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How to use check_test method in avocado
Best Python code snippet using avocado_python
esercizi04.py
Source:esercizi04.py 
...11 ENDC = '\033[0m'12 BOLD = '\033[1m'13 UNDERLINE = '\033[4m'14# Esegue un test e controlla il risultato15def check_test(func: Callable, expected: Any, *args: List[Any]):16 func_str = func.__name__17 args_str = ', '.join(repr(arg) for arg in args)18 try:19 result = func(*args)20 result_str = repr(result)21 expected_str = repr(expected)22 test_outcome = "succeeded" if (result==expected) else "failed"23 color = bcolors.OKGREEN if (result==expected) else bcolors.FAIL24 print(f'{color}Test on {func_str} on input {args_str} {test_outcome}. Output: {result_str} Expected: {expected_str}')25 except BaseException as error:26 error_str = repr(error)27 print(f'{bcolors.FAIL}ERROR: {func_str}({args_str}) => {error_str}')28# Scrivere una funzione che controlla la validita' di una password.29# La password deve avere:30# - Almeno una lettera fra [a-z] e una lettera fra [A-Z]31# - Almeno un numero fra [0-9]32# - Almeno un carattere fra [$#@]33# - Essere lunga almeno 6 caratteri34# - Essere lunga non piu' di 16 caratteri35# - La password non è valida se contiene caratteri diversi da quelli specificati sopra36# o se viola una delle regole specificate.37# La funzione restituisce true/false a seconda se la password sia valida o meno.38def check_pwd(pwd: str) -> bool:39 _min = lambda x: 'a' <= x <= 'z'40 _MAX = lambda x: 'A' <= x <= 'Z'41 _num = lambda x: '0' <= x <= '9'42 simb = '$#@'43 all_used = [False, False, False, False]44 for c in pwd:45 if _min(c):46 all_used[0] = True47 continue48 elif _MAX(c):49 all_used[1] = True50 continue51 elif _num(c):52 all_used[2] = True53 continue54 elif c in simb:55 all_used[3] = True56 continue57 else:58 return False59 return 6 <= len(pwd) <= 16 and all(all_used)60# Scrivere una funzione che data una tupla (x, y, z)61# restituisca la tupla (z+1, x-1, y+2)62def tuple_ex(t: tuple) -> tuple:63 return t[2] + 1, t[0] - 1, t[1] + 264# Scrivere una funzione che calcola l'intersezione fra due liste.65# Date due liste, deve restituire una nuova lista contenente solo gli66# elementi presenti in entrambe le liste.67def intersect(a: list, b: list) -> list:68 res = []69 minore = a if len(a) <= len(b) else b70 magg = a if len(a) > len(b) else b71 for i, el in enumerate(minore):72 if el in magg:73 res.append(el)74 return res75# Scrivere una funzione che data una lista contenente valori >= 0, 76# crei una nuova lista contentente soltanto gli elementi della lista 77# originale tali che soddisfano la seguente proprietà :78# lista[i] > 2*media(lista[0:i])79# (Il primo elemento non viene quindi mai inserito)80# Ad esempio, si consideri la lista [5, 3, 10, 0]81# Il primo elemento è 5. Non viene inserito82# Il secondo elemento è 3, e la media degli elementi nel range [0, 0] è 5. Poichè 3 < 5*2, l'elemento non viene inserito nella nuova lista83# Il terzo elemento è 10, e la media degli elementi nel range [0, 1] è 4. Poichè 10 > 4*2, l'elemento viene inserito nella nuova lista84# Il quarto elemento è 0, e la media degli elementi nel range [0, 2] è 6. Poichè 0 < 6*2, l'elemento non viene inserito nella nuova lista85def media(lista):86 if len(lista) > 0:87 return sum(lista) / len(lista)88def remove_avg(a: list) -> list:89 res = []90 for i, x in enumerate(a):91 if i == 0:92 continue93 if x > 2*media(a[0:i]):94 res.append(x)95 return res96# Data una lista di interi (ciascun intero è compreso fra 0 e 99), scrivere una97# funzione che restituisca una lista di tuple (x, y),98# dove x è un intero, e y è il numero di volte che questo99# intero appare nella lista