def main():
#各種ファイル名
data_dir = "./data/"
json_name = data_dir + "data.json"
csv_name = data_dir + "score_list.csv"
tweet7_json = data_dir + "tweet7.json"
day7_json = data_dir + "day7.json"
week7_json = data_dir + "week7.json"
pn_dict = data_dir + "pn_ja.dic"
setting_name = data_dir + "setting.txt"
#変数
prev_tweet_num = 10
get_tweet_num_by_time = 3
sum_score = 0
#データ関係のファイルを消去
remove_file_names = [
json_name, csv_name, setting_name, tweet7_json, day7_json, week7_json
]
removeFiles(remove_file_names)
#フロント側の設定完了を待つ
account_id, goal = waitSetting(setting_name)
#各種オブジェクトを生成，初期設定
#account オブジェクト
user = account.Account(account_id)
#dataManager オブジェクト
dm = dataManager.DataManager()
dm.setJson(json_name)
dm.loadJson()
dm.setCsv(csv_name)
dm.loadCsv()
#dataSaverオブジェクト
ds = dataSaver.DataSaver(tweet7_json, day7_json, week7_json)
#analyzer オブジェクト
anlzr = analyzer.Analyzer()
anlzr.loadPnDict(pn_dict)
#監視の前にデータを取得しておく
for tweet_id, tweet_info in user.getTimeline(prev_tweet_num, 1).items():
date = tweet_info["date"]
tweet_text = tweet_info["tweet"]
mentions = tweet_info["mentions"]
#score = anlzr.pnDictScore(tweet_text)
score = anlzr.gcnlScore(tweet_text)
sum_score += score
high_score_words = []
json_dict = {
tweet_id: {
"date": date,
"tweet": tweet_text,
"mentions": mentions,
"score": score,
"sum_score": sum_score,
"high_score_words": high_score_words
}
}
dm.updateDatabase(json_dict)
date_score_list = dm.getCsv()
ds.updateSubTotalJsons(date_score_list)
while True:
print("monitoring timeline...")
time.sleep(10)
for tweet_id, tweet_info in user.getTimeline(get_tweet_num_by_time,
1).items():
date = tweet_info["date"]
tweet_text = tweet_info["tweet"]
mentions = tweet_info["mentions"]
if dm.hasData(tweet_id):
print("Attayo!")
continue
#score = anlzr.pnDictScore(tweet_text)
score = anlzr.gcnlScore(tweet_text)
sum_score += score
high_score_words = []
json_dict = {
tweet_id: {
"date": date,
"tweet": tweet_text,
"mentions": mentions,
"score": score,
"sum_score": sum_score,
"high_score_words": high_score_words
}
}
dm.updateDatabase(json_dict)
date_score_list = dm.getCsv()
ds.updateSubTotalJsons(date_score_list)
def manageData():
# Get current MP based on current date
currentDate = datetime.datetime.today()
currentDateObj = date.Date(currentDate.day, currentDate.month,
currentDate.year)
# currentMPindex = 3
# for i in range(0,4):
# mpStartDate = date.Date(mpStartDates[i][0],mpStartDates[i][1],mpStartDates[i][2])
# if(currentDateObj.compareToDateObj(mpStartDate)<0):
# currentMPindex = i-1
# if(currentMPindex<0): # Current date is outside of school year
# currentMPindex = 3 # 4th marking period
numDaysInSchoolYear = 0
for mp in range(0, currentMPindex + 1):
# print("MP: " + str(mp+1))
startDate = date.datetime.date(mpStartDates[mp][2],
mpStartDates[mp][1],
mpStartDates[mp][0])
endDate = date.datetime.date(mpStartDates[mp + 1][2],
mpStartDates[mp + 1][1],
mpStartDates[mp + 1][0] - 1)
daysInMP[mp] = endDate.__sub__(startDate).days
numDaysInSchoolYear += daysInMP[mp] #endDate.__sub__(startDate).days
firstDay = datetime.date(mpStartDates[0][2], mpStartDates[0][1],
mpStartDates[0][0])
print("FIRST DAY: ")
print(firstDay)
# for mp in range(len(coursesAllMPs)):
# for c in range(len(coursesAllMPs[mp])):
# print(coursesAllMPs[mp][c].courseName)
# print("\n")
# exit(0)
print("NUM DAYS IN SCHOOL YEAR: " + str(numDaysInSchoolYear))
data = [[[list]]] * (numDaysInSchoolYear + 1)
for m in range(0, currentMPindex + 1):
print("M: " + str(m))
courseDataAllMPs = [[]]
dm = dataManager.DataManager(studentName, studentID, coursesAllMPs[m],
m)
for c in range(len(coursesAllMPs[m])):
dailyGrades = dm.getDailyCourseGradesForMP(c, m)
for d in range(len(dailyGrades)):
daysSinceStartOfYear = dailyGrades[d][0].__sub__(
firstDay).days - 1
# print(str(firstDay) + "\t" + str(dailyGrades[d][0]) + "\t" + str(daysSinceStartOfYear))
# for asdf in range(len(dailyGrades[d])):
# print(dailyGrades[d][asdf])
# print(daysSinceStartOfYear)
try:
data[daysSinceStartOfYear - 1].append(dailyGrades[d])
except IndexError:
print("IndexError")
# exit(0)
allData = [
[[[]]]
] # [course][[[date,grade,ptsRec,ptsW],...] for mp1, [[date,grade,ptsRec,ptsW],...] for mp2, ...]
