diff --git a/src/unitgrade_devel.egg-info/PKG-INFO b/src/unitgrade_devel.egg-info/PKG-INFO
index 47f493df27ddd9609505e1c14a5dde43aa92c5eb..6bce4bce797771b7a41e1df61b63893ca6f8c3fc 100644
--- a/src/unitgrade_devel.egg-info/PKG-INFO
+++ b/src/unitgrade_devel.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: unitgrade-devel
-Version: 0.1.54
+Version: 0.1.55
Summary: A set of tools to develop unitgrade tests and reports and later evaluate them
Home-page: https://lab.compute.dtu.dk/tuhe/unitgrade_private
Author: Tue Herlau
diff --git a/src/unitgrade_private/hidden_gather_upload.py b/src/unitgrade_private/hidden_gather_upload.py
index 3af81e7f30d986148fbf795a15432134f351f035..773afee9af8f3a789263ca913b4b498696713af3 100644
--- a/src/unitgrade_private/hidden_gather_upload.py
+++ b/src/unitgrade_private/hidden_gather_upload.py
@@ -1,4 +1,5 @@
-from unitgrade.evaluate import evaluate_report, python_code_str_id
+import sys
+from unitgrade.evaluate import evaluate_report, python_code_str_id, _print_header
import textwrap
import bz2
import pickle
@@ -6,6 +7,7 @@ import os
import zipfile
import io
from unitgrade.utils import picklestring2dict, dict2picklestring, load_token, token_sep
+from unitgrade import Report
def bzwrite(json_str, token): # to get around obfuscation issues
with getattr(bz2, 'open')(token, "wt") as f:
@@ -23,9 +25,6 @@ def gather_imports(imp):
if isinstance(im, list):
print("im is a list")
print(im)
- # the __path__ attribute *may* be a string in some cases. I had to fix this.
- # print("path.:", __import__(m.__name__.split('.')[0]).__path__)
- # top_package = __import__(m.__name__.split('.')[0]).__path__._path[0]
top_package = __import__(m.__name__.split('.')[0]).__path__[0]
module_import = False
@@ -58,13 +57,20 @@ parser = argparse.ArgumentParser(description='Evaluate your report.', epilog="""
> python report1_grade.py
-Finally, note that if your report is part of a module (package), and the report script requires part of that package, the -m option for python may be useful.
+Depending on the course configuration, you get the online evaluation/verification results using the `--evaluation`-flag. Example:
+
+> python report1_grade.py --evaluation
+
+The evaluation is of course only available after the teacher has made it available.
+
+Note that if your report is part of a module (package), and the report script requires part of that package, the -m option for python may be useful.
For instance, if the report file is in Documents/course_package/report3_complete.py, and `course_package` is a python package, then change directory to 'Documents/` and run:
> python -m course_package.report1
see https://docs.python.org/3.9/using/cmdline.html
""", formatter_class=argparse.RawTextHelpFormatter)
+parser.add_argument('--evaluation', action="store_true", help='Get the remote evaluation of this report')
parser.add_argument('--noprogress', action="store_true", help='Disable progress bars')
parser.add_argument('--autolab', action="store_true", help='Show Autolab results')
parser.add_argument('--force_kill', action="store_true", help='Forcefully quit on exit.')
@@ -96,6 +102,63 @@ def gather_report_source_include(report):
def gather_upload_to_campusnet(report, output_dir=None, token_include_plaintext_source=False):
# n = report.nL
args = parser.parse_args()
+
+ if report.remote_url is not None and args.evaluation:
+ import datetime
+ _print_header(now=datetime.datetime.now(), big_header=True)
+
+ # get the remote evaluation.
+ # os.path.dirname(report._artifact_file())
+ mf = report._manifest_file()
+ if os.path.isfile(mf):
+ with open(mf, 'r') as f:
+ s = f.read()
+ from unitgrade.utils import checkout_remote_results
+
+
+ results = checkout_remote_results(report.remote_url, s.strip())
+ if results['html'] is None:
+ print("""Oy! I failed to download the verified results.
+There can be three reasons why this this command failed:
+
+ * You have not yet uploaded a .token file (i.e., you have not yet handed in)
+ * You did not upload a .token file, but rather a file in some other format.
+ * You have reset/deleted files from the unitgrade_data directory. In this case I don't know what file to look for remotely.
+ * The .token files associated with this project have not yet been processed by the course responsible.
+
+Assuming you have handed in, and it has been announced the results should be available online, you should contact a TA
+for more information about what happened to your evaluation. Please don't contact the teachers directly.
+""")
+ sys.exit()
+ print("Your verified results are:")
+ import tabulate
+ # import sys
+ print( tabulate.tabulate(results['df'], showindex=False) )
+ print("These scores are based on our internal tests.")
+ print("To see all your results, visit:")
+ print(">", results['url'])
+ print("Your total score was", results['total'])
+ sys.exit()
+ # import tabulate
+ # print( tabulate.tabulate(df, showindex=False) )
+
+
+ else:
+
+
+
+ print(f"""
+The --evaluation flag is used to download and display the (teacher) verification of your result from:
+> %s
+That means you should only use this command after you have uploaded your .token file.
+
+Error: I could not find information about previously generated tokens. The likely causes are:
+
+ * You have not generated a token and uploaded a .token-file yet
+ * You have uploaded a .token file, but it has not yet been evaluated.
+"""%(report.remote_url,))
+ sys.exit()
+
results, table_data = evaluate_report(report, show_help_flag=False, show_expected=False, show_computed=False, silent=True,
show_progress_bar=not args.noprogress,
big_header=not args.autolab,
@@ -141,10 +204,22 @@ def gather_upload_to_campusnet(report, output_dir=None, token_include_plaintext_
obtain, possible = results['total']
vstring = f"_v{report.version}" if report.version is not None else ""
- token = "%s_%i_of_%i%s.token"%(payload_out_base, obtain, possible,vstring)
+ token = "%s_%i_of_%i%s.token"%(payload_out_base, obtain, possible, vstring)
token = os.path.normpath(os.path.join(output_dir, token))
- save_token(results, "\n".join(s_include), token)
+ b_hash = save_token(results, "\n".join(s_include), token)
+ try:
+ with open(report._manifest_file(), 'a') as _file:
+ _file.write("\n"+token + " " + b_hash)
+ except Exception as e:
+ print(e)
+
+ # ug_dir = os.path.dirname(report._artifact_file())
+ # ug_name = os.path.basename(report._artifact_file())
+ # MANIFEST = ug_dir + "/manifest."
+
+
+
if not args.autolab:
print("> Testing token file integrity...", sep="")
@@ -157,7 +232,7 @@ def gather_upload_to_campusnet(report, output_dir=None, token_include_plaintext_
if args.force_kill:
print("Running sys.exit...")
import threading
- import sys
+ # import sys
# import os
print("These are all the threads:")
for thread in threading.enumerate():
@@ -184,9 +259,7 @@ def gather_upload_to_campusnet(report, output_dir=None, token_include_plaintext_
os.kill(pid, 9)
print("Raising exception instead...")
# raise Exception("Quitting this shit.")
-
os._exit(0)
-
sys.exit()
print("Sys.exit ran.")
@@ -205,7 +278,7 @@ def save_token(dictionary, plain_text, file_out):
out = [plain_text, token_sep, f"{b_hash} {b_l1}", token_sep, b]
with open(file_out, 'w') as f:
f.write("\n".join(out))
-
+ return b_hash
diff --git a/src/unitgrade_private/pipelines/dtulearn.py b/src/unitgrade_private/pipelines/dtulearn.py
index d5f0243fc1ea1aed9efa915cf4ee31de0ff99d86..20baeefdae0991563179d6008c9c660a98b0f204 100644
--- a/src/unitgrade_private/pipelines/dtulearn.py
+++ b/src/unitgrade_private/pipelines/dtulearn.py
@@ -481,6 +481,12 @@ def docker_stagewise_evaluation(base_directory, Dockerfile=None, instructor_grad
for tf in glob.glob(fid +"/**/*.token", recursive=True):
rs[id]['token_downloaded'] = tf
+ tdata, _ = load_token(tf)
+ blake_hash = tdata['metadata']['file_reported_hash']
+
+ rs[id]['token_downloaded_hash'] = blake_hash
+
+
for cid in glob.glob(f"{stage4_dir}/{id}-*"):
type = os.path.basename(cid).split("-")[1]
tokens = glob.glob(f"{cid}/*.token")
diff --git a/src/unitgrade_private/pipelines/dummy_handins.py b/src/unitgrade_private/pipelines/dummy_handins.py
index fa366f1632909115257a247ee7c9bd35a1e294da..1e640e1126c43c89bf76ed5f09a258c686e3134f 100644
--- a/src/unitgrade_private/pipelines/dummy_handins.py
+++ b/src/unitgrade_private/pipelines/dummy_handins.py
@@ -16,6 +16,8 @@ def make_dummies(zip_file_path="zip1.zip", n_handins=3, screwups=4, student_base
# Deploy to this dir and create handins. Turn it all into a .zip file and return it.
np.random.seed(42)
+ manifests = {}
+
def copy_and_mutate():
if os.path.isdir(tmp + "/students"):
shutil.rmtree(tmp + "/students")
@@ -60,42 +62,33 @@ def make_dummies(zip_file_path="zip1.zip", n_handins=3, screwups=4, student_base
cmd = f"cd {tmp}/students && python -m {module}"
print(cmd)
o = subprocess.run(cmd, shell=True, capture_output=True, check=True)
- print(o)
+ # print(o)
token = glob.glob(os.path.dirname(tmp +"/students/"+os.path.relpath(student_grade_file, student_base_dir)) + "/*.token")[0]
- print(token)
- return token
- names = ['Alice', 'Bob', 'Charlie', 'Doris', 'Ebert']
-
- # def zipfolder(foldername, target_dir):
- # zipobj = zipfile.ZipFile(foldername + '.zip', 'w', zipfile.ZIP_DEFLATED)
- # rootlen = len(target_dir) + 1
- # for base, dirs, files in os.walk(target_dir):
- # for file in files:
- # fn = os.path.join(base, file)
- # zipobj.write(fn, fn[rootlen:])
+ if os.path.isfile(f_ := f"""{os.path.dirname(token)}/unitgrade_data/token_{module.split(".")[-1]}.manifest"""):
+ with open(f_) as f:
+ manifest = f.read()
+ else:
+ manifests = None
+ print(token)
+ return token, manifest
+ names = ['Alice', 'Bob', 'Charlie', 'Doris', 'Ebert']
for k in range(n_handins):
- token = copy_and_mutate()
-
+ token, manifest = copy_and_mutate()
# Now make directory for handin and create the .zip file.
-
id = 221000 + k
handin_folder = f"116607-35260 - s{id} - {names[k % len(names)]} - 1 March, 2022 518 PM"
os.makedirs(tmp + "/zip/"+handin_folder)
-
shutil.copy(token, tmp + "/zip/"+handin_folder +"/"+os.path.basename(token))
+ manifests[id] = manifest
- shutil.make_archive(zip_file_path[:-4],
- 'zip',
- tmp+ "/zip",
- '')
-
- return zip_file_path
+ shutil.make_archive(zip_file_path[:-4], 'zip', tmp + "/zip", '')
+ return zip_file_path, manifests
# zipfolder('zip1', tmp + "/zip/"+handin_folder) # insert your variables here
diff --git a/src/unitgrade_private/pipelines/tmp/students/.coveragerc b/src/unitgrade_private/pipelines/tmp/students/.coveragerc
new file mode 100644
index 0000000000000000000000000000000000000000..fadf1accc6e96d2c65cb3e96f8b30cd5cd04929a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/.coveragerc
@@ -0,0 +1,3 @@
+[report]
+exclude_also =
+ if __name__ == .__main__.:
diff --git a/src/unitgrade_private/pipelines/tmp/students/.gitignore b/src/unitgrade_private/pipelines/tmp/students/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..27c08e98bdc12548da2f5d05d05cfb798b1e609b
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/.gitignore
@@ -0,0 +1,5 @@
+__pycache__
+.idea
+*.token
+cp.egg-info
+cache.db
diff --git a/src/unitgrade_private/pipelines/tmp/students/.vscode/keybindings.json b/src/unitgrade_private/pipelines/tmp/students/.vscode/keybindings.json
new file mode 100644
index 0000000000000000000000000000000000000000..8ca821e489d1a5a7c751770333ca352f88eb2ff7
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/.vscode/keybindings.json
@@ -0,0 +1,6 @@
+[
+ {
+ "key": "shift+alt+e",
+ "command": "editor.debug.action.selectionToRepl"
+ }
+]
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/.vscode/settings.json b/src/unitgrade_private/pipelines/tmp/students/.vscode/settings.json
new file mode 100644
index 0000000000000000000000000000000000000000..52ab133400251310f86e2e72aab23734d6c63902
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/.vscode/settings.json
@@ -0,0 +1,11 @@
+{
+ "python.testing.unittestArgs": [
+ "-v",
+ "-s",
+ "./cp",
+ "-p", "*tests*.py"
+ ],
+ "python.testing.pytestEnabled": false,
+ "python.testing.unittestEnabled": true,
+
+}
diff --git a/src/unitgrade_private/pipelines/tmp/students/README.md b/src/unitgrade_private/pipelines/tmp/students/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e74098d5a312c978fe2e78c78f12b68586250f6a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/README.md
@@ -0,0 +1,6 @@
+# 001122 Introduction to programming in python
+This repository contains code for 001122.
