Zhoutian Tao fish8
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| 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], |
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| 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") |
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| 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 | |
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| #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 |
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| 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 |
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| #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... |
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| #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 |
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| """ | |
| Overlapping densities ('ridge plot') | |
| ==================================== | |
| """ | |
| import numpy as np | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt |
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| #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" |
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| # 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 |
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