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How to use d_b method in fMBT

Best Python code snippet using fMBT_python

models.py

Source:models.py

1"""Class for user and bookmark template"""2#from app import DB3def get_users(d_b):4    """This function fetches users table.5    Prevents a cyclical import"""6    class Users(d_b.Model):7        """Can create a Person with username and score. Letter won't be used"""8        id = d_b.Column(d_b.String(80), primary_key=True)9        email = d_b.Column(d_b.String(80), unique=True, nullable=False)10        firstName = d_b.Column(d_b.String, unique=False, nullable=False)11        familyName = d_b.Column(d_b.String(80), unique=False, nullable=False)12        imageURL = d_b.Column(d_b.String(80), unique=False, nullable=True)13        def __repr__(self):14            '''This function returns the users class'''15            return '<User %r>' % self.id16    return Users17def get_bookmarks(d_b):18    """Fetches Bookmarks Table"""19    class Bookmarks(d_b.Model):20        """Can create a Bookmark with username and event_id"""21        id = d_b.Column(d_b.Integer, primary_key=True)22        clientId = d_b.Column(d_b.String(80), unique=False, nullable=False)23        event_id = d_b.Column(d_b.String(80), unique=False, nullable=False)24        def __repr__(self):25            return '<Bookmark %r>' % self.id26    return Bookmarks27def get_likes_dislikes(d_b):28    '''Can create DB with eventID, num of likes and dislikes'''29    class LikesDislikes(d_b.Model):30        '''Can create DB with eventID, num of likes and dislikes'''31        eventID = d_b.Column(d_b.String(80), primary_key=True)32        likes = d_b.Column(d_b.Integer, nullable=False)33        dislikes = d_b.Column(d_b.Integer, nullable=False)34        def __repr__(self):35            return '<LikesDislikes %r>' % self.eventID36    return LikesDislikes37def get_comments(d_b):38    """ defines comment table"""39    class Comments(d_b.Model):40        """creates a comment"""41        commentId = d_b.Column(d_b.String(80), primary_key=True)42        event_id = d_b.Column(d_b.String(80), nullable=False)43        username = d_b.Column(d_b.String(80), nullable=False)44        text = d_b.Column(d_b.String(150), nullable=False)45        head = d_b.Column(d_b.String(80), nullable=True)46        tail = d_b.Column(d_b.String(80), nullable=True)47        depth = d_b.Column(d_b.Integer, nullable=False)48        def __repr__(self):49            return '<Comment %r>' % self.commentId...

plot.py

Source:plot.py

1import json2import numpy as np3from sklearn.linear_model import LinearRegression4from sklearn.ensemble import RandomForestRegressor5import joblib6from scipy.ndimage import median_filter as mf7f1 = open("input.json")8inputs = json.load(f1)9a_b = []10d_b = []11gt_theta = []12gt_dis = []13af = []14for i in inputs:15    a_b.append(i["angle_base"])16    d_b.append(i["distance"])17    gt_theta.append(i["sptheta"])18    gt_dis.append(i["spdis"])19    af.append(i["angleFiltered"])20d_b = np.asarray(d_b)21gt_dis = np.asarray(gt_dis)22a_b = np.asarray(a_b)23gt_dis = gt_dis[np.where(d_b > 0.7)[0]]24d_b = d_b[np.where(d_b > 0.7)[0]]25a_b = a_b[np.where(d_b > 0.7)[0]]26gt_dis = gt_dis[np.where(d_b < 4)[0]].reshape((-1, 1))27d_b = d_b[np.where(d_b < 4)[0]].reshape((-1, 1))28a_b = a_b[np.where(d_b < 4)[0]].reshape((-1, 1))29a_b = mf(a_b, size=10)30d_b = mf(d_b, size=10)31X = np.concatenate((d_b, a_b), 1)32reg = LinearRegression().fit(X, gt_dis)33d_pred = reg.predict(X)34regr = RandomForestRegressor(max_depth=10, random_state=0)35# regr = joblib.load('rf5.joblib')36regr.fit(X, gt_dis)37joblib.dump(regr, "rf10.joblib", protocol=2)38ranFor_pred = regr.predict(X)39L = np.linalg.norm(ranFor_pred.reshape((-1, 1)) - gt_dis)40print(L)41# plt.plot(ranFor_pred)42# # plt.plot(d_pred)43# plt.plot(gt_dis)44# # plt.plot(a_b)...

encr3pt.py

Source:encr3pt.py

1from PIL import Image, ImageDraw2import os3import sys4from random import SystemRandom5random = SystemRandom()6xrange = range7I3 = Image.open8if len(sys.argv) != 2:9    exit()10fl = str(sys.argv[1])11O1 = os.path.isfile12O2 = os.path.splitext13if not O1(fl):14    exit()15i = I3(fl)16f, e = O2(fl)17O_A = f+"_A.png"18O_B = f+"_B.png"19i = i.convert('1')20I1 = Image.new21I2 = ImageDraw.Draw22a = i.size[0]*223b = i.size[1]*224i_A = I1('1', (a, b))25d_A = I2(i_A)26i_B = I1('1', (a, b))27d_B = I2(i_B)28ps = ((1, 1, 0, 0), (1, 0, 1, 0), (1, 0, 0, 1),29      (0, 1, 1, 0), (0, 1, 0, 1), (0, 0, 1, 1))30for x in xrange(0, int(a/2)):31    for y in xrange(0, int(b/2)):32        pix = i.getpixel((x, y))33        p = random.choice(ps)34        d_A.point((x*2, y*2), p[0])35        d_A.point((x*2+1, y*2), p[1])36        d_A.point((x*2, y*2+1), p[2])37        d_A.point((x*2+1, y*2+1), p[3])38        if pix == 0:39            d_B.point((x*2, y*2), 1-p[0])40            d_B.point((x*2+1, y*2), 1-p[1])41            d_B.point((x*2, y*2+1), 1-p[2])42            d_B.point((x*2+1, y*2+1), 1-p[3])43        else:44            d_B.point((x*2, y*2), p[0])45            d_B.point((x*2+1, y*2), p[1])46            d_B.point((x*2, y*2+1), p[2])47            d_B.point((x*2+1, y*2+1), p[3])48i_A.save(O_A, 'PNG')...

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