def getChart2():
try:
data = fishData.stream()
dataDict = {}
for data1 in data:
data1 = data1.to_dict()
month = int(data1['date'].split("-")[1])
weight = data1['totalWeight']
if month in dataDict:
dataDict[month] = dataDict[month] + weight
else:
dataDict[month] = weight
plt.bar(list(dataDict.keys()), dataDict.values(), color='g')
plt.xlabel('Month')
plt.ylabel('Weight of catch caught')
a = 'temp' + ''.join(
[str(random.randint(0, 999)).zfill(3) for _ in range(2)]) + '.png'
imageTempPath = BASE_PATH + a
plt.title('Month vs Weight of catch caught')
plt.savefig(imageTempPath)
storage.child('charts/{}'.format(a)).put(imageTempPath)
chartUrl = storage.child('charts/{}'.format(a)).get_url(None)
print(chartUrl)
return jsonify({'chartUrl': chartUrl})
except Exception as e:
print(e)
return f"An Error Occured", 500
def addProducts(category_id, sub_category_id, product_name, product_desc,
product_price, business_id, img):
_, product = firestore_client.collection('businesses').document(
business_id).collection('categories').document(category_id).collection(
'subcategories').document(sub_category_id).collection(
'products').add({
'name': product_name,
'description': product_desc,
'price': product_price
})
storage.child(business_id + '/' + category_id + '/' + sub_category_id +
'/' + product.id + '/' + 'hh.jpg').put(img[0])
firestore_client.collection('businesses').document(business_id).collection(
'categories').document(category_id).collection(
'subcategories').document(sub_category_id).collection(
'products').document(product.id).update({
'image':
storage.child(business_id + '/' + category_id + '/' +
sub_category_id + '/' + product.id + '/' +
'hh.jpg').get_url(None)
})
# links = storage.child(business_id+'/'+category_id+'/'+sub_category_id+'/'+product.id+'/'+'hh.jpg').get_url(None)
# print(links)
return True
def uploadFile(file, prev_ref):
prev = str(prev_ref).encode()
# print("prev --- ", prev)
# print("string type --- ", type(prev) == str)
# print("not empty --- ", prev != '')
# print("has valid filename --- ", allowed_photo(prev))
filename = uniqueValue() + secure_filename(file.filename)
filepath = os.path.join(current_dir, filename)
file.save(filepath)
storage = firebase.storage()
if prev and type(prev) == str and prev != '' and allowed_photo(prev):
try:
storage.delete(prev)
except Exception:
print("could not delete prev --- ", prev)
# pass
storage.child(filename).put(filepath)
link = 'https://firebasestorage.googleapis.com/v0/b/' + config[
'storageBucket'] + '/o/' + filename + '?alt=media&token=None'
os.remove(filepath)
return link
def send_to_db(image_path, Longitude=77, Latitude=13):
name = time.time()
name *= 1000
name = int(name)
storage.child(str(name)).put(image_path)
url = storage.child(str(name)).get_url(2)
db = firestore.client()
ref = db.collection('accidents')
ref.add({
"URL": url,
"Longitude": Longitude,
"Latitude": Latitude,
"is_dismissed": False,
"is_reported": False,
"timestamp": name
})
send_to_whatsapp(
url, "https://www.google.com/maps/dir/?api=1&destination=" +
str(Latitude) + "," + str(Longitude))
def getChart1():
try:
data = fishData.stream()
dataDict = {}
for data1 in data:
data1 = data1.to_dict()
month = int(data1['date'].split("-")[1])
count = 0
for i in data1['catches']:
count += int(i['quantity'])
if month in dataDict:
dataDict[month] = dataDict[month] + count
else:
dataDict[month] = count
plt.bar(list(dataDict.keys()), dataDict.values(), color='g')
plt.xlabel('Months')
plt.ylabel('No of Catches')
plt.title('Month Vs No of catches caught')
a = 'temp' + ''.join(
[str(random.randint(0, 999)).zfill(3) for _ in range(2)]) + '.png'
imageTempPath = BASE_PATH + a
plt.savefig(imageTempPath)
storage.child('charts/{}'.format(a)).put(imageTempPath)
chartUrl = storage.child('charts/{}'.format(a)).get_url(None)
print(chartUrl)
return jsonify({
'chartUrl': chartUrl,
'average': res,
'moneySaved': res * 2
})
except Exception as e:
return f"An Error Occured", 500
def updateCatch():
try:
data = request.json
print(data)
print(data['image'])
convert(data['image'], data['imageFileName'])
imagePath = BASE_PATH + data['imageFileName']
storage.child('fishes/{}'.format(data['imageFileName'])).put(imagePath)
fish_url = storage.child('fishes/{}'.format(
data['imageFileName'])).get_url(None)
# The url of the image sent from app
print(fish_url)
request_url = 'http://35.240.219.8:8080/register?key=' + fish_url
print(request_url)
try:
predictedData = requests.get(request_url).json()
except Exception as e:
print(e)
print(predictedData)
temp = int(''.join(
[str(random.randint(0, 999)).zfill(3) for _ in range(2)]))
data2 = {
'date': data['date'],
'description': data['description'],
'image': fish_url,
'hours': float(data['hours']),
'latitude': float(data['latitude']),
'longitude': float(data['longitude']),
'name': data['name'] + " " + str(temp),
'weight': float(data['weight']),
'number': data['number'],
'catchId': temp,
'catches': predictedData['fishData'],
'boundedImageUrl': predictedData['boundedUrl']
}
ref = userCatches.document(str(data['number']))
ref.update({u'catches': firestore.ArrayUnion([data2])})
print(data2)
data1 = {
'latitude': float(data2['latitude']),
'longitude': float(data2['longitude']),
'totalWeight': float(data2['weight']),
'date': data2['date'],
'catchId': data2['catchId'],
'hours': data2['hours'],
'catches': predictedData['fishData'],
'boundedImageUrl': predictedData['boundedUrl']
}
print(data1)
fishData.document(str(data1['catchId'])).set(data1)
return jsonify({"success": True})
except Exception as e:
print(e)
return jsonify({"success": False}), 400
def getChart4():
print(request.args)
try:
number = str(request.args['number'])
catchDetails = userCatches.document(number).get().to_dict()["catches"]
# print(catchDetails)
resDict = dict()
monthDict = dict()
for i in catchDetails:
hours = float(i['hours'])
weight = float(i['weight'])
month = int(i['date'].split("-")[1])
print(month)
CPUE = weight / hours
print(CPUE)
if month in resDict:
resDict[month] = resDict[month] + CPUE
else:
resDict[month] = CPUE
if month in monthDict:
monthDict[month] += 1
else:
monthDict[month] = 1
print(resDict, monthDict)
ans = {}
total = 0
for i in resDict.keys():
ans[i] = resDict[i] / monthDict[i]
total += ans[i]
# This is the average weight/hour.
# Money saved is random.
res = total / len(ans)
print(str(ans.keys()) + " " + str(ans.values()) + "hi")
plt.bar(list(ans.keys()), ans.values(), color='g')
plt.title("Month Vs CPUE - For Fisherman")
plt.xlabel('Months')
plt.ylabel('CPUE(weight of catch/hour)')
a = 'temp' + ''.join(
[str(random.randint(0, 999)).zfill(3) for _ in range(2)]) + '.png'
imageTempPath = BASE_PATH + a
plt.savefig(imageTempPath)
storage.child('charts/{}'.format(a)).put(imageTempPath)
chartUrl = storage.child('charts/{}'.format(a)).get_url(None)
print(chartUrl)
return jsonify({
'chartUrl': chartUrl,
'average': res,
'moneySaved': res * 2
})
except Exception as e:
return f"An Error Occured", 500
def imageUpload(request):
# try:
with open('./secrets.json') as json_file:
json_data = json.load(json_file)
firebaseConfig = json_data['FIREBASE_CONFIG']
firebase = pyrebase.initialize_app(firebaseConfig)
storage = firebase.storage()
image_file = request.FILES['image']
storage.child(request.user.profile.nickname).put(image_file)
msg = {'status': 'true', 'message': '이미지가 성공적으로 저장되었습니다.'}
return JsonResponse(msg, status=200)
def put_image(dirname):
filenames = os.listdir(dirname)
# folder_num = 1
for i, filename in enumerate(filenames):
# print(i, filename)
# if i % 2000 == 0:
# folder_num += 1
if i < 82800:
continue
# directory = 'img' + str(folder_num)
# if not os.path.exists(directory):
# os.makedirs(directory)
full_name = os.path.join(dirname, filename)
storage_file_name = os.path.join("images/", filename)
print(full_name)
storage.child(storage_file_name).put(full_name)
print(i, "Image Uploaded")
def put_image(dirname):
filenames = os.listdir(dirname)
# folder_num = 1
for i, filename in enumerate(filenames):
# print(i, filename)
# if i % 2000 == 0:
# folder_num += 1
# if i < 82800:
# continue
# directory = 'img' + str(folder_num)
# if not os.path.exists(directory):
# os.makedirs(directory)
print(filename)
full_name = os.path.join(dirname, filename)
name = filename.split('.')
if len(name[0]) == 0:
continue
print(full_name)
storage.child(name[0]).put(full_name)
print(i, "Image Uploaded")
def send_to_db(image_path, Longitude=77, Latitude=13):
name = time.time()
name *= 1000
name = int(name)
storage.child(str(name)).put(image_path)
url = storage.child(str(name)).get_url(2)
db = firestore.client()
ref = db.collection('accidents')
ref.add({
"URL": url,
"Longitude": Longitude,
"Latitude": Latitude,
"is_dismissed": False,
"is_reported": False,
"timestamp": name
})
def getChart3():
data = fishData.stream()
dataDict = {}
monthCountDict = {}
try:
for data1 in data:
data1 = data1.to_dict()
print(data1)
month = int(data1['date'].split("-")[1])
weight = data1['totalWeight']
hours = data1['hours']
CPUE = weight / hours
if month in monthCountDict:
monthCountDict[month] += 1
else:
monthCountDict[month] = 1
if month in dataDict:
dataDict[month] = dataDict[month] + CPUE
else:
dataDict[month] = CPUE
ans = {}
for i in dataDict.keys():
ans[i] = dataDict[i] / monthCountDict[i]
plt.bar(list(ans.keys()), ans.values(), color='g')
plt.xlabel('Months')
plt.ylabel('CPUE(weight of catch/hour)')
a = 'temp' + ''.join(
[str(random.randint(0, 999)).zfill(3) for _ in range(2)]) + '.png'
imageTempPath = BASE_PATH + a
plt.savefig(imageTempPath)
plt.title("Month Vs CPUE")
storage.child('charts/{}'.format(a)).put(imageTempPath)
chartUrl = storage.child('charts/{}'.format(a)).get_url(None)
print(chartUrl)
return jsonify({
'chartUrl': chartUrl,
})
except Exception as e:
return f"An Error Occured", 500
def get_Song(songName):
ref = db.collection('songs').document(songName).get()
filePath = ref.to_dict()["filepath"]
storage.child(filePath).download("intermediate.mid")
original_score = converter.parse("intermediate.mid").chordify()
subset = original_score[0:35]
notes, durations = process_Chords(subset, notes_dict, durations_dict)
midi_stream = stream.Stream()
midi_stream.append(subset)
for i in range(200):
subset_notes = notes[len(notes) - 29:len(notes) - 1]
subset_durations = durations[len(durations) - 29:len(durations) - 1]
feed_notes = np.array(subset_notes).reshape((1, len(subset_notes)))
feed_duration = np.array(subset_durations).reshape(
(1, len(subset_durations)))
prediction = model.predict([feed_notes, feed_duration])
notes.append(np.argmax(prediction[0]))
durations.append(np.argmax(prediction[1]))
prediction_note = notes_dict_reverse[np.argmax(prediction[0])]
prediction_duration = durations_dict_reverse[np.argmax(prediction[1])]
print(process_output(prediction_note, prediction_duration))
midi_stream.append(process_output(prediction_note,
prediction_duration))
midi_stream = midi_stream.chordify()
midi_stream.write('mid', "output.mid")
storage.child("gen_" + filePath).put("output.mid")
return songName
def viewPatient(request, pid):
curr_u = retrieveCurrU(request)
u_name = database.child('user').child(curr_u).child('details').child('name').get().val()
idtoken = request.session['uid']
patient = database.child('patient').child(pid).get().val()
screening = database.child('patient').child(pid).child('screening').get().val()
screen_list = []
if screening is not None:
for p in screening:
temp_class = database.child('patient').child(pid).child('screening').child(p).get().val()
# Convert classes
if temp_class['classes'] == 4:
str_class = "No prediction yet"
elif temp_class['classes'] == 1:
str_class = "Predicted as Class I"
elif temp_class['classes'] == 2:
str_class = "Predicted as Class II"
else:
str_class = "Predicted as Class III"
# Convert date
pf = float(p)
date = datetime.fromtimestamp(pf).strftime('%H:%M %d-%m-%Y')
p_data = {
"date": date,
"datetime": p,
"classes": str_class,
"class1": temp_class['class1'],
