fish8

case class TimeMatrixCase(
                            userId: Long,
                            windowDateList: List[String],
                            sourceCellIdPlaceId: Map[String, Int],
                            timeMatrix: List[List[Int]],
                            time_map: Map[String, Int],
                            weekdayDaySeconds: Map[String, Int],
                            weekdayEveningSeconds: Map[String, Int],
                            weekendDaySeconds: Map[String, Int],
                            weekendEveningSeconds: Map[String, Int],

fish8

from networkx.drawing.nx_agraph import graphviz_layout, to_agraph
from PIL import Image
from io import BytesIO

A = to_agraph(g2)
A.layout(prog='dot')
# graphviz_layout(prog=neato)
img = A.draw(format='png')
image = Image.open(BytesIO(img))
image.show(title="Graph")

fish8

大集群：  hdfs://10.244.12.215:8020
小集群：  hdfs://10.244.12.75:8020
花园集群： hdfs://10.244.13.106:8020

fish8

#bash command completion
source <(sq completion bash)

#add source
sq add ./CBDB_20200528.db -h @cbdb_20200528

#list source
sq ls

#inspecting, list table names

fish8

import scala.math.Integral.Implicits._

def location2utm(location_id:Long):(Long,Long,Long) = {
    // convert a location id to utm coordination
    // THIS IS THE UTM VALUE AS IS IN SS OUTPUTS. TO GET CORRECTED UTM/LAT-LON USE sstool.ssutm.SSUtm
    // return a tuple of (x, y, size) as left-bottom corner of a rectange

    // if location_id < 100000000000:
    //     return None

fish8

#itables
from itables import init_notebook_mode
init_notebook_mode(all_interactive=True)
import world_bank_data as wb

df = wb.get_countries()
df

#qgrid
qgrid.show...

fish8

#pynb-code, show all codes in a jupyter notebook
jq -j '.cells | map( select(.cell_type == "code") | .source +["\n\n"])  | .[][]  ' $@

#beautify code in notebook and copy to clipboard as rich text
pynb-code xxx.ipynb |pygmentize -f rtf | xclip -t text/rtf -selection clipboard

#find print
find . -name "*.ipynb" -exec pynb-code {} +

#find grep

fish8

"""
Overlapping densities ('ridge plot')
====================================


"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

fish8

#to csv
#via csvkit
in2csv --sheet 区县级  基站工参统计.xlsx #to stdout

# via Gnumeric
ssconvert -T Gnumeric_stf:stf_csv 基站工参统计.xlsx

#select columns
in2csv --sheet 区县级  基站工参统计.xlsx |csvcut -c "城市名称","人数"

fish8

# via Tesseract
pdfsandwich -lang chi_sim zgsy.pdf

#merging pages
pdftk A=zgsy.pdf B=zgsy_ocr.pdf cat A1 B2-27 A28 B29-57 A58 B59-140 A141 B142-end output zzz.pdf