def main():
counter = word_count()
filereader = file_reader()
db_access = database_access()
# load folder linked to read the word documents
if (os.environ.get('MONITOREDFOLDER') == None):
monitoredfolder = './words/'
else:
monitoredfolder = os.environ.get('MONITOREDFOLDER')
while True:
# List all files in the folder
onlyfiles = [
f for f in listdir(monitoredfolder)
if isfile(join(monitoredfolder, f))
]
# create log folder
if not exists(monitoredfolder + 'logs/'):
makedirs(monitoredfolder + 'logs/')
# configure Log
logging.basicConfig(filename=monitoredfolder +
'logs/document_import.log',
level=logging.INFO)
logging.info(str(len(onlyfiles)) + " New Files")
# Process Single new File if there is one
if len(onlyfiles) != 0:
words = filereader.read(onlyfiles[0])
result = counter.count(words)
for key in result:
# update count for each word in the dictionary of the currenct file
db_access.add_value_to_word_count(key, result[key])
else:
time.sleep(60)
def scatterplot():
print("""
================================================================================
= Creating scatterplot =
================================================================================
""")
base_url = "https://api.teleport.org/api/urban_areas/slug:new-york/scores/"
r = requests.get(base_url)
d = r.json()
city_score = d["teleport_city_score"]
avg_sentiment = 0
t = file_reader(ask_for_csv_filename())
for sentiment in t[1]:
avg_sentiment += sentiment
avg_sentiment = avg_sentiment / len(t[1])
fig_scatter = px.scatter(x=t[0],
y=t[1],
color=t[1],
color_continuous_scale='Magma')
fig_scatter.add_shape(
# Line Horizontal
go.layout.Shape(type="line",
x0=-5,
y0=avg_sentiment,
x1=105,
y1=avg_sentiment,
line=dict(color="black", width=3)))
fig_scatter.add_shape(
# Line Horizontal
go.layout.Shape(type="line",
x0=-5,
y0=city_score,
x1=105,
y1=city_score,
line=dict(color="black", width=3)))
# Create scatter trace of text labels
fig_scatter.add_trace(
go.Scatter(
x=[5, 5],
y=[avg_sentiment + 2, city_score + 2],
text=[
"Average Sentiment Score", "Quality of Life Score for New York"
],
mode="text",
))
fig_scatter.update_layout(
title="Sentiment Score for Each Headline/Abstract",
xaxis_title="Headline/Abstract ID",
yaxis_title="Sentiment Score (out of 100)")
fig_scatter.show()
def __init__(self, params):
self.dict_ = {'title': [], 'paper_id': [],
'abstract': [], 'body_text': []}
self.root_path = params['root_path']
self.all_json = self.get_all_json()
for idx, entry in enumerate(self.all_json):
if idx % (len(self.all_json) // 10) == 0:
print(f'Processing index: {idx} of {len(self.all_json)}')
content = file_reader(entry)
self.dict_['paper_id'].append(content.paper_id)
self.dict_['title'].append(content.title)
self.dict_['abstract'].append(content.abstract)
self.dict_['body_text'].append(content.body_text)
def train_model_azi(fea_dir, record_dir, azi, model_dir, pb_share):
""" Train GMMs model for a given sound azimuth
Args:
fea_dir: directory where features are saved
record_dir: directory of synthesized recordings corresponding to
features
azi: azimuth of sound source
model_dir: directory where models(frequency bands) are saved
Return: Null
"""
img_dir = os.path.join(model_dir, 'images')
os.makedirs(img_dir, exist_ok=True)
for band_i in range(n_band):
model_fpath = os.path.join(model_dir, '{}_{}.npy'.format(azi, band_i))
fig_fpath = os.path.join(img_dir, '{}_{}.png'.format(azi, band_i))
gif_fpath = os.path.join(img_dir, '{}_{}.gif'.format(azi, band_i))
sample_all = [
sample for sample in file_reader(fea_dir,
azi_tar=azi,
band_tar=band_i,
is_screen=True,
record_dir=record_dir)
]
if len(sample_all) > 0:
data = np.concatenate(sample_all, axis=0)
else:
raise Exception('Empty sample')
model = GMMs.GMMs(x=data, k=15, lh_theta=1e-5, max_iter=300)
if not os.path.exists(model_fpath):
model.EM(init_method='norm_0',
is_log=False,
is_plot=False,
fig_fpath=fig_fpath,
is_gif=False,
gif_fpath=gif_fpath)
model.save(model_fpath)
if True: # not os.path.exists(fig_fpath):
model.load(model_fpath)
model.plot_record(fig_fpath)
pb_share.update()
from file_reader import file_reader
#par
def part(arr):
i = len(arr) - 1
p = arr[0]
for j in range(len(arr) - 1, 0, -1):
if arr[j] > p:
arr[j], arr[i] = arr[i], arr[j]
i -= 1
arr[i], arr[0] = arr[0], arr[i]
i -= 1
for j in range(i, -1, -1):
if arr[j] == p:
arr[j], arr[i] = arr[i], arr[j]
i -= 1
return arr
if __name__ == "__main__":
input_list = file_reader("rosalind_par3.txt")
s = [4, 5, 6, 4, 1, 2, 5, 7, 4]
array = list(map(int, input_list[1].split()))
part(array)
par(s)
print(s)
answer = " ".join(list(map(str, array)))
print(answer)