"class2": temp_class['class2'],
"class3": temp_class['class3'],
"xray_url": storage.child(patient_path+pid+"/"+p+".jpg").get_url(idtoken)
}
screen_list.append(p_data)
data = {
"name": u_name,
"pname": patient["pname"],
"pid": pid,
"age": patient["age"],
"gender": patient["gender"],
"pic": patient["pic"],
"screening": screen_list
}
return render(request, "profileP.html", data)
def classifier(request, pid=None, dt=None):
idtoken = request.session['uid']
# Open patient image
xray_url = storage.child(patient_path+pid+"/"+dt+".jpg").get_url(idtoken)
img = Image.open(urlopen(xray_url))
img_tensor = T.ToTensor()(img)
img_fastai = I(img_tensor)
# Prediction
classifier = load_learner('./')
if classifier:
pred_class, pred_idx, output = classifier.predict(img_fastai)
else:
print("no model")
class1 = round(output.numpy()[0], 4)
class2 = round(output.numpy()[1], 4)
class3 = round(output.numpy()[2], 4)
classes = 4
if class1 > class2:
if class1 > class3:
classes = 1
else:
if class2 > class1:
if class2 > class3:
classes = 2
else:
classes = 3
updated = {
"classes": classes,
"class1": str(class1),
"class2": str(class2),
"class3": str(class3)
}
database.child("patient").child(pid).child("screening").child(dt).update(updated)
print("before redirect")
return redirect('/patient_list/patient_profile/'+pid)
def getProductsInfo(business_id, category_id, sub_category_id):
product_list = []
for k in firestore_client.collection('businesses').document(
business_id).collection('categories').document(
category_id).collection('subcategories').document(
sub_category_id).collection('products').get():
product_category_id = k.id
product_category_name = k.to_dict()['name']
product_category_description = k.to_dict()['description']
product_category_price = k.to_dict()['price']
product_image = storage.child(business_id + '/' + category_id + '/' +
sub_category_id + '/' +
product_category_id + '/' +
'hh.jpg').get_url(None)
product_list.append({
'name': product_category_name,
'id': product_category_id,
'description': product_category_description,
'price': product_category_price,
'image': product_image
})
return product_list
def doctor(pid, did):
import datetime
import pandas as pd
import numpy as np
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from firebase_admin import storage
import pyrebase
from datetime import date, timedelta
import urllib.request, json
import time
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import os
import csv
from IPython.display import display
from Model import trainData
import random
# from google.cloud import storage
from matplotlib.patches import Ellipse
import seaborn as sns
import matplotlib.patches as mpatches
# signal processing
from scipy import signal
from scipy.ndimage import label
from scipy.stats import zscore
from scipy.interpolate import interp1d
from scipy.integrate import trapz
# misc
import warnings
# generate pdf
from reportlab.pdfgen import canvas
from reportlab.lib.colors import Color, lightblue, black
# In[97]:
if not firebase_admin._apps:
cred = credentials.Certificate("serene-firebase-adminsdk.json")
app = firebase_admin.initialize_app(
cred, {
'storageBucket': 'serene-2dfd6.appspot.com',
},
name='[DEFAULT]')
else:
app = firebase_admin.get_app()
db = firestore.client()
# In[98]:
today = datetime.datetime.now()
timestamp = today.strftime("%Y-%m-%d")
bucket = storage.bucket(app=app)
# ## Get data from storage and get list of dates (2 weeks)
# In[99]:
# get a a list of date between start and end date
userID = pid
doctorID = did
duration = 15 # two weeks
dates = []
for x in range(0, duration):
today = date.today() # revert to original
# yesterday = today - datetime.timedelta(days=1)
start_date = (today - timedelta(days=duration - x)).isoformat()
dates.append(start_date)
# In[100]:
df = pd.DataFrame()
notAvailableDates = []
# loop through the storage and get the data
sleep = []
for x in range(0, len(dates)):
# Sleep
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-sleep.json")
# download the file
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
sleepMinutes = data['summary']["totalMinutesAsleep"]
except:
notAvailableDates.append(dates[x])
pass
# Activity (Steps)
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-activity.json")
# download the file
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
steps = data['summary']["steps"]
except:
notAvailableDates.append(dates[x])
pass
# heartrate
heart_rate = {}
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-heartrate.json")
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
df_heartrate = pd.DataFrame(
data['activities-heart-intraday']['dataset'])
df_heartrate.time.apply(str)
df_heartrate['time'] = pd.to_datetime(df_heartrate['time'])
df_heartrate['hour'] = df_heartrate['time'].apply(
lambda time: time.strftime('%H'))
df_heartrate.drop(['time'], axis=1, inplace=True)
heart_rate = df_heartrate.groupby(["hour"], as_index=False).mean()
heart_rate['sleepMin'] = sleepMinutes
heart_rate['TotalSteps'] = steps
heart_rate['date'] = dates[x]
heart_rate = heart_rate.astype({"hour": int})
except:
notAvailableDates.append(dates[x])
pass
# append dataframe
df = df.append(heart_rate, ignore_index=True)
# In[101]:
notAvailableDates
notSyncedDates = pd.DataFrame()
notSyncedDates['date'] = notAvailableDates
# In[102]:
notSyncedDates = notSyncedDates.drop_duplicates()
# In[103]:
notSyncedDates
# ### Get user location
# In[104]:
# get location from database
loc_df = pd.DataFrame()
locID = []
locations = db.collection(u'PatientLocations').where(
u'patientID', u'==', userID).stream()
for location in locations:
loc1 = location.to_dict()
locID.append(location.id)
loc_df = loc_df.append(pd.DataFrame(loc1, index=[0]),
ignore_index=True)
loc_df['id'] = locID
# In[105]:
loc_df.drop(['anxietyLevel', 'lat', 'lng', 'patientID'],
axis=1,
inplace=True)
# In[106]:
loc_df.time.apply(str)
loc_df['time'] = pd.to_datetime(loc_df['time'])
loc_df['date'] = pd.to_datetime(loc_df['time'], format='%Y:%M:%D').dt.date
loc_df['hour'] = loc_df['time'].apply(lambda time: time.strftime('%H'))
loc_df.drop(['time'], axis=1, inplace=True)
loc_df.hour = loc_df.hour.astype(int)
loc_df.date = loc_df.date.astype(str)
df.date = df.date.astype(str)
# In[107]:
dfinal = pd.merge(left=df,
right=loc_df,
how='left',
left_on=['hour', 'date'],
right_on=['hour', 'date']).ffill()
# ### Test data into model
# In[108]:
# test model
train_df = dfinal.rename(columns={'value': 'Heartrate'})
# In[109]:
Labeled_df = pd.DataFrame()
Labeled_df = trainData(train_df)
# In[110]:
Labeled_df.drop(['lon'], axis=1, inplace=True)
# In[111]:
Labeled_df['name'].fillna("Not given", inplace=True)
Labeled_df['id'].fillna("Not given", inplace=True)
Labeled_df['anxiety_assigned'].fillna('Not given', inplace=True)
# In[112]:
# Update firebase with the user anxiety level
for row in Labeled_df.itertuples():
if row.id != 'Not given':
if row.Label == 'Low' or row.Label == 'LowA':
anxietyLevel = '1'
elif row.Label == 'Meduim':
anxietyLevel = '2'
else:
anxietyLevel = '3'
if row.anxiety_assigned == False or row.anxiety_assigned == 'Not given':
doc_ref = db.collection(u'PatientLocations').document(row.id)
doc_ref.update({
u'anxietyLevel': anxietyLevel,
u'anxiety_assigned': True
})
# ### Show the places with highest anxiety level
# In[113]:
# Show the highest level
df_high = pd.DataFrame()
df_high = Labeled_df[Labeled_df.Label == 'High']
# # Get patient information
# In[114]:
docDf = pd.DataFrame()
doc_ref = db.collection(u'Patient').document(userID)
doc = doc_ref.get().to_dict()
docDf = docDf.append(pd.DataFrame(doc, index=[0]), ignore_index=True)
# In[115]:
age1 = docDf['age'].values
name1 = docDf['name'].values
emp1 = docDf['employmentStatus'].values
mar1 = docDf['maritalStatus'].values
income1 = docDf['monthlyIncome'].values
chronicD1 = docDf['chronicDiseases'].values
smoke1 = docDf['smokeCigarettes'].values
gad1 = docDf['GAD-7ScaleScore'].values
gender1 = docDf['gender'].values
age = age1[0]
name = name1[0]
emp = emp1[0]
mar = mar1[0]
income = income1[0]
chronicD = chronicD1[0]
smoke = smoke1[0]
gad = gad1[0]
gender = gender1[0]
# ## Storage intiliazation
# In[116]:
firebaseConfig = {
"apiKey": "AIzaSyBoxoXwFm9TuFysjQYag0GB1NEPyBINlTU",
"authDomain": "serene-2dfd6.firebaseapp.com",
"databaseURL": "https://serene-2dfd6.firebaseio.com",
"projectId": "serene-2dfd6",
"storageBucket": "serene-2dfd6.appspot.com",
"messagingSenderId": "461213981433",
"appId": "1:461213981433:web:62428e3664182b3e58e028",
"measurementId": "G-J66VP2Y3CR"
}
firebase = pyrebase.initialize_app(firebaseConfig)
storage = firebase.storage()
# # HR
# In[117]:
sns.set(rc={'axes.facecolor': '#fcfeff'})
# In[118]:
# Take the highest heartrate in a day
dfhr = pd.DataFrame()
hr = []
hrr = 0
rowCount = 1
for x in range(0, len(dates)):
rowCount = 0
for row in df.itertuples():
if (row.date == dates[x]):
if (row.value > rowCount):
rowCount = row.value
hr.append(rowCount)
dfhr['date'] = dates
dfhr['hr'] = hr
# In[119]:
plt.figure(figsize=(20, 7))
plt.plot(dfhr['date'], dfhr['hr'], color="#a03e3e", linewidth=4)
plt.xlabel("Date", fontsize=16)
plt.ylabel("Amplitude (bpm)")
plt.tick_params(axis='x', rotation=70)
plt.tight_layout()
plt.savefig('hr.png', dpi=None)
# plt.show()
# plt.draw()
# # Steps
# In[120]:
dfstep = pd.DataFrame()
avgSteps = []
totalsteps = 0
rowCount = 1
for x in range(0, len(dates)):
for row in Labeled_df.itertuples():
if (row.date == dates[x]):
rowCount += 1
totalsteps += row.TotalSteps
avgSteps.append(totalsteps / rowCount)
dfstep['date'] = dates
dfstep['Steps'] = avgSteps
# In[121]:
# Plot Steps
plt.figure(figsize=(20, 7))
# plt.fill_between(dfstep['date'], dfstep['Steps'], color="#ffd6b0", linewidth=1)
plt.plot(dfstep['date'],
dfstep['Steps'],
color="#ff5900",
linewidth=4,
marker='o',
markerfacecolor='#ffd6b0')
plt.xlabel("Date", fontsize=16)
plt.ylabel("Total steps")
plt.tick_params(axis='x', rotation=70)
plt.tight_layout()
plt.savefig('steps.png', dpi=None)
# plt.show()
# plt.draw()
# # Sleep
# In[122]:
dfsleep = pd.DataFrame()
sleeps = []
totalsleep = 0
rowCount = 1
for x in range(0, len(dates)):
for row in df.itertuples():
if (row.date == dates[x]):
totalsleep = row.sleepMin
sleeps.append(totalsleep / 60)
dfsleep['date'] = dates
dfsleep['sleep'] = sleeps
# In[123]:
figs = dfsleep.plot.bar(x='date',
y='sleep',
rot=70,
width=0.25,
color='#3629a6',
capstyle='round').get_figure()
figs.set_size_inches(20, 10)
plt.xlabel('Date')
plt.ylabel('Sleep (hr)')
# plt.show()
# plt.draw()
plt.tight_layout()
figs.savefig('sleep.png', dpi=None)
# # AL
# In[124]:
# Change Label values to num, to represent them in a barchart
nums = []
for row in Labeled_df.itertuples():
if row.Label == 'Low' or row.Label == 'LowA':
nums.append(1)
elif row.Label == 'Meduim':
nums.append(2)
else:
nums.append(3)
Labeled_df['numLabel'] = nums
# In[125]:
# Get anxiety level by day and store it in a new data frame
plot_df = pd.DataFrame()
avgAnxiety = []
totalAnxiety = 0
rowCount = 1
for x in range(0, len(dates)):
totalAnxiety = 0
rowCount = 1
for row in Labeled_df.itertuples():
if (row.date == dates[x]):
rowCount += 1
totalAnxiety += row.numLabel
avgAnxiety.append(totalAnxiety / rowCount)
plot_df['date'] = dates
plot_df['Anxiety'] = avgAnxiety
# In[126]:
fig, ax = plt.subplots()
# Draw the stem and circle
c1 = '#9dd6f5'
c2 = '#4ba0d1'
c3 = '#23495f'
bar_width = 0.25
for t, y in zip(plot_df["date"], plot_df["Anxiety"]):
c = ""
if (y <= 1):
c = c1
elif (1 < y <= 2):
c = c2
elif (y > 2):
c = c3
plt.bar([t, t], [0, y], bar_width, color=c)
colors = [[c1, c1], [c2, c2], [c3, c3]]
categories = ['Low', 'Meduim', 'High']
# create dict
legend_dict = dict(zip(categories, colors))
# create patches
patchList = []
for key in legend_dict:
data_key = mpatches.Patch(facecolor=legend_dict[key][0],
edgecolor=legend_dict[key][1],
label=key)
patchList.append(data_key)
ax.legend(handles=patchList, ncol=len(categories), fontsize=12)