originale.100# La lista di tuple deve essere ordinata in base al primo elemento.101# Ad esempio, per l'input [5, 4, 1, 4], restituisce la lista [(1, 1), (4, 2), (5, 1)]102# (ordinata in base al primo elemento perché 1 < 4 < 5)103def frequency(a: list) -> list:104 els = list(dict.fromkeys(a))105 els.sort()106 res = [(x, 0) for x in els]107 for i, el in enumerate(els):108 for el2 in a:109 if el == el2:110 res[i] = (el, res[i][1]+1)111 return res112# Scrivere una funzione che restituisce True113# se la lista è palindroma, o False altrimenti114def is_palindrome(a: list) -> bool:115 rev = a.copy()116 rev.reverse()117 for i, el in enumerate(a):118 if el != rev[i]:119 return False120 return True121# Scrivere una funzione che prende in input una lista, e 122# restituisce True se la lista è ordinata in ordine123# crescente o decrescente, e False altrimenti.124# Suggerimento: fare attenzione ai valori duplicati125# Utilizzare un solo ciclo e non utilizzare sorted/sort.126def is_sorted(a: list) -> bool:127 if len(a) <= 1:128 return True129 i = 1130 while i < len(a):131 if a[0] > a[i]:132 a.reverse()133 if a[0] < a[i]:134 break135 i += 1136 for i, el in enumerate(a[:-1]):137 if el > a[i+1]:138 return False139 return True140# Scrivere una funzione che restituisce True se una lista di interi141# è composta da una prima parte ordinata in modo crescente, seguita142# da una seconda parte ordinata in modo decrescente (o viceversa).143# Le due parti non devono avere necessariamente la stessa lunghezza.144# Utilizzare un solo ciclo e non utilizzare sorted/sort, ne la funzione145# is_sorted implementata precedentemente.146# Si assuma che la lista abbia almeno sempre 3 elementi.147def is_sorted_half(a: list) -> bool:148 changed = False149 verso = 0150 for i in range(len(a)-1):151 if verso == 0:152 if a[i] > a[i+1]:153 verso = 2154 if a[i] < a[i + 1]:155 verso = 1156 elif verso == 1:157 if not changed:158 if a[i] > a[i+1]:159 changed = True160 verso = 2161 else:162 if a[i] > a[i+1]:163 return False164 elif verso == 2:165 if not changed:166 if a[i] < a[i+1]:167 changed = True168 verso = 2169 else:170 if a[i] < a[i+1]:171 return False172 return changed173# Test funzioni174check_test(check_pwd, False, "a")175check_test(check_pwd, False, "000000000000000000")176check_test(check_pwd, False, "almeno6")177check_test(check_pwd, False, "Aa@09asng2/")178check_test(check_pwd, True, "Aa@09asng2")179check_test(tuple_ex, (3, -2, 1), (-1, -1, 2))180check_test(intersect, [2, 3], [1, 2, 3], [2, 3, 4])181check_test(intersect, [], [1, 2, 3], [10, 11, 12])182check_test(intersect, [], [1, 2, 3], [])183check_test(intersect, [], [], [1, 2, 3])184check_test(remove_avg, [10], [5, 3, 10, 0])185check_test(remove_avg, [20, 1000], [5, 20, 10, 1000])186check_test(remove_avg, [], [])187check_test(frequency, [(1, 1), (4, 2), (5, 1)], [5, 4, 1, 4])188check_test(frequency, [(0, 1), (23, 3), (99, 1)], [23, 99, 0, 23, 23])189check_test(is_palindrome, True, [])190check_test(is_palindrome, True, [1])191check_test(is_palindrome, True, [1, 2, 8, 2, 1])192check_test(is_palindrome, True, [1, 2, 8, 8, 2, 1])193check_test(is_palindrome, False, [1, 3, 8, 8, 2, 1])194check_test(is_sorted, True, [1])195check_test(is_sorted, True, [1, 1, 1])196check_test(is_sorted, True, [1, 2, 3, 4])197check_test(is_sorted, True, [4, 3, 2, 1])198check_test(is_sorted, True, [1, 1, 2, 3, 3, 4])199check_test(is_sorted, True, [4, 4, 3, 2, 2, 1])200check_test(is_sorted, False, [1, 1, 3, 3, 2])201check_test(is_sorted, False, [4, 4, 3, 3, 5])202check_test(is_sorted_half, False, [1, 2, 3])203check_test(is_sorted_half, False, [3, 2, 1])204check_test(is_sorted_half, True, [1, 3, 2])205check_test(is_sorted_half, True, [3, 1, 2])...