+Please see our public homepage for reading material and installation instructions, including how you download and run the code on your own computer:
+
+ - https://tuhe.pages.compute.dtu.dk/001122public/installation/installation.html
+
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa34d6c981bce5c3485e78d2e5dcef9e6a052a92
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/__init__.py
@@ -0,0 +1,253 @@
+""" Source code for 02466, Introduction to reinforcement learning and control, offered at DTU """
+__version__ = "0.0.1"
+
+# Do not import Matplotlib (or imports which import matplotlib) in case you have to run in headless mode.
+import shutil
+import inspect
+import lzma, pickle
+import numpy as np
+import os
+
+# Global imports from across the API. Allows imports like
+# > from cp import Agent, train
+# from cp.utils.irlc_plot import main_plot as main_plot
+# from cp.utils.irlc_plot import plot_trajectory as plot_trajectory
+# try:
+# from cp.ex01.agent import Agent as Agent, train as train
+# from cp.ex09.rl_agent import TabularAgent, ValueAgent
+# except ImportError:
+# pass
+# from cp.utils.player_wrapper import interactive as interactive
+# from cp.utils.lazylog import LazyLog # This one is unclear. Is it required?
+# from cp.utils.timer import Timer
+
+
+def get_ipp_base():
+ dir_path = os.path.dirname(os.path.realpath(__file__))
+ return dir_path
+
+# def get_students_base():
+# return os.path.join(get_ipp_base(), "../../../02465students/")
+
+
+def pd2latex_(pd, index=False, escape=False, column_spec=None, **kwargs): # You can add column specs.
+ for c in pd.columns:
+ if pd[c].values.dtype == 'float64' and all(pd[c].values - np.round(pd[c].values)==0):
+ pd[c] = pd[c].astype(int)
+ ss = pd.to_latex(index=index, escape=escape, **kwargs)
+ return fix_bookstabs_latex_(ss,column_spec=column_spec)
+
+def fix_bookstabs_latex_(ss, linewidth=True, first_column_left=True, column_spec=None):
+ to_tabular_x = linewidth
+
+ if to_tabular_x:
+ ss = ss.replace("tabular", "tabularx")
+ lines = ss.split("\n")
+ hd = lines[0].split("{")
+ if column_spec is None:
+ adj = (('l' if to_tabular_x else 'l') if first_column_left else 'C') + ("".join(["C"] * (len(hd[-1][:-1]) - 1)))
+ else:
+ adj = column_spec
+
+ # adj = ( ('l' if to_tabular_x else 'l') if first_column_left else 'C') + ("".join(["C"] * (len(hd[-1][:-1])-1)))
+ if linewidth:
+ lines[0] = "\\begin{tabularx}{\\linewidth}{" + adj + "}"
+ else:
+ lines[0] = "\\begin{tabular}{" + adj.lower() + "}"
+
+ ss = '\n'.join(lines)
+ return ss
+
+
+def _savepdf_env(file, env):
+ from PIL import Image
+ import matplotlib.pyplot as plt
+ if hasattr(env, 'render_mode') and not env.render_mode == 'rgb_array':
+ env.render_mode, rmt = 'rgb_array', env.render_mode
+ frame = env.render()
+ if hasattr(env, 'render_mode') and not env.render_mode == 'rgb_array':
+ env.render_mode = rmt
+
+ im = Image.fromarray(frame)
+ snapshot_base = file
+ if snapshot_base.endswith(".png"):
+ sf = snapshot_base[:-4]
+ fext = 'png'
+ else:
+ fext = 'pdf'
+ if snapshot_base.endswith(".pdf"):
+ sf = snapshot_base[:-4]
+ else:
+ sf = snapshot_base
+
+ sf = f"{sf}.{fext}"
+ dn = os.path.dirname(sf)
+ if len(dn) > 0 and not os.path.isdir(dn):
+ os.makedirs(dn)
+ print("Saving snapshot of environment to", os.path.abspath(sf))
+ if fext == 'png':
+ im.save(sf)
+ from cp import _move_to_output_directory
+ _move_to_output_directory(sf)
+ else:
+ plt.figure(figsize=(16, 16))
+ plt.imshow(im)
+ plt.axis('off')
+ plt.tight_layout()
+ from cp import savepdf
+ savepdf(sf, verbose=True)
+ # plt.show()
+
+
+
+def savepdf(pdf, verbose=False, watermark=False, env=None, _png=False):
+ """
+ Convenience function for saving PDFs. Just call it after you have created your plot as ``savepdf('my_file.pdf')``
+ to save a PDF of the plot.
+ You can also pass an environment, in which case the environment will be stored to a pdf file.
+
+
+ :param pdf: The file to save to, for instance ``"my_pdf.pdf"``
+ :param verbose: Print output destination (optional)
+ :param watermark: Include a watermark (optional)
+ :return: Full path of the created PDF.
+ """
+ if env is not None:
+ _savepdf_env(pdf, env)
+ return
+
+ import matplotlib.pyplot as plt
+ pdf = os.path.normpath(pdf.strip())
+ pdf = pdf+".pdf" if not pdf.endswith(".pdf") else pdf
+
+ if os.sep in pdf:
+ pdf = os.path.abspath(pdf)
+ else:
+ pdf = os.path.join(os.getcwd(), "pdf", pdf)
+ if not os.path.isdir(os.path.dirname(pdf)):
+ os.makedirs(os.path.dirname(pdf))
+
+ modules = [inspect.getmodule(s[0]) for s in inspect.stack()]
+ files = [m.__file__ for m in modules if m is not None]
+ if any( [f.endswith("RUN_OUTPUT_CAPTURE.py") for f in files] ):
+ return
+
+ # for s in stack:
+ # print(s)
+ # print(stack)
+ # for k in range(len(stack)-1, -1, -1):
+ # frame = stack[k]
+ # module = inspect.getmodule(frame[0])
+ # filename = module.__file__
+ # print(filename)
+ # if not any([filename.endswith(f) for f in ["pydev_code_executor.py", "pydevd.py", "_pydev_execfile.py", "pydevconsole.py", "pydev_ipython_console.py"] ]):
+ # # print("breaking c. debugger", filename)
+ # break
+ # if any( [filename.endswith(f) for f in ["pydevd.py", "_pydev_execfile.py"]]):
+ # print("pdf path could not be resolved due to debug mode being active in pycharm", filename)
+ # return
+ # print("Selected filename", filename)
+ # wd = os.path.dirname(filename)
+ # pdf_dir = wd +"/pdf"
+ # if filename.endswith("_RUN_OUTPUT_CAPTURE.py"):
+ # return
+ # if not os.path.isdir(pdf_dir):
+ # os.mkdir(pdf_dir)
+ wd = os.getcwd()
+ ipp_base = os.path.dirname(__file__)
+ if False:
+ pass
+ else:
+ plt.savefig(fname=pdf)
+ outf = os.path.normpath(os.path.abspath(pdf))
+ print("> [savepdf]", pdf + (f" [full path: {outf}]" if verbose else ""))
+
+ return outf
+
+
+def _move_to_output_directory(file):
+ """
+ Hidden function: Move file given file to static output dir.
+ """
+ if not is_this_my_computer():
+ return
+ CDIR = os.path.dirname(os.path.realpath(__file__)).replace('\\', '/')
+ shared_output_dir = CDIR + "/../../shared/output"
+ shutil.copy(file, shared_output_dir + "/"+ os.path.basename(file) )
+
+
+# def bmatrix(a):
+# if False:
+# return a.__str__()
+# else:
+# np.set_printoptions(suppress=True)
+# """Returns a LaTeX bmatrix
+# :a: numpy array
+# :returns: LaTeX bmatrix as a string
+# """
+# if len(a.shape) > 2:
+# raise ValueError('bmatrix can at most display two dimensions')
+# lines = str(a).replace('[', '').replace(']', '').splitlines()
+# rv = [r'\begin{bmatrix}']
+# rv += [' ' + ' & '.join(l.split()) + r'\\' for l in lines]
+# rv += [r'\end{bmatrix}']
+# return '\n'.join(rv)
+
+
+def is_this_my_computer():
+ CDIR = os.path.dirname(os.path.realpath(__file__)).replace('\\', '/')
+ return os.path.exists(CDIR + "/../../Exercises")
+
+# def cache_write(object, file_name, only_on_professors_computer=False, verbose=True, protocol=-1): # -1 is default protocol. Fix crash issue with large files.
+# if only_on_professors_computer and not is_this_my_computer():
+# """ Probably for your own good :-). """
+# return
+#
+# dn = os.path.dirname(file_name)
+# if not os.path.exists(dn):
+# os.mkdir(dn)
+# if verbose: print("Writing cache...", file_name)
+# with lzma.open(file_name, 'wb') as f:
+# pickle.dump(object, f)
+# # compress_pickle.dump(object, f, compression="lzma", protocol=protocol)
+# if verbose:
+# print("Done!")
+
+
+def cache_exists(file_name):
+ return os.path.exists(file_name)
+
+# def cache_read(file_name):
+# if os.path.exists(file_name):
+# with lzma.open(file_name, 'rb') as f:
+# return pickle.load(f)
+# else:
+# return None
+
+inputs = []
+def minput(ls):
+ """
+ Higher-order function to fix input.
+
+ The function is given a list of keyboard input strings it will dutifully enter into input. If one of the inputs in the
+ list is None, the function will throw an error. This is useful to terminate early and show what the user sees as
+ part of the interaction.
+
+ This function should eventually be absorbed into unitgrade once potential kinks involving stdout has been worked out.
+
+ :param ls: Input strings.
+ :return: An alternative input-function (for use in mocking)
+ """
+ inputs.clear()
+ for l in ls:
+ inputs.append(l)
+ def _input(x):
+ l = inputs[0]
+ if l is None:
+ print(x)
+ raise GeneratorExit()
+ # raise Exception()
+ print(x, l)
+ del inputs[0]
+ return l
+ return _input
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex00/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex00/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0a1c8a1e98b6a3b4b18289ba45e535e5449533d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex00/__init__.py
@@ -0,0 +1 @@
+"""The exercises in this folder are intended as preparation for the course start."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex00/fruit_homework.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex00/fruit_homework.py
new file mode 100644
index 0000000000000000000000000000000000000000..42e8b77b7613ac5a940b61b7a899ee02f5bf46f4
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex00/fruit_homework.py
@@ -0,0 +1,10 @@
+"""the fruit homework example problem for project 0. It should probably be among the exercises."""
+def add(a,b):
+ """Add the two numbers a and b and return their sum.
+
+ :param a: First number to add
+ :param b: Second number to add
+ :return: their sum, :math:`a + b`
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex00/say_hello.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex00/say_hello.py
new file mode 100644
index 0000000000000000000000000000000000000000..95c65896ee83e746fc11f37b02bd6bd7eecfcb6d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex00/say_hello.py
@@ -0,0 +1,7 @@
+"""Problem 0.1. Print out 'Hello' and 'World' on two separate lines."""