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
fig.set_size_inches(15.5, 10)
plt.tight_layout()
plt.xlabel('Date')
ax.yaxis.set_label_coords(-0.02, 0.48)
# volo insert
# plt.show()
fig.savefig('AL.png', dpi=None)
# # Location Analysis
# In[127]:
# get location from database
new_loc = pd.DataFrame()
locID = []
locations = db.collection(u'PatientLocations').where(
u'patientID', u'==', userID).stream()
for location in locations:
loc = location.to_dict()
locID.append(location.id)
new_loc = new_loc.append(pd.DataFrame(loc, index=[0]),
ignore_index=True)
new_loc['id'] = locID
# In[128]:
new_loc.time.apply(str)
new_loc['time'] = pd.to_datetime(new_loc['time'])
new_loc['date'] = pd.to_datetime(new_loc['time'],
format='%Y:%M:%D').dt.date
new_loc.drop(['time'], axis=1, inplace=True)
new_loc.date = new_loc.date.astype(str)
# In[129]:
new_loc = new_loc[(new_loc.date >= dates[0])
& (new_loc.date <= dates[len(dates) - 1])]
# In[130]:
names = []
Name = ""
for row in new_loc.itertuples():
Name = row.nearestLoc
names.append(Name)
# In[131]:
new_name = pd.DataFrame()
new_name['name'] = names
# In[132]:
new_name = new_name.drop_duplicates()
new_name.dropna()
# In[133]:
fnames = []
fName = ""
for row in new_name.itertuples():
fName = row.name
fnames.append(fName)
# In[134]:
analysis = pd.DataFrame()
count = 0
i = 0
# label = ""
locationName = ""
near = ''
nearLocs = []
counts = []
# labels = []
locationNames = []
for x in range(0, len(fnames)):
count = 0
locName = fnames[i]
for row in new_loc.itertuples():
if (locName == row.nearestLoc):
if (row.anxietyLevel == '3'):
count += 1
# label = row.anxietyLevel
locationName = row.name
near = row.nearestLoc
i += 1
counts.append(count)
# labels.append(label)
locationNames.append(locationName)
nearLocs.append(near)
analysis['Location'] = locationNames
analysis['Frequency'] = counts
# analysis ['Anxiety Level'] = labels
analysis['Nearest Location'] = nearLocs
# In[135]:
# newA = analysis.drop(analysis[analysis['Number of occurrences'] == 0].index, inplace=True)
# In[136]:
import six
import arabic_reshaper
from bidi.algorithm import get_display
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
from reportlab.lib import colors
# In[137]:
def render_mpl_table(data,
col_width=5.0,
row_height=0.625,
font_size=14,
header_color='#23495f',
row_colors=['#e1eff7', 'w'],
edge_color='#23495f',
bbox=[0, 0, 1, 1],
header_columns=0,
ax=None,
**kwargs):
if ax is None:
size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array(
[col_width, row_height])
fig, ax = plt.subplots(figsize=size)
ax.axis('off')
mpl_table = ax.table(cellText=data.values,
bbox=bbox,
colLabels=data.columns,
cellLoc='center',
**kwargs)
mpl_table.auto_set_font_size(False)
mpl_table.set_fontsize(font_size)
for k, cell in six.iteritems(mpl_table._cells):
cell.set_edgecolor(edge_color)
if k[0] == 0 or k[1] < header_columns:
cell.set_text_props(weight='bold', color='w')
cell.set_facecolor(header_color)
else:
cell.set_facecolor(row_colors[k[0] % len(row_colors)])
cell.alignment = 'center'
fig.savefig('Location.png', dpi=100)
return ax
# In[138]:
if (len(analysis) > 0):
for ind, row in analysis.iterrows():
analysis.loc[ind, 'Nearest Location'] = get_display(
arabic_reshaper.reshape(analysis.loc[ind, 'Nearest Location']))
# In[139]:
if (len(analysis) > 0):
render_mpl_table(analysis, header_columns=0, col_width=4)
# # improvement
# In[153]:
# get yesterday improvement
today_al = float("{:.2f}".format(plot_df['Anxiety'].mean()))
improvement = -1
# In[165]:
try:
doc_ref = db.collection(u'Patient').document(userID)
doc = doc_ref.get().to_dict()
prev = float("{:.2f}".format(doc['anxiety_level']))
# calculate the improvement
improvement = float("{:.2f}".format(((prev - today_al) / 3) * 100))
except:
improvement = 404
# ## Generate doctor report pdf and store it in database
#
# In[143]:
pdf = canvas.Canvas('Doctor.pdf')
pdf.setTitle('Patient report')
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 400, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#23495f'))
pdf.drawString(100, 570, "Patient Information")
pdf.setFont("Helvetica-Bold", 15)
pdf.drawString(150, 540, "Name: ")
pdf.drawString(150, 520, "Age: ")
pdf.drawString(150, 500, "Employment Status: ")
pdf.drawString(150, 480, "Martial Status: ")
pdf.drawString(150, 460, "Monthly Income: ")
pdf.drawString(150, 440, "Chronic Diseases: ")
pdf.drawString(150, 420, "Cigarette Smoker: ")
pdf.drawString(150, 410, "Gender: ")
pdf.setFont("Helvetica", 15)
pdf.setFillColor(black)
pdf.drawString(210, 540, name)
pdf.drawString(210, 520, age)
pdf.drawString(310, 500, emp)
pdf.drawString(260, 480, mar)
pdf.drawString(290, 460, income)
pdf.drawString(290, 440, chronicD)
pdf.drawString(290, 420, smoke)
pdf.drawString(220, 410, gender)
pdf.setFillColor(colors.HexColor('#bfbfbf'))
pdf.roundRect(370, 560, 125, 30, 4, fill=1, stroke=0)
pdf.setFillColorRGB(1, 1, 1)
pdf.drawString(375, 570, "GAD-7 Score = ")
pdf.setFont("Helvetica-Bold", 15)
pdf.drawString(480, 570, gad)
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 160, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 16)
pdf.setFillColor(colors.HexColor('#23495f'))
pdf.drawString(
115, 330, "Report Duration From: (" + dates[0] + " To: " +
dates[len(dates) - 1] + ")")
pdf.setFont("Helvetica-Bold", 14)
pdf.drawString(250, 300, "Improvements: ")
pdf.setFont("Helvetica-Bold", 20)
if (improvement == 404):
pdf.drawString(290, 260, "--")
else:
pdf.drawString(280, 260, str(improvement) + '%')
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#808080'))
pdf.drawString(100, 650, "Anxiety Level")
pdf.drawImage("AL.png", 57, 400, width=485, height=200)
pdf.drawString(100, 330, "Heart Rate")
pdf.drawImage("hr.png", 57, 100, width=485, height=200)
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#808080'))
pdf.drawString(100, 650, "Activity Rate")
pdf.drawImage("steps.png", 57, 400, width=485, height=200)
pdf.drawString(100, 350, "Sleep Analysis")
pdf.drawImage("sleep.png", 57, 100, width=485, height=200)
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#808080'))
pdf.drawString(100, 650, "Locations With Highest Level of Anxiety")
if (len(analysis) > 0):
pdf.drawImage("Location.png", 30, 200, width=570, height=100)
else:
pdf.setFont("Helvetica", 15)
pdf.setFillColor(colors.HexColor('#23495f'))
t = pdf.beginText(130, 550)
text = [
name + " condition was stable through this period,",
"no locations with high anxiety level were detected."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
if (len(notSyncedDates) != 0):
pdf.setFont("Helvetica", 12)
pdf.setFillColor(colors.HexColor('#d40027'))
pdf.drawString(
75, 100,
"Note: Below dates are missing, because they were not synced correctly:"
)
i = 70
for row in notSyncedDates.itertuples():
pdf.drawString(85, i, '- ' + row.date)
i = i - 20
pdf.save()
# In[144]:
# new method
doct = storage.child(userID +
"/DoctorReport/doctorReport").put('Doctor.pdf')
# In[145]:
linkDF = pd.DataFrame()
linkDF = linkDF.append(pd.DataFrame(doct, index=[0]), ignore_index=True)
# In[146]:
token1 = linkDF['downloadTokens'].values
token = token1[0]
link = storage.child(userID + "/DoctorReport/doctorReport").get_url(token)
# In[149]:
from datetime import datetime
# In[150]:
date = datetime.now()
# In[161]:
# store the data
random_id = random.randint(0, 1000)
ID = "Doctor" + userID + str(random_id)
doc_rec = db.collection(u'DoctorReports').document(str(ID))
doc_rec.set({
u'doctorId': doctorID,
u'emailsent': True,
u'patientId': userID,
u'reportTime': date,
u'reportUrl': link,
u'improvement': improvement
})
doc_rec = db.collection(u'Patient').document(userID)
doc_rec.update({u'anxiety_level': today_al})
# In[152]:
os.remove("hr.png")
os.remove("sleep.png")
os.remove("AL.png")
os.remove("steps.png")
os.remove("Doctor.pdf")
if (len(analysis) > 0):
os.remove("Location.png")
return {"Doctor": "Yaaay"}
def addFiletoFirebase(filename):
storage.child('json_keys/' + filename).put('json_keys/' + filename)
"apiKey": "AIzaSyCE_8CVn1Zio1f-EXpEgZtp-8aoyxpteVk",
"authDomain": "bestcamera-7e901.firebaseapp.com",
"databaseURL": "https://bestcamera-7e901.firebaseio.com/",
"projectId": "bestcamera-7e901",
"storageBucket": "bestcamera-7e901.appspot.com",
"messagingSenderId": "1087271452402",
"appId": "1:1087271452402:web:9c34a3417a21237ebe5a6b",
"measurementId": "G-3J20J96NZX"
}
firebase = pyrebase.initialize_app(config)
storage = firebase.storage()
add = random.randrange(9999999)
faceURL = "imagine/dumi" + str(add) + ".jpg"
path_local = "./saved/face/img.jpg"
imgurl = storage.child(faceURL).put(path_local)
db = firebase.database()
camera = cv2.VideoCapture(-1)
_, frame = camera.read()
camera.release()
time.sleep(1)
while True:
camera = cv2.VideoCapture(-1)
_, frame = camera.read()
camera.release()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades +
from firebase_admin import storage
from pyrebase.pyrebase import Firebase
from PIL import Image
config = {
"apiKey": "AIzaSyCbwjD-zqfeO2-5hsHIhF6jHoFTnzFuGQg",
"authDomain": "project-1085473150640.firebaseapp.com",
"serviceAccount":
"/Users/goncalocardoso/PycharmProjects/OpenCV/tese-3ed7d-firebase-adminsdk-4xm5b-3176ad9835.json",
"databaseURL": "https://tese-3ed7d.firebaseio.com/",
"storageBucket": "tese-3ed7d.appspot.com"
}
firebase = Firebase(config)
storage = firebase.storage()
storage.child("images").download("downloaded.jpg")
#image = Image.open(r"downloaded.jpg")
#crop = image.crop((655, 1841, 835, 2034))
#crop.save('myphoto.jpg', 'JPEG')
image = cv2.imread("downloaded.jpg")
#image = cv2.imread("/Users/goncalocardoso/Desktop/black.png")
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower_blue = np.array([100, 50, 50])
upper_blue = np.array([130, 255, 255])
lower_green, upper_green = np.array([36, 25, 25]), np.array([70, 255, 255])
# Threshold the HSV image to get only blue colors
#mask = cv2.inRange (hsv, np.array([36,25,25]), np.array([70, 255,255]))
# mask_blue = cv2.inRange (hsv, lower_blue,upper_blue )
#fileName = 'C:\\Projects\\RubberDuck\\speech-emotion-recognition-ravdess-data\\num25\\audiofile.wav'
#bucket = storage.bucket()
#blob = bucket.blob(fileName)
#blob.upload_from_filename(fileName)
config = {
"apiKey": "AIzaSyBqEF41l5P_hMo3lkupd8YiJhwPpT3ew4o",
"authDomain": "rubberduck-65f9c.firebaseapp.com",
"databaseURL": "https://rubberduck-65f9c-default-rtdb.firebaseio.com",
"storageBucket": "rubberduck-65f9c.appspot.com"
}
firebase = Firebase(config)
storage = firebase.storage()
storage.child("audiofile.wav").download(
'C:\\Projects\\RubberDuck\\speech-emotion-recognition-ravdess-data\\num25\\audiofile.wav'
)
#make data usable
ownFeature = [load_own()]
#ownUsable = ownFeature.reshape(1, -1)
#print (loadedModel)
#print (ownFeature)
#load model
modelName = 'C:\\Projects\\RubberDuck\\rubberduckfinal\\rubberduckalg.sav'
loadedModel = pickle.load(open(modelName, 'rb'))
ownSound = loadedModel.predict(ownFeature)
print(ownSound)
db = firebase_admin.initialize_app(
def hello(id, du):
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import datetime
import pandas as pd
import numpy as np
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from firebase_admin import storage
import pyrebase
from datetime import date, timedelta
import urllib.request, json
import time
#get_ipython().run_line_magic('matplotlib', 'inline')
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import os
import csv
from IPython.display import display
from Model import trainData
import random
#from google.cloud import storage
from matplotlib.patches import Ellipse
import seaborn as sns
# signal processing
from scipy import signal
from scipy.ndimage import label
from scipy.stats import zscore
from scipy.interpolate import interp1d
from scipy.integrate import trapz
# misc
import warnings
#generate pdf
from reportlab.pdfgen import canvas
from reportlab.lib.colors import Color, lightblue, black, HexColor
# In[2]:
cred = credentials.Certificate(