# allData[0][0][0][1] = [AP ENG/LANG & COMP][mp1][2019-09-01][grade]
for c in range(0, len(coursesAllMPs[0])):
courseDataAllMPs = [[[]]]
for x in range(0, currentMPindex + 1):
mp = x
dm = dataManager.DataManager(studentName, studentID,
coursesAllMPs[mp], mp)
dailyGrades = dm.getDailyCourseGradesForMP(c, mp)
courseDataAllMPs.append(dailyGrades)
courseDataAllMPs.pop(0)
allData.append(courseDataAllMPs)
allData.pop(0)
# allData2 = [list]*len(allData[0][0])
allData2 = [[[]]] # [[date[course[infotype]]]...for all dates]
for m in range(0, len(allData[0])):
for d in range(len(allData[0][m])): # for every day in MP
if (d <= numDaysInSchoolYear):
arrC = []
for c in range(len(coursesAllMPs[m])): # for every course
arr = [
allData[c][m][d][1], allData[c][m][d][2],
allData[c][m][d][3]
]
arrC.append(arr)
arrDC = [allData[0][m][d][0],
arrC] # [date,[info1,info2,info3]]
allData2.append(arrDC)
allData2.pop(0)
# OUTPUT DATA TO FILE
try:
outfile = open("dailyGradesFile.txt", "x")
except FileExistsError:
outfile = open("dailyGradesFile.txt", "w")
try:
# json_outfile = open("gradesDictionary.json",'rb+')
# except:
# print("RB+ DID NOT WORK ----------------------------------")
# try:
json_outfile = open("gradesDictionary.json", 'x')
except:
json_outfile = open("gradesDictionary.json", 'w')
json_outfile.write("[")
mpIndexPlus1 = 1
for d in range(len(allData2)):
dDate = date.Date(allData2[d][0].day, allData2[d][0].month,
allData2[d][0].year)
endDate = date.Date(mpStartDates[4][0], mpStartDates[4][1],
mpStartDates[4][2])
if (dDate.compareToDateObj(endDate) >= 0):
break # Stop at last day of school
# print(str(allData2[d][0]))
outfile.write(str(allData2[d][0]) + "\n") # write date
for c in range(len(allData2[d][1])):
try:
if (date.Date(allData2[d][0].day, allData2[d][0].month,
allData2[d][0].year).compareToDMY(
mpStartDates[mpIndexPlus1][0],
mpStartDates[mpIndexPlus1][1],
mpStartDates[mpIndexPlus1][2]) >= 0):
mpIndexPlus1 += 1
lstLbls = ["Date", "Course", "Grd", "PR", "PW"]
lst = []
if (len(coursesAllMPs[mpIndexPlus1 - 1][c].code) > 0):
lst = [
str(allData2[d][0]),
coursesAllMPs[mpIndexPlus1 - 1][c].courseName,
allData2[d][1][c][0], allData2[d][1][c][1],
allData2[d][1][c][2]
]
# print("\t{0:30} {1:15} {2:15} {3:15}".format(coursesAllMPs[mpIndexPlus1-1][c].courseName+":: ","Grd:"+str(allData2[d][1][c][0]),"PR:"+str(allData2[d][1][c][1]),"PW:"+str(allData2[d][1][c][2])))
outfile.write("\t{0:30} {1:15} {2:15} {3:15}\n".format(
coursesAllMPs[mpIndexPlus1 - 1][c].courseName + ":: ",
"Grd:" + str(allData2[d][1][c][0]),
"PR:" + str(allData2[d][1][c][1]),
"PW:" + str(allData2[d][1][c][2])))
else:
lst = [
str(allData2[d][0]),
coursesAllMPs[mpIndexPlus1 - 1][c].courseName, "----",
"----", "----"
]
# print("\t{0:30} {1:15} {2:15} {3:15}".format(coursesAllMPs[mpIndexPlus1-1][c].courseName+":: ","Grd:----","PR:----","PW:----"))
outfile.write("\t{0:30} {1:15} {2:15} {3:15}\n".format(