+print("Hello")
+# Put your code here so the program outputs
+# > Hello
+# > World
+# on two lines.
+# TODO: 1 lines missing.
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex01/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex01/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..228c82bfb2677fe8da2cacfe3ca4f3d41f692ebe
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex01/__init__.py
@@ -0,0 +1 @@
+"""This package represents exercise 1."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex01/examples.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex01/examples.py
new file mode 100644
index 0000000000000000000000000000000000000000..724bde662cb0e5d1a5962e9c87a04a9a659119c6
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex01/examples.py
@@ -0,0 +1,30 @@
+"""A handful of python programming examples."""
+# Example 1
+print("Hello world")
+
+# Example 2
+print("hello")
+print("world")
+
+a = 2
+b = 3
+c = 1
+x = 3
+y = a*x**2 + b * x + c
+
+# Example 3
+def second_poly(x):
+ """Compute a first order polynomial.
+
+ :param x: Input value :math:`x`
+ :return: :math:`y = 2x + 4`
+ """
+ return 2 * x + 4
+
+# example 4
+def say_hello(name):
+ """Print out "Hello {name}.
+
+ :param name: The name to say hello to
+ """
+ print("Hello " + name)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex02/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..228c82bfb2677fe8da2cacfe3ca4f3d41f692ebe
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/__init__.py
@@ -0,0 +1 @@
+"""This package represents exercise 1."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex02/fibonacci.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/fibonacci.py
new file mode 100644
index 0000000000000000000000000000000000000000..5eae0a753c3aea6854c84552f9cb27b331b60e34
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/fibonacci.py
@@ -0,0 +1,26 @@
+"""Exercise 2.3: The Fibonacci sequence and recursion."""
+
+def fibonacci_number(k):
+ """Compute the :math:`k`'th Fibonacci number :math:`x_k`.
+
+ The function use the recursive relationship :math:`x_{k+1} = x_k + x_{k-1}`, along with the initial conditions
+ :math:`x_0 =0, x_1 = 1`, to complete the sequence up to :math:`k`.
+
+ :param k: An integer
+ :return: The corresponding term :math:`x_k` of the Fibonacci sequence as an integer.
+ """
+ if k == 0:
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ elif k == 1:
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ else:
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Fix this stuff")
+
+if __name__ == "__main__":
+ print("The term x_0 should be 0, you got:", fibonacci_number(0))
+ print("The term x_1 should be 1, you got:", fibonacci_number(1))
+ print("The term x_2 should be 1, you got:", fibonacci_number(1))
+ print("The term x_6 should be 8, you got:", fibonacci_number(6))
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor.py
new file mode 100644
index 0000000000000000000000000000000000000000..51d6473cbd23b79626b2da00a9ce0a824b01c297
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor.py
@@ -0,0 +1,12 @@
+"""Problem 1.1."""
+def evaluate_taylor(x: float) -> float:
+ r"""Compute the third order taylor approximation.
+
+ Compute (and return) the Taylor approximation of :math:`\log(x)`. Remember the function must return a ``float``.
+
+ :param x: The input to evaluate.
+ :return: The taylor approximation of :math:`\log(x)`, i.e. :math:`y`
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Define y = (x-1) - 1/2 * ... here.")
+ return y
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor_variant1.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor_variant1.py
new file mode 100644
index 0000000000000000000000000000000000000000..7304776f8aa94de32b753482c92067e0f95e874d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor_variant1.py
@@ -0,0 +1,3 @@
+"""Problem 1.2. Implement a function that computes (and return) the third order Taylor-approximation here."""
+# TODO: Code has been removed from here.
+raise NotImplementedError("Insert your solution and remove this error.")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor_variant2.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor_variant2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c1b2f1ee332d3d812d0109f8f244063c3ed1baa
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex02/taylor_variant2.py
@@ -0,0 +1,7 @@
+"""Problem 1.3. Compute the third order taylor approximation evaluated in x here and store it in a variable y."""
+
+x = 1.5
+# Define y = ... here.
+# TODO: 1 lines missing.
+raise NotImplementedError("Insert your solution and remove this error.")
+# print(y) should work.
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex03/BAC_calculator.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/BAC_calculator.py
new file mode 100644
index 0000000000000000000000000000000000000000..19cc37e786d557c143d612d2b22389024a3fa558
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/BAC_calculator.py
@@ -0,0 +1,13 @@
+"""Exercise 3.5: BAC Calculator."""
+def BAC_calculator(alcohol_consumed: float, weight: float, gender: bool, time: float) -> float:
+ """Calculate the blood alcohol concentration (BAC) based on the total alcohol consumed, body weight, and time elapsed since consumption.
+
+ :param alcohol_consumed: The total amount of alcohol consumed in grams (float)
+ :param weight: The person's body weight in kilograms (float)
+ :param gender: The person's gender, where 0 indicate man and 1 indicate woman (bool)
+ :param time: The time elapsed since alcohol consumption in hours (float)
+ :return: The calculated blood alcohol concentration (BAC) as a float value.
+ """
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return bac
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex03/body_temperature.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/body_temperature.py
new file mode 100644
index 0000000000000000000000000000000000000000..1476088de2e4ff00a359ad58627f8c6a27955404
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/body_temperature.py
@@ -0,0 +1,10 @@
+"""Exercise 3.4: Body Temperature."""
+def body_temperature(temperature:float) -> str:
+ """Calculate the body's response based on the given temperature.
+
+ :param temperature: The temperature in degrees Celsius (float).
+ :return: The body's response as a string (str).
+ """
+ # TODO: 10 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return response
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex03/compare_numbers.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/compare_numbers.py
new file mode 100644
index 0000000000000000000000000000000000000000..375469301d6e4ea369339ddb4b8061f87ea8397b
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/compare_numbers.py
@@ -0,0 +1,11 @@
+"""Exercise 3.3: Compare numbers."""
+
+def compare_numbers(num1:int, num2:int) -> str:
+ """Return a string based on which number has the greatest numerical value.
+
+ :param num1: first number (int).
+ :param num2: second number (int).
+ :return: string with which number is greatest (str).
+ """
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex03/dance_class.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/dance_class.py
new file mode 100644
index 0000000000000000000000000000000000000000..77d73f33db0934503e11da51779ec2e985edf149
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex03/dance_class.py
@@ -0,0 +1,11 @@
+"""Exercise 3.6: Dance class."""
+
+def danceClass(participants:list)->str:
+ """Determine whether a dance class is valid or invalid.
+
+ :param participants: Class registration (list containing zeros and ones) (list)
+ :return: Class validity (str)
+ """
+ # TODO: 8 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return dance_class
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/bug.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/bug.py
new file mode 100644
index 0000000000000000000000000000000000000000..95560e52f925429b3368b1c4ed20bb19e1e20ecb
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/bug.py
@@ -0,0 +1,24 @@
+"""Exercise 4.2-4.3: Let the world know you have written your last bug."""
+
+def last_bug():
+ """Write a nice message enclosed by lines and pipes that clearly indicate you have written your last bug."""
+ # TODO: 5 lines missing.
+ raise NotImplementedError("Use print(...) to print the output here.")
+
+
+def nice_sign(msg):
+ """Enclose the input string in a nice sign surrounded with pipes and dashes. Note the sign should change size.
+
+ >>> from cp.ex04.bug import nice_sign
+ >>> nice_sign('Hello world!')
+
+ :param msg: The message to enclose.
+ """
+ # You can use len(msg) to get the number of characters and "-"*10 to repeat a character (try in the console!)
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Use print(...) to print the output here.")
+
+
+if __name__ == "__main__":
+ last_bug() # Done with the bugs
+ nice_sign("Hello world")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/files/5000-words.txt b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/files/5000-words.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d44bab9bbb4dcc26e61a0ac68f8904936cf5bc9a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/files/5000-words.txt
@@ -0,0 +1,5000 @@
+species
+practice
+natural
+measure
+present
+form
+order
+quality
+condition
+state
+action
+element
+part
+division
+subject
+ground
+power
+station
+register
+person
+place
+ordinary
+strong
+under
+make
+light
+exercise
+exchange
+parallel
+charge
+support
+judgment
+substance
+figure
+strength
+sound
+service
+quantity
+standard
+right
+point
+character
+strike
+discharge
+force
+return
+spring
+square
+between
+without
+water
+spirit
+distance
+contract
+positive
+position
+straight
+moderate
+double
+superior
+certain
+compound
+interest
+language
+passage
+business
+through
+manner
+relation
+general
+process
+strain
+delicate
+bearing
+property
+advance
+account
+original
+religion
+round
+over
+principal
+sharp
+surface
+line
+degree
+report
+course
+matter
+sentence
+body
+express
+close
+quarter
+head
+negative
+take
+plant
+argument
+increase
+house
+movement
+table
+balance
+separate
+small
+back
+entrance
+settle
+reason
+machine
+common
+material
+scale
+authority
+capable
+anything
+regular
+stock
+break
+opposite
+into
+distress
+work
+standing
+cross
+color
+number
+stroke
+convert
+radical
+relative
+function
+stand
+press
+question
+peculiar
+progress
+together
+touch
+capacity
+physical
+horse
+specific
+external
+produce
+incapable
+passion
+represent
+promise
+tender
+issue
+family
+range
+domestic
+shoulder
+change
+approach
+transfer
+carriage
+feeling
+security
+something
+direction
+pressure
+frame
+like
+free
+company
+inferior
+distinct
+variety
+solution
+capital
+grain
+deposit
+circular
+receive
+pleasure
+particular
+office
+faculty
+motion
+personal
+country
+narrow
+occasion
+open
+addition
+second
+complete
+short
+ancient
+contrary
+serve
+disorder
+crown
+mark
+weight
+large
+white
+tongue
+mountain
+address
+vessel
+throw
+science
+system
+turn
+object
+temper
+internal
+base
+pass
+familiar
+principle
+another
+that
+hold
+compass
+pitch
+influence
+enter
+command
+reduce
+level
+surprise
+time
+bottom
+face
+flower
+extreme
+raise
+purpose
+nature
+answer
+down
+against
+stick
+clear
+record
+article
+discourse
+string
+shell
+side
+fall
+backward
+determine
+several
+forward
+period
+absolute
+taste
+draw
+waste
+running
+spread
+chamber
+cause
+official
+province
+flat
+instrument
+native
+conduct
+maintain
+descent
+block
+presence
+simple
+lead
+weather
+direct
+which
+opinion
+justice
+guard
+master
+appearance
+season
+regard
+check
+shoot
+society
+liquid
+rule
+pattern
+sphere
+directly
+cast
+life
+definite
+private
+give
+knowledge
+cover
+weak
+draught
+section
+sovereign
+stop
+organic
+screw
+violence
+decision
+render
+beat
+rise
+current
+affection
+evidence
+great
+court
+silver
+hand
+correct
+roll
+honor
+wind
+plate