"/Users/raghadaziz/Desktop/GP2/SereneReports/SereneReport/serene-firebase-adminsdk.json"
)
app = firebase_admin.initialize_app(
cred, {
'storageBucket': 'serene-2dfd6.appspot.com',
},
name='[DEFAULT]')
db = firestore.client()
# In[3]:
duration = du
userID = id #"UqTdL3T7MteuQHBe1aNfSE9u0Na2"
# In[4]:
today = datetime.datetime.now()
timestamp = today.strftime("%Y-%m-%d %H:%M:%S")
bucket = storage.bucket(app=app)
# ## Get data from storage and get list of dates
# In[5]:
dates = []
for x in range(0, duration):
today = date.today()
yesterday = today - datetime.timedelta(days=1)
start_date = (yesterday - timedelta(days=duration - x)).isoformat()
dates.append(start_date)
# In[6]:
df = pd.DataFrame()
# loop through the storage and get the data
sleep = []
for x in range(0, len(dates)):
#Sleep
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-sleep.json")
# download the file
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
sleepMinutes = data['summary']["totalMinutesAsleep"]
except:
pass
#Activity (Steps)
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-activity.json")
# download the file
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
steps = data['summary']["steps"]
except:
pass
#heartrate
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-heartrate.json")
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
df_heartrate = pd.DataFrame(
data['activities-heart-intraday']['dataset'])
df_heartrate.time.apply(str)
df_heartrate['time'] = pd.to_datetime(df_heartrate['time'])
df_heartrate['hour'] = df_heartrate['time'].apply(
lambda time: time.strftime('%H'))
df_heartrate.drop(['time'], axis=1, inplace=True)
heart_rate = df_heartrate.groupby(["hour"], as_index=False).mean()
heart_rate['sleepMin'] = sleepMinutes
heart_rate['TotalSteps'] = steps
heart_rate['date'] = dates[x]
heart_rate = heart_rate.astype({"hour": int})
except:
pass
# append dataframe
df = df.append(heart_rate, ignore_index=True)
# In[7]:
df
# ### Get user location
# In[8]:
# get location from database
loc_df = pd.DataFrame()
locID = []
locations = db.collection(u'PatientLocations').where(
u'patientID', u'==', userID).stream()
for location in locations:
loc = location.to_dict()
locID.append(location.id)
loc_df = loc_df.append(pd.DataFrame(loc, index=[0]), ignore_index=True)
loc_df['id'] = locID
# In[9]:
loc_df.drop(['anxietyLevel', 'lat', 'lng', 'patientID'],
axis=1,
inplace=True)
# In[10]:
loc_df.time.apply(str)
loc_df['time'] = pd.to_datetime(loc_df['time'])
loc_df['date'] = pd.to_datetime(loc_df['time'], format='%Y:%M:%D').dt.date
loc_df['hour'] = loc_df['time'].apply(lambda time: time.strftime('%H'))
loc_df.drop(['time'], axis=1, inplace=True)
loc_df.hour = loc_df.hour.astype(int)
loc_df.date = loc_df.date.astype(str)
df.date = df.date.astype(str)
# In[11]:
dfinal = pd.merge(left=df,
right=loc_df,
how='left',
left_on=['hour', 'date'],
right_on=['hour', 'date']).ffill()
# ### Test data into model
# In[12]:
#test model
train_df = dfinal.rename(columns={'value': 'Heartrate'})
# In[13]:
Labeled_df = pd.DataFrame()
Labeled_df = trainData(train_df)
# In[14]:
Labeled_df.drop(['lon'], axis=1, inplace=True)
# In[15]:
# Replace missing values because it doesn't exist
Labeled_df['name'].fillna("Not given", inplace=True)
Labeled_df['id'].fillna("Not given", inplace=True)
# In[16]:
# Update firebase with the user anxiety level
for row in Labeled_df.itertuples():
if row.id != 'Not given':
if row.Label == 'Low' or row.Label == 'LowA':
anxietyLevel = 1
elif row.Label == 'Meduim':
anxietyLevel = 2
else:
anxietyLevel = 3
doc_ref = db.collection(u'PatientLocations').document(row.id)
doc_ref.update({u'anxietyLevel': anxietyLevel})
# ### Show the places with highest anxiety level
# In[17]:
# Show the highest level
df_high = pd.DataFrame()
df_high = Labeled_df[Labeled_df.Label == 'High']
# In[18]:
df_high.head(5)
# # Improvements
# # Recommendation
# In[19]:
docDf = pd.DataFrame()
doc_ref = db.collection(u'Patient').document(userID)
doc = doc_ref.get().to_dict()
docDf = docDf.append(pd.DataFrame(doc, index=[0]), ignore_index=True)
# In[20]:
age1 = docDf['age'].values
name1 = docDf['name'].values
emp1 = docDf['employmentStatus'].values
mar1 = docDf['maritalStatus'].values
income1 = docDf['monthlyIncome'].values
chronicD1 = docDf['chronicDiseases'].values
smoke1 = docDf['smokeCigarettes'].values
gad1 = docDf['GAD-7ScaleScore'].values
age = age1[0]
name = name1[0]
emp = emp1[0]
mar = mar1[0]
income = income1[0]
chronicD = chronicD1[0]
smoke = smoke1[0]
gad = gad1[0]
compareAge = int(age)
# In[21]:
sleepMin = Labeled_df['sleepMin'].mean()
totalSteps = Labeled_df['TotalSteps'].mean()
sleepRecomendation = False
stepsRecomendation = False
recomendedSteps = 'No recomendation'
if sleepMin < 360:
sleepRecomendation = True
if compareAge < 20 and compareAge > 11:
if totalSteps < 6000:
stepsRecomendation = True
recomendedSteps = '6000'
if compareAge < 66 and compareAge > 19:
if totalSteps < 3000:
stepsRecomendation = True
recomendedSteps = '3000'
sleepMin = sleepMin / 60
float("{:.2f}".format(sleepMin))
float("{:.2f}".format(totalSteps))
# In[22]:
# store recomendation in database
ID = random.randint(1500000, 10000000)
doc_rec = db.collection(u'LastGeneratePatientReport').document(str(ID))
doc_rec.set({
u'steps': totalSteps,
u'patientID': userID,
u'sleepMin': sleepMin,
u'sleepRecomendation': sleepRecomendation,
u'stepsRecomendation': stepsRecomendation,
u'recommended_steps': recomendedSteps
})
# ## Storage intilization
# In[113]:
firebaseConfig = {
"apiKey": "AIzaSyBoxoXwFm9TuFysjQYag0GB1NEPyBINlTU",
"authDomain": "serene-2dfd6.firebaseapp.com",
"databaseURL": "https://serene-2dfd6.firebaseio.com",
"projectId": "serene-2dfd6",
"storageBucket": "serene-2dfd6.appspot.com",
"messagingSenderId": "461213981433",
"appId": "1:461213981433:web:62428e3664182b3e58e028",
"measurementId": "G-J66VP2Y3CR"
}
firebase = pyrebase.initialize_app(firebaseConfig)
storage = firebase.storage()
# # AL
# In[114]:
# Change Label values to num, to represent them in a barchart
nums = []
for row in Labeled_df.itertuples():
if row.Label == 'Low' or row.Label == 'LowA':
nums.append(1)
elif row.Label == 'Meduim':
nums.append(2)
else:
nums.append(3)
Labeled_df['numLabel'] = nums
# In[115]:
# Get anxiety level by day and store it in a new data frame
plot_df = pd.DataFrame()
avgAnxiety = []
totalAnxiety = 0
rowCount = 1
for x in range(0, len(dates)):
for row in Labeled_df.itertuples():
if (row.date == dates[x]):
rowCount += 1
totalAnxiety += row.numLabel
avgAnxiety.append(totalAnxiety / rowCount)
plot_df['date'] = dates
plot_df['Anxiety'] = avgAnxiety
# ## To generate graphs for Android application
# In[116]:
#divide dataframe into 15 rows (2 weeks)
df1 = pd.DataFrame()
df2 = pd.DataFrame()
df3 = pd.DataFrame()
df4 = pd.DataFrame()
df5 = pd.DataFrame()
df6 = pd.DataFrame()
df7 = pd.DataFrame()
df8 = pd.DataFrame()
df9 = pd.DataFrame()
df10 = pd.DataFrame()
df11 = pd.DataFrame()
df12 = pd.DataFrame()
dfarray = []
count = 0
if (len(plot_df) > 15):
df1 = plot_df[:15]
df2 = plot_df[15:]
dfarray.append(df1)
dfarray.append(df2)
if (len(df2) > 15):
count = (df2.last_valid_index() - (len(df2) - 15))
df3 = df2[count:]
dfarray.append(df3)
if (len(df3) > 15):
count = (df3.last_valid_index() - (len(df3) - 15))
df4 = df3[count:]
dfarray.append(df4)
if (len(df4) > 15):
count = (df4.last_valid_index() - (len(df4) - 15))
df5 = df4[count:]
dfarray.append(df5)
if (len(df5) > 15):
count = (df5.last_valid_index() - (len(df5) - 15))
df6 = df5[count:]
dfarray.append(df6)
if (len(df6) > 15):
count = (df6.last_valid_index() - (len(df6) - 15))
df7 = df6[count:]
dfarray.append(df7)
if (len(df7) > 15):
count = (df7.last_valid_index() -
(len(df7) - 15))
df8 = df7[count:]
dfarray.append(df8)
if (len(df8) > 15):
count = (df8.last_valid_index() -
(len(df8) - 15))
df9 = df8[count:]
dfarray.append(df9)
if (len(df9) > 15):
count = (df9.last_valid_index() -
(len(df9) - 15))
df10 = df9[count:]
dfarray.append(df10)
if (len(df10) > 15):
count = (df10.last_valid_index() -
(len(df10) - 15))
df11 = df10[count:]
dfarray.append(df11)
if (len(df11) > 15):
count = (
df11.last_valid_index() -
(len(df11) - 15))
df12 = df11[count:]
dfarray.append(df12)
# In[117]:
# Plot AL
if (len(plot_df) < 15):
fig, ax = plt.subplots()
# Draw the stem and circle
ax.stem(plot_df.date, plot_df.Anxiety, basefmt=' ')
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
ax.set_title('Anxiety level (Throughout week)')
plt.xlabel('Date')
plt.ylabel('Low Meduim High', fontsize=12)
ax.yaxis.set_label_coords(-0.1, 0.47)
(markers, stemlines, baseline) = plt.stem(plot_df.date,
plot_df.Anxiety)
plt.setp(stemlines, linestyle="-", color="#4ba0d1", linewidth=2)
plt.setp(markers,
marker='o',
markersize=5,
markeredgecolor="#4ba0d1",
markeredgewidth=1)
plt.setp(baseline, linestyle="-", color="#4ba0d1", linewidth=0)
conv = str(x)
fig.savefig('AL.png', dpi=100)
imagePath = 'AL.png'
storage.child(userID +
"/lastGeneratedPatientReport/AL.png").put('AL.png')
os.remove('AL.png')
else:
for x in range(0, len(dfarray)):
fig, ax = plt.subplots()
# Draw the stem and circle
ax.stem(dfarray[x].date, dfarray[x].Anxiety, basefmt=' ')
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
ax.set_title('Anxiety level (Throughout week)')
plt.xlabel('Date')
plt.ylabel('Low Meduim High', fontsize=12)
ax.yaxis.set_label_coords(-0.1, 0.47)
(markers, stemlines, baseline) = plt.stem(dfarray[x].date,
dfarray[x].Anxiety)
plt.setp(stemlines, linestyle="-", color="#4ba0d1", linewidth=2)
plt.setp(markers,
marker='o',
markersize=5,
markeredgecolor="#4ba0d1",
markeredgewidth=1)
plt.setp(baseline, linestyle="-", color="#4ba0d1", linewidth=0)
conv = str(x)
fig.savefig('ALP' + str(x) + '.png', dpi=100)
imagePath = 'ALP' + str(x) + '.png'
storage.child(userID + "/lastGeneratedPatientReport/ALP" + str(x) +
'.png').put('ALP' + str(x) + '.png')
os.remove('ALP' + str(x) + '.png')
# ## To generate graphs for PDF report
# In[108]:
df1 = pd.DataFrame()
df2 = pd.DataFrame()
dfarray = []
count = 0
if (len(plot_df) > 90):
df1 = plot_df[:90]
df2 = plot_df[90:]
dfarray.append(df1)
dfarray.append(df2)
# In[111]:
# Plot AL
if (len(plot_df) <= 90):
fig, ax = plt.subplots()
# Draw the stem and circle
ax.stem(plot_df.date, plot_df.Anxiety, basefmt=' ')
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
ax.set_title('Anxiety level (Throughout week)')
plt.xlabel('Date')
plt.ylabel('Low Meduim High', fontsize=12)
ax.yaxis.set_label_coords(-0.1, 0.47)
(markers, stemlines, baseline) = plt.stem(plot_df.date,
plot_df.Anxiety)
plt.setp(stemlines, linestyle="-", color="#4ba0d1", linewidth=2)
plt.setp(markers,
marker='o',
markersize=5,
markeredgecolor="#4ba0d1",
markeredgewidth=1)
plt.setp(baseline, linestyle="-", color="#4ba0d1", linewidth=0)
conv = str(x)
fig.savefig('ALpdf.png', dpi=100)
else:
for x in range(0, len(dfarray)):
fig, ax = plt.subplots()
# Draw the stem and circle
ax.stem(dfarray[x].date, dfarray[x].Anxiety, basefmt=' ')
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
ax.set_title('Anxiety level (Throughout week)')
plt.xlabel('Date')
plt.ylabel('Low Meduim High', fontsize=12)
ax.yaxis.set_label_coords(-0.1, 0.47)
(markers, stemlines, baseline) = plt.stem(dfarray[x].date,
dfarray[x].Anxiety)
plt.setp(stemlines, linestyle="-", color="#4ba0d1", linewidth=2)
plt.setp(markers,
marker='o',
markersize=5,
markeredgecolor="#4ba0d1",
markeredgewidth=1)
plt.setp(baseline, linestyle="-", color="#4ba0d1", linewidth=0)
fig.savefig('AL' + str(x) + 'pdf.png', dpi=100)
# # Location Analysis
# In[41]:
loc = pd.DataFrame()
loc = Labeled_df[Labeled_df.name != 'Not given']
# In[42]:
loc.drop(['Heartrate', 'sleepMin', 'TotalSteps', 'id'],
axis=1,
inplace=True)
# In[43]:
names = []
Name = ""
for row in loc.itertuples():
Name = row.name
names.append(Name)
# In[44]:
new_name = pd.DataFrame()
new_name['name'] = names
# In[45]:
new_name = new_name.drop_duplicates()
# In[46]:
new_name
# In[47]:
fnames = []