coursesAllMPs[mpIndexPlus1 - 1][c].courseName + ":: ",
"Grd:----", "PR:----", "PW:----"))
with open("gradesDictionary.json", 'a') as json_outfile:
json.dump(Convert(lstLbls, lst), json_outfile, indent=2)
if (d != len(allData2) - 1
or c != len(allData2[d][1]) - 1):
json_outfile.write(",\n")
except IndexError:
break
# json_outfile.close()
# # Delete last comma
# with open("gradesDictionary.json",'rb+') as json_outfile:
# # move to end, then scan forward until a non-continuation byte is found
# json_outfile.seek(-1, os.SEEK_END)
# while json_outfile.read(1) & 0xC0 == 0x80:
# # we just read 1 byte, which moved the file position forward,
# # skip back 2 bytes to move to the byte before the current.
# json_outfile.seek(-2, os.SEEK_CUR)
# # last read byte is our truncation point, move back to it.
# json_outfile.seek(-1, os.SEEK_CUR)
# json_outfile.truncate()
# json_outfile.close()
with open("gradesDictionary.json", 'a') as json_outfile:
json_outfile.write("]")
json_outfile.close()
def train_SPHash(modelSavedPath = '../largeData/model', inputType='Cropped', bitSize=16, num_epochs=30, weightSM = 1.0, weightApp = 1.0, weightFire = 1.0):
tf.reset_default_graph()
prefix = 'JH_SPDH' + inputType + '_' + str(bitSize) + '_wSM_' + str(weightSM) + '_wApp_' + str(weightApp) + '_wFire_' + str(weightFire)
batchSize = 32
dropout_rate = 0.5
display_step = 20
# For ASAN Data
num_classes = 6
# filewriter_path = '../modelStore/ASAN/tensorboardPath'
# checkpoint_path = '../modelStore/ASAN/modelTrainedPath'
filewriter_path = modelSavedPath + '/tensorboardPath'
checkpoint_path = modelSavedPath + '/modelTrainedPath'
if(not os.path.exists(filewriter_path)):
os.mkdir(filewriter_path)
if(not os.path.exists(checkpoint_path)):
os.mkdir(checkpoint_path)
if (inputType == 'Cropped'):
trainImg_path = '../largeData/data/exp_jh/bbox_search_img.npy'
trainLabel_path = '../largeData/data/exp_jh/bbox_search_label.npy'
testImg_path = '../largeData/data/exp_jh/bbox_query_img.npy'
testLabel_path = '../largeData/data/exp_jh/bbox_query_label.npy'
elif (inputType == 'Activated'):
trainImg_path = '../largeData/data/exp_jh/activated_search_img.npy'
trainLabel_path = '../largeData/data/exp_jh/activated_search_label.npy'
testImg_path = '../largeData/data/exp_jh/activated_query_img.npy'
testLabel_path = '../largeData/data/exp_jh/activated_query_label.npy'
elif (inputType == 'Origin'):
trainImg_path = '../largeData/data/exp_jh/origin_search_img.npy'
trainLabel_path = '../largeData/data/exp_jh/origin_search_label.npy'
testImg_path = '../largeData/data/exp_jh/origin_query_img.npy'
testLabel_path = '../largeData/data/./exp_jh/origin_query_label.npy'
else:
print ('No available option found for ' + inputType)
return
print('load train data...')
trainData = dataManager.DataManager(imgNpyPath=trainImg_path, labelNpyPath=trainLabel_path, batchSize=batchSize, classNumber=num_classes)
print('load test data...')