+channel
+single
+keep
+heat
+cylinder
+divide
+release
+after
+respect
+military
+stomach
+some
+root
+timber
+structure
+school
+exterior
+syllable
+piece
+feather
+pledge
+supply
+christian
+branch
+religious
+necessary
+father
+execution
+affected
+knot
+carry
+other
+circle
+kind
+worship
+style
+example
+effect
+obscure
+well
+violent
+horn
+english
+drive
+outside
+better
+church
+mixture
+with
+equal
+reproach
+bear
+volatile
+equivalent
+slender
+remove
+being
+preserve
+grace
+earth
+elevation
+design
+stream
+furnish
+dress
+little
+writing
+spiritual
+black
+twist
+picture
+from
+smooth
+estate
+fine
+anchor
+joint
+heart
+expression
+reach
+center
+contest
+trust
+special
+shall
+friend
+high
+call
+train
+traverse
+irregular
+wrong
+sign
+watch
+catch
+animal
+stone
+dependent
+before
+relief
+thought
+fortune
+will
+active
+formation
+valuable
+soft
+about
+operation
+bank
+heavy
+solid
+ring
+proportion
+symbol
+opposition
+apparent
+abstract
+portion
+dignity
+revolution
+post
+voluntary
+length
+market
+harmony
+prepare
+perform
+land
+board
+shaft
+neglect
+iron
+value
+attempt
+hard
+judge
+proper
+mouth
+moral
+district
+interior
+genus
+union
+purple
+husband
+wing
+broken
+foreign
+divine
+perfect
+last
+compact
+escape
+extract
+demand
+flexible
+still
+wheel
+long
+burden
+labor
+note
+interval
+column
+destitute
+reference
+letter
+control
+freedom
+swallow
+medicine
+history
+difference
+people
+strip
+contempt
+handle
+decline
+good
+liberty
+brilliant
+fashion
+engage
+rest
+impression
+offensive
+drawing
+purchase
+minute
+metal
+texture
+doctrine
+cutting
+lower
+paper
+sight
+hollow
+follow
+judicial
+formal
+salt
+blow
+member
+fellow
+mean
+mother
+thread
+corrupt
+failure
+associate
+relieve
+display
+full
+distant
+disease
+sense
+liberal
+intended
+gather
+play
+blood
+swell
+stuff
+bill
+opening
+quarrel
+world
+secure
+stage
+government
+plane
+public
+composition
+allowance
+thing
+establish
+essential
+gentle
+consider
+examine
+attack
+benefit
+height
+exhibit
+movable
+voice
+tropical
+platform
+alternate
+have
+catholic
+bound
+advantage
+bright
+thrust
+marble
+mercury
+alcohol
+making
+throat
+foot
+shelter
+elastic
+slight
+painting
+inclose
+stem
+reduction
+vegetable
+breast
+drum
+guide
+generation
+study
+within
+succession
+dressing
+powerful
+staff
+notice
+downward
+upright
+favor
+thick
+trade
+extend
+custom
+faith
+mechanical
+turning
+front
+agreement
+glass
+extension
+normal
+green
+exposure
+medium
+product
+application
+term
+vertical
+shape
+choice
+more
+somewhat
+chance
+result
+residence
+party
+wear
+confidence
+soldier
+continue
+powder
+found
+learning
+situation
+bitter
+struggle
+finger
+shield
+officer
+rough
+field
+space
+privilege
+delicacy
+manifest
+sacred
+race
+organ
+behind
+breath
+belonging
+phrase
+border
+marine
+breathing
+flesh
+stretch
+musical
+imperfect
+keeping
+trouble
+fresh
+different
+image
+plaster
+numerous
+existence
+surround
+commerce
+passing
+rank
+around
+mill
+popular
+counter
+fruit
+praise
+hence
+beneath
+loose
+hammer
+amount
+pack
+secondary
+sweet
+meeting
+tone
+inclined
+refuse
+game
+floating
+mineral
+devotion
+memory
+individual
+withdraw
+size
+severe
+declare
+warrant
+desire
+fish
+provide
+title
+smoke
+suffer
+social
+bond
+virtue
+doubtful
+speech
+sympathy
+ready
+feed
+metallic
+fast
+step
+leave
+poison
+worthy
+entire
+easily
+steel
+colored
+disguise
+variable
+speaking
+rising
+indicate
+band
+primary
+agitation
+worm
+quick
+flight
+discipline
+careless
+secret
+lock
+profession
+affect
+minister
+excess
+flourish
+pole
+tooth
+situated
+mind
+disgrace
+censure
+estimate
+treat
+dead
+chemical
+grave
+deliver
+tail
+drop
+attention
+surrender
+hydrogen
+american
+sheet
+building
+preparation
+attend
+plain
+fabric
+limit
+appear
+fasten
+bind
+swelling
+show
+striking
+witness
+ornament
+possession
+bishop
+name
+collect
+hinder
+beyond
+beginning
+case
+covenant
+marriage
+foul
+yield
+type
+handsome
+uniform
+whatever
+earnest
+proof
+himself
+segment
+former
+critical
+difficulty
+proceed
+walk
+restrain
+prince
+setting
+trick
+leader
+female
+universal
+floor
+fire
+false
+depression
+seal
+class
+similar
+stamp
+sleep
+destroy
+following
+resemble
+formerly
+match
+management
+growth
+suit
+patient
+aggregate
+various
+south
+projection
+habit
+tissue
+dispute
+move
+sacrifice
+become
+agreeable
+credit
+partial
+chain
+broad
+northern
+heavenly
+pipe
+arrangement
+impulse
+sport
+apply
+live
+tension
+write
+above
+wood
+furnace
+circuit
+token
+decree
+trial
+shade
+child
+engaged
+imitation
+meet
+truth
+fair
+offer
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+blister
+developed
+claw
+episcopal
+undivided
+redress
+sparkle
+stumble
+coffee
+boisterous
+reproof
+ramble
+encourage
+twice
+thrush
+triangular
+tapeworm
+fitness
+lodging
+appointed
+confer
+manure
+basic
+patriarch
+parties
+phenomena
+expel
+dried
+idiom
+theme
+stimulate
+admitting
+drapery
+genera
+countries
+stamens
+previously
+involving
+separating
+murderous
+seizing
+successful
+frighten
+set
+hoop
+printed
+supported
+spiritless
+plumb
+shoal
+pavilion
+endurance
+encumber
+pendent
+turkey
+conviction
+receiving
+faction
+limber
+disagree
+stupefy
+prevail
+enlarged
+cage
+clasp
+conveying
+mammals
+protoplasm
+engross
+exert
+emperor
+extending
+porous
+consequent
+horseback
+froth
+settled
+bachelor
+reduced
+landscape
+knock
+republican
+partaking
+suspended
+edifice
+capability
+buckle
+vanish
+righteous
+lawful
+maiden
+telltale
+cellular
+convict
+soothe
+snail
+brake
+sparrow
+vast
+chaff
+camp
+filament
+chill
+glassy
+tracing
+elect
+accusation
+fishes
+envy
+hunger
+polite
+lumber
+freely
+flashing
+discredit
+profess
+accretion
+brightness
+moist
+affirm
+illuminate
+melody
+tire
+pliant
+effusion
+exclusion
+text
+blessed
+enrich
+utensil
+frugality
+revenge
+park
+implied
+in
+corona
+differential
+fusion
+impotent
+dissection
+tragedy
+woody
+cleanse
+deceit
+longer
+yearly
+almost
+pendulum
+twin
+indicator
+respiration
+infantry
+hail
+consume
+distrain
+reducing
+chord
+spectator
+illegal
+swiftness
+lever
+prosperous
+colony
+selection
+illusion
+ventilate
+parietal
+arctic
+bismuth
+cranium
+formative
+flax
+solar
+undertaking
+behave
+urinary
+assistance
+but
+worsted
+coral
+humorous
+nineteen
+sameness
+resentment
+gaseous
+lion
+sure
+grecian
+textile
+stagger
+vivacity
+iniquity
+summon
+clause
+velvet
+smear
+encompass
+swollen
+fashionable
+intrude
+deprived
+garnish
+composing
+loom
+preaching
+eloquence
+decayed
+silurian
+parchment
+infection
+magnesium
+prelude
+discontent
+thumb
+tympanum
+distract
+team
+rehearse
+dictate
+abortion
+seminary
+skip
+manager
+selfish
+loathing
+suck
+obsequious
+pyramid
+observant
+establishment
+penance
+trinity
+busy
+springing
+weeping
+gayety
+rugged
+stave
+total
+flask
+concurrence
+mood
+chart
+inscription
+clerk
+viscid
+baptism
+calculus
+enduring
+foretell
+reprove
+eucharist
+madness
+stupidity
+catalogue
+hurry
+atom
+collation
+pillow
+calculate
+eject
+housing
+peel
+location
+inhuman
+tuft
+least
+substantial
+sustained
+thwart
+unlawful
+cabinet
+ventricle
+rigging
+determined
+trip
+assize
+driven
+carpet
+hurt
+smoky
+pump
+longing
+radius
+ambiguous
+exalted
+albumin
+oppression
+frugal
+gothic
+stately
+hooked
+shears
+porcelain
+benzene
+franchise
+although
+fraction
+flavored
+substitution
+distinctly
+entitle
+transit
+since
+nine
+risk
+ghost
+divorce
+prophecy
+representative
+changeable
+bohemian
+unable
+explicit
+advise
+greedy
+squander
+requisite
+enroll
+revolt
+scold
+foreshow
+evaporate
+warfare
+pearly
+vertebral
+swiftly
+stereotype
+herb
+impartial
+hardy
+authorize
+corrosive
+laborious
+infinity
+absorption
+conjunction
+cloister
+reservoir
+acrid
+commodity
+million
+uterus
+eleventh
+fifteen
+pretender
+diatonic
+jury
+hydroxyl
+varieties
+surgery
+commons
+upon
+invent
+responsible
+requite
+prompt
+those
+haul
+reside
+lathe
+allege
+consort
+mentally
+licentious
+excavate
+bury
+babble
+feudal
+canvass
+comprehend
+dissolute
+pension
+blessing
+switch
+effeminate
+repeating
+educate
+balsam
+shuffle
+stifle
+outrage
+invasion
+appease
+tilt
+stoop
+vexatious
+wager
+orbit
+resound
+drawn
+subdued
+dagger
+indication
+pickle
+hackney
+hearty
+relapse
+concealed
+tame
+speculum
+persistent
+blanch
+carbonic
+prickly
+syllogism
+luxury
+childish
+provincial
+knit
+forced
+wasting
+dismiss
+designation
+corporation
+coupling
+venus
+ballast
+lavish
+recording
+phenomenon
+boring
+curvature
+bully
+ruff
+bowsprit
+bastion
+braid
+consult
+harrow
+hasten
+deputy
+suspicious
+betray
+puppet
+playing
+privately
+famous
+assert
+chisel
+permission
+reformed
+ratio
+mustard
+hissing
+hurtful
+leaning
+reversal
+committee
+treason
+villain
+corresponding
+resulting
+treble
+pushing
+examining
+ductile
+affirmation
+flux
+guttural
+remission
+congregation
+annoy
+criticise
+said
+mortify
+heedless
+tabular
+shiver
+uneven
+displeasure
+antique
+fatal
+characteristic
+zigzag
+audible
+reversion
+canker
+shark
+respecting
+surveyor
+pistil
+premature
+agility
+sulphate
+wreath
+inflate
+presage
+lustful
+eighth
+capstan
+confined
+rendered
+chalk
+engender
+twofold
+attractive
+fell
+diligent
+burlesque
+vacancy
+bustle
+misery
+luxurious
+holland
+silicon
+antagonist
+fright
+concretion
+overpower
+dissent
+lover
+introduction
+abandoned
+pottery
+hunt
+lives
+desperate
+roving
+birch
+density
+bunch
+overturn
+revival
+turnpike
+operative
+finishing
+spine
+dismount
+headdress
+veil
+overwhelm
+unhappy
+coarsely
+perplexity
+ellipse
+oration
+tributary
+informal
+herbaceous
+sanctity
+undo
+restoration
+artist
+vicinity
+soak
+vacation
+median
+pivot
+mohammedan
+negligence
+uniformly
+echo
+modify
+testify
+absurd
+seaweed
+collector
+projectile
+aberration
+piston
+saturate
+fallow
+marrow
+vomiting
+infirm
+testing
+enjoy
+languish
+precedent
+stationary
+rudder
+inanimate
+payable
+seventy
+clutch
+china
+peaceful
+sister
+sucking
+preach
+prosecution
+jaundice
+gradient
+hypothesis
+adjoining
+penitent
+comment
+concealment
+perpetual
+airy
+rostrum
+dying
+accuse
+talking
+inherit
+tumbler
+thrown
+palatine
+defraud
+basin
+calmness
+symphony
+rudimentary
+negotiate
+wandering
+quail
+revelation
+greatness
+answerable
+besides
+widow
+alter
+falsify
+autumn
+oval
+crossbow
+recede
+broker
+hatch
+erection
+adjunct
+tense
+habitation
+umbrella
+inclining
+manuscript
+realize
+mild
+folly
+ascending
+patronage
+wedlock
+getting
+snake
+coordinate
+abridge
+dismal
+senior
+oblong
+sequester
+organization
+diseased
+vacuum
+bryozoa
+immersion
+focus
+plait
+founded
+offered
+dioxide
+unfeeling
+fluke
+chicken
+eleven
+maintenance
+puddling
+moisten
+hound
+russian
+perforated
+retainer
+olfactory
+roundish
+warehouse
+granite
+frozen
+launch
+quarry
+splinter
+gunpowder
+molding
+heighten
+surveying
+trough
+searching
+stab
+elevator
+extraction
+refinement
+crotchet
+use
+mound
+rhythm
+ivory
+desirous
+attainment
+rectify
+indolent
+jerk
+umbilicus
+smack
+muse
+auction
+allegiance
+define
+apparently
+tarsus
+inability
+baggage
+boarding
+folding
+junior
+might
+clamp
+changed
+possessed
+passable
+bromine
+sent
+spill
+delirium
+coffin
+trickery
+withered
+polar
+utterly
+stagnant
+phosphorus
+coalesce
+consecrate
+straddle
+submissive
+shatter
+ulcer
+toss
+shameful
+daily
+disengage
+ruler
+bearer
+bony
+calyx
+wickedness
+placed
+changing
+felicity
+poll
+vocation
+attendance
+falsely
+clip
+wonderful
+potash
+bodies
+substantive
+liberate
+ritual
+prevailing
+telegraph
+carrier
+courteous
+waist
+herd
+aspirate
+intend
+potent
+enhance
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/hangman.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/hangman.py
new file mode 100644
index 0000000000000000000000000000000000000000..7907d8ebebed3198b2e1d9f7404d975aa7d6413d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/hangman.py
@@ -0,0 +1,79 @@
+"""Exercise 4.12-4.16."""