fName = ""
for row in new_name.itertuples():
fName = row.name
fnames.append(fName)
# In[61]:
analysis = pd.DataFrame()
count = 0
i = 0
label = ""
locationName = ""
counts = []
labels = []
locationNames = []
for x in range(0, len(fnames)):
count = 0
locName = fnames[i]
for row in loc.itertuples():
if (locName == row.name):
if (row.Label == 'High'):
count += 1
label = row.Label
locationName = row.name
i += 1
counts.append(count)
labels.append(label)
locationNames.append(locationName)
analysis['Location'] = locationNames
analysis['Frequency'] = counts
analysis['Anxiety Level'] = labels
# In[62]:
analysis
# In[63]:
newA = analysis.drop(analysis[analysis['Frequency'] == 0].index,
inplace=True)
# In[64]:
analysis
# In[65]:
import six
# In[66]:
def render_mpl_table(data,
col_width=5.0,
row_height=0.625,
font_size=14,
header_color='#23495f',
row_colors=['#e1eff7', 'w'],
edge_color='#23495f',
bbox=[0, 0, 1, 1],
header_columns=0,
ax=None,
**kwargs):
if ax is None:
size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array(
[col_width, row_height])
fig, ax = plt.subplots(figsize=size)
ax.axis('off')
mpl_table = ax.table(cellText=data.values,
bbox=bbox,
colLabels=data.columns,
cellLoc='center',
**kwargs)
mpl_table.auto_set_font_size(False)
mpl_table.set_fontsize(font_size)
for k, cell in six.iteritems(mpl_table._cells):
cell.set_edgecolor(edge_color)
if k[0] == 0 or k[1] < header_columns:
cell.set_text_props(weight='bold', color='w')
cell.set_facecolor(header_color)
else:
cell.set_facecolor(row_colors[k[0] % len(row_colors)])
cell.alignment = 'center'
fig.savefig('Location.png', dpi=100)
return ax
# In[67]:
if (len(analysis) > 0):
render_mpl_table(analysis, header_columns=0, col_width=4)
# # Genertate patient report and save it in storage
# In[71]:
pdf = canvas.Canvas('Patient.pdf')
pdf.setTitle('Patient report')
#sleepRecomendation
#recomendedSteps
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFillColor(HexColor('#e1eff7'))
pdf.roundRect(57, 400, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 16)
pdf.setFillColor(HexColor('#23495f'))
pdf.drawString(
115, 570,
"Report Duration From: " + dates[0] + " To: " + dates[len(dates) - 1])
pdf.setFont("Helvetica-Bold", 15)
pdf.drawString(250, 540, "Improvments: ")
pdf.drawString(200, 500, "Highest day of anxiety level: ")
pdf.setFillColor(HexColor('#e1eff7'))
pdf.roundRect(57, 160, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 16)
pdf.setFillColor(HexColor('#23495f'))
pdf.drawString(130, 330, "Recommendations: ")
pdf.drawString(150, 300, "Sleep Recomendation: ")
pdf.drawString(150, 260, "Steps Recomendation: ")
pdf.setFont("Helvetica", 16)
pdf.setFillColor(black)
if (sleepRecomendation == True):
pdf.drawString(180, 280, "we reccomend you to sleep from 7-9 hours")
else:
pdf.drawString(180, 280, "keep up the good work")
if (stepsRecomendation == True):
pdf.drawString(180, 240,
"we reccomend you to walk at least " + recomendedSteps)
else:
pdf.drawString(180, 240, "keep up the good work")
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(HexColor('#808080'))
pdf.drawString(100, 650, "Anxiety Level")
if (len(plot_df) <= 90):
pdf.drawImage("ALpdf.png", 57, 400, width=485, height=200)
pdf.drawString(100, 350, "Location Analysis")
if (len(analysis) > 0):
pdf.drawImage("Location.png", 57, 100, width=485, height=200)
else:
pdf.setFont("Helvetica", 15)
pdf.setFillColor(HexColor('#23495f'))
t = pdf.beginText(130, 250)
text = [
name + " condition was stable through this period,",
"no locations with high anxiety level were detected."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
pdf.showPage()
else:
j = 400
for x in range(0, len(dfarray)):
pdf.drawImage('AL' + str(x) + 'pdf.png',
57,
j,
width=485,
height=200)
j = j - 300
pdf.showPage()
pdf.drawImage("serene .png",
150,
730,
width=300,
height=130,
mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(HexColor('#808080'))
pdf.drawString(100, 650, "Location Analysis")
if (len(analysis) > 0):
pdf.drawImage("Location.png", 57, 400, width=485, height=200)
else:
pdf.setFont("Helvetica", 15)
pdf.setFillColor(HexColor('#23495f'))
t = pdf.beginText(130, 550)
text = [
name + " condition was stable through this period,",
"no locations with high anxiety level were detected."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
pdf.save()
# In[ ]:
#new method
doct = storage.child(userID +
"/lastGeneratedPatientReport/patientReport").put(
'Patient.pdf')
# In[73]:
os.remove('Patient.pdf')
if (len(plot_df) <= 90):
os.remove('ALpdf.png')
else:
for x in range(0, len(dfarray)):
os.remove('AL' + str(x) + 'pdf.png')
# In[ ]:
return "HI"
def reportP(pid, dates, is_true):
import datetime
import pandas as pd
import numpy as np
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from firebase_admin import storage
import pyrebase
from datetime import date, timedelta
import urllib.request, json
import time
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import os
import csv
from IPython.display import display
from Model import trainData
import random
from matplotlib.patches import Ellipse
import matplotlib.patches as mpatches
import seaborn as sns
# signal processing
from scipy import signal
from scipy.ndimage import label
from scipy.stats import zscore
from scipy.interpolate import interp1d
from scipy.integrate import trapz
# misc
import warnings
#generate pdf
from reportlab.pdfgen import canvas
from reportlab.lib.colors import Color, lightblue, black
# In[2]:
if not firebase_admin._apps:
cred = credentials.Certificate("serene-firebase-adminsdk.json")
app = firebase_admin.initialize_app(
cred, {
'storageBucket': 'serene-2dfd6.appspot.com',
},
name='[DEFAULT]')
else:
app = firebase_admin.get_app()
db = firestore.client()
# In[3]:
userID = pid
GoogleCalendar = is_true
# In[4]:
today = datetime.datetime.now()
timestamp = today.strftime("%Y-%m-%d %H:%M:%S")
bucket = storage.bucket(app=app)
# ## Get data from storage and get list of dates
# In[5]:
df = pd.DataFrame()
notAvailableDates = []
# loop through the storage and get the data
sleep = []
for x in range(0, len(dates)):
#Sleep
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-sleep.json")
#download the file
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
sleepMinutes = data['summary']["totalMinutesAsleep"]
except:
notAvailableDates.append(dates[x])
pass
#Activity (Steps)
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-activity.json")
#download the file
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
steps = data['summary']["steps"]
except:
notAvailableDates.append(dates[x])
pass
#heartrate
blob = bucket.blob(userID + "/fitbitData/" + dates[x] + "/" +
dates[x] + "-heartrate.json")
u = blob.generate_signed_url(datetime.timedelta(seconds=300),
method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
df_heartrate = pd.DataFrame(
data['activities-heart-intraday']['dataset'])
df_heartrate.time.apply(str)
df_heartrate['time'] = pd.to_datetime(df_heartrate['time'])
df_heartrate['hour'] = df_heartrate['time'].apply(
lambda time: time.strftime('%H'))
df_heartrate.drop(['time'], axis=1, inplace=True)
heart_rate = df_heartrate.groupby(["hour"], as_index=False).mean()
heart_rate['sleepMin'] = sleepMinutes
heart_rate['TotalSteps'] = steps
heart_rate['date'] = dates[x]
heart_rate = heart_rate.astype({"hour": int})
except:
notAvailableDates.append(dates[x])
pass
#append dataframe
df = df.append(heart_rate, ignore_index=True)
notAvailableDates
notSyncedDates = pd.DataFrame()
notSyncedDates['date'] = notAvailableDates
# In[16]:
notSyncedDates = notSyncedDates.drop_duplicates()
# ### Get user location
# In[17]:
# get location from database
loc_df = pd.DataFrame()
locID = []
locations = db.collection(u'PatientLocations').where(
u'patientID', u'==', userID).stream()
for location in locations:
loc = location.to_dict()
locID.append(location.id)
loc_df = loc_df.append(pd.DataFrame(loc, index=[0]), ignore_index=True)
loc_df['id'] = locID
# In[18]:
loc_df.drop(['anxietyLevel', 'lat', 'lng', 'patientID'],
axis=1,
inplace=True)
# In[19]:
loc_df.time.apply(str)
loc_df['time'] = pd.to_datetime(loc_df['time'])
loc_df['date'] = pd.to_datetime(loc_df['time'], format='%Y:%M:%D').dt.date
loc_df['hour'] = loc_df['time'].apply(lambda time: time.strftime('%H'))
loc_df.drop(['time'], axis=1, inplace=True)
loc_df.hour = loc_df.hour.astype(int)
loc_df.date = loc_df.date.astype(str)
df.date = df.date.astype(str)
# In[20]:
dfinal = pd.merge(left=df,
right=loc_df,
how='left',
left_on=['hour', 'date'],
right_on=['hour', 'date']).ffill()
# ### Test data into model
# In[23]:
#test model
train_df = dfinal.rename(columns={'value': 'Heartrate'})
# In[24]:
Labeled_df = pd.DataFrame()
Labeled_df = trainData(train_df)
# In[25]:
Labeled_df.drop(['lon'], axis=1, inplace=True)
# In[26]:
# Replace missing values because it doesn't exist
Labeled_df['name'].fillna("Not given", inplace=True)
Labeled_df['id'].fillna("Not given", inplace=True)
#Labeled_df['anxiety_assigned'].fillna('Not given', inplace = True)
# In[27]:
# In[28]:
# Update firebase with the user anxiety level
for row in Labeled_df.itertuples():
if row.id != 'Not given':
if row.Label == 'Low' or row.Label == 'LowA':
anxietyLevel = '1'
elif row.Label == 'Meduim':
anxietyLevel = '2'
else:
anxietyLevel = '3'
if ((row.anxiety_assigned == False)
or (row.anxiety_assigned == 'Not given')):
doc_ref = db.collection(u'PatientLocations').document(row.id)
doc_ref.update({
u'anxietyLevel': anxietyLevel,
u'anxiety_assigned': True
})
# ### Show the places with highest anxiety level
# In[29]:
# Show the highest level
df_high = pd.DataFrame()
df_high = Labeled_df[Labeled_df.Label == 'High']
# # Recommendation
# In[30]:
docDf = pd.DataFrame()
doc_ref = db.collection(u'Patient').document(userID)
doc = doc_ref.get().to_dict()
docDf = docDf.append(pd.DataFrame(doc, index=[0]), ignore_index=True)
# In[31]:
age1 = docDf['age'].values
name1 = docDf['name'].values
emp1 = docDf['employmentStatus'].values
mar1 = docDf['maritalStatus'].values
income1 = docDf['monthlyIncome'].values
chronicD1 = docDf['chronicDiseases'].values
smoke1 = docDf['smokeCigarettes'].values
gad1 = docDf['GAD-7ScaleScore'].values
gender1 = docDf['gender'].values
age = age1[0]
name = name1[0]
emp = emp1[0]
mar = mar1[0]
income = income1[0]
chronicD = chronicD1[0]
smoke = smoke1[0]
gad = gad1[0]
gender = gender1[0]
compareAge = int(age)
# In[32]:
sleepMin = Labeled_df['sleepMin'].mean()
totalSteps = Labeled_df['TotalSteps'].astype(float).mean()
sleepRecomendation = False
stepsRecomendation = False
recomendedSteps = 'No recomendation'
if sleepMin < 360:
sleepRecomendation = True
if compareAge < 20 and compareAge > 11:
if totalSteps < 6000:
stepsRecomendation = True
recomendedSteps = '6000'
if compareAge < 66 and compareAge > 19:
if totalSteps < 3000:
stepsRecomendation = True
recomendedSteps = '3000'
sleepMin = sleepMin / 60
sleepMin = float("{:.1f}".format(sleepMin))
totalSteps = int(totalSteps)
# ## Storage intilization
# In[33]:
firebaseConfig = {
"apiKey": "AIzaSyBoxoXwFm9TuFysjQYag0GB1NEPyBINlTU",
"authDomain": "serene-2dfd6.firebaseapp.com",
"databaseURL": "https://serene-2dfd6.firebaseio.com",
"projectId": "serene-2dfd6",
"storageBucket": "serene-2dfd6.appspot.com",
"messagingSenderId": "461213981433",
"appId": "1:461213981433:web:62428e3664182b3e58e028",
"measurementId": "G-J66VP2Y3CR"
}
firebase = pyrebase.initialize_app(firebaseConfig)
storage = firebase.storage()
# # AL
# In[34]:
sns.set(rc={'axes.facecolor': '#fcfeff'})
# In[35]:
# Change Label values to num, to represent them in a barchart
nums = []
for row in Labeled_df.itertuples():
if row.Label == 'Low' or row.Label == 'LowA':
nums.append(1)
elif row.Label == 'Meduim':
nums.append(2)
else:
nums.append(3)
Labeled_df['numLabel'] = nums
# In[36]:
# Get anxiety level by day and store it in a new data frame
plot_df = pd.DataFrame()
avgAnxiety = []
totalAnxiety = 0
rowCount = 1