testData = dataManager.DataManager(imgNpyPath=testImg_path, labelNpyPath=testLabel_path, batchSize=batchSize, classNumber=num_classes)
trainDataSize = np.shape(trainData.label_data)[0]
testDataSize = np.shape(testData.label_data)[0]
x = tf.placeholder(tf.float32, [batchSize, 227, 227, 3], name='input_network')
y = tf.placeholder(tf.float32, [batchSize, num_classes], name='label_network')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
model = SPDH_tf(bitSize=bitSize, x_shape=x, y_shape=y, keep_prob=keep_prob, num_classes=num_classes,
skip_layer=['fc8', 'fc7', 'fc6'], weights_path='../largeData/modelAlexnet/bvlc_alexnet.npy',
batchSize=batchSize, weightSC=weightSM, weightApp=weightApp, weightFire=weightFire)
model.build_basic_graph()
model.design_loss()
writer = tf.summary.FileWriter(filewriter_path)
saver = tf.train.Saver(max_to_keep=savor_max_to_keep)
train_batches_per_epoch = int(np.floor(trainDataSize / batchSize))
val_batches_per_epoch = int(np.floor(testDataSize / batchSize))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
writer.add_graph(sess.graph)
model.load_initial_weights(sess)
print("{} Start training...".format(datetime.datetime.now()))
print("{} Open Tensorboard at --logdir {}".format(datetime.datetime.now(),
filewriter_path))
# Loop over number of epochs
for epoch in range(num_epochs):
print('trainData initializer on GPU')
q = trainData.getTrueData()
sess.run(trainData.getInitializer(), feed_dict={trainData.x: q[0], trainData.y: q[1]})
print("{} Epoch number: {}".format(datetime.datetime.now(), epoch + 1))
for step in range(train_batches_per_epoch):
input_value, label_value = sess.run(trainData.getNextBatchPlaceholder())
# And run the training op
# trainLoss, _ = sess.run([loss, optimizer], feed_dict={x: input_value,y: label_value,keep_prob: dropout_rate})
# for TEST
p = sess.run(model.latentLayer, feed_dict={model.x: input_value, model.keep_prob: 1.0})
lossTotal, lossSC, lossApp, lossFire, _ = sess.run([model.loss_total, model.loss_sc, model.loss_app, model.loss_fire, model.train_op],
feed_dict={model.x: input_value, model.y: label_value, model.keep_prob: dropout_rate})
# trainLoss = sess.run(train_op, feed_dict={x: input_value,y: label_value,keep_prob: dropout_rate})
# Generate summary with the current
#
# cat batch of data and write to file
if step % display_step == 0:
s = sess.run(model.merged_summary, feed_dict={model.x: input_value,
model.y: label_value,
model.keep_prob: 1.})
print('At ' + str(step) + ', train loss: ' + str(lossTotal) + ', train SC: ' + str(lossSC) + ', train App: ' + str(lossApp) + ', trainFire: ' + str(lossFire))
writer.add_summary(s, epoch * train_batches_per_epoch + step)
# Validate the model on the entire validation set
print("{} Start validation".format(datetime.datetime.now()))
test_acc = 0.
