+       [-0.74081486,  0.22763649],
+       [ 0.02037839, -0.36898269]])
+
+
+
+
+
+
+
nsubset=10
 
+subset
+
+
+
+
+
array([[ 1.08149552, -0.75473902],
+       [-0.15064708, -0.88421426],
+       [ 0.03331803,  0.51951422],
+       [ 0.58669726,  0.51967288],
+       [ 0.42511267,  0.47888931],
+       [-0.05371048,  0.15998988],
+       [ 0.13428352,  0.24026582],
+       [-0.13976786,  0.57151718],
+       [-0.74081486,  0.22763649],
+       [ 0.02037839, -0.36898269]])
+
+
+
+
+

We now estimate \(\pi\) by using the length of the number points in the circle and in the quarter as approximation to their area.

@@ -1091,7 +1203,7 @@

1.3 Monte Carlo -
We estimate the value of pi to be: 3.04.
+
We estimate the value of pi to be: 3.28.
 
@@ -1111,35 +1223,50 @@

2.1 Plate Motion - Geodetic Data
# The station designation
 sta="P395"
-file_url="http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/"+ sta + ".tenv"
-r = requests.get(file_url).text.splitlines()  # download, read text, split lines into a list
-ue=[];un=[];uv=[];se=[];sn=[];sv=[];date=[];date_year=[];df=[]
-for iday in r:  # this loops through the days of data
-    crap=iday.split()
-    if len(crap)<10:
-      continue
-    date.append((crap[1]))
-    date_year.append(float(crap[2]))
-    ue.append(float(crap[7])*1000)
-    un.append(float(crap[8])*1000)
-    uv.append(float(crap[9])*1000)
+
+print("http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/" + sta + ".tenv")
+zip_file_url="http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/"+ sta + ".tenv"
+r = requests.get(zip_file_url)
+
+
+# create a list of strings with itemized list above
+ll = ['station ID (SSSS)','date (yymmmdd)',
+'decimal year','modified Julian day','GPS week','day of GPS week',
+'longitude (degrees) of reference meridian','delta e (m)',
+'delta n (m)','delta v (m)','antenna height (m)',
+'sigma e (m)','sigma n (m)','sigma v (m)',
+'correlation en','correlation ev','correlation nv']
+      
+
+# transform r.content into a pandas dataframe
+# first split r.content with \n separator
+# Decode the content if it's in bytes
+content_str = r.content.decode('utf-8')
+
+# Split the content by the newline character
+lines = content_str.split('\n')
+
+# Now `lines` is a list of strings, each representing a line from the content
+print(lines[0])
+
+# then transform lines into a pandas dataframe
+df = pd.DataFrame([x.split() for x in lines])
+# assign column names to df a
+df.columns = ll
+
+#convert columns to numeric
+df = df.apply(pd.to_numeric, errors='ignore')
+
+df.dropna()
+df.head()
 

- -
-
-
# We now make a data frame
-crap={'station':sta,'date':date,'date_year':date_year,'east':ue,'north':un,'up':uv}
-if len(df)==0:
-    df = pd.DataFrame(crap, columns = ['station', 'date','date_year','east','north','up'])
-else:
-    df=pd.concat([df,pd.DataFrame(crap, columns = ['station', 'date','date_year','east','north','up'])])
-df.describe()
+
+
http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/P395.tenv
+P395 06JAN25 2006.0671 53760 1359 3 -123.9  3347.67917   4987420.31375   53.03678  0.0083 0.00069 0.00105 0.00327 -0.04832  0.01695 -0.31816
 
-
-
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - + + + + + + + + + + + + + + + + + + + + + - - - - - + + + + + + + + + + + + + + + + + + + + + - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
station ID (SSSS)date (yymmmdd)decimal yearmodified Julian dayGPS weekday of GPS weeklongitude (degrees) of reference meridiandelta e (m)delta n (m)delta v (m)antenna height (m)sigma e (m)sigma n (m)sigma v (m)correlation encorrelation evcorrelation nvnew delta e (m)new delta n (m)new delta v (m)
0P39506JAN252006.067153760.01359.03.0-123.93347.679174.987420e+0653.036780.00830.000690.001050.00327-0.048320.01695-0.318160.000000.000000.00000
50%2015.3922003.347626e+064.987420e+0953038.6700001P39506JAN262006.069853761.01359.04.0-123.93347.680864.987420e+0653.030030.00830.000690.001040.00321-0.046480.00271-0.309700.00169-0.00067-0.00675
75%2020.0670503.347653e+064.987420e+0953042.4500002P39506JAN272006.072653762.01359.05.0-123.93347.680724.987420e+0653.039060.00830.000690.001050.00326-0.023670.00817-0.319410.001550.001010.00228
max2024.7420003.347683e+064.987420e+0953065.4400003P39506JAN282006.075353763.01359.06.0-123.93347.679384.987420e+0653.043820.00830.000690.001050.00324-0.036810.00908-0.305150.00021-0.001500.00704
4P39506JAN292006.078053764.01360.00.0-123.93347.680424.987420e+0653.035130.00830.000680.001050.00328-0.048150.00619-0.330290.00125-0.00162-0.00165
@@ -1227,20 +1588,15 @@

2.1 Plate Motion - Geodetic Data
-
# Plot the GPS time series
-fig,ax=plt.subplots(3,1,figsize=(11,8),sharex=True)
-ax[0].plot(df['date_year'][df['station']==sta],df['east'][df['station']==sta]);ax[0].grid(True);ax[0].set_ylabel('Easting (mm)')
-ax[1].plot(df['date_year'][df['station']==sta],df['north'][df['station']==sta]);ax[1].grid(True);ax[1].set_ylabel('Northing (mm)')
-ax[2].plot(df['date_year'][df['station']==sta],df['up'][df['station']==sta]);ax[2].grid(True);ax[2].set_ylabel('Up (mm)')
-ax[2].set_xlabel('Time (years)')
+
plt.plot(df['decimal year'], df['new delta e (m)'], label='East displacement')
 
-
Text(0.5, 0, 'Time (years)')
+
[<matplotlib.lines.Line2D at 0x319394a90>]
 
-../_images/2.6_resampling_37_1.png +../_images/2.6_resampling_46_1.png

@@ -1260,21 +1616,389 @@

2.2 Linear regression\(R^2\) is to one.

+
# remove nans with dropna for the specific delta e column and replace df with the new dataframe
+df = df.dropna(subset=['delta e (m)'])
+
+
+
+
+
+
+
df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
station ID (SSSS)date (yymmmdd)decimal yearmodified Julian dayGPS weekday of GPS weeklongitude (degrees) of reference meridiandelta e (m)delta n (m)delta v (m)antenna height (m)sigma e (m)sigma n (m)sigma v (m)correlation encorrelation evcorrelation nvnew delta e (m)new delta n (m)new delta v (m)
0P39506JAN252006.067153760.01359.03.0-123.93347.679174.987420e+0653.036780.00830.000690.001050.00327-0.048320.01695-0.318160.000000.000000.00000
1P39506JAN262006.069853761.01359.04.0-123.93347.680864.987420e+0653.030030.00830.000690.001040.00321-0.046480.00271-0.309700.00169-0.00067-0.00675
2P39506JAN272006.072653762.01359.05.0-123.93347.680724.987420e+0653.039060.00830.000690.001050.00326-0.023670.00817-0.319410.001550.001010.00228
3P39506JAN282006.075353763.01359.06.0-123.93347.679384.987420e+0653.043820.00830.000690.001050.00324-0.036810.00908-0.305150.00021-0.001500.00704
4P39506JAN292006.078053764.01360.00.0-123.93347.680424.987420e+0653.035130.00830.000680.001050.00328-0.048150.00619-0.330290.00125-0.00162-0.00165
+
+
+
+
+
plt.plot(df['decimal year'], df['new delta e (m)'], label='East displacement')
+
+
+
+
+
[<matplotlib.lines.Line2D at 0x3195279a0>]
+
+
+../_images/2.6_resampling_50_1.png +
+
+
+
+
df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
station ID (SSSS)date (yymmmdd)decimal yearmodified Julian dayGPS weekday of GPS weeklongitude (degrees) of reference meridiandelta e (m)delta n (m)delta v (m)antenna height (m)sigma e (m)sigma n (m)sigma v (m)correlation encorrelation evcorrelation nvnew delta e (m)new delta n (m)new delta v (m)
0P39506JAN252006.067153760.01359.03.0-123.93347.679174.987420e+0653.036780.00830.000690.001050.00327-0.048320.01695-0.318160.000000.000000.00000
1P39506JAN262006.069853761.01359.04.0-123.93347.680864.987420e+0653.030030.00830.000690.001040.00321-0.046480.00271-0.309700.00169-0.00067-0.00675
2P39506JAN272006.072653762.01359.05.0-123.93347.680724.987420e+0653.039060.00830.000690.001050.00326-0.023670.00817-0.319410.001550.001010.00228
3P39506JAN282006.075353763.01359.06.0-123.93347.679384.987420e+0653.043820.00830.000690.001050.00324-0.036810.00908-0.305150.00021-0.001500.00704
4P39506JAN292006.078053764.01360.00.0-123.93347.680424.987420e+0653.035130.00830.000680.001050.00328-0.048150.00619-0.330290.00125-0.00162-0.00165
+
+
+
+
# now let's find the trends and detrend the data.
 from scipy import stats
 # linear regression such that: displacement = Velocity * time
 # velocity in the East component.
-Ve, intercept, r_value, p_value, std_err = stats.linregress(df['date_year'][df['station']==sta],df['east'][df['station']==sta])
+
+Ve, intercept, r_value, p_value, std_err = stats.linregress(df['decimal year'],df['new delta e (m)'])
 # horizontal plate motion:
-print(sta,"overall plate motion there",Ve,'mm/year')
+print(sta,"overall plate motion there",Ve,'m/year')
 print("parameters: Coefficient of determination %f4.2, P-value %f4.2, standard deviation of errors %f4.2"\
       %(r_value,p_value,std_err))
 
-
P395 overall plate motion there 0.0 mm/year
-parameters: Coefficient of determination 0.0000004.2, P-value 1.0000004.2, standard deviation of errors 0.0000004.2
+
P395 overall plate motion there -0.0064397312911273945 m/year
+parameters: Coefficient of determination -0.9970084.2, P-value 0.0000004.2, standard deviation of errors 0.0000064.2
+
+
+
+
+
@@ -1363,16 +2087,18 @@

2.3 Bootstrapping# the data shows clearly a trend, so the predictions of the trends are close to each other: print("mean of the velocity estimates %f4.2 and the standard deviation %f4.2"%(np.mean(vel),np.std(vel))) -plt.hist(vel,50);plt.title('Distribution of eastward velocities (mm/year)');plt.grid(True) +plt.hist(vel,10);plt.title('Distribution of eastward velocities (mm/year)');plt.grid(True) +# only show a few values in the x-axis + plt.show()

-
mean of the velocity estimates 0.0000004.2 and the standard deviation 0.0000004.2
+
mean of the velocity estimates -0.0064404.2 and the standard deviation 0.0000064.2
 
-../_images/2.6_resampling_45_1.png +../_images/2.6_resampling_59_1.png
@@ -1414,10 +2140,10 @@

2.4 Cross validation -
<matplotlib.legend.Legend at 0x30baba520>
+
<matplotlib.legend.Legend at 0x3196996a0>
 
-../_images/2.6_resampling_47_1.png +../_images/2.6_resampling_61_1.png

Now fit the data and evaluate the error

@@ -1452,16 +2178,16 @@

2.4 Cross validation -
Training set: Coefficient / Velocity eastward (mm/year):  0.0
+
Training set: Coefficient / Velocity eastward (mm/year):  -0.006437910455226973
 MSE (mean square error) on training set (mm): 0.00
-Coefficient of determination on training set: 1.00
-MSE on validation set (mm): 0.00 and coefficient of determiniation on 1.00
+Coefficient of determination on training set: 0.99
+MSE on validation set (mm): 0.00 and coefficient of determiniation on 0.99
 
Text(0.5, 1.0, 'Random selection for data split')
 
-../_images/2.6_resampling_49_2.png +../_images/2.6_resampling_63_2.png

We can also select the training and validation to be chronological. If the “state” of the data changes through time, this may induce a bias in the training. But let’s see.

@@ -1498,14 +2224,14 @@

2.4 Cross validation -
 Training set: Coefficient / Velocity eastward (mm/year):  0.0
-Validation set MSE (mm) and Coef of Determination: 0.00,1.00
+
 Training set: Coefficient / Velocity eastward (mm/year):  -0.006250720119447979
+Validation set MSE (mm) and Coef of Determination: 0.00,0.92
 
Text(0.5, 1.0, 'Chronological selection for data split')
 
-../_images/2.6_resampling_51_2.png +../_images/2.6_resampling_65_2.png

Now you see that the choice of training vs validating data is important to fit a model that will generalize.

@@ -1560,7 +2286,7 @@

2.5 Leave One Out Cross Validation -
mean of the velocity estimates 0.0000004.2 and the standard deviation 0.0000004.2
+
mean of the velocity estimates -0.0064404.2 and the standard deviation 0.0000004.2
 CV = 0.00
 
@@ -1612,7 +2338,7 @@

2.6 K-fold cross validation -
mean of the velocity estimates 0.00 and the standard deviation 0.00
+
mean of the velocity estimates -0.01 and the standard deviation 0.00
 mean MSE for training set : 0.00 and the validation set: 0.00
 