for x in range(0, len(dates)):
totalAnxiety = 0
rowCount = 1
for row in Labeled_df.itertuples():
if (row.date == dates[x]):
rowCount += 1
totalAnxiety += row.numLabel
avgAnxiety.append(totalAnxiety / rowCount)
plot_df['date'] = dates
plot_df['Anxiety'] = avgAnxiety
# In[37]:
#divide dataframe into 15 rows (2 weeks) max is 3 months
df1 = pd.DataFrame()
df2 = pd.DataFrame()
df3 = pd.DataFrame()
df4 = pd.DataFrame()
df5 = pd.DataFrame()
df6 = pd.DataFrame()
dfarray = []
count = 0
if (len(plot_df) > 15):
df1 = plot_df[:15]
df2 = plot_df[15:]
dfarray.append(df1)
dfarray.append(df2)
if (len(df2) > 15):
count = (df2.last_valid_index() - (len(df2) - 15))
df3 = df2[count:]
dfarray.append(df3)
if (len(df3) > 15):
count = (df3.last_valid_index() - (len(df3) - 15))
df4 = df3[count:]
dfarray.append(df4)
if (len(df4) > 15):
count = (df4.last_valid_index() - (len(df4) - 15))
df5 = df4[count:]
dfarray.append(df5)
if (len(df5) > 15):
count = (df5.last_valid_index() - (len(df5) - 15))
df6 = df5[count:]
dfarray.append(df6)
# In[38]:
# Plot AL
if (len(plot_df) <= 15):
fig, ax = plt.subplots()
c1 = '#9dd6f5'
c2 = '#4ba0d1'
c3 = '#23495f'
bar_width = 0.25
for t, y in zip(plot_df["date"], plot_df["Anxiety"]):
c = ""
if (y <= 1):
c = c1
elif (1 < y <= 2):
c = c2
elif (y > 2):
c = c3
plt.bar([t, t], [0, y], bar_width, color=c)
colors = [[c1, c1], [c2, c2], [c3, c3]]
categories = ['Low', 'Meduim', 'High']
#create dict
legend_dict = dict(zip(categories, colors))
#create patches
patchList = []
for key in legend_dict:
data_key = mpatches.Patch(facecolor=legend_dict[key][0],
edgecolor=legend_dict[key][1],
label=key)
patchList.append(data_key)
ax.legend(handles=patchList, ncol=len(categories), fontsize=12)
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
#fig.set_size_inches(15.5, 10)
plt.tight_layout()
plt.xlabel('Date')
ax.yaxis.set_label_coords(-0.02, 0.48)
fig.savefig('AL0.png', dpi=None)
imagePath = 'AL0.png'
link = storage.child(
userID + "/lastGeneratedPatientReport/AL0.png").put('AL0.png')
os.remove('AL0.png')
else:
links = []
for x in range(0, len(dfarray)):
fig, ax = plt.subplots()
c1 = '#9dd6f5'
c2 = '#4ba0d1'
c3 = '#23495f'
bar_width = 0.25
for t, y in zip(dfarray[x]["date"], dfarray[x]["Anxiety"]):
c = ""
if (y <= 1):
c = c1
elif (1 < y <= 2):
c = c2
elif (y > 2):
c = c3
plt.bar([t, t], [0, y], bar_width, color=c)
colors = [[c1, c1], [c2, c2], [c3, c3]]
categories = ['Low', 'Meduim', 'High']
#create dict
legend_dict = dict(zip(categories, colors))
#create patches
patchList = []
for key in legend_dict:
data_key = mpatches.Patch(facecolor=legend_dict[key][0],
edgecolor=legend_dict[key][1],
label=key)
patchList.append(data_key)
ax.legend(handles=patchList, ncol=len(categories), fontsize=12)
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
#fig.set_size_inches(15.5, 10)
plt.tight_layout()
plt.xlabel('Date')
ax.yaxis.set_label_coords(-0.02, 0.48)
fig.savefig('AL' + str(x) + '.png', dpi=None)
imagePath = 'AL' + str(x) + '.png'
link = storage.child(userID + "/lastGeneratedPatientReport/AL" +
str(x) + '.png').put('AL' + str(x) + '.png')
links.append(link)
os.remove('AL' + str(x) + '.png')
# ### save file to database first
# In[39]:
if (len(plot_df) <= 15):
linkDF = pd.DataFrame()
linkDF = linkDF.append(pd.DataFrame(link, index=[0]),
ignore_index=True)
token1 = linkDF['downloadTokens'].values
token = token1[0]
AlLink = storage.child(
userID + "/lastGeneratedPatientReport/AL0.png").get_url(token)
doc_rec = db.collection(u'LastGeneratePatientReport').document(
'report' + userID)
doc_rec.set({
u'average_steps': totalSteps,
u'patient_id': userID,
u'average_sleep_hours': sleepMin,
u'sleepRecomendation': sleepRecomendation,
u'stepsRecomendation': stepsRecomendation,
u'recommended_steps': recomendedSteps,
u'number_of_AL_graphs': 1,
u'AL_graph_0': AlLink,
u'recommended_sleep_hours': '7-9'
})
else:
firebase_links = []
for x in range(0, len(links)):
linkDF = pd.DataFrame()
linkDF = linkDF.append(pd.DataFrame(link, index=[0]),
ignore_index=True)
token1 = linkDF['downloadTokens'].values
token = token1[0]
AlLink = storage.child(userID + "/lastGeneratedPatientReport/AL" +
str(x) + '.png').get_url(token)
firebase_links.append(AlLink)
doc_rec = db.collection(u'LastGeneratePatientReport').document(
'report' + userID)
doc_rec.set({
u'average_steps': totalSteps,
u'patient_id': userID,
u'average_sleep_hours': sleepMin,
u'sleepRecomendation': sleepRecomendation,
u'stepsRecomendation': stepsRecomendation,
u'recommended_steps': recomendedSteps,
u'number_of_AL_graphs': len(links),
u'recommended_sleep_hours': '7-9'
})
for x in range(0, len(links)):
doc_rec = db.collection(u'LastGeneratePatientReport').document(
'report' + userID)
doc_rec.update({u'AL_graph_' + str(x): firebase_links[x]})
# ## To generate graphs for PDF report
# In[40]:
fig, ax = plt.subplots()
c1 = '#9dd6f5'
c2 = '#4ba0d1'
c3 = '#23495f'
bar_width = 0.25
for t, y in zip(plot_df["date"], plot_df["Anxiety"]):
c = ""
if (y <= 1):
c = c1
elif (1 < y <= 2):
c = c2
elif (y > 2):
c = c3
plt.bar([t, t], [0, y], bar_width, color=c)
colors = [[c1, c1], [c2, c2], [c3, c3]]
categories = ['Low', 'Meduim', 'High']
#create dict
legend_dict = dict(zip(categories, colors))
#create patches
patchList = []
for key in legend_dict:
data_key = mpatches.Patch(facecolor=legend_dict[key][0],
edgecolor=legend_dict[key][1],
label=key)
patchList.append(data_key)
ax.legend(handles=patchList, ncol=len(categories), fontsize=12)
plt.tick_params(axis='x', rotation=70)
# Start the graph at 0
ax.set_ylim(0, 3)
fig.set_size_inches(15.5, 10)
plt.tight_layout()
plt.xlabel('Date')
ax.yaxis.set_label_coords(-0.02, 0.48)
fig.savefig('ALpdf.png', dpi=None)
# # Location Analysis
# In[41]:
# get location from database
new_loc = pd.DataFrame()
locID = []
locations = db.collection(u'PatientLocations').where(
u'patientID', u'==', userID).stream()
for location in locations:
loc = location.to_dict()
locID.append(location.id)
new_loc = new_loc.append(pd.DataFrame(loc, index=[0]),
ignore_index=True)
new_loc['id'] = locID
# In[42]:
new_loc.time.apply(str)
new_loc['time'] = pd.to_datetime(new_loc['time'])
new_loc['date'] = pd.to_datetime(new_loc['time'],
format='%Y:%M:%D').dt.date
new_loc.drop(['time'], axis=1, inplace=True)
new_loc.date = new_loc.date.astype(str)
# In[43]:
new_loc = new_loc[(new_loc.date >= dates[0])
& (new_loc.date <= dates[len(dates) - 1])]
# In[44]:
names = []
Name = ""
for row in new_loc.itertuples():
Name = row.nearestLoc
names.append(Name)
# In[45]:
new_name = pd.DataFrame()
new_name['name'] = names
# In[46]:
new_name = new_name.drop_duplicates()
new_name.dropna()
# In[47]:
fnames = []
fName = ""
for row in new_name.itertuples():
fName = row.name
fnames.append(fName)
# In[48]:
analysis_EN = pd.DataFrame()
analysis_AR = pd.DataFrame()
count = 0
i = 0
#label = ""
locationName = ""
near = ''
nearLocs = []
counts = []
labels = []
locationNames = []
for x in range(0, len(fnames)):
count = 0
locName = fnames[i]
for row in new_loc.itertuples():
if (locName == row.nearestLoc):
if (row.anxietyLevel == '3'):
count += 1
label = row.anxietyLevel
locationName = row.name
near = row.nearestLoc
i += 1
counts.append(count)
#labels.append(label)
locationNames.append(locationName)
nearLocs.append(near)
analysis_EN['Location'] = locationNames
analysis_EN['Number of occurrences'] = counts
#analysis_EN ['Anxiety Level'] = labels
analysis_EN['Nearest Location'] = nearLocs
analysis_AR['الموقع'] = locationNames
analysis_AR['عدد مرات الزيارة'] = counts
#analysis_AR ['مستوى القلق'] = labels
analysis_AR['أقرب موقع'] = nearLocs
# In[49]:
newEn = analysis_EN.drop(
analysis_EN[analysis_EN['Number of occurrences'] == 0].index,
inplace=True)
newAr = analysis_AR.drop(
analysis_AR[analysis_AR['عدد مرات الزيارة'] == 0].index, inplace=True)
# In[50]:
#analysis_EN ['Anxiety Level'] = 'High'
#analysis_AR ['مستوى القلق'] = 'مرتفع'
# In[51]:
import six
import arabic_reshaper
from googletrans import Translator
from bidi.algorithm import get_display
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
# In[52]:
def render_mpl_table(data,
col_width=5.0,
row_height=0.625,
font_size=14,
tran='',
header_color='#23495f',
row_colors=['#e1eff7', 'w'],
edge_color='#23495f',
bbox=[0, 0, 1, 1],
header_columns=0,
ax=None,
**kwargs):
if ax is None:
size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array(
[col_width, row_height])
fig, ax = plt.subplots(figsize=size)
ax.axis('off')
mpl_table = ax.table(cellText=data.values,
bbox=bbox,
colLabels=data.columns,
cellLoc='center',
**kwargs)
mpl_table.auto_set_font_size(False)
mpl_table.set_fontsize(font_size)
for k, cell in six.iteritems(mpl_table._cells):
cell.set_edgecolor(edge_color)
if k[0] == 0 or k[1] < header_columns:
cell.set_text_props(weight='bold', color='w')
cell.set_facecolor(header_color)
else:
cell.set_facecolor(row_colors[k[0] % len(row_colors)])
cell.alignment = 'center'
fig.savefig(tran + '.png', dpi=100)
return ax
# In[53]:
if (len(analysis_EN) > 0):
for ind, row in analysis_EN.iterrows():
analysis_EN.loc[ind, 'Nearest Location'] = get_display(
arabic_reshaper.reshape(analysis_EN.loc[ind,
'Nearest Location']))
if (len(analysis_AR) > 0):
for ind, row in analysis_AR.iterrows():
analysis_AR.loc[ind, 'أقرب موقع'] = get_display(
arabic_reshaper.reshape(analysis_AR.loc[ind, 'أقرب موقع']))
# analysis_AR.loc[ind,'مستوى القلق']=get_display(arabic_reshaper.reshape(analysis_AR.loc[ind,'مستوى القلق']))
analysis_AR = analysis_AR.rename(
columns={'الموقع': get_display(arabic_reshaper.reshape('الموقع'))})
analysis_AR = analysis_AR.rename(
columns={
'التكرار': get_display(arabic_reshaper.reshape('التكرار'))
})
#analysis_AR = analysis_AR.rename(columns={'مستوى القلق': get_display(arabic_reshaper.reshape('مستوى القلق')) })
analysis_AR = analysis_AR.rename(
columns={
'أقرب موقع': get_display(arabic_reshaper.reshape('أقرب موقع'))
})
# In[54]:
if (len(analysis_EN) > 0):
render_mpl_table(analysis_EN,
header_columns=0,
col_width=6,
tran='Location-EN')
if (len(analysis_AR) > 0):
render_mpl_table(analysis_AR,
header_columns=0,
col_width=6,
tran='Location-AR')
# # Events with highest level of anxiety
# In[59]:
from dateutil.parser import parse
# In[60]:
if GoogleCalendar == 'true':
events_df = pd.DataFrame()
finalEvents_EN = pd.DataFrame()
finalEvents_AR = pd.DataFrame()
eventID = []
events = db.collection(u'PatientEvents').where(u'patientID', u'==',
userID).stream()
for event in events:
ev = event.to_dict()
eventID.append(event.id)
events_df = events_df.append(pd.DataFrame(ev, index=[0]),
ignore_index=True)
events_df['idEvent'] = eventID
# In[61]:
if len(events_df) > 0:
newDates = []
for row in events_df.itertuples():
newDate = parse(row.date)
newDates.append(newDate)
events_df['newDate'] = newDates
events_df = events_df.rename(columns={'name': 'eventName'})
events_df.newDate.apply(str)
events_df['date'] = pd.to_datetime(events_df['newDate'],
format='%Y:%M:%D').dt.date
events_df['hour'] = events_df['newDate'].apply(
lambda time: time.strftime('%H'))
events_df.hour = events_df.hour.astype(int)
events_df.date = events_df.date.astype(str)
Test = Labeled_df
merge_df = pd.merge(left=Test,
right=events_df,
how='left',
left_on=['hour', 'date'],
right_on=['hour', 'date']).ffill()
merge_df['eventName'].fillna("Not given", inplace=True)
merge_df = merge_df[merge_df.eventName != 'Not given']
#finalEvents_EN = pd.DataFrame()
#finalEvents_AR = pd.DataFrame()
ev_name = ''
evNames = []
#evLabels = []
evDate = []
for row in merge_df.itertuples():
if row.eventName != ev_name:
if row.Label == 'High':
ev_name = row.eventName
#ev_label = row.Label
ev_date = row.date
if (ev_name != ''):
evNames.append(ev_name)
#evLabels.append(ev_label)
evDate.append(ev_date)
finalEvents_EN['Event Name'] = evNames
#finalEvents_EN['Anxiety Level'] = evLabels
finalEvents_EN['Date'] = evDate
finalEvents_AR['اسم الحدث'] = evNames
#finalEvents_AR['مستوى القلق'] = evLabels
finalEvents_AR['تاريخ الحدث'] = evDate