+def is_word_guessed(secret_word, letters_guessed):
+ """Determine if the word has been guessed.
+
+ :param secret_word: The word to guess
+ :param letters_guessed: A ``str`` containing the letters that have currently been guessed
+ :return: True if and only if all letters in ``secret_word`` have been guessed.
+ """
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return guessed
+
+
+def get_guessed_word(secret_word, letters_guessed):
+ """Format the secret word for the user by removing letters that have not been guessed yet.
+
+ Given a list of the available letters, the function will replace the letters in the secret word with `'_ '`
+ (i.e., a lower-case followed by a space). For instance, if the secret word is ``"cat"``, and the
+ available letters are ``"ct"``, then the function should return ``"c_ t"``.
+
+ :param secret_word: A ``str``, the word the user is guessing
+ :param letters_guessed: A ``str`` containing which letters have been guessed so far
+ :return: A ``str``, comprised of letters, underscores (_), and spaces that represents which letters in secret_word have been guessed so far.
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return word
+
+
+def get_available_letters(letters_guessed):
+ """
+ Return the letters which are available, i.e. have not been guessed so far.
+
+ The input string represents the letters the user have guessed, and the output should then be the lower-case
+ letters which are not contained in that string. The function is used to show the user which
+ letters are available in each round.
+
+ :param letters_guessed: A `str` representing the letters the user has already guessed
+ :return: A `str` containing the letters the user has not guessed at yet.
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return letters
+
+
+
+def hangman(secret_word, guesses):
+ """
+ Play an interactive game of Hangman.
+
+ This function should launch an interactive game of Hangman. The details of the game is defined in the
+ project description available online, and should be read carefully.
+
+ * The game should first display how many letters are in the secret word. You should start by generating this output.
+ * Before each round, the user should see how many guesses that are left and which letters are not yet used
+ * In each round, the user is prompted to input a letter. Use the ``input('..')`` function for this.
+ * The user is given feedback based on whether the letter is in the word or not. The program also performs error handling.
+ * The game terminates when the user win, has exceeded the number of guesses, or if the user makes an illegal input.
+ in this case the user is shown a score.
+
+ :param secret_word: The secret word to guess, for instance ``"cow"``
+ :param guesses: The number of available guesses, for instance ``6``
+ """
+ from cp.ex05.hangman_hints import _hangman
+ # TODO: Code has been removed from here.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+
+if __name__ == "__main__":
+ print("This should return True: ", is_word_guessed("dog", "tdohg"))
+ print("This should return False: ", is_word_guessed("dog", "dthk"))
+
+ print("This should be 'c_ w': ", get_guessed_word('cow', 'kcwt'))
+
+ print("Available letters when we have tried 'abcdefghijk'; this should be about half the alphabet: ", get_available_letters('abcdefghijk'))
+
+ print("Lets launch hangman. Try the inputs in the exercise description and see if you get the same")
+ hangman("cow", 4)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/math.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/math.py
new file mode 100644
index 0000000000000000000000000000000000000000..13fb48b8daa481dbb379f0b60ba38b18b7b55141
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/math.py
@@ -0,0 +1,25 @@
+"""Exercise 4.1 and 4.2."""
+import math
+
+def square_root(a):
+ r"""Compute the square root, see section 7.5 in Think Python.
+
+ :param a: A number to compute the square root of.
+ :return: :math:`\sqrt{a}`.
+ """
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return sqrt_a
+
+def ramanujan():
+ r"""Compute the Ramanujan approximation of :math:`\pi` using a sufficient number of terms.
+
+ :return: A high-quality approximation of :math:`\pi` as a ``float``.
+ """
+ # TODO: 9 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return pi
+
+if __name__ == "__main__":
+ print("approximate pi", ramanujan())
+ print("square root of 2 is", square_root(2))
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/palindrome.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/palindrome.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c25e4cbf50595e994523503d82dcb21ab7257e6
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/palindrome.py
@@ -0,0 +1,15 @@
+"""Exercise 4.1: Checking if a word is a palindrome."""
+
+def is_palindrome(word):
+ """Check if ``word`` is a palindrome.
+
+ :param word: The word to check
+ :return: ``True`` if input is a palindrome and otherwise ``False``
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return is_a_palindrome
+
+if __name__ == "__main__":
+ print("Is Madam a palindrome?", is_palindrome('madam'))
+ print("Is gentleman a palindrome?", is_palindrome('gentleman'))
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/parenthesis.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/parenthesis.py
new file mode 100644
index 0000000000000000000000000000000000000000..ced89d285e6c4b0ee69a5d822c078fe0cd9489ac
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/parenthesis.py
@@ -0,0 +1,82 @@
+"""Exercise 4.x-4.y."""
+def matching(expression):
+ """Tell if the parenthesis match in a mathematical expression.
+
+ For instance, the parenthesis match in ``"3x(y-1)(3y+(x-1))"`` but not in ``"3x(y-4))"``
+
+ :param expression: An expression containing zero or more parenthesis.
+ :return: ``True`` if the number of open/close parenthesis match, otherwise ``False``
+ """
+ # TODO: 9 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return matching
+
+def find_index_of_equality(expression):
+ """Find an index ``i`` which split the expression into two balanced parts.
+
+ Given an expression containing opening and closing parenthesis, for instance ``"(()())"``, this function should
+ return an index ``i``, such that when the string is split at ``i``, the
+ number of opening parenthesis ``(`` in the left-hand part equal the number of closing parenthesis ``)`` in the
+ right-hand part. For instance, if ``i=2``, the expression is split into the right, left hand parts:
+
+ - ``"(()"``
+ - ``"())"``
+
+ In this case the left-hand part contains ``2`` opening parenthesis and ``2`` closign parenthesis so ``i`` is the right index.
+ Similarly, for ``"()"``, the answer would be ``1``.
+
+ :param expression: An expression only consisting of opening and closing parenthesis.
+ :return: The index ``i`` as an int.
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return i
+
+
+def print_the_dialogue(s):
+ """Print all dialogue in a manuscript.
+
+ Given a manuscript (as a ``str``), this function will find all lines of dialogue to the console, one line of
+ dialogue per printed line. Dialogue is enclosed by double ticks, i.e. as
+ ``"He said ''so far so good!'', as he fell from the top of the building"``.
+
+ :param s: The manuscript as a ``str``.
+ """
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def find_innermost_part(s):
+ """Find the innermost part of a mathematical expression.
+
+ The innermost part is a substring enclosed in parenthessis but not containing parenthesis.
+ For instance, given ``"3(x+(4-y^2))"``, then ``"4-y^2"`` is an inner-most part.
+ The parenthesis can be assumed to match.
+
+ :param s: The mathematical expression as a ``str``
+ :return: The innermost part as a ``str``
+ """
+ # is_inner = True
+ # cur_inner = ""
+ # inner_words = []
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return inner_part
+
+
+
+
+
+if __name__ == "__main__":
+ print("Does the parenthesis match?", matching("2x(x+2)"))
+ print("Does the parenthesis match?", matching("2x(x+(2-y)^2)"))
+ print("Does the parenthesis match?", matching("4x"))
+
+ print("Does the parenthesis match?", matching("2x(x+2"))
+ print("Does the parenthesis match?", matching("2x)(x"))
+ print("Does the parenthesis match?", matching("4x()(()))"))
+
+ s = "(())))("
+
+ print("Index of equality for", s, "is", find_index_of_equality(s))
+ dialogue = "He said: ''How are you old wife''? She answered, perplexed, ''I am not your wife''"
+ print_the_dialogue(dialogue)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/play_hangman.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/play_hangman.py
new file mode 100644
index 0000000000000000000000000000000000000000..295c77b20e01eea4cd0d544dee5d9de30a7af767
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/play_hangman.py
@@ -0,0 +1,37 @@
+"""Play a game of hangman by loading a random word."""
+import random
+import os
+
+def load_words():
+ """
+ Return a list of valid words. Words are strings of lowercase letters.
+
+ Depending on the size of the word list, this function may
+ take a while to finish.
+
+ :return: The available words as a list.
+ """
+ from cp.ex08.words import load_words
+ wordlist = load_words(os.path.join(os.path.dirname(__file__), "files", "5000-words.txt")).split()
+ return wordlist
+
+
+def choose_word(wordlist):
+ """Select a word at random.
+
+ wordlist (list): list of words (strings)
+ Returns a word from wordlist at random
+
+ :param wordlist: The list of words to sample from.
+ :return: A randomly selected word.
+ """
+ return random.choice(wordlist)
+
+
+# end of helper code
+
+# -----------------------------------
+
+# Load the list of words into the variable wordlist
+# so that it can be accessed from anywhere in the program
+wordlist = load_words(os.path.join(os.path.dirname(__file__), "files", "5000-words.txt"))
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/prefix.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/prefix.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9f8790ea4162fe6965c639ee58e306566742d0b
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/prefix.py
@@ -0,0 +1,52 @@
+"""Exercise XX."""
+
+
+def common_prefix(word1, word2):
+ """
+ Return the longest string so that both ``word1``, and ``word2`` begin with that string.
+
+ :param word1: First word
+ :param word2: Second word
+ :return: The longest common prefix.
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return prefix
+
+def common_prefix3(word1, word2, word3):
+ """
+ Return the longest string so that both ``word1``, ``word2``, and ``word3`` begin with that string.
+
+ :param word1: First word
+ :param word2: Second word
+ :param word3: Third word
+ :return: The longest common prefix.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return prefix
+
+
+def common_prefix_list(words):
+ """Return a common prefix of a list of words. I.e. the longest string all words begin with.
+
+ :param words: A list of words.
+ :return: The longest common prefix as a ``str``.
+ """
+ if len(words) == 0:
+ return ""
+ l = min(len(w) for w in words)
+ prefix = ""
+ for i in range(l):
+ c = words[0][i]
+ if all(w[i] == c for w in words):
+ prefix += words[0][i]
+ else:
+ break
+ return prefix
+
+
+# Driver Code
+if __name__ == "__main__":
+ print("The longest Common Prefix is :", common_prefix("egregious", "egg"))
+ print("The longest Common Prefix is :", common_prefix3("egg", "egregious", "eggplant"))
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex04/reverso.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/reverso.py
new file mode 100644
index 0000000000000000000000000000000000000000..953ec459b242186926d6922aef6c597427f73f87
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex04/reverso.py
@@ -0,0 +1,15 @@
+"""Exercise 4.1."""
+
+def reverso(text):
+ """Return a new text where each word has been reversed.
