# #keras implimentation
batch_size = 1
tsteps = 50
data_dim = 25
#epochs
epochs = 50
# # #data spliting
data_source_train = data_source[:100000, :]
data_source_valid = data_source[100000:, :]
data_target_train = data_target[:100000, :]
data_target_valid = data_target[100000:, :]
# #zeros padding
#source data
data_source_train = reshape(data_source_train, tsteps, data_dim)
data_source_valid = reshape(data_source_valid, tsteps, data_dim)
#target data
data_target_train = reshape(data_target_train, tsteps, data_dim)
data_target_valid = reshape(data_target_valid, tsteps, data_dim)
#checking shape
print('source data', np.shape(data_source_train))
print('target data', np.shape(data_target_train))
#model selection
model = Sequential()
model.add(
GRU(units=70,
batch_input_shape=(batch_size, tsteps, data_dim),
return_sequences=True,
stateful=True))
def main(argv):
# Define input, output, and archive directory names.
turbine_dir_name = 'reshaped_turbine'
weather_dir_name = 'raw_weather'
archive_dir_name = 'archived'
output_dir_name = 'output'
# Process raw turbine files into the eshape format.
# [datetime, sensor id, value]
reshape()
# Define current working directory.
current_dir = os.path.dirname(os.path.abspath(__file__))
# Define input, output, and archive directories.
turbine_dir = os.path.join(current_dir, turbine_dir_name)
weather_dir = os.path.join(current_dir, weather_dir_name)
turbine_archive_dir = os.path.join(turbine_dir, archive_dir_name)
weather_archive_dir = os.path.join(weather_dir, archive_dir_name)
output_dir = os.path.join(current_dir, output_dir_name)
# Process all of the turbine data.
# Scan for all CSV files within the turbine data folder.
for item in os.listdir(turbine_dir):
if item.split('.')[-1].lower() == 'csv':
windows = [Window('t14'), Window('t15'), Window('t16'), \
Window('t17'), Window('t18')]
# Open input and output files.
with open(os.path.join(turbine_dir, item)) as input_file:
input_reader = csv.reader(input_file)
with open(os.path.join(output_dir, item), 'wb') as output_file:
output_writer = csv.writer(output_file)
print 'Processing ' + item
# Write header into the output file.
output_writer.writerow(input_reader.next())
for row in input_reader:
for window in windows:
if row[1] == window.who_am_i():
# Add row if the buffer is empty.
if window.size() == 0:
window.append(row)
continue
if compare_minute(window.startTime(), row[0]) < 9:
window.append(row)
continue
if compare_minute(window.startTime(), row[0]) == 9:
window.append(row)
if window.size() >= 7:
output_writer.writerow([window.startTime(), window.who_am_i(), window.average()])
if compare_minute(window.startTime(), row[0]) > 9:
if window.size() >= 7:
output_writer.writerow([window.startTime(), window.who_am_i(), window.average()])
window.empty()
window.append(row)
continue
window.empty()
shutil.move(os.path.join(turbine_dir, item), os.path.join(turbine_archive_dir, item))
# Process all of the weather data.
# Scan for all CSV files within the weather data folder.
for item in os.listdir(weather_dir):
if item.split('.')[-1].lower() == 'csv':
window = Window('speed_avg')
# Open input and output files.
with open(os.path.join(weather_dir, item)) as input_file:
input_reader = csv.reader(input_file)
with open(os.path.join(output_dir, item), 'wb') as output_file:
output_writer = csv.writer(output_file)
print 'Processing ' + item
input_reader.next()
# Write header into the output file.
output_writer.writerow(['datetime', 'value'])
for row in input_reader:
