def get_analysis_id(self,options):
selections=""; where_clause=""
if options["INSTRUMENT"]:
firstpart, secondpart = add_parameters.selections_conditions("INSTRUMENT", options["INSTRUMENT"], selections); selections="," + firstpart; where_clause+=" and " +secondpart
if options["PAIR"]:
firstpart, secondpart = add_parameters.selections_conditions("PAIR", options["PAIR"], selections); selections="," + firstpart; where_clause+=" and " +secondpart
if options["LANE"]:
firstpart, secondpart = add_parameters.selections_conditions("LANE", options["LANE"], selections); selections="," + firstpart; where_clause+=" and " +secondpart
if options["BARCODE"]:
if os.path.exists(options["BARCODE"]):
list_of_barcodes=read_file.read_file(options["BARCODE"])
else:
list_of_barcodes=options["BARCODE"]
firstpart, secondpart = add_parameters.selections_conditions("BARCODE", list_of_barcodes, selections); selections="," + firstpart; where_clause+=" and " +secondpart
if options["SAMPLE_NAME"]:
if os.path.exists(options["SAMPLE_NAME"]):
list_of_samplenames=read_file.read_file(options["SAMPLE_NAME"])
else:
list_of_samplenames=options["SAMPLE_NAME"]
firstpart, secondpart = add_parameters.selections_conditions("SAMPLE_NAME", list_of_samplenames, selections); selections="," + firstpart; where_clause+=" and " +secondpart
statement = "SELECT analysis_id FROM run WHERE " + where_clause[5:]
statement = statement + " GROUP BY analysis_id"
return self.execute_and_return(statement)
def test_output_1(self):
out_file = 'D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB5\\out\\out1'
array = read_file('D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB5\\data\\data1')[1]
word_num = read_file('D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB5\\data\\data1')[0]
paths = {}
self.assertEqual(find_max(array, paths, out_file, word_num), 6, 'An error occurred while processing your '
'request')
def test_output_1(self):
graph = read_file(
"D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB3\\resources\\data1"
)[0]
vertex_num = read_file(
"D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB3\\resources\\data1"
)[1]
self.assertEqual(check_cycle(graph, vertex_num, 0), [0, 5, 10],
'An error occurred while processing your '
'request')
def test_output_3(self):
graph = read_file(
"D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB3\\resources\\data3"
)[0]
vertex_num = read_file(
"D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB3\\resources\\data3"
)[1]
self.assertEqual(check_cycle(graph, vertex_num, 20),
[20, 127, 131, 154, 46, 70, 73, 163, 125, 98],
'An error occurred while processing your '
'request')
def main(argv):
C = read_file.read_file(argv[0])
C.pop(0)
C = sp.array(C,float)
density,natoms,molmass = read_file.read_file(argv[1])[0]
density = float(density) # kg/m**3
natoms = int(natoms)
molmass = float(molmass) # kg/mol
integral = spherical_integral(C,density) # (s/m)**3
mean_vel = (integral/12./sp.pi)**(-1/3.)
debeye_temp = PLANCKCONST/BOLTZCONST*(3.*natoms*AVONUM*
density/4./sp.pi/molmass)**(1/3.)*mean_vel
print debeye_temp,mean_vel
def main(argv):
C = read_file.read_file(argv[0])
C.pop(0)
C = sp.array(C, float)
density, natoms, molmass = read_file.read_file(argv[1])[0]
density = float(density) # kg/m**3
natoms = int(natoms)
molmass = float(molmass) # kg/mol
integral = spherical_integral(C, density) # (s/m)**3
mean_vel = (integral / 12. / sp.pi)**(-1 / 3.)
debeye_temp = PLANCKCONST / BOLTZCONST * (3. * natoms * AVONUM * density /
4. / sp.pi /
molmass)**(1 / 3.) * mean_vel
print debeye_temp, mean_vel
def render_page():
my_list = read_file('tintern_abbey.txt')
chain = MarkovChain(my_list)
num_words = int(10) - 1
my_sentence = chain.walk(num_words)
my_list2 = read_file("the_rime.txt")
chain2 = MarkovChain(my_list2)
num_words2 = int(10) - 1
my_sentence2 = chain2.walk(num_words2)
return render_template('index.html',
sentence=my_sentence,
sentence2=my_sentence2)
def requests_file():
r = read_file()
urls_list = r.read_urls_from_file()
i = 1
for url in urls_list:
r = requests.get(url,
allow_redirects=True,
stream=True,
headers={'User-agent': 'Mozilla/5.0'})
filename = str(r.headers['Content-Disposition'][22:-1])
if r.status_code == 200:
print('downloading ', filename, '...')