+
+ :param text: Input text as a ``str``
+ :return: The same text with the words reversed
+ """
+ reversed = " ".join( [reversed(w) for w in text.split(" ")] )
+ return reversed
+
+if __name__ == "__main__":
+ text = "The cat sat on the hat"
+ print(text)
+ print(reverso(text))
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex05/hangman_hints.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/hangman_hints.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecfa4b8ecebc6aa112ec9e6ab4cfc36162af5406
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/hangman_hints.py
@@ -0,0 +1,58 @@
+"""Hangman game with hints.
+
+https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/resources/ps2/
+(note only second part of this project is here. Use short word list)
+"""
+import sys
+import os
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.dirname(SCRIPT_DIR))
+
+from ex04.hangman import is_word_guessed, get_guessed_word, get_available_letters
+from ex08.words import load_words
+
+
+
+def match_with_gaps(my_word:str, other_word:str) -> bool:
+ """Return True if ``other_word`` match ``my_word``. Underscores and spaces are taken into account.
+
+ :param my_word: The current (guessed) word such as ``"c_ t"``
+ :param other_word: The other word to match, for instance ``"cat"``.
+ :return: True if the guess match the other wor.
+ """
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+def show_possible_matches(my_word:str) -> None:
+ """Print out all the words that matches ``my_word``.
+
+ The string variable ``my_word`` represents the information currently available to the user and it is in the
+ format ``"c_ _ "``.
+
+ :param my_word: The guess.
+ """
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def _hangman(secret_word:str, guesses:int, use_hints:bool=False) -> None:
+ # TODO: 24 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def hangman_with_hints(secret_word:str, guesses:int) -> None:
+ """
+ Play a version of hangman where the user can also get hints.
+
+ The format of this function follows the previous hang-man program, with one change: If the user type in ?,
+ the program should display the possible words as a hint.
+
+ :param secret_word: The word to guess.
+ :param guesses: How many guesses are available.
+ """
+ # TODO: Code has been removed from here.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+
+if __name__ == "__main__":
+ hangman_with_hints(secret_word="hello",guesses=10)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex05/indexing.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..61c7af446e8cd4a4682e3962ee135e692846a08b
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/indexing.py
@@ -0,0 +1,67 @@
+"""Exercise 5.2: Indexing."""
+
+from typing import Union
+
+
+def middle_element(in_list:list) -> object:
+ """Return middle element of ``in_list`` is ``in_list`` has odd length and return two middle elements if ``in_list`` has even length.
+
+ :param in_list: list
+ :return: middle element of ``in_list``
+ """
+ assert len(in_list) > 2
+ # TODO: 5 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_list
+
+def add_first_and_last(in_list:list) -> object:
+ """Return sum of first and last element in ``in_list``.
+
+ :param in_list: list
+ :return: middle element of ``in_list``
+ """
+ assert len(in_list) >= 2
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_val
+
+def slice_list_in_percent(in_list:list, start_p: float, end_p: float) -> list:
+ """Slice a list such that the first ``start_p`` percent and last ``end_p`` is removed.
+
+ :param in_list: list
+ :param start_p: start percentage, decimal number between 0 and 1
+ :param end_p: end percentage, decimal number between 0 and 1
+ :return: middle element of ``in_list``
+ """
+ assert len(in_list) >= 2
+ assert 0 <= start_p < end_p <= 1
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_list
+
+def get_longest_sublist(in_list:list) -> list:
+ """Get longest sublist from a list of lists.
+
+ :param in_list: a list of lists
+ :return: longest sublist in ``in_list``
+ """
+ assert len(in_list) > 0
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return longest_list
+
+if __name__ == "__main__":
+ in_list = [1,2,3,4,5]
+ out_list = middle_element(in_list)
+ print(f"The middle elements of {in_list} is {out_list}")
+
+ result = add_first_and_last(in_list)
+ print(f"The sum of first and last elements is {result}")
+
+ in_list = [1,2,3,4,5,6,7,8,9,10]
+ out_list = slice_list_in_percent(in_list,0.3,0.6)
+ print(f"Slicing the list {in_list} gives {out_list}")
+
+ in_list = [[1,2],[3,4,5],[6,7,8,9],[10]]
+ out_list = get_longest_sublist(in_list)
+ print(f"The longest sublist of {in_list} is {out_list}")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex05/primes.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/primes.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba84aba4b58ade67084e6f2c980496113b506ea9
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/primes.py
@@ -0,0 +1,32 @@
+"""Exercise 5.1: Prime number exercise."""
+
+def is_prime(n:int) -> bool:
+ """Check if ``n`` is a prime number.
+
+ :param n: integer
+ :return: ``True`` if ``n`` is a prime number and ``False`` if not.
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def create_prime_list(lower_int:int,upper_int:int) -> list:
+ """Create a list of all prime numbers between and including ``lower_int`` and ``upper_int``.
+
+ :param lower_int: lower bound to range
+ :param upper_int: upper bound to range
+ :return: ``out_list`` cotaining all the prime numbers between
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return prime_list
+
+
+if __name__ == "__main__":
+ num = 2503
+ check = is_prime(2503)
+ if check:
+ print(f"{num} is a prime number")
+ else:
+ print(f"{num} is not a prime number")
+ prime_list = create_prime_list(2,11)
+ print(prime_list)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex05/updating_lists.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/updating_lists.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cac948ca0f44bd4af5a76e6cb3a099303205646
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex05/updating_lists.py
@@ -0,0 +1,33 @@
+"""Exercise 5.3: Updating lists."""
+
+from typing import Union
+
+
+def remove_at(in_list:list,pos:int) -> list:
+ """Remove the ``pos`` index in ``in_list`` and return the resulting list.
+
+ :param in_list: input list
+ :param pos: integer position of where to be removed
+ :return: ``in_list``
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_list
+
+def remove_value(in_list:list,val:object) -> list:
+ """Return sum of first and last element in ``in_list``.
+
+ :param in_list: input list
+ :param val: value of what to be removed from the list
+ :return: middle element of ``in_list``
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_list
+
+if __name__ == "__main__":
+ in_list = ["one","two","three","four","five","six","seven","eight"]
+ out_list = remove_at(in_list,2)
+ print(out_list)
+ out_list = remove_value(in_list,"six")
+ print(out_list)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/5000-words.txt b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/5000-words.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d44bab9bbb4dcc26e61a0ac68f8904936cf5bc9a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/5000-words.txt
@@ -0,0 +1,5000 @@
+species
+practice
+natural
+measure
+present
+form
+order
+quality
+condition
+state
+action
+element
+part
+division
+subject
+ground
+power
+station
+register
+person
+place
+ordinary
+strong
+under
+make
+light
+exercise
+exchange
+parallel
+charge
+support
+judgment
+substance
+figure
+strength
+sound
+service
+quantity
+standard
+right
+point
+character
+strike
+discharge
+force
+return
+spring
+square
+between
+without
+water
+spirit
+distance
+contract
+positive
+position
+straight
+moderate
+double
+superior
+certain
+compound
+interest
+language
+passage
+business
+through
+manner
+relation
+general
+process
+strain
+delicate
+bearing
+property
+advance
+account
+original
+religion
+round
+over
+principal
+sharp
+surface
+line
+degree
+report
+course
+matter
+sentence
+body
+express
+close
+quarter
+head
+negative
+take
+plant
+argument
+increase
+house
+movement
+table
+balance
+separate
+small
+back
+entrance
+settle
+reason
+machine
+common
+material
+scale
+authority
+capable
+anything
+regular
+stock
+break
+opposite
+into
+distress
+work
+standing
+cross
+color
+number
+stroke
+convert
+radical
+relative
+function
+stand
+press
+question
+peculiar
+progress
+together
+touch
+capacity
+physical
+horse
+specific
+external
+produce
+incapable
+passion
+represent
+promise
+tender
+issue
+family
+range
+domestic
+shoulder
+change
+approach
+transfer
+carriage
+feeling
+security
+something
+direction
+pressure
+frame
+like
+free
+company
+inferior
+distinct
+variety
+solution
+capital
+grain
+deposit
+circular
+receive
+pleasure
+particular
+office
+faculty
+motion
+personal
+country
+narrow
+occasion
+open
+addition
+second
+complete
+short
+ancient
+contrary
+serve
+disorder
+crown
+mark
+weight
+large
+white
+tongue
+mountain
+address
+vessel
+throw
+science
+system
+turn
+object
+temper
+internal
+base
+pass
+familiar
+principle
+another
+that
+hold
+compass
+pitch
+influence
+enter
+command
+reduce
+level
+surprise
+time
+bottom
+face
+flower
+extreme
+raise
+purpose
+nature
+answer
+down
+against
+stick
+clear
+record
+article
+discourse
+string
+shell
+side
+fall
+backward
+determine
+several
+forward
+period
+absolute
+taste
+draw
+waste
+running
+spread
+chamber
+cause
+official
+province
+flat
+instrument
+native
+conduct
+maintain
+descent
+block
+presence
+simple
+lead
+weather
+direct
+which
+opinion
+justice
+guard
+master
+appearance
+season
+regard
+check
+shoot
+society
+liquid
+rule
+pattern
+sphere
+directly
+cast
+life
+definite
+private
+give
+knowledge
+cover
+weak
+draught
+section
+sovereign
+stop
+organic
+screw
+violence
+decision
+render
+beat
+rise
+current
+affection
+evidence
+great
+court
+silver
+hand
+correct
+roll
+honor
+wind
+plate
+channel
+single
+keep
+heat
+cylinder
+divide
+release
+after
+respect
+military
+stomach
+some
+root
+timber
+structure
+school
+exterior
+syllable
+piece
+feather
+pledge
+supply
+christian
+branch
+religious
+necessary
+father
+execution
+affected
+knot
+carry
+other
+circle
+kind
+worship
+style
+example
+effect
+obscure
+well
+violent
+horn
+english
+drive
+outside
+better
+church
+mixture
+with
+equal
+reproach
+bear
+volatile
+equivalent
+slender
+remove
+being
+preserve
+grace
+earth
+elevation
+design
+stream
+furnish
+dress
+little
+writing
+spiritual
+black
+twist
+picture
+from
+smooth
+estate
+fine
+anchor
+joint
+heart
+expression
+reach
+center
+contest
+trust
+special
+shall
+friend
+high
+call
+train
+traverse
+irregular
+wrong
+sign
+watch
+catch
+animal
+stone
+dependent
+before
+relief
+thought
+fortune
+will
+active
+formation
+valuable
+soft
+about
+operation
+bank
+heavy
+solid
+ring
+proportion
+symbol
+opposition
+apparent
+abstract
+portion
+dignity
+revolution
+post
+voluntary
+length
+market
+harmony
+prepare
+perform
+land
+board
+shaft
+neglect
+iron
+value
+attempt
+hard
+judge
+proper
+mouth
+moral
+district
+interior
+genus
+union
+purple
+husband
+wing
+broken
+foreign