# Add row if the buffer is empty.
if window.size() == 0:
window.append([row[0], window.who_am_i(), row[2]])
continue
if compare_minute(window.startTime(), row[0]) < 9:
window.append([row[0], window.who_am_i(), row[2]])
continue
if compare_minute(window.startTime(), row[0]) == 9:
window.append([row[0], window.who_am_i(), row[2]])
if window.size() >= 7:
output_writer.writerow([window.startTime(), window.average()])
if compare_minute(window.startTime(), row[0]) > 9:
if window.size() >= 7:
output_writer.writerow([window.startTime(), window.average()])
window.empty()
window.append([row[0], window.who_am_i(), row[2]])
continue
window.empty()
shutil.move(os.path.join(weather_dir, item), os.path.join(weather_archive_dir, item))
return 1
import h5py
import numpy as np
from keras.models import model_from_json
from reshape import reshape
from convert_nn import convert_waveform
batch_size = 1
tsteps = 50
data_dim = 25
print('loadig model')
with open('model/BidirLSTM.json', 'r') as model_json:
model = model_from_json(model_json.read())
model.load_weights('model/BidirLSTM.h5')
f = h5py.File('data/h5/100001.h5', 'r')
data_source_test = f.get('jnt')
data_source_test = np.array(data_source_test)
print(data_source_test.shape)
data_source_test = reshape(data_source_test, tsteps, data_dim)
print('Predicting')
prediction_test = model.predict(data_source_test, batch_size=batch_size)
print(prediction_test.shape)
prediction_test = prediction_test.reshape(-1, data_dim)
print(prediction_test.shape)
convert_waveform(prediction_test, "'bidir_lstm.wav'")
cnnConv2LayerMatrix = re.RELU_3D(
conv.convolution_RGB(cnnConv1LayerMatrix, kernelConvLayer2))
cnnConv2LayerMatrix = pool.pooling(cnnConv2LayerMatrix,
poolingSizeConvLayer2,
poolingStrideConvLayer2,
poolType='max')
# Fully connected Layer1 processing
#first layer vector(7*7*36,128)
fcVectorLayer1 = np.zeros((1764, 128))
initVector(fcVectorLayer1, 1)
cnnConv2LayerReshape = rshape.reshape(cnnConv2LayerMatrix)
#print('FullyConnectedLayer0 (reshape) = ',cnnConv2LayerReshape)
cnnFullyConnectedLayer1 = matmult.matrixMult_CHn(cnnConv2LayerReshape,
fcVectorLayer1)
#print('FullyConnectedLayer1 = ',cnnFullyConnectedLayer1)
#second layer vector(128,10)
fcVectorLayer2 = np.zeros((128, 10))
initVector(fcVectorLayer2, 2)
cnnFullyConnectedLayer2 = matmult.matrixMult_CHn(cnnFullyConnectedLayer1,
fcVectorLayer2)
#print('FullyConnectedLayer2 = ',cnnFullyConnectedLayer2)
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from reshape import reshape
import base64
url = 'https://github.com/Mahyarazad/mahyarazad/raw/master/Base.xlsx'
df = pd.read_excel(url)
mapbox_secrect_key = os.environ.get('mapBox')
##### DataFrame Reshaping #####
sd = reshape(df).summation
city_data = reshape(df).dict_data_gen
cv = reshape(df).geo
cvt = cv.drop(columns = [cv.columns[3],cv.columns[4]])
cvt['MOM Ratio'] = ((cvt[cvt.columns[-1]]- cvt[cvt.columns[-2]])/cvt[cvt.columns[-2]]).apply('{:.0%}'.format)
cvt = cvt.sort_values(by=cvt.columns[3],ascending = False)
#################################
north_list = [
'As Sulaymānīyah',
'Altameem',
'Dahūk',
'Arbīl',
#garder que le rouge
#kernel[0,0]=np.array([1,0,0,0,0,0,0,0,0]).reshape(3,3)
#kernel[1,0]=np.array([1,0,0,0,0,0,0,0,0]).reshape(3,3)
#kernel[2,0]=np.array([1,0,0,0,0,0,0,0,0]).reshape(3,3)
print("start")
print(time.localtime())
new_image = np.zeros((cnnLayer1Matrix.shape[0], cnnLayer1Matrix.shape[2], 1))
new_image = re.RELU_3D(conv.convolution_RGB(cnnLayer1Matrix, kernel))
new_image2 = pool.pooling(new_image, 4, 4, 'max')
new_image5 = re.RELU_3D(conv.convolution_nb_kernel(new_image2, kernel2))
new_image6 = pool.pooling(new_image5, 4, 4, 'max')
new_image3 = resha.reshape(new_image2)
b = np.zeros((1, 1000))
new_image4 = multi.matrixMult(new_image3, b)
print("end")
print(time.localtime())
#print("input image")