with open("./answer1.txt", "w") as file:
file.write(answer)
def compose_bar_chart():
# Print opening message for this program action
print(
"""
================================================================================
= Compose histogram chart =
================================================================================
"""
)
# Gets the lists of ids and quality of life scores
ids, scores = file_reader(ask_for_csv_filename())
# Get NY quality of life score
base_url = "https://api.teleport.org/api/urban_areas/slug:new-york/scores/"
r = requests.get(base_url)
d = r.json()
city_score = d["teleport_city_score"]
# Get average sentiment score from csv file
avg_sentiment = sum(scores) / len(scores)
fig = go.Figure()
fig.add_trace(go.Histogram(
x=scores,
name='Quality of Life Name', # name used in legend and hover labels
xbins=dict( # bins used for histogram
start=0,
end=100,
size=5
),
#marker_color='Red',
opacity=1,
marker=dict(
line=dict(
width = 2
),
cmin=0,
cmax=1,
colorscale=[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]
#color="Green"
)
))
fig.update_layout(
title_text='Quality of Life Metrics for Headlines and Abstracts', # title of plot
xaxis_title_text='Quality of Life Metric', # xaxis label
yaxis_title_text='Frequency', # yaxis label
# bargap=0.2, # gap between bars of adjacent location coordinates
)
fig.add_shape(
go.layout.Shape(
type="line",
x0=avg_sentiment,
y0=0,
x1=avg_sentiment,
y1=50,
line=dict(
color="red",
width=3
)
)
)
fig.add_trace(go.Scatter(
x=[avg_sentiment + 8],
y=[15],
text=["Average Sentiment Score: ({})".format(round(avg_sentiment, 2))],
mode="text"
))
fig.add_shape(
go.layout.Shape(
type="line",
x0=city_score,
y0=0,
x1=city_score,
y1=50,
line=dict(
color="yellow",
width=3
)
)
)
fig.add_trace(go.Scatter(
x=[city_score + 8],
y=[15],
text=["QOL Score for New York: ({})".format(round(city_score, 2))],
mode="text"
))
fig.show()
def __init__(self, filename):
self.file_reader = file_reader(filename)
self.products = self.file_reader.products
self.driver = webdriver.Chrome("./chromedriver_version_80")
def itemListFunct(event):
#list OnDouble function part
widget = event.widget
selection=widget.curselection()
value = widget.get(selection[0])
# end -> list OnDouble function part
# selection algorithm for selecting correct display item
#for tutorials ^^^^^^^^^
if(tut):
if( value in orderItemDetailsBasDict ):
temp = orderItemDetailsBasDict[value]
placer.setChildSection(1,0)
placer.setSourceSection( list(sourceSelectedBasicDict.keys()).index( temp ) )
placer.setSelectedDispItem( list(lister.toList(placer.get_disp(),
'attrib', 'entry')).index( value ) )
elif( value in orderItemDetailsAdvDict ):
print('\nTHING\n')
print(orderItemDetailsAdvDict)
print(sourceSelectedAdvancedDict)
print('\nTHING')
temp = orderItemDetailsAdvDict[value]
print(temp )
placer.setChildSection(1,1)
print( list(sourceSelectedAdvancedDict.keys()).index( temp ) )
placer.setSourceSection( list(sourceSelectedAdvancedDict.keys()).index( temp ) )
placer.setSelectedDispItem( list(lister.toList(placer.get_disp(),
'attrib', 'entry')).index( value ) )
#for projects ^^^^^^^^^
if(pro):
if( value in orderItemDetailsBasDict):
placer.setChildSection(0,0)
placer.setSelectedDispItem( selection[0] )
else:
placer.setChildSection(0,1)
placer.setSelectedDispItem( selection[0] - len(orderItemDetailsBasDict) )
# itemDetails
#reset contents
itemText.delete(1.0, 'end')
itemDetails.delete(0, 'end')
#variables to be retrieved from the loop
disp_file_name = None
disp_file_location = None
disp_file_non_cloud_location = None
#this loop places all of the file descriptors
for a in placer.getSelectedDispItem():
string = ''
string = '%s: %s' %(a.tag, a.text)
itemDetails.insert(END, string)
if(a.tag == 'name'):
disp_file_name = a.text
if(a.tag == "location"):
disp_file_location = a.text
if(a.tag == "non_cloud"):
disp_file_non_cloud_location = a.text
# itemDetails end
# itemText
if(disp_file_location == 'default' and tut):
disp_file_location = cloudDirectory + placer.getSourceLocation()
#source dir check: print("%s%s" %(self.cloudDirectory, placer.getSourceLocation()))
elif(disp_file_location == 'non cloud'):
disp_file_location = disp_file_non_cloud_location
elif(disp_file_location == None):
#no location found
return None
else:
#set to given location
disp_file_location = cloudDirectory + disp_file_location
#read file
current_file = file_reader(disp_file_name, disp_file_location)
#print to pane
itemText.insert(END, current_file.getText())
# itemText end
# sets the file name and location for 'File -> Open with NPP'
placer.setCurrentFile(disp_file_location, disp_file_name)