Vanilla.py
Source:Vanilla.py
1# Libraries2import csv3import matplotlib.pyplot as plt4import cv25import tensorflow as tf6import keras7from skimage.measure import compare_ssim8from tensorflow.keras.models import Sequential9from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization10from keras.layers import Conv2D, MaxPooling2D, InputLayer, UpSampling2D, Conv2DTranspose, LeakyReLU, AveragePooling2D, \11 GlobalAveragePooling2D, GlobalMaxPooling2D, Input, RepeatVector, Reshape, concatenate12import matplotlib.pyplot as plt13import numpy as np141516images_gray = np.load("gray_scale.npy")17images_lab = np.load("ab1.npy")181920# gray_scale21size_train = 400022size_test = 10023size_test_begin = 024change_pic = 025X_train_l = images_gray[0+change_pic:change_pic+size_train]26X_train_l = X_train_l.reshape(size_train, 224, 224, 1)27X_test_l = images_gray[size_test_begin + size_train+change_pic:change_pic+size_train + size_test_begin + size_test]28X_test_l = X_test_l.reshape(size_test, 224, 224, 1)2930# colored31X_train_lab = images_lab[0+change_pic:change_pic+size_train]32X_train_lab = X_train_lab.reshape(size_train, 224, 224, 2)33X_test_lab = images_lab[size_test_begin + size_train+change_pic:change_pic+size_train + size_test_begin + size_test]34X_test_lab = X_test_lab.reshape(size_test, 224, 224, 2)3536X_gray_chick = np.zeros((1,224,224,2), dtype=float)37X_gray_chick = X_gray_chick.__add__(128)38check_test = 039X_test_lab_temp = X_test_lab40X_test_l_temp = X_test_l41while check_test < size_test:42 if sum(sum(sum(sum(X_gray_chick == X_test_lab_temp[check_test]))))/100352 > 0.4:43 X_test_l = np.zeros((size_test-1, 224, 224, 1), dtype=float)44 X_test_l[0:check_test] = X_test_l_temp[0:check_test]45 X_test_l[check_test:] = X_test_l_temp[(check_test+1):]46 X_test_l_temp = X_test_l4748 X_test_lab = np.zeros((size_test-1, 224, 224, 2), dtype=float)49 X_test_lab[0:check_test] = X_test_lab_temp[0:check_test]50 X_test_lab[check_test:] = X_test_lab_temp[check_test + 1:]51 X_test_lab_temp = X_test_lab52 size_test = size_test-153 check_test = check_test - 154 check_test = check_test + 15556check_train = 057X_train_lab_temp = X_train_lab58X_train_l_temp = X_train_l59while check_train < size_train:60 if sum(sum(sum(sum(X_gray_chick == X_train_lab_temp[check_train]))))/100352 > 0.4:61 X_train_l = np.zeros((size_train-1, 224, 224, 1), dtype=float)62 X_train_l[0:check_train] = X_train_l_temp[0:check_train]63 X_train_l[check_train:] = X_train_l_temp[(check_train+1):]64 X_train_l_temp = X_train_l6566 X_train_lab = np.zeros((size_train-1, 224, 224, 2), dtype=float)67 X_train_lab[0:check_train] = X_train_lab_temp[0:check_train]68 X_train_lab[check_train:] = X_train_lab_temp[check_train + 1:]69 X_train_lab_temp = X_train_lab70 size_train = size_train-171 check_train = check_train - 172 check_train = check_train + 17374X_test_l = X_test_l/25575X_train_l = X_train_l/2557677X_train_l = tf.cast(X_train_l, tf.float32)78X_test_l = tf.cast(X_test_l, tf.float32)7980# #Vanilla CNN Model 181# model = Sequential()82# model.add(Conv2D(strides=1,filters=32,kernel_size=3,activation='relu',use_bias=True,padding='valid'))83# model.add(Conv2D(strides=1,filters=16,kernel_size=3,activation='relu',use_bias=True,padding='valid'))84# model.add(Conv2DTranspose(strides=1,filters=2,kernel_size=5,activation='relu',use_bias=True,padding='valid'))8586#Vanilla CNN Model 287model = Sequential()88model.add(Conv2D(strides=1,filters=32,kernel_size=3,activation='relu',use_bias=True,padding='valid'))89model.add(MaxPooling2D(pool_size=(2,2),strides=2))90model.add(Conv2D(strides=1,filters=16,kernel_size=3,activation='relu',use_bias=True,padding='valid'))91model.add(Conv2DTranspose(strides=2,filters=2,kernel_size=8,activation='relu',use_bias=True,padding='valid'))9293model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])94history = model.fit(x=X_train_l, y=X_train_lab, validation_split=0.2, epochs=250, batch_size=16)95model.summary()96xx = model.predict(X_test_l)97xx = xx.reshape(size_test, 224, 224, 2)9899h = history100plt.plot(h.history['loss'])101plt.plot(h.history['val_loss'])102plt.xlabel('epoch')103plt.ylabel('loss')104plt.title('Model Loss')105plt.legend(['loss', 'val_loss'])106plt.axis([0, 250, 0, 20000])107plt.show()108plt.plot(h.history['accuracy'])109plt.plot(h.history['val_accuracy'])110plt.legend(['accuracy', 'val_accuracy'])111plt.xlabel('epoch')112plt.ylabel('accuracy')113plt.title('Model Accuracy')114plt.show()115for k in range(size_test):116 # predicted image117 img = np.zeros((224, 224, 3))118 kor = X_test_l[k]*255119120 kor = kor[:, :, 0]121 img[:, :, 1:] = xx[k]122 img[:, :, 0] = kor123124 img = img.astype('uint8')125 img_ = cv2.cvtColor(img, cv2.COLOR_LAB2RGB)126 plt.imshow(img_)127 plt.show()128129 # actual image130 img_truth = np.zeros((224, 224, 3))131 kor_truth = X_test_l[k]*255132 kor_truth = kor_truth[:, :, 0]133 img_truth[:, :, 0] = kor_truth134 img_truth[:, :, 1:] = X_test_lab[k]135136 img_truth = img_truth.astype('uint8')137 img_truth_ = cv2.cvtColor(img_truth, cv2.COLOR_LAB2RGB)138 plt.imshow(img_truth_)139 plt.show()140 (score, diff) = compare_ssim(img_truth, img_truth_, full=True, multichannel=True)141 diff = (diff * 255).astype("uint8")142 print("SSIM: {}".format(score))143 print("PSNR: {}".format(cv2.PSNR(img_truth, img_truth_)))144 m = tf.keras.metrics.Accuracy()145 m.update_state(img_truth, img_truth_)
...