diff --git a/_images/2.6_resampling_10_0.png b/_images/2.6_resampling_10_0.png deleted file mode 100644 index 7e477582..00000000 Binary files a/_images/2.6_resampling_10_0.png and /dev/null differ diff --git a/_images/2.6_resampling_12_0.png b/_images/2.6_resampling_12_0.png new file mode 100644 index 00000000..90a3d9c6 Binary files /dev/null and b/_images/2.6_resampling_12_0.png differ diff --git a/_images/2.6_resampling_14_0.png b/_images/2.6_resampling_14_0.png deleted file mode 100644 index bb401bab..00000000 Binary files a/_images/2.6_resampling_14_0.png and /dev/null differ diff --git a/_images/2.6_resampling_16_0.png b/_images/2.6_resampling_16_0.png new file mode 100644 index 00000000..6b5f8a3d Binary files /dev/null and b/_images/2.6_resampling_16_0.png differ diff --git a/_images/2.6_resampling_20_1.png b/_images/2.6_resampling_20_1.png deleted file mode 100644 index 36c18280..00000000 Binary files a/_images/2.6_resampling_20_1.png and /dev/null differ diff --git a/_images/2.6_resampling_24_0.png b/_images/2.6_resampling_24_0.png deleted file mode 100644 index 5297586f..00000000 Binary files a/_images/2.6_resampling_24_0.png and /dev/null differ diff --git a/_images/2.6_resampling_25_1.png b/_images/2.6_resampling_25_1.png new file mode 100644 index 00000000..06dda32b Binary files /dev/null and b/_images/2.6_resampling_25_1.png differ diff --git a/_images/2.6_resampling_29_0.png b/_images/2.6_resampling_29_0.png deleted file mode 100644 index 959be482..00000000 Binary files a/_images/2.6_resampling_29_0.png and /dev/null differ diff --git a/_images/2.6_resampling_32_0.png b/_images/2.6_resampling_32_0.png new file mode 100644 index 00000000..09a0e7f3 Binary files /dev/null and b/_images/2.6_resampling_32_0.png differ diff --git a/_images/2.6_resampling_37_0.png b/_images/2.6_resampling_37_0.png new file mode 100644 index 00000000..0764ca14 Binary files /dev/null and b/_images/2.6_resampling_37_0.png differ diff --git a/_images/2.6_resampling_37_1.png b/_images/2.6_resampling_37_1.png deleted file mode 100644 index 989cae2d..00000000 Binary files a/_images/2.6_resampling_37_1.png and /dev/null differ diff --git a/_images/2.6_resampling_41_1.png b/_images/2.6_resampling_41_1.png deleted file mode 100644 index 3db5eb70..00000000 Binary files a/_images/2.6_resampling_41_1.png and /dev/null differ diff --git a/_images/2.6_resampling_45_1.png b/_images/2.6_resampling_45_1.png deleted file mode 100644 index bb929b97..00000000 Binary files a/_images/2.6_resampling_45_1.png and /dev/null differ diff --git a/_images/2.6_resampling_46_1.png b/_images/2.6_resampling_46_1.png new file mode 100644 index 00000000..679cb4c5 Binary files /dev/null and b/_images/2.6_resampling_46_1.png differ diff --git a/_images/2.6_resampling_47_1.png b/_images/2.6_resampling_47_1.png deleted file mode 100644 index 938276da..00000000 Binary files a/_images/2.6_resampling_47_1.png and /dev/null differ diff --git a/_images/2.6_resampling_49_2.png b/_images/2.6_resampling_49_2.png deleted file mode 100644 index 1de5e1ca..00000000 Binary files a/_images/2.6_resampling_49_2.png and /dev/null differ diff --git a/_images/2.6_resampling_50_1.png b/_images/2.6_resampling_50_1.png new file mode 100644 index 00000000..679cb4c5 Binary files /dev/null and b/_images/2.6_resampling_50_1.png differ diff --git a/_images/2.6_resampling_51_2.png b/_images/2.6_resampling_51_2.png deleted file mode 100644 index f5f57da9..00000000 Binary files a/_images/2.6_resampling_51_2.png and /dev/null differ diff --git a/_images/2.6_resampling_55_2.png b/_images/2.6_resampling_55_2.png new file mode 100644 index 00000000..5dddcd69 Binary files /dev/null and b/_images/2.6_resampling_55_2.png differ diff --git a/_images/2.6_resampling_59_1.png b/_images/2.6_resampling_59_1.png new file mode 100644 index 00000000..5b1ad3cb Binary files /dev/null and b/_images/2.6_resampling_59_1.png differ diff --git a/_images/2.6_resampling_61_1.png b/_images/2.6_resampling_61_1.png new file mode 100644 index 00000000..89812fb1 Binary files /dev/null and b/_images/2.6_resampling_61_1.png differ diff --git a/_images/2.6_resampling_63_2.png b/_images/2.6_resampling_63_2.png new file mode 100644 index 00000000..4b507d15 Binary files /dev/null and b/_images/2.6_resampling_63_2.png differ diff --git a/_images/2.6_resampling_65_2.png b/_images/2.6_resampling_65_2.png new file mode 100644 index 00000000..54807a8e Binary files /dev/null and b/_images/2.6_resampling_65_2.png differ diff --git a/_sources/Chapter2-DataManipulation/2.6_resampling.ipynb b/_sources/Chapter2-DataManipulation/2.6_resampling.ipynb index 6e3b2172..87a1fc7f 100644 --- a/_sources/Chapter2-DataManipulation/2.6_resampling.ipynb +++ b/_sources/Chapter2-DataManipulation/2.6_resampling.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 234, + "metadata": {}, + "outputs": [], + "source": [ + "# fix the random seed for reproducibility. Place it on the top to reproduce the entire notebook exactly once. But use it in every cell to re-run each cells independently.\n", + "np.random.seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 235, "metadata": {}, "outputs": [], "source": [ @@ -47,6 +57,26 @@ "rng = np.random.default_rng()" ] }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Generator(PCG64) at 0x318353900" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rng" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,20 +92,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 237, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The means of A and B are, 4.916816615939592 and 5.629794831291764, respectively.\n", - "The difference of means is, -0.7129782153521722.\n" + "The means of A and B are, 4.874325971290356 and 5.473420405447642, respectively.\n", + "The difference of means is, -0.5990944341572852.\n" ] } ], "source": [ "# We begin with two datasets, A and B\n", + "np.random.seed(42)\n", + "rng = np.random.default_rng(seed=42)\n", "A = rng.normal(5, 2.5, 100)\n", "B = rng.normal(5.5, 2.5, 100)\n", "\n", @@ -101,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 238, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 239, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -152,12 +184,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 241, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -202,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 242, "metadata": {}, "outputs": [], "source": [ @@ -219,12 +251,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 243, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -250,20 +282,50 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 244, + "metadata": {}, + "outputs": [], + "source": [ + "# The correlation matrix of the first and second columns of correlated_data\n", + "correlation_matrix = np.corrcoef(correlated_data[:, 0], correlated_data[:, 1])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1. , -0.72540304],\n", + " [-0.72540304, 1. ]])" + ] + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correlation_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 246, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The correlation coefficient is: -0.7589433978306135.\n" + "The correlation coefficient is: -0.7254030411293304.\n" ] } ], "source": [ - "# The correlation matrix of the first and second columns of correlated_data\n", - "correlation_matrix = np.corrcoef(correlated_data[:, 0], correlated_data[:, 1])\n", "\n", "# Check the correlation coefficient\n", "print('The correlation coefficient is: ' + str(correlation_matrix[0,1]) + '.')" @@ -278,12 +340,41 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 247, "metadata": {}, "outputs": [], "source": [ "nsubset=10\n", - "subset = rng.choice(correlated_data, size=nsubset, replace=False)" + "subset = rng.choice(correlated_data, size=nsubset, replace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.08149552, -0.75473902],\n", + " [-0.15064708, -0.88421426],\n", + " [ 0.03331803, 0.51951422],\n", + " [ 0.58669726, 0.51967288],\n", + " [ 0.42511267, 0.47888931],\n", + " [-0.05371048, 0.15998988],\n", + " [ 0.13428352, 0.24026582],\n", + " [-0.13976786, 0.57151718],\n", + " [-0.74081486, 0.22763649],\n", + " [ 0.02037839, -0.36898269]])" + ] + }, + "execution_count": 248, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset" ] }, { @@ -295,19 +386,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 249, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The correlation coefficient of our sample is: -0.8537303444308123.\n" + "The correlation coefficient of our sample is: -0.19742697173045337.\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -338,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 250, "metadata": {}, "outputs": [], "source": [ @@ -354,12 +445,87 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 251, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an array that will keep track of the outputs of our resampling loop. In this case, we just want to record the correlation coefficient of each new sample. \n", + "corr_coef_collector = np.zeros([number_runs, 1])\n", + "\n", + "# Let's also get the length of the subset\n", + "# When bootstrapping, the size of your resampled dataset should match the size of your original sample!\n", + "length_sub = len(subset)\n", + "length_sub = 100\n" + ] + }, + { + "cell_type": "code", + "execution_count": 252, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "text/plain": [ + "array([[ 1.08149552, -0.75473902],\n", + " [-0.15064708, -0.88421426],\n", + " [ 0.03331803, 0.51951422],\n", + " [ 0.58669726, 0.51967288],\n", + " [ 0.42511267, 0.47888931],\n", + " [-0.05371048, 0.15998988],\n", + " [ 0.13428352, 0.24026582],\n", + " [-0.13976786, 0.57151718],\n", + " [-0.74081486, 0.22763649],\n", + " [ 0.02037839, -0.36898269]])" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset " + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.08149552, -0.75473902],\n", + " [-0.15064708, -0.88421426],\n", + " [ 0.03331803, 0.51951422],\n", + " [ 0.58669726, 0.51967288],\n", + " [ 0.42511267, 0.47888931],\n", + " [-0.05371048, 0.15998988],\n", + " [ 0.13428352, 0.24026582],\n", + " [-0.13976786, 0.57151718],\n", + " [-0.74081486, 0.22763649],\n", + " [ 0.02037839, -0.36898269]])" + ] + }, + "execution_count": 253, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nsubset=10\n", + "\n", + "subset" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", "text/plain": [ "
" ] @@ -369,17 +535,11 @@ } ], "source": [ - "# Create an array that will keep track of the outputs of our resampling loop. In this case, we just want to record the correlation coefficient of each new sample. \n", - "corr_coef_collector = np.zeros([number_runs, 1])\n", - "\n", - "# Let's also get the length of the subset\n", - "# When bootstrapping, the size of your resampled dataset should match the size of your original sample!\n", - "length_sub = len(subset)\n", - "\n", + "nsubset=500\n", "# Now, for each run\n", "for i in range(number_runs):\n", " # We want to draw length_sub samples WITH REPLACEMENT\n", - " new_pairs = rng.choice(subset, size=length_sub, replace=True)\n", + " new_pairs = rng.choice(correlated_data, size=nsubset, replace=True)\n", " # Calculate and store the correlation coefficient\n", " corr_coef_collector[i] = np.corrcoef(new_pairs[:, 0], new_pairs[:, 1])[0,1]\n", "\n", @@ -428,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 255, "metadata": {}, "outputs": [], "source": [ @@ -454,12 +614,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 256, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -489,14 +649,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 257, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "We estimate the value of pi to be: 3.04.\n" + "We estimate the value of pi to be: 3.28.\n" ] } ], @@ -539,29 +699,253 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 258, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/P395.tenv\n", + "P395 06JAN25 2006.0671 53760 1359 3 -123.9 3347.67917 4987420.31375 53.03678 0.0083 0.00069 0.00105 0.00327 -0.04832 0.01695 -0.31816\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
station ID (SSSS)date (yymmmdd)decimal yearmodified Julian dayGPS weekday of GPS weeklongitude (degrees) of reference meridiandelta e (m)delta n (m)delta v (m)antenna height (m)sigma e (m)sigma n (m)sigma v (m)correlation encorrelation evcorrelation nv
0P39506JAN252006.067153760.01359.03.0-123.93347.679174.987420e+0653.036780.00830.000690.001050.00327-0.048320.01695-0.31816
1P39506JAN262006.069853761.01359.04.0-123.93347.680864.987420e+0653.030030.00830.000690.001040.00321-0.046480.00271-0.30970
2P39506JAN272006.072653762.01359.05.0-123.93347.680724.987420e+0653.039060.00830.000690.001050.00326-0.023670.00817-0.31941
3P39506JAN282006.075353763.01359.06.0-123.93347.679384.987420e+0653.043820.00830.000690.001050.00324-0.036810.00908-0.30515
4P39506JAN292006.078053764.01360.00.0-123.93347.680424.987420e+0653.035130.00830.000680.001050.00328-0.048150.00619-0.33029
\n", + "
" + ], + "text/plain": [ + " station ID (SSSS) date (yymmmdd) decimal year modified Julian day \\\n", + "0 P395 06JAN25 2006.0671 53760.0 \n", + "1 P395 06JAN26 2006.0698 53761.0 \n", + "2 P395 06JAN27 2006.0726 53762.0 \n", + "3 P395 06JAN28 2006.0753 53763.0 \n", + "4 P395 06JAN29 2006.0780 53764.0 \n", + "\n", + " GPS week day of GPS week longitude (degrees) of reference meridian \\\n", + "0 1359.0 3.0 -123.9 \n", + "1 1359.0 4.0 -123.9 \n", + "2 1359.0 5.0 -123.9 \n", + "3 1359.0 6.0 -123.9 \n", + "4 1360.0 0.0 -123.9 \n", + "\n", + " delta e (m) delta n (m) delta v (m) antenna height (m) sigma e (m) \\\n", + "0 3347.67917 4.987420e+06 53.03678 0.0083 0.00069 \n", + "1 3347.68086 4.987420e+06 53.03003 0.0083 0.00069 \n", + "2 3347.68072 4.987420e+06 53.03906 0.0083 0.00069 \n", + "3 3347.67938 4.987420e+06 53.04382 0.0083 0.00069 \n", + "4 3347.68042 4.987420e+06 53.03513 0.0083 0.00068 \n", + "\n", + " sigma n (m) sigma v (m) correlation en correlation ev correlation nv \n", + "0 0.00105 0.00327 -0.04832 0.01695 -0.31816 \n", + "1 0.00104 0.00321 -0.04648 0.00271 -0.30970 \n", + "2 0.00105 0.00326 -0.02367 0.00817 -0.31941 \n", + "3 0.00105 0.00324 -0.03681 0.00908 -0.30515 \n", + "4 0.00105 0.00328 -0.04815 0.00619 -0.33029 " + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The station designation\n", "sta=\"P395\"\n", - "file_url=\"http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/\"+ sta + \".tenv\"\n", - "r = requests.get(file_url).text.splitlines() # download, read text, split lines into a list\n", - "ue=[];un=[];uv=[];se=[];sn=[];sv=[];date=[];date_year=[];df=[]\n", - "for iday in r: # this loops through the days of data\n", - " crap=iday.split()\n", - " if len(crap)<10:\n", - " continue\n", - " date.append((crap[1]))\n", - " date_year.append(float(crap[2]))\n", - " ue.append(float(crap[7])*1000)\n", - " un.append(float(crap[8])*1000)\n", - " uv.append(float(crap[9])*1000)" + "\n", + "print(\"http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/\" + sta + \".tenv\")\n", + "zip_file_url=\"http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/\"+ sta + \".tenv\"\n", + "r = requests.get(zip_file_url)\n", + "\n", + "\n", + "# create a list of strings with itemized list above\n", + "ll = ['station ID (SSSS)','date (yymmmdd)',\n", + "'decimal year','modified Julian day','GPS week','day of GPS week',\n", + "'longitude (degrees) of reference meridian','delta e (m)',\n", + "'delta n (m)','delta v (m)','antenna height (m)',\n", + "'sigma e (m)','sigma n (m)','sigma v (m)',\n", + "'correlation en','correlation ev','correlation nv']\n", + " \n", + "\n", + "# transform r.content into a pandas dataframe\n", + "# first split r.content with \\n separator\n", + "# Decode the content if it's in bytes\n", + "content_str = r.content.decode('utf-8')\n", + "\n", + "# Split the content by the newline character\n", + "lines = content_str.split('\\n')\n", + "\n", + "# Now `lines` is a list of strings, each representing a line from the content\n", + "print(lines[0])\n", + "\n", + "# then transform lines into a pandas dataframe\n", + "df = pd.DataFrame([x.split() for x in lines])\n", + "# assign column names to df a\n", + "df.columns = ll\n", + "\n", + "#convert columns to numeric\n", + "df = df.apply(pd.to_numeric, errors='ignore')\n", + "\n", + "df.dropna()\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [], + "source": [ + "# remove first value for delta e, delta n, delta v to make relative position with respect to the first time. Add these as new columns\n", + "df['new delta e (m)'] = df['delta e (m)'] - df['delta e (m)'].values[0]\n", + "df['new delta n (m)'] = df['delta n (m)'] - df['delta n (m)'].values[0]\n", + "df['new delta v (m)'] = df['delta v (m)'] - df['delta v (m)'].values[0]" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 260, "metadata": {}, "outputs": [ { @@ -585,120 +969,214 @@ " \n", " \n", " \n", - " date_year\n", - " east\n", - " north\n", - " up\n", + " station ID (SSSS)\n", + " date (yymmmdd)\n", + " decimal year\n", + " modified Julian day\n", + " GPS week\n", + " day of GPS week\n", + " longitude (degrees) of reference meridian\n", + " delta e (m)\n", + " delta n (m)\n", + " delta v (m)\n", + " antenna height (m)\n", + " sigma e (m)\n", + " sigma n (m)\n", + " sigma v (m)\n", + " correlation en\n", + " correlation ev\n", + " correlation nv\n", + " new delta e (m)\n", + " new delta n (m)\n", + " new delta v (m)\n", " \n", " \n", " \n", " \n", - " count\n", - " 6799.000000\n", - " 6.799000e+03\n", - " 6.799000e+03\n", - " 6799.000000\n", - " \n", - " \n", - " mean\n", - " 2015.400365\n", - " 3.347623e+06\n", - " 4.987420e+09\n", - " 53038.873887\n", - " \n", - " \n", - " std\n", - " 5.394198\n", - " 3.484142e+01\n", - " 1.870622e+01\n", - " 5.637131\n", + " 0\n", + " P395\n", + " 06JAN25\n", + " 2006.0671\n", + " 53760.0\n", + " 1359.0\n", + " 3.0\n", + " -123.9\n", + " 3347.67917\n", + " 4.987420e+06\n", + " 53.03678\n", + " 0.0083\n", + " 0.00069\n", + " 0.00105\n", + " 0.00327\n", + " -0.04832\n", + " 0.01695\n", + " -0.31816\n", + " 0.00000\n", + " 0.00000\n", + " 0.00000\n", " \n", " \n", - " min\n", - " 2006.067100\n", - " 3.347558e+06\n", - " 4.987420e+09\n", - " 52997.270000\n", + " 1\n", + " P395\n", + " 06JAN26\n", + " 2006.0698\n", + " 53761.0\n", + " 1359.0\n", + " 4.0\n", + " -123.9\n", + " 3347.68086\n", + " 4.987420e+06\n", + " 53.03003\n", + " 0.0083\n", + " 0.00069\n", + " 0.00104\n", + " 0.00321\n", + " -0.04648\n", + " 0.00271\n", + " -0.30970\n", + " 0.00169\n", + " -0.00067\n", + " -0.00675\n", " \n", " \n", - " 25%\n", - " 2010.739250\n", - " 3.347592e+06\n", - " 4.987420e+09\n", - " 53035.100000\n", + " 2\n", + " P395\n", + " 06JAN27\n", + " 2006.0726\n", + " 53762.0\n", + " 1359.0\n", + " 5.0\n", + " -123.9\n", + " 3347.68072\n", + " 4.987420e+06\n", + " 53.03906\n", + " 0.0083\n", + " 0.00069\n", + " 0.00105\n", + " 0.00326\n", + " -0.02367\n", + " 0.00817\n", + " -0.31941\n", + " 0.00155\n", + " 0.00101\n", + " 0.00228\n", " \n", " \n", - " 50%\n", - " 2015.392200\n", - " 3.347626e+06\n", - " 4.987420e+09\n", - " 53038.670000\n", + " 3\n", + " P395\n", + " 06JAN28\n", + " 2006.0753\n", + " 53763.0\n", + " 1359.0\n", + " 6.0\n", + " -123.9\n", + " 3347.67938\n", + " 4.987420e+06\n", + " 53.04382\n", + " 0.0083\n", + " 0.00069\n", + " 0.00105\n", + " 0.00324\n", + " -0.03681\n", + " 0.00908\n", + " -0.30515\n", + " 0.00021\n", + " -0.00150\n", + " 0.00704\n", " \n", " \n", - " 75%\n", - " 2020.067050\n", - " 3.347653e+06\n", - " 4.987420e+09\n", - " 53042.450000\n", - " \n", - " \n", - " max\n", - " 2024.742000\n", - " 3.347683e+06\n", - " 4.987420e+09\n", - " 53065.440000\n", + " 4\n", + " P395\n", + " 06JAN29\n", + " 2006.0780\n", + " 53764.0\n", + " 1360.0\n", + " 0.0\n", + " -123.9\n", + " 3347.68042\n", + " 4.987420e+06\n", + " 53.03513\n", + " 0.0083\n", + " 0.00068\n", + " 0.00105\n", + " 0.00328\n", + " -0.04815\n", + " 0.00619\n", + " -0.33029\n", + " 0.00125\n", + " -0.00162\n", + " -0.00165\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " date_year east north up\n", - "count 6799.000000 6.799000e+03 6.799000e+03 6799.000000\n", - "mean 2015.400365 3.347623e+06 4.987420e+09 53038.873887\n", - "std 5.394198 3.484142e+01 1.870622e+01 5.637131\n", - "min 2006.067100 3.347558e+06 4.987420e+09 52997.270000\n", - "25% 2010.739250 3.347592e+06 4.987420e+09 53035.100000\n", - "50% 2015.392200 3.347626e+06 4.987420e+09 53038.670000\n", - "75% 2020.067050 3.347653e+06 4.987420e+09 53042.450000\n", - "max 2024.742000 3.347683e+06 4.987420e+09 53065.440000" + " station ID (SSSS) date (yymmmdd) decimal year modified Julian day \\\n", + "0 P395 06JAN25 2006.0671 53760.0 \n", + "1 P395 06JAN26 2006.0698 53761.0 \n", + "2 P395 06JAN27 2006.0726 53762.0 \n", + "3 P395 06JAN28 2006.0753 53763.0 \n", + "4 P395 06JAN29 2006.0780 53764.0 \n", + "\n", + " GPS week day of GPS week longitude (degrees) of reference meridian \\\n", + "0 1359.0 3.0 -123.9 \n", + "1 1359.0 4.0 -123.9 \n", + "2 1359.0 5.0 -123.9 \n", + "3 1359.0 6.0 -123.9 \n", + "4 1360.0 0.0 -123.9 \n", + "\n", + " delta e (m) delta n (m) delta v (m) antenna height (m) sigma e (m) \\\n", + "0 3347.67917 4.987420e+06 53.03678 0.0083 0.00069 \n", + "1 3347.68086 4.987420e+06 53.03003 0.0083 0.00069 \n", + "2 3347.68072 4.987420e+06 53.03906 0.0083 0.00069 \n", + "3 3347.67938 4.987420e+06 53.04382 0.0083 0.00069 \n", + "4 3347.68042 4.987420e+06 53.03513 0.0083 0.00068 \n", + "\n", + " sigma n (m) sigma v (m) correlation en correlation ev correlation nv \\\n", + "0 0.00105 0.00327 -0.04832 0.01695 -0.31816 \n", + "1 0.00104 0.00321 -0.04648 0.00271 -0.30970 \n", + "2 0.00105 0.00326 -0.02367 0.00817 -0.31941 \n", + "3 0.00105 0.00324 -0.03681 0.00908 -0.30515 \n", + "4 0.00105 0.00328 -0.04815 0.00619 -0.33029 \n", + "\n", + " new delta e (m) new delta n (m) new delta v (m) \n", + "0 0.00000 0.00000 0.00000 \n", + "1 0.00169 -0.00067 -0.00675 \n", + "2 0.00155 0.00101 0.00228 \n", + "3 0.00021 -0.00150 0.00704 \n", + "4 0.00125 -0.00162 -0.00165 " ] }, - "execution_count": 31, + "execution_count": 260, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# We now make a data frame\n", - "crap={'station':sta,'date':date,'date_year':date_year,'east':ue,'north':un,'up':uv}\n", - "if len(df)==0:\n", - " df = pd.DataFrame(crap, columns = ['station', 'date','date_year','east','north','up'])\n", - "else:\n", - " df=pd.concat([df,pd.DataFrame(crap, columns = ['station', 'date','date_year','east','north','up'])])\n", - "df.describe()" + "df.head()" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 261, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0.5, 0, 'Time (years)')" + "[]" ] }, - "execution_count": 32, + "execution_count": 261, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -706,12 +1184,7 @@ } ], "source": [ - "# Plot the GPS time series\n", - "fig,ax=plt.subplots(3,1,figsize=(11,8),sharex=True)\n", - "ax[0].plot(df['date_year'][df['station']==sta],df['east'][df['station']==sta]);ax[0].grid(True);ax[0].set_ylabel('Easting (mm)')\n", - "ax[1].plot(df['date_year'][df['station']==sta],df['north'][df['station']==sta]);ax[1].grid(True);ax[1].set_ylabel('Northing (mm)')\n", - "ax[2].plot(df['date_year'][df['station']==sta],df['up'][df['station']==sta]);ax[2].grid(True);ax[2].set_ylabel('Up (mm)')\n", - "ax[2].set_xlabel('Time (years)')" + "plt.plot(df['decimal year'], df['new delta e (m)'], label='East displacement')" ] }, { @@ -745,15 +1218,483 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 262, + "metadata": {}, + "outputs": [], + "source": [ + "# remove nans with dropna for the specific delta e column and replace df with the new dataframe\n", + "df = df.dropna(subset=['delta e (m)'])" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
station ID (SSSS)date (yymmmdd)decimal yearmodified Julian dayGPS weekday of GPS weeklongitude (degrees) of reference meridiandelta e (m)delta n (m)delta v (m)antenna height (m)sigma e (m)sigma n (m)sigma v (m)correlation encorrelation evcorrelation nvnew delta e (m)new delta n (m)new delta v (m)
0P39506JAN252006.067153760.01359.03.0-123.93347.679174.987420e+0653.036780.00830.000690.001050.00327-0.048320.01695-0.318160.000000.000000.00000
1P39506JAN262006.069853761.01359.04.0-123.93347.680864.987420e+0653.030030.00830.000690.001040.00321-0.046480.00271-0.309700.00169-0.00067-0.00675
2P39506JAN272006.072653762.01359.05.0-123.93347.680724.987420e+0653.039060.00830.000690.001050.00326-0.023670.00817-0.319410.001550.001010.00228
3P39506JAN282006.075353763.01359.06.0-123.93347.679384.987420e+0653.043820.00830.000690.001050.00324-0.036810.00908-0.305150.00021-0.001500.00704
4P39506JAN292006.078053764.01360.00.0-123.93347.680424.987420e+0653.035130.00830.000680.001050.00328-0.048150.00619-0.330290.00125-0.00162-0.00165
\n", + "
" + ], + "text/plain": [ + " station ID (SSSS) date (yymmmdd) decimal year modified Julian day \\\n", + "0 P395 06JAN25 2006.0671 53760.0 \n", + "1 P395 06JAN26 2006.0698 53761.0 \n", + "2 P395 06JAN27 2006.0726 53762.0 \n", + "3 P395 06JAN28 2006.0753 53763.0 \n", + "4 P395 06JAN29 2006.0780 53764.0 \n", + "\n", + " GPS week day of GPS week longitude (degrees) of reference meridian \\\n", + "0 1359.0 3.0 -123.9 \n", + "1 1359.0 4.0 -123.9 \n", + "2 1359.0 5.0 -123.9 \n", + "3 1359.0 6.0 -123.9 \n", + "4 1360.0 0.0 -123.9 \n", + "\n", + " delta e (m) delta n (m) delta v (m) antenna height (m) sigma e (m) \\\n", + "0 3347.67917 4.987420e+06 53.03678 0.0083 0.00069 \n", + "1 3347.68086 4.987420e+06 53.03003 0.0083 0.00069 \n", + "2 3347.68072 4.987420e+06 53.03906 0.0083 0.00069 \n", + "3 3347.67938 4.987420e+06 53.04382 0.0083 0.00069 \n", + "4 3347.68042 4.987420e+06 53.03513 0.0083 0.00068 \n", + "\n", + " sigma n (m) sigma v (m) correlation en correlation ev correlation nv \\\n", + "0 0.00105 0.00327 -0.04832 0.01695 -0.31816 \n", + "1 0.00104 0.00321 -0.04648 0.00271 -0.30970 \n", + "2 0.00105 0.00326 -0.02367 0.00817 -0.31941 \n", + "3 0.00105 0.00324 -0.03681 0.00908 -0.30515 \n", + "4 0.00105 0.00328 -0.04815 0.00619 -0.33029 \n", + "\n", + " new delta e (m) new delta n (m) new delta v (m) \n", + "0 0.00000 0.00000 0.00000 \n", + "1 0.00169 -0.00067 -0.00675 \n", + "2 0.00155 0.00101 0.00228 \n", + "3 0.00021 -0.00150 0.00704 \n", + "4 0.00125 -0.00162 -0.00165 " + ] + }, + "execution_count": 263, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['decimal year'], df['new delta e (m)'], label='East displacement')" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
station ID (SSSS)date (yymmmdd)decimal yearmodified Julian dayGPS weekday of GPS weeklongitude (degrees) of reference meridiandelta e (m)delta n (m)delta v (m)antenna height (m)sigma e (m)sigma n (m)sigma v (m)correlation encorrelation evcorrelation nvnew delta e (m)new delta n (m)new delta v (m)
0P39506JAN252006.067153760.01359.03.0-123.93347.679174.987420e+0653.036780.00830.000690.001050.00327-0.048320.01695-0.318160.000000.000000.00000
1P39506JAN262006.069853761.01359.04.0-123.93347.680864.987420e+0653.030030.00830.000690.001040.00321-0.046480.00271-0.309700.00169-0.00067-0.00675
2P39506JAN272006.072653762.01359.05.0-123.93347.680724.987420e+0653.039060.00830.000690.001050.00326-0.023670.00817-0.319410.001550.001010.00228
3P39506JAN282006.075353763.01359.06.0-123.93347.679384.987420e+0653.043820.00830.000690.001050.00324-0.036810.00908-0.305150.00021-0.001500.00704
4P39506JAN292006.078053764.01360.00.0-123.93347.680424.987420e+0653.035130.00830.000680.001050.00328-0.048150.00619-0.330290.00125-0.00162-0.00165
\n", + "
" + ], + "text/plain": [ + " station ID (SSSS) date (yymmmdd) decimal year modified Julian day \\\n", + "0 P395 06JAN25 2006.0671 53760.0 \n", + "1 P395 06JAN26 2006.0698 53761.0 \n", + "2 P395 06JAN27 2006.0726 53762.0 \n", + "3 P395 06JAN28 2006.0753 53763.0 \n", + "4 P395 06JAN29 2006.0780 53764.0 \n", + "\n", + " GPS week day of GPS week longitude (degrees) of reference meridian \\\n", + "0 1359.0 3.0 -123.9 \n", + "1 1359.0 4.0 -123.9 \n", + "2 1359.0 5.0 -123.9 \n", + "3 1359.0 6.0 -123.9 \n", + "4 1360.0 0.0 -123.9 \n", + "\n", + " delta e (m) delta n (m) delta v (m) antenna height (m) sigma e (m) \\\n", + "0 3347.67917 4.987420e+06 53.03678 0.0083 0.00069 \n", + "1 3347.68086 4.987420e+06 53.03003 0.0083 0.00069 \n", + "2 3347.68072 4.987420e+06 53.03906 0.0083 0.00069 \n", + "3 3347.67938 4.987420e+06 53.04382 0.0083 0.00069 \n", + "4 3347.68042 4.987420e+06 53.03513 0.0083 0.00068 \n", + "\n", + " sigma n (m) sigma v (m) correlation en correlation ev correlation nv \\\n", + "0 0.00105 0.00327 -0.04832 0.01695 -0.31816 \n", + "1 0.00104 0.00321 -0.04648 0.00271 -0.30970 \n", + "2 0.00105 0.00326 -0.02367 0.00817 -0.31941 \n", + "3 0.00105 0.00324 -0.03681 0.00908 -0.30515 \n", + "4 0.00105 0.00328 -0.04815 0.00619 -0.33029 \n", + "\n", + " new delta e (m) new delta n (m) new delta v (m) \n", + "0 0.00000 0.00000 0.00000 \n", + "1 0.00169 -0.00067 -0.00675 \n", + "2 0.00155 0.00101 0.00228 \n", + "3 0.00021 -0.00150 0.00704 \n", + "4 0.00125 -0.00162 -0.00165 " + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 266, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "P395 overall plate motion there 0.0 mm/year\n", - "parameters: Coefficient of determination 0.0000004.2, P-value 1.0000004.2, standard deviation of errors 0.0000004.2\n" + "P395 overall plate motion there -0.0064397312911273945 m/year\n", + "parameters: Coefficient of determination -0.9970084.2, P-value 0.0000004.2, standard deviation of errors 0.0000064.2\n" ] } ], @@ -762,13 +1703,34 @@ "from scipy import stats\n", "# linear regression such that: displacement = Velocity * time\n", "# velocity in the East component.\n", - "Ve, intercept, r_value, p_value, std_err = stats.linregress(df['date_year'][df['station']==sta],df['east'][df['station']==sta])\n", + "\n", + "Ve, intercept, r_value, p_value, std_err = stats.linregress(df['decimal year'],df['new delta e (m)'])\n", "# horizontal plate motion:\n", - "print(sta,\"overall plate motion there\",Ve,'mm/year')\n", + "print(sta,\"overall plate motion there\",Ve,'m/year')\n", "print(\"parameters: Coefficient of determination %f4.2, P-value %f4.2, standard deviation of errors %f4.2\"\\\n", " %(r_value,p_value,std_err))\n" ] }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.isnan(df['new delta e (m)']).any()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -778,19 +1740,29 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 268, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Coefficient / Velocity eastward (mm/year): 0.0\n" + "Coefficient / Velocity eastward (mm/year): -0.006439731291127403\n" ] }, { "data": { - "image/png": "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", + "text/plain": [ + "[]" + ] + }, + "execution_count": 268, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", "text/plain": [ "
" ] @@ -802,8 +1774,10 @@ "source": [ "from sklearn.linear_model import LinearRegression\n", "# convert the data into numpy arrays.\n", - "E = np.asarray(df['east'][df['station']==sta]).reshape(-1, 1)# reshaping was necessary to be an argument of Linear regress\n", - "t = np.asarray(df['date_year'][df['station']==sta]).reshape(-1, 1)\n", + "E = np.asarray(df['new delta e (m)']).reshape(-1, 1)# reshaping was necessary to be an argument of Linear regress\n", + "# E = np.asarray(df['east'][df['station']==sta]).reshape(-1, 1)# reshaping was necessary to be an argument of Linear regress\n", + "# make a new time array\n", + "t = np.asarray(df['decimal year']).reshape(-1, 1)\n", "tt = np.linspace(np.min(t),np.max(t),1000)\n", "\n", "# perform the linear regression. First we will use the entire available data\n", @@ -815,13 +1789,8 @@ "\n", "# The coefficients\n", "print('Coefficient / Velocity eastward (mm/year): ', regr.coef_[0][0])\n", - "\n", - "plt.plot(t,E);ax[0].grid(True);ax[0].set_ylabel('East (mm)')\n", - "plt.plot(t,Epred,color=\"red\")\n", - "plt.grid(True)\n", - "plt.xticks(())\n", - "plt.yticks(())\n", - "plt.show()\n" + "# plot the data\n", + "plt.plot(t,E,'b',label='data')" ] }, { @@ -833,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 269, "metadata": {}, "outputs": [ { @@ -841,7 +1810,7 @@ "output_type": "stream", "text": [ "Mean squared error (mm): 0.00\n", - "Coefficient of determination: 1.00\n" + "Coefficient of determination: 0.99\n" ] } ], @@ -872,19 +1841,19 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 270, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean of the velocity estimates 0.0000004.2 and the standard deviation 0.0000004.2\n" + "mean of the velocity estimates -0.0064404.2 and the standard deviation 0.0000064.2\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAGxCAYAAACTN+exAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA8Q0lEQVR4nO3deVyVZf7/8fcRDmtAggmSqOSYWaKWlkuaC4I5LjnWOGWZlZWNaZGamlZi8w0n+6bMaNbUzxHNXGZMm7ZxhEzTkdzN1Ha3TJEyAtzgCNfvD7+cPLIIxuKFr+fj4ePhuc7nvu/rfLjP4c29gMMYYwQAAGCZOjU9AQAAgAtBiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIqUVSUlLkcDjc//z8/BQREaHu3btr6tSpyszMLLZMYmKiHA5HhbZz4sQJJSYmavXq1RVarqRtNWnSRH379q3Qes5n4cKFSk5OLvE5h8OhxMTESt1eZfvwww/Vrl07BQYGyuFw6O23367pKXlISkq66OZUmiZNmui+++6rdds9d/2HDh1SYmKitm/fXqz2Qt7jleWBBx7QrbfeWiPbPp8dO3bI4XBo27ZtNT2VUt1yyy1KSEio6Wlc3Axqjblz5xpJZu7cuSY9Pd18/PHHZunSpSYhIcGEhISY0NBQk5qa6rHMd999Z9LT0yu0nR9++MFIMpMnT67QciVtq3HjxqZPnz4VWs/59OnTxzRu3LjE59LT0813331XqdurTIWFhSY0NNR06NDBpKWlmfT0dPPTTz/V9LQ8BAYGmqFDh9b0NMqlcePGNTLXqt7u1q1bzTfffON+vGnTJvd7/1wX8h6vDFu3bjV16tQxmzZtqvZtl8ezzz5roqOja3oaZVq9erVxOp3miy++qOmpXLS8azhDoQq0bNlS7dq1cz++/fbb9cQTT6hz584aOHCgvv76a4WHh0uSGjZsqIYNG1bpfE6cOKGAgIBq2db5dOjQoUa3fz6HDh3STz/9pN/97neKjY2t6elc9Ir2rUvN9ddfX+7amnrf/fnPf9ZNN93k8Vl0MVm6dKluv/32mp5GiYr2665du6p58+Z66aWX9Nprr9X0tC5KnE66RDRq1EgvvfSScnNz9be//c09XtKh5lWrVqlbt24KCwuTv7+/GjVqpNtvv10nTpzQvn37dMUVV0iSpkyZ4j51VXRou2h9W7du1R133KG6deuqadOmpW6ryPLly9WqVSv5+fnpqquu0l//+leP54tOle3bt89jfPXq1XI4HO5TW926ddP777+v/fv3e5xaK1LS6aSdO3fqtttuU926deXn56c2bdpo3rx5JW5n0aJFmjRpkiIjIxUcHKyePXvqyy+/LL3xZ1m3bp1iY2MVFBSkgIAAderUSe+//777+cTERPc3m/Hjx8vhcKhJkyZlrjMnJ0djx45VdHS0fHx8dOWVVyohIUHHjx/3qHv55Zd1yy23qH79+goMDFRMTIymTZsml8vlUbdt2zb17dtX9evXl6+vryIjI9WnTx8dPHjQ3b/jx49r3rx57t5269ZNOTk58vb21osvvuhe148//qg6deooJCREp0+fdo8/9thjuuKKK2T+72/Ppqam6rbbblPDhg3l5+en3/zmNxo+fLh+/PFHj7mVtW+5XC6NGzdOERERCggIUOfOnbVx48bzfk1cLpfq16+vIUOGFHvu559/lr+/v0aPHl3hfpfkwIEDuueee9y9bdGihV566SUVFhZ61OXl5em5555TixYt5Ofnp7CwMHXv3l3r169315x9Omn16tW68cYbJUn333+/++tStJ+X9r5bsmSJOnbsqMDAQF122WXq1atXsVMre/bs0Z133qnIyEj5+voqPDxcsbGxJZ62OtuRI0e0fPnyYn0teh8tXLhQ48ePV4MGDXTZZZepX79+OnLkiHJzc/Xwww+rXr16qlevnu6//34dO3bMYx0Oh0MjR47U3Llz1bx5c/n7+6tdu3b65JNPZIzRiy++qOjoaF122WXq0aOHvvnmm2Lz++KLL7R7927dfvvtMsaoWbNm6tWrV7G6Y8eOKSQkRI8++qh7rLLfc926dVPLli318ccfq1OnTgoICNADDzzgfn7IkCFauHChcnNzy+z5pYojMZeQ3/72t/Ly8tLHH39cas2+ffvUp08fdenSRX//+991+eWX6/vvv9eKFSuUn5+vBg0aaMWKFbr11ls1bNgwPfjgg5LkDjZFBg4cqDvvvFOPPPLIeT/gt2/froSEBCUmJioiIkJvvvmmHn/8ceXn52vs2LEVeo2zZ8/Www8/rG+//VbLly8/b/2XX36pTp06qX79+vrrX/+qsLAwLViwQPfdd5+OHDmicePGedRPnDhRN998s/7f//t/ysnJ0fjx49WvXz99/vnn8vLyKnU7a9asUVxcnFq1aqU5c+bI19dXs2fPVr9+/bRo0SL94Q9/0IMPPqjWrVtr4MCBGjVqlAYPHixfX99S13nixAl17dpVBw8e1MSJE9WqVSvt2rVLzz77rD777DOlpaW5v3l9++23Gjx4sPuD99NPP9Xzzz+vL774Qn//+98lScePH1dcXJyio6P18ssvKzw8XBkZGfroo4/cH6Dp6enq0aOHunfvrmeeeUaSFBwcrODgYN14441KS0vTk08+KenMtT2+vr7Kzc3Vxo0b1alTJ0lSWlqaevTo4TG3jh076sEHH1RISIj27dun6dOnq3Pnzvrss8/kdDo9XndJ+9ZDDz2k+fPna+zYsYqLi9POnTs1cODA837wO51O3XPPPXr11Vf18ssvKzg42P3cokWLdOrUKd1///0V7ve5fvjhB3Xq1En5+fn605/+pCZNmui9997T2LFj9e2332r27NmSpNOnT6t3795au3atEhIS1KNHD50+fVqffPKJDhw44O7h2W644QbNnTtX999/v55++mn16dNHkso8+pKUlKSnn37avUx+fr5efPFFdenSRRs3btS1114r6cxnRkFBgaZNm6ZGjRrpxx9/1Pr16/Xzzz+X2deVK1fK5XKpe/fuJT4/ceJEde/eXSkpKdq3b5/Gjh2ru+66S97e3mrdurUWLVqkbdu2aeLEiQoKCir2Q817772nbdu26c9//rMcDofGjx+vPn36aOjQodqzZ49mzZql7OxsjR49Wrfffru2b9/u8bV56623dOWVV6p9+/ZyOBwaNWqUEhIS9PXXX6tZs2buuvnz5ysnJ8cdYir7PVfk8OHDuueeezRu3DglJSWpTp1fji9069ZN48eP1+rVq9WvX78y+35JquHTWahERdfElHUOOjw83LRo0cL9ePLkyebs3WDp0qVGktm+fXup6yjrmpii9T377LOlPne2xo0bG4fDUWx7cXFxJjg42Bw/ftzjte3du9ej7qOPPjKSzEcffeQeK+uamHPnfeeddxpfX19z4MABj7revXubgIAA8/PPP3ts57e//a1H3T/+8Q8j6bzXHHTo0MHUr1/f5ObmusdOnz5tWrZsaRo2bGgKCwuNMcbs3bvXSDIvvvhimeszxpipU6eWeM1B0dfwgw8+KHG5goIC43K5zPz5842Xl5f7mpvNmzcbSebtt98uc7ulXRPz9NNPG39/f3Pq1CljjDEPPvigufXWW02rVq3MlClTjDHGfP/990aSee2110pcd2FhoXG5XGb//v1GkvnXv/7lfq60fevzzz83kswTTzzhMf7mm28aSee9NmXHjh0lzummm24ybdu2dT+uSL/PvSZmwoQJRpLZsGGDx7J//OMfjcPhMF9++aUxxpj58+cbSeb1118vc87nrr+sa2LOfd8dOHDAeHt7m1GjRnnU5ebmmoiICDNo0CBjjDE//vijkWSSk5PLnEtJ/vjHPxp/f3/3fl2k6H3Ur18/j/GEhAQjyTz22GMe4wMGDDChoaEeY5JMRESEOXbsmHvs7bffNpJMmzZtPLaZnJxsJJkdO3Z4rKNNmzYerz8nJ8cEBQWZxx9/3KPu2muvNd27d3c/ruz3nDHGdO3a1UgyH374YYnL5ufnG4fDYcaPH1/i85c6TiddYsz/HcIvTZs2beTj46OHH35Y8+bN0549ey5oOxU513zdddepdevWHmODBw9WTk6Otm7dekHbL69Vq1YpNjZWUVFRHuP33XefTpw4ofT0dI/x/v37ezxu1aqVJGn//v2lbuP48ePasGGD7rjjDl122WXucS8vLw0ZMkQHDx4s9ymps7333ntq2bKl2rRpo9OnT7v/9erVy+MUm3TmNFH//v0VFhYmLy8vOZ1O3XvvvSooKNBXX30lSfrNb36junXravz48Xr11Ve1e/fuCs0nNjZWJ0+edJ/2SEtLU1xcnHr27KnU1FT3mCT17NnTvVxmZqYeeeQRRUVFydvbW06nU40bN5Ykff7558W2c+6+9dFHH0mS7r77bo/xQYMGydv7/AebY2Ji1LZtW82dO9c99vnnn2vjxo0eh/Ur0u9zrVq1Stdee61uuukmj/H77rtPxhitWrVKkvTvf/9bfn5+HtutbP/5z390+vRp3XvvvR6vw8/PT127dnW/jtDQUDVt2lQvvviipk+frm3bthU79VWaQ4cO6Yorrij1yNS5dyS2aNFCktxHkc4e/+mnn4qdUurevbsCAwOLLd+7d2+PbRaNn/3+3LNnj7Zv3+6xHwUFBen+++9XSkqK++jeqlWrtHv3bo0cOdJdV9nvuSJ169ZVjx49SuyV0+l0HxFHcYSYS8jx48d19OhRRUZGllrTtGlTpaWlqX79+nr00UfVtGlTNW3aVH/5y18qtK0GDRqUuzYiIqLUsaNHj1ZouxV19OjREuda1KNztx8WFubxuOh0z8mTJ0vdRlZWlowxFdpOeRw5ckQ7duyQ0+n0+BcUFCRjjPuakgMHDqhLly76/vvv9Ze//EVr167Vpk2b9PLLL3vMPSQkRGvWrFGbNm00ceJEXXfddYqMjNTkyZOLnccvSdH5/LS0NH3zzTfat2+fO8Rs2LBBx44dU1pamq666ipFR0dLkgoLCxUfH69ly5Zp3Lhx+vDDD7Vx40Z98sknHnM727l9LOrdufuRt7d3sa9XaR544AGlp6friy++kCTNnTtXvr6+uuuuu9w15e13Scq7n/3www+KjIz0OJ1Q2Y4cOSJJuvHGG4u9liVLlrhfh8Ph0IcffqhevXpp2rRpuuGGG3TFFVfoscceO+9pupMnT8rPz6/U50NDQz0e+/j4lDl+6tSpSlt+6dKlql+/vjp37uxRO2rUKOXm5urNN9+UJM2aNUsNGzbUbbfd5q6p7PdckfN9Xvr5+ZX5GXMp45qYS8j777+vgoICdevWrcy6Ll26qEuXLiooKNDmzZs1c+ZMJSQkKDw8XHfeeWe5tlWR30uRkZFR6ljRN6GiD8S8vDyPurK+cZRHWFiYDh8+XGz80KFDkqR69er9qvVLZ37KqlOnTqVvp169evL39y92fv3s5yXp7bff1vHjx7Vs2TL3EQ5JJV6cGRMTo8WLF8sYox07diglJUXPPfec/P39NWHChDLn4+Pjo86dOystLU0NGzZURESEYmJidNVVV0k6c1Hnhx9+6PFT+M6dO/Xpp58qJSVFQ4cOdY+XdDFmkXP3raJ9JCMjQ1deeaV7/PTp0+UOh3fddZdGjx6tlJQUPf/883rjjTc0YMAA1a1b111T3n6XpLz72RVXXKF169apsLCwyoJM0baWLl3qsT+UpHHjxpozZ44k6auvvtI//vEPJSYmKj8/X6+++mqZ26jqo6gX6q233tKAAQOKXcP2m9/8Rr1799bLL7+s3r1765133tGUKVM86qriPSed//MyKyurUj6LaiOOxFwiDhw4oLFjxyokJETDhw8v1zJeXl5q3769+6eHog+l8hx9qIhdu3bp008/9RhbuHChgoKCdMMNN0iS+y6dHTt2eNS98847xdbn6+tb7rnFxsZq1apV7m8mRebPn6+AgIBKuSU7MDBQ7du317JlyzzmVVhYqAULFqhhw4a6+uqrK7zevn376ttvv1VYWJjatWtX7F9Rz4o+IM++SNgYo9dff73UdTscDrVu3VozZszQ5Zdf7vENqaz+9uzZU1u2bNFbb73lPmUUGBioDh06aObMmTp06JDHqaSS5ibJ4w668ykK5UU/QRf5xz/+4XFXVFnq1q2rAQMGaP78+XrvvfeUkZFR7JROeftdktjYWO3evbvYN/b58+fL4XC4L4Dt3bu3Tp06pZSUlHLNu0hF3pO9evWSt7e3vv322xJfR2m3RF999dV6+umnFRMTc96Acs011+jo0aPKzs6u0Ouoat999502bdpU6unuxx9/XDt27NDQoUPl5eWlhx56yOP5qnzPlebQoUM6deqU+2JreOJITC20c+dO97nazMxMrV27VnPnzpWXl5eWL19e7E6is7366qtatWqV+vTpo0aNGunUqVPunzqKvvkEBQWpcePG+te//qXY2FiFhoaqXr16570duDSRkZHq37+/EhMT1aBBAy1YsECpqal64YUX3L8D5MYbb1Tz5s01duxYnT59WnXr1tXy5cu1bt26YuuLiYnRsmXL9Morr6ht27aqU6dOqR/MkydP1nvvvafu3bvr2WefVWhoqN588029//77mjZtmkJCQi7oNZ1r6tSpiouLU/fu3TV27Fj5+Pho9uzZ2rlzpxYtWnRBv1E1ISFBb731lm655RY98cQTatWqlQoLC3XgwAGtXLlSY8aMUfv27RUXFycfHx/dddddGjdunE6dOqVXXnlFWVlZHut77733NHv2bA0YMEBXXXWVjDFatmyZfv75Z8XFxbnrYmJitHr1ar377rtq0KCBgoKC1Lx5c0lnvlkXFBToww8/9LhNvWfPnpo8ebIcDofHuf9rrrlGTZs21YQJE2SMUWhoqN599133NTTl0aJFC91zzz1KTk6W0+lUz549tXPnTv3v//6vx91G5/PAAw9oyZIlGjlypBo2bOgRtirS75I88cQTmj9/vvr06aPnnntOjRs31vvvv6/Zs2frj3/8ozvE3nXXXZo7d64eeeQRffnll+revbsKCwu1YcMGtWjRotQjoU2bNpW/v7/efPNNtWjRQpdddpkiIyNLPHXcpEkTPffcc5o0aZL27NmjW2+9VXXr1tWRI0e0ceNGBQYGasqUKdqxY4dGjhyp3//+92rWrJl8fHy0atUq7dix47xH5bp16yZjjDZs2KD4+PjytL9avPXWW7r88stLvWsqLi5O1157rT766CP37fBnq+z3XHkUnVotbc6XvJq5nhhVoegOnqJ/Pj4+pn79+qZr164mKSnJZGZmFlvm3DsX0tPTze9+9zvTuHFj4+vra8LCwkzXrl3NO++847FcWlqauf76642vr6/HHSBF6/vhhx/Ouy1jfvmNvUuXLjXXXXed8fHxMU2aNDHTp08vtvxXX31l4uPjTXBwsLniiivMqFGjzPvvv1/s7qSffvrJ3HHHHebyyy83DofDY5sq4a6qzz77zPTr18+EhIQYHx8f07p162J3eRTdVfHPf/7TY7zobqKS7go519q1a02PHj1MYGCg8ff3Nx06dDDvvvtuiesrz91Jxhhz7Ngx8/TTT5vmzZsbHx8fExISYmJiYswTTzxhMjIy3HXvvvuuad26tfHz8zNXXnmlefLJJ82///1vj9598cUX5q677jJNmzY1/v7+JiQkxNx0000mJSXFY5vbt283N998swkICDCSTNeuXd3PFRYWmnr16hlJ5vvvv3eP//e//zWSzA033FDsNezevdvExcWZoKAgU7duXfP73//eHDhwoNjXqqx9Ky8vz4wZM8bUr1/f+Pn5mQ4dOpj09PQK/ebcgoICExUVZSSZSZMmlVhT3n6XtN39+/ebwYMHm7CwMON0Ok3z5s3Niy++aAoKCjzqTp48aZ599lnTrFkz4+PjY8LCwkyPHj3M+vXry1z/okWLzDXXXGOcTqdH70p63xlz5o6e7t27m+DgYOPr62saN25s7rjjDpOWlmaMMebIkSPmvvvuM9dcc40JDAw0l112mWnVqpWZMWOGOX369Hl72aRJEzNixAiP8dLeR6XdWVnS11ySefTRRz3qSnvfnLu9zp07n3d/SExMNJLMJ598UuLzlfmeM+bM3UnXXXddqfMZMmSIiYmJKXPOlzKHMee5XQUAgAp66aWX9Pzzz+v777+Xv79/TU/Hfc3U22+/XebvW2nXrp0cDoc2bdpUjbMrWU5OjiIjIzVjxoxip7ZwBtfEAAAq3aOPPqqQkBD3NXU1LSIiQgUFBSUGmJycHK1fv14TJ07Uli1bNGnSpBqYYXEzZsxQo0aN3L9wEcVxTQwAoNL5+fnpjTfeuKj/SnSRrVu3qnv37goLC9PkyZM1YMCAmp6SpDO/DTslJaVcv+/oUsXpJAAAYCVOJwEAACsRYgAAgJUIMQAAwEq19mqhwsJCHTp0SEFBQRf0i8QAAED1M8YoNze3XH9HrNaGmEOHDhX7y8QAAMAO3333nRo2bFhmTa0NMUFBQZLONKEiv3q8tnK5XFq5cqXi4+PldDprejq1Fn2uHvS5etDn6kOvf5GTk6OoqCj39/Gy1NoQU3QKKTg4mBCjM2+QgIAABQcHX/JvkKpEn6sHfa4e9Ln60OviynMpCBf2AgAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVKhxiPv74Y/Xr10+RkZFyOBx6++23PZ43xigxMVGRkZHy9/dXt27dtGvXLo+avLw8jRo1SvXq1VNgYKD69++vgwcPetRkZWVpyJAhCgkJUUhIiIYMGaKff/65wi8QAADUThUOMcePH1fr1q01a9asEp+fNm2apk+frlmzZmnTpk2KiIhQXFyccnNz3TUJCQlavny5Fi9erHXr1unYsWPq27evCgoK3DWDBw/W9u3btWLFCq1YsULbt2/XkCFDLuAlAgCA2qjCfwCyd+/e6t27d4nPGWOUnJysSZMmaeDAgZKkefPmKTw8XAsXLtTw4cOVnZ2tOXPm6I033lDPnj0lSQsWLFBUVJTS0tLUq1cvff7551qxYoU++eQTtW/fXpL0+uuvq2PHjvryyy/VvHnzC329AACglqjUv2K9d+9eZWRkKD4+3j3m6+urrl27av369Ro+fLi2bNkil8vlURMZGamWLVtq/fr16tWrl9LT0xUSEuIOMJLUoUMHhYSEaP369SWGmLy8POXl5bkf5+TkSDrzl0FdLldlvkwrFfWAXlQt+lw96HP1oM/Vh17/oiI9qNQQk5GRIUkKDw/3GA8PD9f+/fvdNT4+Pqpbt26xmqLlMzIyVL9+/WLrr1+/vrvmXFOnTtWUKVOKja9cuVIBAQEVfzG1VGpqak1P4ZJAn6sHfa4e9Ln60GvpxIkT5a6t1BBTxOFweDw2xhQbO9e5NSXVl7Wep556SqNHj3Y/zsnJUVRUlOLj4xUcHFyR6ddKLpdLqampiouLk9PprOnp1Fr0uXoU9fmZzXWUV1j6Z8vOxF7VOKvah/25+tDrXxSdSSmPSg0xERERks4cSWnQoIF7PDMz0310JiIiQvn5+crKyvI4GpOZmalOnTq5a44cOVJs/T/88EOxozxFfH195evrW2zc6XRe8jvE2ehH9aDP1SOv0KG8gtJDDF+DysH+XH3odcXet5X6e2Kio6MVERHhcTgsPz9fa9ascQeUtm3byul0etQcPnxYO3fudNd07NhR2dnZ2rhxo7tmw4YNys7OdtcAAIBLW4WPxBw7dkzffPON+/HevXu1fft2hYaGqlGjRkpISFBSUpKaNWumZs2aKSkpSQEBARo8eLAkKSQkRMOGDdOYMWMUFham0NBQjR07VjExMe67lVq0aKFbb71VDz30kP72t79Jkh5++GH17duXO5MAAICkCwgxmzdvVvfu3d2Pi65DGTp0qFJSUjRu3DidPHlSI0aMUFZWltq3b6+VK1cqKCjIvcyMGTPk7e2tQYMG6eTJk4qNjVVKSoq8vLzcNW+++aYee+wx911M/fv3L/V30wAAgEtPhUNMt27dZIwp9XmHw6HExEQlJiaWWuPn56eZM2dq5syZpdaEhoZqwYIFFZ0eAAC4RPC3kwAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwUqWHmNOnT+vpp59WdHS0/P39ddVVV+m5555TYWGhu8YYo8TEREVGRsrf31/dunXTrl27PNaTl5enUaNGqV69egoMDFT//v118ODByp4uAACwVKWHmBdeeEGvvvqqZs2apc8//1zTpk3Tiy++qJkzZ7prpk2bpunTp2vWrFnatGmTIiIiFBcXp9zcXHdNQkKCli9frsWLF2vdunU6duyY+vbtq4KCgsqeMgAAsJB3Za8wPT1dt912m/r06SNJatKkiRYtWqTNmzdLOnMUJjk5WZMmTdLAgQMlSfPmzVN4eLgWLlyo4cOHKzs7W3PmzNEbb7yhnj17SpIWLFigqKgopaWlqVevXpU9bQAAYJlKDzGdO3fWq6++qq+++kpXX321Pv30U61bt07JycmSpL179yojI0Px8fHuZXx9fdW1a1etX79ew4cP15YtW+RyuTxqIiMj1bJlS61fv77EEJOXl6e8vDz345ycHEmSy+WSy+Wq7JdpnaIe0IuqRZ+rR1F/feuYctXhwrA/Vx96/YuK9KDSQ8z48eOVnZ2ta665Rl5eXiooKNDzzz+vu+66S5KUkZEhSQoPD/dYLjw8XPv373fX+Pj4qG7dusVqipY/19SpUzVlypRi4ytXrlRAQMCvfl21RWpqak1P4ZJAn6vHn9oVlvn8Bx98UE0zqd3Yn6sPvZZOnDhR7tpKDzFLlizRggULtHDhQl133XXavn27EhISFBkZqaFDh7rrHA6Hx3LGmGJj5yqr5qmnntLo0aPdj3NychQVFaX4+HgFBwf/ildUO7hcLqWmpiouLk5Op7Omp1Nr0efqUdTnZzbXUV5h6Z8bOxM59fxrsD9XH3r9i6IzKeVR6SHmySef1IQJE3TnnXdKkmJiYrR//35NnTpVQ4cOVUREhKQzR1saNGjgXi4zM9N9dCYiIkL5+fnKysryOBqTmZmpTp06lbhdX19f+fr6Fht3Op2X/A5xNvpRPehz9cgrdCivoPQQw9egcrA/Vx96XbH3baXfnXTixAnVqeO5Wi8vL/ct1tHR0YqIiPA4ZJafn681a9a4A0rbtm3ldDo9ag4fPqydO3eWGmIAAMClpdKPxPTr10/PP/+8GjVqpOuuu07btm3T9OnT9cADD0g6cxopISFBSUlJatasmZo1a6akpCQFBARo8ODBkqSQkBANGzZMY8aMUVhYmEJDQzV27FjFxMS471YCAACXtkoPMTNnztQzzzyjESNGKDMzU5GRkRo+fLieffZZd824ceN08uRJjRgxQllZWWrfvr1WrlypoKAgd82MGTPk7e2tQYMG6eTJk4qNjVVKSoq8vLwqe8oAAMBClR5igoKClJyc7L6luiQOh0OJiYlKTEwstcbPz08zZ870+CV5AAAARfjbSQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwUpWEmO+//1733HOPwsLCFBAQoDZt2mjLli3u540xSkxMVGRkpPz9/dWtWzft2rXLYx15eXkaNWqU6tWrp8DAQPXv318HDx6siukCAAALVXqIycrK0s033yyn06l///vf2r17t1566SVdfvnl7ppp06Zp+vTpmjVrljZt2qSIiAjFxcUpNzfXXZOQkKDly5dr8eLFWrdunY4dO6a+ffuqoKCgsqcMAAAs5F3ZK3zhhRcUFRWluXPnuseaNGni/r8xRsnJyZo0aZIGDhwoSZo3b57Cw8O1cOFCDR8+XNnZ2ZozZ47eeOMN9ezZU5K0YMECRUVFKS0tTb169arsaQMAAMtUeoh555131KtXL/3+97/XmjVrdOWVV2rEiBF66KGHJEl79+5VRkaG4uPj3cv4+vqqa9euWr9+vYYPH64tW7bI5XJ51ERGRqply5Zav359iSEmLy9PeXl57sc5OTmSJJfLJZfLVdkv0zpFPaAXVYs+V4+i/vrWMeWqw4Vhf64+9PoXFelBpYeYPXv26JVXXtHo0aM1ceJEbdy4UY899ph8fX117733KiMjQ5IUHh7usVx4eLj2798vScrIyJCPj4/q1q1brKZo+XNNnTpVU6ZMKTa+cuVKBQQEVMZLqxVSU1NregqXBPpcPf7UrrDM5z/44INqmkntxv5cfei1dOLEiXLXVnqIKSwsVLt27ZSUlCRJuv7667Vr1y698soruvfee911DofDYzljTLGxc5VV89RTT2n06NHuxzk5OYqKilJ8fLyCg4Mv9OXUGi6XS6mpqYqLi5PT6azp6dRa9Ll6FPX5mc11lFdY+ufGzkROPf8a7M/Vh17/ouhMSnlUeohp0KCBrr32Wo+xFi1a6K233pIkRURESDpztKVBgwbumszMTPfRmYiICOXn5ysrK8vjaExmZqY6depU4nZ9fX3l6+tbbNzpdF7yO8TZ6Ef1oM/VI6/QobyC0kMMX4PKwf5cfeh1xd63lX530s0336wvv/zSY+yrr75S48aNJUnR0dGKiIjwOGSWn5+vNWvWuANK27Zt5XQ6PWoOHz6snTt3lhpiAADApaXSj8Q88cQT6tSpk5KSkjRo0CBt3LhRr732ml577TVJZ04jJSQkKCkpSc2aNVOzZs2UlJSkgIAADR48WJIUEhKiYcOGacyYMQoLC1NoaKjGjh2rmJgY991KAADg0lbpIebGG2/U8uXL9dRTT+m5555TdHS0kpOTdffdd7trxo0bp5MnT2rEiBHKyspS+/bttXLlSgUFBblrZsyYIW9vbw0aNEgnT55UbGysUlJS5OXlVdlTBgAAFqr0ECNJffv2Vd++fUt93uFwKDExUYmJiaXW+Pn5aebMmZo5c2YVzBAAANiOv50EAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJWqPMRMnTpVDodDCQkJ7jFjjBITExUZGSl/f39169ZNu3bt8lguLy9Po0aNUr169RQYGKj+/fvr4MGDVT1dAABgiSoNMZs2bdJrr72mVq1aeYxPmzZN06dP16xZs7Rp0yZFREQoLi5Oubm57pqEhAQtX75cixcv1rp163Ts2DH17dtXBQUFVTllAABgCe+qWvGxY8d099136/XXX9f//M//uMeNMUpOTtakSZM0cOBASdK8efMUHh6uhQsXavjw4crOztacOXP0xhtvqGfPnpKkBQsWKCoqSmlpaerVq1ex7eXl5SkvL8/9OCcnR5Lkcrnkcrmq6mVao6gH9KJq0efqUdRf3zqmXHW4MOzP1Yde/6IiPXAYY8r+FLhAQ4cOVWhoqGbMmKFu3bqpTZs2Sk5O1p49e9S0aVNt3bpV119/vbv+tttu0+WXX6558+Zp1apVio2N1U8//aS6deu6a1q3bq0BAwZoypQpxbaXmJhY4vjChQsVEBBQFS8RAABUshMnTmjw4MHKzs5WcHBwmbVVciRm8eLF2rp1qzZt2lTsuYyMDElSeHi4x3h4eLj279/vrvHx8fEIMEU1Rcuf66mnntLo0aPdj3NychQVFaX4+PjzNuFS4HK5lJqaqri4ODmdzpqeTq1Fn6tHUZ+f2VxHeYWOUut2JhY/aovyY3+uPvT6F0VnUsqj0kPMd999p8cff1wrV66Un59fqXUOh+cHjzGm2Ni5yqrx9fWVr69vsXGn03nJ7xBnox/Vgz5Xj7xCh/IKSv/c4GtQOdifqw+9rtj7ttIv7N2yZYsyMzPVtm1beXt7y9vbW2vWrNFf//pXeXt7u4/AnHtEJTMz0/1cRESE8vPzlZWVVWoNAAC4tFV6iImNjdVnn32m7du3u/+1a9dOd999t7Zv366rrrpKERERSk1NdS+Tn5+vNWvWqFOnTpKktm3byul0etQcPnxYO3fudNcAAIBLW6WfTgoKClLLli09xgIDAxUWFuYeT0hIUFJSkpo1a6ZmzZopKSlJAQEBGjx4sCQpJCREw4YN05gxYxQWFqbQ0FCNHTtWMTEx7ruVAADApa3KbrEuy7hx43Ty5EmNGDFCWVlZat++vVauXKmgoCB3zYwZM+Tt7a1Bgwbp5MmTio2NVUpKiry8vGpiygAA4CJTLSFm9erVHo8dDocSExOVmJhY6jJ+fn6aOXOmZs6cWbWTAwAAVuJvJwEAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASpUeYqZOnaobb7xRQUFBql+/vgYMGKAvv/zSo8YYo8TEREVGRsrf31/dunXTrl27PGry8vI0atQo1atXT4GBgerfv78OHjxY2dMFAACWqvQQs2bNGj366KP65JNPlJqaqtOnTys+Pl7Hjx9310ybNk3Tp0/XrFmztGnTJkVERCguLk65ubnumoSEBC1fvlyLFy/WunXrdOzYMfXt21cFBQWVPWUAAGAh78pe4YoVKzwez507V/Xr19eWLVt0yy23yBij5ORkTZo0SQMHDpQkzZs3T+Hh4Vq4cKGGDx+u7OxszZkzR2+88YZ69uwpSVqwYIGioqKUlpamXr16Vfa0AQCAZSo9xJwrOztbkhQaGipJ2rt3rzIyMhQfH++u8fX1VdeuXbV+/XoNHz5cW7Zskcvl8qiJjIxUy5YttX79+hJDTF5envLy8tyPc3JyJEkul0sul6tKXptNinpAL6oWfa4eRf31rWPKVYcLw/5cfej1LyrSgyoNMcYYjR49Wp07d1bLli0lSRkZGZKk8PBwj9rw8HDt37/fXePj46O6desWqyla/lxTp07VlClTio2vXLlSAQEBv/q11Bapqak1PYVLAn2uHn9qV1jm8x988EE1zaR2Y3+uPvRaOnHiRLlrqzTEjBw5Ujt27NC6deuKPedwODweG2OKjZ2rrJqnnnpKo0ePdj/OyclRVFSU4uPjFRwcfAGzr11cLpdSU1MVFxcnp9NZ09Optehz9Sjq8zOb6yivsPTPjZ2JnHr+Ndifqw+9/kXRmZTyqLIQM2rUKL3zzjv6+OOP1bBhQ/d4RESEpDNHWxo0aOAez8zMdB+diYiIUH5+vrKysjyOxmRmZqpTp04lbs/X11e+vr7Fxp1O5yW/Q5yNflQP+lw98godyisoPcTwNagc7M/Vh15X7H1b6XcnGWM0cuRILVu2TKtWrVJ0dLTH89HR0YqIiPA4ZJafn681a9a4A0rbtm3ldDo9ag4fPqydO3eWGmIAAMClpdKPxDz66KNauHCh/vWvfykoKMh9DUtISIj8/f3lcDiUkJCgpKQkNWvWTM2aNVNSUpICAgI0ePBgd+2wYcM0ZswYhYWFKTQ0VGPHjlVMTIz7biUAAHBpq/QQ88orr0iSunXr5jE+d+5c3XfffZKkcePG6eTJkxoxYoSysrLUvn17rVy5UkFBQe76GTNmyNvbW4MGDdLJkycVGxurlJQUeXl5VfaUAQCAhSo9xBhT9i2P0pmLehMTE5WYmFhqjZ+fn2bOnKmZM2dW4uwAAEBtwd9OAgAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVLvoQM3v2bEVHR8vPz09t27bV2rVra3pKAADgInBRh5glS5YoISFBkyZN0rZt29SlSxf17t1bBw4cqOmpAQCAGnZRh5jp06dr2LBhevDBB9WiRQslJycrKipKr7zySk1PDQAA1DDvmp5AafLz87VlyxZNmDDBYzw+Pl7r168vVp+Xl6e8vDz34+zsbEnSTz/9JJfLVbWTtYDL5dKJEyd09OhROZ3Omp5OrUWfq0dRn71ddVRQ6Ci17ujRo9U4q9qH/bn60Otf5ObmSpKMMeetvWhDzI8//qiCggKFh4d7jIeHhysjI6NY/dSpUzVlypRi49HR0VU2RwAXt3ov1fQMAFyo3NxchYSElFlz0YaYIg6H509ZxphiY5L01FNPafTo0e7HhYWF+umnnxQWFlZi/aUmJydHUVFR+u677xQcHFzT06m16HP1oM/Vgz5XH3r9C2OMcnNzFRkZed7aizbE1KtXT15eXsWOumRmZhY7OiNJvr6+8vX19Ri7/PLLq3KKVgoODr7k3yDVgT5XD/pcPehz9aHXZ5zvCEyRi/bCXh8fH7Vt21apqake46mpqerUqVMNzQoAAFwsLtojMZI0evRoDRkyRO3atVPHjh312muv6cCBA3rkkUdqemoAAKCGXdQh5g9/+IOOHj2q5557TocPH1bLli31wQcfqHHjxjU9Nev4+vpq8uTJxU65oXLR5+pBn6sHfa4+9PrCOEx57mECAAC4yFy018QAAACUhRADAACsRIgBAABWIsQAAAArEWIAAICVCDG1VFZWloYMGaKQkBCFhIRoyJAh+vnnn8u9/PDhw+VwOJScnFxlc6wtKtprl8ul8ePHKyYmRoGBgYqMjNS9996rQ4cOVd+kLTB79mxFR0fLz89Pbdu21dq1a8usX7Nmjdq2bSs/Pz9dddVVevXVV6tppnarSJ+XLVumuLg4XXHFFQoODlbHjh31n//8pxpna6+K7s9F/vvf/8rb21tt2rSp2glaihBTSw0ePFjbt2/XihUrtGLFCm3fvl1Dhgwp17Jvv/22NmzYUK6/W4GK9/rEiRPaunWrnnnmGW3dulXLli3TV199pf79+1fjrC9uS5YsUUJCgiZNmqRt27apS5cu6t27tw4cOFBi/d69e/Xb3/5WXbp00bZt2zRx4kQ99thjeuutt6p55napaJ8//vhjxcXF6YMPPtCWLVvUvXt39evXT9u2bavmmdulon0ukp2drXvvvVexsbHVNFMLGdQ6u3fvNpLMJ5984h5LT083kswXX3xR5rIHDx40V155pdm5c6dp3LixmTFjRhXP1m6/ptdn27hxo5Fk9u/fXxXTtM5NN91kHnnkEY+xa665xkyYMKHE+nHjxplrrrnGY2z48OGmQ4cOVTbH2qCifS7Jtddea6ZMmVLZU6tVLrTPf/jDH8zTTz9tJk+ebFq3bl2FM7QXR2JqofT0dIWEhKh9+/busQ4dOigkJETr168vdbnCwkINGTJETz75pK677rrqmKr1LrTX58rOzpbD4eCPlkrKz8/Xli1bFB8f7zEeHx9fak/T09OL1ffq1UubN2+Wy+Wqsrna7EL6fK7CwkLl5uYqNDS0KqZYK1xon+fOnatvv/1WkydPruopWu2i/rMDuDAZGRmqX79+sfH69esX+6vgZ3vhhRfk7e2txx57rCqnV6tcaK/PdurUKU2YMEGDBw/mr9dK+vHHH1VQUFDsr9WHh4eX2tOMjIwS60+fPq0ff/xRDRo0qLL52upC+nyul156ScePH9egQYOqYoq1woX0+euvv9aECRO0du1aeXvzbbosHImxSGJiohwOR5n/Nm/eLElyOBzFljfGlDguSVu2bNFf/vIXpaSklFpzKanKXp/N5XLpzjvvVGFhoWbPnl3pr8Nm5/bvfD0tqb6kcXiqaJ+LLFq0SImJiVqyZEmJQR6eytvngoICDR48WFOmTNHVV19dXdOzFhHPIiNHjtSdd95ZZk2TJk20Y8cOHTlypNhzP/zwQ7GfBoqsXbtWmZmZatSokXusoKBAY8aMUXJysvbt2/er5m6bqux1EZfLpUGDBmnv3r1atWoVR2H+T7169eTl5VXsp9TMzMxSexoREVFivbe3t8LCwqpsrja7kD4XWbJkiYYNG6Z//vOf6tmzZ1VO03oV7XNubq42b96sbdu2aeTIkZLOnLYzxsjb21srV65Ujx49qmXuNiDEWKRevXqqV6/eees6duyo7Oxsbdy4UTfddJMkacOGDcrOzlanTp1KXGbIkCHFPox69eqlIUOG6P777//1k7dMVfZa+iXAfP311/roo4/4RnsWHx8ftW3bVqmpqfrd737nHk9NTdVtt91W4jIdO3bUu+++6zG2cuVKtWvXTk6ns0rna6sL6bN05gjMAw88oEWLFqlPnz7VMVWrVbTPwcHB+uyzzzzGZs+erVWrVmnp0qWKjo6u8jlbpQYvKkYVuvXWW02rVq1Menq6SU9PNzExMaZv374eNc2bNzfLli0rdR3cnVQ+Fe21y+Uy/fv3Nw0bNjTbt283hw8fdv/Ly8uriZdw0Vm8eLFxOp1mzpw5Zvfu3SYhIcEEBgaaffv2GWOMmTBhghkyZIi7fs+ePSYgIMA88cQTZvfu3WbOnDnG6XSapUuX1tRLsEJF+7xw4ULj7e1tXn75ZY/99ueff66pl2CFivb5XNydVDpCTC119OhRc/fdd5ugoCATFBRk7r77bpOVleVRI8nMnTu31HUQYsqnor3eu3evkVTiv48++qja53+xevnll03jxo2Nj4+PueGGG8yaNWvczw0dOtR07drVo3716tXm+uuvNz4+PqZJkybmlVdeqeYZ26kife7atWuJ++3QoUOrf+KWqej+fDZCTOkcxvzf1W8AAAAW4e4kAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFjp/wPwJQqStBRmGgAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -916,7 +1885,9 @@ "# the data shows clearly a trend, so the predictions of the trends are close to each other:\n", "print(\"mean of the velocity estimates %f4.2 and the standard deviation %f4.2\"%(np.mean(vel),np.std(vel)))\n", "\n", - "plt.hist(vel,50);plt.title('Distribution of eastward velocities (mm/year)');plt.grid(True)\n", + "plt.hist(vel,10);plt.title('Distribution of eastward velocities (mm/year)');plt.grid(True)\n", + "# only show a few values in the x-axis\n", + "\n", "plt.show()" ] }, @@ -956,22 +1927,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 271, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 271, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1007,17 +1978,17 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 272, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Training set: Coefficient / Velocity eastward (mm/year): 0.0\n", + "Training set: Coefficient / Velocity eastward (mm/year): -0.006437910455226973\n", "MSE (mean square error) on training set (mm): 0.00\n", - "Coefficient of determination on training set: 1.00\n", - "MSE on validation set (mm): 0.00 and coefficient of determiniation on 1.00\n" + "Coefficient of determination on training set: 0.99\n", + "MSE on validation set (mm): 0.00 and coefficient of determiniation on 0.99\n" ] }, { @@ -1026,13 +1997,13 @@ "Text(0.5, 1.0, 'Random selection for data split')" ] }, - "execution_count": 26, + "execution_count": 272, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1078,15 +2049,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 273, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Training set: Coefficient / Velocity eastward (mm/year): 0.0\n", - "Validation set MSE (mm) and Coef of Determination: 0.00,1.00\n" + " Training set: Coefficient / Velocity eastward (mm/year): -0.006250720119447979\n", + "Validation set MSE (mm) and Coef of Determination: 0.00,0.92\n" ] }, { @@ -1095,13 +2066,13 @@ "Text(0.5, 1.0, 'Chronological selection for data split')" ] }, - "execution_count": 27, + "execution_count": 273, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1174,14 +2145,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 274, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean of the velocity estimates 0.0000004.2 and the standard deviation 0.0000004.2\n", + "mean of the velocity estimates -0.0064404.2 and the standard deviation 0.0000004.2\n", "CV = 0.00\n" ] } @@ -1242,14 +2213,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 275, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean of the velocity estimates 0.00 and the standard deviation 0.00\n", + "mean of the velocity estimates -0.01 and the standard deviation 0.00\n", "mean MSE for training set : 0.00 and the validation set: 0.00\n" ] } diff --git a/searchindex.js b/searchindex.js index 62ab07a1..93be874a 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({docnames:["Chapter1-GettingStarted/1.1_open_reproducible_science","Chapter1-GettingStarted/1.2_jupyter_environment","Chapter1-GettingStarted/1.3_python_environment","Chapter1-GettingStarted/1.4_computational_environments","Chapter1-GettingStarted/1.5_version_control_git","Chapter1-GettingStarted/1.6_data_gallery","Chapter1-GettingStarted/readme","Chapter2-DataManipulation/2.10_feature_engineering","Chapter2-DataManipulation/2.11_dimensionality_reduction","Chapter2-DataManipulation/2.12_MLready_data","Chapter2-DataManipulation/2.1_Data_Definitions","Chapter2-DataManipulation/2.2_data_formats_rendered","Chapter2-DataManipulation/2.3_pandas_rendered","Chapter2-DataManipulation/2.4_dataframes_prep","Chapter2-DataManipulation/2.5_Arrays","Chapter2-DataManipulation/2.6_resampling","Chapter2-DataManipulation/2.7_statistical_considerations","Chapter2-DataManipulation/2.8_data_spectral_transforms","Chapter2-DataManipulation/2.9_synthetic_noise","Chapter3-MachineLearning/3.1_clustering","Chapter3-MachineLearning/3.2_classification_regression","Chapter3-MachineLearning/3.3_binary_classification","Chapter3-MachineLearning/3.4_multiclass_classification","Chapter3-MachineLearning/3.5_logistic_regression","Chapter3-MachineLearning/3.6_randomForest_regression","Chapter3-MachineLearning/3.7_hyperparameter_tuning","Chapter3-MachineLearning/3.8_ensemble_learning","Chapter3-MachineLearning/3.9_autoML","Chapter4-DeepLearning/mlgeo_4.0_perceptrons","Chapter4-DeepLearning/mlgeo_4.1_neural_networks","Chapter4-DeepLearning/mlgeo_4.2_MultiLayerPerceptron","Chapter4-DeepLearning/mlgeo_4.3_CNN","Chapter4-DeepLearning/mlgeo_4.3_ModelTraining","Chapter4-DeepLearning/mlgeo_4.3_PINN","Chapter4-DeepLearning/mlgeo_4.5_RNN","Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder","Chapter4-DeepLearning/mlgeo_4.6_NAS","Chapter5-ModelWorkflows/readme","about_this_book/0_mlgeo_project","about_this_book/about_this_book","about_this_book/acknowledgements","reference/bibliography","reference/glossary"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":5,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinxcontrib.bibtex":9,sphinx:56},filenames:["Chapter1-GettingStarted/1.1_open_reproducible_science.md","Chapter1-GettingStarted/1.2_jupyter_environment.md","Chapter1-GettingStarted/1.3_python_environment.md","Chapter1-GettingStarted/1.4_computational_environments.md","Chapter1-GettingStarted/1.5_version_control_git.md","Chapter1-GettingStarted/1.6_data_gallery.md","Chapter1-GettingStarted/readme.md","Chapter2-DataManipulation/2.10_feature_engineering.ipynb","Chapter2-DataManipulation/2.11_dimensionality_reduction.ipynb","Chapter2-DataManipulation/2.12_MLready_data.ipynb","Chapter2-DataManipulation/2.1_Data_Definitions.md","Chapter2-DataManipulation/2.2_data_formats_rendered.ipynb","Chapter2-DataManipulation/2.3_pandas_rendered.ipynb","Chapter2-DataManipulation/2.4_dataframes_prep.ipynb","Chapter2-DataManipulation/2.5_Arrays.ipynb","Chapter2-DataManipulation/2.6_resampling.ipynb","Chapter2-DataManipulation/2.7_statistical_considerations.ipynb","Chapter2-DataManipulation/2.8_data_spectral_transforms.ipynb","Chapter2-DataManipulation/2.9_synthetic_noise.ipynb","Chapter3-MachineLearning/3.1_clustering.ipynb","Chapter3-MachineLearning/3.2_classification_regression.ipynb","Chapter3-MachineLearning/3.3_binary_classification.ipynb","Chapter3-MachineLearning/3.4_multiclass_classification.ipynb","Chapter3-MachineLearning/3.5_logistic_regression.ipynb","Chapter3-MachineLearning/3.6_randomForest_regression.ipynb","Chapter3-MachineLearning/3.7_hyperparameter_tuning.ipynb","Chapter3-MachineLearning/3.8_ensemble_learning.ipynb","Chapter3-MachineLearning/3.9_autoML.ipynb","Chapter4-DeepLearning/mlgeo_4.0_perceptrons.ipynb","Chapter4-DeepLearning/mlgeo_4.1_neural_networks.ipynb","Chapter4-DeepLearning/mlgeo_4.2_MultiLayerPerceptron.ipynb","Chapter4-DeepLearning/mlgeo_4.3_CNN.ipynb","Chapter4-DeepLearning/mlgeo_4.3_ModelTraining.ipynb","Chapter4-DeepLearning/mlgeo_4.3_PINN.ipynb","Chapter4-DeepLearning/mlgeo_4.5_RNN.ipynb","Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder.ipynb","Chapter4-DeepLearning/mlgeo_4.6_NAS.ipynb","Chapter5-ModelWorkflows/readme.md","about_this_book/0_mlgeo_project.md","about_this_book/about_this_book.md","about_this_book/acknowledgements.md","reference/bibliography.md","reference/glossary.md"],objects:{},objnames:{},objtypes:{},terms:{"0":[2,4,7,8,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,29,30,32,33,34,35,38],"00":[12,13,15,16,17,18,19,22,26,32,35],"000":[3,8,12,29],"000000":[12,13,15,16,24],"0000004":15,"000000e":13,"000000z":[17,18],"000009":13,"000055":13,"000081":13,"0000ff":30,"0001":28,"000111":13,"00015211022576318623":18,"0003":19,"0005":32,"000590":13,"0006263194954954088":33,"00077540e":25,"0009188263163747461":14,"00092988":25,"00095":25,"000951":25,"00095572":25,"00096354":25,"00096431":25,"00097799":25,"00097928":25,"000986":25,"00098691":25,"00098772":25,"00098853":25,"00099373":25,"00099473":25,"00099483":25,"00099893":25,"000e":32,"001":[22,23,28,29,30,31,32,33,35],"00100203":25,"00100331":25,"00100341":25,"0010047":25,"00100555":25,"00100613":25,"00100646":25,"00100651":25,"00100737":25,"00100942":25,"00101366":25,"00101843":25,"00102372":25,"00102644":25,"00103245":25,"00103259":25,"00103416":25,"00103521":25,"00103917":25,"00103965":25,"00104094":25,"00104437":25,"00104656":25,"00104809":25,"00104914":25,"00105019":25,"00105281":25,"00105462":25,"00105872":25,"00105924e":19,"00106049":25,"00106201":25,"00106554":25,"00106707":25,"00106764":25,"00106878":25,"00106978":25,"0010725":25,"0010726":25,"00107574":25,"00107622":25,"0010766":25,"00107718":25,"0010776":25,"00107789":25,"00107832":25,"00108519":25,"001086":25,"00108638":25,"00108681":25,"00108762":25,"00109024":25,"00109105":25,"00109315":25,"00109363":25,"00109911":25,"00110121":25,"00110216":25,"00110488":25,"00110974":25,"00111265":25,"00111418":25,"00111895":25,"001121":25,"0011219":25,"0011241":25,"00112505":25,"00112543":25,"00112891":25,"0011312":25,"00113409e":19,"00113697":25,"00114322":25,"00114336":25,"00114398":25,"00114417":25,"00114655":25,"00114799":25,"00114856":25,"00115099":25,"0011518":25,"00115423":25,"00115576":25,"00116277":25,"00116334":25,"00116444":25,"00116515":25,"00116839":25,"00117":13,"00117087":25,"00117283":25,"00117745":23,"00117784":25,"00117974":25,"00118184":25,"0011826":25,"00118356":25,"00118542":25,"00118771":25,"00118823":25,"00118876":25,"00118885":25,"00119176":25,"001193":25,"00120039":25,"00120368":25,"00120587":25,"0012094":25,"00122175":25,"00122461":25,"00123711":25,"00124407":25,"00125222":25,"00125556":25,"00125637":25,"00126467":25,"00126867":25,"00127654":25,"00128045":25,"00129204":25,"00129838":25,"00133281":25,"00133452":25,"00134025":25,"00134029":25,"0013628":25,"00136886":25,"00137367":25,"00137486":25,"00137854":25,"00138397":25,"00139098":25,"00139213":25,"00139284":25,"00139289":25,"00139799":25,"00140023":25,"00140777":25,"00141668":25,"001417":19,"00142055":25,"00144706":25,"00144835":25,"00151439":25,"00153746e":25,"00155859":25,"00157524e":19,"00189348e":25,"00219419e":25,"00219507":8,"003069":19,"0030715920533113":14,"00329":13,"003468":19,"00380339e":19,"003938":19,"004136":13,"004774":13,"004932182490752158":23,"005":[29,30,31,35],"00523":28,"005311":19,"00643465e":25,"006434906304516136":18,"007":7,"00744489":25,"007495006078435612":18,"00800":12,"008060063544843572":14,"00821080e":25,"00878350e":25,"008924":19,"00896047e":19,"008967":19,"00917668e":19,"009565":19,"009791921664626684":23,"01":[12,15,17,19,24,26,32,33],"010":12,"010000":[16,17],"01008876e":19,"0101010101010102":14,"010145":13,"01026":13,"010370":13,"010845":13,"010848":25,"0109":23,"01107147":25,"011097410604192354":23,"01124606":25,"0114562":25,"01150443e":19,"01150772":25,"01192058":25,"011982":19,"01206191":25,"01231908":25,"01241204":25,"01258343":25,"012774":13,"01284938":25,"01286752":25,"01287":13,"01289171":25,"01293827":25,"01298769":25,"01303417":25,"01304742":25,"01311811":25,"01314302e":25,"01317991e":19,"0132973926342017":18,"01330530e":25,"01338":13,"01357734":25,"01360224e":25,"0136416":25,"013847e":13,"01396018":8,"013998e":13,"01407104":25,"0141058":25,"01414058":25,"01417596":25,"0142139":25,"01432905":25,"0143383":25,"01434685":25,"01434876":25,"01437521":25,"01445313":25,"01446302":25,"01450577":25,"01455708":25,"01457076":25,"01462278":25,"01472216":25,"01476016":25,"01483183":25,"01484245e":19,"01484685":25,"01485596":25,"01493359":25,"01494575":25,"01497216":25,"01497725":25,"015":29,"01503067":25,"01503139":25,"01508956":25,"01510458":25,"01511592":25,"0151176":25,"01515818":25,"01519043":25,"01521006":25,"0152317":25,"015246388348034143":18,"0152606":25,"01526103":25,"01527653":25,"01528138":25,"0153625":25,"01540737":25,"01547699":25,"015568":19,"01559561":25,"01565228":25,"01576381":25,"01581057":25,"01630097e":25,"01647874":25,"01660968e":25,"01667988":25,"0169":12,"01691109":25,"01692484":25,"01693117":23,"01693139":25,"017019":13,"0171665":25,"0171936":25,"01721282":25,"01724968":25,"01750928":25,"01756458":25,"01758518":25,"01762635":25,"01790279e":19,"01795520e":25,"01812615":25,"018297":25,"01831212":25,"01833758":25,"01843243":25,"01845818":25,"018471":25,"01847563":25,"01862588":25,"01873102":25,"01874394":25,"01875048":25,"01880918":25,"01882305":25,"01882615":25,"01883609":25,"01885603":25,"01887064":25,"01887522":25,"01888666":25,"01897316":25,"01899896":25,"01901288":25,"01901824":25,"01904299":25,"01905718":25,"01908689":25,"01911283":25,"01913071":25,"01913462":25,"01921201":25,"01921757":25,"01922411":25,"0192246":25,"01923843":23,"01928401":25,"01928897":25,"01929832":25,"0192997":25,"01936684":25,"01936893":25,"01947122":25,"01948614":25,"01954636":25,"0195549":25,"01957004":25,"01957202":25,"0195775":25,"01961274":25,"0196908":25,"01971579":25,"01971922":25,"01973233":25,"01975498":25,"01980486":25,"0198133":25,"0198184":25,"01983519":25,"01985712":25,"01990356":25,"01991944":25,"01998029":25,"01998377":25,"02":[12,19,24,25,30],"02001534":25,"02002053":25,"02005477":25,"02011108":25,"02012463":25,"02015944":25,"02019405":25,"02020388":25,"02026519":25,"02032981":25,"02041302":25,"02054709":25,"02068725":25,"02095075":25,"02111402":25,"0211823":25,"02136241":25,"02145925":25,"02156137":25,"02172494":25,"021897810218978103":23,"0220214":25,"02207262":25,"02210412":25,"02210692":25,"02214394":25,"02232589":25,"02234629":25,"022358":19,"02243166":25,"02244415":25,"02267064":25,"02275643":25,"02293578":25,"02294638e":19,"02297895":25,"02298839":25,"02304196":25,"02311587":25,"02313848":25,"02317823":25,"02318607":25,"02328081":25,"02333975":25,"02335019":25,"02344284":25,"02348842":25,"02351891":25,"02353102":14,"0235342":25,"02355308":25,"02355585":25,"02355795":25,"02358461":25,"02359171":25,"02365112":25,"02366824":25,"02368955":25,"02369399":25,"02371317":25,"02377076":25,"02379055":25,"02379142":25,"02380972":25,"02381821":25,"02385512":25,"02386303":25,"02392955":25,"02400956":25,"02411374":25,"02415328":25,"02421236":25,"02424603":25,"02431536":25,"02442665":25,"02446957":25,"02453833":25,"02459221":25,"02464504":25,"02480783":25,"0248394":25,"02488422":25,"02499743":25,"025":21,"025000":24,"02505026":25,"0250979":25,"02514329":25,"02515011":25,"02530203":25,"02541871":25,"02555343":25,"02562881":25,"0257":12,"02574883":25,"02577629":25,"02578182":25,"0258419":25,"02588782":25,"02608404":25,"02635684":25,"02642287e":19,"02672602":25,"02673278":25,"02683849":25,"02696":12,"027":29,"02883570e":25,"02902726e":19,"029486":19,"02948785":25,"02956544":23,"02959356":25,"02991104":25,"02993002":8,"02998571":25,"03":[12,13,15,19,24,25,33],"030":35,"03006983":25,"03009968":25,"0301":12,"03030303":14,"03058095e":19,"03060265":25,"030637":19,"03071399":25,"030896":25,"03093848e":25,"03100":12,"03103009":23,"03337844e":19,"03416":13,"0344":12,"03440":12,"03447569e":25,"034483":24,"034800":13,"03494963e":19,"03501758e":19,"03566152e":19,"035698":13,"036229":19,"03760092e":19,"03815835e":19,"0386919":25,"039697":13,"03978648e":25,"04":[12,13,15,25,35],"04101425":23,"041580e":13,"041600":13,"04240":13,"042591":13,"043":29,"04303176034198916":14,"04314":12,"04350267e":19,"043535":13,"04372530e":25,"04460142e":25,"0454577":23,"04572756":23,"04648":13,"04690610e":19,"04767375e":19,"04798007e":19,"047994":19,"04842":13,"04955":13,"04966072e":19,"04984011e":19,"049902":19,"049911":13,"05":[7,12,14,15,17,18,19,22,24,25,31],"05010276e":19,"05152703e":19,"05211":13,"05213662e":25,"05242661":8,"052873":28,"05394413e":19,"05411581e":25,"056":29,"05651231":14,"05658198e":25,"05707018e":19,"05826066e":25,"05836797e":19,"05876537e":19,"05876856e":25,"05888410e":25,"05915033e":25,"06":[7,12,13,15,16,24,25],"060":12,"06025041e":19,"060298649528828":16,"06060606":14,"06078202e":25,"06092507e":25,"06168176e":19,"06172":13,"0625":22,"06278148e":25,"0635":12,"06380531e":19,"064":29,"06419769e":19,"06490653e":25,"06615794e":25,"067050":15,"067100":15,"06741252e":25,"067648":13,"067764":13,"06848084e":19,"069400":13,"07":[12,13,14,16,17,18,33,35],"07094755358789713":18,"07107204e":19,"07107968e":19,"07132985e":19,"07226722":14,"07230686e":19,"072650":13,"07493105e":19,"07508417348193797":16,"07561688e":25,"0777344413103096":17,"077800":12,"07841108":8,"07951792e":25,"07t03":7,"08":[12,16,33,35],"080":29,"0803":12,"08064872e":25,"08072608e":19,"080897":13,"08113310e":19,"08186501e":25,"08200949e":25,"0823":12,"08269":13,"082983":7,"08310640e":25,"08484803":23,"08537423e":19,"08593236e":19,"08732":13,"08764086e":19,"08831":13,"08846372e":25,"088800":13,"089693":13,"08998769e":19,"08t15":7,"09":[7,12,15,33],"09090909":14,"09091501e":19,"091":29,"092579":13,"0935":19,"09399618e":25,"094121":13,"09478493e":19,"09610147e":19,"09689906e":19,"098":29,"09816631e":19,"09887886e":25,"099000":7,"09t23":7,"0_wavelet":7,"0f":22,"0ffh4r23mitn2dz":7,"0m":[12,13,18,28],"0s":[22,25],"0x1060f17f0":23,"0x126f0e7f0":23,"0x145352250":25,"0x16b4d22e0":32,"0x17a5efa60":8,"0x17a96cb80":8,"0x17a9ebeb0":18,"0x17ca1a580":8,"0x1c90d4820":7,"0x1e1e75e10":30,"0x2af90f8b0":35,"0x2b17a0f10":19,"0x2b1edcd60":19,"0x2b26d6080":31,"0x2ba38ac10":19,"0x2ba44ab50":19,"0x2ba534b80":19,"0x2ba5f8bb0":19,"0x2bc2b4eb0":19,"0x2bdd92640":19,"0x2bde1d5e0":19,"0x2d0523130":35,"0x30baba520":15,"0x685db3790":14,"0x78d4ee490":14,"0x7f27e3ee9c00":21,"0x7f27e600fd90":21,"0x7f27e62d2980":21,"0x7fd65cb311b0":22,"1":[7,9,13,23,28,33,34,35,39,41],"10":[2,3,7,11,12,13,14,15,16,17,18,19,21,22,24,25,26,28,29,30,31,32,33,34,35,38,39],"100":[1,7,11,12,13,14,15,16,17,18,19,21,24,25,26,28,29,30,31,32,33,35,39,42],"1000":[8,9,10,12,13,14,15,16,17,24,26,28,29,31,32,33,35],"10000":[8,12,13,14,15,17,28,33,34],"100000":[12,15,16,24,28],"10000543e":19,"10013437e":19,"100ka":5,"101":[12,29,30],"10116954e":19,"10184613e":19,"102":30,"10227580e":19,"102557":13,"1029":31,"103":30,"1030":22,"1031":22,"1032":22,"1033":22,"1034":22,"10349180e":25,"1035":22,"1036":22,"1037":22,"1038":22,"103932":16,"104":[25,30],"1040":22,"1041":22,"10420561e":19,"10427394e":25,"10476158":14,"10487413e":19,"105":[12,25,29,30],"105000":12,"106":[25,30],"10639533e":25,"107":[25,30],"108":[13,25,30],"109":[12,13,25,30],"1094":12,"109400":12,"10942":13,"10974988":14,"10hz":17,"10k":34,"11":[7,8,12,13,14,15,16,17,18,24,26,28,29,30,35,39],"110":[11,12,25,30],"11022491e":19,"11046253e":25,"11049848e":19,"1106":12,"1107":12,"11070377e":25,"1108":12,"1109":12,"111":[8,12,13,17,19,25,29,30],"1110":12,"1111":12,"11111111":14,"1112":12,"11128084e":25,"1113":22,"1114":22,"1115":22,"1116":22,"11162756":14,"1117":22,"112":30,"11215633e":25,"113":[12,25,30],"113026":16,"1136":14,"114":[25,30],"1140":13,"1142":22,"1143":22,"1144":22,"1145":22,"1146":22,"1147":22,"1148":22,"1149":22,"1149757":14,"114x80":31,"115":30,"1150":22,"11504743e":25,"1151":22,"1152":22,"11535082e":19,"1159":22,"116":[25,30],"1160":22,"1162":22,"1164":22,"1166":22,"117":[24,30],"117873":13,"11794405e":19,"118":[25,29,30],"1181":22,"1182":22,"1183":[22,32],"1184":22,"1185":22,"119":[7,30],"1190913055":19,"1198":23,"12":[7,12,13,14,17,18,19,24,27,28,29,30,31,33,35],"120":[7,12,30,31],"12000":[12,33],"12045035":14,"120542":24,"121":[7,11,12,25,30],"12121212":14,"122":[25,30],"1225":23,"12263265e":19,"123":[25,27,30],"123111":13,"1233":22,"12332403":14,"1234":[11,22],"1235":22,"123783465":19,"1239":13,"124":30,"1240":22,"1241":22,"1242":22,"1244":22,"1245":22,"1246":22,"125":[8,12,14,16,22,29,30],"12571585":14,"126":[7,12,14,22,25,30],"126185":13,"12618569":14,"12662":13,"127":[11,14,22,30],"12701549e":25,"12745122e":25,"12750503e":25,"127k":12,"128":[14,25,30,31,34],"128000":7,"129":[25,30,31],"129629":12,"12977":13,"13":[7,12,13,14,16,17,19,25,28,29,30,33,35],"130":[25,30],"13003767e":25,"130547":13,"13058067e":19,"131":[25,30],"13147540e":19,"13176935e":25,"132":[25,30],"13200349e":25,"1320124":14,"13219411":14,"133":[12,13,25,29,30,31],"1331":13,"134":[25,30,31],"13455924e":25,"13463788e":25,"134956":13,"135":[25,30],"135285":13,"13535513e":25,"13559614e":19,"13571728e":19,"136":[13,25,30],"136646":13,"136778":19,"1369":27,"137":[25,30],"1377602":13,"138":[25,30],"13813089e":25,"13848864":14,"138856":13,"139":[25,30],"13m":35,"14":[7,12,13,14,17,18,19,29,30,31,33,35],"140":[13,25,30],"14000":33,"141":[25,29,30],"14118360e":19,"1412":13,"141447":13,"1416":19,"14169642e":19,"141790":13,"141805":13,"14186":13,"142":[12,13,25,30],"142010":13,"14262720e":19,"1427":13,"143":[25,30],"143726":13,"14398":13,"144":[25,30],"14403563e":19,"1445":11,"1447":11,"1448":11,"145":[12,13,25,30,35],"14508288":14,"14542351286436":32,"14581385e":25,"1459":[13,23],"14593293e":25,"146":[12,25,30],"1460":13,"146988":12,"147":[25,30],"14788620e":25,"148":[12,25,30],"148057":13,"14831120e":25,"149":[25,30],"14m":35,"15":[2,5,7,12,13,14,17,18,22,23,24,25,26,29,30,33,35,39],"150":[17,25,29,30],"15000":33,"15001":7,"150651":13,"15089793e":19,"15094544e":25,"151":[25,30],"1510":13,"1511":13,"15123857e":25,"15151515":14,"15199111":14,"152":[25,30],"15201264e":25,"152151":13,"15293862e":25,"153":[13,30],"154":[25,30],"15443469":14,"1548":8,"1549214831474401":32,"155":[25,30],"15525786e":19,"1553671407878263":19,"15547294e":19,"156":[25,26,30],"15607835e":19,"1561":32,"156750":7,"15688101":17,"157":[13,25,30],"157000":7,"158":[19,25,30],"158000":7,"159":[25,30],"1590":12,"15908766e":25,"15910416e":19,"15922828":14,"15924":16,"15975960e":19,"15m":35,"16":[7,12,13,14,15,16,18,19,28,29,30,31,32,33,35],"160":[13,25,29,30,35],"16000":33,"161":30,"16116":13,"16121009e":19,"161700":12,"162":[13,30],"16296722254477":32,"163":[13,30,35],"163786":13,"16392710e":19,"164":30,"165":[7,13,30],"16500907e":19,"165398":24,"166":[12,17,30],"1664":12,"16681005":14,"167":[29,30],"16783219e":25,"168":30,"16816806e":19,"16827078e":19,"169":30,"16900":12,"1691094658":17,"169214":13,"16m":35,"16x16":31,"17":[7,12,13,14,18,24,25,28,29,30,33,35],"170":30,"170000":16,"17002010e":19,"1703":11,"1704":11,"17049426e":19,"1705":11,"1706":11,"1707":11,"1708":11,"1709":11,"171":[29,30],"1710":11,"1711":11,"1712":11,"1713":11,"1714":11,"1715":11,"1716":11,"1719":35,"171915":13,"172":[13,30],"1720":7,"173":[19,30],"173233":13,"173333":13,"173370":13,"174":30,"17468526e":19,"1747":13,"17475284":14,"1748":13,"1749":13,"175":[13,30],"175485":13,"176":30,"176494":13,"176529":13,"1767563":17,"17675743e":19,"17681195":14,"177":[13,30],"17762":13,"178":[12,30],"1780":12,"178035":13,"178060":13,"1781":12,"1782":12,"1783":12,"1784":12,"1785":12,"178772":13,"1788":13,"179":[12,30],"1790":22,"1791":22,"179191":13,"1792":22,"1793":22,"1794":22,"179630":13,"17968626e":25,"1797":[22,25,26,29,30],"18":[2,7,13,14,16,19,29,30,33,35],"180":[8,13,30],"18000":33,"18035155e":19,"18068457e":25,"18092002e":19,"181":30,"18115211e":25,"18126811e":25,"181545":13,"18159":13,"18181818":14,"18181892e":19,"182":[29,30],"18209788e":25,"18281851e":25,"183":[13,30],"18307383":14,"18314361e":19,"1836":33,"18381967e":19,"184":[13,30,35],"18452379e":19,"18470":19,"18493938e":19,"185":[12,13,30],"186":[13,30],"1861":22,"1862":22,"1863":22,"1865":22,"1866":22,"1867":22,"1868":[13,22],"1869":22,"187":[11,13,30],"1870":22,"18712198e":19,"187675":12,"18767507003":12,"187840":7,"188":[22,30],"18899":13,"189":[22,30],"19":[7,12,13,14,28,29,30,31,35],"190":[22,30],"19044712e":25,"191":[22,30,33],"19169":13,"19179103":14,"192":[13,22,30],"19239174e":25,"19274190e":19,"193":[22,30],"19350":13,"194":[22,30],"19450078e":25,"19479893e":19,"195":[12,22,29,30],"196":[22,30],"19612071e":25,"197":[22,30],"1976":12,"1977":12,"198":30,"1981":12,"1984":12,"1985":12,"198503":13,"1987":12,"19876832e":19,"199":[12,13,22,30],"1990":11,"1995":38,"1998":31,"1d":[10,17,22,30,32,33],"1e":[14,17,28,32,33],"1e4":17,"1hz":[17,18],"1j":18,"1m":[12,13,18,28],"1pko9ormcllaxipzoa3aoztgzfpad2iwj":[24,27],"1s":[13,17,22,25],"1st":[8,19],"2":[1,2,3,4,23,27,28,35,39,41],"20":[7,12,13,14,16,18,19,22,24,27,28,29,30,32,33,34,35],"200":[1,12,13,17,19,23,30,32,39],"2000":[8,14,26,30,33],"20000":33,"200000":24,"20002856e":19,"2006":15,"2007":3,"20082908e":19,"20086303e":19,"2009233":14,"201":[13,22],"2010":[12,15],"2013":14,"2014":7,"2015":15,"20158980e":19,"20159493e":19,"2016":24,"2017":[7,12,18],"2018":[13,31,35],"2019":[19,35],"2019gl085870":31,"202":22,"2020":[2,7,13,15,28,31,35],"2021":[7,17,18],"2022":[4,7,13,17,28,35,40],"2023":[1,4,7,12,13,18,28,39],"2024":[12,13,15],"2026":12,"20269556e":19,"20294639e":25,"203":22,"203094":13,"203188":13,"20555138e":19,"206":13,"206_51_39":13,"207":[13,39],"20704562e":25,"207073":19,"208":[13,29,39],"208269":19,"20pm":13,"21":[12,14,18,29,30],"210":13,"21000":33,"21049041":14,"210926":13,"21113694e":25,"2113147933":28,"21153578e":19,"212":31,"21210983e":19,"21212121":14,"212207":13,"21236235e":19,"21307490e":19,"213167":13,"21362874e":19,"21400829":14,"21449":13,"21450115e":25,"21452746e":25,"21619273e":25,"21621959e":25,"21687591e":25,"2171b5":8,"21882842e":25,"21892":13,"21907473":32,"22":[11,12,13,14,18,28,29,30,32,33,35],"220":13,"2201005948153714":17,"22051307":14,"22080902":14,"221":29,"22140377e":19,"222":29,"22222222":14,"222370e":13,"222400e":13,"22247136e":25,"22501940e":25,"22531":13,"228":[11,13],"22872158e":19,"23":[7,12,13,14,18,19,24,28,29,30,33,35],"2300000":17,"23062432e":19,"23094":13,"23101297":14,"232053040564":32,"232080e":13,"23233":35,"232646":13,"232740e":13,"23284674":14,"23296":35,"23299271e":19,"234":14,"2343":12,"23439988e":25,"2344":12,"2346":12,"2347":12,"2348":12,"2349":12,"2350":12,"2351":12,"235100":13,"2352":12,"2354":12,"2355":12,"2356":12,"2357":12,"2358":12,"23605235e":19,"236217054087575":17,"237445":13,"237650e":13,"23810334e":25,"238506":24,"2389":13,"239270e":13,"2399":11,"24":[12,13,14,28,29,30],"240":14,"24000":33,"24052539":8,"24059":13,"241000":7,"241273":7,"24201283":14,"24203":13,"24211860e":25,"24242424":14,"24248697e":19,"242735":13,"24279855e":19,"24400":12,"24402698e":19,"2445":13,"24529984e":25,"24568202e":19,"24663131e":25,"24715790e":25,"24755566e":25,"24893725e":19,"24931771e":19,"24937819e":25,"24t10":7,"25":[12,13,14,15,16,17,19,21,24,25,28,29,30,35,38],"250":3,"251":13,"25170509e":25,"2519":35,"252":[7,12],"2520":35,"2521":35,"2522":35,"2523":35,"2524":35,"2525":35,"2526":35,"2527":35,"253":12,"2531":35,"2533":35,"25353645":14,"2539":35,"254":12,"2540":35,"2541":35,"2543":35,"254590":13,"2548":35,"25485059e":25,"2549":35,"255":12,"2550":35,"2551":35,"255175":13,"2552":35,"25525":13,"2553":35,"2554":35,"2555":35,"2556":35,"2557":35,"2558":[13,35],"2559":13,"256":[12,13,29],"2564":35,"2565":35,"25700":12,"25701972":14,"258550":13,"258610":13,"259070":13,"259232":13,"25hrequir":26,"26":[12,13,14,17,18,19,26,28,29,30,33,35],"260":13,"2600000":17,"261":24,"26122375e":19,"26138302e":25,"261756":13,"26189863e":19,"26342":13,"264":13,"26560878":14,"26564294e":19,"266":13,"266950":13,"267":13,"268":13,"269":13,"26974":13,"27":[7,12,13,14,25,29,30,32,35],"270":13,"27000":33,"270000":15,"27112239e":25,"271863":13,"272373":13,"27272727":14,"273":[13,14],"27390039e":25,"274":[7,13],"27408835e":25,"27419865e":25,"27454916":14,"274761":13,"27481244e":19,"274861":13,"27594":12,"27641682e":25,"27723895e":19,"277725":19,"278":29,"27825594":14,"27834627e":25,"27882059e":19,"2789":12,"27894486e":19,"27937344e":25,"28":[13,14,19,24,29,30,31,32,35],"28000":33,"28012":13,"28029144":14,"281325":13,"282202506964987":32,"28344035e":19,"28410467e":19,"28460883e":25,"28512736e":25,"28546337e":19,"28566571e":19,"28581684e":25,"28597742e":19,"286":11,"2862":13,"28664273e":25,"28679745e":19,"287":13,"2877":13,"2884356107":17,"28855255e":19,"28857":13,"28913069":8,"28915345e":25,"28975461e":19,"29":[12,14,17,18,24,25,29,30,33],"29150531":14,"29154967":14,"29233218e":19,"2926":13,"2934":13,"29352404e":19,"29363134e":25,"29365818e":25,"29377350344869":14,"2937735034487":14,"29385795e":19,"294":11,"29434":12,"29435":12,"29436":12,"29437":12,"29438":12,"29439":12,"294510e":13,"295":11,"296560":13,"297":11,"2970118992746436":16,"29739056e":19,"29746":13,"298149":13,"298557385097123":14,"29866468e":25,"299":[11,13],"29t04":[17,18],"29t06":[17,18],"2_multilayerperceptron":30,"2bcurli":[17,22],"2d":[8,10,19,28,30,31],"2dt":17,"2f":[8,15,16,22,30,33],"2fa":4,"2m":12,"2nd":19,"2p":16,"2x":30,"2x2":21,"3":[3,4,7,13,18,28,33,34,35,39],"30":[7,8,12,14,15,16,19,25,28,29,30,32,33,35],"300":[7,11,19,27,30,33],"3000":[12,33],"30000":33,"300000":12,"30014826e":25,"301":13,"30100":12,"302":[11,13],"3020249866927003":32,"302031":12,"30217568":14,"3030303":14,"303440e":13,"30379":13,"30468018e":19,"30472588e":19,"305024":13,"30538555":14,"305700":12,"3068295825":19,"307":39,"30767943e":25,"3077":13,"30773284e":25,"308":[13,39],"30845379e":19,"30t04":7,"31":[7,12,13,14,18,24,28,29,30],"31048681e":25,"311":13,"311191":13,"31177":13,"31240204e":19,"31248":13,"3125":22,"312878":13,"3133":11,"314213":13,"31478216e":19,"31551975e":19,"3159":12,"31675465e":19,"31707406e":19,"31771048e":25,"318":13,"318070e":13,"31840023":8,"31874":13,"31875618e":19,"31992671":14,"31993257e":25,"31m":13,"32":[7,12,13,14,18,28,29,30,31,32,35],"32000":33,"3200000":17,"3201":[11,17],"32081209e":19,"32138028e":19,"32268686e":25,"323":13,"3238846712":12,"32410070e":25,"3246":11,"32622034e":19,"32625132e":25,"32655998e":25,"32876128":14,"32968954e":25,"33":[7,12,14,19,29,30,32,38],"330000z":7,"33004834e":25,"33014":13,"330439":13,"3306":13,"33071950e":25,"33087274e":19,"331":[12,13],"33194":13,"333":29,"33333333":14,"33333333333333":19,"333333333333332":19,"3333333333333335":19,"333333333333334":19,"333333333333336":19,"33333333333334":19,"33335384e":25,"33459512e":25,"33516027":14,"335168":13,"33518319e":25,"33553382e":19,"33613971e":19,"33724303e":25,"33814553e":19,"33900561e":25,"33992835e":19,"34":[12,13,14,18,25,28,29,30,32],"34043406":8,"341243":13,"34277804e":19,"34298459e":25,"34351":13,"34400":12,"34400217e":25,"34510049e":19,"34570272":8,"34573837e":19,"34593598e":25,"347558e":15,"347592e":15,"347623e":15,"347626e":15,"347653e":15,"347683e":15,"348":24,"348280":13,"34913307e":19,"34929719e":19,"349380":13,"35":[12,13,14,24,25,29,30,32],"351":[13,39],"351101":13,"35111917":14,"35171223e":19,"352":39,"35214245e":25,"35235665e":19,"35258262":8,"35265098e":25,"35284738e":25,"353":13,"35304777":14,"353854":13,"35440":12,"35445482e":25,"35469237e":19,"354773":7,"355876":13,"35710083e":19,"35851976e":25,"35863021e":25,"36":[12,14,19,29,30,32],"360":[8,14],"3600":17,"36000":33,"360007":13,"36095430e":25,"36127618e":19,"36191":13,"36335407e":25,"36363636":14,"36448941":14,"365":32,"36608":31,"367375":13,"367623":19,"36783798":14,"368":7,"369437":13,"36967973e":19,"37":[7,12,14,25,26,28,29,30,32],"370":[7,12],"37029163e":19,"37087279e":25,"370946":13,"371":7,"371931":13,"372414":13,"373":13,"373563":24,"375":22,"37570999e":25,"37813526e":19,"37841317":8,"378927":13,"379":13,"38":[12,14,29,30],"380":[7,13],"380100":13,"38063281e":19,"38104":13,"381880":7,"38236679e":25,"382403":12,"382632":13,"38298":13,"38320642e":25,"38346417e":25,"3835":13,"38358033e":19,"38469443e":19,"38535286":14,"38561943":8,"38837774e":19,"388774":13,"389":[29,30],"389000":7,"389220e":13,"389248e":13,"38942222e":25,"389945":13,"389960":13,"39":[7,8,12,13,14,16,18,28,29,30,33],"390000":16,"39001736e":19,"39061200e":25,"390620e":13,"39150":13,"391540e":13,"391759":13,"391800":12,"39198186e":25,"392200":15,"3931145617":19,"39322864":23,"393638":13,"39393795e":19,"39393939":14,"394198":15,"394514":13,"39486211e":25,"39610759e":25,"397651":13,"39781005e":25,"398300":13,"39961":13,"39975399e":19,"3d":[7,10,14,17,19,22,30,31,32],"3f":[29,30,31,35],"3m":13,"3x3":31,"4":[2,7,11,16,21,25,39],"40":[11,12,14,16,17,19,24,26,29,30,33],"400":[7,12,31,32],"4000":33,"40000":33,"400000":24,"400365":15,"40079389e":19,"400x300":19,"403":12,"40331959e":19,"40352714e":25,"40367004e":19,"40370173":14,"40401":13,"404166":13,"40552474e":25,"40575570e":25,"405891":13,"40732739":8,"407906":13,"40928480e":19,"40953573e":25,"41":[13,14,24,25,29,30,33],"41001253e":25,"41018":13,"41197511e":25,"413":13,"41369110e":25,"414":13,"41413700e":19,"41443296e":25,"416752":13,"41679880e":19,"416857":19,"41747416":14,"41774748e":19,"41781333e":19,"41992596e":25,"42":[4,7,14,21,22,24,29,30,31,32,33],"420000z":7,"42113730e":25,"42195117e":25,"42250976e":25,"422833":13,"42292429":14,"423153":13,"42359606e":19,"423691":11,"42373896e":19,"42424242":14,"42674044e":25,"42788150e":19,"42819812e":19,"42892166e":19,"429012":12,"43":[12,13,14,18,19,24,25,28,29,30],"43035011e":25,"43046929":14,"431600":13,"431687":13,"4325":13,"43350268e":19,"43447":13,"43585916e":19,"43651998e":25,"436909":13,"43711":13,"437923":13,"43869":13,"44":[7,11,12,13,14,24,29,30],"440000":15,"44012308e":25,"441":13,"44265462e":19,"44278":13,"443":[12,13],"44306215":14,"444":[7,13,29],"44444444":14,"44483783e":25,"44506361e":25,"44576096e":25,"44617376e":25,"448":33,"44819809e":25,"44882771e":19,"44952179e":25,"45":[7,14,24,25,29,30,33],"450000":15,"45044537e":19,"45055":13,"4513":12,"45216271e":25,"45270114e":19,"45319":12,"4545":13,"45454545":14,"457200":13,"45789629e":19,"45857469e":19,"45899":13,"46":[7,14,19,24,29,30,35],"46154748e":25,"46166602e":19,"462018":19,"46227722":14,"46310032e":19,"46415888":14,"46473182e":19,"4649":12,"46554711e":19,"46725669e":19,"46769":13,"468830e":13,"469":39,"46903645e":19,"47":[7,11,13,14,17,24,29,30,31],"471614":13,"47165930e":25,"47192869":8,"47232592":8,"47406":13,"47428":13,"47469585e":25,"4757":13,"476825":13,"47691796e":19,"477011":24,"47707636":14,"47792":13,"47805102e":19,"47855870e":19,"478654":19,"478900":12,"479":13,"47963855e":19,"48":[14,24,29,30,33],"48002":13,"48032570e":19,"48094800e":25,"48252852e":25,"484142e":15,"48484848":14,"48496826":14,"48530636e":25,"48626016":14,"487":12,"488":12,"48879817e":19,"48922":13,"48948642e":19,"49":[14,16,18,22,24,25,29,30,33],"490":12,"4901":[11,17],"49084638e":19,"49099562e":25,"491":[12,13],"492":12,"49249982e":19,"49268":13,"493":12,"494":12,"495":12,"495126":8,"495135":13,"495150":13,"49539290e":19,"49559702e":25,"496":12,"496600":13,"497":12,"498380":24,"498535":13,"49858699e":25,"499":12,"49969726e":25,"49m":[12,13,18,28],"49m23":[12,13,18,28],"49m24":[12,13,18,28],"49mnotic":[12,13,18,28],"49mpip":[12,13,18,28],"4_multiclass_classif":22,"4d":[10,14],"4ducqnd7mfihnh7d":3,"4fb140e9d4b0":32,"4p":16,"5":[2,3,7,11,13,16,17,18,19,21,22,25,26,27,28,33,34,39],"50":[7,12,13,14,15,16,18,19,21,22,24,25,26,28,29,30,31,32,33,34,35],"500":[3,7,8,12,29,35],"5000":[21,28,34],"500000":24,"5007397612756534":17,"50087464e":25,"500935":13,"501":[12,17],"50128686e":25,"502":12,"50243315e":25,"50274156e":19,"503260e":13,"50342":13,"50654833e":19,"50777458":8,"50807":13,"50838706e":25,"5094138":14,"50998857e":25,"51":[12,14,17,29,30,34],"510":13,"51122842e":25,"512":13,"512675":13,"512870":13,"513":13,"51344574e":19,"51397":13,"514368":24,"51463199e":19,"515":13,"51515152":14,"51578":13,"51615":13,"51665":13,"517":[12,31],"517213":13,"518":4,"51900":13,"51909":13,"51942":13,"51997":13,"52":[12,14,29,30],"520":7,"52023":13,"52077":13,"52197574e":19,"52368":13,"52395":13,"52575621e":19,"526":7,"5268":23,"527306":24,"52807567e":19,"52854687e":19,"529":35,"52941":13,"52943":13,"52976973e":19,"529880":13,"52997":15,"529987":13,"53":[14,19,24,25,29,30],"53035":15,"53038":15,"53042":15,"53065":15,"531119":13,"53120750e":19,"531326":13,"53254559e":19,"532844":19,"533300":13,"53366992":14,"53412077e":19,"53487851":14,"53528":13,"53553593e":25,"53575704e":19,"53637802e":19,"5364":23,"53659577e":19,"538958":13,"539035":13,"539264":13,"54":[14,24,25,29,30,33],"54062965e":19,"540813":19,"541":13,"541062":13,"54140":13,"54158392e":19,"54175147e":25,"542":7,"54208":13,"54213":13,"542265":13,"54237061e":25,"543":7,"543103":24,"54375250e":19,"544":12,"54400":12,"54468":13,"54471":13,"54478269e":25,"54501126e":25,"5452":23,"54523":13,"54534953":8,"54545455":14,"54548457":14,"54573":13,"546":12,"54606369e":25,"54625":13,"54626":13,"54700385e":25,"547279":13,"54746":13,"547774":13,"548094":19,"548885":13,"54922":13,"549381":24,"54990193e":19,"55":[12,14,29,30],"550":12,"55025":13,"55140244e":25,"55171570e":25,"55188227e":19,"552":12,"55222759e":19,"55258449e":19,"55285629e":25,"55492":13,"554985":13,"5550078":8,"555270":13,"55538743e":19,"55555556":14,"55567437e":19,"55567614":14,"55570":13,"556":[29,30],"55617":13,"55729680e":19,"55799770e":19,"55808":13,"558363":12,"55846061e":19,"55908102":14,"55957757e":25,"559689":13,"56":[14,19,24,29,30],"560":[7,12],"56111":13,"56112":13,"561237":13,"561819":13,"5625":22,"56331337e":25,"56463328":14,"56598296e":19,"56653506e":19,"567089":13,"56749":13,"568":13,"56801387e":19,"569":[35,39],"569208":13,"56933621e":25,"57":[14,16,24,29,30],"570":[12,35],"57037499e":25,"571":35,"571252":13,"572":35,"57278414e":25,"57373200e":25,"574":35,"57472288e":19,"57481":13,"57545":13,"57569875e":19,"57575758":14,"57593123e":19,"57708":13,"577122":19,"577763":13,"5777710951796506":14,"578800":13,"579":7,"5792":12,"579308":12,"57933225":14,"57933225e":25,"57949544e":19,"58":[14,24,25,29,30,33],"58000":12,"580796":19,"583579":13,"583973":13,"58428941e":19,"58464736e":19,"58570208":14,"58690191e":25,"58804467e":19,"588516":11,"58880384e":25,"589":7,"58953285e":25,"59":[7,14,16,17,18,24,29,30,39],"59051900e":19,"59103728e":19,"59120796e":19,"591k":12,"59265875e":19,"593":35,"59316":13,"59364427e":19,"59380863e":19,"59381366":14,"594":35,"594047":13,"59461":13,"59461729e":19,"59470943e":25,"59473293e":25,"595":35,"59502421":14,"596":35,"59655748e":19,"59660":13,"597441":13,"59758546e":25,"598":22,"598370":13,"59843":13,"59924246e":25,"59939861e":19,"59943803e":25,"599900":13,"59995190e":19,"59k":12,"5e":33,"5x5":30,"6":[4,7,11,13,17,18,19,21,22,25,27,28,29,30,31,33,35,39],"60":[12,14,16,19,22,24,29,30],"600":12,"6000":33,"600000":12,"60009622e":19,"6001989060169071":23,"601":[12,13],"60272":13,"60324221e":25,"60365269e":19,"605":13,"60519969e":19,"605746":24,"60605548e":19,"60606061":14,"608":[11,12],"608510":12,"60857280e":25,"608674":12,"609":[7,35],"60928335e":19,"6098":12,"61":[14,22,24,29,30],"610":11,"610410":13,"611":11,"61107851e":25,"612":7,"61267658e":19,"613":11,"61326":13,"61359073":14,"613687":16,"613763":13,"614":11,"614986":13,"61636910e":25,"6173489951795071":17,"617955":13,"61819897e":19,"618285":13,"61908328e":25,"619206":13,"619355":13,"619527":13,"61971214e":19,"619920":13,"62":[14,22,24,29,30,33],"620":22,"621":[4,22],"621145":13,"62141":13,"622":[13,22],"62246648e":25,"6228":12,"623":22,"624":35,"625":22,"625297":13,"62606":13,"626179":24,"62618254e":25,"628":32,"62891172e":25,"62928":13,"62941687e":19,"62975083":14,"629794831291764":15,"63":[7,14,19,22,25,26,29,30,33],"63007403e":19,"63028776e":19,"633146":13,"63412378e":19,"63469071e":19,"6359":13,"6360":13,"63636364":14,"637131":15,"638140e":13,"63943160e":19,"64":[14,22,29,30,31,35],"640":35,"64019598":8,"64033033e":19,"64054926e":19,"64075181e":25,"64228344e":19,"64280731":14,"643135e":13,"64354655e":25,"643588":19,"643981":13,"645022e":13,"64575262e":19,"64575823e":19,"646886":8,"64705215e":19,"6480666066247558":17,"648700":13,"649397":13,"649801":13,"649835":13,"65":[7,8,12,14,22,29,30],"65073966e":19,"65095025e":19,"65140128e":19,"652299":24,"65301846e":19,"653386":19,"6541":12,"65587265e":25,"65681801e":19,"659365":13,"65958117e":19,"66":[13,14,19,24,29,30,35,38],"660":12,"661046":13,"6611":12,"66200710e":19,"66215858e":25,"66280":13,"66284056e":19,"6660137226595727":19,"666183":13,"66666666666666":19,"666666666666664":19,"6666666666666666":23,"666666666666667":19,"666666666666668":19,"66666666666667":19,"66666667":14,"666667":12,"666876":13,"667":29,"66848983e":19,"66887625e":19,"66980625e":25,"66982019e":19,"67":[7,14,16,25,29,30],"670000":15,"67049934e":25,"67071852e":25,"67081402e":19,"67131969e":25,"67262241e":25,"67295307e":19,"673280":13,"67341507":14,"67392941e":25,"67524768e":25,"67580740e":19,"67637":13,"6771":8,"67752249e":25,"678714":13,"6799":15,"68":[12,13,14,16,18,25,29,30,33],"68001571e":25,"680207":13,"68104510e":19,"68179228e":19,"68189771e":19,"68216093e":25,"685500":12,"68576456e":19,"68738041e":19,"68808391e":19,"6881":12,"689780e":13,"689790e":13,"69":[7,12,14,24,29,30,33],"690000z":7,"69033981e":19,"69039912e":19,"690544":13,"69104212e":19,"693652":13,"69490068e":19,"69565340e":19,"69596561e":25,"69664895e":19,"6969697":14,"69749003":14,"699557":13,"6_51_39":13,"7":[2,3,7,8,11,12,13,14,15,18,19,22,28,29,30,35,39],"70":[12,14,16,19,29,30],"700":17,"7000":34,"70089548e":19,"701149":24,"70130848e":19,"70181608e":19,"70308463e":19,"70522483e":25,"705256":[13,24],"70548023":14,"70611057e":25,"70684726e":19,"70735265":14,"70751":13,"708":7,"70801967e":19,"70832949e":19,"7093":13,"7094":13,"70985658e":19,"71":[14,16,24,29,30,33],"710000":16,"71049697e":19,"71090116e":25,"7129782153521722":15,"71456":13,"7159964058294":14,"71624672":8,"71644621e":25,"71659203e":25,"71775288e":19,"71858824":14,"719977":13,"72":[14,16,24,25,29,30],"7200":[17,18],"720000":[17,18],"72028530e":25,"72048413e":19,"721282":13,"72189621e":19,"722":29,"722360e":13,"72236766":14,"722370e":13,"72436206e":25,"72581564e":25,"72637647e":19,"72699":13,"72706875e":