+divine
+perfect
+last
+compact
+escape
+extract
+demand
+flexible
+still
+wheel
+long
+burden
+labor
+note
+interval
+column
+destitute
+reference
+letter
+control
+freedom
+swallow
+medicine
+history
+difference
+people
+strip
+contempt
+handle
+decline
+good
+liberty
+brilliant
+fashion
+engage
+rest
+impression
+offensive
+drawing
+purchase
+minute
+metal
+texture
+doctrine
+cutting
+lower
+paper
+sight
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+follow
+judicial
+formal
+salt
+blow
+member
+fellow
+mean
+mother
+thread
+corrupt
+failure
+associate
+relieve
+display
+full
+distant
+disease
+sense
+liberal
+intended
+gather
+play
+blood
+swell
+stuff
+bill
+opening
+quarrel
+world
+secure
+stage
+government
+plane
+public
+composition
+allowance
+thing
+establish
+essential
+gentle
+consider
+examine
+attack
+benefit
+height
+exhibit
+movable
+voice
+tropical
+platform
+alternate
+have
+catholic
+bound
+advantage
+bright
+thrust
+marble
+mercury
+alcohol
+making
+throat
+foot
+shelter
+elastic
+slight
+painting
+inclose
+stem
+reduction
+vegetable
+breast
+drum
+guide
+generation
+study
+within
+succession
+dressing
+powerful
+staff
+notice
+downward
+upright
+favor
+thick
+trade
+extend
+custom
+faith
+mechanical
+turning
+front
+agreement
+glass
+extension
+normal
+green
+exposure
+medium
+product
+application
+term
+vertical
+shape
+choice
+more
+somewhat
+chance
+result
+residence
+party
+wear
+confidence
+soldier
+continue
+powder
+found
+learning
+situation
+bitter
+struggle
+finger
+shield
+officer
+rough
+field
+space
+privilege
+delicacy
+manifest
+sacred
+race
+organ
+behind
+breath
+belonging
+phrase
+border
+marine
+breathing
+flesh
+stretch
+musical
+imperfect
+keeping
+trouble
+fresh
+different
+image
+plaster
+numerous
+existence
+surround
+commerce
+passing
+rank
+around
+mill
+popular
+counter
+fruit
+praise
+hence
+beneath
+loose
+hammer
+amount
+pack
+secondary
+sweet
+meeting
+tone
+inclined
+refuse
+game
+floating
+mineral
+devotion
+memory
+individual
+withdraw
+size
+severe
+declare
+warrant
+desire
+fish
+provide
+title
+smoke
+suffer
+social
+bond
+virtue
+doubtful
+speech
+sympathy
+ready
+feed
+metallic
+fast
+step
+leave
+poison
+worthy
+entire
+easily
+steel
+colored
+disguise
+variable
+speaking
+rising
+indicate
+band
+primary
+agitation
+worm
+quick
+flight
+discipline
+careless
+secret
+lock
+profession
+affect
+minister
+excess
+flourish
+pole
+tooth
+situated
+mind
+disgrace
+censure
+estimate
+treat
+dead
+chemical
+grave
+deliver
+tail
+drop
+attention
+surrender
+hydrogen
+american
+sheet
+building
+preparation
+attend
+plain
+fabric
+limit
+appear
+fasten
+bind
+swelling
+show
+striking
+witness
+ornament
+possession
+bishop
+name
+collect
+hinder
+beyond
+beginning
+case
+covenant
+marriage
+foul
+yield
+type
+handsome
+uniform
+whatever
+earnest
+proof
+himself
+segment
+former
+critical
+difficulty
+proceed
+walk
+restrain
+prince
+setting
+trick
+leader
+female
+universal
+floor
+fire
+false
+depression
+seal
+class
+similar
+stamp
+sleep
+destroy
+following
+resemble
+formerly
+match
+management
+growth
+suit
+patient
+aggregate
+various
+south
+projection
+habit
+tissue
+dispute
+move
+sacrifice
+become
+agreeable
+credit
+partial
+chain
+broad
+northern
+heavenly
+pipe
+arrangement
+impulse
+sport
+apply
+live
+tension
+write
+above
+wood
+furnace
+circuit
+token
+decree
+trial
+shade
+child
+engaged
+imitation
+meet
+truth
+fair
+offer
+supreme
+constant
+sensible
+properly
+share
+device
+pair
+sugar
+solemn
+restore
+commission
+happiness
+word
+exact
+criminal
+model
+belief
+delivery
+spot
+suitable
+commonly
+ceremony
+convey
+search
+behavior
+printing
+measured
+frequent
+ball
+profit
+especially
+whole
+admit
+muscular
+thin
+singular
+related
+vapor
+crowd
+quiet
+method
+living
+first
+fault
+skin
+reverse
+college
+royal
+chief
+want
+battle
+appetite
+disposition
+recover
+beam
+drink
+reverence
+converse
+essence
+same
+deprive
+deep
+nervous
+wide
+hawk
+compose
+skeleton
+error
+nucleus
+proposition
+bend
+ship
+drinking
+difficult
+whip
+important
+swimming
+hanging
+empty
+wash
+obligation
+cotton
+nerve
+generally
+prayer
+century
+view
+servant
+coloring
+exertion
+incline
+unite
+observation
+middle
+community
+territory
+bone
+story
+colorless
+shallow
+solitary
+doubt
+acid
+slip
+speak
+deceive
+apparatus
+flow
+even
+dominion
+introduce
+severity
+region
+intimate
+becoming
+meaning
+philosophy
+extremity
+gravity
+forth
+diameter
+near
+scratch
+assembly
+east
+shoe
+prevent
+success
+spindle
+what
+activity
+bolt
+extensive
+brief
+project
+book
+cunning
+drift
+accident
+indirect
+protection
+evil
+group
+burning
+yellow
+fancy
+particle
+confusion
+document
+energy
+exhaust
+series
+reception
+utter
+yielding
+remote
+stiff
+wild
+discover
+policy
+angle
+below
+tree
+literary
+rate
+pursuit
+clothing
+engine
+trunk
+claim
+previous
+grant
+tribe
+extinct
+obtain
+furniture
+sequence
+imperial
+petition
+rope
+pleasing
+remedy
+oblique
+warm
+composed
+fill
+blue
+economy
+sprinkle
+smell
+promote
+money
+resolve
+covering
+needle
+blind
+acute
+foolish
+fossil
+european
+worth
+combine
+companion
+volume
+rather
+gain
+exciting
+look
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/hamlet.txt b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/hamlet.txt
new file mode 100644
index 0000000000000000000000000000000000000000..522e7bdc77e2761a53ee023c806f118aa91a8e6b
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/hamlet.txt
@@ -0,0 +1,22 @@
+The Lord Chamberlain walked busily among the courtiers assembled in the main hall, fussing about the arrangements, making sure that everyone was in his or her place. He had summoned two lords, Voltimand and Cornelius, because the king wished to send them on an important mission. His son, Laertes, was there too. There had been some tension between them because Laertes wanted to return to Paris. He had come to Elsinore for the funeral of the late King Hamlet but he was missing the life he had made for himself in Paris. The Lord Chamberlain disapproved of his son’s Parisian lifestyle and was considering opposing his departure, which required King Claudius’ permission.
+
+The great doors opened. The trumpeters raised their instruments and played a fanfare. The king and queen swept in, followed by the queen’s son, Prince Hamlet. The royal couple were splendidly dressed in colourful robes of state. Hamlet wore a black mourning suit.
+
+The king and queen mounted the dais and sat down on their chairs of state. Hamlet joined the courtiers as they stood facing them, and stared at his uncle.
+
+Claudius stood to address the assembly.
+
+‘Although the memory of our dear brother Hamlet’s death is still green,’ he began, ‘and although we have mourned him in company with all our subjects, we should now find a balance. We should temper our public sorrow and remember him privately as we return to the consideration of our normal affairs. Therefore our former sister, now our queen, our imperial partner in the conduct of this warlike nation, we have, as it were, with mixed emotions, married. It was something of a low key wedding, with some happiness in the face of a funeral, and pain on the occasion of a marriage, in equal measure, weighing delight and solemnity. Nor have we declined to take your advice, which fully supported this marriage.’
+
+
+He bowed graciously, embracing the company with a warm smile. ‘For all that, our thanks.’
+
+He became businesslike. ‘Now. You know that Fortinbras, the young Norwegian prince, underestimating us, thinks that because of our late dear brother’s death our state is in disarray. That, together with what he imagines to be his military superiority, inspires him to pester us with the demand that we surrender those lands that his father lost, quite legally, to our most brave brother.’ He snapped his fingers. ‘So much for him. Now, as for ourself and the reason for this meeting: this is what the business is: we have written to the king of Norway, the uncle of young Fortinbras. He is weak and bedridden and doesn’t know what his nephew is up to so he’s unable to check him. He doesn’t know that his nephew is using his resources to build up his own army. Therefore we are sending you, good Cornelius, and you Voltimand, to take this letter to old Norway, giving you no further personal power to do business with the king other than the instructions set out in detail here. Farewell, and let your haste show your sense of duty.’
+
+The ambassadors went forward and took the letter. They bowed and assured the king that they would show their duty in all things.
+
+Claudius nodded graciously. ‘We don’t doubt that. With all our heart, farewell.’
+
+When they had gone the king beckoned to Laertes, smiling to put him at his ease.
+
+‘And now, Laertes, what’s the news with you?’ he said. ‘You told us of some suit. What is it, Laertes?’ Laertes cleared his throat and Claudius smiled more broadly. ‘If you want to speak reason to the Dane you mustn’t lose your voice. What could you ask, Laertes, that I wouldn’t give you? The head and the heart, the hand and the mouth, don’t work more closely together than the throne of Denmark and your father do. What would you like, Laertes?’
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/names.txt b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a7ff72c1fa2102c3b2e7724064fdb3c8cd4c2572
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/files/names.txt
@@ -0,0 +1,1406 @@
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+Mikki
+Xiao
+Nore
+Jiayi
+Shannon
+Hüda
+Sondos
+Amine
+Majlind
+Sui
+Francis
+Nickie
+Cassiel
+Xiao
+Elke
+Acelya
+Iliya
+Maxime
+Ayomide
+Shu
+Huyen
+Keita
+Iraj
+Melikas
+Anele
+Dara
+Jino
+Leith
+Sadaf
+Najah
+Ke
+Fu
+Sabri
+Gry
+Rei
+Anur
+Mawuli
+Tri
+Muhan
+Soya
+Eri
+Anuk
+Maris
+Shuo
+Ngun
+Cecil
+Ilay
+Lamar
+Rin
+Ching
+Jackie
+Lali
+Naher
+Jood
+Asa
+Svale
+Diamond
+Maj
+Tin
+Leone
+Bertie
+Sun
+Liel
+Vinter
+Alix
+Hosanna
+Jamie
+Caspian
+Thais
+Erle
+Brooklyn
+Ty
+Mung
+Silva
+Ilian
+Ryle
+Sri
+Samar
+Zephyr
+Hajar
+Finley
+Lykke
+Yong
+Shah
+Brooklyn
+Hasti
+Sandi
+Vivan
+Roma
+Sasi
+Emaan
+Nicol
+Manda
+Zema
+Iraj
+Nile
+Arta
+Dane
+Jiayi
+Sree
+Charley
+Lin
+Kami
+Elias
+Juno
+Iman
+Tri
+Trong
+Chriss
+Ville
+Nisan
+Kari
+Halle
+Joa
+Ngun
+Chriss
+Akira
+Andrea
+Yixuan
+Elo
+Trin
+Shad
+Noël
+Ricci
+Truc
+Falke
+Hoa
+An
+Shiva
+Gün
+Shannon
+Karli
+Tian
+Heaven
+Bera
+Soul
+Aria
+Tawan
+Masih
+Anne
+Chansa
+Felice
+Rehan
+Perri
+Samsam
+Yuki
+Deep
+Maru
+Justice
+Hui
+Jazz
+Alem
+Renee
+Marlowe
+Gin
+Marlon
+Kiran
+Zihan
+Mattie
+Mire
+Benni
+Zana
+Cilla
+Liel
+Zaia
+Marie
+Noha
+Lind
+Kei
+Ezrah
+Hami
+Willow
+Ilia
+Toba
+Ilay
+Selam
+Elvan
+Sølve
+El
+Karly
+Nicci
+Tha
+Abel
+Bailey
+Sakhi
+Obi
+Iraj
+Maui
+Rukan
+Kha
+Aputsiaq
+Lingxi
+Aslaug
+Veera
+Hajin
+Mizan
+Nhyira
+Helmi
+Em
+Mischa
+Gui
+Rene
+Assana
+Sari
+Hejar
+Bowie
+Veera
+Nami
+Milou
+Miral
+Truc
+Valley
+Zana
+Monroe
+Man
+Rana
+Kuni
+Tonni
+Ivik
+Jannie
+Riyaan
+Sisu
+Mica
+Dominique
+Blue
+Sari
+Jules
+Mani
+Nika
+Dixi
+Lamar
+Ryan
+Vian
+Amine
+Ananda
+Mattie
+Mikah
+Levian
+Reddy
+Misal
+Sada
+Afnan
+Yilin
+Xin
+Seven
+Muhan
+Elisha
+Rama
+Hawi
+Eddy
+Lindi
+Nicki
+Yan
+Vanja
+Brooklyn
+Dhwani
+Miku
+Brooklyn