test_count = 0
print('testData initializer on GPU')
p = testData.getTrueData()
sess.run(testData.getInitializer(), feed_dict={testData.x: p[0], testData.y: p[1]})
for _ in range(val_batches_per_epoch):
img_batch, label_batch = sess.run(testData.getNextBatchPlaceholder())
acc = sess.run(model.accuracy, feed_dict={x: img_batch, y: label_batch, keep_prob: 1.})
test_acc += acc
test_count += 1
test_acc /= test_count
print("{} Validation Accuracy = {:.4f}".format(datetime.datetime.now(),
test_acc))
print("{} Saving checkpoint of model...".format(datetime.datetime.now()))
# save checkpoint of the model
if(epoch % model_save_step == 0):
checkpoint_name = os.path.join(checkpoint_path, prefix + 'model_epoch' + str(epoch + 1) + '.ckpt')
save_path = saver.save(sess, checkpoint_name)
print("{} Model checkpoint saved at {}".format(datetime.datetime.now(), checkpoint_name))
"""
pdata = pd.DataFrame(date_score_list)
pdata.columns = ["date", "score"]
pdata["date"] = pd.to_datetime(pdata["date"], format="%Y%m%d_%H%M%S")
sorted_pdata = pdata.sort_values("date")
return sorted_pdata
def sendSumScore(self, save_file_name, sum_score):
try:
f = codecs.open(save_file_name, "w", encoding="utf-8")
except OSError as e:
print("sendSumScore Error")
else:
f.write(str(sum_score + 10))
if __name__ == "__main__":
csv_name = "./data/score_list.csv"
tweets_name = "./data/seven_tweets_scores.json"
days_name = "./data/seven_days_scores.json"
weeks_name = "./data/seven_weeks_scores.json"
import dataManager
dm = dataManager.DataManager()
dm.setCsv(csv_name)
dm.loadCsv()
date_score_list = dm.getCsv()
ds = DataSaver(tweets_name, days_name, weeks_name)
ds.updateSubTotalJsons(date_score_list, data_num=7)
def __init__(self):
# some fonts
self.headerfont = 'TkHeadingFont'
self.cellfont = 'TkTextFont'
# initialiye the main window and configure the overall layout
self.window = Tk()
self.window.title("SkinnerBox Control")
self.window.columnconfigure(0, weight=1)
self.window.rowconfigure(0, weight=1)
self.n = ttk.Notebook(self.window)
n = self.n
n.columnconfigure(0, weight=1)
n.rowconfigure(0, weight=1)
w = Frame(n) # first tab: all fish overview
self.f2 = Frame(n) # second tab: single fish view
self.ctlTab = Frame(n) # third tab: controls
n.add(w, text='All Fish')
n.add(self.f2, text='Single Fish')
n.add(self.ctlTab, text='controls')
n.grid(row=0, column=0)
w.columnconfigure(0, weight=1)
w.columnconfigure(1, weight=1)
w.rowconfigure(0, weight=0)
w.rowconfigure(1, weight=1)
w.rowconfigure(2, weight=1)
w.rowconfigure(3, weight=1)
Label(w, text='SkinnerBox control',
font='TkCaptionFont').grid(row=0,
column=0,
columnspan=2,
sticky=W)
# subframes:
self.upf = LabelFrame(
w, text='controls'
) # update indicator: just a labelled canvas that changes color when the datagrabber is getting new data
self.cpf = LabelFrame(
w, text='Currently playing fish'
) # currently playing fish: a table of all fish currently running trials
self.atf = LabelFrame(
w, text='All trials'
) # all trials frame: a dictionary display for summary data accross all trials
self.cfd = LabelFrame(
w, text='Current fish'
) # current fish display: shows data about a selected fish
self.grf = LabelFrame(self.f2, text='Individual Fish')
self.rstFrame = LabelFrame(self.ctlTab, text='Reset controllers')
# configure the layout of the frames and subframes
self.upf.grid(row=1, column=0, columnspan=10, sticky=W + N)
self.upf.grid_columnconfigure(0, weight=1)
self.upf.grid_columnconfigure(1, weight=1)
self.upf.grid_rowconfigure(0, weight=1)
self.upf.grid_rowconfigure(1, weight=1)
self.cpf.grid(row=2, column=1, sticky=W + N)
for i in range(0, 21):
self.cpf.grid_rowconfigure(i, weight=1)
for i in range(0, 4):
self.cpf.grid_columnconfigure(i, weight=1)
self.atf.grid(row=2, column=0, sticky=W + N)
for i in range(0, 10):
self.atf.grid_rowconfigure(i, weight=1)
for i in range(0, 2):
self.atf.grid_columnconfigure(i, weight=1)
self.cfd.grid(row=2, column=2, sticky=W + N)
for i in range(0, 10):
self.cfd.grid_rowconfigure(i, weight=1)
for i in range(0, 2):
