This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import pandas as pd | |
import csv | |
glove_df = pd.read_csv("glove.6B.50d.txt.zip", sep=" ", index_col=0, header=None, | |
quoting=csv.QUOTE_NONE) | |
"new york" in glove_df.index | |
vocabs = list(glove_df.index) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10 | |
import plotly.graph_objs as go | |
import plotly.graph_objs as go | |
import plotly.offline as offline | |
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot | |
offline.init_notebook_mode() |
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import os | |
directory = "sample_dir" | |
if not os.path.exists(directory): | |
os.makedirs(directory) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
ls *.png | awk '{print("mv "$1" "$1)}' | sed 's/png/jpg/2' | /bin/sh |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import os | |
from PIL import Image | |
src_path = "./testing2/" | |
out_path = "./" | |
basewidth = 75 | |
image_type = (".jpg", ".png", ".JPG", ".PNG", ".JPEG", ".tif", ".tiff", ".TIFF") | |
image_paths = [] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import pandas as pd | |
import os | |
import shutil | |
import sys | |
if len(sys.argv) < 1: | |
print "provide CSV file argument, followed by target directory argument,",\ | |
" followed by which column to use from csv file (optional)" | |
elif len(sys.argv) < 4: | |
in_file = sys.argv[1] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import os | |
import shutil | |
src_path = "/Users/myazdaniUCSD/Desktop/hourly_colors/" | |
num_chunks = 6 | |
target_path = "/Users/myazdaniUCSD/Desktop/some_dir/" | |
def chunks(l, n): | |
""" Yield successive n-sized chunks from l. | |
""" |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
def chunks(l, n): | |
""" Yield successive n-sized chunks from l. | |
""" | |
for i in xrange(0, len(l), n): | |
yield l[i:i+n] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
normalized.probs = function(x) {return(x/sum(x))} | |
res.H = relevant.images[,normalized.probs(.SD), by = filename.path, .SDcols = paste0("H", c(1:180))] |