def csv_upload_visualization(filename=''):
"""
This function is designed for users to upload
a file to visualize
"""
# TODO
# choose a file stored in the server
# grab the url of the csv file
# obtain row_offset and column_offset
csv_row_offset = request.form['row_offset']
csv_column_offset = request.form['column_offset']
item_name_list = []
# get the current app location
# app/visualization
app_root = os.path.dirname(os.path.abspath(__file__))
download_dir = app_root + '/tempData/'
# getItemName five variables: folder, file name, item in the csv,
# first row offset, second row offset, coloumn offset
util.getItemName(download_dir, filename, item_name_list, \
csv_row_offset, 0, csv_column_offset)
input_filename = 'temp' + filename
return render_template('visualization/csv_visualization_results.html', \
item_name_list=item_name_list, \
input_filename=input_filename)
def csv_new_upload_visualization(filename=''):
"""
This function is designed for users to upload
a file to visualize
"""
# TODO
# choose a file stored in the server
# grab the url of the csv file
# obtain row_offset and column_offset
csv_row_offset = request.form['row_offset']
csv_column_offset = request.form['column_offset']
item_name_list = []
# get the current app location
# app/visualization
app_root = os.path.dirname(os.path.abspath(__file__))
download_dir = app_root + '/templates/UPLOAD_FOLDER/'
# getItemName five variables: folder, file name, item in the csv,
# first row offset, second row offset, coloumn offset
util.getItemName(download_dir, filename, item_name_list, \
csv_row_offset, 0, csv_column_offset)
# remove .csv from the end of file name
file_origin_name = filename[:-4]
input_filename = 'temp' + filename
file_name = filename
file_path = download_dir + input_filename
#print file_path
cpu_num = multiprocessing.cpu_count()
# I don't know why funcion separate_csv does not work here, I used code directly
# and it works
#chunk = util.separate_csv(file_path, cpu_num)
filename = file_path
csv_handle = pd.read_csv(filename)
csv_len = len(csv_handle)
if csv_len%cpu_num == 0:
chunk_size = csv_len/cpu_num
elif csv_len%(cpu_num-1) == 0:
chunk_size = (csv_len/(cpu_num-1))-1
else:
chunk_size = csv_len/(cpu_num-1)
chunk=[None]*cpu_num
count=0
for chunk_piece in pd.read_csv(filename, chunksize=chunk_size):
chunk[count] = chunk_piece
count = count+1
# print chunk[0]
#
column_bool = [1]*len(csv_handle.columns)
x_scale = util.x_date_max_min_of_csv(filename)
y_scale = util.y_max_min_of_csv(filename,column_bool)
# create a list of input class objects for the pool
input_objects = [None]*cpu_num
for i in range(cpu_num):
input_objects[i] = CSVChunkInformation(file_origin_name,i,chunk[i],x_scale,y_scale,column_bool)
# test
# cpu_num =
# separate work into cpu_number pieces
pool = Pool(cpu_num)
pool.map(util.plot, input_objects)
#close pool
pool.close()
pool.join()
# combine temp imgs and delete temp imgs
temp_img_dir = app_root + '/data/img/'
#temp_img_name = 'temp_fig_'+file_origin_name+'_0.png'
result_img_name = file_origin_name+'_result.png'
result_img_path = temp_img_dir+'../'+result_img_name
util.combine_delete_image(temp_img_dir, result_img_path)
return render_template('csv_new_visualization_results.html', \
item_name_list=item_name_list, \
input_filename=result_img_name, \
filename = file_name)