19,"72716534e":19,"72727273":14,"727430e":13,"73":[12,14,19,29,30],"73013118e":19,"7307":23,"73096308e":19,"73109557e":19,"731839":13,"732093":13,"7321":23,"73319":13,"73334082e":19,"73339668e":19,"73388":13,"73416701e":19,"73430":13,"73434481e":25,"7354":13,"73608":13,"73633":13,"73669760e":19,"73789973e":25,"73823302e":25,"7390722":14,"739250":15,"74":[12,14,16,29,30,33],"74009149e":25,"74095585e":19,"741430":13,"74176784":8,"742000":15,"74254149e":25,"74368878e":19,"7453":13,"74778663e":25,"749052":13,"75":[12,13,14,15,16,22,24,29,30,33],"750":12,"750000":[13,16,24],"75081016":14,"75116445e":25,"752":13,"752860e":13,"752870e":13,"75307036e":25,"75354527e":19,"754":17,"755898":13,"756":[13,17],"75642317e":19,"75732367e":19,"75757576":14,"75788452e":25,"7589433978306135":15,"76":[14,16,19,24,29,30],"76033":13,"760632":24,"76323776e":19,"7647wqr96rhr49q":[17,22],"76493581":14,"76536":13,"76546160e":19,"767943601369907":17,"768":13,"7682":12,"76846118e":19,"76971":13,"76975214e":19,"77":[12,14,24,29,30],"77200563e":19,"772982":24,"772989":24,"77316345e":25,"773574":13,"77388106e":25,"77426368":14,"774447":13,"77578831e":19,"7763568394002505e":16,"77653136e":25,"77777778":14,"777778":12,"77784322e":19,"778":29,"778371":13,"7793":13,"78":[12,14,29,30,33,35],"780000":16,"78193582e":19,"782500":12,"783439":13,"784":[30,35],"78425838e":25,"78721":13,"78724266e":25,"78787879":14,"788435":13,"78864953":14,"7889":23,"78909912e":25,"79":[14,24,29,30,35],"79046":13,"79400":13,"794146":24,"79615554e":19,"797":7,"799000e":15,"79905757e":19,"79932403e":25,"799550":13,"7999":13,"7_data_spectral_transform":17,"7_day_avg":12,"7_modeltrain":32,"8":[2,3,7,8,11,12,13,14,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35,39],"80":[8,12,14,19,25,29,30],"800":25,"8000":33,"80046686e":19,"80125":13,"80184096e":19,"802":[23,29],"802040":13,"80232020e":25,"803":29,"804":29,"80414214e":19,"80467402e":19,"805":29,"805389":13,"8055555555555556":26,"806":29,"807":29,"8073373888433721":32,"80752904e":19,"808":29,"809":29,"81":[14,16,29,30,35],"810":29,"810000z":7,"811":29,"81113083":14,"81141063e":25,"812":29,"813":29,"81304750e":25,"814":[29,35],"815":29,"815100":13,"815140":13,"81525401e":25,"816":29,"81692591e":19,"817":29,"817346":13,"818":29,"8181818181818182":23,"81818182":14,"819":29,"81909580e":25,"81970102e":19,"82":[14,24,25,29,30,33],"820":29,"820579":13,"821":29,"82123482e":25,"822":29,"822220":13,"82295974e":25,"823":29,"824":29,"825":29,"826":29,"8263632119514472":26,"82639626e":19,"827":29,"82773":12,"828":7,"828204":13,"828656":13,"829":29,"82903172e":19,"82940078e":25,"83":[7,14,19,29,30],"830":29,"8309308755379773":17,"83111864e":25,"832":29,"832200":12,"83254010e":25,"83286861e":19,"833":[29,30],"833060":13,"834":29,"835":29,"836148":13,"837":29,"838":29,"83847536e":19,"84":[14,16,22,29,30,31,33],"840":29,"84020133e":19,"84028749e":19,"8406":12,"840700":12,"840963":13,"841":29,"8410":13,"841475":13,"843":29,"84315":13,"84388478e":19,"84424972e":25,"844845":13,"845":29,"8453316591120342":17,"84575843":8,"84583932e":19,"847":29,"847409":13,"84803587":14,"84805874e":19,"84848485":14,"849":29,"84975344":14,"85":[14,19,22,29,30,33],"850":[12,29],"852405":13,"852980":13,"853":29,"853095":13,"853705":13,"8537303444308123":15,"85382366e":25,"853950":13,"85430983e":25,"8545072":8,"85451879e":19,"855":29,"857":[7,29],"85702362e":25,"85775":13,"858":11,"85836044e":19,"858770":13,"859":11,"859240":13,"85946":13,"85955127e":19,"86":[14,19,22,25,29,30],"860":[11,29],"86090316e":25,"861":11,"86149975e":25,"862":11,"86269050e":25,"863":[11,29],"86349029869899":17,"864":11,"865":[11,29],"866":11,"86637":13,"867":[11,31],"8679961043240163":17,"868":11,"869":[11,29],"87":[14,22,24,25,29,30],"870":11,"870000z":7,"870529":13,"870622e":15,"871":11,"872":11,"87265918e":25,"873":29,"87381742":14,"873887":15,"875000":21,"87549147e":25,"876":29,"87810165e":19,"87812831e":19,"87876889e":19,"87878788":14,"88":[14,22,29,30],"880":[13,29],"880100e":13,"881180e":13,"881300e":13,"88163672":8,"88218788e":25,"88306815e":25,"883288":13,"88405566e":25,"884382":13,"884605":13,"88499613e":19,"885":29,"8861111111111111":26,"886273":13,"88678261e":19,"8888":27,"88888889":14,"89":[14,22,29,30,33],"890":29,"890000":16,"89021509":14,"89038735e":19,"89098648e":19,"89181306e":19,"8926121":14,"89347960e":25,"89595643e":19,"896":29,"89751882e":19,"89813112e":19,"898166":19,"899":[22,30],"89950044e":25,"8x8":[29,30],"9":[2,11,12,13,14,15,16,19,22,23,24,25,26,28,29,30,31,32,35,39],"90":[8,12,14,19,22,29,30,35],"900":30,"9000":[33,34],"901":[17,30],"902":30,"90265774":8,"903":[29,30],"90305907e":25,"904":30,"90438":13,"905":30,"905100":12,"90535107e":19,"906":[25,30,33],"90625":25,"907":30,"90700158e":25,"90781154e":19,"908":30,"909":30,"90909091":14,"90950553e":19,"90972222":25,"91":[14,22,29,30],"910":[25,30],"911":[12,30],"912":[7,30],"91289199":25,"913":[25,30],"91319444":25,"91336627e":25,"91363892e":25,"914":[29,30],"91468729e":19,"91615":13,"91666667":25,"916816615939592":15,"917":25,"918970":13,"91910361e":19,"9192":13,"91938238e":25,"91949590e":19,"91986063":25,"92":[14,22,24,29,30,31],"920":25,"92013889":25,"921":7,"92233225e":25,"923":25,"92334495":25,"92361111":25,"924":25,"92482898":14,"92495460e":25,"925":26,"925120":19,"92678":13,"92682927":25,"927":25,"92708333":25,"92742328e":19,"92780079e":25,"92810648e":19,"92820725e":25,"92855":13,"92880978865406":22,"929816":13,"93":[14,16,19,22,24,25,29,30],"930":25,"93031359":25,"93043423e":25,"93055556":25,"931":25,"93108787e":25,"932":29,"93205588e":19,"93260335":14,"93289237e":19,"93379791":25,"9339":23,"934":25,"93402778":25,"935":11,"936":[11,31],"9365":13,"937":[11,25],"93728223":25,"9375":25,"938":25,"93914670e":19,"93939394":14,"93946235":25,"94":[7,11,14,22,25,29,30],"94015921":25,"94076655":25,"94086334":25,"94097222":25,"941":[7,25],"94155536":25,"942135":16,"94225223":25,"94268501e":25,"94288096e":19,"942928":13,"94293215":25,"94293457":25,"94294425":25,"94337144e":25,"944":[11,25,29],"94420606":14,"94425087":25,"94433556":25,"94444444":25,"945":11,"94503":25,"94503242":25,"94503968":25,"945234":13,"945457":13,"945635":13,"946":11,"94642373":25,"94657351":8,"94699":13,"947":[7,29],"94711334":25,"94712544":25,"947210":13,"94773519":25,"94780778":25,"94781746":25,"9478223":25,"94782472":25,"94791667":25,"948":[11,25],"94850707":25,"94850949":25,"94851916":25,"94852158":25,"94915864":8,"94919909":25,"94921361":25,"94921603":25,"94921845":25,"94990321":25,"94990563":25,"94990805":25,"94991047":25,"95":[8,14,22,24,25,29,30,31],"95001412e":25,"9500949":8,"95032425e":25,"9506025":25,"95060492":25,"951":25,"95121951":25,"9512921":25,"95129694":25,"95129936":25,"95130178":25,"95138889":25,"95181":13,"95198897":25,"95199139":25,"95199381":25,"95199623":25,"95199864":25,"95200106":25,"95214107e":19,"95239386e":19,"95269793":25,"95338269":25,"95338753":25,"95407714":25,"95470383":25,"95476432":25,"95486111":25,"955":25,"95514458e":25,"95547329":25,"95547571":25,"9555061179087876":22,"956":29,"95616531":25,"95616773":25,"95617015":25,"95685734":25,"95686218":25,"95686943":25,"957200":12,"95754694":25,"95755662":25,"958":25,"95818815":25,"95825348":25,"95833333":25,"95894551":25,"95894793":25,"95895035":25,"9599473847106159":26,"96":[3,14,19,22,25,29,30,31],"9609":12,"96124":13,"9615219421101775":26,"96167247":25,"96180501e":25,"96180556":25,"962":[25,29],"96242741":25,"96243225":25,"96304065":14,"96312911":25,"9638163":25,"96382356":25,"96408":13,"965":25,"96515679":25,"96521003":25,"96527778":25,"96590447":25,"96590689":25,"96591415":25,"966550e":13,"966580e":13,"96660376":25,"9666666666666667":25,"96723179e":25,"968":29,"96841261e":25,"96864111":25,"96868709":25,"96875":25,"9687815126050421":26,"9688542825361512":22,"969":25,"96938637":25,"96969697":14,"97":[8,14,22,29,30],"97071249e":19,"972":25,"9720":23,"97212544":25,"97222222":25,"97260277e":25,"97287069":25,"975":29,"975000":[21,24],"97560976":25,"97569444":25,"976":25,"97699003e":25,"97700996":14,"97702356":14,"979":25,"97909408":25,"97916667":25,"9793790849673202":26,"98":[14,19,22,29,30],"98076449e":25,"981":[7,13],"981000e":13,"98100851e":19,"98156960e":25,"982":29,"98233":13,"9825784":25,"98263889":25,"9826966":8,"983":25,"98364724":14,"98533885":8,"986":25,"98606272":25,"98611111":25,"98629590e":25,"986400":13,"987420e":15,"98750850e":19,"98780850e":19,"988":29,"988460":13,"988970":13,"989040":13,"98945863e":19,"98954704":25,"98958333":25,"98965852e":25,"99":[12,14,22,29,30],"990":25,"990000z":[17,18],"99469224e":25,"9948425":14,"99549186e":19,"995780e":13,"996":29,"99606599e":25,"99685":13,"99702835e":19,"99746591":8,"99780493":8,"9981":13,"998100":12,"99908489e":19,"99918722e":19,"99947629e":25,"99958587e":19,"99964465e":25,"9999":13,"\u00b5s":17,"boolean":[11,20],"break":[1,28,35],"case":[8,11,13,14,15,18,20,24,27,28,31,32,35,37,38],"class":[5,6,7,8,9,12,13,14,15,16,17,18,19,20,21,22,23,25,26,28,30,31,32,33,35],"default":[2,3,4,8,12,14,19,26,32,34],"do":[4,8,9,11,12,15,16,17,19,21,22,23,25,26,28,29,30,31,32,34,35,38,39],"export":[24,27],"final":[9,19,22,26,31,33,34,37,38,39],"float":[8,13,14,15,19,22,23,29,30,31,32,33,35],"function":[2,4,6,7,8,13,14,15,16,17,18,19,20,21,22,23,25,26,27,28,31,33,34,35,38],"ga\u00ebl":21,"import":[2,4,7,8,11,12,13,14,15,16,17,18,19,21,22,23,25,26,27,28,29,30,31,32,33,34,35],"int":[7,8,14,15,17,18,19,22,24,29,30,31,33,35],"long":[15,16,17,29,30,31,34],"m\u00fcller":21,"new":[8,12,13,14,15,16,18,19,21,23,24,26,27,28,36,38,42],"null":[11,13,15],"public":[1,4,42],"return":[7,8,11,12,13,16,19,21,22,23,27,28,29,30,31,32,33,34,35],"short":[4,12,34,35,39],"super":[22,29,30,33,35],"switch":8,"transient":35,"true":[7,8,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"try":[12,14,15,19,21,23,25,26,27,28,31,32,34,38],"var":[12,15,16,17,19,23,28],"while":[12,19,23,25],A:[1,2,4,8,9,10,12,13,14,15,16,18,19,20,21,22,23,25,26,28,29,30,32,34,35,36,42],AND:12,And:[7,15],As:[8,10,12,15,26,27,31,32],At:[3,4,14,15,31,32,38],Be:4,But:[15,16,19,32],By:[4,8,15,26,32,39],For:[1,3,8,10,12,13,14,15,18,19,23,26,27,28,29,30,31,32,36],If:[3,4,8,9,13,14,15,17,19,20,22,23,24,27,28,29,30,32,42],In:[3,4,7,10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,28,29,31,32,33,34,35,36,37],Is:[1,9,14,19,28,38],It:[1,3,4,6,7,8,10,11,12,13,14,15,16,17,19,20,21,22,23,25,27,29,30,31,32,34,35,37,38,42],Its:19,NEAR:12,No:[11,17,19,38],Not:[4,17],OF:12,On:[4,25],One:[1,3,8,9,16,17,19,23,25,26,27,30,31,35],Or:[1,9,31,34],THe:19,TO:[24,35],That:[23,26],The:[1,3,4,5,6,7,8,9,11,12,13,14,15,17,18,20,21,22,23,24,25,26,27,29,30,32,33,34,35,36,37,39,40,42],Their:3,Then:[4,19,29,31],There:[3,4,8,11,14,15,17,19,20,22,23,25,26,27,30,31,32,38],These:[2,3,5,8,18,19,27,30,31,35,38],To:[0,2,3,4,5,7,8,12,13,15,17,18,19,20,23,26,27,28,30,32,34,39],WITH:15,Will:[24,38],With:[12,16,26,32],_1:[8,32],_2:[14,16,32],_:[8,15,22,28,29,30,31,35],_________________________________________________________________:35,__call__:22,__class__:[26,31],__getitem__:[29,30],__init__:[11,28,29,30,33,35],__len__:[29,30],__main__:12,__name__:[26,31],__none:28,__sigmoid:28,__step:28,_array_strptime_with_fallback:12,_asarray_with_ord:22,_assert_all_finit:22,_base:[22,26],_c:30,_check_estimator_nam:22,_check_i:22,_convert_listlike_datetim:12,_coordinate_desc:32,_engin:11,_ensure_no_complex_data:22,_fit_and_predict:22,_fit_context:22,_funcwrapp:22,_get_sequential_output:22,_i:[20,23,32],_k:32,_kmean:19,_lib:12,_lock:22,_make_engin:11,_maybe_cach:12,_n:8,_read:11,_refine_defaults_read:11,_stochastic_gradi:32,_valid:22,_validate_data:22,_validate_nam:11,_validate_param:22,_validate_target:22,_with_config:22,a_:8,a_nparrai:14,aa:14,aar:12,ab:[14,17,18,19,23,24,31],abcseri:12,abil:[6,15,42],abl:[4,19,22,23,27,32,33,38,39],about:[2,4,9,11,12,14,15,17,18,19,23,27,31,33,34,39],abov:[4,8,17,19,21,30,31,32],absolut:[18,19,24,28,32],abund:19,ac29:32,academ:4,academia:27,acc:21,acceler:14,accentu:31,accept:[4,15],accept_large_spars:22,accept_spars:22,access:[3,4,5,10,11,14,24,38,39],accord:[8,29,30],accordingli:33,account:[3,8,15,19],accur:[17,38],accuraci:[9,15,21,22,23,24,25,26,29,30,31],accuracy_scor:[22,25,26],accuracy_tim:[29,30,31],accurraci:23,achiev:[9,25,27],acknowledg:4,acoust:3,across:[10,19,26,38,42],act:[26,32],actic:19,action:4,activ:[2,28,29,32,34,35],actual:[9,19,23,24,27,28,31,32,37],actualfalsevalu:21,actualtruevalu:21,ad:[3,6,14,30,31,32,33,34,35],ada_clf:26,adaboost:21,adaboostclassifi:[21,26],adagrad:32,adam:[30,31,32,33,34,35],adapt:[25,31,40],add:[2,4,8,9,10,11,12,14,16,18,19,24,28,29,30,31,33,35],add_subplot:[8,19],add_to:11,addit:[9,10,11,12,18,19,22,30,33],address:[1,4,7,9,15,39],adequ:9,adher:4,adityakadiw:23,adjac:15,adjust:[15,25,29,30,32],admiralti:12,adopt:[14,39],advanc:[3,39],advantag:[3,8,15,25,26,32],affect:[12,19],affin:30,afford:3,after:[4,13,14,15,19,29,30,31,32,35],again:[3,8,11,12,15,32],against:[15,19,21,30,32],agenc:42,agg:12,agglom:19,agglomerativeclust:19,aggreg:[14,19,26],agnost:33,ago:24,ahead:[14,27],ai:[10,13,27,32,37],aid:[19,40],aim:[1,4,5,12,32,34],air:14,air_temperatur:[11,14],aircraft:10,aka:[14,21],akash:39,akmehra:39,akshai:[39,40],al:[4,31,35],alana:4,alanabd:13,alaska:17,algebra:[14,20],algorithm:[8,9,19,23,25,26,28,30,31,32,34,35,37,38],alias:2,align:8,all:[1,4,6,7,8,9,11,12,14,15,17,18,19,21,22,23,24,25,26,27,29,30,31,32,33,38,39],allcorr:8,allevi:15,alloc:[3,14,31],allow:[1,3,4,8,10,11,12,13,14,15,23,25,31,32,33,34,38,42],allow_nd:22,allow_object:12,along:[11,19],alow:17,alpha:[8,15,16,19,21,23,24,30,31,32,33],alreadi:[2,3,7,12,13,14,18,23,26,27,28,31,33,37],also:[3,4,7,8,11,12,13,14,15,16,17,19,20,21,26,27,29,30,31,32,34,35,37,42],alter:37,although:23,alwai:[19,23],amath301:39,amath:39,amaz:27,amazon:3,ambient:[7,33],among:[7,10,13,15,19,30,32],amount:[8,11,15,32],amplitud:[17,18,31,38],an:[1,3,6,7,8,10,11,12,13,14,15,17,18,19,20,21,22,23,25,26,27,28,29,30,32,33,34,35,38,39,42],anaconda:2,analog:20,analys:9,analysi:[7,10,11,12,19,20,31,37,39],analyz:[4,17,18,32],anchor:8,andrea:21,android:4,angl:[17,19],ani:[4,6,7,11,12,15,16,17,26,30,34,35,37,42],annoi:27,annot:[8,13],annotate_heatmap:8,anomal:24,anonym:12,anoth:[8,9,12,17,19,26,29,32,35,37],answer:[12,14,15,16,27,32,39],anthoni:39,anticorrel:15,anyon:14,api:[4,11,32],app:42,appear:[8,14,17],append:[8,11,14,15,21,28,30,32,33],appli:[8,12,14,16,17,19,20,24,29,31,35,37,38,39],applic:[3,4,8,10,11,13,15,16,17,19,30,42],appnop:[7,28],approach:[8,9,12,15,19,25,26,30,36],appropri:[4,13,14,27,32,38,39],approxim:[8,15,17,23,30],ar:[3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,34,35,37,38,39],arang:[7,8,14,15,17,19,22,29,30,31,32,34],architectur:[3,6,30,31,32,35],archiv:[3,10,38],arcitectur:29,area:[3,4,14,15,21,22],arendt:39,arg:[12,22,33],argmax:8,argmin:19,argument:[8,15,26,30,32],arian:40,aris:17,arithmet:14,around:[3,8,16,19,30],arr:14,arra:14,arrai:[6,7,8,9,12,15,17,18,19,21,22,23,24,25,26,28,31,32,33],arrang:9,array_of_diff:15,array_to_datetim:12,arriv:3,arrow:5,art3d:19,art:[9,10],artefact:17,articl:17,artifici:24,artist:7,asarrai:[8,15,17,19,21,22,31,32],asid:32,ask:[4,23],aspect:[17,31,32],assembl:[9,19],assert:11,assess:[13,15,19,21,25,32],assign:[12,14,15,19,20,24,27,30,31],associ:[3,7,11,29,32],assum:[7,15,17,19,23,32],assumpt:[15,32],ast:8,asttoken:[7,28],astyp:[19,29,30,34],asynchron:12,atan2:8,atlow:17,atmospher:[3,5,10],attach:14,attach_respons:[17,18],attain:19,attempt:[32,35,39],attent:26,attr:14,attribut:[7,9,10,12,13,14,19,22,32,38],attriobut:7,auc:[21,22],augment:[9,32],author:[4,31],autmn22:4,autmn:39,auto:[17,19,25,27,36],autofmt_xd:24,autograd:33,autokera:27,autom:[4,27,36],automat:[2,4,7,8,9,14,27,32,38,39],autumn22:4,avail:[2,3,11,12,13,15,18,19,26,28,35,38],avalanch:7,avenu:19,averag:[12,15,19,23,24,26,27,29,30,32,38],averagepooling2d:31,avg:22,avgpool2d:31,avoid:[4,12,13,15,25,34,38],await:[12,13],awar:[4,17],awesom:[4,27,28],ax11:14,ax12:14,ax13:14,ax14:14,ax1:[8,14,19,24,29,30,31],ax2:[8,14,19,24,29,30,31],ax3:[14,24],ax4:[14,24],ax:[8,14,15,16,17,18,19,21,22,28,30,31,32],axes3d:19,axesimag:[8,25],axhlin:31,axi:[5,6,7,8,12,14,15,17,19,21,24,29,30,31,35,38],axisgrid:19,axvlin:[15,19,31],azimuth:[8,12],b:[8,9,11,12,14,15,17,19,21,23,24,28,29,30,31,32,34],b_i:29,b_j:29,b_k:29,b_w:28,back:[8,12,27,30,32,35],backcal:[7,28],backend:4,background:21,backprop:14,backpropag:[30,34],backward:[8,9,23,29,30,31,32,33,35],bad:[21,26,32,34,35],badg:4,bag_clf:26,baggingclassif:26,baggingclassifi:26,balanc:[7,12,22,32],ball_tre:25,band:[13,17,18],bandpass:17,bar:[4,17,24],barcontain:7,base:[8,10,11,12,14,15,18,19,20,22,23,25,30,32,42],base_estim:26,baseestim:22,baselibsvm:22,baselin:38,baseline_error:24,baseline_pr:24,bash:3,basi:[9,17,19,20,28,32],basic:[3,4,16,19,20,21,25,30,32,36,38,39],basin:10,batch:[19,29,30,31],batch_data:29,batch_label:29,batch_siz:[29,30,31,34,35],bay:[20,21],bayesian:25,bbox_to_anchor:15,beat:38,beauti:42,becaus:[4,10,12,14,15,19,21,23,29,30,31,32,34,35,37,38,39],becom:[3,4,10,17,20,23,34],been:[14,23,27,38],befor:[4,9,13,14,17,20,21,28,32,38,39],begin:[11,15,16,21,31,32],beginn:39,behav:[14,30,32],behavior:[16,21,25,32,38],behind:[26,42],being:[20,29],belong:[12,19,21,23,32],below:[1,3,4,6,7,8,11,12,14,16,17,18,19,20,25,26,30,32,33,35,39],benchmark:9,benefit:40,best:[4,14,19,21,23,25,30,31,32,36,38],best_epoch:32,best_i:32,best_model:32,best_params_:25,beta:35,better:[8,9,15,19,27,30,32,34],between:[3,4,6,7,12,13,14,15,16,17,18,19,21,22,23,26,29,30,31,32,35,38],beyond:[32,40],bia:[15,26,28,29,30,31,32],bias:[28,29,30,32],biasweightupd:28,bibtex:4,big:[3,11,23],bin:[12,14,16,18],binari:[2,11,18,20,23,26,29,30,32,35],binder:[4,42],binderhub:42,biomed:35,bit:32,bitbucket:4,black:[8,14,15,16,19,30,31],blank:[1,19],blast:7,blob:12,block:[34,35],blog:16,blue:[11,13,15,29,30,31],blurr:14,bmatrix:31,bo:19,bold:[1,19],book:[3,14,17,22,30,32,38,39,40,42],boost:27,bootstrap:[9,26],both:[9,14,15,18,19,20,21,23,27,32],bottleneck:35,bottom:8,boulder:40,bound:[11,30,38],boundari:[11,21,30,31],boundedfloattext:28,bourn:3,box:[4,15],bp:17,br:1,bracket:14,branc:35,branch:[4,6,30,35,39],branch_nam:4,breadth:39,brew:3,brief:4,briefli:4,bring:17,broader:31,broadli:15,broken:32,browser:[1,4,5,42],brute:25,bsd:21,bucket:7,bucketx:7,bug:4,build:[4,13,15,19,23,25,26,30,32,42],build_meta:4,built:[11,14,19,30,35,38],bundl:30,burden:15,butter:17,butterworth:17,button:[5,28],c1:[17,32],c2:[17,32],c:[8,14,15,16,19,21,22,28,30,31,33,35,38,42],c_:[8,30,32],cach:[7,12,17],cache_arrai:12,cache_d:[11,12],calcul:[7,8,12,13,14,15,16,17,18,19,20,21,22,23,24,27,28,29,30,31,32,33,35,38],calculat:18,california:[3,12],call:[3,8,11,12,14,17,18,20,21,22,23,26,27,28,29,30,32,34,35,38,42],callabl:30,callback:22,calucl:33,camcol:13,can:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,25,26,27,29,30,31,32,33,35,36,37,38,39,42],candid:25,cannot:[12,17,20,30,35],canon:[17,35,39],canva:39,cap:19,capabl:3,capit:28,captur:[15,16,26,30],carbon:19,carefulli:[13,15,17],carlo:9,carpentri:[3,4,6,14,40],carri:31,carto:12,cascad:19,cascadia:15,casino:15,cast:29,cast_to_ndarrai:22,catalog:[3,12,31],categor:[10,11,24,30],categori:[7,12,20],causal:[17,32],caveat:19,cbar:[8,13],cbar_kw:8,cbarlabel:8,cc:[19,21,32],cd:4,cd_fast:32,cdot:[8,23,29,32],cell:[1,11,12,17,22,25,27,30,32,34],celsiu:[12,14],center:[3,4,8,14,15,19],central:[3,12,15,16,17],central_mo:16,centroid:19,certain:[19,26],certifi:18,cfg:7,chain:30,challeng:11,chang:[4,8,10,12,14,15,16,19,26,28,32,33,35,37],channel:[2,12,17,18,30,31,42],chapter2:17,chapter3:22,chapter4:[30,32],chapter:[3,27,32,39],charact:[11,13],character:[9,13,32],characterist:[17,21,22,32],charset:18,chart:24,chatgpt:40,cheat:4,chebyshev:[17,25],check:[2,4,11,12,14,15,17,18,30,32,35],check_arrai:22,check_consistent_length:22,check_param:22,check_x_i:22,check_y_param:22,checkout:4,checkpoint:[30,31,35],chemistri:10,chile:12,chine:39,choic:[12,15,25,26,28,32],choos:[3,4,8,12,17,18,19,20,29,30,31],choosealicens:4,chronolog:15,chunksiz:11,ci:4,cienc:39,circ:[14,23],circl:[15,19],citat:4,cite:4,classic:[17,19,20,25,31,32],classif:[5,9,18,19,23,26,29,30,31],classifi:[7,12,20,22,23,25,29,30,31,32,35],classificaiton:[20,26],classification_report:[21,22],claus:21,clean:[4,13,15,16,18,32,35,38],cleaned_data:13,cleanest:4,clear:[19,27,39],clear_output:[17,28],clearli:[15,19],clf2:25,clf:[21,22,25,26,28,30],cli:4,click:[4,5,9,28],client:[17,18],climat:[10,11,12,14],clipboard:4,clone:[4,6,22,32,39],close:[15,19,21,23,35],closer:[15,19],closest:19,cloud:[1,4,6,8,10,11,14,38,39,42],cloudstor:3,cluster:[3,5,31,38,39],cluster_centers_:19,cluster_label:19,clusterid:19,clusters_new:19,clusters_old:19,cm:[8,17,19,22,28,30],cm_bright:30,cmap:[7,8,13,17,21,22,30,31,35],cnn:[34,35],cnnautoencod:35,co2:5,co:[8,14,19,21],coarsen:14,coast:12,cobli:14,code:[1,3,4,8,12,13,19,21,23,27,31,32,38,39,42],codespac:[17,22],coef:15,coef_:[15,23,32],coeff:8,coeffic:15,coeffici:[8,13,14,15,17,19,32],cogt:10,coher:19,cohes:19,cohort:4,coin:31,col:14,col_index:19,col_label:8,colab:4,coll:33,collabor:42,colleagu:4,collect:[5,7,8,10,11,13,14,16,17,19,21,26,27,38],color:[8,11,12,13,14,15,16,18,19,21,28,29,30,31],color_threshold:19,colorbar:8,colorblind:14,colorinterp:11,colormap:8,column:[7,8,10,13,15,16,17,19,22,23,24,32],column_or_1d:[22,32],column_stack:19,columnar:11,com:[1,4,5,7,11,12,13,16,17,19,23,24,27,31,33,39,42],combin:[8,15,16,18,19,25,26,29,31,32,34],come:[2,10,14,19,27],comic:4,comm:28,comma:10,command:[3,4,23,24,30,42],comment:[11,19,33],commit:4,common:[4,7,10,11,12,13,16,17,19,20,21,32,35],commonli:[10,19],commun:[4,11,40,42],compact:[12,17],compar:[6,12,14,17,18,19,20,21,26,29,30,31,35,38],compare_model:27,compareoutput:28,comparison:[8,27],compil:[9,30,34],complet:[12,16,17,19,21,38],completeness_scor:19,complex:[8,10,11,14,15,17,19,20,26,30,31,32,38],complex_:18,complic:[23,34],compon:[7,15,35,37,39],components_:8,compos:[1,3,12,26,31,35],composit:16,compress:[11,12,17,32,35],compris:31,comput:[1,2,4,6,8,9,10,11,15,17,19,20,22,23,29,30,32,33,38,39,42],computation:[8,15,25],compute_cent:19,compute_clust:19,compute_dist:19,compute_elbow:19,compute_object:19,cona:2,concat:[7,15],concaten:[15,18,19,31],conceit:15,concept:[10,15,19,35,39],concurr:22,conda:[4,42],condit:[19,20,32,33,36,38],conduct:4,conern:3,conf:4,confid:[15,21],config:[4,14,22],config_context:22,configur:[4,11,25,27],confirm:27,conflict:4,confus:[21,22],confusion_matrix:[21,22],confusionmatrixdisplai:22,congrat:4,conj:18,connect:[3,4,12,13,29,30,34,35],consid:[9,17,20,21,27,28,31,32],consist:[12,19,25,32,36],console_script:4,constant:32,constrain:[17,30,32,33],constraint:32,construct:[5,18,19],consum:[25,36],contact:[4,10],contain:[1,2,4,8,10,11,12,13,14,15,19,24,32,35,39,42],contamin:18,content:[3,4,5,6,11,12,16,18],context:[13,14,18,19,29,30,31,35],continu:[4,8,11,15,17,32],contourf:[11,17,30],contourpi:[13,18,28],contribut:[3,6,8,19,26,30,32,40],contributor:4,control:[39,42],conv1:30,conv2:30,conv2d:[30,31,35],converg:[15,19,32,35],convergencewarn:32,convers:[12,19,28],convert:[8,11,12,14,15,17,22,23,24,25,26,29,33],convert_listlik:12,convert_str_to_tsobject:12,convien:11,convnetquak:31,convolut:30,convolve2d:31,convout:31,convtranspose2d:35,cool:[3,27,33],cooling_law:33,coolwarm:13,coord:14,coordin:[11,14,15,17,20,30,38],copi:[2,4,9,12,14,19,22,25,26,29,30,38,39],copilot:12,core:[3,12,13,14,19,26,42],corner:[4,11,17],corr:[7,8,13],corr_coef_collector:15,corr_matrix:13,corrcoef:[14,15],correct:[23,26,27,29,30,31],correctli:[21,23,27],correl:[7,8,9,14,15,19,32,38],correlated_data:15,correlation_matrix:15,correspond:[7,10,12,19,23,28,30,39],cosin:[17,19],cost:[3,15,19,20,27,28,30,32],costfunct:28,costum:12,could:[3,4,10,12,13,22,31,35],count:[12,13,15,16,17,18,21,22,24],cours:[1,4,5,6,11,12,14,15,40],cov:14,covari:[15,19],cover:[17,19,38,39],cpu:[3,14,26,33,35],cr:11,craft:33,crap2:35,crap:[7,8,15,18,35],creat:[2,6,8,9,11,13,14,15,16,17,18,19,20,22,23,24,26,27,30,31,32,33,34,35,38,39,42],create_graph:33,creation:11,credit:4,crispi:18,cristea:[39,40],criterion:[28,29,30,31,35],critic:[4,15,16,17],cross:[9,14,22,23,29,30,31,32,38],cross_val_predict:[22,26],cross_val_scor:[21,26],crossentropyloss:[29,30,31],crucial:[13,14,19],cruis:19,cryospher:[5,39],cs160:39,cs163:39,cs230:31,csr:22,csv:[1,5,7,9,10,13,16,19,23,24,27],cu:40,cubic:16,cuda:[3,14,33,35],cumsum:8,cumul:8,cumulativevarianceexplain:8,curat:5,curl:5,current:[2,8,19,22,23,24,34,38],curriculum:[30,32],curv:[21,22,26,30,31,32,35],curvatur:17,curve_fit:14,custom:[12,30],custom_dataset:[29,30],customdataset:[29,30],cutoff:17,cv:[13,15,22,25,26],cv_results_:25,cwt:[9,17,38],cwtm:17,cwtmatr:17,cyan:8,cycler:[2,13,18,28],cyto:19,cytomet:19,d1:14,d2:14,d2dl:34,d:[8,12,14,17,18,19,20,24,29,30,31,33,35],da:3,dai:[8,12,15,17,24,27],daili:[8,12,24],dall:10,dash:28,dask:14,data:[1,3,4,7,17,20,23,25,26,29,32,33,34,36,37,39,42],data_faith:19,data_loader_test:[29,30],data_loader_train:[29,30],data_rang:12,data_shrink:19,dataarrai:14,databas:[10,11,14,16],dataconversionwarn:32,datafram:[8,10,11,14,15,16,19,21,24,34],dataload:[30,31,35],datamanipul:17,dataset:[7,8,9,10,11,12,13,14,15,16,17,19,21,22,23,24,25,26,30,31,32,35,38],dataseta:25,datatyp:13,date:[4,8,10,12,15,19,24],date_format:11,date_pars:11,date_rang:12,date_year:[8,15,32],dateparseerror:12,datetim:[12,24],datetime64:12,datetimearrai:12,dateutil:[2,7,12,13,18,28],dateutil_pars:12,dayfirst:[11,12],db:23,de:8,deactiv:2,deal:[8,12,14,15,24,32],debug:38,dec:13,decai:18,decid:[13,19,21,23,30],decim:11,decis:[20,21,26,30],decision_funct:[22,30],decisionboundarydisplai:21,decisiontre:26,decisiontreeclassifi:[21,26],declar:[14,15],decod:12,decoder_cnn:35,decompos:[8,17,35],decomposit:[8,17,19,20],decor:[7,18,22,28],decreas:[8,9,19,21,23],dedic:30,deep:[3,14,19,31,35,37,39],deepdenois:35,deeplearn:[30,32],def:[7,8,12,13,16,19,21,23,28,29,30,31,32,33,34,35],default_rng:[15,28],defin:[2,4,9,12,14,15,16,18,19,21,23,25,28,29,30,32,33,35,42],definit:[8,9,19],degc:14,degre:[8,17,24,32],delai:22,delayed_func:22,delet:2,delim_whitespac:11,delimit:11,delta:[17,18,23,28],dem:17,demarc:19,demographisc:3,demonstr:[6,7,8,14,15,21,27,32,39],dendogram:19,dendrogram:19,denoisecnna:35,denol:[39,40],denot:[12,19],dens:[30,31,34],densiti:[13,19],depend:[2,4,8,9,10,13,17,19,30,42],deploi:[3,12,39,42],deploy:[4,12,39],deprec:[15,28],deprecationwarn:28,depth:[10,12,19,26,31,35],depth_km:12,deriv:[10,23,30,32,33,42],descent:[14,20,28,29,30,31],descr:[22,25],describ:[4,9,11,12,13,14,15,16,24,25,35,37,39],descript:[4,12,24,28],design:[11,14,15,19,22,26,31,32,33,39],desir:32,detach:[23,33,35],detail:[3,4,19,25,27,30,39],detect:[7,31,32,35],determin:[9,15,17,24,28,29,30,32],determini:15,detrend:[15,17,18],dev:[12,17],develop:[2,3,4,32,39,40,42],deviat:[14,15,16,17,38],devic:[3,14,33,35],devid:15,df:[7,8,11,12,13,15,16,19,21],df_clean:13,df_numer:13,df_read:11,diag:8,diagon:[8,21],dialect:11,diam:19,diam_lwr:19,diam_mid:19,diam_upr:19,diamet:19,dict:[8,22],dict_kei:[22,25],dictat:31,dictionari:[8,11,12,14,22,25,26,32],did:[12,15,27,32],diff:[4,15],diff_mean:15,differ:[2,4,6,7,11,12,14,15,17,18,19,21,25,26,32,35,37,38,42],differenti:[14,30,32,33],difficult:[17,19,37],diffus:[33,35],digit:[11,12,13,17,22,25,26,29,30,39],dim:14,dimens:[8,9,10,11,12,14,15,17,30,31,32,34,35,38],dimension:[9,10,11,12,14,19,20,32,39],dip:12,dir0:30,dir1:35,direct:[5,7,8,10,13,14,32],directli:[3,4,10,12,22,27],directori:[11,35],disabl:[29,30,31,35],disadvantag:[15,32],discern:19,disciplin:39,disciplinari:5,discov:19,discoveri:33,discret:[17,31],discrimin:[8,13,16,18,20],discriminant_analysi:21,discriminatori:17,discuss:[3,4,10,12,15,19],disp:22,displac:[8,15,32],displai:[12,13,14,16,17,24,28,31],display_styl:14,dissimilar:[14,19],dist:[14,19],distanc:[17,25],distance_threshold:19,distancemetr:19,distinct:19,distinguish:[13,14],distort:19,distribut:[3,7,8,9,12,14,15,17,18,19,25,32,38,42],distrubut:13,dive:[14,31],divers:[5,10,25,26,32],diversifi:32,divid:[9,15,16,17,18,38,39],divis:19,djf:14,dl:[7,11,17,19,23,31],dmatrix:26,dmc:[17,18],dn:19,doc:[4,12,14,15,19,24,27,30,32],docker:[4,27,39,42],docmument:4,document:[4,6,9,11,12,17,19,21,32,38,42],doe:[4,11,12,15,18,19,21,23,26,28,31,38,42],doi:[4,11,31,38],domain:[18,25,30,35],domin:[11,17],don:[27,34],done:[3,4,11,14,15,17,22,27,30,31],dot:[8,23,28,31,32],doublequot:11,dowload:11,down:[1,4,11,29,32],download:[2,4,7,8,11,12,13,15,17,18,19,23,26,27,31,35,42],downsampl:[14,30,31],dozen:42,draft:21,dramat:3,drastic:21,draw:[9,15,19],drawn:32,drive:39,driven:42,drlat:17,drlon:17,drone:10,drop:[4,7,13,16,23,24,28,30,38],dropbox:[4,7,11,17,19,30,31,32],dropdown:28,dropna:[12,13,16,23],dropout:[30,31,32,35],ds2:14,ds:[1,4,14,39,42],dt:[14,17,33],dta:12,dtype:[7,11,12,13,14,18,19,22,25,31],dtype_backend:11,du:14,dualiti:32,ducelli:40,due:[13,15,17,19,32,39],dummi:31,dump:11,duo:4,duplic:[4,15],durat:17,dure:[27,29,30,32,34,39],dw:23,dwa82x6xhjkhyw8:19,dynam:5,dz:16,e2019gl085870:31,e:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,19,23,25,26,27,29,30,31,32,35,37,38],e_b:15,e_train:15,e_val:15,each:[2,3,4,7,8,10,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,39,42],earli:[9,11,22,34,39],earlier:[30,35],earliest:12,early_stop:[30,32],earth:[4,7,10,14,19,39,40],earthchem:16,earthdatasci:[4,11,14,40],earthquak:[7,8,16,17,18,20,31,32,35],earthquake_magnitud:16,earthrocgranit:[5,16],eas:[15,32],easi:[4,11,14,23,27],easier:[8,14,23,30],easili:[15,35,38],east:[8,11,12,15,32],eastward:15,ecosystem:[25,39],edg:[17,31],edgecolor:[16,19,21,30],edit:[3,4,27,35],editor:4,edu:[4,8,15,39],educ:39,effect:[10,15,17,21,25,26,29,32],effici:[3,10,11,12,14,15,25,27,32,35],effort:[4,14,27,36],ehz:12,eigen:8,eigenvalu:8,eigenvector:8,either:[1,3,5,8,14,15,19,23,30,32,35,39],ela_reg:32,elaps:22,elasticnet:32,element:[11,12,14,15,19,21,28,31,32],elev:[11,12,17],elevation_m:11,elif:[11,12],elimin:32,ellips:19,els:[4,7,8,11,12,14,15,19,21,22,28,29,30,31,33,35],elsewher:31,email:[4,39],emb:32,embed:[8,11,37],emerg:[7,11],empir:19,emploi:[15,25],empti:[12,15,19,22,34],enabl:[3,17],encod:[11,27],encoder_cnn:35,encoding_error:11,encount:[5,10,17],end:[0,8,9,12,15,16,19,20,21,25,29,30,31,35,39],end_dim:[29,30],end_tim:12,enddat:12,endeavor:19,endtim:[17,18],energi:17,enet_coordinate_desc:32,enforc:17,engin:[11,15,32,39],enhanc:11,enivron:2,enough:[9,14,32,38],ensembl:[21,22,24],ensur:[4,12,15,24,28],ensure_2d:22,ensure_min_featur:22,ensure_min_sampl:22,enter:[12,16,17,27],entir:[4,12,15,19,26,29,31,32,37],entri:[4,8,11,13],entropi:[23,29,30,31,32],enumer:[8,13,18,19,21],env:[2,7,11,12,13,18,19,26,28,32],environ:[4,6,10,11,25,38,39,42],environment:[11,15],ep:[21,33],epal:28,epoch:[26,29,30,31,32,33,34,35],epr:15,epred_train:15,epred_v:15,epsilon:23,eq1:31,eq2:31,eq:[7,33],equal:[8,15,16,17,18,28],equat:[10,20,30,33,42],equial:14,equival:[10,12,14,23,29,30,32],er:28,err:21,errno:11,error:[8,9,11,12,13,15,19,20,21,23,24,28,29,30,31,32,35,38],error_scor:25,erupt:19,escapechar:11,escienc:3,esourc:39,espear:31,especi:[14,15,19,25,27,32,38],ess490:[30,32],ess:[1,4,39,42],essenc:19,essenti:[12,15],est:19,estim:[8,15,19,22,24,25,26,29,31,32,35,38],estimator__algorithm:25,estimator__leaf_s:25,estimator__metr:25,estimator__metric_param:25,estimator__n_job:25,estimator__n_neighbor:25,estimator__p:25,estimator__weight:25,estimator_nam:22,et:[4,31,35],eta0:32,eta:[26,28],etc:[1,4,10,12,14,27,29,32,38],euclidean:[19,25],euclidian:[14,19],eval:[7,28,33],evalu:[15,19,20,25,26,29,30,31,32,35,39],evaluate_model:27,evapor:12,evaporation_mm:12,even:[10,17,18,19,37],evenli:14,event:[5,7,12,16,17,19,31,42],event_id:7,eventu:42,everi:[9,14,17,25,31,37,39],everyth:[2,28,37],everytim:4,evolut:[29,32],exact:[2,12,15,23,37],exactli:15,exampl:[3,7,8,10,11,13,14,16,17,19,20,21,22,23,24,26,29,30,33,34,36,39],exce:29,excel:[4,14],except:[1,30],exceptiongroup:28,excess:32,exchang:11,exclud:[13,27],exclus:30,execut:[1,2,7,28],exercis:[5,7,11,15,19,21],exess:32,exhibit:15,exist:[9,15,17,19,23,25,42],exist_ok:[11,30,31,35],exp:[12,14,17,18,23,28,29,30,32,33],exp_reg101:27,expand:1,expans:3,expect:[6,12,21,27,32,38],expens:[3,25],experi:[3,19,25,27,36,42],expert:[9,25,39],expertis:38,expir:4,explain:[8,9,19,23,30,31,33],explained_variance_:8,explained_variance_ratio_:8,explanatori:12,explicitli:[15,19],explor:[9,15,16,18,19,22,25,26,28,29,36,38],explort:13,explos:7,expon:16,exponenti:[16,32],express:[12,19],extend:[21,29,31],extens:[2,6,25,28,42],extent:[9,17],extern:[2,25],extract:[7,9,16,17,18,24,27,28,29,35,39],extractal:11,extrem:[5,15],f0e1ywupdbuv3l3:7,f1:[21,22,23,32,34],f1_score:21,f2:34,f4:15,f:[7,11,13,14,16,17,18,21,22,23,24,25,26,28,29,30,31,33],f_1:21,f_:17,f_a:17,f_c:17,f_path:[30,31,35],f_x:23,facecolor:19,facet:14,facil:38,facilit:[11,14],fact:[15,19,31],factor:[4,13,17,18,30],fahrenheit:12,fail:[12,30],fair:38,faith:19,fake:19,fall:12,faller:4,fals:[8,11,12,13,15,16,19,21,22,23,24,25,26],false_valu:11,famili:17,familiar:[14,29],far:15,fashion:31,fashionmnist:35,fast:[3,12],faster:[11,14],fastica:[8,9],fc1:[30,35],fc2:[30,35],fc3:30,fc4e2a:8,fc:19,fdsn:[17,18],fdsn_client:[17,18],feather:19,featur:[9,10,12,14,17,18,19,20,23,25,27,30,31,32,33,35,38,39,42],feature_import:24,feature_importances_:24,feature_list:24,feature_nam:[8,22,25,28],fee:4,feed:[13,35],fetch:[4,39],fetch_openml:[22,31,35],few:[12,13,15,19,32],fewer:8,ff0000:30,ff:12,fft2:17,fft:[9,17,18],fftfreq:[17,18],fftpack:[17,18],fftshift:17,fiberid:13,fidel:35,field:[8,10,12,13,14,27,31,32,33,35,36],fig1:14,fig2:14,fig:[8,12,14,15,16,17,18,19,24,28,29,30,31,32],figsiz:[8,13,14,15,16,17,18,19,22,24,28,30,31,32,34],figur:[5,8,11,12,13,18,19,22,27,28,29,30,31],figure_:22,figure_format:14,fiji:12,file1:[4,11,17],file2:[4,11,17],file:[1,3,7,9,11,13,14,16,19,21,22,31,38,39],file_url:[8,15],filenam:[11,19],filenotfounderror:11,filepath_or_buff:11,fill:[13,17,18,31,38],fill_betweenx:19,fill_valu:25,filter:[12,13,14,31],filterdimens:31,filtered_imag:31,find:[3,4,7,8,9,12,13,14,15,17,19,20,21,23,25,26,29,30,31,32,35,36,38,39],finder:27,fine:[25,32],finit:[7,17,31],first:[1,3,4,7,8,9,12,13,14,15,16,17,18,19,21,22,23,26,28,29,30,31,32,33,34,35,38,39],fisher:16,fit:[8,14,15,19,21,22,23,24,25,26,30,33,34,36],fit_method:22,fit_param:22,fit_predict:19,fit_transform:[8,19,21,22,25,26,32,35],fivethirtyeight:24,fix:[4,12,15,30,31,32,38],flag:19,flat:18,flatten:[14,26,29,30,31,34,35],flavor:42,flexibl:[10,14],flip:14,float32:[14,29,30,31,34],float64:[12,13,22],float_precis:11,floatlogslid:28,floor:17,flouresc:19,flow:[10,16,19],fluctuat:12,fluor:19,fly:14,fmi:19,fn:[19,21,23],fname:[11,12],focu:[3,7,11,14,15,25,27],focus:[13,19,39],fold:[22,25,32],fold_shuffl:27,folder:[9,11,12,17,19,28,31,38],folium:11,follow:[0,3,4,5,7,8,11,15,16,19,21,26,27,28,31,32,39],font:[14,18,19],fontsiz:[14,19],fonttool:[13,18,28],fontweight:19,forc:[4,23,25,39],force_all_finit:22,forecast:[24,27],forecast_acc:24,forecast_noaa:[24,27],forecast_und:24,forest:[20,21,22,26,27,30,39],forestri:5,forg:2,forget:32,fork:4,forlin:28,form:[10,11,14,15,18,19,20,32],format:[1,5,7,8,9,12,19,21,22,24,29,30,31,35,38,39],formatt:8,former:40,formul:[9,20,23],formula:[19,22,23],forth:27,fortran:42,forward:[8,9,14,29,31,33,35],found:[4,11,14,23,29,31,32,39],foundat:[10,14,39],four:7,fourier:[7,9,18,35,39],fourth:[1,14,16,18],fowlk:19,fp:[19,21,23],fpr:[21,22],frac:[8,14,16,17,18,19,20,21,23,29,30,31,32,33],fraction:[19,21],frame:[7,12,13,15,16,20,22,25],framework:[4,11,17,39],free:[3,4,35,38,42],freedom:32,freq:[12,17],frequenc:[14,16,17,18],frequent:[8,12,19],freqvec1:17,freqvec:[17,18],fri:24,friend:[2,24,27],friendli:[4,30],fritz:32,from:[1,2,4,7,8,9,10,11,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,38,39,42],from_estim:[21,22],from_numpi:[23,33],fs:[7,17,18],ft:17,ftp:12,full:[12,27,31,32],full_matric:8,fulli:[29,30,34,35],func:22,functool:33,fundament:[9,10,15,19,21,32,33],further:[4,30,35],futur:[4,10,28],futurewarn:[19,26],g2:32,g:[1,2,3,4,5,8,9,10,11,12,13,14,15,19,20,23,25,26,27,29,30,31,32,37,38],gain:42,galaxi:13,game:3,gamma:[21,22],gap:[13,17,32,38],gate:[19,34],gather:[3,20,32],gaussian:[14,21,32,38],gaussian_filt:14,gaussian_process:21,gaussiannb:[21,26],gaussiannois:35,gaussianprocessclassifi:21,gb:26,gca:[8,15,16],gcp:3,gd:32,gdoutput:28,gdrive:4,ge:14,gener:[4,8,9,10,11,14,15,16,19,20,26,28,30,31,32,34,35,36,37,38,39],generaliz:[26,38],generate_time_seri:34,geo:39,geodes:19,geodesi:[8,15,39],geodet:[5,19],geograph:[10,11],geographi:19,geohackweek:[11,40],geolog:[11,17],geologi:[11,17,39],geometr:10,geometri:11,geophys:[5,10,16,19,31],georeferenc:11,georg:40,geoscien:39,geoscienc:[3,5,11,12,13,16,17,19,32,37,38,40],geoscientif:[10,12,13,17],geoscientist:12,geosmart:[4,39],geospati:[10,15,17,19,26,30,32,37,38,39],geostatist:19,geotiff:10,geq:23,get:[3,4,5,8,9,11,12,13,14,15,16,19,22,23,24,26,29,30,31,32,35],get_arrai:8,get_config:22,get_dummi:[24,27],get_features_by_domain:7,get_handl:11,get_param:25,get_waveform:[17,18],get_xticklabel:8,getattr:22,getitem:29,geyser:19,gh:12,gi:10,giant:37,gist_dir:12,git:[6,26,39,42],github:[1,3,5,6,12,25,27,31,33,35,38,40,42],githubusercont:[5,12,13,16],gitignor:4,gitlab:4,give:[1,2,4,15,17,18,19,21,23,27,32],given:[1,8,10,12,15,19,21,25,31,33,38],glacier:19,glass:1,glob:[8,15],global:[3,4,12,19,31,32],global_quakes_iri:12,global_skip_valid:22,go:[4,7,11,12,14,15,17,23,26,27,28,29,30,32],goal:[15,19,25],goe:[29,32,33],gone:27,good:[4,9,14,15,19,25,32,38],googl:[24,27,39],got:22,gouraud:17,gov:[11,24],gp:[8,15,19],gpd:11,gps_timeseri:[8,15],gpu:[3,14,35],grad:[23,33],grad_a:14,grad_b:14,grad_c:14,grad_d:14,grad_fn:23,grad_output:33,grad_y_pr:14,gradient:[14,20,28,29,30,31,33,34,35],gradientdesc:28,graduat:40,grai:[28,31],granit:16,grant:4,grant_sudo:27,graph:[14,24,30],graphic:[3,31],graviti:10,gray_r:22,great:[3,4,27,28,30,31,35],greater:[17,24,31,32],green:[11,13,15,19],grei:29,grid:[8,10,11,12,14,15,17,18,19,21,22,28,29,30,31,32,33,34],gridsearchcv:25,gridsearchcvgridsearchcv:25,grobler:21,ground:[7,10,16,17,19,32,38],group:[3,7,11,12,14,15,19,20,22,38],groupbi:[12,14],growth:3,gudelin:4,gueron:26,guess:11,gui:4,guid:[4,12],guidelin:[4,38],gzip:[8,15],h2o:27,h5:[5,7,9,11,31],h5py:[7,11,31,35],h:[8,12,23,30,33],h_w:20,h_y:23,ha:[4,7,8,11,12,14,15,18,19,20,23,25,27,29,30,31,32,34,35,38],hackweek:42,had:[4,29],half:39,hand:[26,29,42],handl:[3,4,10,12,20,29,30],handwritten:22,happen:[8,12,14,15,17,19,21,32],happi:[28,30],hard:[4,26,39],hardwar:[1,3,42],harmon:21,has_index_nam:11,hasattr:30,hasn:28,hat:[15,17,20,23,29,32],have:[2,3,4,7,9,11,12,13,14,15,16,17,18,19,23,25,26,27,29,30,31,32,33,34,35,37,38,40],hazard:16,hdf5:7,hdf:[10,11],head:[1,4,7,8,11,12,13,16,19,24,27],headach:4,header:[11,12],heartbeat:32,heat:33,heatmap:[8,13],heavi:[3,16],height:[11,12,31],help:[4,6,14,15,19,25,32,38],henderson:[39,40],here:[3,4,7,8,11,12,14,15,16,17,18,19,20,22,23,25,27,28,29,30,31,32,34,35,39],heterogen:11,hhz:[17,18],hidden:[30,33,34],hidden_layer_s:30,hide:8,hierarch:[10,31],hierarchi:19,high:[3,10,13,15,16,17,19,21,25,32,35,39],higher:[12,21],highest:[8,17],highli:13,highlight:13,highpass:17,hint:12,hist:[7,12,14,15,16,17,18],histogram:[7,9,12,13,14,16,17,18],histor:[24,27],histori:[9,29,30,31,34,35],histplot:13,hnz:12,hold:11,home:[3,4,9,11],homepag:4,homewgrown:38,homework:4,homgeneity_scor:19,homogen:[19,32],homogeneity_completeness_v_measur:19,honshu:12,hood:[19,27],hook:23,hopefulli:19,horizont:[3,8,15,31],horizontalalign:[8,30],host:[4,39,42],hot:27,hour:[3,12,17],hover_data:12,hover_nam:12,how:[3,4,7,8,9,10,11,12,13,14,15,16,19,20,21,22,23,28,29,30,37,38,39],howev:[3,4,8,9,11,13,15,23,30],hp:17,hpc:[6,14],href:[17,22,30,32],html:[1,4,8,12,14,19,25,30,32,35,38],htop:3,http:[1,3,4,5,7,8,11,12,13,14,15,16,17,19,21,23,24,27,30,31,32,33,35,36,38,39,42],hub:[3,6,11,39],hue:19,hulbert:31,human:[10,11,38],hyak:3,hydrolog:[5,8,10],hydrotherm:19,hyp_50m_sr:11,hyper:[20,25,27,30],hyperbol:30,hyperparamet:[15,26,27,31,32,38],hyperplan:20,hyperspectr:10,hypothesi:[15,20],hz:[17,18],i2:17,i6tv3ug15oe6yh:17,i8:12,i:[3,7,8,12,13,15,17,18,19,20,22,23,28,29,30,31,32,35,38],i_it:23,i_j:28,ic:[19,31],ica:[8,9,38],iclass:21,icon:4,id:[4,7,19,22,24,27],idai:[8,15],idea:[19,26,27,35],ideal:[8,10,14,21,32],ident:[14,21],identifi:[5,8,19,22,24,31,32,38],idna:18,idx:35,ifft2:17,ifft:[17,18],ifram:12,ignor:[4,17,19],ignore_index:7,igs14:[8,15],ii:[8,15,21],iik:15,il:13,illustr:[4,15,17,19,35],iloc:[7,11,12,24,27],im:8,imag:[1,8,9,10,11,16,17,18,19,22,25,27,29,30,32,35,39],image_index:35,imageri:[10,11,14],imagin:[15,35],imaginari:35,img:[1,31],immedi:22,impact:[25,26,28,32,37],imperfect:26,implement:[3,19,20,25,26,28,29,30,32,35],impli:[8,19,31],important_indic:24,importantli:16,importlib:[13,18,28],impos:32,improv:[26,32,35,38],impuls:7,imput:13,imputation_typ:27,imshow:[8,17,22,31,35],in_channel:31,in_circl:15,in_featur:[29,30],in_squar:15,inaccess:4,includ:[1,4,5,6,10,16,19,30,31,32,35,38,39],incomplet:4,incoropor:39,incorpor:[19,42],incorrect:13,increas:[14,15,19,21,23,26,32,33,34],increment:33,ind:17,inde:[15,21,35],independ:[4,11,22,23,26,32],index:[4,5,7,11,12,13,14,19,24,29,30],index_col:11,indic:[13,15,16,19,32],indirect:10,individu:[3,12,14,19,26,29,32],induc:15,industri:27,ineffici:11,inertia:19,inexact:23,inf:[13,32],infer:12,infer_datetime_format:[11,12],infin:23,infinit:17,influenc:[15,27],info:[2,12,13],inform:[4,8,9,10,11,30,32,37,38,42],infrastructur:42,infti:[16,17,31],inher:19,inherit:[29,35],inhomogen:32,init:[4,15,19],init_cent:19,initi:[4,13,14,19,23,25,28,29,30,31,32,33,35,38],initiali:19,inlin:[7,8,14,15,24,28,30,32],inlinebackend:14,inplac:[12,13,23],input:[7,8,9,12,13,14,17,27,28,29,30,31,32,33,34,35,38],input_dim:33,input_nam:22,input_s:35,input_shap:[30,31,34],inquiri:11,inscrib:15,insert:[1,19],insid:[2,8,15],insight:[13,19],inspect:21,inspir:[4,33],instal:[2,3,4,6,7,12,13,17,18,26,27,28],instanc:[1,3,8,10,12,18,21,22,26,30,31,32,38,39],instanti:[24,35],instead:[4,12,16,17,19,21,22,23,26,32,35,38],institut:[3,38],instruct:4,instructor:[4,12,17,22,39],instrument:[10,17,19],int32:[19,25],int64:[12,13,22,25,26],int_:[16,17],integ:[14,22,29],integr:[4,9,14,27],intend:[10,39,42],intens:[8,15],interact:[1,3,11,12,30,42],intercept:15,intercept_:[23,32],interest:[7,10,13,17,35,40],interfac:[1,2,4,42],intermedi:[4,17,29,30,31,35,38,39],intermingl:19,intern:34,internet:42,interpol:[13,22],interpret:[2,11,19,20,30,31,32,39],interpret_model:27,interrupt:22,intersect:14,interv:[15,16,30],intro:[12,14,39],introduc:[4,14,15,27,30,34,35,37,39],introduct:[3,12],introductori:11,intuit:[19,25,30],inv_test_indic:22,invari:31,invers:[8,20],inverse_transform:8,invert:20,investig:17,involv:[9,14,15,25,26,32,33],io:[4,8,11,12,15,31,32,35,36],ioarg:11,iohandl:11,ipykernel_26544:12,ipykernel_47043:17,ipykernel_60185:19,ipykernel_70516:28,ipynb:[17,22,30,32],ipython:[7,17,28],ipywidget:28,iq1:31,iq2:31,iq:31,iri:[8,17,18],irisdf:8,irreduc:32,irrelev:13,is_avail:[33,35],is_text:11,isel:14,isfinit:31,isin:12,isinst:[8,11,12],island:12,isnul:13,isol:[2,8,37,42],isomap:8,issu:[4,6,9,27,34,38],issue_templ:4,ital:1,item:[1,4,12,14,28,29,30,31,33,35],iter:[7,11,15,19,22,23,24,25,27,28,29,30,31,32,35],iterable_with_config:22,iterationsran:28,iterationsslid:28,ith_cluster_silhouette_valu:19,its:[2,5,7,9,14,15,18,19,25,26,27,30,31,32,34,38],itself:[4,11,19,32],j5lxhd8uxrtsxko:11,j:[8,19,23,28,29,32],jaqu:21,java:42,javascript:42,jedi:[7,28],jja:14,job:3,joblib:[22,28],johnson:31,journal:41,js:[11,12,17,19,28],json:1,juli:17,julia:[38,42],jump:[9,29],jupyt:[2,3,6,24,25,27,30,38,39,42],jupyterhub:[3,4,42],jupyterlab:[2,4,12,28,42],jupyternotebook:42,just:[4,7,12,15,16,19,23,28,29,30,31,32,34,37],k:[8,16,20,21,22,23,24,26,28,29,31,32],k_means_cyto_3:19,k_means_cyto_8:19,k_means_cyto_bad_init:19,kaggl:[13,23],katherin:19,kb:[12,13],kcbp:12,kcpb:12,kd_tree:25,kde:13,keep:[4,14,15,17,23,28,32,35],keep_date_col:11,keep_default_na:11,kei:[7,10,11,12,13,14,22,24,25,26],kelvin:14,kept:15,kera:[26,29,30,31,32,34,35,36,39],keras_tun:36,kerasclassif:30,kerasregressor:30,kernel:[1,20,21,22,30,31],kernel_s:[31,35],kf:15,kfold:15,kharita:39,khbb:12,kilomet:12,kind:11,kit:[19,26],kiwisolv:[2,13,18,28],klat:17,klon:17,km:[12,17],kmean:19,kmeans_model:19,kneighborsclassifi:[21,22,25,26],kneighborsclassifierkneighborsclassifi:25,knn:[20,21,25,26],knn_clf:22,knn_predict:[22,25],know:[1,3,20,23,32],knowledg:[25,35,39],known:[15,19,22,27,31,32],ko:19,koehrsen:24,kurtosi:[13,17],kurtosis_valu:16,kw:8,kwarg:[8,22],kwd:11,kwds_default:11,kxk:21,l1:[19,32],l1_ratio:32,l2:[14,32,33],l2_reg:33,l:[23,29,31,32,35],l_1:14,l_2:14,l_old:23,lab:[4,23,40],label:[6,7,8,9,12,13,14,18,19,20,21,22,26,27,29,30,31,32,33,34,35,38],label_binar:22,labelbottom:8,labelcolor:[29,30,31],labels:14,labels_:19,labeltop:8,laden:26,lambda:[24,32],lambda_meters2kilomet:12,land:[3,11],landsat:[3,10],landslid:[7,16],languag:[1,3,4,38,42],laplac:[16,32],laps:10,laptop:[2,4],larg:[3,4,10,12,14,16,17,23,25,26,30,32,38],larger:[3,8,11,16,19],largest:[8,17],lasso_reg:32,last:[4,11,12,14,17,22,23,24,26,27,30,32,34],lat:[12,14,17,19],later:[2,15,19,23,24,30,34],latex:1,latitud:[11,12,14,17,30],latter:28,launch:1,law:[14,16,32,33],layer1:[29,30],layer2:30,layer:[4,11,29,32,33,34,35],layout:[14,24],lbfg:30,lda:[20,21],ldata:7,lead:[8,15,17,36],leader:3,leaf_siz:25,learn:[2,3,8,9,11,12,13,14,15,20,21,22,23,24,25,27,28,29,31,33,34,35,38,40,42],learn_rat:26,learner:[26,32],learning_r:[14,26,29,30,31,32,35],learningr:28,learningrateslid:28,leas:38,least:[17,32,39],leav:[1,12],leaveoneout:15,lectur:[7,13,14,15,17,19,31,39],lecun:31,leduc:31,left:[4,8,12,14,15,20,30,31,32],legend:[8,13,14,15,17,18,22,24,28,30,31,32,33,34],legend_el:[8,28],len:[7,8,11,12,15,17,18,19,21,22,23,24,26,28,29,30,31,32,33,35],lenet:32,lenet_checkpoint:31,length:[8,12,13,14,15,18,20,23,28,31],length_a:15,length_sub:15,leq:[19,23],less:[8,10,12,15,17,26,27,28,38],lesson:[3,6,40],let:[7,8,11,12,14,15,16,17,18,19,20,21,22,23,29,30,32,34],letter:31,level:[1,7,12,21,39],leverag:31,lh:[12,14],lib:[7,11,12,13,18,19,22,26,28,32],librari:[2,10,11,14,27,38],licens:[4,5,21],lidar:10,lie:[19,30],lighgbm:27,light:19,lightgbm:27,lightweight:11,like:[3,4,9,10,11,12,13,14,15,16,18,19,23,25,26,28,31,32],likewis:9,lil:35,lilianweng:35,limit:[11,14,15,17,20,25,26,30,31,35,38],linalg:[8,14],line2d:[7,18