#print(cnnLayer1Matrix)
#print("somme")
#print(new_image)
#print(new_image.shape)
#print("test")
#print(test_image)
#print(test_image.shape)
def handle_rdd(rdd):
global ss, now, cnt_in, cnt_out
if not rdd.isEmpty():
data_in = "data_in.txt"
data_out = "data_out.txt"
def has_column(df, col):
try:
df[col]
return True
except:
return False
dataframe = ss.createDataFrame(rdd, Schema.tweet_data)
## data_in count
with open(data_in, "a") as di:
cnt_in += dataframe.count()
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
di.write(now + '\n')
di.write('per 5 seconds: ' + str(dataframe.count()) +
' total count: ' + str(cnt_in) + '\n')
## remove 'matchin_rules' column
dataframe = dataframe.drop('matching_rules')
#dataframe.show(3)
## dataframe to JSON(list)
df = dataframe.toJSON().map(lambda x: json.loads(x)).collect()
## JSON reshape return
reshaped_json = reshape.reshape(df,
'covid-19') ## json(list), topic
## convert list to RDD to Json
new_df = sc.parallelize(reshaped_json).map(lambda x: json.dumps(x))
## convert Json to Dataframe
new_df = ss.read.json(new_df)
new_df.show(3)
print()
## data_out count
with open(data_out, "a") as do:
cnt_out += new_df.count()
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
do.write(now + '\n')
do.write('per 5 seconds: ' + str(new_df.count()) +
' total count: ' + str(cnt_out) + '\n')
output_file_name = "error_message.txt"
try:
new_df.write \
.format("org.apache.spark.sql.cassandra") \
.mode('append') \
.options(table="tweets", keyspace="tweettrend") \
.save()
except Exception as e:
with open(output_file_name, "a",
encoding="utf-8") as output_file:
output_file.write(type(e), e)
output_file.write(reshaped_json)
def main(argv):
start_time = datetime.datetime.now()
# Define input, output, and archive directory names.
input_dir_name = 'reshaped'
archive_dir_name = 'archived'
output_dir_name = 'output'
# Must have two input parameters.
if len(argv) != 2:
print 'usage: driver.py <sample size (hours)> <threshold for std>'
return
# Save the input parameters.
sample_size = int(argv[0])
threshold = int(argv[1])
# Process raw data files into the eshape format.
# [datetime, sensor id, value]
reshape()
# Define current working directory.
current_dir = os.path.dirname(os.path.abspath(__file__))
# Define input, output, and archive directories
input_dir = os.path.join(current_dir, input_dir_name)
archive_dir = os.path.join(input_dir, archive_dir_name)
output_dir = os.path.join(current_dir, output_dir_name)
# Scans for all CSV files within the input directory.
for item in os.listdir(input_dir):
if item.split('.')[len(item.split('.'))-1].lower() == 'csv':
windows = [Window('t14'), Window('t15'), Window('t16'), \
Window('t17'), Window('t18')]
# Open input and output files.
with open(os.path.join(input_dir, item)) as input_file:
input_reader = csv.reader(input_file)
with open(os.path.join(output_dir, item), 'wb') as output_file:
output_writer = csv.writer(output_file)
print 'Processing ' + item
# Write header into the output file.
output_writer.writerow(input_reader.next())
for row in input_reader:
for window in windows:
if row[1] == window.who_am_i():
# Add row if the buffer is empty.
if window.size() == 0:
window.append(row)
continue
# Add rows until the buffer contains points
# within the window size.
if compare_hour(window.startTime(), row[0]) < sample_size:
window.append(row)
continue
# Grow the buffer if the std is below
# threshold. This is to capture ranges of bad
# data that could be larger than the window.
# Otherwise, pop the first item off and write
# it to the output file and shift the window.
if window.std() > threshold:
output_writer.writerow(window.pop())
window.append(row)
continue
window.append(row)
if window.std() > threshold:
print 'Sensor ' + window.who_am_i()
print 'Standard deviation below threshold...'