with open(filename, "wb") as f:
dl = 0
total_length = int(r.headers.get('content-length'))
for data in r.iter_content(chunk_size=4096):
dl += len(data)
f.write(data)
done = int(100 * dl / total_length)
sys.stdout.write("\r[%s%s] %.2f/%.2f MB" %
('=' * done, ' ' * (100 - done), dl /
(1024 * 1024), total_length /
(1024 * 1024)))
sys.stdout.flush()
print('\nfile', filename, 'saved successfully!')
print('waiting for 1 second....')
time.sleep(10)
i = i + 1
def change_password(ls):
old_password = ls[2]
flag = verify_old_password(old_password)
if flag == '0':
new_password = input("\nEnter the new password: "******"Out of range of try ... press Enter")
def test_minhash_with_testdata(self):
sets = read_file('testdata.csv')
similarity = similarity_matrix(sets, minhash_similarity)
self.assertEqual(recommendations(1, sets, similarity, 0.75), {42})
self.assertFalse(recommendations(3, sets, similarity, 0.75))
self.assertEqual(recommendations(1, sets, similarity, 0.15),
(sets[2] | sets[3]) - sets[1])
def get_post_by_date(self):
content = self.text.get("1.0", tk.END)
dateFormat = re.compile(r"\d\d\d\d.\d\d.\d\d")
try:
result = dateFormat.search(content).group()
except:
result = "xxxx.xx.xx"
#print("date in text box:", result)
diaryContent = "".join(read_file(self.name, False))
try:
datesIn = dateFormat.findall(diaryContent)
except:
datesIn = []
datesIn = list(set(datesIn)) #remove duplicates
datesIn.sort()
startPoint = len(diaryContent)
stopPoint = len(diaryContent)
#print("result:", result)
if result in datesIn:
startPoint = diaryContent.find(result)
if not datesIn[-1] == result:
nextDate = datesIn[datesIn.index(result)+1] #if no duplicates and sorted it should be greater than result
stopPoint = diaryContent.find(nextDate) #-6 if colored
chosenDay = diaryContent[startPoint:stopPoint] #-6 if colored
#print(chosenDay)
self.text.delete("1.0", tk.END)
self.text.insert("1.0", chosenDay)
else:
#print("No such date. These are availabe:\n", ", ".join(datesIn))
textData = "Available dates:\n" + ", ".join(datesIn) + "\n"
self.text.delete("1.0", tk.END)
self.text.insert("1.0", textData)
def shan_chu():
'''
删除学生条目
'''
l = Rf.read_file()
# print(l)
sc = input('输入需要删除的学生的姓名：')
for i in l:
if i['姓名'] == sc:
print('您要删除的是：', i['姓名'], '请确认(y/n)')
qr = input()
if qr == 'y':
shan_chu = l.pop(l.index(i))
print('删除了：', shan_chu)
# from imp import reload
# reload(get_path)
Wf.write_file(l, 'w')
return
else:
print('已放弃操作')
ts.tishi('即将返回目录', 4)
return
print('没有此人')
ts.tishi('返回目录', 5)
def test_recommendations_with_zero_cutoff_returns_all_other_products(self):
sets = read_file('testdata.csv')
similarity = similarity_matrix(sets)
for i in sets.keys():
self.assertEqual(
recommendations(i, sets, similarity, 0),
reduce(lambda a, b: a | b, sets.values(), set()) - sets[i])
def field_to_str():
'''(list) -> (str)
Converts list into str and writes it in a file
0 is for ' ' 1 is for '*' 2 s for 'X'
'''
board = read_file.read_file('field.txt')
board_str1 = ""
board_str = []
for i in board:
if 0 in i:
board_str1 += " "
elif 1 in i:
board_str1 += "*"
elif 2 in i:
board_str1 += "X"
for i in range(0, 91, 10):
board_str.append(board_str1[i:i + 10])
print('Do you want to see a field here', end='')
print(' or you want to write it in a file field1.txt?')
control = int(input('Print 1 to see a field or 2 to write in a file '))
if control == 1:
for i in board_str:
print(i)
elif control == 2:
with open('field1.txt', 'w') as file:
for i in board_str:
file.write(i + '\n')
else:
print('You are cheating!')