,19,32,35],line:[1,2,4,7,8,11,12,14,15,17,18,19,20,22,24,27,30,32,35,42],linear:[8,14,17,18,21,22,23,25,28,29,30,31,32,33,35],linear_model:[15,20,23,28,32],lineardiscriminantanalysi:21,linearli:[14,17,28],linearly_separ:30,linearregress:[15,20,28],linestyl:[8,14,19,28,31],linetermin:11,linewidth:[8,14,15,21,28,31,32],link:[1,4,7,12,39],linkag:19,linregress:15,linspac:[8,14,15,17,18,28,30,31,33,34],linux:[3,6,38],list:[1,2,3,4,6,7,8,11,12,14,15,19,22,24,27,38],listedcolormap:[21,30],literatur:[9,31,38],littl:[20,21,32],live:42,ll:[4,13],ln:8,load:[8,16,25,30,31],load_dataset:14,load_digit:[22,25,26,29,30,31,35],load_iri:[8,28],loaded_test:[31,35],loaded_train:[31,35],loc:[15,16,19,22,28],local:[4,6,12,17,19,22,31,32,39],locat:[1,7,9,10,11,12,16,17,18,19,24,31],log10:[7,17,19],log:[14,16,17,18,19,23,29,30,32,33,35],log_:18,log_i:19,log_x:19,logarithm:38,logic:29,logist:[20,29,32,39],logistic_regress:23,logisticregress:[23,32],logit:29,lognorm:16,logspac:[14,17,30],lon:[12,14,19],longer:[17,19,23,32,34,35],longitud:[11,12,14,17,30],loo:15,loocv:15,look:[4,7,8,9,11,12,13,14,16,18,21,26,27,28,29,31,32],loop:[6,8,12,15,17,19,29,30,31,35],loos:[3,42],loot:28,loss2:33,loss2_weight:33,loss:[8,9,14,19,20,23,25,30,31,33,34,35],loss_funct:[30,31],loss_tim:[29,30,31,35],loss_val:35,loss_val_tim:35,losses2:33,lot:[3,26,27,32,34,38],low:[3,15,17,18,19,21,25,26,27,32],low_memori:11,lower:[8,15,17,20,22,26,28,32,35],lower_critical_valu:15,lowest:[19,35],lowpass:17,lp:17,lr:[29,30,31,33,35],ls:[12,14],lstrip:30,lt:[31,35],lua:42,lucidlenn:13,lucki:3,lunch:38,lw:19,lxml:18,lzmy975n0l5bjbmr9db291m00000gn:[12,17,19,28],m1:27,m2km:12,m8:17,m:[4,8,11,12,14,17,20,24,26,32,35],m_3:16,m_4:16,ma:39,mac:27,mach:29,machin:[3,9,11,13,14,15,19,20,22,25,26,32,38,40],machinelearn:22,maco:3,macosx:[3,4],macosx_12_0_arm64:26,macro:22,made:[4,5,8,12,29],madison:40,mae:[24,32],mag:12,magic:24,magmat:19,magnesium:16,magnet:10,magnitud:[16,17,26,32],magnitude_bin:12,mai:[1,2,3,4,7,8,9,11,12,13,15,17,18,19,23,26,30,32,33,34,35,38],mail:4,main:[5,11,12,13,16,19,29,39],maintain:15,major:[8,19,26,27],make:[2,4,6,7,11,12,14,15,17,18,21,22,23,24,27,28,30,31,32,33,34,37,38,39],make_circl:[21,30],make_classif:[21,30],make_moon:[21,30],make_pipelin:30,makedir:[11,30,31,35],malfunct:13,mallow:19,mam:14,manag:[2,12,29,30,31,35,42],manhattan:[19,25],mani:[2,3,7,8,11,12,15,17,19,20,21,22,23,27,28,29,31,32,37,38,40,42],manifold:35,manipul:[6,7,9,10,11,24,27,39],manual:[25,38],manual_se:[30,31,33],map:[8,10,11,30,31],mapbox_styl:12,mape:24,march:32,margin:20,mariana:12,marin:[39,40],marinedenol:[7,12,13,18,19,26,28,30,32],mark:39,markdown:[4,42],markedli:42,marker:[11,12,14,15,19],marker_clust:11,marker_s:12,markerclust:11,markers:12,market:27,mask:[11,25,35],masked_arrai:25,mass:19,master:4,match:[15,19,27,32],materi:[11,14,31,39,40,42],math:[8,14,19,23,39],mathbf:[8,19,20,30,32],mathcal:[23,29,32],mathemat:[14,23],matlab:[38,39],matmul:8,matplolib:39,matplotlib:[2,6,7,8,11,12,13,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35],matric:9,matrix:[13,15,17,19,20,21,22,23,30],matshow:[7,25],max:[8,11,12,13,14,15,16,17,18,19,22,24,25,26,28,29,30,31,32,35,38],max_depth:[21,26],max_featur:21,max_it:[23,30,32],max_percentag:[17,18],max_pool2d:30,max_sampl:26,maxim:[20,25,29,33],maximum:[12,19,23,24,27,29,30,31,32,38],maxlat:17,maxlon:17,maxpool2d:31,maxpool:31,maxpooling2d:31,mayb:30,mb:[12,13,26,35],mcbrearti:31,md:[1,4,38],mdenol:39,mean:[3,4,11,12,13,14,15,17,18,20,21,23,24,26,28,29,31,32,33,38],mean_a:15,mean_b:15,mean_filt:31,mean_fit_tim:25,mean_score_tim:25,mean_squared_error:[15,32],mean_temp:16,mean_test_scor:25,meaning:13,meant:42,measur:[8,10,12,13,15,16,19,21,24,27,30,32,38],mechan:32,media:10,median:[12,13,15,38],meet:[27,38],mehra:[39,40],member:[15,19,42],memori:[3,11,12,13,14,23,29,30,31,34],memory_map:11,mention:4,menu:[1,4],mere:30,merg:[1,4,17,18,19,39],mesh:30,meshgrid:30,messag:4,messi:4,meta:26,metadat:14,metadata:[4,9,10,11,12,14,38],meter:12,meters2kilomet:12,meters2kilometers2:12,metho:9,method:[5,8,9,10,12,13,14,16,17,20,22,23,24,25,26,29,31,35,36,39],methodolog:39,metric:[15,17,19,22,23,24,25,26,30,31,32,34,36],metric_param:25,mgo:16,microsoft:[3,27],microstoft:3,middl:[8,18,19,30],might:[8,9,11,12,13,15,16,17,18,21,26,27,31,32],mimic:[11,35],min:[11,12,13,14,15,16,17,19,22,24,25,28,30,32,38],mini:[19,29,30],minibatch:32,miniconda3:[7,11,12,13,18,19,26,28,32],miniconda:2,minim:[12,13,19,20,23,29,32,33,38],minima:[19,32],minimum:[8,12,19,29,32,38],minimum_val_error:32,minipnw_metadata:7,minipnw_waveform:7,minkowski:25,minlat:17,minlon:17,minmax:[32,38],minmaxscal:[22,25,26,38],minor:[8,12],minu:[24,27],minut:[19,39],mirror:[9,16],misclassifi:[19,21,26],misenterpret:17,misinterpret:17,miss:[38,41],mistak:23,mit:4,mitig:[15,17,26,30,32],mix:[8,10,12],mixing_:8,mjd:13,ml:[3,7,13,19,21,27,32,34,36,38,39],mlgeo2022:4,mlgeo2022_uwnetid:4,mlgeo2023:4,mlgeo2023_uwnetid:[4,39],mlgeo:[1,4,6,7,11,12,13,16,17,18,19,22,26,28,32,39],mlgeo_4:[30,32],mlhub:3,mllab:2,mlp:34,mlpclassifi:30,mlxtend:28,mm:[8,15,32],mnist:[22,31],mobil:4,mode:[11,14,20,26,31],model:[8,10,11,13,14,16,17,19,20,22,23,25,26,28,34,36,39],model_lenet:31,model_select:[15,21,22,24,25,26,30,31,32,35],moder:[12,30],modern:4,modif:4,modifi:[4,9,12,14,21,22,24,25,26,29,30,31,35,39],modul:[2,4,7,8,9,12,14,15,16,17,19,20,29,30,31,32,33,35,38,39],module1:4,module2:4,modulenotfounderror:17,moistur:[10,13],moment:[16,18,31],momentum:32,mon:24,monaco:15,mondai:[20,39],monitor:3,monoton:13,mont:9,month:[3,17,24],monthli:14,more:[2,4,8,9,10,11,12,13,14,15,16,17,19,20,21,23,25,26,30,32,35,36,38,42],morlet2:17,morlet:17,morn:39,most:[2,3,4,6,7,8,9,11,12,16,17,18,19,20,22,24,26,27,30,32,34,38],mostli:[3,7,15,19],mother:17,motion:[7,8,10,16,17,38],motiv:38,mousavi:35,move:[3,10,11,14,29,31],movement:19,mpl_toolkit:19,mplot3d:19,ms:[12,17],mse:[15,20,28,32,34,35],mse_train:15,mse_val:15,msecost:28,mseed:5,mseloss:[33,35],mt:19,mu:16,much:[3,8,19,23,27,30,32],mulbackward0:23,mult:35,multi:[12,14,17,20,21,26,29,32],multi_output:22,multiclass:[20,30],multiclassif:31,multicollinear:13,multidimension:[8,10,11,14,19,34],multilabel:35,multilay:31,multinomi:32,multipl:[3,4,8,10,14,15,17,26,29,30,31,32,34,38,42],multipli:[8,12,18],multiscal:32,multispectr:10,multivariate_norm:15,must:31,mxn:28,my:[1,4,14],my_kmean:19,my_metadata:12,my_mlp:30,my_mlp_checkpoint:30,my_pd:12,mybind:42,mycod:4,myenv:2,myst:42,n:[7,8,11,14,15,17,18,19,20,21,22,23,27,32,33],n_cluster:19,n_clusters_per_class:30,n_completed_task:22,n_compon:[8,19],n_dispatched_batch:22,n_dispatched_task:22,n_epoch:[29,30,31,35],n_estim:[21,24,26],n_featur:30,n_imag:35,n_inform:30,n_init:19,n_iter:25,n_j:19,n_job:[22,25,26],n_neighbor:25,n_redund:30,n_sampl:[8,15,32,33],n_select:27,n_split:15,n_step:34,n_unit:33,na:13,na_filt:11,na_valu:11,naiv:[20,21,34],naive_bay:[21,26],name:[2,4,7,8,11,12,14,15,17,19,21,24,30,31,38,42],nameerror:30,nan:[7,12,16,25],narr:42,narrai:7,narrow:9,nat:18,nation:[3,11],nativ:[12,20],natur:[7,10,15,16],navig:[4,5,19],nb:[20,26],nb_clf:26,nbin:12,nbviewer:25,nc:[11,12,14,17],ncedc:12,nclass:[22,26],ncluster:19,ncm_geologicframeworkgrid:[11,17],ncm_spatialgrid:[11,17],ncol:[22,24],ndarrai:[8,14,22,25,26],ndata:7,ndim:28,ndimag:14,nearest:[14,19,20,21,22],necessari:[4,15,16,23,32],necessarili:9,need:[4,7,8,9,12,13,15,17,19,20,23,26,27,28,29,30,31,32,34,35,38,39],neg:[16,19,21,23,32],negbackward:23,neighbor:[8,19,20,21,22,25,26],neighborhood:31,neither:27,nenad:4,neonscienc:11,neq1:31,nest:[11,30],net:[12,30,33],net_water_bal:12,netcdf4:17,netcdf:[5,10,14],netdisc:33,netdiscoveri:33,netreg:33,network:[3,10,11,12,17,18,23,28,32,35,37],neural:[17,23,28,32,35,37],neural_network:30,neuron:[29,30,31,34],nevada:15,never:26,new_a:15,new_b:15,new_nois:18,new_pair:15,newaxi:34,newcrap:18,newdata:[22,25,26],newer:4,newfil:4,newi:32,newli:15,newlin:11,newnoisef:18,newweight:28,next:[15,19,22,26,27,28,29,30,31,32,34,35,42],next_fast_len:[17,18],nf:17,nfft1:17,nfft:[17,18],nhat:[17,18],ni:39,nice:[4,11,18,23],nicoleta:[39,40],nipy_spectr:19,nlabel:31,nn1:29,nn:[18,29,30,31,32,33,35],no12:31,no1:31,no2:31,no_grad:[29,30,31,35],no_val_x:22,noaa:[24,27],node:[3,11],nois:[7,8,17,19,20,21,26,28,30,31,32,33,34,35,38],noise2:31,noise_factor:28,noisi:[8,16,18,26,32,33,35],noisy_sign:18,nomal:23,non:[1,13,17,35],none:[7,8,11,12,14,17,19,22,25,26,28,29,30,31,32,33,34,35],nonlinear:30,nor:27,norm:[8,19,32,33],norm_x1:14,norm_x1_norm:14,normal:[5,8,13,14,15,16,18,19,20,21,22,25,26,30,31,32,35,38],north:[8,11,15],northern:12,northwest:[7,15],note:[11,15,16,17,22,23,25,26,30,32,35,39],notebook:[2,3,4,6,12,14,17,22,24,25,27,30,32,38,39,42],noth:[14,18,23],notic:[14,15,17,32],notifi:4,notion:19,noverlap:17,now:[4,7,8,11,12,13,14,15,16,17,18,19,21,23,25,28,29,32,33],np:[2,7,8,11,12,14,15,16,17,18,19,21,22,23,24,25,26,28,29,30,31,32,33,34,35],np_to_th:33,nperseg:17,npmap:11,npoint:8,npt:[17,18],npts1:17,nqso:13,nrow:[11,22,24],nstar:13,nsubset:15,nt:7,num:28,num_class:[26,29,30],num_images_to_displai:31,number:[2,7,8,9,11,12,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38],number_run:15,numel:14,numer:[10,11,13,14,20,24],numinput:28,numiter:28,numpi:[2,4,7,8,9,11,12,13,15,16,17,18,19,20,21,22,23,24,25,26,28,29,30,31,32,33,34,35,39],numsv:8,nvidia:3,nyq:17,o:[3,12,14,15,19,28,33,38],obei:[32,33],obj:19,object:[7,10,12,13,14,19,20,23,24,25,26,30,31,32,35],object_:12,objective_new:19,objective_old:19,objects_to_datetime64n:12,objid:13,observ:[8,9,10,12,15,16,19,23,24,28,33],obspi:[17,18],obtain:[15,23,24,32],obviou:32,occur:32,occurr:16,ocean:[3,5,10],oceanographi:[3,39],oct:12,off1:34,off2:34,off:[8,21,31,32,35],offer:[3,4,10,12,14,39],offic:14,offici:4,offset:5,ofr20191081:11,often:[2,3,10,11,13,14,15,16,17,19,25,31],ok027:31,ok029:31,ok:[12,13,18],okai:[15,21,32],ol:28,old:19,olsoutput:28,omega:23,on_bad_lin:11,on_click:28,onc:[4,12,15,23,27,29,32],one:[1,3,4,7,8,9,10,11,12,13,14,15,16,17,19,20,21,23,24,26,27,28,29,30,31,32,35,36,37],ones:[7,8,20,26,31,32],ones_lik:33,oneself:9,onevsrestclassifi:22,onli:[3,4,8,9,11,13,15,17,19,24,26,28,30,31,32,35,39],onlin:[4,28],onto:[8,17],oob_scor:26,open:[1,3,4,5,6,11,38,39,42],openeew:3,oper:[3,11,12,14,21,22,28,30,31],opportun:32,opt:[7,11,12,13,18,19,26,28,32],optic:19,optim:[3,10,11,14,19,20,23,25,26,27,31,33,34,35,36,38],optimis:33,optimist:15,optimizi:20,option:[4,8,11,13,23,28],oracl:14,orang:[8,16,19],ord:14,order:[1,12,14,17,22,30,42],ordinai:33,oregon:8,org:[8,11,12,14,19,21,23,25,30,31,32,38,42],organ:[4,8,9,11],orient:[8,24],origin:[4,7,8,11,12,15,16,17,19,21,31,32,35,39],origin_dist:15,orthogon:8,orthograph:12,orthonorm:17,os:[3,4,7,8,11,15,17,19,30,31,35],oscillatori:32,other:[1,2,4,5,7,9,10,11,13,14,15,17,19,20,21,26,27,28,29,30,34,37,38,40],otherwis:[29,32],ouput:32,our:[0,8,13,15,19,21,22,23,24,30,32],ourperceptron:28,out:[2,3,4,11,16,17,22,24,27,30,31,33,34],out_channel:31,out_featur:[29,30],outcom:12,outfil:11,outlier:[26,32,38],outofboundsdatetim:12,output:[1,8,9,10,11,12,14,15,17,20,22,26,28,29,30,31,32,33,34,35,38,39],output_dim:33,output_lay:30,output_pad:35,outputwidget:28,outstand:38,over:[1,4,5,8,10,12,15,16,17,18,19,29,30,31,32],overal:[6,15,19,31,32],overestim:15,overfit:[15,25,26,27,30],overlai:[7,8,17],overlap:[17,19],overli:15,overview:[4,14,35],overwrit:[4,9,12],overwritten:8,ovject:32,own:[3,4,5,14,19,23,38,39,42],owner:4,p395:[8,15],p:[14,15,16,19,21,23,25,27,28,29,31,32,33,35],p_valu:15,pacif:[7,15],packag:[2,4,7,8,11,12,13,14,15,17,18,19,21,22,23,26,28,32,36,39,42],pad:[24,31,35],page:[4,11,12,25,27],pai:26,pair:[8,11,19,24,38],pairgrid:19,pairplot:19,panda:[2,4,7,8,9,10,11,13,14,15,16,17,19,21,23,24,27,28,31,32,34,35,39],pandrarrai:21,panopt:31,paper:[4,9,31,37,39],paragraph:1,parallel:[3,22],param:[14,25,26,35],param_algorithm:25,param_distribut:25,param_grid:25,param_metr:25,param_n_neighbor:25,param_weight:25,paramet:[14,15,16,19,20,23,25,26,27,29,30,32,33,35],parameter:[17,32],parametr:[13,15],parent:35,pareto:16,park:11,parks_wa:11,pars:[12,27],parse_d:11,parse_datetime_str:12,parser:11,parso:[7,28],part:[4,29,30,31,32,34,35,39],partial:[23,32,33],particip:42,particl:19,particular:[7,11,14,32,38],particularli:[3,10,11,13,14,15,19,26,30,32],partit:[15,19,21],pass:[3,12,14,17,22,29,30,31,32,33,35],password:4,past:[15,17,34],path3dcollect:19,path:[4,13,19],path_or_buf:11,pathcollect:[8,19,21],pathwai:39,pattern:[5,24,31],pc:[8,19],pca:[9,20,35,38],pcolor:14,pcolormesh:17,pd:[7,8,11,12,13,15,16,17,19,21,23,24,27,31,32,34,35],pde:33,pdf:[17,39],pdist:19,pdt:39,peak:[19,31],pearson:[14,15,19],peform:21,penal:32,penalti:32,peopl:[4,27,37],pep:4,per:[10,12,17,22,30],percentag:[23,24,29],percept:28,perceptron:[29,31],perfect:[15,19,28],perfectli:32,perform:[3,4,12,13,14,15,17,19,20,25,26,28,29,30,31,32,35,36,38,39],period:[12,17],permiss:[4,39],permut:28,perol:31,person:4,perspect:19,petal:28,pexpect:[7,28],pga:38,phase:[17,18],phd:4,phenomena:16,phi:30,phone:4,physi:33,physic:32,physics_loss:33,physics_loss_discoveri:33,phytoplankton:19,pi:[8,14,15,17,18,19],pi_est:15,pick:31,pickl:22,pickleshar:[7,28],picoeuk:19,piec:4,pillow:[13,18,28],pinn:32,pio:12,pip3:2,pip:[4,7,12,13,17,18,26,27,28],pipelin:[9,13,19,20,30,32],pitfal:17,pivot:19,pixel:[8,11,29,31,35],piyg:21,place:[15,22,31],plai:[4,19],plain:[11,12,13],planetari:[3,39],plate:[8,13,18],plateau:[19,32],platform:[3,4,38,42],pleas:[0,3,4,6,12,17,25,27,32],plenti:[3,14],plot:[6,7,8,9,11,12,13,15,16,17,18,19,21,22,24,25,27,28,29,30,31,32,33,34,35,38],plot_decision_region:28,plot_imag:35,plot_learning_curv:32,plot_test:14,plotli:[19,39],plotoutput:28,plt:[7,8,11,12,13,14,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35],pltcount:28,plu:[24,27,32,34],plugin:11,pm:[13,39],png:[1,12,14],point:[4,8,10,11,14,15,16,17,18,19,20,26,27,28,30,31,34],poisson:14,polar:32,poly_featur:32,poly_scal:32,polynomi:[17,20,32],polynomialfeatur:32,polyquadmesh:14,pool:30,poorli:[19,26,32],pop:19,popul:[3,15,19],popular:[7,10,19,20,26,27,30,32,35,42],popup:[11,27],porotomo:3,port:3,portion:[15,26],posit:[8,12,14,15,16,19,21,22,23,29,30,31,32,38],positron:12,possibl:[6,9,14,16,19,23,25,26,32,35,38,42],possibleactiv:28,post1:[13,28],post:4,potabl:23,potenti:[17,38],pow:[14,33],power:[12,14,16,18,19,30,32],power_law_data:16,pq:12,pr:21,practic:[3,4,6,12,14,15,17,20,22,32,35],practition:36,pre:[8,9,16,17,19,21],pre_dispatch:[22,25],precalcul:19,precipit:[10,12,13],precis:[21,22,23,32],precision_recall_curv:22,precision_recall_fscore_support:23,precision_scor:21,precisionrecallcollector:21,precisionrecalldisplai:22,precison:21,precondit:38,precursor:17,pred:[15,26,33],predecessor:26,predefin:25,predict:[7,9,10,14,15,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,35],predict_proba:[21,26,30],predictions_data:24,predictor:26,predsreg:33,prefer:4,prefer_skip_nested_valid:22,preferred_source_magnitud:7,preferred_source_magnitude_typ:7,preferred_source_magnitude_uncertainti:7,preinstal:2,preliminari:38,prep:32,prepar:[9,13,29,32,39],preprocess:[8,14,19,21,22,23,25,26,30,31,32,35,38],prescrib:19,presenc:[26,35],present:[0,17,18],preserv:17,pressur:10,preval:19,prevent:22,previou:[9,15,20,22,25,26,30,32,34,37],previous:[19,31],previouscost:28,primari:[15,19],primarili:[14,15],princip:39,principl:[33,39],print:[7,8,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,29,30,31,32,33,34,35],print_progress:22,printer:14,prior:[18,24,25,27],privat:[4,39],pro:[4,19],proba:26,probabilist:[15,25],probabl:[19,20,22,23,26,29,30,31,32,35],probe:[10,31],problem:[9,14,17,18,19,20,23,25,27,29,30,31,38],proce:30,procedur:15,process:[1,3,7,8,9,10,12,15,16,17,19,21,25,31,37],prochloro:19,produc:[11,20,27,28,37],product:[12,17,20,27,31,37],profession:27,profit:1,program:[1,3,4,10,39,42],progress:[26,32,39],project:[1,4,8,9,11,14,17,19,39,42],projection_typ:12,promis:[25,38],promot:0,prompt:[7,12,28],propag:[9,15,29,30,31,35],proper:[2,17,32],properli:32,properti:[9,10,14,16,17,18,19,23,26,32,35],proport:18,propos:[7,35,38],proven:38,provid:[2,3,4,5,8,12,13,14,15,19,20,26,27,29,30,31,33,36,38,39,42],proxim:14,pseudo:17,pseudoinvers:20,pseuo:17,psi:17,pt:[30,31,35],ptyprocess:[7,28],pub:12,publish:[37,42],puget:17,pull:[4,6,12,27,39],pull_request_templ:4,puor:8,pure:[7,18,28],purpos:[3,4,28],push:[4,6],put:[23,30],px:[12,19],py3:[7,17,26],py:[2,4,11,12,17,19,22,26,28,32],pycr:11,pydata:19,pygment:[7,28],pylab:14,pypars:[2,13,18,28],pypi:4,pyplot:[7,8,11,12,13,14,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35],pytest:4,python3:[2,7,11,12,13,18,19,22,26,28,32],python:[3,4,6,7,9,10,11,13,14,16,17,18,19,23,28,30,38,39,42],pytorch:[2,32,35,36,39],pytz:[2,7,13,28],pyx:12,q:12,qc:19,qc_lwr:19,qc_mid:19,qc_upr:19,qda:21,qi:19,qso:13,quadrat:32,quadraticdiscriminantanalysi:21,quak:[12,31],quakes2:31,quakes2plot:12,qualit:[20,23,39],qualiti:[13,19,27,32,42],quantifi:[13,19,21,32],quantil:15,quantit:[20,23],quarri:7,quarter:[15,39],queri:[11,17,18],question:[9,37,38,39],queue:3,quick:[12,38],quickli:12,quot:11,quota:19,quotechar:11,r2:[15,27],r2_score:15,r:[7,8,10,12,13,14,15,24,26,28,30,31,32,33,34,38,39,42],r_valu:15,ra:13,radar:10,radial:20,radiant:3,radiu:[15,19],rain:39,rainfal:[10,12,13,16],rainfall_mm:12,rais:22,ran:3,rand:[14,18,23,31,34],randint:[21,25],randn:[8,14,18,19,33],random:[7,8,16,19,20,21,22,23,26,27,28,30,31,32,33,34,37,38,39],random_project:8,random_split:[29,30,31,35],random_st:[15,19,21,22,23,24,25,30,32],randomforestclassifi:[21,22,26],randomforestregressor:24,randomizedsearchcv:[25,30],randomizedsearchcvrandomizedsearchcv:25,randomli:[14,15,17,19,21,25,27,30,32],randomst:[22,30],rang:[7,8,11,12,14,15,18,19,21,22,24,28,29,30,31,32,33,34,35,38],range_color:12,rangeindex:13,rank:[13,19,20],rank_test_scor:25,ransac:27,rare:[15,16,17,32],raster:10,rate:[10,12,17,18,21,22,26,28,29,30,31,33],rather:[3,35],ratio:[8,15,18,21],ratt:[17,18],ravel:[30,32],raw:[5,7,9,12,13,16,17,18,22,25,26],raw_moment:16,rbf:21,rcparam:[14,19],rdbu:30,rdylbu:17,re:[4,14,15,28,32,33],reach:[3,19,23,32],read:[7,8,9,14,15,17,19,24,27,29,32,37,38],read_csv:[7,11,12,13,16,19,23,24,27],read_data:7,read_feath:19,read_fil:11,read_parquet:11,readabl:[9,10,11,29,38],reader:11,readi:[13,29,39],readm:[1,4,38],real:[3,17,18,30,35,38],realist:9,realli:[4,32],realpython:12,reason:[13,19,27,30,32],reassign:38,rec:21,recal:[21,22,23,32],recalcul:15,recall_scor:21,receiv:[8,21,22,31,34,37],recent:[11,12,17,22,30,32],recogn:[4,6,9,12,27],recommend:[2,3,8,19,39,42],reconstruct:[8,32,35],record:[7,8,12,15,16,24],recov:[8,12,35],recreat:17,rectifi:[30,31],recurs:19,red:[8,11,13,14,15,16,19,29,30,31],redefin:15,redshift:13,redshift_zero:13,reduc:[4,8,9,14,15,17,19,21,26,31,32,35,38],reduct:[9,20,26,32,35,39],redund:13,ref:[12,13],refer:[3,14,15,20,24,28,30],refit:25,refresh:39,regard:30,region:[3,10,11,12,25],register_hook:23,regr:15,regress:[5,9,14,25,26,27,29,30,33,34,39],regressor:25,regrid:14,regular:[26,30,31,33,34],regularis:32,regularli:[14,17,33],reinstal:27,reject:15,rel:[16,18,25,28,32,34],relat:[2,9,15,17,19],relationship:[13,15,19,26],releas:[12,13,18,28],relev:[19,32,38],reli:[31,42],reliabl:[13,15,26,27,38],relu1:30,relu2:30,relu:[29,30,31,33,35],remain:[4,15,32],remedi:34,rememb:23,remot:[3,4,6,10,14,39,42],remov:[2,4,8,9,13,14,16,17,24,26,32,35,38],removablehandl:23,remove_anyth:12,renam:[12,26],render:[12,25],reno:15,repeat:[15,17,27,31],repeat_kmean:19,repeatedli:15,replac:[4,11,12,13,15,19,26,31,42],replic:19,repo:[1,4],report:[7,11,15,16,21,22,30,39],reposistori:39,repositori:[3,6,27,39],repres:[5,8,10,13,15,17,20,35],represent:[17,19,25,30,31,35],reproduc:[2,9,15,19,30,31,32,38,39,42],request:[3,4,6,8,11,12,13,15,18,32],requir:[2,3,4,7,12,13,14,17,18,20,22,25,26,28,30,32,35,38],require_grad:33,requires_grad:[23,33],requires_grad_:33,rerun:[13,25],resampl:[14,26],rescal:12,research:[3,4,10,31,35,38,40],reset:[4,22,34,39],reset_index:[11,23],reshap:[7,8,14,15,17,19,22,28,29,30,31,32,33,35],resid:4,residu:[14,15,20,26,32,33,35],resnet:35,resolut:[12,35],resolv:[4,12,13,17],resour:3,resourc:[3,6,11,13,18,28,38,40],respect:[7,8,14,15,19,21,23,28,30,32,33],respons:[13,17,20,39],rest:32,restart:1,restrict:19,result:[12,14,15,19,20,21,23,25,26,27,29,30,31,32,35,37,38],result_horizont:31,result_vert:31,retain:[8,13],retain_grad:23,retina:14,retrain:32,retriev:14,return_gener:22,return_sequ:34,return_train_scor:25,returnprecisionandrecal:21,reus:[4,38],revers:[14,23,24,30],revert:30,review:[0,6,9,30,38],rf:[20,24,26],rf_clf:[22,26],rf_most_import:24,rf_predict:22,rich:42,ricker:18,ridg:33,ridge2:32,ridge_reg2:32,ridge_reg:32,right:[2,4,8,20,22,28,30,32],rise:5,risk:[15,26,32],river:[10,16],rlat:17,rlon:17,rmse:[27,32],rmsprop:32,rng:[15,28,30],ro:[19,24],robust:[8,19,25,26,32],roc:[21,22],roc_auc:22,roc_auc_scor:22,roc_curv:[21,22],roccurvedisplai:22,role:[19,31],roll:[12,14],root:[11,16,31,35],rotat:[8,9,19,24,32],rotation_mod:8,rouet:31,round:[24,30],routin:14,row:[7,8,10,11,12,13,14,16,17,18,19,23,24,27,28],row_index:19,row_label:8,rs:15,rst:4,rstudio:1,rt:33,rubi:42,rule:[20,30],run:[1,3,4,5,12,13,14,15,17,18,19,21,22,23,28,32,37,38,39,42],runner:27,running_loss:[29,30,31,35],running_val_loss:35,runtimewarn:17,rv:25,rw:12,s1:8,s2:8,s3:[3,8],s6x10hzdyra:3,s:[4,7,8,10,11,12,13,14,15,16,17,18,19,21,22,24,25,26,27,30,32,33,34,35],s_:8,s_i:32,s_k:32,sa:18,sagemak:3,sai:[7,9,31,32],sake:15,same:[2,4,8,9,12,14,15,18,19,22,26,27,28,31,35,37,38],samm:26,sampl:[8,10,11,12,14,15,16,17,18,19,20,21,22,25,26,29,30,31,32,35],sample_data:[29,30],sample_label:[29,30],sample_silhouette_valu:19,sample_weight:22,sampler:[31,35],sampling_r:17,sat:24,satellit:[10,11,14],satisfi:[7,12,13,18,26,28,32,33,36],save:[2,4,8,9,11,12,13,14,21,24,27,29,31,32,35,38],savefig:14,saw:8,sawtooth:8,sc:19,scala:42,scalar:[23,28,30,34],scale:[3,5,8,12,14,16,17,19,26,29,30,31,32,38],scaleogram:17,scaler:[19,22,23,25,26],scan:11,scatter:[8,15,16,19,21,28,30,35],scatter_3d:19,scatter_geo:12,scatter_mapbox:12,scatterplot:14,scenario:[12,19,26],schedul:3,schemat:31,scheme:[19,25],scholarship:39,sci:[19,26],scienc:[1,5,11,14,33,39,42],scientif:[1,10,11,14,17,38,39],scikit:[2,8,9,15,20,21,25,28,31,32,38],scikitlearn:[21,23],scipi:[2,7,8,9,13,14,15,16,17,18,19,25,26,28,31,33],scitkit:9,score:[19,20,21,22,25,26,30,31,32],scott:[39,40],scratch:[1,3,12,33],screen:[29,30,31,35],script:[4,9,39],se:[8,15],sea:[10,16],seaborn:[2,13,19],seaflow:19,search:[30,31],season:[5,14],seasonal_mean:14,seattl:[14,24],second:[1,5,8,10,14,15,16,17,18,23,26,31,33,34,35,39],section:[17,39],see:[1,3,4,8,11,14,15,17,18,19,21,27,30,32,42],seed:[8,19,30,31,33,38],seeed:30,seek:19,seem:[15,27,31],seemingli:32,seen:[16,28,32],segment:[9,35],seismic:[3,5,7,10,12,14,16,17,18,19],seismogram:[10,31,32,35],seismolog:[5,39],seismomet:[3,10,17],sel:14,select:[1,4,7,9,12,14,15,17,19,24,25,27,31,38],select_dtyp:13,self:[11,12,22,28,29,30,31,33,35],selu:35,semant:31,send:[3,30,35],sens:[3,9,10,14,19,32],sensit:[16,17,18,19,21,23,32,38],sensor:[12,13,17,32],sent:[12,13],sep:[11,12],sepal:[8,28],separ:[8,10,19,20,28,35,38,42],sequenc:[10,14,32,35],sequenti:[26,30,31,33,34,35],sequential_15:35,seri:[4,5,7,8,10,14,15,17,18,31,32,33,34,35,38,39],serv:19,server:[3,4,17,18],servic:[3,42],session:4,session_id:27,set:[3,5,7,8,11,12,13,14,15,17,21,22,25,26,28,29,30,31,34,35,38,39],set_aspect:[8,15,16,28],set_axis_off:22,set_axisbelow:[8,28],set_grad:23,set_opt:14,set_size_inch:19,set_them:19,set_titl:[8,14,17,18,19,22,24,28,30,31],set_vis:8,set_xlabel:[8,14,15,16,17,18,19,24,28,29,30,31],set_xlim:[16,17,19,30],set_xscal:[17,18],set_xtick:[8,19,30],set_ylabel:[8,14,15,16,17,19,24,28,29,30,31,32],set_ylim:[16,17,19,30,32],set_yscal:[14,17],set_ytick:[8,19,30],set_zlabel:8,setp:8,settingwithcopywarn:19,settl:27,setup:[4,27],setuptool:[4,7,18],setw:28,sever:[4,9,11,12,13,14,15,17,18,19,20,22,25,26,31,35,36,38],sgd:[20,29,30,32],sgd_reg:32,sgdclassifi:32,sgdregressor:32,sh:14,shade:17,shap:27,shape:[7,8,11,14,16,17,18,19,22,24,25,26,28,29,30,31,32,34,35],shapefil:10,share:[10,11,39,42],sharex:[15,17],sheet:4,shell:[3,39],shift:[16,17,32],shortcut:12,shorter:17,shortest:19,should:[0,4,8,9,14,15,22,25,27,28,30,31,32,38,39],show:[8,11,12,13,14,15,16,17,18,19,21,22,25,26,27,28,29,30,31],show_reconstruct:35,showcountri:12,shown:[12,35],shuffl:[15,22,25,26,28,29,30,34,35],shufflesplit:15,si:3,side:[15,16],sidebar:4,sig:[8,16,18],sigm:30,sigma:[8,14,16,23,30,32],sigmoid:[28,29,30,31,32,35],sign:[8,19,32],signal:[5,8,16,17,31,32,35],signatur:[17,32],signifi:19,significantli:[25,37],silhouett:19,silhouette_avg:19,silhouette_sampl:19,silhouette_scor:19,silic:16,silica:16,sim:15,similar:[5,10,14,17,18,19,24,26,32],similarli:[14,17,23,29],simpl:[1,6,7,9,10,11,12,15,19,20,23,27,30,31,32,33,34,38,39],simpleinput:28,simpleoutput:28,simpleperceptron:28,simpler:[8,32],simplernn:34,simplest:[29,34],simpli:[11,14,18,30,31],simplifi:[14,19,31],simul:[10,14,15,16,21],sin:[8,14,19,34],sinc:[11,12,15,22,30,32,34],sine:[14,17],singl:[7,8,9,10,11,19,26,27,28,29,30,32,34],singular:[8,20],sinusoid:[8,14],sio2:16,sio:16,sit:[15,16],site:[2,7,10,11,12,13,18,19,22,26,28,32],situ:10,six:[2,7,13,18,28],size:[8,11,12,14,15,16,17,19,23,25,26,28,29,30,31,32,35],size_cluster_i:19,size_img:[29,30],skew:[9,13,17],skicit:24,skill:[6,12],skimag:31,skip:35,skip_blank_lin:11,skip_parameter_valid:22,skipfoot:11,skipinitialspac:11,skiprow:[11,12],sklean:8,sklearn:[8,15,19,20,21,22,23,24,25,26,27,28,29,30,31,32,35,38,39],sky:13,skyblu:16,skyserv:13,skyserver_sql2_27_2018:13,skyserver_sql2_27_2:13,slack:42,sleep:17,slice:[14,15,19],slick:4,slide:9,slightli:[30,37],sloan:13,slope:15,slow:[19,31],small:[5,8,9,11,12,16,19,23,25,26,32,34],smaller:[8,9,11,15,19,23,26,30,32,38],smallest:[19,35],smi:3,smooth:[12,14,17,18,30],sn:[8,13,15,19],snapshot:10,snow:19,snr:18,so:[2,4,8,9,12,13,15,17,22,23,27,29,30,31,32,34,35,38],sobel:31,sobel_filter_horizont:31,sobel_filter_vert:31,societ:38,soft:26,softmax:[26,29,30,31,32],softplu:30,softwar:[3,6,10,11,16,40,42],software_carpentries_intermedi:41,sofwar:14,soil:[10,11,13],solid:27,solut:[9,19,20,27,28,29,32,33,38,39],solv:[10,11,19,20,23,34,38],solver:30,some:[2,3,4,8,9,11,13,14,15,17,18,19,20,23,27,30,32,39,40],somebodi:4,sometim:[13,37],somewhat:34,somewher:4,son:[11,14],sonic_boom:7,soon:32,sophist:[4,13],sort:[7,12,17,19,24,33],sort_valu:12,sosfilt:17,sound:17,sourc:[1,4,6,7,8,10,11,18,21,32,38,39,42],source_depth_km:7,source_duration_magnitud:7,source_duration_magnitude_uncertainti:7,source_hand_magnitud:7,source_latitude_deg:7,source_local_magnitud:7,source_local_magnitude_uncertainti:7,source_longitude_deg:7,source_origin_tim:7,source_typ:7,source_type_pnsn_label:7,south:[11,12],southern:[3,12],space:[8,9,10,14,16,19,25,30,32,33,36,42],span:17,spars:[30,32],spatial:[10,12,14,17,19,31],spatiotempor:19,spearman:[13,19],spearman_corr_matrix:13,speci:8,special:[11,14],specif:[1,2,3,4,5,7,9,10,11,12,14,16,17,18,21,22,23],specifi:[4,12,18,23,25,30],specobjid:13,spectra:17,spectral:[7,9,18],spectrogram:[17,31],spectrum:[17,18],speed:[16,17,18,19],spend:39,sphere:19,sphinx:4,spine:8,split0_test_scor:25,split1_test_scor:25,split2_test_scor:25,split3_test_scor:25,split4_test_scor:25,split:[7,8,15,19,21,25,26,30,31,34],splitlin:[8,15],spread:16,spreadsheet:10,spyder:2,sql2:13,sqlalchemi:18,sqrt:[1,8,14,17,18,19,31,32],squar:[8,14,15,16,19,20,28,30,31,32],squeez:[31,35],src:1,ssh:4,st:[7,16],sta:[8,15],stabil:26,stabl:[8,14,17,19,26,30,32,38],stack:[7,28,31,34,35],stacked_a:35,stacked_ae_checkpoint:35,stacked_cnna:35,stacked_decod:35,stacked_encod:35,stackeda:35,stackeddecod:35,stackedencod:35,stacklevel:22,staff:12,stage:[4,35],stai:35,stain:19,stamp:7,standalon:12,standard:[4,8,11,12,14,15,16,17,20,25,38,39,42],standardscal:[19,21,23,30,31,32,35,38],stanford:31,star:[2,13],start:[3,4,8,9,12,14,15,19,21,23,27,28,29,30,32,35,38],start_dim:[29,30],start_tim:12,startdat:12,startingbia:28,startingweight:28,starttim:[17,18],stat:[7,13,15,16,17,18,25],state:[9,11,15,32,34,38],state_dict:[30,31,35],statement:6,station:[10,12,15,17,18,31],stationari:35,statist:[7,9,10,12,13,14,15,17,18,20,24,26],statu:4,std:[8,12,13,14,15,16,17,24],std_err:15,std_fit_tim:25,std_scaler:32,std_score_tim:25,std_temp:16,std_test_scor:25,steelblu:8,stefan:[39,40],step:[5,9,14,16,19,23,25,27,28,29,30,31,32,33,35,38],step_ahead:34,stft:[17,38],still:[4,19,22,30,42],stochast:[8,20,29,31,38],stop:[14,19,28],stop_alg:19,stoppingcriterion:28,stoppingcriterionslid:28,storag:[3,4,7,10,11,14,38],storage_opt:11,store:[3,5,7,9,10,11,12,14,15,22,25,26,30,38],str:[8,11,12,15,18,19,24,27],straight:19,strategi:[15,19,31,32,35,38,39],strength:13,stretch:[9,21],strict:11,strictli:[20,30,31],stride:[31,35],string:[7,8,11,12,13,14],stringio:12,strive:[0,42],strmethodformatt:8,strong:[15,26,32],stronger:26,strongli:[15,19,26],strptime:24,structur:[9,10,11,12,13,14,15,18,19,31,35,38,42],strutur:19,student:[5,6,40],studi:[10,19,38],studio:[3,4],style:[19,21,24],sub:4,subarrai:14,subclass:29,subdata:19,subduct:15,subgroup:[19,26],submiss:[4,39],subplot:[6,8,13,14,15,16,17,18,19,21,22,24,28,29,30,31,32,35],subplots_adjust:30,subsequ:30,subset:[11,13,14,15,19,22,25,26,27,29,30,32,35,38],subsetrandomsampl:[31,35],substanti:25,subsurfac:10,subtract:[12,28],success:19,successfulli:[7,17,26],suffici:[3,20,32],suggest:[4,19,38],suitabl:19,sum:[8,12,13,14,16,19,23,26,28,29,30,31,32,33,35],sum_:[15,19,20,23,29,32],sum_i:14,sumatra:12,summar:[13,19,20,39],summari:[12,13,19,31,34,35],summarize_distribut:13,summary_galaxi:13,summary_qso:13,summary_star:13,sumup:30,sumvalu:21,sun:[24,39,40],sundai:24,superimpos:17,superposit:32,superseismo:4,supervis:[8,20,38],supplementari:31,suppli:8,support:[1,10,11,14,20,22,25,39],suppos:[12,38],suppress:19,suptitl:[19,22],sure:[2,4,7,14,15,22,34,38],surfac:[3,7,8,10,11,17,19],surface_ev:7,survei:[10,13],suscept:26,sv:[8,15],svc:[21,22,25,26],svc_clf:26,svc_predict:22,svg:21,svm:[20,21,22,26],symm:31,symmetr:[8,16,35],synchron:4,synecho:19,syntax:[14,15,30],synthet:[14,19,33,34,38],synthetics_pca:19,synthetics_sc:19,system:[3,4,6,8,10,11,14,15,20,37,42],systemat:[14,21,25,30,36],t0:33,t20:19,t:[8,12,14,15,16,17,18,19,20,23,27,28,30,31,33,34,39],t_0:33,t_1:33,t_b:15,t_train:15,t_val:15,tab10:35,tab:[29,30,31],tabl:[10,12,27],tabular:[10,12],tacc:3,tackl:19,tag:[8,11,39],tail:[16,17],take:[7,9,12,14,15,17,18,19,23,26,28,29,31,32,33,34,35],taken:[10,16,19],takewai:4,talk:23,tangent:30,tanh:[30,34],taper:[17,18],target:[8,15,22,25,26,27,28,29,30,38],target_nam:[8,22,25,28],task:[3,4,9,12,22,30,38],tau:31,taught:39,team:[31,37,39,42],technic:42,techniqu:[13,26,27,32,39],technolog:38,tecton:19,tediou:27,tell:[12,27],temp:[24,27,33],temp_1:[24,27],temp_2:[24,27],temperatur:[10,12,14,16,24,27,32,33],temperature_c:[11,12],temperature_f:12,templat:[4,31,39],templates_027:31,templates_029:31,tempor:[5,7,9,10,12,17],temporari:3,tenac:12,tend:[17,23,26,29,32,38],tensor:[3,23,29,30,33],tensorflow:[26,29,30,31,34],tenv:[8,15,33],term:[8,9,12,15,16,21,34],termin:[1,3,4,5,27],terminolog:4,test1:19,test:[4,12,15,19,21,23,25,26,30,31,32,34,35,38],test_dat:24,test_dataset:[29,30],test_df:27,test_featur:24,test_import:24,test_index:15,test_indic:22,test_label:24,test_module1:4,test_module2:4,test_siz:[15,21,22,24,25,26,29,30,31,32],testarrai:28,testcost:28,testinput:28,testload:[29,30,31,35],testpredict:28,texa:3,text:[1,4,8,11,13,14,15,17,18,19,27,28,30,32,33,42],textcolor:8,textfileread:11,textkw:8,textur:9,tf:[17,34],th:[19,23],than:[8,9,15,17,18,19,20,21,25,26,28,31,32,34,38],the_turing_way_community_2022_6909298:41,thei:[3,8,10,11,12,15,17,18,19,20,23,30,31,32,34,35,38],them:[4,7,8,9,10,12,18,19,21,27,32,39],themselv:7,thenarrai:7,theodorewolf:33,theorem:38,theoret:33,theori:39,therefor:[15,17,19,30,35],theta:[8,19],thi:[1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,36,38,39,40,42],thing:[9,28,38,42],think:[15,27],third:[1,16,18,23,26],thiscost:28,thisinput:28,thispandr:21,thistarget:28,thorough:25,those:[8,14,27],though:[3,6,37],thought:[10,31],thousand:11,threadpoolctl:28,three:[3,8,11,13,14,19,23,31,39],thresh:[17,19],threshod:19,threshold:[8,19,21,29,30,31,32],through:[3,8,10,13,15,19,29,30,31,32,37,39],throughout:[5,39],thu:[8,21,23,32],thunder:7,ti:10,tick:[8,19,24],tick_param:[8,14,29,30,31],ticker:8,tier:3,tif:11,tiff:11,tight:[14,19],tight_layout:[8,13,14,16,24,28,29,30,31],tightli:3,tile:8,tiledb:[5,10],time:[3,5,7,8,9,10,11,14,15,16,18,19,20,25,27,29,30,31,32,33,34,35,36,38,39],time_series_features_extractor:7,timedelta:12,timedistribut:34,timeit:[12,14,17],timestamp:12,tip:14,titl:[8,12,13,14,15,16,17,18,19,22,24,28,30,32],tlu:29,tn:[7,21,23],tnr:21,to_csv:[11,12,13],to_datetim:12,to_netcdf:14,to_numpi:[19,23],to_parquet:[11,12],to_zarr:14,todai:[24,27],todoran:[39,40],togeth:2,toi:[8,19,32,33,38],token:4,tol:32,toler:32,too:[12,15,21,23,32,34,38],tool:[2,4,11,12,19,21,25,38,39],toolbox:[17,19],toolkit:[7,28,39],tooth:8,top3:27,top:[1,3,4,8,12,31],topic:39,toplot:28,topographi:[10,11],torch:[14,23,29,30,31,33,35],torchinfo:35,torchvis:[31,35],total:[4,13,14,21,25,26,29,30,31,35],totalfals:21,totaltru:21,totensor:[31,35],touch:4,toward:9,towardsdatasci:24,towfish_001:19,towfish_002:19,towfish_003:19,towfish_004:19,towfish_005:19,towfish_006:19,towfish_007:19,towfish_008:19,towfish_009:19,towfish_010:19,towfish_011:19,towfish_012:19,towfish_013:19,towfish_014:19,towfish_015:19,towfish_016:19,towfish_017:19,towfish_018:19,towfish_019:19,towfish_020:19,tp:[19,21,23],tpr:[21,22],tpu:3,trace:7,trace_nam:7,trace_p_onset:7,trace_s_onset:7,trace_snr_db:7,traceback:[11,12,17,22,30,32],track:[4,5,14,15],trade:21,tradeoff:32,tradit:4,train:[15,19,20,21,23,25,26,28,33,34,35,37,39],train_data:35,train_dataset:[29,30],train_df:27,train_error:32,train_featur:24,train_import:24,train_index:15,train_label:[24,31],train_set:31,train_siz:[29,30],train_test_split:[21,22,24,25,26,30,31,32,35],trainabl:35,traininput:28,trainload:[29,30,31,35],traintarget:28,traitlet:[7,28],transform:[7,8,9,11,12,13,14,19,22,23,25,26,30,31,32,35,38,39],transit:14,translat:[17,31],transmit:37,transpos:8,treat:[13,31],tree:[11,20,21,26,30],tremend:40,trend:[15,32],trial:30,trick:32,tricki:27,trim:17,triplic:19,true_data:24,true_label:19,true_valu:11,trust:25,truth:[19,32,34],ts:33,tsfel:[7,9],tsfresh:[7,9],tslib:12,tsne:35,tstart:[17,18],tt:15,tue:24,tunabl:19,tune:[15,18,27,32,35,38],tuner:31,tupl:24,ture:[4,40],turn:[8,17,30,34],tutori:[3,4,11,12,14,15,22,24,25,27,30,33,35,40],twin:18,twinx:[29,30,31],two:[4,8,10,11,12,13,14,15,16,17,19,20,21,24,28,31,35,36],txt:[2,4],type:[1,4,6,7,10,11,12,14,17,18,19,20,22,25,26,27,28,30,31,35,38,39],typic:[3,4,5,7,10,12,13,14,15,16,17,19,26,32,35],tz_local:12,tz_pars:12,tz_to_dtyp:12,tzdata:[7,13,28],u:[1,8,13,19,32],u_:15,u_n:15,uc:[24,27],ue:[8,15,32],ug3_fcm_distribut:19,un:[8,15],unabl:[12,25],uncertain:[15,26],uncertainti:[9,15,19,34],unclear:31,uncom:14,under:[4,7,19,21,27],underestim:15,underfit:[25,26,27,32],underli:[14,15,42],underlin:1,understand:[8,10,13,19,25,31,39],undertak:8,underwai:19,underway_002:19,underway_003:19,underway_004:19,underway_005:19,underway_006:19,underway_007:19,underway_008:19,underway_009:19,underway_010:19,underway_011:19,underway_012:19,underway_013:19,underway_014:19,underway_015:19,underway_017:19,underway_018:19,underway_019:19,underway_020:19,underway_021:19,underway_022:19,underway_023:19,underway_024:19,underway_025:19,underway_026:19,underway_027:19,underway_028:19,underway_029:19,underway_030:19,underway_031:19,underway_032:19,underway_033:19,underway_034:19,underway_035:19,underway_036:19,underway_037:19,underway_038:19,underway_039:19,underway_040:19,underway_041:19,underway_042:19,underway_043:19,underway_044:19,underway_045:19,underway_046:19,underway_047:19,underway_048:19,underway_049:19,underway_050:19,underway_051:19,underway_052:19,underway_053:19,underway_054:19,underway_055:19,underway_056:19,underway_057:19,underway_058:19,underway_059:19,underway_060:19,underway_061:19,underway_062:19,underway_063:19,underway_064:19,underway_065:19,underway_066:19,underway_067:19,underway_068:19,underway_069:19,underway_070:19,underway_071:19,underway_072:19,underway_073:19,underway_074:19,underway_075:19,underway_076:19,underway_077:19,underway_078:19,underway_079:19,underway_080:19,underway_081:19,underway_082:19,underway_083:19,underway_084:19,underway_085:19,underway_086:19,underway_087:19,underway_088:19,underway_g3:19,unecessari:8,unifi:[3,4],uniform:[14,15,16,18,19,25,30,38],uniqu:[7,8,12,19,22,26,31],unique_d:12,unit:[3,4,8,12,14,17,19,29,30,31,33],univers:[15,30,39,40],unix:[6,12],unknown:12,unlik:[15,17],unnam:7,unord:1,unr:[8,15],unread:14,unrealist:17,unseen:[15,27],unstag:4,unsupervis:[8,19,38,39],unsur:14,until:[17,18,19,23,26,28,31,32],unveil:19,unzip:11,up:[8,12,15,17,18,20,21,26,27,31],updat:[8,11,12,13,14,18,19,23,28,29,30,31,32,34,35,39],update_geo:12,update_layout:12,updatebtn:28,updateclick:28,upgrad:[12,13,18,28],upload:[4,39],upper:[4,15],upper_critical_valu:15,upsampl:14,upstream:37,url:[1,4,5,11,12,16],urllib3:18,us:[1,2,3,5,6,7,8,9,10,11,13,14,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,37,38,39,42],usabl:30,usag:[3,4,13],usecol:[11,12],user:[1,2,4,7,11,12,13,14,18,19,26,28,30,32,42],user_guid:19,usernam:4,userwarn:12,usg:11,usual:[3,7,15,19,20,27,30,32],utc:12,utcdatetim:[17,18],utf:[11,12],util:[15,22,23,29,30,31,32,35,42],uv:[8,15,32],uw60888282:7,uw61361706:7,uw61639436:7,uw61669232:7,uw61735446:7,uw:[1,3,4,5,6,12,13,16,17,18,19,39,40,42],v:[8,19],v_e:15,v_measure_scor:19,v_n:15,v_size:15,va:8,vae:35,val:[32,35],val_data:35,val_error:32,val_index:15,val_loss:35,val_set:31,valfmt:8,valid:[13,22,32,34,35,38,39],validate_separ:22,validation_data:34,valu:[8,10,12,14,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,38],valuabl:[13,15,19],value_count:12,valueerror:[22,32],vanish:[30,34],vanuatu:12,varaibl:15,vari:[7,10,14,18,21],variabl:[6,8,9,10,11,12,13,14,15,16,17,19,22,23,24,27,30,32],varianc:[8,15,17,19,20,26,32],variance_1:7,variat:[14,16,19,32],varieti:11,variogram:19,variou:[3,4,5,7,10,15,17,18,19,21,26],varoquaux:21,ve:[13,15],vector:[8,10,14,15,17,19,20,22,23,25,28,30,32,33,39],veget:13,vel:15,veloc:[15,17],venv:2,verbos:[11,22,25],veri:[2,4,12,14,17,18,19,20,23,26,27,30,34,38],verifi:[12,15],version:[1,2,11,26,30,31,37,42],versu:19,vertic:[3,7,8,17,19,24,31,32],verticalalign:8,vi9gmjy8d4zd5jv:31,via:[2,10,19,30,39],vibrat:7,video:[3,42],view:[5,12,19,30,31,33,35,39],viewoutput:30,virginica:28,viridi:17,virtual:[3,4],visit:2,visual:[3,4,8,11,14,15,16,19,21,24,27,35,38,39,42],vmax:[7,8,14,17],vmin:[7,8,14,17],volcan:19,volcano:19,volum:19,voting_clf:26,votingclassifi:26,vs:[4,8,11,15,16,26],vscode:[3,12,17,22,30,32],vstack:[8,19],vt:8,w1sdnnjb2rllxjlbw90zq:17,w:[8,11,14,17,20,23,28,29,30,31,32],w_0:20,w_1:20,w_ela:32,w_i:[29,32],w_j:[23,28,29,32],w_k:29,w_lasso:32,w_n:20,w_ridg:32,w_sgd:32,wa:[11,12,15,16,19,20,21,22,24,25,26,31,32,35],wai:[2,4,8,11,12,13,14,15,19,23,25,27,32,35,38,40],wait:[17,19,28],walk:13,want:[11,12,14,15,17,23,24,29,30,32,33,34],ward:19,warm_start:32,warn:[8,17,19,22,23,26,32],washington:[11,39],watch:[3,22,25,26],water:[10,12,23],water_pot:23,wave:[5,10,34,35],wavedecompnet:35,wavefield:[10,35],waveform:[7,17,18,19,35],wavelength:10,wavelet:[7,9,18,39],wavenumb:17,wavespe:10,wcwidth:[7,28],wdb25puxh3u07dj:[11,17],we:[0,2,3,4,5,7,8,10,11,12,13,14,15,16,17,18,19,20,21,22,23,25,26,28,29,30,32,33,34,35,39,42],weak:26,weather:[5,10,11],web:[3,5,11,42],wednesdai:39,week:[24,39],week_fri:24,week_mon:24,week_sat:24,week_sun:24,week_thur:24,week_tu:24,week_w:24,weigh:32,weight:[14,16,21,22,25,26,28,29,30,31,32,33,34],weightedsum:28,weightupd:28,weka:27,welcom:[4,42],well:[4,14,15,19,20,21,22,23,26,28,30,32,38],went:37,were:[3,11,12,14,19,21,22,23,26,28,31,32,40],west:[11,14],westward:15,wget:[5,7,11,12,13,17,19,24,27,31],what:[3,4,7,8,9,12,13,14,15,16,17,18,19,21,23,25,26,27,31,33,38],wheel:4,when:[3,4,11,12,13,14,15,19,21,22,23,25,26,27,28,30,32,35,38,39],where:[1,3,4,7,8,10,11,12,13,15,16,17,19,20,23,27,28,29,30,31,32,33,37,38],whether:[9,11,15,21,30,32],which:[2,4,8,10,11,12,13,14,15,16,17,18,19,20,23,25,26,27,29,30,31,32,35,36,38,39,42],whichitem:28,white:[8,14,18,19],whl:[7,17,26],who:[4,14],who_is_awesom:14,whole:[11,16,23],why:[3,15,27,31,32],wich:14,wide:[10,11,15,16,21,25,27,30,35,42],widget:[27,28],widgetsnbextens:28,width:[1,8,11,12,17,18,28,31],wiggl:32,wiki:21,wikimedia:21,wildfir:16,willustr:32,wind:16,window:[3,4,12,13,14,17,18,30,31],winner:[17,22],wise:[14,27],within:[3,11,12,14,15,17,19,24,26,27,30,31],without:[4,8,9,12,18,26,30,32,38],wmp:12,won:34,work:[0,2,3,8,11,12,13,14,15,19,23,25,26,27,31,34,38],worker:22,workflow:[3,7,9,15,17,27,39],workspac:[17,22],world:[3,27,38],worlpert:38,would:[3,4,7,8,14,17,23,24,26,28,30,31,32,35,37,38],wrap:[14,29],wrapper:22,write:[4,9,11,12,14,23,31,33,38,39],written:1,wrong:[19,27,32],wrote:3,wsl:12,wt:16,wwvb:12,www:[7,11,13,14,17,19,23,24,31],wx:[29,30],x1:[14,19,31],x1_norm:14,x25sdnnjb2rllxjlbw90zq:22,x27:25,x2:[14,19],x2_smooth:14,x30szmlszq:32,x3:[14,19],x41szmlszq:30,x4:14,x:[1,5,7,8,9,11,12,14,15,16,17,19,20,21,22,23,24,28,29,30,31,32,33,34,35,38],x_1:20,x_:[15,23],x_a:15,x_b:15,x_i:[14,15,19,23,29],x_int:14,x_label_list:17,x_lat:14,x_lon:14,x_max:30,x_min:30,x_n:20,x_new:[8,30],x_p:19,x_pca:8,x_poli:32,x_scale:38,x_std:38,x_t:14,x_test:[21,22,25,26,30,31,34],x_tl:14,x_train:[21,22,25,26,30,31,32,34],x_train_poly_sc:32,x_val:[32,34,35],x_val_2d:35,x_val_compress:35,x_val_poly_sc:32,x_valu:24,xarrai:[9,11],xarrrai:14,xavg:8,xaxi:28,xc:8,xgb:26,xgboost:[26,30],xkcd:21,xlabel:[8,12,14,15,16,17,18,19,21,22,24,28,30,32,33,34],xlat:17,xlim:[8,16,17,18,19,22,30,32],xlon:17,xp:22,xr:14,xrang:28,xscale:18,xt:33,xtick:[8,14,15,17,24],xx:[7,30],y2:19,y3:19,y:[5,7,8,9,11,12,14,15,17,19,20,21,22,23,24,25,26,28,29,30,31,32,33,34,35],y_ela:32,y_i:[14,19,20,23,32],y_k:29,y_lasso:32,y_lower:19,y_max:30,y_min:30,y_numer:22,y_pred:[14,22,25,26,34],y_predict:32,y_proba:30,y_relu:31,y_ridg:32,y_ridge2:32,y_score:[21,22],y_sgd:32,y_sridg:32,y_test:[21,22,25,26,30,31,34],y_test_pr:21,y_train:[21,22,25,26,30,31,32,34],y_train_pr:22,y_train_predict:32,y_true:[22,25],y_upper:19,y_val:[32,34,35],y_val_predict:32,yaxi:19,ye:[2,23,27],year:[4,5,7,8,12,15,17,24,30,38],yearfirst:12,yellowston:[11,19],yesterdai:[24,27],yet:[4,32],yhat:23,yield:[17,32,36],yin:35,yiyu:39,ylabel:[8,14,15,16,17,18,19,21,22,24,28,32,33],ylim:[8,17,18,19,22,32],yml:[4,38,39],you:[1,3,4,5,7,8,9,11,12,13,14,15,17,18,19,21,23,26,27,28,29,30,31,32,35,37,38,42],your:[3,5,9,11,12,13,14,15,17,19,21,23,27,28,29,30,32,38,39,42],your_command:4,your_packag:4,yourusernam:4,youtu:3,yrang:28,yscale:[17,18,33],yt:33,ytick:[14,15,17],yy:30,z:[7,11,12,13,16,17,18,19,23,29,30],zarr:[9,10,14],zel:17,zenodo:4,zero:[7,8,15,17,18,19,21,23,28,29,30,31,32,35,38],zero_grad:[29,30,31,33,35],zf:17,zhat:[17,18],zhu:35,ziheng:[39,40],zip:[8,11,22,24,30],zipfil:[8,11,15],zipp:[13,18,28],zone:15,zoom_start:11,zorder:8,zp:16,zsort:17,zxx:17},titles:["1.1 Open Reproducible Science","1.3 Jupyter Environment","1.3 Python Ecosystem","1.4 Computing Environments","1.5 Version Control & GitHub","1.6 Data Gallery","Getting Started","2.9 Feature engineering","2.10 Dimensionality Reduction","2.11 ML-ready data","2.1 Data Definitions","2.2 Data Formats","2.3 Pandas","2.4 DataFrame Exploration","2.5 Data Arrays","2.6 Resampling Methods","2.7 Statistical Considerations for geoscientific Data and Noise","2.7 Spectral Transforms","2.9 Synthetic noise","3.1 Clustering","3.2 Classification and Regression","3.3 Binary classification","3.4 Multiclass Classification","3.5 Logistic regression","3.6 Random Forests","3.7 Hyperparameter Tuning","3.8 Ensemble learning","3.9 AutoML","4.0 The Perceptron","4.1 Neural Networks","4.2 Multi Layer Perceptrons","4.3 Convolutional Neural Networks","4.3 Model Training","4.2 Physics-Informed Neural Networks","4.4 Recurrent Neural Networks: Processing sequences","4.5 Auto-encoders","4.6 NAS: Network Architecture Search","This chapter focuces on model workflow and ML reproducibility","The MLGeo Project","Machine Learning in the Geosciences","Acknowledgements from