self.cfd.grid_columnconfigure(i, weight=1)
self.grf.grid(row=0, column=0)
self.rstFrame.grid(row=0, column=0)
# create update indicator label and indicator light
Label(self.upf, text='Loading').grid(row=0, column=0, sticky=W)
self.upind = Canvas(self.upf, bg='grey', width=20, height=20)
self.upind.grid(row=0, column=1, sticky=E)
# create buttons on update indicator
Button(self.upf, text='Show Graph',
command=self.show_graph_callback).grid(row=1,
column=0,
sticky=W)
# create button on conrtol tab
Button(self.rstFrame,
text='Reset Laptop',
command=self.reset_laptop_button_callback).grid(row=0,
column=0,
sticky=W)
Button(self.rstFrame,
text='Reset PI_001',
command=self.reset_pi_001_button_callback).grid(row=1,
column=0,
sticky=W)
Button(self.rstFrame,
text='Reset PI_002',
command=self.reset_pi_002_button_callback).grid(row=2,
column=0,
sticky=W)
Button(self.rstFrame,
text='Reset PI_003',
command=self.reset_pi_003_button_callback).grid(row=3,
column=0,
sticky=W)
Button(self.rstFrame,
text='Reset All',
command=self.reset_all_button_callback).grid(row=4,
column=0,
sticky=W)
# an area to draw the graphics to
self.grf_canvas = Frame(self.grf, border=10, relief=SUNKEN)
self.grf_canvas.grid(row=1, column=0, columnspan=3)
# make a title for the graphics frame (the name of the fish)
self.grf_title = Label(master=self.grf, text="")
self.grf_title.grid(row=2, column=1)
# create buttons on graphics frame
Button(master=self.grf, text='<<',
command=self.previous_fish).grid(row=2, column=0)
Button(master=self.grf, text='>>',
command=self.next_fish).grid(row=2, column=2)
Button(master=self.grf,
text='show schedules',
command=self.show_schedules).grid(row=3, column=2)
Button(master=self.grf, text='show dates',
command=self.show_dates).grid(row=4, column=2)
Button(master=self.grf,
text='clear selection',
command=self.clear_selection).grid(row=5, column=2)
self.schedList = Listbox(self.grf, selectmode=MULTIPLE, width=40)
self.schedList.grid(row=3, column=0, rowspan=3, sticky=W)
self.dateList = Listbox(self.grf, selectmode=MULTIPLE, width=30)
self.dateList.grid(row=3, column=1, rowspan=3, sticky=W)
# create the data grabber. the member function passed will display a string on the output widget.
self.dm = dataManager.DataManager()
self.skp = skinnerplot.skinnerplot()
def manageData():
# Get current MP based on current date
currentDate = datetime.datetime.today()
currentDateObj = date.Date(currentDate.day, currentDate.month,
currentDate.year)
currentMPindex = 3
for i in range(0, 4):
mpStartDate = date.Date(mpStartDates[i][0], mpStartDates[i][1],
mpStartDates[i][2])
if (currentDateObj.compareToDateObj(mpStartDate) < 0):
currentMPindex = i - 1
if (currentMPindex < 0): # Current date is outside of school year
currentMPindex = 3 # 4th marking period
numDaysInSchoolYear = 0
for mp in range(len(coursesAllMPs)):
# print("MP: " + str(mp+1))
startDate = date.datetime.date(mpStartDates[mp][2],
mpStartDates[mp][1],
mpStartDates[mp][0])
endDate = date.datetime.date(mpStartDates[mp + 1][2],
mpStartDates[mp + 1][1],
mpStartDates[mp + 1][0] - 1)
daysInMP[mp] = endDate.__sub__(startDate).days
numDaysInSchoolYear += daysInMP[mp] #endDate.__sub__(startDate).days
firstDay = datetime.date(mpStartDates[0][2], mpStartDates[0][1],
mpStartDates[0][0])
# for mp in range(len(coursesAllMPs)):
# for c in range(len(coursesAllMPs[mp])):
# print(coursesAllMPs[mp][c].courseName)
# print("\n")
# exit(0)
data = [[[list]]] * (numDaysInSchoolYear + 1)
for m in range(len(coursesAllMPs)):
courseDataAllMPs = [[]]
dm = dataManager.DataManager(studentName, studentID, coursesAllMPs[m],
m + 1)
for c in range(len(coursesAllMPs[m])):
dailyGrades = dm.getDailyCourseGradesForMP(c, m)
for d in range(len(dailyGrades)):
daysSinceStartOfYear = dailyGrades[d][0].__sub__(
firstDay).days - 1
try:
data[daysSinceStartOfYear - 1].append(dailyGrades[d])
except IndexError:
print("IndexError")
allData = [
[[[]]]
] # [course][[[date,grade,ptsRec,ptsW],...] for mp1, [[date,grade,ptsRec,ptsW],...] for mp2, ...]
