vfive fivejjs

## exploratory_data_analysis.py
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline

## countries_codes_and_coordinates.csv

          
            Country
            Alpha-2 code
            Alpha-3 code
            Numeric code
            Latitude (average)
            Longitude (average)

            
              Afghanistan
              AF
              AFG
              4
              33
              65

            
              Albania
              AL
              ALB
              8
              41
              20

            
              Algeria
              DZ
              DZA
              12
              28
              3

            
              American Samoa
              AS
              ASM
              16
              -14.3333
              -170

            
              Andorra
              AD
              AND
              20
              42.5
              1.6

            
              Angola
              AO
              AGO
              24
              -12.5
              18.5

            
              Anguilla
              AI
              AIA
              660
              18.25
              -63.1667

            
              Antarctica
              AQ
              ATA
              10
              -90
              0

            
              Antigua and Barbuda
              AG
              ATG
              28
              17.05
              -61.8

## gist:08b6299be5dd2c6f428b68f0e4d6bc96

      
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                fivejjs
                / gist:08b6299be5dd2c6f428b68f0e4d6bc96
            
            
              Created
              April 30, 2021 12:09
                — forked from bcherny/gist:de24955c15430efd99f1
            
              
                How to squash commits with git
              
          
    Let's say you have a Pull Request from myBranch to master with 3 commits, and you want them to appear as a single commit in master.
If you're merging on the command line, this is trivial:
git checkout master
git merge --squash myBranch
git commit

  
## odc-dscache-csv-odd-tiles-to-geojson.py
from collections import defaultdict
import pandas as pd


df_new = pd.read_csv('s2_l2a_all.csv') # add absolut path of csv

xy_t = defaultdict(list)

for row in df_2019.itertuples():
    xy_t[(row.X, row.Y)].append(row.T)

## keybase.md

      
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                fivejjs
                / keybase.md
            
            
              Last active
              April 13, 2021 05:56
            
          
    Keybase proof

I hereby claim:

I am fivejjs on github.
I am jinjunsun (https://keybase.io/jinjunsun) on keybase.
I have a public key ASBIkHDrGNWV9gLbFeUfFBGomvnPI2ivtyEgj_TmeW50Tgo

To claim this, I am signing this object:

  
## 1.html.md

      
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              1 star
            
          
                fivejjs
                / 1.html.md
            
            
              Created
              November 3, 2019 10:51
                — forked from hongyangqin/1.html.md
            
              
                傅立叶变换的物理意义 From:https://mp.weixin.qq.com/s/B0267BKDutJhUQ5I9wquqg##
              
          
    傅立叶变换的物理意义

原创 2016-04-08 yangboy2
昨天我们分享了一篇《FFT结果的物理意义》一文，有些网友表示看不大明白，为此我们整理重发一篇去年分享过的《傅里叶变换的物理意义》一文，以便大家更好的理解。
1. 先看下面的两幅动画，增加对傅立叶变换的印象


## dummy-indexing.ipynb

      
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                fivejjs
                / dummy-indexing.ipynb
            
            
              Created
              February 23, 2021 05:15
            
              
                sample-ipynb
              
          
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## crop_mask_feature_layer.py
def gm_mads_two_seasons(ds1, ds2):
    """
    Feature layer function for production run of
    eastern crop-mask
    """
    def fun(ds, era):
        #normalise SR and edev bands
        for band in ds.data_vars:
            if band not in ['sdev', 'bcdev']:
                ds[band] = ds[band] / 10000

## librosa_parallel.py
# The script illustartes stunning difference on my machine with processing of signal with multiprocessing and joblib.
# The slowness of multiprocessing is likely caused by oversubscription

import time
import numpy as np
import librosa
from joblib import Parallel, delayed
from functools import partial
from multiprocessing import Pool

## 1.html.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                fivejjs
                / 1.html.md
            
            
              Created
              November 3, 2019 10:51
                — forked from hongyangqin/1.html.md
            
              
                傅立叶变换的物理意义 From:https://mp.weixin.qq.com/s/B0267BKDutJhUQ5I9wquqg##
              
          
    傅立叶变换的物理意义

原创 2016-04-08 yangboy2
昨天我们分享了一篇《FFT结果的物理意义》一文，有些网友表示看不大明白，为此我们整理重发一篇去年分享过的《傅里叶变换的物理意义》一文，以便大家更好的理解。
1. 先看下面的两幅动画，增加对傅立叶变换的印象
	import pandas as pd
	import numpy as np
	import matplotlib
	import matplotlib.pyplot as plt
	import seaborn as sns
	import missingno
	import warnings
	warnings.filterwarnings("ignore")
	%matplotlib inline
Country	Alpha-2 code	Alpha-3 code	Numeric code	Latitude (average)	Longitude (average)
Afghanistan	AF	AFG	4	33	65
Albania	AL	ALB	8	41	20
Algeria	DZ	DZA	12	28	3
American Samoa	AS	ASM	16	-14.3333	-170
Andorra	AD	AND	20	42.5	1.6
Angola	AO	AGO	24	-12.5	18.5
Anguilla	AI	AIA	660	18.25	-63.1667
Antarctica	AQ	ATA	10	-90	0
Antigua and Barbuda	AG	ATG	28	17.05	-61.8
	from collections import defaultdict
	import pandas as pd


	df_new = pd.read_csv('s2_l2a_all.csv') # add absolut path of csv

	xy_t = defaultdict(list)

	for row in df_2019.itertuples():
	xy_t[(row.X, row.Y)].append(row.T)
	def gm_mads_two_seasons(ds1, ds2):
	"""
	Feature layer function for production run of
	eastern crop-mask
	"""
	def fun(ds, era):
	#normalise SR and edev bands
	for band in ds.data_vars:
	if band not in ['sdev', 'bcdev']:
	ds[band] = ds[band] / 10000
	# The script illustartes stunning difference on my machine with processing of signal with multiprocessing and joblib.
	# The slowness of multiprocessing is likely caused by oversubscription

	import time
	import numpy as np
	import librosa
	from joblib import Parallel, delayed
	from functools import partial
	from multiprocessing import Pool