def withdraw(ls):
current_balance = int(ls[3])
print('Your current balance: ' + ls[3])
withdraw_amount = int(input('Enter withdraw amount: '))
if withdraw_amount > current_balance:
print("ERROR: You can't withdraw more than your current balance")
else:
current_balance -= abs(withdraw_amount)
file_name = ls[0] + '.txt'
process_list = read_file.read_file(file_name)
id_file = open(file_name, 'a')
if len(process_list) == 0:
last_id = 1
else:
last_id = int(process_list[len(process_list) - 1][0]) + 1
id_file.write('{0}\twithdraw\t\t\t{1}\t{2}\t{3}\n'.format(
str(last_id), str(time.ctime()), ls[3], str(current_balance)))
id_file.close()
ls[3] = str(current_balance)
print('Your current balance: ' + ls[3])
return ls
def jia_zai():
jia_zai_f = tkinter.filedialog.askopenfilename(title='选择加载的文件',
filetypes=[('TXT格式', '.txt')
])
if jia_zai_f == ():
print('已取消')
else:
fn = os.path.basename(jia_zai_f) #截取文件名
r_path = os.path.dirname(jia_zai_f) #截取文件所在文件夹名称
local_path = os.getcwd() #获取本地文件夹
f_local = fn + local_path
print(jia_zai_f)
Tisi.tishi('读入中', 1)
sfp.safe_file_path(fn, r_path)
from imp import reload
reload(read_file)
ld = read_file.read_file(jia_zai_f)
if ld is None:
return
else:
print('数据文件切换成功！即将退出系统，请重新进入系统查看和删改！')
Tisi.tishi('正在关闭系统...', 2)
sys.exit()
def change_password(ls):
# Get the old password
old_password = ls[2]
# Ask to old password to enter new one
flag = gate_x(old_password)
# Security flag get the output flag
if flag == '0':
new_password = input("\nEnter the new password: "******"Out of range of try ... press Enter")
def get_bands():
control_raw, patient_raw, channel_names = read_file.read_file()
control_res = []
count = 0
for (eid, raw) in control_raw.items():
temp = handle_raw(raw)
if temp is None:
print('too large')
continue
control_res.append(temp)
print('control: ' + str(count))
count += 1
patient_res = []
count = 0
for (eid, raw) in patient_raw.items():
temp = handle_raw(raw)
if temp is None:
print('too large')
continue
patient_res.append(temp)
print('patient: ' + str(count))
count += 1
return control_res, patient_res
def deposit(ls):
current_balance = int(ls[3])
print('Your current balance: ' + ls[3])
deposit_amount = int(input('Enter the amount to deposit: '))
current_balance += abs(deposit_amount)
file_name = ls[0] + '.txt'
process_list = read_file.read_file(file_name)
id_file = open(file_name, 'ab')
if len(process_list) == 0:
last_id = 1
else:
last_id = int(process_list[len(process_list) - 1][0]) + 1
id_file.write(('{0}\tdeposit\t\t\t\t{1}\t{2}\t{3}\n'.format(str(last_id), str(time.ctime()), ls[3], str(current_balance))).encode('utf-8'))
id_file.close()
ls[3] = str(current_balance)
print('Your current balance: ' + ls[3])
return ls
def deposit(ls):
# ls is a list of the information of the account
# ls elements are of type string
# ls[0] id
# ls[1] name
# ls[2] password
# ls[3] balance
current_balance = int(
ls[3]) # make changes to another variable to keep the previous balance
# to print it later, then save ls[3] = current_balance
print('Your current balance: ' + ls[3])
deposit_amount = int(input('Enter deposit amount: '))
current_balance += abs(deposit_amount) # to guarantee the entered value
file_name = ls[0] + '.txt'
process_list = read_file.read_file(file_name)
id_file = open(file_name, 'a')
if len(process_list) == 0: # if there are no processes in the file
last_id = 1
else:
last_id = int(process_list[len(process_list) -
1][0]) + 1 # get last id and increment it
id_file.write('{0}\tdeposit\t\t\t\t{1}\t{2}\t{3}\n'.format(
str(last_id), str(time.ctime()), ls[3], str(current_balance)))
# write-> process_id process_name process_date_and_time before_process after_process
id_file.close()
ls[3] = str(current_balance)
print('Your current balance: ' + ls[3])
return ls
def test_output_2(self):
order_list = []
graph = read_file("D:\\Projects\\Data-Structures-and-Algorithms-Uni\\LAB4\\resources\\data2")
visited = {k: False for k in graph}
self.assertEqual(dfs("visa", graph, visited, order_list), ['foreignpassport', 'visa'],
'An error occurred while processing your '
'request')
def main():
positions_file = "Positions - Performance.txt"
image_filepath = "C:\\Users\\Joshua\\Documents\\Development\\FIFAStats\\Pictures - Performance\\Mason.jpg"
image = Image.open(image_filepath)
scores = read_file(positions_file)
indi_image(image, scores)
def test_asymmetric_similarity_returns_superset_of_jaccard(self):
sets = read_file('testdata.csv')
similarity1 = similarity_matrix(sets)
similarity2 = similarity_matrix(sets, asymmetric_similarity)
for i in sets.keys():
self.assertTrue(
recommendations(i, sets, similarity1, 0.25).issubset(
recommendations(i, sets, similarity2, 0.25)))
def __init__(self, word_list):
self.histogram = {}
self.text = read_file("tintern_abbey.txt")
self.word_list = word_list
self.dictionary_histogram = self.build_dictogram()
self.tokens = sum(self.dictionary_histogram.values())
self.types = self.unique_words()
def load(self):
"""
Load data from mongodb.