# allData[0][0][0][1] = [AP ENG/LANG & COMP][mp1][2019-09-01][grade]
for c in range(0, len(coursesAllMPs[0])):
courseDataAllMPs = [[[]]]
for x in range(0, currentMPindex + 1):
mp = x
dm = dataManager.DataManager(studentName, studentID,
coursesAllMPs[mp], mp)
dailyGrades = dm.getDailyCourseGradesForMP(c, mp)
courseDataAllMPs.append(dailyGrades)
courseDataAllMPs.pop(0)
allData.append(courseDataAllMPs)
allData.pop(0)
# allData2 = [list]*len(allData[0][0])
allData2 = [[[]]] # [[date[course[infotype]]]...for all dates]
for m in range(0, len(allData[0])):
for d in range(len(allData[0][m])): # for every day in MP
if (d <= numDaysInSchoolYear):
arrC = []
for c in range(len(coursesAllMPs[m])): # for every course
arr = [
allData[c][m][d][1], allData[c][m][d][2],
allData[c][m][d][3]
]
arrC.append(arr)
arrDC = [allData[0][m][d][0],
arrC] # [date,[info1,info2,info3]]
allData2.append(arrDC)
allData2.pop(0)
# OUTPUT DATA TO FILE
try:
outfile = open("dailyGradesFile.txt", "x")
except FileExistsError:
outfile = open("dailyGradesFile.txt", "w")
with open("gradesDictionary.txt", 'a') as json_outfile:
json_outfile.write("[")
mpIndexPlus1 = 1
for d in range(len(allData2)):
dDate = date.Date(allData2[d][0].day, allData2[d][0].month,
allData2[d][0].year)
endDate = date.Date(mpStartDates[4][0], mpStartDates[4][1],
mpStartDates[4][2])
if (dDate.compareToDateObj(endDate) >= 0):
break # Stop at last day of school
print(str(allData2[d][0]))
outfile.write(str(allData2[d][0]) + "\n") # write date
for c in range(len(allData2[d][1])):
try:
if (date.Date(allData2[d][0].day, allData2[d][0].month,
allData2[d][0].year).compareToDMY(
mpStartDates[mpIndexPlus1][0],
mpStartDates[mpIndexPlus1][1],
mpStartDates[mpIndexPlus1][2]) >= 0):
mpIndexPlus1 += 1
lstLbls = ["Date", "Course", "Grd", "PR", "PW"]
lst = []
if (len(coursesAllMPs[mpIndexPlus1 - 1][c].code) > 0):
lst = [
str(allData2[d][0]),
coursesAllMPs[mpIndexPlus1 - 1][c].courseName,
allData2[d][1][c][0], allData2[d][1][c][1],
allData2[d][1][c][2]
]
print("\t{0:30} {1:15} {2:15} {3:15}".format(
coursesAllMPs[mpIndexPlus1 - 1][c].courseName + ":: ",
"Grd:" + str(allData2[d][1][c][0]),
"PR:" + str(allData2[d][1][c][1]),
"PW:" + str(allData2[d][1][c][2])))
outfile.write("\t{0:30} {1:15} {2:15} {3:15}\n".format(
coursesAllMPs[mpIndexPlus1 - 1][c].courseName + ":: ",
"Grd:" + str(allData2[d][1][c][0]),
"PR:" + str(allData2[d][1][c][1]),
"PW:" + str(allData2[d][1][c][2])))
else:
lst = [
str(allData2[d][0]),
coursesAllMPs[mpIndexPlus1 - 1][c].courseName, "----",
"----", "----"
]
print("\t{0:30} {1:15} {2:15} {3:15}".format(
coursesAllMPs[mpIndexPlus1 - 1][c].courseName + ":: ",
"Grd:----", "PR:----", "PW:----"))
outfile.write("\t{0:30} {1:15} {2:15} {3:15}\n".format(
coursesAllMPs[mpIndexPlus1 - 1][c].courseName + ":: ",
"Grd:----", "PR:----", "PW:----"))
with open("gradesDictionary.txt", 'a') as json_outfile:
json.dump(Convert(lstLbls, lst), json_outfile, indent=2)
if (d < len(allData2) or c < len(allData2[d][1])):
json_outfile.write(",\n")
except IndexError:
break
with open("gradesDictionary.txt", 'a') as json_outfile:
json_outfile.write("]")
outfile.close()