finalEvents_EN = finalEvents_EN.drop_duplicates()
finalEvents_AR = finalEvents_AR.drop_duplicates()
Final_duplicated = pd.DataFrame()
eventId = ''
eventIds = []
event_Labels = []
event_names = []
hours = []
for row in merge_df.itertuples():
if eventId != row.idEvent:
eventId = row.idEvent
if eventId != '':
eventIds.append(eventId)
event_Labels.append(row.Label)
event_names.append(row.eventName)
hours.append(row.hour)
Final_duplicated['id'] = eventIds
Final_duplicated['Label'] = event_Labels
Final_duplicated['name'] = event_names
Final_duplicated['hour'] = hours
Final_duplicated = Final_duplicated.drop_duplicates(subset='id')
# Update firebase with the user anxiety level
for row in Final_duplicated.itertuples():
if row.Label == 'High':
doc_ref = db.collection(u'PatientEvents').document(row.id)
doc_ref.update({u'anxietyLevel': '3'})
else:
doc_ref = db.collection(u'PatientEvents').document(row.id)
doc_ref.delete()
checkEvents_df = pd.DataFrame()
cEventsID = []
checkEvents = db.collection(u'PatientEvents').where(
u'patientID', u'==', userID).stream()
for event in checkEvents:
cEv = event.to_dict()
cEventsID.append(event.id)
checkEvents_df = checkEvents_df.append(pd.DataFrame(cEv,
index=[0]),
ignore_index=True)
checkEvents_df['id'] = cEventsID
checkEvents_df.fillna("Not given", inplace=True)
if len(checkEvents_df) > 0:
checkEvents_df = checkEvents_df[checkEvents_df.anxietyLevel ==
'Not given']
for row in checkEvents_df.itertuples():
doc_ref = db.collection(u'PatientEvents').document(row.id)
doc_ref.delete()
# In[62]:
if (len(finalEvents_EN) > 0):
finalEvents_EN = finalEvents_EN.drop_duplicates()
finalEvents_AR = finalEvents_AR.drop_duplicates()
# In[63]:
if (len(finalEvents_EN) > 0):
for ind, row in finalEvents_EN.iterrows():
try:
finalEvents_EN.loc[ind, 'Event Name'] = get_display(
arabic_reshaper.reshape(
finalEvents_EN.loc[ind, 'Event Name']))
except:
pass
if (len(finalEvents_AR) > 0):
#finalEvents_AR['مستوى القلق'] = 'مرتفع'
for ind, row in finalEvents_AR.iterrows():
try:
finalEvents_AR.loc[ind, 'اسم الحدث'] = get_display(
arabic_reshaper.reshape(
finalEvents_AR.loc[ind, 'اسم الحدث']))
#finalEvents_AR.loc[ind,'مستوى القلق']=get_display(arabic_reshaper.reshape(finalEvents_AR.loc[ind,'مستوى القلق']))
except:
pass
finalEvents_AR = finalEvents_AR.rename(columns={
'اسم الحدث':
get_display(arabic_reshaper.reshape('اسم الحدث'))
})
#finalEvents_AR = finalEvents_AR.rename(columns={'مستوى القلق': get_display(arabic_reshaper.reshape('مستوى القلق')) })
finalEvents_AR = finalEvents_AR.rename(
columns={
'تاريخ الحدث':
get_display(arabic_reshaper.reshape('تاريخ الحدث'))
})
# In[64]:
if (len(finalEvents_EN) > 0):
render_mpl_table(finalEvents_EN,
header_columns=0,
col_width=4,
tran='Events-EN')
if (len(finalEvents_AR) > 0):
render_mpl_table(finalEvents_AR,
header_columns=0,
col_width=4,
tran='Events-AR')
# In[ ]:
# # Genertate patient report and save it in storage
# In[65]:
from reportlab.lib import colors
# In[66]:
pdfmetrics.registerFont(TTFont('Arabic', 'traditional-arabic/tradbdo.TTF'))
# In[67]:
pdf = canvas.Canvas('Patient-EN.pdf')
pdf.setTitle('Patient report')
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 620, 485, 50, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 16)
pdf.setFillColor(colors.HexColor('#23495f'))
pdf.drawString(
115, 638,
"Report Duration From: " + dates[0] + " To: " + dates[len(dates) - 1])
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 400, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#23495f'))
pdf.drawString(100, 570, "Patient Information")
pdf.setFont("Helvetica-Bold", 15)
pdf.drawString(150, 550, "Name: ")
pdf.drawString(150, 530, "Age: ")
pdf.drawString(150, 510, "Employment Status: ")
pdf.drawString(150, 490, "Martial Status: ")
pdf.drawString(150, 470, "Monthly Income: ")
pdf.drawString(150, 450, "Chronic Diseases: ")
pdf.drawString(150, 430, "Cigarette Smoker: ")
pdf.drawString(150, 410, "Gender: ")
pdf.setFont("Helvetica", 15)
pdf.setFillColor(black)
pdf.drawString(210, 550, name)
pdf.drawString(210, 530, age)
pdf.drawString(310, 510, emp)
pdf.drawString(260, 490, mar)
pdf.drawString(290, 470, income)
pdf.drawString(290, 450, chronicD)
pdf.drawString(290, 430, smoke)
pdf.drawString(220, 410, gender)
pdf.setFillColor(colors.HexColor('#bfbfbf'))
pdf.roundRect(370, 560, 125, 30, 4, fill=1, stroke=0)
pdf.setFillColorRGB(1, 1, 1)
pdf.drawString(375, 570, "GAD-7 Score = ")
pdf.setFont("Helvetica-Bold", 15)
pdf.drawString(480, 570, gad)
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 160, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Helvetica-Bold", 16)
pdf.setFillColor(colors.HexColor('#23495f'))
pdf.drawString(80, 330, "Recommendations: ")
pdf.drawString(100, 300, "Sleep Recomendation: ")
pdf.drawString(100, 220, "Steps Recomendation: ")
pdf.setFont("Helvetica", 11)
pdf.setFillColor(black)
if (sleepRecomendation == True):
t = pdf.beginText(100, 280)
text = [
"It is recommended to sleep at least 7-9 hours per day to get lower chance of anxiety.",
"Getting a good night’s sleep can improve your mental well-being and help you to better",
"manage your anxiety."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
else:
t = pdf.beginText(100, 280)
text = [
"Good News! It seems you're getting enough sleep!",
"Getting a good night’s sleep can improve your mental well-being and help you to better",
"manage your anxiety."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
if (stepsRecomendation == True):
t = pdf.beginText(100, 200)
text = [
"It is recommended to walk at least " + recomendedSteps +
" steps per day to get lower chance of anxiety.",
"Keep in mind that active people have lower rates of anxiety than sedentary people."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
else:
t = pdf.beginText(100, 200)
text = [
"Great Work! You're considered an active person based on your average steps per day.",
"Keep in mind that active people have lower rates of anxiety than sedentary people."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#808080'))
pdf.drawString(100, 650, "Anxiety Level")
pdf.drawImage("ALpdf.png", 57, 400, width=485, height=200)
pdf.drawString(100, 320, "Locations With Highest Level of Anxiety")
if (len(analysis_EN) > 0):
pdf.drawImage("Location-EN.png", 30, 200, width=570, height=100)
else:
pdf.setFont("Helvetica", 15)
pdf.setFillColor(colors.HexColor('#23495f'))
t = pdf.beginText(130, 250)
text = [
name + " condition was stable through this period,",
"no locations with high anxiety level were detected."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Helvetica-Bold", 20)
pdf.setFillColor(colors.HexColor('#808080'))
pdf.drawString(100, 650, "Events With Highest Level of Anxiety")
if (GoogleCalendar == 'true' and len(finalEvents_EN) > 0):
pdf.drawImage("Events-EN.png", 30, 500, width=550, height=100)
elif (GoogleCalendar == 'true' and len(finalEvents_EN) < 0):
pdf.setFont("Helvetica", 15)
pdf.setFillColor(colors.HexColor('#23495f'))
t = pdf.beginText(130, 550)
text = ["No events found associated with high anxiety level"]
for line in text:
t.textLine(line)
pdf.drawText(t)
else:
pdf.setFont("Helvetica", 15)
pdf.setFillColor(colors.HexColor('#23495f'))
t = pdf.beginText(130, 550)
text = [
name + " have not synced Google Calendar events,",
"with thier account."
]
for line in text:
t.textLine(line)
pdf.drawText(t)
if (len(notSyncedDates) != 0):
pdf.setFont("Helvetica", 12)
pdf.setFillColor(colors.HexColor('#d40027'))
pdf.drawString(
75, 95,
"Note: Below dates are missing, because they were not synced correctly:"
)
i = 70
for row in notSyncedDates.itertuples():
pdf.drawString(85, i, '- ' + row.date)
i = i - 20
pdf.save()
# In[68]:
def translate(text):
reshaped_text = arabic_reshaper.reshape(text)
bidi_text = get_display(reshaped_text)
return bidi_text
# In[69]:
def from_en_to_ar(text):
translator = Translator()
result = translator.translate(text, dest='ar')
return result.text
# In[70]:
pdf = canvas.Canvas('Patient-AR.pdf')
pdf.setTitle('تقرير المريض')
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 620, 485, 50, 4, fill=1, stroke=0)
pdf.setFont("Arabic", 22)
pdf.setFillColor(colors.HexColor('#23495f'))
reportDuration = translate(u'مدة التقرير من')
to = translate(u'إلى')
pdf.drawString(
125, 638, dates[len(dates) - 1] + ' ' + to + ' ' + dates[0] + ' ' +
reportDuration)
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 400, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Arabic", 22)
pdf.setFillColor(colors.HexColor('#23495f'))
patient = translate(u'معلومات المريض')
pdf.drawString(400, 570, patient)
na = translate(u'الاسم:')
ag = translate(u'العمر:')
Es = translate(u'الحالة الوظيفية:')
Ms = translate(u'الحالة الإجتماعية:')
Mi = translate(u'الدخل الشهري:')
Cd = translate(u'الأمراض المزمنة:')
Cs = translate(u'مدخن:')
ge = translate(u'الجنس:')
pdf.setFont("Arabic", 18)
pdf.drawString(445, 550, na)
pdf.drawString(446, 530, ag)
pdf.drawString(400, 510, Es)
pdf.drawString(390, 490, Ms)
pdf.drawString(400, 470, Mi)
pdf.drawString(395, 450, Cd)
pdf.drawString(445, 430, Cs)
pdf.drawString(446, 410, ge)
pdf.setFont("Arabic", 15)
pdf.setFillColor(black)
pdf.drawString(400, 550, name)
pdf.drawString(420, 530, age)
pdf.drawString(350, 510, translate(from_en_to_ar(emp)))
pdf.drawString(355, 490, translate(from_en_to_ar(mar)))
pdf.drawString(360, 470, income)
pdf.drawString(360, 450, translate(from_en_to_ar(chronicD)))
pdf.drawString(420, 430, translate(from_en_to_ar(smoke)))
pdf.drawString(420, 410, translate(from_en_to_ar(gender)))
pdf.setFillColor(colors.HexColor('#bfbfbf'))
pdf.roundRect(100, 560, 120, 30, 4, fill=1, stroke=0)
ga = translate(u'مقياس GAD-7 =')
pdf.setFillColorRGB(1, 1, 1)
pdf.drawString(120, 570, ga)
pdf.setFont("Helvetica-Bold", 15)
pdf.drawString(105, 570, gad)
pdf.setFillColor(colors.HexColor('#e1eff7'))
pdf.roundRect(57, 160, 485, 200, 4, fill=1, stroke=0)
pdf.setFont("Arabic", 22)
pdf.setFillColor(colors.HexColor('#23495f'))
rec = translate(u' التوصيات:')
stepRec = translate(u'توصيات النشاط:')
sleepRec = translate(u'توصيات النوم:')
pdf.drawString(420, 330, rec)
pdf.drawString(380, 300, sleepRec)
pdf.drawString(360, 220, stepRec)
pdf.setFont("Arabic", 15)
pdf.setFillColor(black)
if (sleepRecomendation == True):
text = translate(
u'يوصى بالنوم لمدة تتراوح بين ٧-٩ ساعات في اليوم للحصول على أقل معدل قلق.'
)
text2 = translate(
u'يمكن أن يحسن النوم الجيد ليلاً من صحتك العقلية ويساعدك على إدارة قلقك بشكل أفضل.'
)
pdf.drawString(120, 280, text)
pdf.drawString(80, 260, text2)
else:
text = translate(u'أخبار جيدة! يبدو أنك تحصل على قسط كافٍ من اليوم.')
text2 = translate(
u'يمكن أن يحسن النوم الجيد ليلاً من صحتك العقلية ويساعدك على إدارة قلقك بشكل أفضل.'
)
pdf.drawString(230, 280, text)
pdf.drawString(80, 260, text2)
if (stepsRecomendation == True):
btwnText = translate(u'خطوة في اليوم للحصول على أقل معدل قلق. ')
text = translate(u'يوصى بالمشي على الأقل')
text2 = translate(
u'تذكر أن الأشخاص الأكثر نشاطاً لديهم معدلات قلق أقل من أولئك الغير نشطين.'
)
pdf.drawString(130, 200, btwnText + recomendedSteps + text)
pdf.drawString(120, 180, text2)
else:
text = translate(
u'عمل رائع! بناءاً على معدل خطواتك في اليوم الواحد، أنت تعتبر من الأشخاص النشطين.'
)
text2 = translate(
u'تذكر أن الأشخاص الأكثر نشاطاً لديهم معدلات قلق أقل من أولئك الغير نشطين.'
)
pdf.drawString(100, 200, text)
pdf.drawString(130, 180, text2)
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Arabic", 28)
pdf.setFillColor(colors.HexColor('#808080'))
AL = translate(u'مستوى القلق')
pdf.drawString(400, 650, AL)
loc = translate(u'تحليل المواقع ذات مستوى قلق مرتفع ')
pdf.drawImage("ALpdf.png", 57, 400, width=485, height=200)
pdf.drawString(210, 320, loc)
if (len(analysis_AR) > 0):
pdf.drawImage("Location-AR.png", 10, 200, width=570, height=100)
else:
pdf.setFont("Arabic", 18)
pdf.setFillColor(colors.HexColor('#23495f'))
text = translate(
u'كانت الحالة مستقرة خلال هذه الفترة, لم يتم الكشف عن أي مواقع ذات مستوى قلق مرتفع.'