INPUT: None.
OUTPUT: None.
"""
#self.df = read_file("../data/yelp_academic_dataset_user.json") #Full Data.
self.df = read_file("../data/user300.json") #For local machine.
def main():
"""
主函数
"""
info = read_file.read_file("mission.txt")
currency = translate_to_roman_numerals.translate_to_roman_numerals(info)
get_price = price.cal_unit_price(info, currency)
answers.get_answers(info, currency, get_price)
def main():
"""
主函数
"""
info = read_file.read_file("mission.txt")
currency = translate_to_roman_numerals.translate_to_roman_numerals(info)
get_price = price.cal_unit_price(info, currency)
answers.get_answers(info,currency,get_price)
def map(fi_name):
matrix = read_file(fi_name)
print('start mapping...')
print('...')
map_matrix = {}
for e in matrix:
map_matrix[e] = exp_map(matrix[e])
print('mapping finished.')
return map_matrix
def train(self, training_data_file):
self.train_file_str = read_file(training_data_file)
self.train_pfs = preprocess_line(self.train_file_str)
self.train_counts = count_ngrams(3, self.train_pfs)
self.train_discounts = gt_discount(self.train_counts)
self.train_probs = estimate_probs(self.train_discounts)
write_file(self.train_probs)
def generate_sentence():
my_file = read_file("tintern_abbey.txt")
my_histogram = histogram(my_file.split("\n"))
sentence = ""
num_words = 10
for i in range(num_words):
word = random_word(my_histogram)
sentence += " " + word
return sentence
def calculate_library_weight_main():
final_list = []
meta_data = read_file("input/d_tough_choices.txt")
scanned_books = {}
libraries = meta_data[4]
num_days = meta_data[2]
while libraries and num_days:
libraries, num_days, final_list = calculate_scores(libraries, num_days, final_list)
return final_list
def main():
content = read_file()
bn = BayesNet(content)
# for node in bn.nodes:
# print(node)
# X = 'G'
# e = {'O': True, 'A': True, 'X':True, 'N': True, 'H':True}
# # print(markov_blanket_sample(X, e, bn))
# print(gibbs_ask(X, e, bn, 100))
print(bn.markov_blanket('A'))
def read_table(current_path):
# Input File
input_file_path = os.path.join(current_path, "TableA.txt")
print(input_file_path)
# Read File
name, f = read_file(input_file_path)
return name, f
def get_analysis_id(self, options):
selections = ""
where_clause = ""
if options["INSTRUMENT"]:
firstpart, secondpart = add_parameters.selections_conditions(
"INSTRUMENT", options["INSTRUMENT"], selections)
selections = "," + firstpart
where_clause += " and " + secondpart
if options["PAIR"]:
firstpart, secondpart = add_parameters.selections_conditions(
"PAIR", options["PAIR"], selections)
selections = "," + firstpart
where_clause += " and " + secondpart
if options["LANE"]:
firstpart, secondpart = add_parameters.selections_conditions(
"LANE", options["LANE"], selections)
selections = "," + firstpart
where_clause += " and " + secondpart
if options["BARCODE"]:
if os.path.exists(options["BARCODE"]):
list_of_barcodes = read_file.read_file(options["BARCODE"])
else:
list_of_barcodes = options["BARCODE"]
firstpart, secondpart = add_parameters.selections_conditions(
"BARCODE", list_of_barcodes, selections)
selections = "," + firstpart
where_clause += " and " + secondpart
if options["SAMPLE_NAME"]:
if os.path.exists(options["SAMPLE_NAME"]):
list_of_samplenames = read_file.read_file(
options["SAMPLE_NAME"])
else:
list_of_samplenames = options["SAMPLE_NAME"]
firstpart, secondpart = add_parameters.selections_conditions(
"SAMPLE_NAME", list_of_samplenames, selections)
selections = "," + firstpart
where_clause += " and " + secondpart
statement = "SELECT analysis_id FROM run WHERE " + where_clause[5:]
statement = statement + " GROUP BY analysis_id"
return self.execute_and_return(statement)