Contributors","Bibliography","Glossaries"],titleterms:{"0":[28,31],"1":[0,1,2,3,4,5,8,10,11,12,14,15,16,17,18,19,20,21,22,24,25,26,29,30,31,32,38],"10":8,"11":9,"1d":14,"2":[7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,29,30,31,32,33,34,38],"2d":[14,17],"3":[1,2,8,11,12,14,15,16,17,19,20,21,22,23,24,25,26,27,29,30,31,32,38],"3d":8,"4":[3,8,12,13,14,15,17,18,19,22,24,26,28,29,30,31,32,33,34,35,36,38],"5":[4,8,12,14,15,23,24,29,30,31,32,35,38],"6":[5,8,12,14,15,24,32,36,38],"7":[16,17,24,25,38],"8":26,"9":[7,18,27],"class":29,"do":2,"final":13,"function":[12,29,30,32],"import":24,"new":4,"short":17,A:31,But:11,One:[15,24],The:[16,19,28,31,38],To:31,about:28,account:4,acknowledg:40,activ:[30,31],adaboost:26,addit:4,advanc:12,aggreg:12,ahead:34,algorithm:20,an:[2,4,31],analysi:[8,13],app:4,appendix:23,ar:2,architectur:36,arrai:[10,11,14,29],asid:31,assembl:31,assess:24,authent:4,auto:35,autoencod:35,automat:23,automl:27,aw:3,azur:3,bag:26,baselin:24,basic:[1,2,12,14,17],batch:32,befor:19,best:27,bibliographi:41,binari:21,bonu:14,boost:26,bootstrap:15,build:39,can:34,canon:16,carlo:15,chapt:38,chapter:[37,38],check:[13,24,27],checklist:32,choic:[19,31],choos:14,classic:12,classif:[20,21,22,32],classifi:[21,26],clever:32,cloud:3,cluster:19,cnn:31,code:2,colab:3,column:[12,27],comma:11,command:[1,2],compar:27,comparison:14,compon:8,comput:[3,14,24],conda:2,condit:12,connect:31,consider:16,contribut:4,contributor:40,control:4,convers:14,convex:32,convolut:[31,35],correl:13,cours:39,covari:8,creat:[4,12,28,29],cross:[15,25],csv:[11,12],custom:29,data:[5,8,9,10,11,12,13,14,15,16,18,19,21,22,24,27,28,30,31,35,38],datafram:[12,13],dataload:29,dataset:[5,28,29],deal:2,decis:24,decod:35,deep:32,defin:31,definit:[10,31,42],denois:35,descent:[23,32],descript:5,design:[29,30,38],desktop:4,determin:8,differenti:23,dimension:[8,35,38],directori:4,discov:33,displai:27,distanc:[14,19],distribut:[13,16],download:[5,24,38],earli:32,earthquak:12,ecosystem:2,elast:32,elbow:19,encod:[24,35],engin:7,enhanc:4,ensembl:26,environ:[1,2,3],evalu:27,event:18,exampl:[2,4,12,15,31,32,35],exercis:[12,13,14,18,32],exist:4,explan:4,explor:[7,8,13,21,24],extract:[8,14],far:34,featur:[7,8,13,16,24],file:[2,4,5,12],filter:17,fine:30,first:[24,27],fit:[28,32],focuc:37,fold:15,forecast:34,forest:24,format:[10,11],forward:30,fourier:17,frame:[10,38],freez:2,from:[5,6,12,40],fulli:31,fundament:12,futur:34,galleri:5,gener:[12,42],geodet:15,geojson:11,geolog:16,geopanda:11,geoscienc:[10,39],geoscientif:[14,16],geospati:11,geotiff:11,get:[6,27],git:4,github:[4,39],glossari:42,googl:3,gradient:[23,26,32],grid:25,handl:[11,13],hassl:27,hdf5:11,hierarch:[11,19],high:14,homework:39,hot:24,how:[2,5,31,34],hpc:3,hub:1,hyperparamet:[25,30],i:11,imag:31,implement:[23,31],independ:8,infer:15,inform:[18,33],initi:24,intermedi:12,interpret:27,introduct:23,iri:28,javascript:11,json:11,jupyt:1,k:[15,19],kei:4,kurtosi:16,lab:1,label:24,lambda:12,larg:11,lasso:32,latent:35,layer:[30,31],learn:[6,19,26,30,32,39],leav:15,lectur:10,lenet:31,let:[28,31],level:[14,15,16,17,18],line:28,linear:[15,20],littl:28,load:29,local:3,logic:12,logist:23,loss:[29,32],low:35,lstm:34,machin:39,magnitud:12,main:4,manipul:[12,14],map:12,markdown:1,matplotlib:14,matrix:8,mean:[8,16,19],measur:14,metadata:7,method:[15,19],metric:[21,27],mini:32,miss:13,ml:[9,31,37],mlgeo:[5,38],mlp:30,modal:10,model:[15,21,24,27,29,30,31,32,33,35,37,38],mont:15,more:27,motion:15,multi:[30,35],multiclass:22,multipl:12,na:36,nan:13,need:2,net:[32,35],netcdf4:11,netcdf:11,network:[29,30,31,33,34,36],neural:[29,30,31,33,34],nois:[16,18],norm:14,notat:11,note:[4,31],notebook:1,number:19,numpi:14,nyquist:17,o:11,object:[6,11,39],open:0,optim:[29,30,32],organ:38,other:[8,32],our:[28,31],out:[15,28],outcom:8,overfit:32,overview:39,panda:12,paramet:[8,31],parquet:[11,12],past:26,pca:[8,19],perceptron:[28,30],perform:[21,24],physic:[18,33],pinn:33,plate:15,plot:14,plotli:12,pool:31,practic:[19,31],predict:34,prep:31,prepar:[22,24,30,38],prerequisit:39,princip:8,problem:[32,34],process:34,project:38,publish:[4,31],pycaret:27,pyproject:4,python:[2,12],pytorch:[14,23,29,30,31],qualiti:24,random:[14,15,18,24,25,29],randomli:24,raster:11,rasterio:11,rate:32,re:22,read:[11,12,13,31,39],readi:[9,27],realist:18,recod:31,recommend:4,recurr:34,reduct:[8,38],regress:[15,20,23,24,32],regular:32,rememb:28,repeat:19,repositori:[4,5],reproduc:[0,37],resampl:15,resourc:4,respons:12,restor:30,review:31,ridg:32,right:14,rnn:34,robust:15,rule:28,run:[2,27],s:[28,31],save:30,scale:22,scienc:0,scikit:[19,30],search:[25,36],section:12,segment:31,seismic:31,seismolog:35,select:8,separ:[11,24],sequenc:34,seri:12,set:[4,16,19,24,32],sever:34,shape:27,signal:18,skew:16,skill:39,slide:10,slow:11,softwar:4,solut:34,some:[28,31],space:[7,17,35],specif:39,spectral:17,split:[22,24,27,29],stack:26,start:[6,31],statist:16,step:[4,8,34],stochast:32,stop:32,structur:[4,30],student:[12,14,39],subtract:8,svd:8,syllabu:39,synthet:[18,21],tabular:11,task:[14,35],team:4,technic:39,techniqu:[8,15],technolog:42,tensor:14,test:[22,24,27,28,29],text:12,thi:[2,28,37],tier:39,time:[12,17],tip:19,toml:4,tool:[14,42],train:[22,24,27,29,30,31,32,38],transform:17,tree:24,troubleshoot:27,tune:[25,30,31],tutori:[19,39],typic:30,u:35,uncertainti:17,under:32,up:[4,19,24,32],us:[4,12,15,28],valid:[15,25],valu:[11,13],varianc:16,version:[4,39],via:8,virtual:2,vote:26,vs:12,wavelet:17,we:[24,27,31],webinar:39,what:[1,2,28],why:14,work:4,workflow:[4,37],xarrai:14,yml:2,you:[2,34],your:[2,4],zarr:11,zero:[6,13]}}) \ No newline at end of file +Search.setIndex({docnames:["Chapter1-GettingStarted/1.1_open_reproducible_science","Chapter1-GettingStarted/1.2_jupyter_environment","Chapter1-GettingStarted/1.3_python_environment","Chapter1-GettingStarted/1.4_computational_environments","Chapter1-GettingStarted/1.5_version_control_git","Chapter1-GettingStarted/1.6_data_gallery","Chapter1-GettingStarted/readme","Chapter2-DataManipulation/2.10_feature_engineering","Chapter2-DataManipulation/2.11_dimensionality_reduction","Chapter2-DataManipulation/2.12_MLready_data","Chapter2-DataManipulation/2.1_Data_Definitions","Chapter2-DataManipulation/2.2_data_formats_rendered","Chapter2-DataManipulation/2.3_pandas_rendered","Chapter2-DataManipulation/2.4_dataframes_prep","Chapter2-DataManipulation/2.5_Arrays","Chapter2-DataManipulation/2.6_resampling","Chapter2-DataManipulation/2.7_statistical_considerations","Chapter2-DataManipulation/2.8_data_spectral_transforms","Chapter2-DataManipulation/2.9_synthetic_noise","Chapter3-MachineLearning/3.1_clustering","Chapter3-MachineLearning/3.2_classification_regression","Chapter3-MachineLearning/3.3_binary_classification","Chapter3-MachineLearning/3.4_multiclass_classification","Chapter3-MachineLearning/3.5_logistic_regression","Chapter3-MachineLearning/3.6_randomForest_regression","Chapter3-MachineLearning/3.7_hyperparameter_tuning","Chapter3-MachineLearning/3.8_ensemble_learning","Chapter3-MachineLearning/3.9_autoML","Chapter4-DeepLearning/mlgeo_4.0_perceptrons","Chapter4-DeepLearning/mlgeo_4.1_neural_networks","Chapter4-DeepLearning/mlgeo_4.2_MultiLayerPerceptron","Chapter4-DeepLearning/mlgeo_4.3_CNN","Chapter4-DeepLearning/mlgeo_4.3_ModelTraining","Chapter4-DeepLearning/mlgeo_4.3_PINN","Chapter4-DeepLearning/mlgeo_4.5_RNN","Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder","Chapter4-DeepLearning/mlgeo_4.6_NAS","Chapter5-ModelWorkflows/readme","about_this_book/0_mlgeo_project","about_this_book/about_this_book","about_this_book/acknowledgements","reference/bibliography","reference/glossary"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":5,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,"sphinxcontrib.bibtex":9,sphinx:56},filenames:["Chapter1-GettingStarted/1.1_open_reproducible_science.md","Chapter1-GettingStarted/1.2_jupyter_environment.md","Chapter1-GettingStarted/1.3_python_environment.md","Chapter1-GettingStarted/1.4_computational_environments.md","Chapter1-GettingStarted/1.5_version_control_git.md","Chapter1-GettingStarted/1.6_data_gallery.md","Chapter1-GettingStarted/readme.md","Chapter2-DataManipulation/2.10_feature_engineering.ipynb","Chapter2-DataManipulation/2.11_dimensionality_reduction.ipynb","Chapter2-DataManipulation/2.12_MLready_data.ipynb","Chapter2-DataManipulation/2.1_Data_Definitions.md","Chapter2-DataManipulation/2.2_data_formats_rendered.ipynb","Chapter2-DataManipulation/2.3_pandas_rendered.ipynb","Chapter2-DataManipulation/2.4_dataframes_prep.ipynb","Chapter2-DataManipulation/2.5_Arrays.ipynb","Chapter2-DataManipulation/2.6_resampling.ipynb","Chapter2-DataManipulation/2.7_statistical_considerations.ipynb","Chapter2-DataManipulation/2.8_data_spectral_transforms.ipynb","Chapter2-DataManipulation/2.9_synthetic_noise.ipynb","Chapter3-MachineLearning/3.1_clustering.ipynb","Chapter3-MachineLearning/3.2_classification_regression.ipynb","Chapter3-MachineLearning/3.3_binary_classification.ipynb","Chapter3-MachineLearning/3.4_multiclass_classification.ipynb","Chapter3-MachineLearning/3.5_logistic_regression.ipynb","Chapter3-MachineLearning/3.6_randomForest_regression.ipynb","Chapter3-MachineLearning/3.7_hyperparameter_tuning.ipynb","Chapter3-MachineLearning/3.8_ensemble_learning.ipynb","Chapter3-MachineLearning/3.9_autoML.ipynb","Chapter4-DeepLearning/mlgeo_4.0_perceptrons.ipynb","Chapter4-DeepLearning/mlgeo_4.1_neural_networks.ipynb","Chapter4-DeepLearning/mlgeo_4.2_MultiLayerPerceptron.ipynb","Chapter4-DeepLearning/mlgeo_4.3_CNN.ipynb","Chapter4-DeepLearning/mlgeo_4.3_ModelTraining.ipynb","Chapter4-DeepLearning/mlgeo_4.3_PINN.ipynb","Chapter4-DeepLearning/mlgeo_4.5_RNN.ipynb","Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder.ipynb","Chapter4-DeepLearning/mlgeo_4.6_NAS.ipynb","Chapter5-ModelWorkflows/readme.md","about_this_book/0_mlgeo_project.md","about_this_book/about_this_book.md","about_this_book/acknowledgements.md","reference/bibliography.md","reference/glossary.md"],objects:{},objnames:{},objtypes:{},terms:{"0":[2,4,7,8,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,29,30,32,33,34,35,38],"00":[12,13,15,16,17,18,19,22,26,32,35],"000":[3,8,12,29],"00000":15,"000000":[12,13,16,24],"0000004":15,"000000e":13,"000000z":[17,18],"0000064":15,"000009":13,"000055":13,"000081":13,"0000ff":30,"0001":28,"000111":13,"00015211022576318623":18,"00021":15,"0003":19,"0005":32,"000590":13,"0006263194954954088":33,"00067":15,"00068":15,"00069":15,"00077540e":25,"0009188263163747461":14,"00092988":25,"00095":25,"000951":25,"00095572":25,"00096354":25,"00096431":25,"00097799":25,"00097928":25,"000986":25,"00098691":25,"00098772":25,"00098853":25,"00099373":25,"00099473":25,"00099483":25,"00099893":25,"000e":32,"001":[22,23,28,29,30,31,32,33,35],"00100203":25,"00100331":25,"00100341":25,"0010047":25,"00100555":25,"00100613":25,"00100646":25,"00100651":25,"00100737":25,"00100942":25,"00101":15,"00101366":25,"00101843":25,"00102372":25,"00102644":25,"00103245":25,"00103259":25,"00103416":25,"00103521":25,"00103917":25,"00103965":25,"00104":15,"00104094":25,"00104437":25,"00104656":25,"00104809":25,"00104914":25,"00105":15,"00105019":25,"00105281":25,"00105462":25,"00105872":25,"00105924e":19,"00106049":25,"00106201":25,"00106554":25,"00106707":25,"00106764":25,"00106878":25,"00106978":25,"0010725":25,"0010726":25,"00107574":25,"00107622":25,"0010766":25,"00107718":25,"0010776":25,"00107789":25,"00107832":25,"00108519":25,"001086":25,"00108638":25,"00108681":25,"00108762":25,"00109024":25,"00109105":25,"00109315":25,"00109363":25,"00109911":25,"00110121":25,"00110216":25,"00110488":25,"00110974":25,"00111265":25,"00111418":25,"00111895":25,"001121":25,"0011219":25,"0011241":25,"00112505":25,"00112543":25,"00112891":25,"0011312":25,"00113409e":19,"00113697":25,"00114322":25,"00114336":25,"00114398":25,"00114417":25,"00114655":25,"00114799":25,"00114856":25,"00115099":25,"0011518":25,"00115423":25,"00115576":25,"00116277":25,"00116334":25,"00116444":25,"00116515":25,"00116839":25,"00117":13,"00117087":25,"00117283":25,"00117745":23,"00117784":25,"00117974":25,"00118184":25,"0011826":25,"00118356":25,"00118542":25,"00118771":25,"00118823":25,"00118876":25,"00118885":25,"00119176":25,"001193":25,"00120039":25,"00120368":25,"00120587":25,"0012094":25,"00122175":25,"00122461":25,"00123711":25,"00124407":25,"00125":15,"00125222":25,"00125556":25,"00125637":25,"00126467":25,"00126867":25,"00127654":25,"00128045":25,"00129204":25,"00129838":25,"00133281":25,"00133452":25,"00134025":25,"00134029":25,"0013628":25,"00136886":25,"00137367":25,"00137486":25,"00137854":25,"00138397":25,"00139098":25,"00139213":25,"00139284":25,"00139289":25,"00139799":25,"00140023":25,"00140777":25,"00141668":25,"001417":19,"00142055":25,"00144706":25,"00144835":25,"00150":15,"00151439":25,"00153746e":25,"00155":15,"00155859":25,"00157524e":19,"00162":15,"00165":15,"00169":15,"00189348e":25,"00219419e":25,"00219507":8,"00228":15,"00271":15,"003069":19,"0030715920533113":14,"00321":15,"00324":15,"00326":15,"00327":15,"00328":15,"00329":13,"003468":19,"00380339e":19,"003938":19,"004136":13,"004774":13,"004932182490752158":23,"005":[29,30,31,35],"00523":28,"005311":19,"00619":15,"006250720119447979":15,"00643465e":25,"006434906304516136":18,"006437910455226973":15,"0064397312911273945":15,"006439731291127403":15,"0064404":15,"00675":15,"007":7,"00704":15,"00744489":25,"007495006078435612":18,"00800":12,"008060063544843572":14,"00817":15,"00821080e":25,"0083":15,"00878350e":25,"008924":19,"00896047e":19,"008967":19,"00908":15,"00917668e":19,"009565":19,"009791921664626684":23,"01":[12,15,17,19,24,26,32,33],"010":12,"010000":[16,17],"01008876e":19,"0101010101010102":14,"010145":13,"01026":13,"010370":13,"010845":13,"010848":25,"0109":23,"01107147":25,"011097410604192354":23,"01124606":25,"0114562":25,"01150443e":19,"01150772":25,"01192058":25,"011982":19,"01206191":25,"01231908":25,"01241204":25,"01258343":25,"012774":13,"01284938":25,"01286752":25,"01287":13,"01289171":25,"01293827":25,"01298769":25,"01303417":25,"01304742":25,"01311811":25,"01314302e":25,"01317991e":19,"0132973926342017":18,"01330530e":25,"01338":13,"01357734":25,"01360224e":25,"0136416":25,"013847e":13,"01396018":8,"013998e":13,"01407104":25,"0141058":25,"01414058":25,"01417596":25,"0142139":25,"01432905":25,"0143383":25,"01434685":25,"01434876":25,"01437521":25,"01445313":25,"01446302":25,"01450577":25,"01455708":25,"01457076":25,"01462278":25,"01472216":25,"01476016":25,"01483183":25,"01484245e":19,"01484685":25,"01485596":25,"01493359":25,"01494575":25,"01497216":25,"01497725":25,"015":29,"01503067":25,"01503139":25,"01508956":25,"01510458":25,"01511592":25,"0151176":25,"01515818":25,"01519043":25,"01521006":25,"0152317":25,"015246388348034143":18,"0152606":25,"01526103":25,"01527653":25,"01528138":25,"0153625":25,"01540737":25,"01547699":25,"015568":19,"01559561":25,"01565228":25,"01576381":25,"01581057":25,"01630097e":25,"01647874":25,"01660968e":25,"01667988":25,"0169":12,"01691109":25,"01692484":25,"01693117":23,"01693139":25,"01695":15,"017019":13,"0171665":25,"0171936":25,"01721282":25,"01724968":25,"01750928":25,"01756458":25,"01758518":25,"01762635":25,"01790279e":19,"01795520e":25,"01812615":25,"018297":25,"01831212":25,"01833758":25,"01843243":25,"01845818":25,"018471":25,"01847563":25,"01862588":25,"01873102":25,"01874394":25,"01875048":25,"01880918":25,"01882305":25,"01882615":25,"01883609":25,"01885603":25,"01887064":25,"01887522":25,"01888666":25,"01897316":25,"01899896":25,"01901288":25,"01901824":25,"01904299":25,"01905718":25,"01908689":25,"01911283":25,"01913071":25,"01913462":25,"01921201":25,"01921757":25,"01922411":25,"0192246":25,"01923843":23,"01928401":25,"01928897":25,"01929832":25,"0192997":25,"01936684":25,"01936893":25,"01947122":25,"01948614":25,"01954636":25,"0195549":25,"01957004":25,"01957202":25,"0195775":25,"01961274":25,"0196908":25,"01971579":25,"01971922":25,"01973233":25,"01975498":25,"01980486":25,"0198133":25,"0198184":25,"01983519":25,"01985712":25,"01990356":25,"01991944":25,"01998029":25,"01998377":25,"02":[12,19,24,25,30],"02001534":25,"02002053":25,"02005477":25,"02011108":25,"02012463":25,"02015944":25,"02019405":25,"02020388":25,"02026519":25,"02032981":25,"02037839":15,"02041302":25,"02054709":25,"02068725":25,"02095075":25,"02111402":25,"0211823":25,"02136241":25,"02145925":25,"02156137":25,"02172494":25,"021897810218978103":23,"0220214":25,"02207262":25,"02210412":25,"02210692":25,"02214394":25,"02232589":25,"02234629":25,"022358":19,"02243166":25,"02244415":25,"02267064":25,"02275643":25,"02293578":25,"02294638e":19,"02297895":25,"02298839":25,"02304196":25,"02311587":25,"02313848":25,"02317823":25,"02318607":25,"02328081":25,"02333975":25,"02335019":25,"02344284":25,"02348842":25,"02351891":25,"02353102":14,"0235342":25,"02355308":25,"02355585":25,"02355795":25,"02358461":25,"02359171":25,"02365112":25,"02366824":25,"02367":15,"02368955":25,"02369399":25,"02371317":25,"02377076":25,"02379055":25,"02379142":25,"02380972":25,"02381821":25,"02385512":25,"02386303":25,"02392955":25,"02400956":25,"02411374":25,"02415328":25,"02421236":25,"02424603":25,"02431536":25,"02442665":25,"02446957":25,"02453833":25,"02459221":25,"02464504":25,"02480783":25,"0248394":25,"02488422":25,"02499743":25,"025":21,"025000":24,"02505026":25,"0250979":25,"02514329":25,"02515011":25,"02530203":25,"02541871":25,"02555343":25,"02562881":25,"0257":12,"02574883":25,"02577629":25,"02578182":25,"0258419":25,"02588782":25,"02608404":25,"02635684":25,"02642287e":19,"02672602":25,"02673278":25,"02683849":25,"02696":12,"027":29,"02883570e":25,"02902726e":19,"029486":19,"02948785":25,"02956544":23,"02959356":25,"02991104":25,"02993002":8,"02998571":25,"03":[12,13,19,24,25,33],"030":35,"03003":15,"03006983":25,"03009968":25,"0301":12,"03030303":14,"03058095e":19,"03060265":25,"030637":19,"03071399":25,"030896":25,"03093848e":25,"03100":12,"03103009":23,"03331803":15,"03337844e":19,"03416":13,"0344":12,"03440":12,"03447569e":25,"034483":24,"034800":13,"03494963e":19,"03501758e":19,"03513":15,"03566152e":19,"035698":13,"036229":19,"03678":15,"03681":15,"03760092e":19,"03815835e":19,"0386919":25,"03906":15,"039697":13,"03978648e":25,"04":[12,13,25,35],"04101425":23,"041580e":13,"041600":13,"04240":13,"042591":13,"043":29,"04303176034198916":14,"04314":12,"04350267e":19,"043535":13,"04372530e":25,"04382":15,"04460142e":25,"0454577":23,"04572756":23,"04648":[13,15],"04690610e":19,"04767375e":19,"04798007e":19,"047994":19,"04815":15,"04832":15,"04842":13,"04955":13,"04966072e":19,"04984011e":19,"049902":19,"049911":13,"05":[7,12,14,15,17,18,19,22,24,25,31],"05010276e":19,"05152703e":19,"05211":13,"05213662e":25,"05242661":8,"052873":28,"05371048":15,"05394413e":19,"05411581e":25,"056":29,"05651231":14,"05658198e":25,"05707018e":19,"05826066e":25,"05836797e":19,"05876537e":19,"05876856e":25,"05888410e":25,"05915033e":25,"06":[7,12,13,15,16,24,25],"060":12,"06025041e":19,"060298649528828":16,"06060606":14,"06078202e":25,"06092507e":25,"06168176e":19,"06172":13,"0625":22,"06278148e":25,"0635":12,"06380531e":19,"064":29,"06419769e":19,"06490653e":25,"06615794e":25,"0671":15,"06741252e":25,"067648":13,"067764":13,"06848084e":19,"069400":13,"0698":15,"06jan25":15,"06jan26":15,"06jan27":15,"06jan28":15,"06jan29":15,"07":[12,13,14,16,17,18,33,35],"07094755358789713":18,"07107204e":19,"07107968e":19,"07132985e":19,"07226722":14,"07230686e":19,"0726":15,"072650":13,"07493105e":19,"07508417348193797":16,"0753":15,"07561688e":25,"0777344413103096":17,"077800":12,"0780":15,"07841108":8,"07951792e":25,"07t03":7,"08":[12,16,33,35],"080":29,"0803":12,"08064872e":25,"08072608e":19,"080897":13,"08113310e":19,"08149552":15,"08186501e":25,"08200949e":25,"0823":12,"08269":13,"082983":7,"08310640e":25,"08484803":23,"08537423e":19,"08593236e":19,"08732":13,"08764086e":19,"08831":13,"08846372e":25,"088800":13,"089693":13,"08998769e":19,"08t15":7,"09":[7,12,33],"09090909":14,"09091501e":19,"091":29,"092579":13,"0935":19,"09399618e":25,"094121":13,"09478493e":19,"09610147e":19,"09689906e":19,"098":29,"09816631e":19,"09887886e":25,"099000":7,"09t23":7,"0_wavelet":7,"0f":22,"0ffh4r23mitn2dz":7,"0m":[12,13,18,28],"0s":[22,25],"0x1060f17f0":23,"0x126f0e7f0":23,"0x145352250":25,"0x16b4d22e0":32,"0x17a5efa60":8,"0x17a96cb80":8,"0x17a9ebeb0":18,"0x17ca1a580":8,"0x1c90d4820":7,"0x1e1e75e10":30,"0x2af90f8b0":35,"0x2b17a0f10":19,"0x2b1edcd60":19,"0x2b26d6080":31,"0x2ba38ac10":19,"0x2ba44ab50":19,"0x2ba534b80":19,"0x2ba5f8bb0":19,"0x2bc2b4eb0":19,"0x2bdd92640":19,"0x2bde1d5e0":19,"0x2d0523130":35,"0x318353900":15,"0x318690940":15,"0x319394a90":15,"0x3195279a0":15,"0x3196996a0":15,"0x685db3790":14,"0x78d4ee490":14,"0x7f27e3ee9c00":21,"0x7f27e600fd90":21,"0x7f27e62d2980":21,"0x7fd65cb311b0":22,"1":[7,9,13,23,28,33,34,35,39,41],"10":[2,3,7,11,12,13,14,15,16,17,18,19,21,22,24,25,26,28,29,30,31,32,33,34,35,38,39],"100":[1,7,11,12,13,14,15,16,17,18,19,21,24,25,26,28,29,30,31,32,33,35,39,42],"1000":[8,9,10,12,13,14,15,16,17,24,26,28,29,31,32,33,35],"10000":[8,12,13,14,15,17,28,33,34],"100000":[12,16,24,28],"10000543e":19,"10013437e":19,"100ka":5,"101":[12,29,30],"10116954e":19,"10184613e":19,"102":30,"10227580e":19,"102557":13,"1029":31,"103":30,"1030":22,"1031":22,"1032":22,"1033":22,"1034":22,"10349180e":25,"1035":22,"1036":22,"1037":22,"1038":22,"103932":16,"104":[25,30],"1040":22,"1041":22,"10420561e":19,"10427394e":25,"10476158":14,"10487413e":19,"105":[12,25,29,30],"105000":12,"106":[25,30],"10639533e":25,"107":[25,30],"108":[13,25,30],"109":[12,13,25,30],"1094":12,"109400":12,"10942":13,"10974988":14,"10hz":17,"10k":34,"11":[7,8,12,13,14,16,17,18,24,26,28,29,30,35,39],"110":[11,12,25,30],"11022491e":19,"11046253e":25,"11049848e":19,"1106":12,"1107":12,"11070377e":25,"1108":12,"1109":12,"111":[8,12,13,17,19,25,29,30],"1110":12,"1111":12,"11111111":14,"1112":12,"11128084e":25,"1113":22,"1114":22,"1115":22,"1116":22,"11162756":14,"1117":22,"112":30,"11215633e":25,"113":[12,25,30],"113026":16,"1136":14,"114":[25,30],"1140":13,"1142":22,"1143":22,"1144":22,"1145":22,"1146":22,"1147":22,"1148":22,"1149":22,"1149757":14,"114x80":31,"115":30,"1150":22,"11504743e":25,"1151":22,"1152":22,"11535082e":19,"1159":22,"116":[25,30],"1160":22,"1162":22,"1164":22,"1166":22,"117":[24,30],"117873":13,"11794405e":19,"118":[25,29,30],"1181":22,"1182":22,"1183":[22,32],"1184":22,"1185":22,"119":[7,30],"1190913055":19,"1198":23,"12":[7,12,13,14,17,18,19,24,27,28,29,30,31,33,35],"120":[7,12,30,31],"12000":[12,33],"12045035":14,"120542":24,"121":[7,11,12,25,30],"12121212":14,"122":[25,30],"1225":23,"12263265e":19,"123":[15,25,27,30],"123111":13,"1233":22,"12332403":14,"1234":[11,22],"1235":22,"123783465":19,"1239":13,"124":30,"1240":22,"1241":22,"1242":22,"1244":22,"1245":22,"1246":22,"125":[8,12,14,16,22,29,30],"12571585":14,"126":[7,12,14,22,25,30],"126185":13,"12618569":14,"12662":13,"127":[11,14,22,30],"12701549e":25,"12745122e":25,"12750503e":25,"127k":12,"128":[14,25,30,31,34],"128000":7,"129":[25,30,31],"129629":12,"12977":13,"13":[7,12,13,14,16,17,19,25,28,29,30,33,35],"130":[25,30],"13003767e":25,"130547":13,"13058067e":19,"131":[25,30],"13147540e":19,"13176935e":25,"132":[25,30],"13200349e":25,"1320124":14,"13219411":14,"133":[12,13,25,29,30,31],"1331":13,"134":[25,30,31],"13428352":15,"13455924e":25,"13463788e":25,"134956":13,"135":[25,30],"135285":13,"13535513e":25,"13559614e":19,"13571728e":19,"1359":15,"136":[13,25,30],"1360":15,"136646":13,"136778":19,"1369":27,"137":[25,30],"1377602":13,"138":[25,30],"13813089e":25,"13848864":14,"138856":13,"139":[25,30],"13976786":15,"13m":35,"14":[7,12,13,14,17,18,19,29,30,31,33,35],"140":[13,25,30],"14000":33,"141":[25,29,30],"14118360e":19,"1412":13,"141447":13,"1416":19,"14169642e":19,"141790":13,"141805":13,"14186":13,"142":[12,13,25,30],"142010":13,"14262720e":19,"1427":13,"143":[25,30],"143726":13,"14398":13,"144":[25,30],"14403563e":19,"1445":11,"1447":11,"1448":11,"145":[12,13,25,30,35],"14508288":14,"14542351286436":32,"14581385e":25,"1459":[13,23],"14593293e":25,"146":[12,25,30],"1460":13,"146988":12,"147":[25,30],"14788620e":25,"148":[12,25,30],"148057":13,"14831120e":25,"149":[25,30],"14m":35,"15":[2,5,7,12,13,14,17,18,22,23,24,25,26,29,30,33,35,39],"150":[17,25,29,30],"15000":33,"15001":7,"15064708":15,"150651":13,"15089793e":19,"15094544e":25,"151":[25,30],"1510":13,"1511":13,"15123857e":25,"15151515":14,"15199111":14,"152":[25,30],"15201264e":25,"152151":13,"15293862e":25,"153":[13,30],"154":[25,30],"15443469":14,"1548":8,"1549214831474401":32,"155":[25,30],"15525786e":19,"1553671407878263":19,"15547294e":19,"156":[25,26,30],"15607835e":19,"1561":32,"156750":7,"15688101":17,"157":[13,25,30],"157000":7,"158":[19,25,30],"158000":7,"159":[25,30],"1590":12,"15908766e":25,"15910416e":19,"15922828":14,"15924":16,"15975960e":19,"15998988":15,"15m":35,"16":[7,12,13,14,15,16,18,19,28,29,30,31,32,33,35],"160":[13,25,29,30,35],"16000":33,"161":30,"16116":13,"16121009e":19,"161700":12,"162":[13,30],"16296722254477":32,"163":[13,30,35],"163786":13,"16392710e":19,"164":30,"165":[7,13,30],"16500907e":19,"165398":24,"166":[12,17,30],"1664":12,"16681005":14,"167":[29,30],"16783219e":25,"168":30,"16816806e":19,"16827078e":19,"169":30,"16900":12,"1691094658":17,"169214":13,"16m":35,"16x16":31,"17":[7,12,13,14,18,24,25,28,29,30,33,35],"170":30,"170000":16,"17002010e":19,"1703":11,"1704":11,"17049426e":19,"1705":11,"1706":11,"1707":11,"1708":11,"1709":11,"171":[29,30],"1710":11,"1711":11,"1712":11,"1713":11,"1714":11,"1715":11,"1716":11,"1719":35,"171915":13,"172":[13,30],"1720":7,"173":[19,30],"173233":13,"173333":13,"173370":13,"174":30,"17468526e":19,"1747":13,"17475284":14,"1748":13,"1749":13,"175":[13,30],"175485":13,"176":30,"176494":13,"176529":13,"1767563":17,"17675743e":19,"17681195":14,"177":[13,30],"17762":13,"178":[12,30],"1780":12,"178035":13,"178060":13,"1781":12,"1782":12,"1783":12,"1784":12,"1785":12,"178772":13,"1788":13,"179":[12,30],"1790":22,"1791":22,"179191":13,"1792":22,"1793":22,"1794":22,"179630":13,"17968626e":25,"1797":[22,25,26,29,30],"18":[2,7,13,14,16,19,29,30,33,35],"180":[8,13,30],"18000":33,"18035155e":19,"18068457e":25,"18092002e":19,"181":30,"18115211e":25,"18126811e":25,"181545":13,"18159":13,"18181818":14,"18181892e":19,"182":[29,30],"18209788e":25,"18281851e":25,"183":[13,30],"18307383":14,"18314361e":19,"1836":33,"18381967e":19,"184":[13,30,35],"18452379e":19,"18470":19,"18493938e":19,"185":[12,13,30],"186":[13,30],"1861":22,"1862":22,"1863":22,"1865":22,"1866":22,"1867":22,"1868":[13,22],"1869":22,"187":[11,13,30],"1870":22,"18712198e":19,"187675":12,"18767507003":12,"187840":7,"188":[22,30],"18899":13,"189":[22,30],"19":[7,12,13,14,28,29,30,31,35],"190":[22,30],"19044712e":25,"191":[22,30,33],"19169":13,"19179103":14,"192":[13,22,30],"19239174e":25,"19274190e":19,"193":[22,30],"19350":13,"194":[22,30],"19450078e":25,"19479893e":19,"195":[12,22,29,30],"196":[22,30],"19612071e":25,"197":[22,30],"19742697173045337":15,"1976":12,"1977":12,"198":30,"1981":12,"1984":12,"1985":12,"198503":13,"1987":12,"19876832e":19,"199":[12,13,22,30],"1990":11,"1995":38,"1998":31,"1d":[10,17,22,30,32,33],"1e":[14,17,28,32,33],"1e4":17,"1hz":[17,18],"1j":18,"1m":[12,13,18,28],"1pko9ormcllaxipzoa3aoztgzfpad2iwj":[24,27],"1s":[13,17,22,25],"1st":[8,19],"2":[1,2,3,4,23,27,28,35,39,41],"20":[7,12,13,14,16,18,19,22,24,27,28,29,30,32,33,34,35],"200":[1,12,13,17,19,23,30,32,39],"2000":[8,14,26,30,33],"20000":33,"200000":24,"20002856e":19,"2006":15,"2007":3,"20082908e":19,"20086303e":19,"2009233":14,"201":[13,22],"2010":12,"2013":14,"2014":7,"20158980e":19,"20159493e":19,"2016":24,"2017":[7,12,18],"2018":[13,31,35],"2019":[19,35],"2019gl085870":31,"202":22,"2020":[2,7,13,28,31,35],"2021":[7,17,18],"2022":[4,7,13,17,28,35,40],"2023":[1,4,7,12,13,18,28,39],"2024":[12,13],"2026":12,"20269556e":19,"20294639e":25,"203":22,"203094":13,"203188":13,"20555138e":19,"206":13,"206_51_39":13,"207":[13,39],"20704562e":25,"207073":19,"208":[13,29,39],"208269":19,"20pm":13,"21":[12,14,18,29,30],"210":13,"21000":33,"21049041":14,"210926":13,"21113694e":25,"2113147933":28,"21153578e":19,"212":31,"21210983e":19,"21212121":14,"212207":13,"21236235e":19,"21307490e":19,"213167":13,"21362874e":19,"21400829":14,"21449":13,"21450115e":25,"21452746e":25,"21619273e":25,"21621959e":25,"21687591e":25,"2171b5":8,"21882842e":25,"21892":13,"21907473":32,"22":[11,12,13,14,18,28,29,30,32,33,35],"220":13,"2201005948153714":17,"22051307":14,"22080902":14,"221":29,"22140377e":19,"222":29,"22222222":14,"222370e":13,"222400e":13,"22247136e":25,"22501940e":25,"22531":13,"22763649":15,"228":[11,13],"22872158e":19,"23":[7,12,13,14,18,19,24,28,29,30,33,35],"2300000":17,"23062432e":19,"23094":13,"23101297":14,"232053040564":32,"232080e":13,"23233":35,"232646":13,"232740e":13,"23284674":14,"23296":35,"23299271e":19,"234":14,"2343":12,"23439988e":25,"2344":12,"2346":12,"2347":12,"2348":12,"2349":12,"2350":12,"2351":12,"235100":13,"2352":12,"2354":12,"2355":12,"2356":12,"2357":12,"2358":12,"23605235e":19,"236217054087575":17,"237445":13,"237650e":13,"23810334e":25,"238506":24,"2389":13,"239270e":13,"2399":11,"24":[12,13,14,28,29,30],"240":14,"24000":33,"24026582":15,"24052539":8,"24059":13,"241000":7,"241273":7,"24201283":14,"24203":13,"24211860e":25,"24242424":14,"24248697e":19,"242735":13,"24279855e":19,"24400":12,"24402698e":19,"2445":13,"24529984e":25,"24568202e":19,"24663131e":25,"24715790e":25,"24755566e":25,"24893725e":19,"24931771e":19,"24937819e":25,"24t10":7,"25":[12,13,14,16,17,19,21,24,25,28,29,30,35,38],"250":3,"251":13,"25170509e":25,"2519":35,"252":[7,12],"2520":35,"2521":35,"2522":35,"2523":35,"2524":35,"2525":35,"2526":35,"2527":35,"253":12,"2531":35,"2533":35,"25353645":14,"2539":35,"254":12,"2540":35,"2541":35,"2543":35,"254590":13,"2548":35,"25485059e":25,"2549":35,"255":12,"2550":35,"2551":35,"255175":13,"2552":35,"25525":13,"2553":35,"2554":35,"2555":35,"2556":35,"2557":35,"2558":[13,35],"2559":13,"256":[12,13,29],"2564":35,"2565":35,"25700":12,"25701972":14,"258550":13,"258610":13,"259070":13,"259232":13,"25hrequir":26,"26":[12,13,14,17,18,19,26,28,29,30,33,35],"260":13,"2600000":17,"261":24,"26122375e":19,"26138302e":25,"261756":13,"26189863e":19,"26342":13,"264":13,"26560878":14,"26564294e":19,"266":13,"266950":13,"267":13,"268":13,"269":13,"26974":13,"27":[7,12,13,14,25,29,30,32,35],"270":13,"27000":33,"27112239e":25,"271863":13,"272373":13,"27272727":14,"273":[13,14],"27390039e":25,"274":[7,13],"27408835e":25,"27419865e":25,"27454916":14,"274761":13,"27481244e":19,"274861":13,"27594":12,"27641682e":25,"27723895e":19,"277725":19,"278":29,"27825594":14,"27834627e":25,"27882059e":19,"2789":12,"27894486e":19,"27937344e":25,"28":[13,14,15,19,24,29,30,31,32,35],"28000":33,"28012":13,"28029144":14,"281325":13,"282202506964987":32,"28344035e":19,"28410467e":19,"28460883e":25,"28512736e":25,"28546337e":19,"28566571e":19,"28581684e":25,"28597742e":19,"286":11,"2862":13,"28664273e":25,"28679745e":19,"287":13,"2877":13,"2884356107":17,"28855255e":19,"28857":13,"28913069":8,"28915345e":25,"28975461e":19,"29":[12,14,17,18,24,25,29,30,33],"29150531":14,"29154967":14,"29233218e":19,"2926":13,"2934":13,"29352404e":19,"29363134e":25,"29365818e":25,"29377350344869":14,"2937735034487":14,"29385795e":19,"294":11,"29434":12,"29435":12,"29436":12,"29437":12,"29438":12,"29439":12,"294510e":13,"295":11,"296560":13,"297":11,"2970118992746436":16,"29739056e":19,"29746":13,"298149":13,"298557385097123":14,"29866468e":25,"299":[11,13],"29t04":[17,18],"29t06":[17,18],"2_multilayerperceptron":30,"2bcurli":[17,22],"2d":[8,10,19,28,30,31],"2dt":17,"2f":[8,15,16,22,30,33],"2fa":4,"2m":12,"2nd":19,"2p":16,"2x":30,"2x2":21,"3":[3,4,7,13,18,28,33,34,35,39],"30":[7,8,12,14,15,16,19,25,28,29,30,32,33,35],"300":[7,11,19,27,30,33],"3000":[12,33],"30000":33,"300000":12,"30014826e":25,"301":13,"30100":12,"302":[11,13],"3020249866927003":32,"302031":12,"30217568":14,"3030303":14,"303440e":13,"30379":13,"30468018e":19,"30472588e":19,"305024":13,"30515":15,"30538555":14,"305700":12,"3068295825":19,"307":39,"30767943e":25,"3077":13,"30773284e":25,"308":[13,39],"30845379e":19,"30970":15,"30t04":7,"31":[7,12,13,14,18,24,28,29,30],"31048681e":25,"311":13,"311191":13,"31177":13,"31240204e":19,"31248":13,"3125":22,"312878":13,"3133":11,"31375":15,"314213":13,"31478216e":19,"31551975e":19,"3159":12,"31675465e":19,"31707406e":19,"31771048e":25,"318":13,"318070e":13,"31816":15,"31840023":8,"31874":13,"31875618e":19,"31941":15,"31992671":14,"31993257e":25,"31m":13,"32":[7,12,13,14,18,28,29,30,31,32,35],"32000":33,"3200000":17,"3201":[11,17],"32081209e":19,"32138028e":19,"32268686e":25,"323":13,"3238846712":12,"32410070e":25,"3246":11,"32622034e":19,"32625132e":25,"32655998e":25,"32876128":14,"32968954e":25,"33":[7,12,14,19,29,30,32,38],"330000z":7,"33004834e":25,"33014":13,"33029":15,"330439":13,"3306":13,"33071950e":25,"33087274e":19,"331":[12,13],"33194":13,"333":29,"33333333":14,"33333333333333":19,"333333333333332":19,"3333333333333335":19,"333333333333334":19,"333333333333336":19,"33333333333334":19,"33335384e":25,"33459512e":25,"3347":15,"33516027":14,"335168":13,"33518319e":25,"33553382e":19,"33613971e":19,"33724303e":25,"33814553e":19,"33900561e":25,"33992835e":19,"34":[12,13,14,18,25,28,29,30,32],"34043406":8,"341243":13,"34277804e":19,"34298459e":25,"34351":13,"34400":12,"34400217e":25,"34510049e":19,"34570272":8,"34573837e":19,"34593598e":25,"348":24,"348280":13,"34913307e":19,"34929719e":19,"349380":13,"35":[12,13,14,24,25,29,30,32],"351":[13,39],"351101":13,"35111917":14,"35171223e":19,"352":39,"35214245e":25,"35235665e":19,"35258262":8,"35265098e":25,"35284738e":25,"353":13,"35304777":14,"353854":13,"35440":12,"35445482e":25,"35469237e":19,"354773":7,"355876":13,"35710083e":19,"35851976e":25,"35863021e":25,"36":[12,14,19,29,30,32],"360":[8,14],"3600":17,"36000":33,"360007":13,"36095430e":25,"36127618e":19,"36191":13,"36335407e":25,"36363636":14,"36448941":14,"365":32,"36608":31,"367375":13,"367623":19,"36783798":14,"368":7,"36898269":15,"369437":13,"36967973e":19,"37":[7,12,14,25,26,28,29,30,32],"370":[7,12],"37029163e":19,"37087279e":25,"370946":13,"371":7,"371931":13,"372414":13,"373":13,"373563":24,"375":22,"37570999e":25,"37813526e":19,"37841317":8,"378927":13,"379":13,"38":[12,14,29,30],"380":[7,13],"380100":13,"38063281e":19,"38104":13,"381880":7,"38236679e":25,"382403":12,"382632":13,"38298":13,"38320642e":25,"38346417e":25,"3835":13,"38358033e":19,"38469443e":19,"38535286":14,"38561943":8,"38837774e":19,"388774":13,"389":[29,30],"389000":7,"389220e":13,"389248e":13,"38942222e":25,"389945":13,"389960":13,"39":[7,8,12,13,14,16,18,28,29,30,33],"390000":16,"39001736e":19,"39061200e":25,"390620e":13,"39150":13,"391540e":13,"391759":13,"391800":12,"39198186e":25,"3931145617":19,"39322864":23,"393638":13,"39393795e":19,"39393939":14,"394514":13,"39486211e":25,"39610759e":25,"397651":13,"39781005e":25,"398300":13,"39961":13,"39975399e":19,"3d":[7,10,14,17,19,22,30,31,32],"3f":[29,30,31,35],"3m":13,"3x3":31,"4":[2,7,11,16,21,25,39],"40":[11,12,14,16,17,19,24,26,29,30,33],"400":[7,12,31,32],"4000":33,"40000":33,"400000":24,"40079389e":19,"400x300":19,"403":12,"40331959e":19,"40352714e":25,"40367004e":19,"40370173":14,"40401":13,"404166":13,"40552474e":25,"40575570e":25,"405891":13,"40732739":8,"407906":13,"40928480e":19,"40953573e":25,"41":[13,14,24,25,29,30,33],"41001253e":25,"41018":13,"41197511e":25,"413":13,"41369110e":25,"414":13,"41413700e":19,"41443296e":25,"416752":13,"41679880e":19,"416857":19,"41747416":14,"41774748e":19,"41781333e":19,"41992596e":25,"42":[4,7,14,15,21,22,24,29,30,31,32,33],"420000z":7,"42113730e":25,"42195117e":25,"42250976e":25,"422833":13,"42292429":14,"423153":13,"42359606e":19,"423691":11,"42373896e":19,"42424242":14,"42511267":15,"42674044e":25,"42788150e":19,"42819812e":19,"42892166e":19,"429012":12,"43":[12,13,14,18,19,24,25,28,29,30],"43035011e":25,"43046929":14,"431600":13,"431687":13,"4325":13,"43350268e":19,"43447":13,"43585916e":19,"43651998e":25,"436909":13,"43711":13,"437923":13,"43869":13,"44":[7,11,12,13,14,24,29,30],"44012308e":25,"441":13,"44265462e":19,"44278":13,"443":[12,13],"44306215":14,"444":[7,13,29],"44444444":14,"44483783e":25,"44506361e":25,"44576096e":25,"44617376e":25,"448":33,"44819809e":25,"44882771e":19,"44952179e":25,"45":[7,14,24,25,29,30,33],"45044537e":19,"45055":13,"4513":12,"45216271e":25,"45270114e":19,"45319":12,"4545":13,"45454545":14,"457200":13,"45789629e":19,"45857469e":19,"45899":13,"46":[7,14,19,24,29,30,35],"46154748e":25,"46166602e":19,"462018":19,"46227722":14,"46310032e":19,"46415888":14,"46473182e":19,"4649":12,"46554711e":19,"46725669e":19,"46769":13,"468830e":13,"469":39,"46903645e":19,"47":[7,11,13,14,17,24,29,30,31],"471614":13,"47165930e":25,"47192869":8,"47232592":8,"473420405447642":15,"47406":13,"47428":13,"47469585e":25,"4757":13,"476825":13,"47691796e":19,"477011":24,"47707636":14,"47792":13,"47805102e":19,"47855870e":19,"478654":19,"47888931":15,"478900":12,"479":13,"47963855e":19,"48":[14,24,29,30,33],"48002":13,"48032570e":19,"48094800e":25,"48252852e":25,"48484848":14,"48496826":14,"48530636e":25,"48626016":14,"487":12,"488":12,"48879817e":19,"48922":13,"48948642e":19,"49":[14,16,18,22,24,25,29,30,33],"490":12,"4901":[11,17],"49084638e":19,"49099562e":25,"491":[12,13],"492":12,"49249982e":19,"49268":13,"493":12,"494":12,"495":12,"495126":8,"495135":13,"495150":13,"49539290e":19,"49559702e":25,"496":12,"496600":13,"497":12,"498380":24,"498535":13,"49858699e":25,"4987420":15,"499":12,"49969726e":25,"49m":[12,13,18,28],"49m23":[12,13,18,28],"49m24":[12,13,18,28],"49mnotic":[12,13,18,28],"49mpip":[12,13,18,28],"4_multiclass_classif":22,"4d":[10,14],"4ducqnd7mfihnh7d":3,"4fb140e9d4b0":32,"4p":16,"5":[2,3,7,11,13,16,17,18,19,21,22,25,26,27,28,33,34,39],"50":[7,12,13,14,15,16,18,19,21,22,24,25,26,28,29,30,31,32,33,34,35],"500":[3,7,8,12,15,29,35],"5000":[21,28,34],"500000":24,"5007397612756534":17,"50087464e":25,"500935":13,"501":[12,17],"50128686e":25,"502":12,"50243315e":25,"50274156e":19,"503260e":13,"50342":13,"50654833e":19,"50777458":8,"50807":13,"50838706e":25,"5094138":14,"50998857e":25,"51":[12,14,17,29,30,34],"510":13,"51122842e":25,"512":13,"512675":13,"512870":13,"513":13,"51344574e":19,"51397":13,"514368":24,"51463199e":19,"515":13,"51515152":14,"51578":13,"51615":13,"51665":13,"517":[12,31],"517213":13,"518":4,"51900":13,"51909":13,"51942":13,"51951422":15,"51967288":15,"51997":13,"52":[12,14,29,30],"520":7,"52023":13,"52077":13,"52197574e":19,"52368":13,"52395":13,"52575621e":19,"526":7,"5268":23,"527306":24,"52807567e":19,"52854687e":19,"529":35,"52941":13,"52943":13,"52976973e":19,"529880":13,"529987":13,"53":[14,15,19,24,25,29,30],"531119":13,"53120750e":19,"531326":13,"53254559e":19,"532844":19,"533300":13,"53366992":14,"53412077e":19,"53487851":14,"53528":13,"53553593e":25,"53575704e":19,"53637802e":19,"5364":23,"53659577e":19,"53760":15,"53761":15,"53762":15,"53763":15,"53764":15,"538958":13,"539035":13,"539264":13,"54":[14,24,25,29,30,33],"54062965e":19,"540813":19,"541":13,"541062":13,"54140":13,"54158392e":19,"54175147e":25,"542":7,"54208":13,"54213":13,"542265":13,"54237061e":25,"543":7,"543103":24,"54375250e":19,"544":12,"54400":12,"54468":13,"54471":13,"54478269e":25,"54501126e":25,"5452":23,"54523":13,"54534953":8,"54545455":14,"54548457":14,"54573":13,"546":12,"54606369e":25,"54625":13,"54626":13,"54700385e":25,"547279":13,"54746":13,"547774":13,"548094":19,"548885":13,"54922":13,"549381":24,"54990193e":19,"55":[12,14,29,30],"550":12,"55025":13,"55140244e":25,"55171570e":25,"55188227e":19,"552":12,"55222759e":19,"55258449e":19,"55285629e":25,"55492":13,"554985":13,"5550078":8,"555270":13,"55538743e":19,"55555556":14,"55567437e":19,"55567614":14,"55570":13,"556":[29,30],"55617":13,"55729680e":19,"55799770e":19,"55808":13,"558363":12,"55846061e":19,"55908102":14,"55957757e":25,"559689":13,"56":[14,19,24,29,30],"560":[7,12],"56111":13,"56112":13,"561237":13,"561819":13,"5625":22,"56331337e":25,"56463328":14,"56598296e":19,"56653506e":19,"567089":13,"56749":13,"568":13,"56801387e":19,"569":[35,39],"569208":13,"56933621e":25,"57":[14,16,24,29,30],"570":[12,35],"57037499e":25,"571":35,"571252":13,"57151718":15,"572":35,"57278414e":25,"57373200e":25,"574":35,"57472288e":19,"57481":13,"57545":13,"57569875e":19,"57575758":14,"57593123e":19,"57708":13,"577122":19,"577763":13,"5777710951796506":14,"578800":13,"579":7,"5792":12,"579308":12,"57933225":14,"57933225e":25,"57949544e":19,"58":[14,24,25,29,30,33],"58000":12,"580796":19,"583579":13,"583973":13,"58428941e":19,"58464736e":19,"58570208":14,"58669726":15,"58690191e":25,"58804467e":19,"588516":11,"58880384e":25,"589":7,"58953285e":25,"59":[7,14,16,17,18,24,29,30,39],"59051900e":19,"59103728e":19,"59120796e":19,"591k":12,"59265875e":19,"593":35,"59316":13,"59364427e":19,"59380863e":19,"59381366":14,"594":35,"594047":13,"59461":13,"59461729e":19,"59470943e":25,"59473293e":25,"595":35,"59502421":14,"596":35,"59655748e":19,"59660":13,"597441":13,"59758546e":25,"598":22,"598370":13,"59843":13,"5990944341572852":15,"59924246e":25,"59939861e":19,"59943803e":25,"599900":13,"59995190e":19,"59k":12,"5e":33,"5x5":30,"6":[4,7,11,13,17,18,19,21,22,25,27,28,29,30,31,33,35,39],"60":[12,14,16,19,22,24,29,30],"600":12,"6000":33,"600000":12,"60009622e":19,"6001989060169071":23,"601":[12,13],"60272":13,"60324221e":25,"60365269e":19,"605":13,"60519969e":19,"605746":24,"60605548e":19,"60606061":14,"608":[11,12],"608510":12,"60857280e":25,"608674":12,"609":[7,35],"60928335e":19,"6098":12,"61":[14,22,24,29,30],"610":11,"610410":13,"611":11,"61107851e":25,"612":7,"61267658e":19,"613":11,"61326":13,"61359073":14,"613687":16,"613763":13,"614":11,"614986":13,"61636910e":25,"6173489951795071":17,"617955":13,"61819897e":19,"618285":13,"61908328e":25,"619206":13,"619355":13,"619527":13,"61971214e":19,"619920":13,"62":[14,22,24,29,30,33],"620":22,"621":[4,22],"621145":13,"62141":13,"622":[13,22],"62246648e":25,"6228":12,"623":22,"624":35,"625":22,"625297":13,"62606":13,"626179":24,"62618254e":25,"628":32,"62891172e":25,"62928":13,"62941687e":19,"62975083":14,"63":[7,14,19,22,25,26,29,30,33],"63007403e":19,"63028776e":19,"633146":13,"63412378e":19,"63469071e":19,"6359":13,"6360":13,"63636364":14,"638140e":13,"63943160e":19,"64":[14,22,29,30,31,35],"640":35,"64019598":8,"64033033e":19,"64054926e":19,"64075181e":25,"64228344e":19,"64280731":14,"643135e":13,"64354655e":25,"643588":19,"643981":13,"645022e":13,"64575262e":19,"64575823e":19,"646886":8,"64705215e":19,"6480666066247558":17,"648700":13,"649397":13,"649801":13,"649835":13,"65":[7,8,12,14,22,29,30],"65073966e":19,"65095025e":19,"65140128e":19,"652299":24,"65301846e":19,"653386":19,"6541":12,"65587265e":25,"65681801e":19,"659365":13,"65958117e":19,"66":[13,14,19,24,29,30,35,38],"660":12,"661046":13,"6611":12,"66200710e":19,"66215858e":25,"66280":13,"66284056e":19,"6660137226595727":19,"666183":13,"66666666666666":19,"666666666666664":19,"6666666666666666":23,"666666666666667":19,"666666666666668":19,"66666666666667":19,"66666667":14,"666667":12,"666876":13,"667":29,"66848983e":19,"66887625e":19,"66980625e":25,"66982019e":19,"67":[7,14,16,25,29,30],"67049934e":25,"67071852e":25,"67081402e":19,"67131969e":25,"67262241e":25,"67295307e":19,"673280":13,"67341507":14,"67392941e":25,"67524768e":25,"67580740e":19,"67637":13,"6771":8,"67752249e":25,"678714":13,"67917":15,"67938":15,"68":[12,13,14,16,18,25,29,30,33],"68001571e":25,"680207":13,"68042":15,"68072":15,"68086":15,"68104510e":19,"68179228e":19,"68189771e":19,"68216093e":25,"685500":12,"68576456e":19,"68738041e":19,"68808391e":19,"6881":12,"689780e":13,"689790e":13,"69":[7,12,14,24,29,30,33],"690000z":7,"69033981e":19,"69039912e":19,"690544":13,"69104212e":19,"693652":13,"69490068e":19,"69565340e":19,"69596561e":25,"69664895e":19,"6969697":14,"69749003":14,"699557":13,"6_51_39":13,"7":[2,3,7,8,11,12,13,14,18,19,22,28,29,30,35,39],"70":[12,14,16,19,29,30],"700":17,"7000":34,"70089548e":19,"701149":24,"70130848e":19,"70181608e":19,"70308463e":19,"70522483e":25,"705256":[13,24],"70548023":14,"70611057e":25,"70684726e":19,"70735265":14,"70751":13,"708":7,"70801967e":19,"70832949e":19,"7093":13,"7094":13,"70985658e":19,"71":[14,16,24,29,30,33],"710000":16,"71049697e":19,"71090116e":25,"71456":13,"7159964058294":14,"71624672":8,"71644621e":25,"71659203e":25,"71775288e":19,"71858824":14,"719977":13,"72":[14,16,24,25,29,30],"7200":[17,18],"720000":[17,18],"72028530e":25,"72048413e":19,"721282":13,"72189621e":19,"722":29,"722360e":13,"72236766":14,"722370e":13,"72436206e":25,"72540304":15,"7254030411293304":15,"72581564e":25,"72637647e":19,"72699":13,"72706875e":19,"72716534e":19,"72727273":14,"727430e":13,"73":[12,14,19,29,30],"73013118e":19,"7307":23,"73096308e":19,"73109557e":19,"731839":13,"732093":13,"7321":23,"73319":13,"73334082e":19,"73339668e":19,"73388":13,"73416701e":19,"73430":13,"73434481e":25,"7354":13,"73608":13,"73633":13,"73669760e":19,"73789973e":25,"73823302e":25,"7390722":14,"74":[12,14,16,29,30,33],"74009149e":25,"74081486":15,"74095585e":19,"741430":13,"74176784":8,"74254149e":25,"74368878e":19,"7453":13,"74778663e":25,"749052":13,"75":[12,13,14,15,16,22,24,29,30,33],"750":12,"750000":[13,16,24],"75081016":14,"75116445e":25,"752":13,"752860e":13,"752870e":13,"75307036e":25,"75354527e":19,"754":17,"75473902":15,"755898":13,"756":[13,17],"75642317e":19,"75732367e":19,"75757576":14,"75788452e":25,"76":[14,16,19,24,29,30],"76033":13,"760632":24,"76323776e":19,"7647wqr96rhr49q":[17,22],"76493581":14,"76536":13,"76546160e":19,"767943601369907":17,"768":13,"7682":12,"76846118e":19,"76971":13,"76975214e":19,"77":[12,14,24,29,30],"77200563e":19,"772982":24,"772989":24,"77316345e":25,"773574":13,"77388106e":25,"77426368":14,"774447":13,"77578831e":19,"7763568394002505e":16,"77653136e":25,"77777778":14,"777778":12,"77784322e":19,"778":29,"778371":13,"7793":13,"78":[12,14,29,30,33,35],"780000":16,"78193582e":19,"782500":12,"783439":13,"784":[30,35],"78425838e":25,"78721":13,"78724266e":25,"78787879":14,"788435":13,"78864953":14,"7889":23,"78909912e":25,"79":[14,24,29,30,35],"79046":13,"79400":13,"794146":24,"79615554e":19,"797":7,"79905757e":19,"79932403e":25,"799550":13,"7999":13,"7_data_spectral_transform":17,"7_day_avg":12,"7_modeltrain":32,"8":[2,3,7,8,11,12,13,14,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35,39],"80":[8,12,14,19,25,29,30],"800":25,"8000":33,"80046686e":19,"80125":13,"80184096e":19,"802":[23,29],"802040":13,"80232020e":25,"803":29,"804":29,"80414214e":19,"80467402e":19,"805":29,"805389":13,"8055555555555556":26,"806":29,"807":29,"8073373888433721":32,"80752904e":19,"808":29,"809":29,"81":[14,16,29,30,35],"810":29,"810000z":7,"811":29,"81113083":14,"81141063e":25,"812":29,"813":29,"81304750e":25,"814":[29,35],"815":29,"815100":13,"815140":13,"81525401e":25,"816":29,"81692591e":19,"817":29,"817346":13,"818":29,"8181818181818182":23,"81818182":14,"819":29,"81909580e":25,"81970102e":19,"82":[14,24,25,29,30,33],"820":29,"820579":13,"821":29,"82123482e":25,"822":29,"822220":13,"82295974e":25,"823":29,"824":29,"825":29,"826":29,"8263632119514472":26,"82639626e":19,"827":29,"82773":12,"828":7,"828204":13,"828656":13,"829":29,"82903172e":19,"82940078e":25,"83":[7,14,19,29,30],"830":29,"8309308755379773":17,"83111864e":25,"832":29,"832200":12,"83254010e":25,"83286861e":19,"833":[29,30],"833060":13,"834":29,"835":29,"836148":13,"837":29,"838":29,"83847536e":19,"84":[14,16,22,29,30,31,33],"840":29,"84020133e":19,"84028749e":19,"8406":12,"840700":12,"840963":13,"841":29,"8410":13,"841475":13,"843":29,"84315":13,"84388478e":19,"84424972e":25,"844845":13,"845":29,"8453316591120342":17,"84575843":8,"84583932e":19,"847":29,"847409":13,"84803587":14,"84805874e":19,"84848485":14,"849":29,"84975344":14,"85":[14,19,22,29,30,33],"850":[12,29],"852405":13,"852980":13,"853":29,"853095":13,"853705":13,"85382366e":25,"853950":13,"85430983e":25,"8545072":8,"85451879e":19,"855":29,"857":[7,29],"85702362e":25,"85775":13,"858":11,"85836044e":19,"858770":13,"859":11,"859240":13,"85946":13,"85955127e":19,"86":[14,19,22,25,29,30],"860":[11,29],"86090316e":25,"861":11,"86149975e":25,"862":11,"86269050e":25,"863":[11,29],"86349029869899":17,"864":11,"865":[11,29],"866":11,"86637":13,"867":