)
pdf.drawString(50, 250, text)
pdf.showPage()
pdf.drawImage("serene .png", 150, 730, width=300, height=130, mask='auto')
pdf.setFont("Arabic", 28)
pdf.setFillColor(colors.HexColor('#808080'))
EV = translate(u'تحليل الأحداث ذات مستوى قلق مرتفع ')
pdf.drawString(200, 650, EV)
if (GoogleCalendar == 'true' and len(finalEvents_AR) > 0):
pdf.drawImage("Events-AR.png", 20, 500, width=550, height=100)
elif (GoogleCalendar == 'true' and len(finalEvents_EN) < 0):
pdf.setFont("Arabic", 18)
pdf.setFillColor(colors.HexColor('#23495f'))
text = translate(u' لم يتم الكشف أي أحداث ذات مستوى قلق مرتفع.')
pdf.drawString(250, 550, text)
else:
pdf.setFont("Arabic", 18)
pdf.setFillColor(colors.HexColor('#23495f'))
text = translate(
u'المستخدم لم يقم بمزامنة أحداث تقويم قوقل بحساب سيرين.')
pdf.drawString(210, 550, text)
if (len(notSyncedDates) != 0):
pdf.setFont("Arabic", 15)
pdf.setFillColor(colors.HexColor('#d40027'))
note = translate(
u'.ملاحظة: التواريخ المذكورة أدناه غير متضمنة في التحليل لعدم مزامنة البيانات'
)
pdf.drawString(200, 95, note)
i = 70
for row in notSyncedDates.itertuples():
pdf.setFont("Helvetica", 12)
pdf.drawString(450, i, row.date + ' -')
i = i - 20
pdf.save()
# In[71]:
#new method
doct = storage.child(userID +
"/lastGeneratedPatientReport/patientReport-EN").put(
'Patient-EN.pdf')
doct = storage.child(userID +
"/lastGeneratedPatientReport/patientReport-AR").put(
'Patient-AR.pdf')
# In[72]:
os.remove('Patient-EN.pdf')
os.remove('Patient-AR.pdf')
if (len(plot_df) <= 90):
os.remove('ALpdf.png')
else:
for x in range(0, len(dfarray)):
os.remove('AL' + str(x) + 'pdf.png')
if (GoogleCalendar == 'true' and len(finalEvents_AR) > 0):
os.remove('Events-AR.png')
os.remove('Events-EN.png')
if (len(analysis_AR) > 0):
os.remove('Location-AR.png')
os.remove('Location-EN.png')
to_json = plot_df[['date', 'Anxiety']].to_json()
return to_json
def reportD(pid):
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from firebase_admin import storage
import pyrebase
import math
from datetime import date, timedelta
import urllib.request, json
import time
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import os
import csv
from IPython.display import display
from Model import trainData
import random
#from google.cloud import storage
from matplotlib.patches import Ellipse
import matplotlib.patches as mpatches
import seaborn as sns
# signal processing
from scipy import signal
from scipy.ndimage import label
from scipy.stats import zscore
from scipy.interpolate import interp1d
from scipy.integrate import trapz
# misc
import warnings
#generate pdf
from reportlab.pdfgen import canvas
from reportlab.lib.colors import Color, lightblue, black
# In[35]:
if not firebase_admin._apps:
cred = credentials.Certificate("serene-firebase-adminsdk.json")
app = firebase_admin.initialize_app(cred , {
'storageBucket': 'serene-2dfd6.appspot.com',
}, name='[DEFAULT]')
else:
app = firebase_admin.get_app()
db = firestore.client()
# In[36]:
duration = 7
userID = pid
# In[37]:
today = datetime.now()
timestamp = today.strftime("%Y-%m-%d %H:%M:%S")
bucket = storage.bucket(app=app)
# ## Get data from storage and get list of dates
# In[6]:
dates =[]
for x in range(0 ,duration):
today=date.today()
#yesterday = today - datetime.timedelta(days=1)
start_date = (today-timedelta(days=duration-x)).isoformat()
dates.append(start_date)
# In[7]:
df= pd.DataFrame()
# loop through the storage and get the data
sleep =[]
for x in range(0 ,len(dates)):
#Sleep
blob = bucket.blob(userID+"/fitbitData/"+dates[x]+"/"+dates[x]+"-sleep.json")
# download the file
u = blob.generate_signed_url(timedelta(seconds=300), method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
sleepMinutes = data['summary']["totalMinutesAsleep"]
except:
pass
#Activity (Steps)
blob = bucket.blob(userID+"/fitbitData/"+dates[x]+"/"+dates[x]+"-activity.json")
# download the file
u = blob.generate_signed_url(timedelta(seconds=300), method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
steps = data['summary']["steps"]
except:
pass
#heartrate
blob = bucket.blob(userID+"/fitbitData/"+dates[x]+"/"+dates[x]+"-heartrate.json")
u = blob.generate_signed_url(timedelta(seconds=300), method='GET')
try:
with urllib.request.urlopen(u) as url:
data = json.loads(url.read().decode())
df_heartrate = pd.DataFrame(data['activities-heart-intraday']['dataset'])
df_heartrate.time.apply(str)
df_heartrate['time'] = pd.to_datetime(df_heartrate['time'])
df_heartrate['hour'] = df_heartrate['time'].apply(lambda time: time.strftime('%H'))
df_heartrate.drop(['time'],axis=1, inplace = True)
heart_rate = df_heartrate.groupby(["hour"], as_index=False).mean()
heart_rate['sleepMin'] = sleepMinutes
heart_rate['TotalSteps'] = steps
heart_rate['date'] = dates[x]
heart_rate = heart_rate.astype({"hour": int})
except:
pass
# append dataframe
try:
df = df.append(heart_rate, ignore_index = True)
except:
pass
# ### Get user location
# In[8]:
# get location from database
loc_df = pd.DataFrame()
locID = []
locations = db.collection(u'PatientLocations').where(u'patientID', u'==', userID ).stream()
for location in locations:
loc = location.to_dict()
locID.append(location.id)
loc_df = loc_df.append(pd.DataFrame(loc,index=[0]),ignore_index=True)
loc_df['id'] = locID
# In[9]:
loc_df.drop(['anxietyLevel', 'lat','lng', 'patientID' ], axis=1, inplace = True)
# In[10]:
loc_df.time.apply(str)
loc_df['time'] = pd.to_datetime(loc_df['time'])
loc_df['date'] = pd.to_datetime(loc_df['time'], format='%Y:%M:%D').dt.date
loc_df['hour'] = loc_df['time'].apply(lambda time: time.strftime('%H'))
loc_df.drop(['time'], axis=1, inplace = True)
loc_df.hour = loc_df.hour.astype(int)
loc_df.date = loc_df.date.astype(str)
df.date = df.date.astype(str)
# In[11]:
dfinal = pd.merge(left=df,
right = loc_df,
how = 'left',
left_on=['hour','date'],
right_on=['hour','date']).ffill()
# ### Test data into model
# In[12]:
#test model
train_df = dfinal.rename(columns={'value': 'Heartrate'})
# In[13]:
Labeled_df = pd.DataFrame()
Labeled_df = trainData(train_df)
# In[14]:
# Labeled_df.drop(['lon'],axis=1, inplace = True)
# In[15]:
# Replace missing values because it doesn't exist
Labeled_df['name'].fillna("Not given", inplace=True)
Labeled_df['id'].fillna("Not given", inplace=True)
# ## Storage intilization
# In[16]:
firebaseConfig = {
"apiKey": "AIzaSyBoxoXwFm9TuFysjQYag0GB1NEPyBINlTU",
"authDomain": "serene-2dfd6.firebaseapp.com",
"databaseURL": "https://serene-2dfd6.firebaseio.com",
"projectId": "serene-2dfd6",
"storageBucket": "serene-2dfd6.appspot.com",
"messagingSenderId": "461213981433",
"appId": "1:461213981433:web:62428e3664182b3e58e028",
"measurementId": "G-J66VP2Y3CR"
}
firebase = pyrebase.initialize_app(firebaseConfig)
storage = firebase.storage()
# # AL
# In[17]:
sns.set( rc={'axes.facecolor': '#fcfeff'})
# In[18]:
# i added this code here because it will result an error down because of the datetime
yesterday = today - timedelta(days=1)
timestamp = yesterday.strftime("%Y-%m-%d")
from datetime import datetime
# In[19]:
# Change Label values to num, to represent them in a barchart
nums=[]
for row in Labeled_df.itertuples():
if row.Label == 'Low' or row.Label == 'LowA':
nums.append(1)
elif row.Label == 'Meduim':
nums.append(2)
else:
nums.append(3)
Labeled_df['numLabel'] = nums
# In[20]:
# Get anxiety level by day and store it in a new data frame
plot_df = pd.DataFrame()
avgAnxiety = []
days = []
totalAnxiety = 0
rowCount = 1
for x in range(0 ,len(dates)):
totalAnxiety = 0
rowCount = 1
for row in Labeled_df.itertuples():
if (row.date == dates[x]):
rowCount += 1
totalAnxiety += row.numLabel
day = (datetime.strptime(dates[x], "%Y-%m-%d")).strftime("%a")
days.append(day)
avgAnxiety.append(totalAnxiety/rowCount)
plot_df['date'] = dates
plot_df['Anxiety'] = avgAnxiety
plot_df['day'] = days
# In[42]:
# In[21]:
fig, ax = plt.subplots()
c1 = '#9dd6f5'
c2 = '#4ba0d1'
c3 = '#23495f'
bar_width = 0.25
for t, y in zip(plot_df["day"], plot_df["Anxiety"]):
c=""
if(y <= 1):
c = c1
elif (1 < y <= 2):
c = c2
elif ( y > 2):
c = c3
plt.bar([t,t], [0,y],bar_width, color=c)
colors = [[c1,c1],[c2,c2],[c3,c3]]
categories = ['Low','Meduim','High']
#create dict
legend_dict=dict(zip(categories,colors))
#create patches
patchList = []
for key in legend_dict:
data_key = mpatches.Patch(facecolor=legend_dict[key][0],
edgecolor=legend_dict[key][1], label=key)
patchList.append(data_key)
ax.legend(handles=patchList,ncol=len(categories), fontsize=12)
plt.tick_params(axis='x')
# Start the graph at 0
ax.set_ylim(0, 3)
#fig.set_size_inches(15.5, 10)
plt.tight_layout()
#plt.xlabel('Date')
ax.yaxis.set_label_coords(-0.02, 0.48)
fig.savefig('AL.png', dpi = None)
imagePath = "AL.png"
link = storage.child(userID+"/dailyReport/AL.png").put('AL.png')
os.remove("AL.png")
# In[22]:
linkDF = pd.DataFrame()
linkDF = linkDF.append(pd.DataFrame(link,index=[0]),ignore_index=True)
token1 = linkDF['downloadTokens'].values
token = token1[0]
AlLink = storage.child(userID+"/dailyReport/AL.png").get_url(token)
# # Improvement
# In[77]:
# get yesterday improvement
#today = datetime.datetime.now()
#yesterday = today - datetime.timedelta(days=1)
#timestamp = yesterday.strftime("%Y-%m-%d")
al = float(plot_df[plot_df.date == timestamp].Anxiety)
today_al = float("{:.2f}".format(al))
yesterday = plot_df.iloc[5]['Anxiety']
today = plot_df.iloc[6]['Anxiety']
imp = ((yesterday - today)/3)*100
# get before yesterday to calculate the improvement
try:
doc_ref = db.collection(u'DailyReport').document('daily'+userID)
doc = doc_ref.get().to_dict()
prev = float(doc['anxiety_level'])
# calculate the improvement
#1. get today Al
improvement= ((prev - today_al)/3)*100
doc_rec = db.collection(u'DailyReport').document('daily'+userID)
doc_rec.set({
u'AL_graph': AlLink,
u'improvement': float("{:.2f}".format(improvement)),#float("{:.2f}".format(improvement))
u'anxiety_level' : float(today_al),
u'date': str(datetime.now().strftime("%Y-%m-%d"))
})
yesterday = plot_df.iloc[6]['Anxiety']
today = plot_df.iloc[5]['Anxiety']
except:
doc_rec = db.collection(u'DailyReport').document('daily'+userID)
doc_rec.set({
u'AL_graph': AlLink,
u'improvement': 404 ,
u'anxiety_level' : float(today_al),
u'date': str(datetime.now().strftime("%Y-%m-%d"))
})
# In[ ]:
json_df = plot_df[['Anxiety','date']].to_json()
return json_df
def updateCatch():
try:
data = request.json
print(data)
print(data['image'])
convert(data['image'], data['imageFileName'])
imagePath = BASE_PATH + data['imageFileName']
storage.child('fishes/{}'.format(data['imageFileName'])).put(imagePath)
fish_url = storage.child('fishes/{}'.format(
data['imageFileName'])).get_url(None)
print(fish_url)
data2 = {
'date':
data['date'],
'description':
data['description'],
'image':
fish_url,
'hours':
float(data['hours']),
'latitude':
float(data['latitude']),
'longitude':
float(data['longitude']),
'name':
data['name'],
'weight':
float(data['weight']),
'number':
data['number'],
'catchId':
int(''.join(
[str(random.randint(0, 999)).zfill(3) for _ in range(2)]))
}
ref = userCatches.document(str(data['number']))
ref.update({u'catches': firestore.ArrayUnion([data2])})
print(data2)
data1 = {
'latitude':
float(data2['latitude']),
'longitude':
float(data2['longitude']),
'totalWeight':
float(data2['weight']),
'date':
data2['date'],
'catchId':
data2['catchId'],
'hours':
data2['hours'],
'catches': [{
'cost': 200,
'fishType': 'Shark',
'weight': 50,
'quantity': 10
}, {
'cost': 200,
'fishType': 'Whale',
'weight': 50,
'quantity': 10
}, {
'cost': 200,
'fishType': 'Dolphin',
'weight': 50,
'quantity': 10
}, {
'cost': 200,
'fishType': 'Bluefish',
'weight': 50,
'quantity': 10
}]
}
print(data1)
fishData.document(str(data1['catchId'])).set(data1)
return jsonify({"success": True})
except Exception as e:
return jsonify({"success": False}), 400
auth = firebase.auth()
email = "*****@*****.**"
password = "******"
camera = PiCamera()
while True:
try:
if GPIO.input(10) == GPIO.HIGH:
print("food pushed")
now = datetime.now()
dt = now.strftime("%d%m%Y%H:%M:%S")
food = dt + ".jpg"
camera.capture(food)
print(food + " saved")
storage.child(food).put(food)
user = auth.sign_in_with_email_and_password(email, password)
url = storage.child("fried.jpeg").get_url(user['idToken'])
print(url)
image = {'img': url, 'addedOn': str(date.today())}
db.collection('images').add(image)
print("Image sent")
os.remove(food)
print("File Removed")
sleep(2)
elif GPIO.input(11) == GPIO.HIGH:
print("barcode pushed")
now = datetime.now()
dt = now.strftime("%d%m%Y%H:%M:%S")
barcode = dt + ".jpg"
camera.capture(barcode)
config = {
"apiKey": "AIzaSyDuQJ6RZE6dWs7vToVDCJIRvEWDmMy9GKs",
"authDomain": "pmisha-misha.firebaseapp.com",
"databaseURL": "https://misha-misha.firebaseio.com",
"projectId": "misha-misha",
"storageBucket": "misha-misha.appspot.com",
"serviceAccount": "ServiceAccountKey.json"
}
firebase = pyrebase.initialize_app(config)
storage = firebase.storage()
path_on_cloud = "images/uploadedFile1"
path_local = "downloaded.jpeg"
storage.child(path_on_cloud).download(path_local)
#PUSHING NUMBER
val = 2
doc_ref = db.collection(u'data').document(u'TF')
doc_ref.set({
u'TF' : val,
})