def train(self, training_data_file):
self.train_file_str = read_file(training_data_file)
self.train_pfs = preprocess_line(self.train_file_str)
self.train_counts = count_trigrams(3, self.train_pfs)
ans = raw_input("Smooth the counts? [y/n]")
if ans.lower() == 'y':
self.train_discounts = gt_discount(self.train_counts)
self.train_probs = estimate_probs(self.train_discounts)
else:
self.train_probs = estimate_probs(self.train_counts)
write_file(self.train_probs)
def glitch_1(input_path, output_path="test_folder"):
files = read_file(output_path, input_path, True)
f = files[0]
new_f = files[1]
count = 0
for line in f:
temp = line
if count > 20 and count % 20 == 0 and random.randint(1, 10) > 9:
if random.randint(1, 10) < 8:
temp += temp
else:
temp = temp[random.randint(0, len(temp)):random.randint(0, len(temp))]
new_f.write(temp)
count += 1
def process_des(filename, key, encryption): input_blocks = read_file(filename, encryption) # Return error if file is empty if not input_blocks: return 'file_empty_error' if input_blocks == 'not_encrypted_file': return 'not_encrypted_file' keys = keygen(key) if encryption: # Convert each block to binary if operation is encryption. input_blocks = [string_to_bin(x) for x in input_blocks] # Pad zeroes to the end, if last block has less than 64 bits. if len(input_blocks[-1]) < 64: input_blocks[-1] += '0' * (64 - len(input_blocks[-1])) if not encryption: # Reverse the key schedule if operation is decryption. keys = list(reversed(keys)) encoded_text = '' for block in input_blocks: encoded_text += fiestel(block, keys) if encryption: # Return cipher text as binary. return encoded_text else: # Remove padded zeroes if any. for i in range(64, 0, -8): if encoded_text[-8:] == '0' * 8: encoded_text = encoded_text[:-8] else: break # Convert binary to normal string. return bin_to_string(encoded_text)
for each in check_list:
args.append(options[each])
reports = Reports.Reports(config);tables=reports.get_samples_from_run_lane_barcode(args); display_outputs(tables)
else:
print "You have not supplied one of the option parameter. Make sure you supplied parameters for all of these : ", ",".join(check_list)
exit(1)
elif options["get_analysis_id"]:
check_list=["INSTRUMENT", "RUN", "LANE", "PAIR", "SAMPLE_NAME", "BARCODE"] #supply at least one or all of these options
reports = Reports.Reports(config);tables=reports.get_analysis_id(options); display_outputs(tables)
elif options["get_properties_for_analysis_ids"]:
if options["ids"]:
if os.path.exists(options["ids"]):
list_of_ids=read_file.read_file(options["ids"])
else:
list_of_ids=options["ids"]
reports = Reports.Reports(config);tables=reports.get_properties_for_analysis_ids(list_of_ids); display_outputs(tables); reports.disconnect()
else:
print "Please provide a list of analysis ids which you wish to get the properties"
exit(1)
elif options["get_barcodes_for_sample_name"]:
if options["SAMPLE_NAME"]:
if os.path.exists(options["SAMPLE_NAME"]):
#read the file
list_of_samplenames=read_file.read_file(options["SAMPLE_NAME"])
selections, where_clause=add_parameters.selections_conditions("sample_name", list_of_samplenames, "")
else:
selections, where_clause=add_parameters.selections_conditions("sample_name", options["SAMPLE_NAME"], "")
def test(self, test_data_file):
self.test_file_str = read_file(test_data_file)
self.test_pfs = preprocess_line(self.test_file_str)
self.test_counts = count_trigrams(3, self.test_pfs)
self.perplexity = calc_perplexity(self.test_counts, self.train_probs)
parser.add_argument("--softening",type=int,default=0,
help="softening constant")