main.py
Source:main.py
...4import sys5DEFAULT = "\033[39m"6GREEN = "\033[32m"7RED = "\033[31m"8def check_test(port: int, name_test: str, test: Callable) -> None:9 try:10 str_result = test(port)11 except:12 str_result = "couldn't connect"13 if (len(str_result) == 0):14 check = GREEN + "V" + DEFAULT15 else:16 check = RED + "X" + DEFAULT17 18 print("test : {:65} | result : {} {}".format(name_test, check, str_result))19def run(option: str, port: int) -> None:20 """run tests"""21 22 print("All tests are done requesting on http://localhost:port\n")23 if (option == "basic"):24 check_test(port, "GET / ", simple_get_index)25 check_test(port, "GET /auto autoindex", get_autoindex_subdir)26 check_test(port, "GET /forbidden (403) ", get_forbidden_dir)27 check_test(port, "GET non_existing_dir (404) ", get_404)28 check_test(port, "GET / ports 8080 and 8081", get_index_two_ports)29 check_test(port, "GET / 1 worker 50 times", fifty_get_root)30 check_test(port, "POST / method not authorized (405) ", wrong_method)31 32 check_test(port, "POST /post ", simple_post)33 check_test(port, "POST /post content length = 0", post_size_0)34 check_test(port, "POST /post request too big (413)", post_too_big)35 check_test(port, "POST /post_upload upload in /upload", post_with_upload)36 37 check_test(port, "DELETE /delete_folder/index.html", delete)38 check_test(port, "DELETE /delete_folder/index.html (404)", delete_already_deleted)39 40 check_test(port, "GET http://webserv:port use of server_name ", server_name)41 check_test(port, "GET /redirect_me/please redirect 301 in /redirection/index.html", redirect_get_ok)42 check_test(port, "GET /redirect_me/wrong redirect 301 in /redirection/lol.html", redirect_get_non_existing)43 check_test(port, "POST /post_upload upload in /upload", post_with_upload)44 check_test(port, "POST /post_upload upload in /upload", post_with_upload)45 check_test(port, "POST /post_upload upload in /upload", post_with_upload)46 check_test(port, "GET /use_location_root/", get_loc_root)47 check_test(port, "POST /use_location_root/", post_loc_root)48 49 print("\nCGI TESTS & CHUNKED REQUESTS:")50 check_test(port, "GET /cgi/file.tester ", cgi_tester_get)51 check_test(port, "POST /cgi/file.tester", cgi_tester_post)52 check_test(port, "GET /use_location_root_cgi/file.tester ", cgi_tester_get_loc_root)53 check_test(port, "POST /use_location_root_cgi/file.tester", cgi_tester_post_loc_root)54 check_test(port, "POST /cgi/file.tester 20 bits", chunked_post_no_upload)55 check_test(port, "POST /cgi/file.tester 500 bits", chunked_post_no_upload_size_500)56 check_test(port, "POST /post-upload/another_file.tester 20 bits", chunked_post_upload)57 check_test(port, "POST /post-upload/another_file.tester 500 bits", chunked_post_size_500)58 if (option == "stress"):59 print("STRESS TESTS:")60 check_test(port, "GET / 25 workers 100 times", stress_test1)61 check_test(port, "GET / 100 workers 25 times", stress_test1bis)62 check_test(port, "POST 100k-1M bits 10 workers 100 times", stress_test2)63 check_test(port, "POST 100k-1M bits 100 workers 10 times", stress_test2bis)64 check_test(port, "POST CGI 1M bits 5 workers 20 times", stress_test3)65 check_test(port, "POST CGI 1M bits 20 workers 5 times", stress_test3bis)66 check_test(port, "POST CGI w/ upload 1M bits 5 workers 10 times", stress_test4)67 check_test(port, "POST CGI w/ upload 1M bits 10 workers 5 times", stress_test4bis)68 check_test(port, "POST /post-upload/another_file.tester 100M bits", chunked_post_size_100M)69if (__name__ == "__main__"):70 if (len(sys.argv) != 2):71 print("Usage: python3 main.py tests.py [port]")72 exit(1)73 try:74 port = int(sys.argv[1])75 except:76 print("please input a valid port (int)")77 exit(1)78 os.chdir("tester_documents")79 os.system("rm -rf ./upload/*")80 os.system("echo \"Delete me please\" > ./delete_folder/index.html")81 option = ""82 while (option != 'basic' and option != 'stress'):...
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