[11,31],"8679961043240163":17,"868":11,"869":[11,29],"87":[14,22,24,25,29,30],"870":11,"870000z":7,"870529":13,"871":11,"872":11,"87265918e":25,"873":29,"87381742":14,"874325971290356":15,"875000":21,"87549147e":25,"876":29,"87810165e":19,"87812831e":19,"87876889e":19,"87878788":14,"88":[14,22,29,30],"880":[13,29],"880100e":13,"881180e":13,"881300e":13,"88163672":8,"88218788e":25,"88306815e":25,"883288":13,"88405566e":25,"88421426":15,"884382":13,"884605":13,"88499613e":19,"885":29,"8861111111111111":26,"886273":13,"88678261e":19,"8888":27,"88888889":14,"89":[14,22,29,30,33],"890":29,"890000":16,"89021509":14,"89038735e":19,"89098648e":19,"89181306e":19,"8926121":14,"89347960e":25,"89595643e":19,"896":29,"89751882e":19,"89813112e":19,"898166":19,"899":[22,30],"89950044e":25,"8x8":[29,30],"9":[2,11,12,13,14,15,16,19,22,23,24,25,26,28,29,30,31,32,35,39],"90":[8,12,14,19,22,29,30,35],"900":30,"9000":[33,34],"901":[17,30],"902":30,"90265774":8,"903":[29,30],"90305907e":25,"904":30,"90438":13,"905":30,"905100":12,"90535107e":19,"906":[25,30,33],"90625":25,"907":30,"90700158e":25,"90781154e":19,"908":30,"909":30,"90909091":14,"90950553e":19,"90972222":25,"91":[14,22,29,30],"910":[25,30],"911":[12,30],"912":[7,30],"91289199":25,"913":[25,30],"91319444":25,"91336627e":25,"91363892e":25,"914":[29,30],"91468729e":19,"91615":13,"91666667":25,"917":25,"918970":13,"91910361e":19,"9192":13,"91938238e":25,"91949590e":19,"91986063":25,"92":[14,15,22,24,29,30,31],"920":25,"92013889":25,"921":7,"92233225e":25,"923":25,"92334495":25,"92361111":25,"924":25,"92482898":14,"92495460e":25,"925":26,"925120":19,"92678":13,"92682927":25,"927":25,"92708333":25,"92742328e":19,"92780079e":25,"92810648e":19,"92820725e":25,"92855":13,"92880978865406":22,"929816":13,"93":[14,16,19,22,24,25,29,30],"930":25,"93031359":25,"93043423e":25,"93055556":25,"931":25,"93108787e":25,"932":29,"93205588e":19,"93260335":14,"93289237e":19,"93379791":25,"9339":23,"934":25,"93402778":25,"935":11,"936":[11,31],"9365":13,"937":[11,25],"93728223":25,"9375":25,"938":25,"93914670e":19,"93939394":14,"93946235":25,"94":[7,11,14,22,25,29,30],"94015921":25,"94076655":25,"94086334":25,"94097222":25,"941":[7,25],"94155536":25,"942135":16,"94225223":25,"94268501e":25,"94288096e":19,"942928":13,"94293215":25,"94293457":25,"94294425":25,"94337144e":25,"944":[11,25,29],"94420606":14,"94425087":25,"94433556":25,"94444444":25,"945":11,"94503":25,"94503242":25,"94503968":25,"945234":13,"945457":13,"945635":13,"946":11,"94642373":25,"94657351":8,"94699":13,"947":[7,29],"94711334":25,"94712544":25,"947210":13,"94773519":25,"94780778":25,"94781746":25,"9478223":25,"94782472":25,"94791667":25,"948":[11,25],"94850707":25,"94850949":25,"94851916":25,"94852158":25,"94915864":8,"94919909":25,"94921361":25,"94921603":25,"94921845":25,"94990321":25,"94990563":25,"94990805":25,"94991047":25,"95":[8,14,22,24,25,29,30,31],"95001412e":25,"9500949":8,"95032425e":25,"9506025":25,"95060492":25,"951":25,"95121951":25,"9512921":25,"95129694":25,"95129936":25,"95130178":25,"95138889":25,"95181":13,"95198897":25,"95199139":25,"95199381":25,"95199623":25,"95199864":25,"95200106":25,"95214107e":19,"95239386e":19,"95269793":25,"95338269":25,"95338753":25,"95407714":25,"95470383":25,"95476432":25,"95486111":25,"955":25,"95514458e":25,"95547329":25,"95547571":25,"9555061179087876":22,"956":29,"95616531":25,"95616773":25,"95617015":25,"95685734":25,"95686218":25,"95686943":25,"957200":12,"95754694":25,"95755662":25,"958":25,"95818815":25,"95825348":25,"95833333":25,"95894551":25,"95894793":25,"95895035":25,"9599473847106159":26,"96":[3,14,19,22,25,29,30,31],"9609":12,"96124":13,"9615219421101775":26,"96167247":25,"96180501e":25,"96180556":25,"962":[25,29],"96242741":25,"96243225":25,"96304065":14,"96312911":25,"9638163":25,"96382356":25,"96408":13,"965":25,"96515679":25,"96521003":25,"96527778":25,"96590447":25,"96590689":25,"96591415":25,"966550e":13,"966580e":13,"96660376":25,"9666666666666667":25,"96723179e":25,"968":29,"96841261e":25,"96864111":25,"96868709":25,"96875":25,"9687815126050421":26,"9688542825361512":22,"969":25,"96938637":25,"96969697":14,"97":[8,14,22,29,30],"97071249e":19,"972":25,"9720":23,"97212544":25,"97222222":25,"97260277e":25,"97287069":25,"975":29,"975000":[21,24],"97560976":25,"97569444":25,"976":25,"97699003e":25,"97700996":14,"97702356":14,"979":25,"97909408":25,"97916667":25,"9793790849673202":26,"98":[14,19,22,29,30],"98076449e":25,"981":[7,13],"981000e":13,"98100851e":19,"98156960e":25,"982":29,"98233":13,"9825784":25,"98263889":25,"9826966":8,"983":25,"98364724":14,"98533885":8,"986":25,"98606272":25,"98611111":25,"98629590e":25,"986400":13,"987420e":15,"98750850e":19,"98780850e":19,"988":29,"988460":13,"988970":13,"989040":13,"98945863e":19,"98954704":25,"98958333":25,"98965852e":25,"99":[12,14,15,22,29,30],"990":25,"990000z":[17,18],"99469224e":25,"9948425":14,"99549186e":19,"995780e":13,"996":29,"99606599e":25,"99685":13,"9970084":15,"99702835e":19,"99746591":8,"99780493":8,"9981":13,"998100":12,"99908489e":19,"99918722e":19,"99947629e":25,"99958587e":19,"99964465e":25,"9999":13,"\u00b5s":17,"boolean":[11,20],"break":[1,28,35],"byte":15,"case":[8,11,13,14,15,18,20,24,27,28,31,32,35,37,38],"class":[5,6,7,8,9,12,13,14,15,16,17,18,19,20,21,22,23,25,26,28,30,31,32,33,35],"default":[2,3,4,8,12,14,19,26,32,34],"do":[4,8,9,11,12,15,16,17,19,21,22,23,25,26,28,29,30,31,32,34,35,38,39],"export":[24,27],"final":[9,19,22,26,31,33,34,37,38,39],"float":[8,13,14,19,22,23,29,30,31,32,33,35],"function":[2,4,6,7,8,13,14,15,16,17,18,19,20,21,22,23,25,26,27,28,31,33,34,35,38],"ga\u00ebl":21,"import":[2,4,7,8,11,12,13,14,15,16,17,18,19,21,22,23,25,26,27,28,29,30,31,32,33,34,35],"int":[7,8,14,15,17,18,19,22,24,29,30,31,33,35],"long":[15,16,17,29,30,31,34],"m\u00fcller":21,"new":[8,12,13,14,15,16,18,19,21,23,24,26,27,28,36,38,42],"null":[11,13,15],"public":[1,4,42],"return":[7,8,11,12,13,16,19,21,22,23,27,28,29,30,31,32,33,34,35],"short":[4,12,34,35,39],"super":[22,29,30,33,35],"switch":8,"transient":35,"true":[7,8,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"try":[12,14,15,19,21,23,25,26,27,28,31,32,34,38],"var":[12,15,16,17,19,23,28],"while":[12,19,23,25],A:[1,2,4,8,9,10,12,13,14,15,16,18,19,20,21,22,23,25,26,28,29,30,32,34,35,36,42],AND:12,And:[7,15],As:[8,10,12,15,26,27,31,32],At:[3,4,14,15,31,32,38],Be:4,But:[15,16,19,32],By:[4,8,15,26,32,39],For:[1,3,8,10,12,13,14,15,18,19,23,26,27,28,29,30,31,32,36],If:[3,4,8,9,13,14,15,17,19,20,22,23,24,27,28,29,30,32,42],In:[3,4,7,10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,28,29,31,32,33,34,35,36,37],Is:[1,9,14,19,28,38],It:[1,3,4,6,7,8,10,11,12,13,14,15,16,17,19,20,21,22,23,25,27,29,30,31,32,34,35,37,38,42],Its:19,NEAR:12,No:[11,17,19,38],Not:[4,17],OF:12,On:[4,25],One:[1,3,8,9,16,17,19,23,25,26,27,30,31,35],Or:[1,9,31,34],THe:19,TO:[24,35],That:[23,26],The:[1,3,4,5,6,7,8,9,11,12,13,14,15,17,18,20,21,22,23,24,25,26,27,29,30,32,33,34,35,36,37,39,40,42],Their:3,Then:[4,19,29,31],There:[3,4,8,11,14,15,17,19,20,22,23,25,26,27,30,31,32,38],These:[2,3,5,8,18,19,27,30,31,35,38],To:[0,2,3,4,5,7,8,12,13,15,17,18,19,20,23,26,27,28,30,32,34,39],WITH:15,Will:[24,38],With:[12,16,26,32],_1:[8,32],_2:[14,16,32],_:[8,15,22,28,29,30,31,35],_________________________________________________________________:35,__call__:22,__class__:[26,31],__getitem__:[29,30],__init__:[11,28,29,30,33,35],__len__:[29,30],__main__:12,__name__:[26,31],__none:28,__sigmoid:28,__step:28,_array_strptime_with_fallback:12,_asarray_with_ord:22,_assert_all_finit:22,_base:[22,26],_c:30,_check_estimator_nam:22,_check_i:22,_convert_listlike_datetim:12,_coordinate_desc:32,_engin:11,_ensure_no_complex_data:22,_fit_and_predict:22,_fit_context:22,_funcwrapp:22,_get_sequential_output:22,_i:[20,23,32],_k:32,_kmean:19,_lib:12,_lock:22,_make_engin:11,_maybe_cach:12,_n:8,_read:11,_refine_defaults_read:11,_stochastic_gradi:32,_valid:22,_validate_data:22,_validate_nam:11,_validate_param:22,_validate_target:22,_with_config:22,a_:8,a_nparrai:14,aa:14,aar:12,ab:[14,17,18,19,23,24,31],abcseri:12,abil:[6,15,42],abl:[4,19,22,23,27,32,33,38,39],about:[2,4,9,11,12,14,15,17,18,19,23,27,31,33,34,39],abov:[4,8,15,17,19,21,30,31,32],absolut:[18,19,24,28,32],abund:19,ac29:32,academ:4,academia:27,acc:21,acceler:14,accentu:31,accept:[4,15],accept_large_spars:22,accept_spars:22,access:[3,4,5,10,11,14,24,38,39],accord:[8,29,30],accordingli:33,account:[3,8,15,19],accur:[17,38],accuraci:[9,15,21,22,23,24,25,26,29,30,31],accuracy_scor:[22,25,26],accuracy_tim:[29,30,31],accurraci:23,achiev:[9,25,27],acknowledg:4,acoust:3,across:[10,19,26,38,42],act:[26,32],actic:19,action:4,activ:[2,28,29,32,34,35],actual:[9,19,23,24,27,28,31,32,37],actualfalsevalu:21,actualtruevalu:21,ad:[3,6,14,30,31,32,33,34,35],ada_clf:26,adaboost:21,adaboostclassifi:[21,26],adagrad:32,adam:[30,31,32,33,34,35],adapt:[25,31,40],add:[2,4,8,9,10,11,12,14,15,16,18,19,24,28,29,30,31,33,35],add_subplot:[8,19],add_to:11,addit:[9,10,11,12,18,19,22,30,33],address:[1,4,7,9,15,39],adequ:9,adher:4,adityakadiw:23,adjac:15,adjust:[15,25,29,30,32],admiralti:12,adopt:[14,39],advanc:[3,39],advantag:[3,8,15,25,26,32],affect:[12,19],affin:30,afford:3,after:[4,13,14,15,19,29,30,31,32,35],again:[3,8,11,12,15,32],against:[15,19,21,30,32],agenc:42,agg:12,agglom:19,agglomerativeclust:19,aggreg:[14,19,26],agnost:33,ago:24,ahead:[14,27],ai:[10,13,27,32,37],aid:[19,40],aim:[1,4,5,12,32,34],air:14,air_temperatur:[11,14],aircraft:10,aka:[14,21],akash:39,akmehra:39,akshai:[39,40],al:[4,31,35],alana:4,alanabd:13,alaska:17,algebra:[14,20],algorithm:[8,9,19,23,25,26,28,30,31,32,34,35,37,38],alias:2,align:8,all:[1,4,6,7,8,9,11,12,14,15,17,18,19,21,22,23,24,25,26,27,29,30,31,32,33,38,39],allcorr:8,allevi:15,alloc:[3,14,31],allow:[1,3,4,8,10,11,12,13,14,15,23,25,31,32,33,34,38,42],allow_nd:22,allow_object:12,along:[11,19],alow:17,alpha:[8,15,16,19,21,23,24,30,31,32,33],alreadi:[2,3,7,12,13,14,18,23,26,27,28,31,33,37],also:[3,4,7,8,11,12,13,14,15,16,17,19,20,21,26,27,29,30,31,32,34,35,37,42],alter:37,although:23,alwai:[19,23],amath301:39,amath:39,amaz:27,amazon:3,ambient:[7,33],among:[7,10,13,15,19,30,32],amount:[8,11,15,32],amplitud:[17,18,31,38],an:[1,3,6,7,8,10,11,12,13,14,15,17,18,19,20,21,22,23,25,26,27,28,29,30,32,33,34,35,38,39,42],anaconda:2,analog:20,analys:9,analysi:[7,10,11,12,19,20,31,37,39],analyz:[4,17,18,32],anchor:8,andrea:21,android:4,angl:[17,19],ani:[4,6,7,11,12,15,16,17,26,30,34,35,37,42],annoi:27,annot:[8,13],annotate_heatmap:8,anomal:24,anonym:12,anoth:[8,9,12,17,19,26,29,32,35,37],answer:[12,14,15,16,27,32,39],antenna:15,anthoni:39,anticorrel:15,anyon:14,api:[4,11,32],app:42,appear:[8,14,17],append:[8,11,14,15,21,28,30,32,33],appli:[8,12,14,15,16,17,19,20,24,29,31,35,37,38,39],applic:[3,4,8,10,11,13,15,16,17,19,30,42],appnop:[7,28],approach:[8,9,12,15,19,25,26,30,36],appropri:[4,13,14,27,32,38,39],approxim:[8,15,17,23,30],ar:[3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,34,35,37,38,39],arang:[7,8,14,15,17,19,22,29,30,31,32,34],architectur:[3,6,30,31,32,35],archiv:[3,10,38],arcitectur:29,area:[3,4,14,15,21,22],arendt:39,arg:[12,22,33],argmax:8,argmin:19,argument:[8,15,26,30,32],arian:40,aris:17,arithmet:14,around:[3,8,16,19,30],arr:14,arra:14,arrai:[6,7,8,9,12,15,17,18,19,21,22,23,24,25,26,28,31,32,33],arrang:9,array_of_diff:15,array_to_datetim:12,arriv:3,arrow:5,art3d:19,art:[9,10],artefact:17,articl:17,artifici:24,artist:7,asarrai:[8,15,17,19,21,22,31,32],asid:32,ask:[4,23],aspect:[17,31,32],assembl:[9,19],assert:11,assess:[13,15,19,21,25,32],assign:[12,14,15,19,20,24,27,30,31],associ:[3,7,11,29,32],assum:[7,15,17,19,23,32],assumpt:[15,32],ast:8,asttoken:[7,28],astyp:[19,29,30,34],asynchron:12,atan2:8,atlow:17,atmospher:[3,5,10],attach:14,attach_respons:[17,18],attain:19,attempt:[32,35,39],attent:26,attr:14,attribut:[7,9,10,12,13,14,19,22,32,38],attriobut:7,auc:[21,22],augment:[9,32],author:[4,31],autmn22:4,autmn:39,auto:[17,19,25,27,36],autofmt_xd:24,autograd:33,autokera:27,autom:[4,27,36],automat:[2,4,7,8,9,14,27,32,38,39],autumn22:4,avail:[2,3,11,12,13,15,18,19,26,28,35,38],avalanch:7,avenu:19,averag:[12,15,19,23,24,26,27,29,30,32,38],averagepooling2d:31,avg:22,avgpool2d:31,avoid:[4,12,13,15,25,34,38],await:[12,13],awar:[4,17],awesom:[4,27,28],ax11:14,ax12:14,ax13:14,ax14:14,ax1:[8,14,19,24,29,30,31],ax2:[8,14,19,24,29,30,31],ax3:[14,24],ax4:[14,24],ax:[8,14,15,16,17,18,19,21,22,28,30,31,32],axes3d:19,axesimag:[8,25],axhlin:31,axi:[5,6,7,8,12,14,15,17,19,21,24,29,30,31,35,38],axisgrid:19,axvlin:[15,19,31],azimuth:[8,12],b:[8,9,11,12,14,15,17,19,21,23,24,28,29,30,31,32,34],b_i:29,b_j:29,b_k:29,b_w:28,back:[8,12,27,30,32,35],backcal:[7,28],backend:4,background:21,backprop:14,backpropag:[30,34],backward:[8,9,23,29,30,31,32,33,35],bad:[21,26,32,34,35],badg:4,bag_clf:26,baggingclassif:26,baggingclassifi:26,balanc:[7,12,22,32],ball_tre:25,band:[13,17,18],bandpass:17,bar:[4,17,24],barcontain:7,base:[8,10,11,12,14,15,18,19,20,22,23,25,30,32,42],base_estim:26,baseestim:22,baselibsvm:22,baselin:38,baseline_error:24,baseline_pr:24,bash:3,basi:[9,17,19,20,28,32],basic:[3,4,16,19,20,21,25,30,32,36,38,39],basin:10,batch:[19,29,30,31],batch_data:29,batch_label:29,batch_siz:[29,30,31,34,35],bay:[20,21],bayesian:25,bbox_to_anchor:15,beat:38,beauti:42,becaus:[4,10,12,14,15,19,21,23,29,30,31,32,34,35,37,38,39],becom:[3,4,10,17,20,23,34],been:[14,23,27,38],befor:[4,9,13,14,17,20,21,28,32,38,39],begin:[11,15,16,21,31,32],beginn:39,behav:[14,30,32],behavior:[16,21,25,32,38],behind:[26,42],being:[20,29],belong:[12,19,21,23,32],below:[1,3,4,6,7,8,11,12,14,16,17,18,19,20,25,26,30,32,33,35,39],benchmark:9,benefit:40,best:[4,14,19,21,23,25,30,31,32,36,38],best_epoch:32,best_i:32,best_model:32,best_params_:25,beta:35,better:[8,9,15,19,27,30,32,34],between:[3,4,6,7,12,13,14,15,16,17,18,19,21,22,23,26,29,30,31,32,35,38],beyond:[32,40],bia:[15,26,28,29,30,31,32],bias:[28,29,30,32],biasweightupd:28,bibtex:4,big:[3,11,23],bin:[12,14,16,18],binari:[2,11,18,20,23,26,29,30,32,35],binder:[4,42],binderhub:42,biomed:35,bit:32,bitbucket:4,black:[8,14,15,16,19,30,31],blank:[1,19],blast:7,blob:12,block:[34,35],blog:16,blue:[11,13,15,29,30,31],blurr:14,bmatrix:31,bo:19,bold:[1,19],book:[3,14,17,22,30,32,38,39,40,42],boost:27,bootstrap:[9,26],both:[9,14,15,18,19,20,21,23,27,32],bottleneck:35,bottom:8,boulder:40,bound:[11,30,38],boundari:[11,21,30,31],boundedfloattext:28,bourn:3,box:[4,15],bp:17,br:1,bracket:14,branc:35,branch:[4,6,30,35,39],branch_nam:4,breadth:39,brew:3,brief:4,briefli:4,bring:17,broader:31,broadli:15,broken:32,browser:[1,4,5,42],brute:25,bsd:21,bucket:7,bucketx:7,bug:4,build:[4,13,15,19,23,25,26,30,32,42],build_meta:4,built:[11,14,19,30,35,38],bundl:30,burden:15,butter:17,butterworth:17,button:[5,28],c1:[17,32],c2:[17,32],c:[8,14,15,16,19,21,22,28,30,31,33,35,38,42],c_:[8,30,32],cach:[7,12,17],cache_arrai:12,cache_d:[11,12],calcul:[7,8,12,13,14,15,16,17,18,19,20,21,22,23,24,27,28,29,30,31,32,33,35,38],calculat:18,california:[3,12],call:[3,8,11,12,14,17,18,20,21,22,23,26,27,28,29,30,32,34,35,38,42],callabl:30,callback:22,calucl:33,camcol:13,can:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,25,26,27,29,30,31,32,33,35,36,37,38,39,42],candid:25,cannot:[12,17,20,30,35],canon:[17,35,39],canva:39,cap:19,capabl:3,capit:28,captur:[15,16,26,30],carbon:19,carefulli:[13,15,17],carlo:9,carpentri:[3,4,6,14,40],carri:31,carto:12,cascad:19,cascadia:15,casino:15,cast:29,cast_to_ndarrai:22,catalog:[3,12,31],categor:[10,11,24,30],categori:[7,12,20],causal:[17,32],caveat:19,cbar:[8,13],cbar_kw:8,cbarlabel:8,cc:[19,21,32],cd:4,cd_fast:32,cdot:[8,23,29,32],cell:[1,11,12,15,17,22,25,27,30,32,34],celsiu:[12,14],center:[3,4,8,14,15,19],central:[3,12,15,16,17],central_mo:16,centroid:19,certain:[19,26],certifi:18,cfg:7,chain:30,challeng:11,chang:[4,8,10,12,14,15,16,19,26,28,32,33,35,37],channel:[2,12,17,18,30,31,42],chapter2:17,chapter3:22,chapter4:[30,32],chapter:[3,27,32,39],charact:[11,13,15],character:[9,13,32],characterist:[17,21,22,32],charset:18,chart:24,chatgpt:40,cheat:4,chebyshev:[17,25],check:[2,4,11,12,14,15,17,18,30,32,35],check_arrai:22,check_consistent_length:22,check_param:22,check_x_i:22,check_y_param:22,checkout:4,checkpoint:[30,31,35],chemistri:10,chile:12,chine:39,choic:[12,15,25,26,28,32],choos:[3,4,8,12,17,18,19,20,29,30,31],choosealicens:4,chronolog:15,chunksiz:11,ci:4,cienc:39,circ:[14,23],circl:[15,19],citat:4,cite:4,classic:[17,19,20,25,31,32],classif:[5,9,18,19,23,26,29,30,31],classifi:[7,12,20,22,23,25,29,30,31,32,35],classificaiton:[20,26],classification_report:[21,22],claus:21,clean:[4,13,15,16,18,32,35,38],cleaned_data:13,cleanest:4,clear:[19,27,39],clear_output:[17,28],clearli:[15,19],clf2:25,clf:[21,22,25,26,28,30],cli:4,click:[4,5,9,28],client:[17,18],climat:[10,11,12,14],clipboard:4,clone:[4,6,22,32,39],close:[15,19,21,23,35],closer:[15,19],closest:19,cloud:[1,4,6,8,10,11,14,38,39,42],cloudstor:3,cluster:[3,5,31,38,39],cluster_centers_:19,cluster_label:19,clusterid:19,clusters_new:19,clusters_old:19,cm:[8,17,19,22,28,30],cm_bright:30,cmap:[7,8,13,17,21,22,30,31,35],cnn:[34,35],cnnautoencod:35,co2:5,co:[8,14,19,21],coarsen:14,coast:12,cobli:14,code:[1,3,4,8,12,13,19,21,23,27,31,32,38,39,42],codespac:[17,22],coef:15,coef_:[15,23,32],coeff:8,coeffic:15,coeffici:[8,13,14,15,17,19,32],cogt:10,coher:19,cohes:19,cohort:4,coin:31,col:14,col_index:19,col_label:8,colab:4,coll:33,collabor:42,colleagu:4,collect:[5,7,8,10,11,13,14,16,17,19,21,26,27,38],color:[8,11,12,13,14,15,16,18,19,21,28,29,30,31],color_threshold:19,colorbar:8,colorblind:14,colorinterp:11,colormap:8,column:[7,8,10,13,15,16,17,19,22,23,24,32],column_or_1d:[22,32],column_stack:19,columnar:11,com:[1,4,5,7,11,12,13,16,17,19,23,24,27,31,33,39,42],combin:[8,15,16,18,19,25,26,29,31,32,34],come:[2,10,14,19,27],comic:4,comm:28,comma:10,command:[3,4,23,24,30,42],comment:[11,19,33],commit:4,common:[4,7,10,11,12,13,16,17,19,20,21,32,35],commonli:[10,19],commun:[4,11,40,42],compact:[12,17],compar:[6,12,14,17,18,19,20,21,26,29,30,31,35,38],compare_model:27,compareoutput:28,comparison:[8,27],compil:[9,30,34],complet:[12,16,17,19,21,38],completeness_scor:19,complex:[8,10,11,14,15,17,19,20,26,30,31,32,38],complex_:18,complic:[23,34],compon:[7,15,35,37,39],components_:8,compos:[1,3,12,26,31,35],composit:16,compress:[11,12,17,32,35],compris:31,comput:[1,2,4,6,8,9,10,11,15,17,19,20,22,23,29,30,32,33,38,39,42],computation:[8,15,25],compute_cent:19,compute_clust:19,compute_dist:19,compute_elbow:19,compute_object:19,cona:2,concat:7,concaten:[15,18,19,31],conceit:15,concept:[10,15,19,35,39],concurr:22,conda:[4,42],condit:[19,20,32,33,36,38],conduct:4,conern:3,conf:4,confid:[15,21],config:[4,14,22],config_context:22,configur:[4,11,25,27],confirm:27,conflict:4,confus:[21,22],confusion_matrix:[21,22],confusionmatrixdisplai:22,congrat:4,conj:18,connect:[3,4,12,13,29,30,34,35],consid:[9,17,20,21,27,28,31,32],consist:[12,19,25,32,36],console_script:4,constant:32,constrain:[17,30,32,33],constraint:32,construct:[5,18,19],consum:[25,36],contact:[4,10],contain:[1,2,4,8,10,11,12,13,14,15,19,24,32,35,39,42],contamin:18,content:[3,4,5,6,11,12,15,16,18],content_str:15,context:[13,14,18,19,29,30,31,35],continu:[4,8,11,17,32],contourf:[11,17,30],contourpi:[13,18,28],contribut:[3,6,8,19,26,30,32,40],contributor:4,control:[39,42],conv1:30,conv2:30,conv2d:[30,31,35],converg:[15,19,32,35],convergencewarn:32,convers:[12,19,28],convert:[8,11,12,14,15,17,22,23,24,25,26,29,33],convert_listlik:12,convert_str_to_tsobject:12,convien:11,convnetquak:31,convolut:30,convolve2d:31,convout:31,convtranspose2d:35,cool:[3,27,33],cooling_law:33,coolwarm:13,coord:14,coordin:[11,14,15,17,20,30,38],copi:[2,4,9,12,14,19,22,25,26,29,30,38,39],copilot:12,core:[3,12,13,14,19,26,42],corner:[4,11,17],corr:[7,8,13],corr_coef_collector:15,corr_matrix:13,corrcoef:[14,15],correct:[23,26,27,29,30,31],correctli:[21,23,27],correl:[7,8,9,14,15,19,32,38],correlated_data:15,correlation_matrix:15,correspond:[7,10,12,19,23,28,30,39],cosin:[17,19],cost:[3,15,19,20,27,28,30,32],costfunct:28,costum:12,could:[3,4,10,12,13,22,31,35],count:[12,13,15,16,17,18,21,22,24],cours:[1,4,5,6,11,12,14,15,40],cov:14,covari:[15,19],cover:[17,19,38,39],cpu:[3,14,26,33,35],cr:11,craft:33,crap2:35,crap:[7,8,18,35],creat:[2,6,8,9,11,13,14,15,16,17,18,19,20,22,23,24,26,27,30,31,32,33,34,35,38,39,42],create_graph:33,creation:11,credit:4,crispi:18,cristea:[39,40],criterion:[28,29,30,31,35],critic:[4,15,16,17],cross:[9,14,22,23,29,30,31,32,38],cross_val_predict:[22,26],cross_val_scor:[21,26],crossentropyloss:[29,30,31],crucial:[13,14,19],cruis:19,cryospher:[5,39],cs160:39,cs163:39,cs230:31,csr:22,csv:[1,5,7,9,10,13,16,19,23,24,27],cu:40,cubic:16,cuda:[3,14,33,35],cumsum:8,cumul:8,cumulativevarianceexplain:8,curat:5,curl:5,current:[2,8,19,22,23,24,34,38],curriculum:[30,32],curv:[21,22,26,30,31,32,35],curvatur:17,curve_fit:14,custom:[12,30],custom_dataset:[29,30],customdataset:[29,30],cutoff:17,cv:[13,15,22,25,26],cv_results_:25,cwt:[9,17,38],cwtm:17,cwtmatr:17,cyan:8,cycler:[2,13,18,28],cyto:19,cytomet:19,d1:14,d2:14,d2dl:34,d:[8,12,14,17,18,19,20,24,29,30,31,33,35],da:3,dai:[8,12,15,17,24,27],daili:[8,12,24],dall:10,dash:28,dask:14,data:[1,3,4,7,17,20,23,25,26,29,32,33,34,36,37,39,42],data_faith:19,data_loader_test:[29,30],data_loader_train:[29,30],data_rang:12,data_shrink:19,dataarrai:14,databas:[10,11,14,16],dataconversionwarn:32,datafram:[8,10,11,14,15,16,19,21,24,34],dataload:[30,31,35],datamanipul:17,dataset:[7,8,9,10,11,12,13,14,15,16,17,19,21,22,23,24,25,26,30,31,32,35,38],dataseta:25,datatyp:13,date:[4,8,10,12,15,19,24],date_format:11,date_pars:11,date_rang:12,date_year:[8,32],dateparseerror:12,datetim:[12,24],datetime64:12,datetimearrai:12,dateutil:[2,7,12,13,18,28],dateutil_pars:12,dayfirst:[11,12],db:23,de:8,deactiv:2,deal:[8,12,14,15,24,32],debug:38,dec:13,decai:18,decid:[13,19,21,23,30],decim:[11,15],decis:[20,21,26,30],decision_funct:[22,30],decisionboundarydisplai:21,decisiontre:26,decisiontreeclassifi:[21,26],declar:[14,15],decod:[12,15],decoder_cnn:35,decompos:[8,17,35],decomposit:[8,17,19,20],decor:[7,18,22,28],decreas:[8,9,19,21,23],dedic:30,deep:[3,14,19,31,35,37,39],deepdenois:35,deeplearn:[30,32],def:[7,8,12,13,16,19,21,23,28,29,30,31,32,33,34,35],default_rng:[15,28],defin:[2,4,9,12,14,15,16,18,19,21,23,25,28,29,30,32,33,35,42],definit:[8,9,19],degc:14,degre:[8,15,17,24,32],delai:22,delayed_func:22,delet:2,delim_whitespac:11,delimit:11,delta:[15,17,18,23,28],dem:17,demarc:19,demographisc:3,demonstr:[6,7,8,14,15,21,27,32,39],dendogram:19,dendrogram:19,denoisecnna:35,denol:[39,40],denot:[12,19],dens:[30,31,34],densiti:[13,19],depend:[2,4,8,9,10,13,17,19,30,42],deploi:[3,12,39,42],deploy:[4,12,39],deprec:[15,28],deprecationwarn:28,depth:[10,12,19,26,31,35],depth_km:12,deriv:[10,23,30,32,33,42],descent:[14,20,28,29,30,31],descr:[22,25],describ:[4,9,11,12,13,14,16,24,25,35,37,39],descript:[4,12,24,28],design:[11,14,15,19,22,26,31,32,33,39],desir:32,detach:[23,33,35],detail:[3,4,19,25,27,30,39],detect:[7,31,32,35],determin:[9,15,17,24,28,29,30,32],determini:15,detrend:[15,17,18],dev:[12,17],develop:[2,3,4,32,39,40,42],deviat:[14,15,16,17,38],devic:[3,14,33,35],devid:15,df:[7,8,11,12,13,15,16,19,21],df_clean:13,df_numer:13,df_read:11,diag:8,diagon:[8,21],dialect:11,diam:19,diam_lwr:19,diam_mid:19,diam_upr:19,diamet:19,dict:[8,22],dict_kei:[22,25],dictat:31,dictionari:[8,11,12,14,22,25,26,32],did:[12,15,27,32],diff:[4,15],diff_mean:15,differ:[2,4,6,7,11,12,14,15,17,18,19,21,25,26,32,35,37,38,42],differenti:[14,30,32,33],difficult:[17,19,37],diffus:[33,35],digit:[11,12,13,17,22,25,26,29,30,39],dim:14,dimens:[8,9,10,11,12,14,15,17,30,31,32,34,35,38],dimension:[9,10,11,12,14,19,20,32,39],dip:12,dir0:30,dir1:35,direct:[5,7,8,10,13,14,32],directli:[3,4,10,12,22,27],directori:[11,35],disabl:[29,30,31,35],disadvantag:[15,32],discern:19,disciplin:39,disciplinari:5,discov:19,discoveri:33,discret:[17,31],discrimin:[8,13,16,18,20],discriminant_analysi:21,discriminatori:17,discuss:[3,4,10,12,15,19],disp:22,displac:[8,15,32],displai:[12,13,14,16,17,24,28,31],display_styl:14,dissimilar:[14,19],dist:[14,19],distanc:[17,25],distance_threshold:19,distancemetr:19,distinct:19,distinguish:[13,14],distort:19,distribut:[3,7,8,9,12,14,15,17,18,19,25,32,38,42],distrubut:13,dive:[14,31],divers:[5,10,25,26,32],diversifi:32,divid:[9,15,16,17,18,38,39],divis:19,djf:14,dl:[7,11,17,19,23,31],dmatrix:26,dmc:[17,18],dn:19,doc:[4,12,14,15,19,24,27,30,32],docker:[4,27,39,42],docmument:4,document:[4,6,9,11,12,17,19,21,32,38,42],doe:[4,11,12,15,18,19,21,23,26,28,31,38,42],doi:[4,11,31,38],domain:[18,25,30,35],domin:[11,17],don:[27,34],done:[3,4,11,14,15,17,22,27,30,31],dot:[8,23,28,31,32],doublequot:11,dowload:11,down:[1,4,11,29,32],download:[2,4,7,8,11,12,13,15,17,18,19,23,26,27,31,35,42],downsampl:[14,30,31],dozen:42,draft:21,dramat:3,drastic:21,draw:[9,15,19],drawn:32,drive:39,driven:42,drlat:17,drlon:17,drone:10,drop:[4,7,13,16,23,24,28,30,38],dropbox:[4,7,11,17,19,30,31,32],dropdown:28,dropna:[12,13,15,16,23],dropout:[30,31,32,35],ds2:14,ds:[1,4,14,39,42],dt:[14,17,33],dta:12,dtype:[7,11,12,13,14,18,19,22,25,31],dtype_backend:11,du:14,dualiti:32,ducelli:40,due:[13,15,17,19,32,39],dummi:31,dump:11,duo:4,duplic:[4,15],durat:17,dure:[27,29,30,32,34,39],dw:23,dwa82x6xhjkhyw8:19,dynam:5,dz:16,e2019gl085870:31,e:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,19,23,25,26,27,29,30,31,32,35,37,38],e_b:15,e_train:15,e_val:15,each:[2,3,4,7,8,10,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,39,42],earli:[9,11,22,34,39],earlier:[30,35],earliest:12,early_stop:[30,32],earth:[4,7,10,14,19,39,40],earthchem:16,earthdatasci:[4,11,14,40],earthquak:[7,8,16,17,18,20,31,32,35],earthquake_magnitud:16,earthrocgranit:[5,16],eas:[15,32],easi:[4,11,14,23,27],easier:[8,14,23,30],easili:[15,35,38],east:[8,11,12,15,32],eastward:15,ecosystem:[25,39],edg:[17,31],edgecolor:[16,19,21,30],edit:[3,4,27,35],editor:4,edu:[4,8,15,39],educ:39,effect:[10,15,17,21,25,26,29,32],effici:[3,10,11,12,14,15,25,27,32,35],effort:[4,14,27,36],ehz:12,eigen:8,eigenvalu:8,eigenvector:8,either:[1,3,5,8,14,15,19,23,30,32,35,39],ela_reg:32,elaps:22,elasticnet:32,element:[11,12,14,15,19,21,28,31,32],elev:[11,12,17],elevation_m:11,elif:[11,12],elimin:32,ellips:19,els:[4,7,8,11,12,14,19,21,22,28,29,30,31,33,35],elsewher:31,email:[4,39],emb:32,embed:[8,11,37],emerg:[7,11],empir:19,emploi:[15,25],empti:[12,15,19,22,34],en:15,enabl:[3,17],encod:[11,27],encoder_cnn:35,encoding_error:11,encount:[5,10,17],end:[0,8,9,12,15,16,19,20,21,25,29,30,31,35,39],end_dim:[29,30],end_tim:12,enddat:12,endeavor:19,endtim:[17,18],energi:17,enet_coordinate_desc:32,enforc:17,engin:[11,15,32,39],enhanc:11,enivron:2,enough:[9,14,32,38],ensembl:[21,22,24],ensur:[4,12,15,24,28],ensure_2d:22,ensure_min_featur:22,ensure_min_sampl:22,enter:[12,16,17,27],entir:[4,12,15,19,26,29,31,32,37],entri:[4,8,11,13],entropi:[23,29,30,31,32],enumer:[8,13,18,19,21],env:[2,7,11,12,13,18,19,26,28,32],environ:[4,6,10,11,25,38,39,42],environment:[11,15],ep:[21,33],epal:28,epoch:[26,29,30,31,32,33,34,35],epr:15,epred_train:15,epred_v:15,epsilon:23,eq1:31,eq2:31,eq:[7,33],equal:[8,15,16,17,18,28],equat:[10,20,30,33,42],equial:14,equival:[10,12,14,23,29,30,32],er:28,err:21,errno:11,error:[8,9,11,12,13,15,19,20,21,23,24,28,29,30,31,32,35,38],error_scor:25,erupt:19,escapechar:11,escienc:3,esourc:39,espear:31,especi:[14,15,19,25,27,32,38],ess490:[30,32],ess:[1,4,39,42],essenc:19,essenti:[12,15],est:19,estim:[8,15,19,22,24,25,26,29,31,32,35,38],estimator__algorithm:25,estimator__leaf_s:25,estimator__metr:25,estimator__metric_param:25,estimator__n_job:25,estimator__n_neighbor:25,estimator__p:25,estimator__weight:25,estimator_nam:22,et:[4,31,35],eta0:32,eta:[26,28],etc:[1,4,10,12,14,27,29,32,38],euclidean:[19,25],euclidian:[14,19],ev:15,eval:[7,28,33],evalu:[15,19,20,25,26,29,30,31,32,35,39],evaluate_model:27,evapor:12,evaporation_mm:12,even:[10,17,18,19,37],evenli:14,event:[5,7,12,16,17,19,31,42],event_id:7,eventu:42,everi:[9,14,15,17,25,31,37,39],everyth:[2,28,37],everytim:4,evolut:[29,32],exact:[2,12,15,23,37],exactli:15,exampl:[3,7,8,10,11,13,14,16,17,19,20,21,22,23,24,26,29,30,33,34,36,39],exce:29,excel:[4,14],except:[1,30],exceptiongroup:28,excess:32,exchang:11,exclud:[13,27],exclus:30,execut:[1,2,7,28],exercis:[5,7,11,15,19,21],exess:32,exhibit:15,exist:[9,15,17,19,23,25,42],exist_ok:[11,30,31,35],exp:[12,14,17,18,23,28,29,30,32,33],exp_reg101:27,expand:1,expans:3,expect:[6,12,21,27,32,38],expens:[3,25],experi:[3,19,25,27,36,42],expert:[9,25,39],expertis:38,expir:4,explain:[8,9,19,23,30,31,33],explained_variance_:8,explained_variance_ratio_:8,explanatori:12,explicitli:[15,19],explor:[9,15,16,18,19,22,25,26,28,29,36,38],explort:13,explos:7,expon:16,exponenti:[16,32],express:[12,19],extend:[21,29,31],extens:[2,6,25,28,42],extent:[9,17],extern:[2,25],extract:[7,9,16,17,18,24,27,28,29,35,39],extractal:11,extrem:[5,15],f0e1ywupdbuv3l3:7,f1:[21,22,23,32,34],f1_score:21,f2:34,f4:15,f:[7,11,13,14,16,17,18,21,22,23,24,25,26,28,29,30,31,33],f_1:21,f_:17,f_a:17,f_c:17,f_path:[30,31,35],f_x:23,facecolor:19,facet:14,facil:38,facilit:[11,14],fact:[15,19,31],factor:[4,13,17,18,30],fahrenheit:12,fail:[12,30],fair:38,faith:19,fake:19,fall:12,faller:4,fals:[8,11,12,13,15,16,19,21,22,23,24,25,26],false_valu:11,famili:17,familiar:[14,29],far:15,fashion:31,fashionmnist:35,fast:[3,12],faster:[11,14],fastica:[8,9],fc1:[30,35],fc2:[30,35],fc3:30,fc4e2a:8,fc:19,fdsn:[17,18],fdsn_client:[17,18],feather:19,featur:[9,10,12,14,17,18,19,20,23,25,27,30,31,32,33,35,38,39,42],feature_import:24,feature_importances_:24,feature_list:24,feature_nam:[8,22,25,28],fee:4,feed:[13,35],fetch:[4,39],fetch_openml:[22,31,35],few:[12,13,15,19,32],fewer:8,ff0000:30,ff:12,fft2:17,fft:[9,17,18],fftfreq:[17,18],fftpack:[17,18],fftshift:17,fiberid:13,fidel:35,field:[8,10,12,13,14,27,31,32,33,35,36],fig1:14,fig2:14,fig:[8,12,14,16,17,18,19,24,28,29,30,31,32],figsiz:[8,13,14,16,17,18,19,22,24,28,30,31,32,34],figur:[5,8,11,12,13,18,19,22,27,28,29,30,31],figure_:22,figure_format:14,fiji:12,file1:[4,11,17],file2:[4,11,17],file:[1,3,7,9,11,13,14,16,19,21,22,31,38,39],file_url:8,filenam:[11,19],filenotfounderror:11,filepath_or_buff:11,fill:[13,17,18,31,38],fill_betweenx:19,fill_valu:25,filter:[12,13,14,31],filterdimens:31,filtered_imag:31,find:[3,4,7,8,9,12,13,14,15,17,19,20,21,23,25,26,29,30,31,32,35,36,38,39],finder:27,fine:[25,32],finit:[7,17,31],first:[1,3,4,7,8,9,12,13,14,15,16,17,18,19,21,22,23,26,28,29,30,31,32,33,34,35,38,39],fisher:16,fit:[8,14,15,19,21,22,23,24,25,26,30,33,34,36],fit_method:22,fit_param:22,fit_predict:19,fit_transform:[8,19,21,22,25,26,32,35],fivethirtyeight:24,fix:[4,12,15,30,31,32,38],flag:19,flat:18,flatten:[14,26,29,30,31,34,35],flavor:42,flexibl:[10,14],flip:14,float32:[14,29,30,31,34],float64:[12,13,22],float_precis:11,floatlogslid:28,floor:17,flouresc:19,flow:[10,16,19],fluctuat:12,fluor:19,fly:14,fmi:19,fn:[19,21,23],fname:[11,12],focu:[3,7,11,14,15,25,27],focus:[13,19,39],fold:[22,25,32],fold_shuffl:27,folder:[9,11,12,17,19,28,31,38],folium:11,follow:[0,3,4,5,7,8,11,15,16,19,21,26,27,28,31,32,39],font:[14,18,19],fontsiz:[14,19],fonttool:[13,18,28],fontweight:19,forc:[4,23,25,39],force_all_finit:22,forecast:[24,27],forecast_acc:24,forecast_noaa:[24,27],forecast_und:24,forest:[20,21,22,26,27,30,39],forestri:5,forg:2,forget:32,fork:4,forlin:28,form:[10,11,14,15,18,19,20,32],format:[1,5,7,8,9,12,19,21,22,24,29,30,31,35,38,39],formatt:8,former:40,formul:[9,20,23],formula:[19,22,23],forth:27,fortran:42,forward:[8,9,14,29,31,33,35],found:[4,11,14,23,29,31,32,39],foundat:[10,14,39],four:7,fourier:[7,9,18,35,39],fourth:[1,14,16,18],fowlk:19,fp:[19,21,23],fpr:[21,22],frac:[8,14,16,17,18,19,20,21,23,29,30,31,32,33],fraction:[19,21],frame:[7,12,13,16,20,22,25],framework:[4,11,17,39],free:[3,4,35,38,42],freedom:32,freq:[12,17],frequenc:[14,16,17,18],frequent:[8,12,19],freqvec1:17,freqvec:[17,18],fri:24,friend:[2,24,27],friendli:[4,30],fritz:32,from:[1,2,4,7,8,9,10,11,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,38,39,42],from_estim:[21,22],from_numpi:[23,33],fs:[7,17,18],ft:17,ftp:12,full:[12,27,31,32],full_matric:8,fulli:[29,30,34,35],func:22,functool:33,fundament:[9,10,15,19,21,32,33],further:[4,30,35],futur:[4,10,28],futurewarn:[19,26],g2:32,g:[1,2,3,4,5,8,9,10,11,12,13,14,15,19,20,23,25,26,27,29,30,31,32,37,38],gain:42,galaxi:13,game:3,gamma:[21,22],gap:[13,17,32,38],gate:[19,34],gather:[3,20,32],gaussian:[14,21,32,38],gaussian_filt:14,gaussian_process:21,gaussiannb:[21,26],gaussiannois:35,gaussianprocessclassifi:21,gb:26,gca:[8,15,16],gcp:3,gd:32,gdoutput:28,gdrive:4,ge:14,gener:[4,8,9,10,11,14,15,16,19,20,26,28,30,31,32,34,35,36,37,38,39],generaliz:[26,38],generate_time_seri:34,geo:39,geodes:19,geodesi:[8,15,39],geodet:[5,19],geograph:[10,11],geographi:19,geohackweek:[11,40],geolog:[11,17],geologi:[11,17,39],geometr:10,geometri:11,geophys:[5,10,16,19,31],georeferenc:11,georg:40,geoscien:39,geoscienc:[3,5,11,12,13,16,17,19,32,37,38,40],geoscientif:[10,12,13,17],geoscientist:12,geosmart:[4,39],geospati:[10,15,17,19,26,30,32,37,38,39],geostatist:19,geotiff:10,geq:23,get:[3,4,5,8,9,11,12,13,14,15,16,19,22,23,24,26,29,30,31,32,35],get_arrai:8,get_config:22,get_dummi:[24,27],get_features_by_domain:7,get_handl:11,get_param:25,get_waveform:[17,18],get_xticklabel:8,getattr:22,getitem:29,geyser:19,gh:12,gi:10,giant:37,gist_dir:12,git:[6,26,39,42],github:[1,3,5,6,12,25,27,31,33,35,38,40,42],githubusercont:[5,12,13,16],gitignor:4,gitlab:4,give:[1,2,4,15,17,18,19,21,23,27,32],given:[1,8,10,12,15,19,21,25,31,33,38],glacier:19,glass:1,glob:[8,15],global:[3,4,12,19,31,32],global_quakes_iri:12,global_skip_valid:22,go:[4,7,11,12,14,15,17,23,26,27,28,29,30,32],goal:[15,19,25],goe:[29,32,33],gone:27,good:[4,9,14,15,19,25,32,38],googl:[24,27,39],got:22,gouraud:17,gov:[11,24],gp:[8,15,19],gpd:11,gps_timeseri:[8,15],gpu:[3,14,35],grad:[23,33],grad_a:14,grad_b:14,grad_c:14,grad_d:14,grad_fn:23,grad_output:33,grad_y_pr:14,gradient:[14,20,28,29,30,31,33,34,35],gradientdesc:28,graduat:40,grai:[28,31],granit:16,grant:4,grant_sudo:27,graph:[14,24,30],graphic:[3,31],graviti:10,gray_r:22,great:[3,4,27,28,30,31,35],greater:[17,24,31,32],green:[11,13,15,19],grei:29,grid:[8,10,11,12,14,15,17,18,19,21,22,28,29,30,31,32,33,34],gridsearchcv:25,gridsearchcvgridsearchcv:25,grobler:21,ground:[7,10,16,17,19,32,38],group:[3,7,11,12,14,15,19,20,22,38],groupbi:[12,14],growth:3,gudelin:4,gueron:26,guess:11,gui:4,guid:[4,12],guidelin:[4,38],gzip:[8,15],h2o:27,h5:[5,7,9,11,31],h5py:[7,11,31,35],h:[8,12,23,30,33],h_w:20,h_y:23,ha:[4,7,8,11,12,14,15,18,19,20,23,25,27,29,30,31,32,34,35,38],hackweek:42,had:[4,29],half:39,hand:[26,29,42],handl:[3,4,10,12,20,29,30],handwritten:22,happen:[8,12,14,15,17,19,21,32],happi:[28,30],hard:[4,26,39],hardwar:[1,3,42],harmon:21,has_index_nam:11,hasattr:30,hasn:28,hat:[15,17,20,23,29,32],have:[2,3,4,7,9,11,12,13,14,15,16,17,18,19,23,25,26,27,29,30,31,32,33,34,35,37,38,40],hazard:16,hdf5:7,hdf:[10,11],head:[1,4,7,8,11,12,13,15,16,19,24,27],headach:4,header:[11,12],heartbeat:32,heat:33,heatmap:[8,13],heavi:[3,16],height:[11,12,15,31],help:[4,6,14,15,19,25,32,38],henderson:[39,40],here:[3,4,7,8,11,12,14,15,16,17,18,19,20,22,23,25,27,28,29,30,31,32,34,35,39],heterogen:11,hhz:[17,18],hidden:[30,33,34],hidden_layer_s:30,hide:8,hierarch:[10,31],hierarchi:19,high:[3,10,13,15,16,17,19,21,25,32,35,39],higher:[12,21],highest:[8,17],highli:13,highlight:13,highpass:17,hint:12,hist:[7,12,14,15,16,17,18],histogram:[7,9,12,13,14,16,17,18],histor:[24,27],histori:[9,29,30,31,34,35],histplot:13,hnz:12,hold:11,home:[3,4,9,11],homepag:4,homewgrown:38,homework:4,homgeneity_scor:19,homogen:[19,32],homogeneity_completeness_v_measur:19,honshu:12,hood:[19,27],hook:23,hopefulli:19,horizont:[3,8,15,31],horizontalalign:[8,30],host:[4,39,42],hot:27,hour:[3,12,17],hover_data:12,hover_nam:12,how:[3,4,7,8,9,10,11,12,13,14,15,16,19,20,21,22,23,28,29,30,37,38,39],howev:[3,4,8,9,11,13,15,23,30],hp:17,hpc:[6,14],href:[17,22,30,32],html:[1,4,8,12,14,19,25,30,32,35,38],htop:3,http:[1,3,4,5,7,8,11,12,13,14,15,16,17,19,21,23,24,27,30,31,32,33,35,36,38,39,42],hub:[3,6,11,39],hue:19,hulbert:31,human:[10,11,38],hyak:3,hydrolog:[5,8,10],hydrotherm:19,hyp_50m_sr:11,hyper:[20,25,27,30],hyperbol:30,hyperparamet:[15,26,27,31,32,38],hyperplan:20,hyperspectr:10,hypothesi:[15,20],hz:[17,18],i2:17,i6tv3ug15oe6yh:17,i8:12,i:[3,7,8,12,13,15,17,18,19,20,22,23,28,29,30,31,32,35,38],i_it:23,i_j:28,ic:[19,31],ica:[8,9,38],iclass:21,icon:4,id:[4,7,15,19,22,24,27],idai:8,idea:[19,26,27,35],ideal:[8,10,14,21,32],ident:[14,21],identifi:[5,8,19,22,24,31,32,38],idna:18,idx:35,ifft2:17,ifft:[17,18],ifram:12,ignor:[4,15,17,19],ignore_index:7,igs14:[8,15],ii:[8,15,21],iik:15,il:13,illustr:[4,15,17,19,35],iloc:[7,11,12,24,27],im:8,imag:[1,8,9,10,11,16,17,18,19,22,25,27,29,30,32,35,39],image_index:35,imageri:[10,11,14],imagin:[15,35],imaginari:35,img:[1,31],immedi:22,impact:[25,26,28,32,37],imperfect:26,implement:[3,19,20,25,26,28,29,30,32,35],impli:[8,19,31],important_indic:24,importantli:16,importlib:[13,18,28],impos:32,improv:[26,32,35,38],impuls:7,imput:13,imputation_typ:27,imshow:[8,17,22,31,35],in_channel:31,in_circl:15,in_featur:[29,30],in_squar:15,inaccess:4,includ:[1,4,5,6,10,16,19,30,31,32,35,38,39],incomplet:4,incoropor:39,incorpor:[19,42],incorrect:13,increas:[14,15,19,21,23,26,32,33,34],increment:33,ind:17,inde:[15,21,35],independ:[4,11,15,22,23,26,32],index:[4,5,7,11,12,13,14,19,24,29,30],index_col:11,indic:[13,15,16,19,32],indirect:10,individu:[3,12,14,19,26,29,32],induc:15,industri:27,ineffici:11,inertia:19,inexact:23,inf:[13,32],infer:12,infer_datetime_format:[11,12],infin:23,infinit:17,influenc:[15,27],info:[2,12,13],inform:[4,8,9,10,11,30,32,37,38,42],infrastructur:42,infti:[16,17,31],inher:19,inherit:[29,35],inhomogen:32,init:[4,15,19],init_cent:19,initi:[4,13,14,19,23,25,28,29,30,31,32,33,35,38],initiali:19,inlin:[7,8,14,15,24,28,30,32],inlinebackend:14,inplac:[12,13,23],input:[7,8,9,12,13,14,17,27,28,29,30,31,32,33,34,35,38],input_dim:33,input_nam:22,input_s:35,input_shap:[30,31,34],inquiri:11,inscrib:15,insert:[1,19],insid:[2,8,15],insight:[13,19],inspect:21,inspir:[4,33],instal:[2,3,4,6,7,12,13,17,18,26,27,28],instanc:[1,3,8,10,12,18,21,22,26,30,31,32,38,39],instanti:[24,35],instead:[4,12,16,17,19,21,22,23,26,32,35,38],institut:[3,38],instruct:4,instructor:[4,12,17,22,39],instrument:[10,17,19],int32:[19,25],int64:[12,13,22,25,26],int_:[16,17],integ:[14,22,29],integr:[4,9,14,27],intend:[10,39,42],intens:[8,15],interact:[1,3,11,12,30,42],intercept:15,intercept_:[23,32],interest:[7,10,13,17,35,40],interfac:[1,2,4,42],intermedi:[4,17,29,30,31,35,38,39],intermingl:19,intern:34,internet:42,interpol:[13,22],interpret:[2,11,19,20,30,31,32,39],interpret_model:27,interrupt:22,intersect:14,interv:[15,16,30],intro:[12,14,39],introduc:[4,14,15,27,30,34,35,37,39],introduct:[3,12],introductori:11,intuit:[19,25,30],inv_test_indic:22,invari:31,invers:[8,20],inverse_transform:8,invert:20,investig:17,involv:[9,14,15,25,26,32,33],io:[4,8,11,12,15,31,32,35,36],ioarg:11,iohandl:11,ipykernel_26544:12,ipykernel_47043:17,ipykernel_60185:19,ipykernel_70516:28,ipynb:[17,22,30,32],ipython:[7,17,28],ipywidget:28,iq1:31,iq2:31,iq:31,iri:[8,17,18],irisdf:8,irreduc:32,irrelev:13,is_avail:[33,35],is_text:11,isel:14,isfinit:31,isin:12,isinst:[8,11,12],island:12,isnan:15,isnul:13,isol:[2,8,37,42],isomap:8,issu:[4,6,9,27,34,38],issue_templ:4,ital:1,item:[1,4,12,14,15,28,29,30,31,33,35],iter:[7,11,15,19,22,23,24,25,27,28,29,30,31,32,35],iterable_with_config:22,iterationsran:28,iterationsslid:28,ith_cluster_silhouette_valu:19,its:[2,5,7,9,14,15,18,19,25,26,27,30,31,32,34,38],itself:[4,11,19,32],j5lxhd8uxrtsxko:11,j:[8,19,23,28,29,32],jaqu:21,java:42,javascript:42,jedi:[7,28],jja:14,job:3,joblib:[22,28],johnson:31,journal:41,js:[11,12,17,19,28],json:1,juli:17,julia:[38,42],julian:15,jump:[9,29],jupyt:[2,3,6,24,25,27,30,38,39,42],jupyterhub:[3,4,42],jupyterlab:[2,4,12,28,42],jupyternotebook:42,just:[4,7,12,15,16,19,23,28,29,30,31,32,34,37],k:[8,16,20,21,22,23,24,26,28,29,31,32],k_means_cyto_3:19,k_means_cyto_8:19,k_means_cyto_bad_init:19,kaggl:[13,23],katherin:19,kb:[12,13],kcbp:12,kcpb:12,kd_tree:25,kde:13,keep:[4,14,15,17,23,28,32,35],keep_date_col:11,keep_default_na:11,kei:[7,10,11,12,13,14,22,24,25,26],kelvin:14,kept:15,kera:[26,29,30,31,32,34,35,36,39],keras_tun:36,kerasclassif:30,kerasregressor:30,kernel:[1,20,21,22,30,31],kernel_s:[31,35],kf:15,kfold:15,kharita:39,khbb:12,kilomet:12,kind:11,kit:[19,26],kiwisolv:[2,13,18,28],klat:17,klon:17,km:[12,17],kmean:19,kmeans_model:19,kneighborsclassifi:[21,22,25,26],kneighborsclassifierkneighborsclassifi:25,knn:[20,21,25,26],knn_clf:22,knn_predict:[22,25],know:[1,3,20,23,32],knowledg:[25,35,39],known:[15,19,22,27,31,32],ko:19,koehrsen:24,kurtosi:[13,17],kurtosis_valu:16,kw:8,kwarg:[8,22],kwd:11,kwds_default:11,kxk:21,l1:[19,32],l1_ratio:32,l2:[14,32,33],l2_reg:33,l:[23,29,31,32,35],l_1:14,l_2:14,l_old:23,lab:[4,23,40],label:[6,7,8,9,12,13,14,15,18,19,20,21,22,26,27,29,30,31,32,33,34,35,38],label_binar:22,labelbottom:8,labelcolor:[29,30,31],labels:14,labels_:19,labeltop:8,laden:26,lambda:[24,32],lambda_meters2kilomet:12,land:[3,11],landsat:[3,10],landslid:[7,16],languag:[1,3,4,38,42],laplac:[16,32],laps:10,laptop:[2,4],larg:[3,4,10,12,14,16,17,23,25,26,30,32,38],larger:[3,8,11,16,19],largest:[8,17],lasso_reg:32,last:[4,11,12,14,17,22,23,24,26,27,30,32,34],lat:[12,14,17,19],later:[2,15,19,23,24,30,34],latex:1,latitud:[11,12,14,17,30],latter:28,launch:1,law:[14,16,32,33],layer1:[29,30],layer2:30,layer:[4,11,29,32,33,34,35],layout:[14,24],lbfg:30,lda:[20,21],ldata:7,lead:[8,15,17,36],leader:3,leaf_siz:25,learn:[2,3,8,9,11,12,13,14,15,20,21,22,23,24,25,27,28,29,31,33,34,35,38,40,42],learn_rat:26,learner:[26,32],learning_r:[14,26,29,30,31,32,35],learningr:28,learningrateslid:28,leas:38,least:[17,32,39],leav:[1,12],leaveoneout:15,lectur:[7,13,14,15,17,19,31,39],lecun:31,leduc:31,left:[4,8,12,14,15,20,30,31,32],legend:[8,13,14,15,17,18,22,24,28,30,31,32,33,34],legend_el:[8,28],len:[7,8,11,12,15,17,18,19,21,22,23,24,26,28,29,30,31,32,33,35],lenet:32,lenet_checkpoint:31,length:[8,12,13,14,15,18,20,23,28,31],length_a:15,length_sub:15,leq:[19,23],less:[8,10,12,15,17,26,27,28,38],lesson:[3,6,40],let:[7,8,11,12,14,15,16,17,18,19,20,21,22,23,29,30,32,34],letter:31,level:[1,7,12,21,39],leverag:31,lh:[12,14],lib:[7,11,12,13,18,19,22,26,28,32],librari:[2,10,11,14,27,38],licens:[4,5,21],lidar:10,lie:[19,30],lighgbm:27,light:19,lightgbm:27,lightweight:11,like:[3,4,9,10,11,12,13,14,15,16,18,19,23,25,26,28,31,32],likewis:9,lil:35,lilianweng:35,limit:[11,14,15,17,20,25,26,30,31,35,38],linalg:[8,14],line2d:[7,15,18,19,32,35],line:[1,2,4,7,8,11,12,14,15,17,18,19,20,22,24,27,30,32,35,42],linear:[8,14,17,18,21,22,23,25,28,29,30,31,32,33,35],linear_model:[15,20,23,28,32],lineardiscriminantanalysi:21,linearli:[14,17,28],linearly_separ:30,linearregress:[15,20,28],linestyl:[8,14,19,28,31],linetermin:11,linewidth:[8,14,15,21,28,31,32],link:[1,4,7,12,39],linkag:19,linregress:15,linspac:[8,14,15,17,18,28,30,31,33,34],linux:[3,6,38],list:[1,2,3,4,6,7,8,11,12,14,15,19,22,24,27,38],listedcolormap:[21,30],literatur:[9,31,38],littl:[20,21,32],live:42,ll:[4,13,15],ln:8,load:[8,16,25,30,31],load_dataset:14,load_digit:[22,25,26,29,30,31,35],load_iri:[8,28],loaded_test:[31,35],loaded_train:[31,35],loc:[15,16,19,22,28],local:[4,6,12,17,19,22,31,32,39],locat:[1,7,9,10,11,12,16,17,18,19,24,31],log10:[7,17,19],log:[14,16,17,18,19,23,29,30,32,33,35],log_:18,log_i:19,log_x:19,logarithm:38,logic:29,logist:[20,29,32,39],logistic_regress:23,logisticregress:[23,32],logit:29,lognorm:16,logspac:[14,17,30],lon:[12,14,19],longer:[17,19,23,32,34,35],longitud:[11,12,14,15,17,30],loo:15,loocv:15,look:[4,7,8,9,11,12,13,14,16,18,21,26,27,28,29,31,32],loop:[6,8,12,15,17,19,29,30,31,35],loos:[3,42],loot:28,loss2:33,loss2_weight:33,loss:[8,9,14,19,20,23,25,30,31,33,34,35],loss_funct:[30,31],loss_tim:[29,30,31,35],loss_val:35,loss_val_tim:35,losses2:33,lot:[3,26,27,32,34,38],low:[3,15,17,18,19,21,25,26,27,32],low_memori:11,lower:[8,15,17,20,22,26,28,32,35],lower_critical_valu:15,lowest:[19,35],lowpass:17,lp:17,lr:[29,30,31,33,35],ls:[12,14],lstrip:30,lt:[31,35],lua:42,lucidlenn:13,lucki:3,lunch:38,lw:19,lxml:18,lzmy975n0l5bjbmr9db291m00000gn:[12,17,19,28],m1:27,m2km:12,m8:17,m:[4,8,11,12,14,15,17,20,24,26,32,35],m_3:16,m_4:16,ma:39,mac:27,mach:29,machin:[3,9,11,13,14,15,19,20,22,25,26,32,38,40],machinelearn:22,maco:3,macosx:[3,4],macosx_12_0_arm64:26,macro:22,made:[4,5,8,12,29],madison:40,mae:[24,32],mag:12,magic:24,magmat:19,magnesium:16,magnet:10,magnitud:[16,17,26,32],magnitude_bin:12,mai:[1,2,3,4,7,8,9,11,12,13,15,17,18,19,23,26,30,32,33,34,35,38],mail:4,main:[5,11,12,13,16,19,29,39],maintain:15,major:[8,19,26,27],make:[2,4,6,7,11,12,14,15,17,18,21,22,23,24,27,28,30,31,32,33,34,37,38,39],make_circl:[21,30],make_classif:[21,30],make_moon:[21,30],make_pipelin:30,makedir:[11,30,31,35],malfunct:13,mallow:19,mam:14,manag:[2,12,29,30,31,35,42],manhattan:[19,25],mani:[2,3,7,8,11,12,15,17,19,20,21,22,23,27,28,29,31,32,37,38,40,42],manifold:35,manipul:[6,7,9,10,11,24,27,39],manual:[25,38],manual_se:[30,31,33],map:[8,10,11,30,31],mapbox_styl:12,mape:24,march:32,margin:20,mariana:12,marin:[39,40],marinedenol:[7,12,13,18,19,26,28,30,32],mark:39,markdown:[4,42],markedli:42,marker:[11,12,14,15,19],marker_clust:11,marker_s:12,markerclust:11,markers:12,market:27,mask:[11,25,35],masked_arrai:25,mass:19,master:4,match:[15,19,27,32],materi:[11,14,31,39,40,42],math:[8,14,19,23,39],mathbf:[8,19,20,30,32],mathcal:[23,29,32],mathemat:[14,23],matlab:[38,39],matmul:8,matplolib:39,matplotlib:[2,6,7,8,11,12,13,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35],matric:9,matrix:[13,15,17,19,20,21,22,23,30],matshow:[7,25],max:[8,11,12,13,14,15,16,17,18,19,22,24,25,26,28,29,30,31,32,35,38],max_depth:[21,26],max_featur:21,max_it:[23,30,32],max_percentag:[17,18],max_pool2d:30,max_sampl:26,maxim:[20,25,29,33],maximum:[12,19,23,24,27,29,30,31,32,38],maxlat:17,maxlon:17,maxpool2d:31,maxpool:31,maxpooling2d:31,mayb:30,mb:[12,13,26,35],mcbrearti:31,md:[1,4,38],mdenol:39,mean:[3,4,11,12,13,14,15,17,18,20,21,23,24,26,28,29,31,32,33,38],mean_a:15,mean_b:15,mean_filt:31,mean_fit_tim:25,mean_score_tim:25,mean_squared_error:[15,32],mean_temp:16,mean_test_scor:25,meaning:13,meant:42,measur:[8,10,12,13,15,16,19,21,24,27,30,32,38],mechan:32,media:10,median:[12,13,15,38],meet:[27,38],mehra:[39,40],member:[15,19,42],memori:[3,11,12,13,14,23,29,30,31,34],memory_map:11,mention:4,menu:[1,4],mere:30,merg:[1,4,17,18,19,39],meridian:15,mesh:30,meshgrid:30,messag:4,messi:4,meta:26,metadat:14,metadata:[4,9,10,11,12,14,38],meter:12,meters2kilomet:12,meters2kilometers2:12,metho:9,method:[5,8,9,10,12,13,14,16,17,20,22,23,24,25,26,29,31,35,36,39],methodolog:39,metric:[15,17,19,22,23,24,25,26,30,31,32,34,36],metric_param:25,mgo:16,microsoft:[3,27],microstoft:3,middl:[8,18,19,30],might:[8,9,11,12,13,15,16,17,18,21,26,27,31,32],mimic:[11,35],min:[11,12,13,14,15,16,17,19,22,24,25,28,30,32,38],mini:[19,29,30],minibatch:32,miniconda3:[7,11,12,13,18,19,26,28,32],miniconda:2,minim:[12,13,19,20,23,29,32,33,38],minima:[19,32],minimum:[8,12,19,29,32,38],minimum_val_error:32,minipnw_metadata:7,minipnw_waveform:7,minkowski:25,minlat:17,minlon:17,minmax:[32,38],minmaxscal:[22,25,26,38],minor:[8,12],minu:[24,27],minut:[19,39],mirror:[9,16],misclassifi:[19,21,26],misenterpret:17,misinterpret:17,miss:[38,41],mistak:23,mit:4,mitig:[15,17,26,30,32],mix:[8,10,12],mixing_:8,mjd:13,ml:[3,7,13,19,21,27,32,34,36,38,39],mlgeo2022:4,mlgeo2022_uwnetid:4,mlgeo2023:4,mlgeo2023_uwnetid:[4,39],mlgeo:[1,4,6,7,11,12,13,16,17,18,19,22,26,28,32,39],mlgeo_4:[30,32],mlhub:3,mllab:2,mlp:34,mlpclassifi:30,mlxtend:28,mm:[8,15,32],mnist:[22,31],mobil:4,mode:[11,14,20,26,31],model:[8,10,11,13,14,16,17,19,20,22,23,25,26,28,34,36,39],model_lenet:31,model_select:[15,21,22,24,25,26,30,31,32,35],moder:[12,30],modern:4,modif:4,modifi:[4,9,12,14,15,21,22,24,25,26,29,30,31,35,39],modul:[2,4,7,8,9,12,14,15,16,17,19,20,29,30,31,32,33,35,38,39],module1:4,module2:4,modulenotfounderror:17,moistur:[10,13],moment:[16,18,31],momentum:32,mon:24,monaco:15,mondai:[20,39],monitor:3,monoton:13,mont:9,month:[3,17,24],monthli:14,more:[2,4,8,9,10,11,12,13,14,15,16,17,19,20,21,23,25,26,30,32,35,36,38,42],morlet2:17,morlet:17,morn:39,most:[2,3,4,6,7,8,9,11,12,16,17,18,19,20,22,24,26,27,30,32,34,38],mostli:[3,7,15,19],mother:17,motion:[7,8,10,16,17,38],motiv:38,mousavi:35,move:[3,10,11,14,29,31],movement:19,mpl_toolkit:19,mplot3d:19,ms:[12,17],mse:[15,20,28,32,34,35],mse_train:15,mse_val:15,msecost:28,mseed:5,mseloss:[33,35],mt:19,mu:16,much:[3,8,19,23,27,30,32],mulbackward0:23,mult:35,multi:[12,14,17,20,21,26,29,32],multi_output:22,multiclass:[20,30],multiclassif:31,multicollinear:13,multidimension:[8,10,11,14,19,34],multilabel:35,multilay:31,multinomi:32,multipl:[3,4,8,10,14,15,17,26,29,30,31,32,34,38,42],multipli:[8,12,18],multiscal:32,multispectr:10,multivariate_norm:15,must:31,mxn:28,my:[1,4,14],my_kmean:19,my_metadata:12,my_mlp:30,my_mlp_checkpoint:30,my_pd:12,mybind:42,mycod:4,myenv:2,myst:42,n:[7,8,11,14,15,17,18,19,20,21,22,23,27,32,33],n_cluster:19,n_clusters_per_class:30,n_completed_task:22,n_compon:[8,19],n_dispatched_batch:22,n_dispatched_task:22,n_epoch:[29,30,31,35],n_estim:[21,24,26],n_featur:30,n_imag:35,n_inform:30,n_init:19,n_iter:25,n_j:19,n_job:[22,25,26],n_neighbor:25,n_redund:30,n_sampl:[8,15,32,33],n_select:27,n_split:15,n_step:34,n_unit:33,na:13,na_filt:11,na_valu:11,naiv:[20,21,34],naive_bay:[21,26],name:[2,4,7,8,11,12,14,15,17,19,21,24,30,31,38,42],nameerror:30,nan:[7,12,15,16,25],narr:42,narrai:7,narrow:9,nat:18,nation:[3,11],nativ:[12,20],natur:[7,10,15,16],navig:[4,5,19],nb:[20,26],nb_clf:26,nbin:12,nbviewer:25,nc:[11,12,14,17],ncedc:12,nclass:[22,26],ncluster:19,ncm_geologicframeworkgrid:[11,17],ncm_spatialgrid:[11,17],ncol:[22,24],ndarrai:[8,14,22,25,26],ndata:7,ndim:28,ndimag:14,nearest:[14,19,20,21,22],necessari:[4,15,16,23,32],necessarili:9,need:[4,7,8,9,12,13,15,17,19,20,23,26,27,28,29,30,31,32,34,35,38,39],neg:[16,19,21,23,32],negbackward:23,neighbor:[8,19,20,21,22,25,26],neighborhood:31,neither:27,nenad:4,neonscienc:11,neq1:31,nest:[11,30],net:[12,30,33],net_water_bal:12,netcdf4:17,netcdf:[5,10,14],netdisc:33,netdiscoveri:33,netreg:33,network:[3,10,11,12,17,18,23,28,32,35,37],neural:[17,23,28,32,35,37],neural_network:30,neuron:[29,30,31,34],nevada:15,never:26,new_a:15,new_b:15,new_nois:18,new_pair:15,newaxi:34,newcrap:18,newdata:[22,25,26],newer:4,newfil:4,newi:32,newli:15,newlin:[11,15],newnoisef:18,newweight:28,next:[15,19,22,26,27,28,29,30,31,32,34,35,42],next_fast_len:[17,18],nf:17,nfft1:17,nfft:[17,18],nhat:[17,18],ni:39,nice:[4,11,18,23],nicoleta:[39,40],nipy_spectr:19,nlabel:31,nn1:29,nn:[18,29,30,31,32,33,35],no12:31,no1:31,no2:31,no_grad:[29,30,31,35],no_val_x:22,noaa:[24,27],node:[3,11],nois:[7,8,17,19,20,21,26,28,30,31,32,33,34,35,38],noise2:31,noise_factor:28,noisi:[8,16,18,26,32,33,35],noisy_sign:18,nomal:23,non:[1,13,17,35],none:[7,8,11,12,14,17,19,22,25,26,28,29,30,31,32,33,34,35],nonlinear:30,nor:27,norm:[8,19,32,33],norm_x1:14,norm_x1_norm:14,normal:[5,8,13,14,15,16,18,19,20,21,22,25,26,30,31,32,35,38],north:[8,11],northern:12,northwest:[7,15],note:[11,15,16,17,22,23,25,26,30,32,35,39],notebook:[2,3,4,6,12,14,15,17,22,24,25,27,30,32,38,39,42],noth:[14,18,23],notic:[14,15,17,32],notifi:4,notion:19,noverlap:17,now:[4,7,8,11,12,13,14,15,16,17,18,19,21,23,25,28,29,32,33],np:[2,7,8,11,12,14,15,16,17,18,19,21,22,23,24,25,26,28,29,30,31,32,33,34,35],np_to_th:33,nperseg:17,npmap:11,npoint:8,npt:[17,18],npts1:17,nqso:13,nrow:[11,22,24],nstar:13,nsubset:15,nt:7,num:28,num_class:[26,29,30],num_images_to_displai:31,number:[2,7,8,9,11,12,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,37,38],number_run:15,numel:14,numer:[10,11,13,14,15,20,24],numinput:28,numiter:28,numpi:[2,4,7,8,9,11,12,13,15,16,17,18,19,20,21,22,23,24,25,26,28,29,30,31,32,33,34,35,39],numsv:8,nv:15,nvidia:3,nyq:17,o:[3,12,14,15,19,28,33,38],obei:[32,33],obj:19,object:[7,10,12,13,14,19,20,23,24,25,26,30,31,32,35],object_:12,objective_new:19,objective_old:19,objects_to_datetime64n:12,objid:13,observ:[8,9,10,12,15,16,19,23,24,28,33],obspi:[17,18],obtain:[15,23,24,32],obviou:32,occur:32,occurr:16,ocean:[3,5,10],oceanographi:[3,39],oct:12,off1:34,off2:34,off:[8,21,31,32,35],offer:[3,4,10,12,14,39],offic:14,offici:4,offset:5,ofr20191081:11,often:[2,3,10,11,13,14,15,16,17,19,25,31],ok027:31,ok029:31,ok:[12,13,18],okai:[15,21,32],ol:28,old:19,olsoutput:28,omega:23,on_bad_lin:11,on_click:28,onc:[4,12,15,23,27,29,32],one:[1,3,4,7,8,9,10,11,12,13,14,15,16,17,19,20,21,23,24,26,27,28,29,30,31,32,35,36,37],ones:[7,8,20,26,31,32],ones_lik:33,oneself:9,onevsrestclassifi:22,onli:[3,4,8,9,11,13,15,17,19,24,26,28,30,31,32,35,39],onlin:[4,28],onto:[8,17],oob_scor:26,open:[1,3,4,5,6,11,38,39,42],openeew:3,oper:[3,11,12,14,21,22,28,30,31],opportun:32,opt:[7,11,12,13,18,19,26,28,32],optic:19,optim:[3,10,11,14,19,20,23,25,26,27,31,33,34,35,36,38],optimis:33,optimist:15,optimizi:20,option:[4,8,11,13,23,28],oracl:14,orang:[8,16,19],ord:14,order:[1,12,14,17,22,30,42],ordinai:33,oregon:8,org:[8,11,12,14,19,21,23,25,30,31,32,38,42],organ:[4,8,9,11],orient:[8,24],origin:[4,7,8,11,12,15,16,17,19,21,31,32,35,39],origin_dist:15,orthogon:8,orthograph:12,orthonorm:17,os:[3,4,7,8,11,15,17,19,30,31,35],oscillatori:32,other:[1,2,4,5,7,9,10,11,13,14,15,17,19,20,21,26,27,28,29,30,34,37,38,40],otherwis:[29,32],ouput:32,our:[0,8,13,15,19,21,22,23,24,30,32],ourperceptron:28,out:[2,3,4,11,16,17,22,24,27,30,31,33,34],out_channel:31,out_featur:[29,30],outcom:12,outfil:11,outlier:[26,32,38],outofboundsdatetim:12,output:[1,8,9,10,11,12,14,15,17,20,22,26,28,29,30,31,32,33,34,35,38,39],output_dim:33,output_lay:30,output_pad:35,outputwidget:28,outstand:38,over:[1,4,5,8,10,12,15,16,17,18,19,29,30,31,32],overal:[6,15,19,31,32],overestim:15,overfit:[15,25,26,27,30],overlai:[7,8,17],overlap:[17,19],overli:15,overview:[4,14,35],overwrit:[4,9,12],overwritten:8,ovject:32,own:[3,4,5,14,19,23,38,39,42],owner:4,p395:[8,15],p:[14,15,16,19,21,23,25,27,28,29,31,32,33,35],p_valu:15,pacif:[7,15],packag:[2,4,7,8,11,12,13,14,15,17,18,19,21,22,23,26,28,32,36,39,42],pad:[24,31,35],page:[4,11,12,25,27],pai:26,pair:[8,11,19,24,38],pairgrid:19,pairplot:19,panda:[2,4,7,8,9,10,11,13,14,15,16,17,19,21,23,24,27,28,31,32,34,35,39],pandrarrai:21,panopt:31,paper:[4,9,31,37,39],paragraph:1,parallel:[3,22],param:[14,25,26,35],param_algorithm:25,param_distribut:25,param_grid:25,param_metr:25,param_n_neighbor:25,param_weight:25,paramet:[14,15,16,19,20,23,25,26,27,29,30,32,33,35],parameter:[17,32],parametr:[13,15],parent:35,pareto:16,park:11,parks_wa:11,pars:[12,27],parse_d:11,parse_datetime_str:12,parser:11,parso:[7,28],part:[4,29,30,31,32,34,35,39],partial:[23,32,33],particip:42,particl:19,particular:[7,11,14,32,38],particularli:[3,10,11,13,14,15,19,26,30,32],partit:[15,19,21],pass:[3,12,14,17,22,29,30,31,32,33,35],password:4,past:[15,17,34],path3dcollect:19,path:[4,13,19],path_or_buf:11,pathcollect:[8,19,21],pathwai:39,pattern:[5,24,31],pc:[8,19],pca:[9,20,35,38],pcg64:15,pcolor:14,pcolormesh:17,pd:[7,8,11,12,13,15,16,17,19,21,23,24,27,31,32,34,35],pde:33,pdf:[17,39],pdist:19,pdt:39,peak:[19,31],pearson:[14,15,19],peform:21,penal:32,penalti:32,peopl:[4,27,37],pep:4,per:[10,12,17,22,30],percentag:[23,24,29],percept:28,perceptron:[29,31],perfect:[15,19,28],perfectli:32,perform:[3,4,12,13,14,15,17,19,20,25,26,28,29,30,31,32,35,36,38,39],period:[12,17],permiss:[4,39],permut:28,perol:31,person:4,perspect:19,petal:28,pexpect:[7,28],pga:38,phase:[17,18],phd:4,phenomena:16,phi:30,phone:4,physi:33,physic:32,physics_loss:33,physics_loss_discoveri:33,phytoplankton:19,pi:[8,14,15,17,18,19],pi_est:15,pick:31,pickl:22,pickleshar:[7,28],picoeuk:19,piec:4,pillow:[13,18,28],pinn:32,pio:12,pip3:2,pip:[4,7,12,13,17,18,26,27,28],pipelin:[9,13,19,20,30,32],pitfal:17,pivot:19,pixel:[8,11,29,31,35],piyg:21,place:[15,22,31],plai:[4,19],plain:[11,12,13],planetari:[3,39],plate:[8,13,18],plateau:[19,32],platform:[3,4,38,42],pleas:[0,3,4,6,12,17,25,27,32],plenti:[3,14],plot:[6,7,8,9,11,12,13,15,16,17,18,19,21,22,24,25,27,28,29,30,31,32,33,34,35,38],plot_decision_region:28,plot_imag:35,plot_learning_curv:32,plot_test:14,plotli:[19,39],plotoutput:28,plt:[7,8,11,12,13,14,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35],pltcount:28,plu:[24,27,32,34],plugin:11,pm:[13,39],png:[1,12,14],point:[4,8,10,11,14,15,16,17,18,19,20,26,27,28,30,31,34],poisson:14,polar:32,poly_featur:32,poly_scal:32,polynomi:[17,20,32],polynomialfeatur:32,polyquadmesh:14,pool:30,poorli:[19,26,32],pop:19,popul:[3,15,19],popular:[7,10,19,20,26,27,30,32,35,42],popup:[11,27],porotomo:3,port:3,portion:[15,26],posit:[8,12,14,15,16,19,21,22,23,29,30,31,32,38],positron:12,possibl:[6,9,14,16,19,23,25,26,32,35,38,42],possibleactiv:28,post1:[13,28],post:4,potabl:23,potenti:[17,38],pow:[14,33],power:[12,14,16,18,19,30,32],power_law_data:16,pq:12,pr:21,practic:[3,4,6,12,14,15,17,20,22,32,35],practition:36,pre:[8,9,16,17,19,21],pre_dispatch:[22,25],precalcul:19,precipit:[10,12,13],precis:[21,22,23,32],precision_recall_curv:22,precision_recall_fscore_support:23,precision_scor:21,precisionrecallcollector:21,precisionrecalldisplai:22,precison:21,precondit:38,precursor:17,pred:[15,26,33],predecessor:26,predefin:25,predict:[7,9,10,14,15,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,35],predict_proba:[21,26,30],predictions_data:24,predictor:26,predsreg:33,prefer:4,prefer_skip_nested_valid:22,preferred_source_magnitud:7,preferred_source_magnitude_typ:7,preferred_source_magnitude_uncertainti:7,preinstal:2,preliminari:38,prep:32,prepar:[9,13,29,32,39],preprocess:[8,14,19,21,22,23,25,26,30,31,32,35,38],prescrib:19,presenc:[26,35],present:[0,17,18],preserv:17,pressur:10,preval:19,prevent:22,previou:[9,15,20,22,25,26,30,32,34,37],previous:[19,31],previouscost:28,primari:[15,19],primarili:[14,15],princip:39,principl:[33,39],print:[7,8,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,29,30,31,32,33,34,35],print_progress:22,printer:14,prior:[18,24,25,27],privat:[4,39],pro:[4,19],proba:26,probabilist:[15,25],probabl:[19,20,22,23,26,29,30,31,32,35],probe:[10,31],problem:[9,14,17,18,19,20,23,25,27,29,30,31,38],proce:30,procedur:15,process:[1,3,7,8,9,10,12,15,16,17,19,21,25,31,37],prochloro:19,produc:[11,20,27,28,37],product:[12,17,20,27,31,37],profession:27,profit:1,program:[1,3,4,10,39,42],progress:[26,32,39],project:[1,4,8,9,11,14,17,19,39,42],projection_typ:12,promis:[25,38],promot:0,prompt:[7,12,28],propag:[9,15,29,30,31,35],proper:[2,17,32],properli:32,properti:[9,10,14,16,17,18,19,23,26,32,35],proport:18,propos:[7,35,38],proven:38,provid:[2,3,4,5,8,12,13,14,15,19,20,26,27,29,30,31,33,36,38,39,42],proxim:14,pseudo:17,pseudoinvers:20,pseuo:17,psi:17,pt:[30,31,35],ptyprocess:[7,28],pub:12,publish:[37,42],puget:17,pull:[4,6,12,27,39],pull_request_templ:4,puor:8,pure:[7,18,28],purpos:[3,4,28],push:[4,6],put:[23,30],px:[12,19],py3:[7,17,26],py:[2,4,11,12,17,19,22,26,28,32],pycr:11,pydata:19,pygment:[7,28],pylab:14,pypars:[2,13,18,28],pypi:4,pyplot:[7,8,11,12,13,14,15,16,17,18,19,21,22,24,25,28,29,30,31,32,33,34,35],pytest:4,python3:[2,7,11,12,13,18,19,22,26,28,32],python:[3,4,6,7,9,10,11,13,14,16,17,18,19,23,28,30,38,39,42],pytorch:[2,32,35,36,39],pytz:[2,7,13,28],pyx:12,q:12,qc:19,qc_lwr:19,qc_mid:19,qc_upr:19,qda:21,qi:19,qso:13,quadrat:32,quadraticdiscriminantanalysi:21,quak:[12,31],quakes2:31,quakes2plot:12,qualit:[20,23,39],qualiti:[13,19,27,32,42],quantifi:[13,19,21,32],quantil:15,quantit:[20,23],quarri:7,quarter:[15,39],queri:[11,17,18],question:[9,37,38,39],queue:3,quick:[12,38],quickli:12,quot:11,quota:19,quotechar:11,r2:[15,27],r2_score:15,r:[7,8,10,12,13,14,15,24,26,28,30,31,32,33,34,38,39,42],r_valu:15,ra:13,radar:10,radial:20,radiant:3,radiu:[15,19],rain:39,rainfal:[10,12,13,16],rainfall_mm:12,rais:22,ran:3,rand:[14,18,23,31,34],randint:[21,25],randn:[8,14,18,19,33],random:[7,8,16,19,20,21,22,23,26,27,28,30,31,32,33,34,37,38,39],random_project:8,random_split:[29,30,31,35],random_st:[15,19,21,22,23,24,25,30,32],randomforestclassifi:[21,22,26],randomforestregressor:24,randomizedsearchcv:[25,30],randomizedsearchcvrandomizedsearchcv:25,randomli:[14,15,17,19,21,25,27,30,32],randomst:[22,30],rang:[7,8,11,12,14,15,18,19,21,22,24,28,29,30,31,32,33,34,35,38],range_color:12,rangeindex:13,rank:[13,19,20],rank_test_scor:25,ransac:27,rare:[15,16,17,32],raster:10,rate:[10,12,17,18,21,22,26,28,29,30,31,33],rather:[3,35],ratio:[8,15,18,21],ratt:[17,18],ravel:[30,32],raw:[5,7,9,12,13,16,17,18,22,25,26],raw_moment:16,rbf:21,rcparam:[14,19],rdbu:30,rdylbu:17,re:[4,14,15,28,32,33],reach:[3,19,23,32],read:[7,8,9,14,17,19,24,27,29,32,37,38],read_csv:[7,11,12,13,16,19,23,24,27],read_data:7,read_feath:19,read_fil:11,read_parquet:11,readabl:[9,10,11,29,38],reader:11,readi:[13,29,39],readm:[1,4,38],real:[3,17,18,30,35,38],realist:9,realli:[4,32],realpython:12,reason:[13,19,27,30,32],reassign:38,rec:21,recal:[21,22,23,32],recalcul:15,recall_scor:21,receiv:[8,21,22,31,34,37],recent:[11,12,17,22,30,32],recogn:[4,6,9,12,27],recommend:[2,3,8,19,39,42],reconstruct:[8,32,35],record:[7,8,12,15,16,24],recov:[8,12,35],recreat:17,rectifi:[30,31],recurs:19,red:[8,11,13,14,15,16,19,29,30,31],redefin:15,redshift:13,redshift_zero:13,reduc:[4,8,9,14,15,17,19,21,26,31,32,35,38],reduct:[9,20,26,32,35,39],redund:13,ref:[12,13],refer:[3,14,15,20,24,28,30],refit:25,refresh:39,regard:30,region:[3,10,11,12,25],register_hook:23,regr:15,regress:[5,9,14,25,26,27,29,30,33,34,39],regressor:25,regrid:14,regular:[26,30,31,33,34],regularis:32,regularli:[14,17,33],reinstal:27,reject:15,rel:[15,16,18,25,28,32,34],relat:[2,9,15,17,19],relationship:[13,15,19,26],releas:[12,13,18,28],relev:[19,32,38],reli:[31,42],reliabl:[13,15,26,27,38],relu1:30,relu2:30,relu:[29,30,31,33,35],remain:[4,15,32],remedi:34,rememb:23,remot:[3,4,6,10,14,39,42],remov:[2,4,8,9,13,14,15,16,17,24,26,32,35,38],removablehandl:23,remove_anyth:12,renam:[12,26],render:[12,25],reno:15,repeat:[15,17,27,31],repeat_kmean:19,repeatedli:15,replac:[4,11,12,13,15,19,26,31,42],replic:19,repo:[1,4],report:[7,11,15,16,21,22,30,39],reposistori:39,repositori:[3,6,27,39],repres:[5,8,10,13,15,17,20,35],represent:[17,19,25,30,31,35],reproduc:[2,9,15,19,30,31,32,38,39,42],request:[3,4,6,8,11,12,13,15,18,32],requir:[2,3,4,7,12,13,14,17,18,20,22,25,26,28,30,32,35,38],require_grad:33,requires_grad:[23,33],requires_grad_:33,rerun:[13,25],resampl:[14,26],rescal:12,research:[3,4,10,31,35,38,40],reset:[4,22,34,39],reset_index:[11,23],reshap:[7,8,14,15,17,19,22,28,29,30,31,32,33,35],resid:4,residu:[14,15,20,26,32,33,35],resnet:35,resolut:[12,35],resolv:[4,12,13,17],resour:3,resourc:[3,6,11,13,18,28,38,40],respect:[7,8,14,15,19,21,23,28,30,32,33],respons:[13,17,20,39],rest:32,restart:1,restrict:19,result:[12,14,15,19,20,21,23,25,26,27,29,30,31,32,35,37,38],result_horizont:31,result_vert:31,retain:[8,13],retain_grad:23,retina:14,retrain:32,retriev:14,return_gener:22,return_sequ:34,return_train_scor:25,returnprecisionandrecal:21,reus:[4,38],revers:[14,23,24,30],revert:30,review:[0,6,9,30,38],rf:[20,24,26],rf_clf:[22,26],rf_most_import:24,rf_predict:22,rich:42,ricker:18,ridg:33,ridge2:32,ridge_reg2:32,ridge_reg:32,right:[2,4,8,20,22,28,30,32],rise:5,risk:[15,26,32],river:[10,16],rlat:17,rlon:17,rmse:[27,32],rmsprop:32,rng:[15,28,30],ro:[19,24],robust:[8,19,25,26,32],roc:[21,22],roc_auc:22,roc_auc_scor:22,roc_curv:[21,22],roccurvedisplai:22,role:[19,31],roll:[12,14],root:[11,16,31,35],rotat:[8,9,19,24,32],rotation_mod:8,rouet:31,round:[24,30],routin:14,row:[7,8,10,11,12,13,14,16,17,18,19,23,24,27,28],row_index:19,row_label:8,rs:15,rst:4,rstudio:1,rt:33,rubi:42,rule:[20,30],run:[1,3,4,5,12,13,14,15,17,18,19,21,22,23,28,32,37,38,39,42],runner:27,running_loss:[29,30,31,35],running_val_loss:35,runtimewarn:17,rv:25,rw:12,s1:8,s2:8,s3:[3,8],s6x10hzdyra:3,s:[4,7,8,10,11,12,13,14,15,16,17,18,19,21,22,24,25,26,27,30,32,33,34,35],s_:8,s_i:32,s_k:32,sa:18,sagemak:3,sai:[7,9,31,32],sake:15,same:[2,4,8,9,12,14,15,18,19,22,26,27,28,31,35,37,38],samm:26,sampl:[8,10,11,12,14,15,16,17,18,19,20,21,22,25,26,29,30,31,32,35],sample_data:[29,30],sample_label:[29,30],sample_silhouette_valu:19,sample_weight:22,sampler:[31,35],sampling_r:17,sat:24,satellit:[10,11,14],satisfi:[7,12,13,18,26,28,32,33,36],save:[2,4,8,9,11,12,13,14,21,24,27,29,31,32,35,38],savefig:14,saw:8,sawtooth:8,sc:19,scala:42,scalar:[23,28,30,34],scale:[3,5,8,12,14,16,17,19,26,29,30,31,32,38],scaleogram:17,scaler:[19,22,23,25,26],scan:11,scatter:[8,15,16,19,21,28,30,35],scatter_3d:19,scatter_geo:12,scatter_mapbox:12,scatterplot:14,scenario:[12,19,26],schedul:3,schemat:31,scheme:[19,25],scholarship:39,sci:[19,26],scienc:[1,5,11,14,33,39,42],scientif:[1,10,11,14,17,38,39],scikit:[2,8,9,15,20,21,25,28,31,32,38],scikitlearn:[21,23],scipi:[2,7,8,9,13,14,15,16,17,18,19,25,26,28,31,33],scitkit:9,score:[19,20,21,22,25,26,30,31,32],scott:[39,40],scratch:[1,3,12,33],screen:[29,30,31,35],script:[4,9,39],se:8,sea:[10,16],seaborn:[2,13,19],seaflow:19,search:[30,31],season:[5,14],seasonal_mean:14,seattl:[14,24],second:[1,5,8,10,14,15,16,17,18,23,26,31,33,34,35,39],section:[17,39],see:[1,3,4,8,11,14,15,17,18,19,21,27,30,32,42],seed:[8,15,19,30,31,33,38],seeed:30,seek:19,seem:[15,27,31],seemingli:32,seen:[16,28,32],segment:[9,35],seismic:[3,5,7,10,12,14,16,17,18,19],seismogram:[10,31,32,35],seismolog:[5,39],seismomet:[3,10,17],sel:14,select:[1,4,7,9,12,14,15,17,19,24,25,27,31,38],select_dtyp:13,self:[11,12,22,28,29,30,31,33,35],selu:35,semant:31,send:[3,30,35],sens:[3,9,10,14,19,32],sensit:[16,17,18,19,21,23,32,38],sensor:[12,13,17,32],sent:[12,13],sep:[11,12],sepal:[8,28],separ:[8,10,15,19,20,28,35,38,42],sequenc:[10,14,32,35],sequenti:[26,30,31,33,34,35],sequential_15:35,seri:[4,5,7,8,10,14,15,17,18,31,32,33,34,35,38,39],serv:19,server:[3,4,17,18],servic:[3,42],session:4,session_id:27,set:[3,5,7,8,11,12,13,14,15,17,21,22,25,26,28,29,30,31,34,35,38,39],set_aspect:[8,15,16,28],set_axis_off:22,set_axisbelow:[8,28],set_grad:23,set_opt:14,set_size_inch:19,set_them:19,set_titl:[8,14,17,18,19,22,24,28,30,31],set_vis:8,set_xlabel:[8,14,16,17,18,19,24,28,29,30,31],set_xlim:[16,17,19,30],set_xscal:[17,18],set_xtick:[8,19,30],set_ylabel:[8,14,16,17,19,24,28,29,30,31,32],set_ylim:[16,17,19,30,32],set_yscal:[14,17],set_ytick:[8,19,30],set_zlabel:8,setp:8,settingwithcopywarn:19,settl:27,setup:[4,27],setuptool:[4,7,18],setw:28,sever:[4,9,11,12,13,14,15,17,18,19,20,22,25,26,31,35,36,38],sgd:[20,29,30,32],sgd_reg:32,sgdclassifi:32,sgdregressor:32,sh:14,shade:17,shap:27,shape:[7,8,11,14,16,17,18,19,22,24,25,26,28,29,30,31,32,34,35],shapefil:10,share:[10,11,39,42],sharex:17,sheet:4,shell:[3,39],shift:[16,17,32],shortcut:12,shorter:17,shortest:19,should:[0,4,8,9,14,15,22,25,27,28,30,31,32,38,39],show:[8,11,12,13,14,15,16,17,18,19,21,22,25,26,27,28,29,30,31],show_reconstruct:35,showcountri:12,shown:[12,35],shuffl:[15,22,25,26,28,29,30,34,35],shufflesplit:15,si:3,side:[15,16],sidebar:4,sig:[8,16,18],sigm:30,sigma:[8,14,15,16,23,30,32],sigmoid:[28,29,30,31,32,35],sign:[8,19,32],signal:[5,8,16,17,31,32,35],signatur:[17,32],signifi:19,significantli:[25,37],silhouett:19,silhouette_avg:19,silhouette_sampl:19,silhouette_scor:19,silic:16,silica:16,sim:15,similar:[5,10,14,17,18,19,24,26,32],similarli:[14,17,23,29],simpl:[1,6,7,9,10,11,12,15,19,20,23,27,30,31,32,33,34,38,39],simpleinput:28,simpleoutput:28,simpleperceptron:28,simpler:[8,32],simplernn:34,simplest:[29,34],simpli:[11,14,18,30,31],simplifi:[14,19,31],simul:[10,14,15,16,21],sin:[8,14,19,34],sinc:[11,12,15,22,30,32,34],sine:[14,17],singl:[7,8,9,10,11,19,26,27,28,29,30,32,34],singular:[8,20],sinusoid:[8,14],sio2:16,sio:16,sit:[15,16],site:[2,7,10,11,12,13,18,19,22,26,28,32],situ:10,six:[2,7,13,18,28],size:[8,11,12,14,15,16,17,19,23,25,26,28,29,30,31,32,35],size_cluster_i:19,size_img:[29,30],skew:[9,13,17],skicit:24,skill:[6,12],skimag:31,skip:35,skip_blank_lin:11,skip_parameter_valid:22,skipfoot:11,skipinitialspac:11,skiprow:[11,12],sklean:8,sklearn:[8,15,19,20,21,22,23,24,25,26,27,28,29,30,31,32,35,38,39],sky:13,skyblu:16,skyserv:13,skyserver_sql2_27_2018:13,skyserver_sql2_27_2:13,slack:42,sleep:17,slice:[14,15,19],slick:4,slide:9,slightli:[30,37],sloan:13,slope:15,slow:[19,31],small:[5,8,9,11,12,16,19,23,25,26,32,34],smaller:[8,9,11,15,19,23,26,30,32,38],smallest:[19,35],smi:3,smooth:[12,14,17,18,30],sn:[8,13,19],snapshot:10,snow:19,snr:18,so:[2,4,8,9,12,13,15,17,22,23,27,29,30,31,32,34,35,38],sobel:31,sobel_filter_horizont:31,sobel_filter_vert:31,societ:38,soft:26,softmax:[26,29,30,31,32],softplu:30,softwar:[3,6,10,11,16,40,42],software_carpentries_intermedi:41,sofwar:14,soil:[10,11,13],solid:27,solut:[9,19,20,27,28,29,32,33,38,39],solv:[10,11,19,20,23,34,38],solver:30,some:[2,3,4,8,9,11,13,14,15,17,18,19,20,23,27,30,32,39,40],somebodi:4,sometim:[13,37],somewhat:34,somewher:4,son:[11,14],sonic_boom:7,soon:32,sophist:[4,13],sort:[7,12,17,19,24,33],sort_valu:12,sosfilt:17,sound:17,sourc:[1,4,6,7,8,10,11,18,21,32,38,39,42],source_depth_km:7,source_duration_magnitud:7,source_duration_magnitude_uncertainti:7,source_hand_magnitud:7,source_latitude_deg:7,source_local_magnitud:7,source_local_magnitude_uncertainti:7,source_longitude_deg:7,source_origin_tim:7,source_typ:7,source_type_pnsn_label:7,south:[11,12],southern:[3,12],space:[8,9,10,14,16,19,25,30,32,33,36,42],span:17,spars:[30,32],spatial:[10,12,14,17,19,31],spatiotempor:19,spearman:[13,19],spearman_corr_matrix:13,speci:8,special:[11,14],specif:[1,2,3,4,5,7,9,10,11,12,14,15,16,17,18,21,22,23],specifi:[4,12,18,23,25,30],specobjid:13,spectra:17,spectral:[7,9,18],spectrogram:[17,31],spectrum:[17,18],speed:[16,17,18,19],spend:39,sphere:19,sphinx:4,spine:8,split0_test_scor:25,split1_test_scor:25,split2_test_scor:25,split3_test_scor:25,split4_test_scor:25,split:[7,8,15,19,21,25,26,30,31,34],splitlin:8,spread:16,spreadsheet:10,spyder:2,sql2:13,sqlalchemi:18,sqrt:[1,8,14,17,18,19,31,32],squar:[8,14,15,16,19,20,28,30,31,32],squeez:[31,35],src:1,ssh:4,ssss:15,st:[7,16],sta:[8,15],stabil:26,stabl:[8,14,17,19,26,30,32,38],stack:[7,28,31,34,35],stacked_a:35,stacked_ae_checkpoint:35,stacked_cnna:35,stacked_decod:35,stacked_encod:35,stackeda:35,stackeddecod:35,stackedencod:35,stacklevel:22,staff:12,stage:[4,35],stai:35,stain:19,stamp:7,standalon:12,standard:[4,8,11,12,14,15,16,17,20,25,38,39,42],standardscal:[19,21,23,30,31,32,35,38],stanford:31,star:[2,13],start:[3,4,8,9,12,14,15,19,21,23,27,28,29,30,32,35,38],start_dim:[29,30],start_tim:12,startdat:12,startingbia:28,startingweight:28,starttim:[17,18],stat:[7,13,15,16,17,18,25],state:[9,11,15,32,34,38],state_dict:[30,31,35],statement:6,station:[10,12,15,17,18,31],stationari:35,statist:[7,9,10,12,13,14,15,17,18,20,24,26],statu:4,std:[8,12,13,14,15,16,17,24],std_err:15,std_fit_tim:25,std_scaler:32,std_score_tim:25,std_temp:16,std_test_scor:25,steelblu:8,stefan:[39,40],step:[5,9,14,16,19,23,25,27,28,29,30,31,32,33,35,38],step_ahead:34,stft:[17,38],still:[4,19,22,30,42],stochast:[8,20,29,31,38],stop:[14,19,28],stop_alg:19,stoppingcriterion:28,stoppingcriterionslid:28,storag:[3,4,7,10,11,14,38],storage_opt:11,store:[3,5,7,9,10,11,12,14,15,22,25,26,30,38],str:[8,11,12,15,18,19,24,27],straight:19,strategi:[15,19,31,32,35,38,39],strength:13,stretch:[9,21],strict:11,strictli:[20,30,31],stride:[31,35],string:[7,8,11,12,13,14,15],stringio:12,strive:[0,42],strmethodformatt:8,strong:[15,26,32],stronger:26,strongli:[15,19,26],strptime:24,structur:[9,10,11,12,13,14,15,18,19,31,35,38,42],strutur:19,student:[5,6,40],studi:[10,19,38],studio:[3,4],style:[19,21,24],sub:4,subarrai:14,subclass:29,subdata:19,subduct:15,subgroup:[19,26],submiss:[4,39],subplot:[6,8,13,14,16,17,18,19,21,22,24,28,29,30,31,32,35],subplots_adjust:30,subsequ:30,subset:[11,13,14,15,19,22,25,26,27,29,30,32,35,38],subsetrandomsampl:[31,35],substanti:25,subsurfac:10,subtract:[12,28],success:19,successfulli:[7,17,26],suffici:[3,20,32],suggest:[4,19,38],suitabl:19,sum:[8,12,13,14,16,19,23,26,28,29,30,31,32,33,35],sum_:[15,19,20,23,29,32],sum_i:14,sumatra:12,summar:[13,19,20,39],summari:[12,13,19,31,34,35],summarize_distribut:13,summary_galaxi:13,summary_qso:13,summary_star:13,sumup:30,sumvalu:21,sun:[24,39,40],sundai:24,superimpos:17,superposit:32,superseismo:4,supervis:[8,20,38],supplementari:31,suppli:8,support:[1,10,11,14,20,22,25,39],suppos:[12,38],suppress:19,suptitl:[19,22],sure:[2,4,7,14,15,22,34,38],surfac:[3,7,8,10,11,17,19],surface_ev:7,survei:[10,13],suscept:26,sv:8,svc:[21,22,25,26],svc_clf:26,svc_predict:22,svg:21,svm:[20,21,22,26],symm:31,symmetr:[8,16,35],synchron:4,synecho:19,syntax:[14,15,30],synthet:[14,19,33,34,38],synthetics_pca:19,synthetics_sc:19,system:[3,4,6,8,10,11,14,15,20,37,42],systemat:[14,21,25,30,36],t0:33,t20:19,t:[8,12,14,15,16,17,18,19,20,23,27,28,30,31,33,34,39],t_0:33,t_1:33,t_b:15,t_train:15,t_val:15,tab10:35,tab:[29,30,31],tabl:[10,12,27],tabular:[10,12],tacc:3,tackl:19,tag:[8,11,39],tail:[16,17],take:[7,9,12,14,15,17,18,19,23,26,28,29,31,32,33,34,35],taken:[10,16,19],takewai:4,talk:23,tangent:30,tanh:[30,34],taper:[17,18],target:[8,15,22,25,26,27,28,29,30,38],target_nam:[8,22,25,28],task:[3,4,9,12,22,30,38],tau:31,taught:39,team:[31,37,39,42],technic:42,techniqu:[13,26,27,32,39],technolog:38,tecton:19,tediou:27,tell:[12,27],temp:[24,27,33],temp_1:[24,27],temp_2:[24,27],temperatur:[10,12,14,16,24,27,32,33],temperature_c:[11,12],temperature_f:12,templat:[4,31,39],templates_027:31,templates_029:31,tempor:[5,7,9,10,12,17],temporari:3,tenac:12,tend:[17,23,26,29,32,38],tensor:[3,23,29,30,33],tensorflow:[26,29,30,31,34],tenv:[8,15,33],term:[8,9,12,15,16,21,34],termin:[1,3,4,5,27],terminolog:4,test1:19,test:[4,12,15,19,21,23,25,26,30,31,32,34,35,38],test_dat:24,test_dataset:[29,30],test_df:27,test_featur:24,test_import:24,test_index:15,test_indic:22,test_label:24,test_module1:4,test_module2:4,test_siz:[15,21,22,24,25,26,29,30,31,32],testarrai:28,testcost:28,testinput:28,testload:[29,30,31,35],testpredict:28,texa:3,text:[1,4,8,11,13,14,15,17,18,19,27,28,30,32,33,42],textcolor:8,textfileread:11,textkw:8,textur:9,tf:[17,34],th:[19,23],than:[8,9,15,17,18,19,20,21,25,26,28,31,32,34,38],the_turing_way_community_2022_6909298:41,thei:[3,8,10,11,12,15,17,18,19,20,23,30,31,32,34,35,38],them:[4,7,8,9,10,12,18,19,21,27,32,39],themselv:7,thenarrai:7,theodorewolf:33,theorem:38,theoret:33,theori:39,therefor:[15,17,19,30,35],theta:[8,19],thi:[1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,36,38,39,40,42],thing:[9,28,38,42],think:[15,27],third:[1,16,18,23,26],thiscost:28,thisinput:28,thispandr:21,thistarget:28,thorough:25,those:[8,14,27],though:[3,6,37],thought:[10,31],thousand:11,threadpoolctl:28,three:[3,8,11,13,14,19,23,31,39],thresh:[17,19],threshod:19,threshold:[8,19,21,29,30,31,32],through:[3,8,10,13,15,19,29,30,31,32,37,39],throughout:[5,39],thu:[8,21,23,32],thunder:7,ti:10,tick:[8,19,24],tick_param:[8,14,29,30,31],ticker:8,tier:3,tif:11,tiff:11,tight:[14,19],tight_layout:[8,13,14,16,24,28,29,30,31],tightli:3,tile:8,tiledb:[5,10],time:[3,5,7,8,9,10,11,14,15,16,18,19,20,25,27,29,30,31,32,33,34,35,36,38,39],time_series_features_extractor:7,timedelta:12,timedistribut:34,timeit:[12,14,17],timestamp:12,tip:14,titl:[8,12,13,14,15,16,17,18,19,22,24,28,30,32],tlu:29,tn:[7,21,23],tnr:21,to_csv:[11,12,13],to_datetim:12,to_netcdf:14,to_numer:15,to_numpi:[19,23],to_parquet:[11,12],to_zarr:14,todai:[24,27],todoran:[39,40],togeth:2,toi:[8,19,32,33,38],token:4,tol:32,toler:32,too:[12,15,21,23,32,34,38],tool:[2,4,11,12,19,21,25,38,39],toolbox:[17,19],toolkit:[7,28,39],tooth:8,top3:27,top:[1,3,4,8,12,15,31],topic:39,toplot:28,topographi:[10,11],torch:[14,23,29,30,31,33,35],torchinfo:35,torchvis:[31,35],total:[4,13,14,21,25,26,29,30,31,35],totalfals:21,totaltru:21,totensor:[31,35],touch:4,toward:9,towardsdatasci:24,towfish_001:19,towfish_002:19,towfish_003:19,towfish_004:19,towfish_005:19,towfish_006:19,towfish_007:19,towfish_008:19,towfish_009:19,towfish_010:19,towfish_011:19,towfish_012:19,towfish_013:19,towfish_014:19,towfish_015:19,towfish_016:19,towfish_017:19,towfish_018:19,towfish_019:19,towfish_020:19,tp:[19,21,23],tpr:[21,22],tpu:3,trace:7,trace_nam:7,trace_p_onset:7,trace_s_onset:7,trace_snr_db:7,traceback:[11,12,17,22,30,32],track:[4,5,14,15],trade:21,tradeoff:32,tradit:4,train:[15,19,20,21,23,25,26,28,33,34,35,37,39],train_data:35,train_dataset:[29,30],train_df:27,train_error:32,train_featur:24,train_import:24,train_index:15,train_label:[24,31],train_set:31,train_siz:[29,30],train_test_split:[21,22,24,25,26,30,31,32,35],trainabl:35,traininput:28,trainload:[29,30,31,35],traintarget:28,traitlet:[7,28],transform:[7,8,9,11,12,13,14,15,19,22,23,25,26,30,31,32,35,38,39],transit:14,translat:[17,31],transmit:37,transpos:8,treat:[13,31],tree:[11,20,21,26,30],tremend:40,trend:[15,32],trial:30,trick:32,tricki:27,trim:17,triplic:19,true_data:24,true_label:19,true_valu:11,trust:25,truth:[19,32,34],ts:33,tsfel:[7,9],tsfresh:[7,9],tslib:12,tsne:35,tstart:[17,18],tt:15,tue:24,tunabl:19,tune:[15,18,27,32,35,38],tuner:31,tupl:24,ture:[4,40],turn:[8,17,30,34],tutori:[3,4,11,12,14,15,22,24,25,27,30,33,35,40],twin:18,twinx:[29,30,31],two:[4,8,10,11,12,13,14,15,16,17,19,20,21,24,28,31,35,36],txt:[2,4],type:[1,4,6,7,10,11,12,14,17,18,19,20,22,25,26,27,28,30,31,35,38,39],typic:[3,4,5,7,10,12,13,14,15,16,17,19,26,32,35],tz_local:12,tz_pars:12,tz_to_dtyp:12,tzdata:[7,13,28],u:[1,8,13,19,32],u_:15,u_n:15,uc:[24,27],ue:[8,32],ug3_fcm_distribut:19,un:8,unabl:[12,25],uncertain:[15,26],uncertainti:[9,15,19,34],unclear:31,uncom:14,under:[4,7,19,21,27],underestim:15,underfit:[25,26,27,32],underli:[14,15,42],underlin:1,understand:[8,10,13,19,25,31,39],undertak:8,underwai:19,underway_002:19,underway_003:19,underway_004:19,underway_005:19,underway_006:19,underway_007:19,underway_008:19,underway_009:19,underway_010:19,underway_011:19,underway_012:19,underway_013:19,underway_014:19,underway_015:19,underway_017:19,underway_018:19,underway_019:19,underway_020:19,underway_021:19,underway_022:19,underway_023:19,underway_024:19,underway_025:19,underway_026:19,underway_027:19,underway_028:19,underway_029:19,underway_030:19,underway_031:19,underway_032:19,underway_033:19,underway_034:19,underway_035:19,underway_036:19,underway_037:19,underway_038:19,underway_039:19,underway_040:19,underway_041:19,underway_042:19,underway_043:19,underway_044:19,underway_045:19,underway_046:19,underway_047:19,underway_048:19,underway_049:19,underway_050:19,underway_051:19,underway_052:19,underway_053:19,underway_054:19,underway_055:19,underway_056:19,underway_057:19,underway_058:19,underway_059:19,underway_060:19,underway_061:19,underway_062:19,underway_063:19,underway_064:19,underway_065:19,underway_066:19,underway_067:19,underway_068:19,underway_069:19,underway_070:19,underway_071:19,underway_072:19,underway_073:19,underway_074:19,underway_075:19,underway_076:19,underway_077:19,underway_078:19,underway_079:19,underway_080:19,underway_081:19,underway_082:19,underway_083:19,underway_084:19,underway_085:19,underway_086:19,underway_087:19,underway_088:19,underway_g3:19,unecessari:8,unifi:[3,4],uniform:[14,15,16,18,19,25,30,38],uniqu:[7,8,12,19,22,26,31],unique_d:12,unit:[3,4,8,12,14,17,19,29,30,31,33],univers:[15,30,39,40],unix:[6,12],unknown:12,unlik:[15,17],unnam:7,unord:1,unr:[8,15],unread:14,unrealist:17,unseen:[15,27],unstag:4,unsupervis:[8,19,38,39],unsur:14,until:[17,18,19,23,26,28,31,32],unveil:19,unzip:11,up:[8,12,15,17,18,20,21,26,27,31],updat:[8,11,12,13,14,18,19,23,28,29,30,31,32,34,35,39],update_geo:12,update_layout:12,updatebtn:28,updateclick:28,upgrad:[12,13,18,28],upload:[4,39],upper:[4,15],upper_critical_valu:15,upsampl:14,upstream:37,url:[1,4,5,11,12,16],urllib3:18,us:[1,2,3,5,6,7,8,9,10,11,13,14,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,37,38,39,42],usabl:30,usag:[3,4,13],usecol:[11,12],user:[1,2,4,7,11,12,13,14,18,19,26,28,30,32,42],user_guid:19,usernam:4,userwarn:12,usg:11,usual:[3,7,15,19,20,27,30,32],utc:12,utcdatetim:[17,18],utf:[11,12,15],util:[15,22,23,29,30,31,32,35,42],uv:[8,32],uw60888282:7,uw61361706:7,uw61639436:7,uw61669232:7,uw61735446:7,uw:[1,3,4,5,6,12,13,16,17,18,19,39,40,42],v:[8,15,19],v_e:15,v_measure_scor:19,v_n:15,v_size:15,va:8,vae:35,val:[32,35],val_data:35,val_error:32,val_index:15,val_loss:35,val_set:31,valfmt:8,valid:[13,22,32,34,35,38,39],validate_separ:22,validation_data:34,valu:[8,10,12,14,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,38],valuabl:[13,15,19],value_count:12,valueerror:[22,32],vanish:[30,34],vanuatu:12,varaibl:15,vari:[7,10,14,18,21],variabl:[6,8,9,10,11,12,13,14,15,16,17,19,22,23,24,27,30,32],varianc:[8,15,17,19,20,26,32],variance_1:7,variat:[14,16,19,32],varieti:11,variogram:19,variou:[3,4,5,7,10,15,17,18,19,21,26],varoquaux:21,ve:[13,15],vector:[8,10,14,15,17,19,20,22,23,25,28,30,32,33,39],veget:13,vel:15,veloc:[15,17],venv:2,verbos:[11,22,25],veri:[2,4,12,14,17,18,19,20,23,26,27,30,34,38],verifi:[12,15],version:[1,2,11,26,30,31,37,42],versu:19,vertic:[3,7,8,17,19,24,31,32],verticalalign:8,vi9gmjy8d4zd5jv:31,via:[2,10,19,30,39],vibrat:7,video:[3,42],view:[5,12,19,30,31,33,35,39],viewoutput:30,virginica:28,viridi:17,virtual:[3,4],visit:2,visual:[3,4,8,11,14,15,16,19,21,24,27,35,38,39,42],vmax:[7,8,14,17],vmin:[7,8,14,17],volcan:19,volcano:19,volum:19,voting_clf:26,votingclassifi:26,vs:[4,8,11,15,16,26],vscode:[3,12,17,22,30,32],vstack:[8,19],vt:8,w1sdnnjb2rllxjlbw90zq:17,w:[8,11,14,17,20,23,28,29,30,31,32],w_0:20,w_1:20,w_ela:32,w_i:[29,32],w_j:[23,28,29,32],w_k:29,w_lasso:32,w_n:20,w_ridg:32,w_sgd:32,wa:[11,12,15,16,19,20,21,22,24,25,26,31,32,35],wai:[2,4,8,11,12,13,14,15,19,23,25,27,32,35,38,40],wait:[17,19,28],walk:13,want:[11,12,14,15,17,23,24,29,30,32,33,34],ward:19,warm_start:32,warn:[8,17,19,22,23,26,32],washington:[11,39],watch:[3,22,25,26],water:[10,12,23],water_pot:23,wave:[5,10,34,35],wavedecompnet:35,wavefield:[10,35],waveform:[7,17,18,19,35],wavelength:10,wavelet:[7,9,18,39],wavenumb:17,wavespe:10,wcwidth:[7,28],wdb25puxh3u07dj:[11,17],we:[0,2,3,4,5,7,8,10,11,12,13,14,15,16,17,18,19,20,21,22,23,25,26,28,29,30,32,33,34,35,39,42],weak:26,weather:[5,10,11],web:[3,5,11,42],wednesdai:39,week:[15,24,39],week_fri:24,week_mon:24,week_sat:24,week_sun:24,week_thur:24,week_tu:24,week_w:24,weigh:32,weight:[14,16,21,22,25,26,28,29,30,31,32,33,34],weightedsum:28,weightupd:28,weka:27,welcom:[4,42],well:[4,14,15,19,20,21,22,23,26,28,30,32,38],went:37,were:[3,11,12,14,19,21,22,23,26,28,31,32,40],west:[11,14],westward:15,wget:[5,7,11,12,13,17,19,24,27,31],what:[3,4,7,8,9,12,13,14,15,16,17,18,19,21,23,25,26,27,31,33,38],wheel:4,when:[3,4,11,12,13,14,15,19,21,22,23,25,26,27,28,30,32,35,38,39],where:[1,3,4,7,8,10,11,12,13,15,16,17,19,20,23,27,28,29,30,31,32,33,37,38],whether:[9,11,15,21,30,32],which:[2,4,8,10,11,12,13,14,15,16,17,18,19,20,23,25,26,27,29,30,31,32,35,36,38,39,42],whichitem:28,white:[8,14,18,19],whl:[7,17,26],who:[4,14],who_is_awesom:14,whole:[11,16,23],why:[3,15,27,31,32],wich:14,wide:[10,11,15,16,21,25,27,30,35,42],widget:[27,28],widgetsnbextens:28,width:[1,8,11,12,17,18,28,31],wiggl:32,wiki:21,wikimedia:21,wildfir:16,willustr:32,wind:16,window:[3,4,12,13,14,17,18,30,31],winner:[17,22],wise:[14,27],within:[3,11,12,14,15,17,19,24,26,27,30,31],without:[4,8,9,12,18,26,30,32,38],wmp:12,won:34,work:[0,2,3,8,11,12,13,14,15,19,23,25,26,27,31,34,38],worker:22,workflow:[3,7,9,15,17,27,39],workspac:[17,22],world:[3,27,38],worlpert:38,would:[3,4,7,8,14,17,23,24,26,28,30,31,32,35,37,38],wrap:[14,29],wrapper:22,write:[4,9,11,12,14,23,31,33,38,39],written:1,wrong:[19,27,32],wrote:3,wsl:12,wt:16,wwvb:12,www:[7,11,13,14,17,19,23,24,31],wx:[29,30],x1:[14,19,31],x1_norm:14,x25sdnnjb2rllxjlbw90zq:22,x27:25,x2:[14,19],x2_smooth:14,x30szmlszq:32,x3:[14,19],x41szmlszq:30,x4:14,x:[1,5,7,8,9,11,12,14,15,16,17,19,20,21,22,23,24,28,29,30,31,32,33,34,35,38],x_1:20,x_:[15,23],x_a:15,x_b:15,x_i:[14,15,19,23,29],x_int:14,x_label_list:17,x_lat:14,x_lon:14,x_max:30,x_min:30,x_n:20,x_new:[8,30],x_p:19,x_pca:8,x_poli:32,x_scale:38,x_std:38,x_t:14,x_test:[21,22,25,26,30,31,34],x_tl:14,x_train:[21,22,25,26,30,31,32,34],x_train_poly_sc:32,x_val:[32,34,35],x_val_2d:35,x_val_compress:35,x_val_poly_sc:32,x_valu:24,xarrai:[9,11],xarrrai:14,xavg:8,xaxi:28,xc:8,xgb:26,xgboost:[26,30],xkcd:21,xlabel:[8,12,14,15,16,17,18,19,21,22,24,28,30,32,33,34],xlat:17,xlim:[8,16,17,18,19,22,30,32],xlon:17,xp:22,xr:14,xrang:28,xscale:18,xt:33,xtick:[8,14,17,24],xx:[7,30],y2:19,y3:19,y:[5,7,8,9,11,12,14,15,17,19,20,21,22,23,24,25,26,28,29,30,31,32,33,34,35],y_ela:32,y_i:[14,19,20,23,32],y_k:29,y_lasso:32,y_lower:19,y_max:30,y_min:30,y_numer:22,y_pred:[14,22,25,26,34],y_predict:32,y_proba:30,y_relu:31,y_ridg:32,y_ridge2:32,y_score:[21,22],y_sgd:32,y_sridg:32,y_test:[21,22,25,26,30,31,34],y_test_pr:21,y_train:[21,22,25,26,30,31,32,34],y_train_pr:22,y_train_predict:32,y_true:[22,25],y_upper:19,y_val:[32,34,35],y_val_predict:32,yaxi:19,ye:[2,23,27],year:[4,5,7,8,12,15,17,24,30,38],yearfirst:12,yellowston:[11,19],yesterdai:[24,27],yet:[4,32],yhat:23,yield:[17,32,36],yin:35,yiyu:39,ylabel:[8,14,15,16,17,18,19,21,22,24,28,32,33],ylim:[8,17,18,19,22,32],yml:[4,38,39],you:[1,3,4,5,7,8,9,11,12,13,14,15,17,18,19,21,23,26,27,28,29,30,31,32,35,37,38,42],your:[3,5,9,11,12,13,14,15,17,19,21,23,27,28,29,30,32,38,39,42],your_command:4,your_packag:4,yourusernam:4,youtu:3,yrang:28,yscale:[17,18,33],yt:33,ytick:[14,17],yy:30,yymmmdd:15,z:[7,11,12,13,16,17,18,19,23,29,30],zarr:[9,10,14],zel:17,zenodo:4,zero:[7,8,15,17,18,19,21,23,28,29,30,31,32,35,38],zero_grad:[29,30,31,33,35],zf:17,zhat:[17,18],zhu:35,ziheng:[39,40],zip:[8,11,22,24,30],zip_file_url:15,zipfil:[8,11,15],zipp:[13,18,28],zone:15,zoom_start:11,zorder:8,zp:16,zsort:17,zxx:17},titles:["1.1 Open Reproducible Science","1.3 Jupyter Environment","1.3 Python Ecosystem","1.4 Computing Environments","1.5 Version Control & GitHub","1.6 Data Gallery","Getting Started","2.9 Feature engineering","2.10 Dimensionality Reduction","2.11 ML-ready data","2.1 Data Definitions","2.2 Data Formats","2.3 Pandas","2.4 DataFrame Exploration","2.5 Data Arrays","2.6 Resampling Methods","2.7 Statistical Considerations for geoscientific Data and Noise","2.7 Spectral Transforms","2.9 Synthetic noise","3.1 Clustering","3.2 Classification and Regression","3.3 Binary classification","3.4 Multiclass Classification","3.5 Logistic regression","3.6 Random Forests","3.7 Hyperparameter Tuning","3.8 Ensemble learning","3.9 AutoML","4.0 The Perceptron","4.1 Neural Networks","4.2 Multi Layer Perceptrons","4.3 Convolutional Neural Networks","4.3 Model Training","4.2 Physics-Informed Neural Networks","4.4 Recurrent Neural Networks: Processing sequences","4.5 Auto-encoders","4.6 NAS: Network Architecture Search","This chapter focuces on model workflow and ML reproducibility","The MLGeo Project","Machine Learning in the Geosciences","Acknowledgements from Contributors","Bibliography","Glossaries"],titleterms:{"0":[28,31],"1":[0,1,2,3,4,5,8,10,11,12,14,15,16,17,18,19,20,21,22,24,25,26,29,30,31,32,38],"10":8,"11":9,"1d":14,"2":[7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,29,30,31,32,33,34,38],"2d":[14,17],"3":[1,2,8,11,12,14,15,16,17,19,20,21,22,23,24,25,26,27,29,30,31,32,38],"3d":8,"4":[3,8,12,13,14,15,17,18,19,22,24,26,28,29,30,31,32,33,34,35,36,38],"5":[4,8,12,14,15,23,24,29,30,31,32,35,38],"6":[5,8,12,14,15,24,32,36,38],"7":[16,17,24,25,38],"8":26,"9":[7,18,27],"class":29,"do":2,"final":13,"function":[12,29,30,32],"import":24,"new":4,"short":17,A:31,But:11,One:[15,24],The:[16,19,28,31,38],To:31,about:28,account:4,acknowledg:40,activ:[30,31],adaboost:26,addit:4,advanc:12,aggreg:12,ahead:34,algorithm:20,an:[2,4,31],analysi:[8,13],app:4,appendix:23,ar:2,architectur:36,arrai:[10,11,14,29],asid:31,assembl:31,assess:24,authent:4,auto:35,autoencod:35,automat:23,automl:27,aw:3,azur:3,bag:26,baselin:24,basic:[1,2,12,14,17],batch:32,befor:19,best:27,bibliographi:41,binari:21,bonu:14,boost:26,bootstrap:15,build:39,can:34,canon:16,carlo:15,chapt:38,chapter:[37,38],check:[13,24,27],checklist:32,choic:[19,31],choos:14,classic:12,classif:[20,21,22,32],classifi:[21,26],clever:32,cloud:3,cluster:19,cnn:31,code:2,colab:3,column:[12,27],comma:11,command:[1,2],compar:27,comparison:14,compon:8,comput:[3,14,24],conda:2,condit:12,connect:31,consider:16,contribut:4,contributor:40,control:4,convers:14,convex:32,convolut:[31,35],correl:13,cours:39,covari:8,creat:[4,12,28,29],cross:[15,25],csv:[11,12],custom:29,data:[5,8,9,10,11,12,13,14,15,16,18,19,21,22,24,27,28,30,31,35,38],datafram:[12,13],dataload:29,dataset:[5,28,29],deal:2,decis:24,decod:35,deep:32,defin:31,definit:[10,31,42],denois:35,descent:[23,32],descript:5,design:[29,30,38],desktop:4,determin:8,differenti:23,dimension:[8,35,38],directori:4,discov:33,displai:27,distanc:[14,19],distribut:[13,16],download:[5,24,38],earli:32,earthquak:12,ecosystem:2,elast:32,elbow:19,encod:[24,35],engin:7,enhanc:4,ensembl:26,environ:[1,2,3],evalu:27,event:18,exampl:[2,4,12,15,31,32,35],exercis:[12,13,14,18,32],exist:4,explan:4,explor:[7,8,13,21,24],extract:[8,14],far:34,featur:[7,8,13,16,24],file:[2,4,5,12],filter:17,fine:30,first:[24,27],fit:[28,32],focuc:37,fold:15,forecast:34,forest:24,format:[10,11],forward:30,fourier:17,frame:[10,38],freez:2,from:[5,6,12,40],fulli:31,fundament:12,futur:34,galleri:5,gener:[12,42],geodet:15,geojson:11,geolog:16,geopanda:11,geoscienc:[10,39],geoscientif:[14,16],geospati:11,geotiff:11,get:[6,27],git:4,github:[4,39],glossari:42,googl:3,gradient:[23,26,32],grid:25,handl:[11,13],hassl:27,hdf5:11,hierarch:[11,19],high:14,homework:39,hot:24,how:[2,5,31,34],hpc:3,hub:1,hyperparamet:[25,30],i:11,imag:31,implement:[23,31],independ:8,infer:15,inform:[18,33],initi:24,intermedi:12,interpret:27,introduct:23,iri:28,javascript:11,json:11,jupyt:1,k:[15,19],kei:4,kurtosi:16,lab:1,label:24,lambda:12,larg:11,lasso:32,latent:35,layer:[30,31],learn:[6,19,26,30,32,39],leav:15,lectur:10,lenet:31,let:[28,31],level:[14,15,16,17,18],line:28,linear:[15,20],littl:28,load:29,local:3,logic:12,logist:23,loss:[29,32],low:35,lstm:34,machin:39,magnitud:12,main:4,manipul:[12,14],map:12,markdown:1,matplotlib:14,matrix:8,mean:[8,16,19],measur:14,metadata:7,method:[15,19],metric:[21,27],mini:32,miss:13,ml:[9,31,37],mlgeo:[5,38],mlp:30,modal:10,model:[15,21,24,27,29,30,31,32,33,35,37,38],mont:15,more:27,motion:15,multi:[30,35],multiclass:22,multipl:12,na:36,nan:13,need:2,net:[32,35],netcdf4:11,netcdf:11,network:[29,30,31,33,34,36],neural:[29,30,31,33,34],nois:[16,18],norm:14,notat:11,note:[4,31],notebook:1,number:19,numpi:14,nyquist:17,o:11,object:[6,11,39],open:0,optim:[29,30,32],organ:38,other:[8,32],our:[28,31],out:[15,28],outcom:8,overfit:32,overview:39,panda:12,paramet:[8,31],parquet:[11,12],past:26,pca:[8,19],perceptron:[28,30],perform:[21,24],physic:[18,33],pinn:33,plate:15,plot:14,plotli:12,pool:31,practic:[19,31],predict:34,prep:31,prepar:[22,24,30,38],prerequisit:39,princip:8,problem:[32,34],process:34,project:38,publish:[4,31],pycaret:27,pyproject:4,python:[2,12],pytorch:[14,23,29,30,31],qualiti:24,random:[14,15,18,24,25,29],randomli:24,raster:11,rasterio:11,rate:32,re:22,read:[11,12,13,31,39],readi:[9,27],realist:18,recod:31,recommend:4,recurr:34,reduct:[8,38],regress:[15,20,23,24,32],regular:32,rememb:28,repeat:19,repositori:[4,5],reproduc:[0,37],resampl:15,resourc:4,respons:12,restor:30,review:31,ridg:32,right:14,rnn:34,robust:15,rule:28,run:[2,27],s:[28,31],save:30,scale:22,scienc:0,scikit:[19,30],search:[25,36],section:12,segment:31,seismic:31,seismolog:35,select:8,separ:[11,24],sequenc:34,seri:12,set:[4,16,19,24,32],sever:34,shape:27,signal:18,skew:16,skill:39,slide:10,slow:11,softwar:4,solut:34,some:[28,31],space:[7,17,35],specif:39,spectral:17,split:[22,24,27,29],stack:26,start:[6,31],statist:16,step:[4,8,34],stochast:32,stop:32,structur:[4,30],student:[12,14,39],subtract:8,svd:8,syllabu:39,synthet:[18,21],tabular:11,task:[14,35],team:4,technic:39,techniqu:[8,15],technolog:42,tensor:14,test:[22,24,27,28,29],text:12,thi:[2,28,37],tier:39,time:[12,17],tip:19,toml:4,tool:[14,42],train:[22,24,27,29,30,31,32,38],transform:17,tree:24,troubleshoot:27,tune:[25,30,31],tutori:[19,39],typic:30,u:35,uncertainti:17,under:32,up:[4,19,24,32],us:[4,12,15,28],valid:[15,25],valu:[11,13],varianc:16,version:[4,39],via:8,virtual:2,vote:26,vs:12,wavelet:17,we:[24,27,31],webinar:39,what:[1,2,28],why:14,work:4,workflow:[4,37],xarrai:14,yml:2,you:[2,34],your:[2,4],zarr:11,zero:[6,13]}}) \ No newline at end of file