# parser.add_argument("-o","--outfile",type=argparse.FileType('w'),
# help="save results to file, '-' is stdout")
parser.add_argument("--energy",action="store_const",const=Energy(),
help="calculate and plot the energy of the system")
parser.add_argument("--period",type=int,metavar="i",nargs="?",const=1,
help="calculate and output the orbtal period of body i")
parser.add_argument("--maxpoints",type=int,
help="maximum plotted line length in data points")
parser.add_argument("file",nargs="+",
help="files containing the mass,"
" initial position and velocity of the bodies")
args = parser.parse_args()
m,x,v,names,styles = read_file(args.file)
# change the position and velocity of the Sun so that
# the centre of mass is stationary and at the origin
x[0] = -numpy.sum(x[1:]*m[1:,numpy.newaxis],axis=0)/m[0]
v[0] = -numpy.sum(v[1:]*m[1:,numpy.newaxis],axis=0)/m[0]
solar_system = N_bodies(m,x,v,args.dt,args.softening)
v = solar_system.v_correction()
dt = args.dt
t = args.time
pr = None
# pr = printer() # for solar velocity print out
if args.graph:
plot = Plotter(dict(zip(names,styles)),axis=[-9e11,9e11,-9e11,9e11],position=111)
def translate_to_roman_numerals(info):
"""
转义为罗马数字模块
提取输入信息中对星际货币与罗马数字的转换信息
"""
translate_to_roman=[[],[],[],[],[],[],[]]
currency={}
for key,value in info.items():
if(key=='condition_roman'):
for index,number in enumerate(value):
translate_to_roman[index]=number.split()
for i in translate_to_roman:
if i:
currency[i[0]]=i[2]
return currency
if __name__ == "__main__":
info = read_file.read_file()
translate_to_roman_numerals(info)
def test_read_file():
"""Assert the correct return value from the function read_file()."""
assert read_file('test_read_file.txt') == """ABCDEFGHIJKLMNOPQRSTUVWXYZ?
from Problem import Problem
from solver_vertical_motion import Solver
from read_file import read_file
from scitools.std import plot
from numpy import linspace, array, zeros
import sys
v0 = 0
T = 100
dt = 0.01
N = int(T/float(dt))
parameters = read_file(sys.argv[1:])
problem = Problem(parameters)
problem.set_initial_condition(v0);
c = Solver(problem)
c.set_timestep(dt)
v = c.solve(T)
v = array(v)
F_d = zeros(N)
for i in range(N):
F_d[i] = problem.get_drag_force(v[i])
F_g = zeros(N) + problem.get_gravity_force()
F_b = zeros(N) + problem.get_buoyancy_force()
time = linspace(0,dt*N,N);
plot(time,F_d,time,F_g,time,F_b, legend=['drag','gravity','buoyancy'])
"""
S_vib = 4*debye_func(thetaD/T)-3*sp.log(1-sp.exp(-thetaD/T))
S_vib = natoms*BOLTZCONST*S_vib
return S_vib
def debye_C_V(T,thetaD,natoms):
"""
Returns the heat capacity at constant volume, C_V, of the Debeye model at a
given temperature, T, in meV/atom/K.
"""
C_V = 4*debye_func(thetaD/T)-3*(thetaD/T)/(sp.exp(thetaD/T)-1.)
C_V = 3*natoms*BOLTZCONST*C_V
return C_V
if __name__ == "__main__":
thetaD,natoms = read_file.read_file(sys.argv[1])[0]
thetaD = float(thetaD)
natoms = int(natoms)
temp = sp.linspace(0,2000,201)
temp[0] = 1.0
#reduced_temp = thetaD/temp
E_zp = 9*natoms*BOLTZCONST*thetaD/8.
#E_zp = 9*BOLTZCONST*thetaD/8.
E_vib = sp.array([])
S_vib = sp.array([])
C_V = sp.array([])
for T in temp:
D = debye_func(thetaD/T)
E_vib = sp.append(E_vib,natoms*BOLTZCONST*(E_zp + 3*T*D))
#E_vib = sp.append(E_vib,BOLTZCONST*(E_zp + 3*T*D))
S_vib = sp.append(S_vib,natoms*BOLTZCONST*(4*D -
#!/usr/bin/env python import read_file import parse_prop import get_snpcount import add_seq import append import integrate import add_to_db import run_struct import get_sites file = integrate.integrate() df = read_file.read_file(file) new_df = parse_prop.parse_prop(df) #kage = get_snpcount.enough_info(file) str = append.append(new_df) sites, indices = get_sites.get_sites(new_df) #add_to_db.add_to_base(kage,df) run_struct.call_structure(sites, indices)