print 'Discarding ' + str(window.size()) + ' items...'
print 'Starting from ' + window.startTime() + ' and ending on ' + window.endTime()
window.empty()
window.append(row)
# Check to see if the remaining items are within the
# threshold of good data.
for window in windows:
if window.std() > threshold:
while window.size() != 0:
output_writer.writerow(window.pop())
else:
print 'Sensor ' + window.who_am_i()
print 'Standard deviation below threshold...'
print 'Discarding ' + str(window.size()) + ' items...'
print 'Starting from ' + window.startTime() + ' and ending on ' + window.endTime()
# Move input file to archive directory.
shutil.move(os.path.join(input_dir, item), os.path.join(archive_dir, item))
end_time = datetime.datetime.now()
difference = end_time - start_time
difference = difference.total_seconds() / 60.0 / 60.0
print 'Run Time: ' + difference + ' hours'
def main(argv):
start_time = datetime.datetime.now()
# Define input, output, and archive directory names.
input_dir_name = 'reshaped'
archive_dir_name = 'archived'
output_dir_name = 'output'
# Must have two input parameters.
if len(argv) != 2:
print 'usage: driver.py <sample size (hours)> <threshold for std>'
return
# Save the input parameters.
sample_size = int(argv[0])
threshold = int(argv[1])
# Process raw data files into the eshape format.
# [datetime, sensor id, value]
reshape()
# Define current working directory.
current_dir = os.path.dirname(os.path.abspath(__file__))
# Define input, output, and archive directories
input_dir = os.path.join(current_dir, input_dir_name)
archive_dir = os.path.join(input_dir, archive_dir_name)
output_dir = os.path.join(current_dir, output_dir_name)
# Scans for all CSV files within the input directory.
for item in os.listdir(input_dir):
if item.split('.')[len(item.split('.')) - 1].lower() == 'csv':
windows = [Window('t14'), Window('t15'), Window('t16'), \
Window('t17'), Window('t18')]
# Open input and output files.
with open(os.path.join(input_dir, item)) as input_file:
input_reader = csv.reader(input_file)
with open(os.path.join(output_dir, item), 'wb') as output_file:
output_writer = csv.writer(output_file)
print 'Processing ' + item
# Write header into the output file.
output_writer.writerow(input_reader.next())
for row in input_reader:
for window in windows:
if row[1] == window.who_am_i():
# Add row if the buffer is empty.
if window.size() == 0:
window.append(row)
continue
# Add rows until the buffer contains points
# within the window size.
if compare_hour(window.startTime(),
row[0]) < sample_size:
window.append(row)
continue
# Grow the buffer if the std is below
# threshold. This is to capture ranges of bad
# data that could be larger than the window.
# Otherwise, pop the first item off and write
# it to the output file and shift the window.
if window.std() > threshold:
output_writer.writerow(window.pop())
window.append(row)
continue
window.append(row)
if window.std() > threshold:
print 'Sensor ' + window.who_am_i()
print 'Standard deviation below threshold...'
print 'Discarding ' + str(
window.size()) + ' items...'
print 'Starting from ' + window.startTime(
) + ' and ending on ' + window.endTime()
window.empty()
window.append(row)
# Check to see if the remaining items are within the
# threshold of good data.
for window in windows:
if window.std() > threshold:
while window.size() != 0:
output_writer.writerow(window.pop())
else:
print 'Sensor ' + window.who_am_i()
print 'Standard deviation below threshold...'
print 'Discarding ' + str(
window.size()) + ' items...'
print 'Starting from ' + window.startTime(
) + ' and ending on ' + window.endTime()
# Move input file to archive directory.
shutil.move(os.path.join(input_dir, item),
os.path.join(archive_dir, item))
end_time = datetime.datetime.now()
difference = end_time - start_time
difference = difference.total_seconds() / 60.0 / 60.0
print 'Run Time: ' + difference + ' hours'