+Constantine
+Ami
+Vy
+Chandra
+Tich
+Sami
+Alaa
+Ilja
+Ari
+Jamal
+Alek
+Noël
+Ehsan
+Rojbin
+Sonny
+Noel
+Da
+Rey
+Len
+Xin
+Chang
+Lamar
+Amine
+Amaru
+Sigge
+Len
+Rahel
+Em
+Sian
+Gert
+Vy
+Karam
+Marie
+Na
+Delvin
+Xuan
+Ul
+Marlowe
+Hee
+Danila
+Karly
+Si
+Storm
+Hamdi
+Xian
+Kit
+Liel
+Chau
+Jonny
+Fate
+Charly
+Jaden
+Shan
+Manel
+Cléo
+Assana
+Pi
+Melian
+Soo
+Hang
+Haven
+Cass
+Ha
+Sanam
+Aston
+Ainhoa
+Sale
+Emaan
+Darma
+Kenny
+Vanja
+Roshan
+Ada
+Win
+Melian
+Lavey
+Bernie
+Aria
+Chervin
+Praise
+Rayne
+Nice
+Vic
+Malle
+Amel
+Tale
+Rojan
+Rani
+Bora
+Sasha
+Kai
+Suad
+Sika
+Gisa
+Joann
+Aputsiaq
+Jona
+Hunter
+Ola
+Nawal
+Shad
+Kataya
+Jay
+Echevarria
+Hawi
+Bailey
+Maddy
+Em
+Vigge
+Sølve
+Claude
+Aleksa
+Maes
+Sixuan
+Isa
+Hyun
+Sana
+Elif
+Roa
+Vi
+Shema
+Gigi
+Ajo
+Matti
+Aiko
+Alke
+Raja
+Yin
+Minel
+Aye
+Shaam
+Riyaan
+Görkem
+Tayler
+Alham
+Dali
+Lennie
+Yaran
+Jamie
+Charlie
+Nikola
+Novah
+Arin
+Ty
+Hajin
+Kha
+Addison
+Marlin
+Choi
+Avneet
+Ife
+Noël
+Van
+Elvan
+Shaker
+Thien
+Nukaaka
+Cho
+Danny
+Veera
+Ling
+Lois
+Mackenzie
+Ryle
+Suad
+Edem
+Jian
+Jude
+Frey
+Sree
+Wilke
+Kiwi
+Mavi
+Shuo
+Lilo
+Nouri
+Jess
+Hawi
+Kimm
+Melian
+Fate
+Ajo
+Naz
+Umi
+Elia
+Najah
+Max
+Shaam
+An
+Noah
+Akira
+Rojin
+Ocean
+Jaymi
+Elisa
+Jean
+Ferda
+Lemar
+Merdzhan
+Jan
+Pai
+Charley
+Ezra
+Shema
+Miran
+Alke
+Agni
+Azhar
+Zein
+Vin
+Naseem
+Sanny
+Elliott
+Arjin
+Adhvik
+Noir
+Cali
+Bobbie
+Michell
+Tan
+Kali
+Barri
+Jules
+Sena
+Yuna
+Deep
+Petri
+Yin
+Glenn
+Mu
+Emile
+Cennet
+Maes
+Nana
+Petri
+Tian
+Ryle
+Amal
+Lune
+Hani
+Elo
+Nile
+Zidan
+Sana
+Amani
+Ilya
+Aslaug
+Biruk
+Sanam
+Jose
+Sky
+Anmol
+Eli
+Wai
+Anuk
+Echevarria
+Arian
+Tesla
+Nazir
+Noa
+Io
+Bodhi
+Noé
+Joey
+Elvin
+Samora
+Nahid
+Evian
+Edom
+Dana
+Ravi
+Iliya
+Ran
+Sascha
+Blessing
+Charly
+Emaan
+Mudi
+Alpha
+Haven
+Elo
+Bode
+Satya
+Binh
+Meo
+Seyda
+Jaime
+Mal
+Eman
+Shema
+Minne
+Melian
+Maes
+Delvin
+Noël
+Misal
+Beren
+Dieumerci
+Ngun
+Tin
+Joann
+Sari
+Hayan
+Ul
+Alae
+Peng
+Rhythm
+Tasniim
+Marti
+Yee
+Baraka
+Maria
+Farai
+Xi
+Da
+Soul
+Xian
+Shams
+Najah
+Bennie
+An
+Birke
+Aiden
+Hoa
+Hoa
+Eden
+Hyo
+Benja
+Bennie
+Nour
+Shams
+Kerry
+Zulal
+Ruhan
+Halle
+Nuunu
+Avery
+Kenny
+Pim
+Destiny
+Danny
+Samar
+Nilo
+Alem
+Gry
+Marni
+Sonic
+Aycan
+Evian
+En
+Moksha
+Da
+Dhiya
+Azhar
+Thien
+Eddie
+Amari
+Maitri
+Elaf
+Yao
+Eliya
+Sixuan
+Mai
+Tui
+Joann
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex08/loading.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/loading.py
new file mode 100644
index 0000000000000000000000000000000000000000..b03dac569bca9faaacba9ed30a2d898f9eea1952
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/loading.py
@@ -0,0 +1,60 @@
+"""Exercise 8.X: Loading."""
+import os
+
+def this_path() -> str:
+ """Get the path of the current file.
+
+ This function retrieves the path of the current file.
+
+ :return: A string representing the path of the current file.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return pth
+
+def load_txt2str(path: str) -> str:
+ """Load text from a file.
+
+ This function reads the contents of a file and returns it as a string.
+
+ :param path: A string representing the path of the file to be loaded.
+ :return: The text content of the file as a string.
+ """
+ abs_path = this_path()
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return text
+
+def load_txt2list(path:str) -> list:
+ """Load text file content into a list of strings.
+
+ This function takes a file path as input, reads the content of the text file
+ located at the specified path, and returns a list of strings. Each string in
+ the list represents a line from the text file.
+
+ :param path: A string representing the path to the text file.
+ :return: A list of strings containing the lines of the text file.
+ """
+ abs_path = this_path()
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_list
+
+
+def word_counter(path: str) -> int:
+ """Count the number of words in a text file.
+
+ This function loads the text from a file and counts the number of words.
+
+ :param path: A string representing the path of the file to be counted.
+ :return: The number of words in the file as an integer.
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return num_words
+
+
+if __name__ == "__main__":
+ path = os.path.join("files", "hamlet.txt")
+ num_words = word_counter(path)
+ print(num_words)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex08/saving.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/saving.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc09135f135003c9d5ba864dce06c76781426720
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/saving.py
@@ -0,0 +1,60 @@
+"""Exercise 8.X: Saving."""
+import os
+from cp.ex08.loading import this_path,load_txt2str,load_txt2list
+
+def save_str2txt(content:str, name: str) -> None:
+ """Save a string content to a text file.
+
+ This function takes a string content and a file name as input and saves the content
+ to a text file in the 'files' directory relative to the current script's location.
+
+ :param content: A string representing the content to be saved.
+ :param name: A string representing the name of the file.
+ """
+ abs_path = this_path() + "/files/"
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def save_list2txt(content:list, name: str) -> None:
+ """Save a list of strings to a text file.
+
+ This function takes a list of strings and a file name as input and saves each item
+ of the list as a separate line in a text file. The file is saved in the 'files'
+ directory relative to the current script's location.
+
+ :param content: A list of strings representing the content to be saved.
+ :param name: A string representing the name of the file.
+ """
+ abs_path = this_path() + "/files/"
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def save_names_first_letters()->list:
+ """Save names grouped by their first letters.
+
+ This function reads a list of names from a text file, sorts them, and groups them
+ based on their first letters. Each group of names starting with the same letter
+ is saved to a separate text file in the 'files' directory relative to the current
+ script's location. The function returns a list of lists, where each inner list
+ contains the names starting with the corresponding letter.
+
+ :return: A list of lists containing the names grouped by their first letters.
+ """
+ out_list = []
+ # TODO: 17 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return out_list
+
+
+# def shuffle():
+# import numpy as np
+# names = load_txt2list("/files/names.txt")
+# np.random.seed(0)
+# names = np.array(names)
+# names = np.random.choice(names,size=len(names)).tolist()
+# save_list2txt(names,"names.txt")
+
+
+if __name__ == "__main__":
+ # shuffle()
+ save_names_first_letters()
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex08/words.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/words.py
new file mode 100644
index 0000000000000000000000000000000000000000..f960a5ddf3b3df7b33128b672057640a0207fb7e
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex08/words.py
@@ -0,0 +1,74 @@
+"""Exercise 8.X."""
+
+import os
+
+def load_words(path:str) -> str:
+ """Load a bunch of words.
+
+ This function loads a file containing 5000 words and returns them as a single string.
+ The words are separated by whitespace.
+
+ :param path: A string representing the path of the file to be loaded.
+ :return: A string containing 5000 words, separated by whitespace.
+ """
+ with open(path,'r') as f:
+ return f.read().strip() # Read file content and removing starting/trailing whitespace
+
+def words_starting_with(start):
+ """Return the words starting with ``start`` as a list. For instance, if called with ``"te"``, it should return ``[.., "telephone", "teapot", ..], etc.
+
+ :param start: The prefix of the words to print (as a ``str``)
+ :return: A list of all words starting with ``start``
+ """
+ words = load_words(os.path.join(os.path.dirname(__file__), "files", "5000-words.txt")) # Get a large string of all the words.
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return selected_words
+
+def get_words_of_length(n):
+ """Return a list of all words of length ``n``.
+
+ :param n: The length of the word to return
+ :return: A list of all words of length ``n``.
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return selected_words
+
+
+def words_writable_by_left_hand():
+ """Return a list of words that can be written by the left hand.
+
+ :return: A list of words writeable by the left hand.
+ """
+ words = load_words(os.path.join(os.path.dirname(__file__), "files", "5000-words.txt"))
+ # print(words.split())
+ lwords = []
+ for word in words.split():
+ if len([l for l in word if l in left_hand_letters]) == len(word):
+ lwords.append(word)
+ return lwords
+
+def longest_left_handed_word():
+ """Return the longest word writeable by the left hand.
+
+ :return: A word writable by the left hand.
+ """
+ lwords = words_writable_by_left_hand()
+ max_length = max([len(w) for w in lwords] )
+ longest_words = [w for w in lwords if len(w) == max_length]
+ return longest_words
+
+
+left_hand_letters = "qwertasdfgzxcvb"
+if __name__ == "__main__":
+ starts_with_te = words_starting_with("te")
+ print("There are", len(starts_with_te), "words starting with 'te'. The first two are:", starts_with_te[:2])
+
+ words_of_length_14 = get_words_of_length(14)
+ print("There are", len(words_of_length_14), "words of length 14. They are:", words_of_length_14[:2])
+
+ words = words_writable_by_left_hand()
+ print("A few words that can be written by left hand:", words[:3])
+
+ print("The longest words that can be written by left hand is", longest_left_handed_word())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/appointment.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/appointment.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2eec6cb4b07688b2e0419f6980b90283ddce768
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/appointment.py
@@ -0,0 +1,33 @@
+"""Exercise 9.2: Appointment class."""
+
+import sys
+import os
+
+
+from cp.ex09.patient import Patient
+
+
+class Appointment:
+ """Appointment class."""
+
+ def __init__(self, patient: Patient, procedure:str, scheduled_datetime: str, cost: float):
+ """Initialize an Appointment object.
+
+ :param patient: the patient associated with the appointment
+ :param procedure: the name of the procedure
+ :param scheduled_datetime: the date and time of the appointment
+ :param cost: the cost of the appointment
+ """
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+
+if __name__ == "__main__":
+ patient = Patient("John Doe", "Migraine", 38)
+ appointment = Appointment(patient, "MRI" , "2023-06-01 10:00", 100)
+ print(appointment) # should print: Patient: John Doe, Condition: Migraine, Procedure: MRI, Scheduled Time: 2023-06-01 10:00, Cost: $100.00
+
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/costs_specialty.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/costs_specialty.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bcafb391a5f5e961da7a3f7c79d518899a931e3
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/costs_specialty.py
@@ -0,0 +1,60 @@
+"""Exercise 9.4 + 9.5: Speciality look up and total cost computation."""
+import sys
+import os
+
+
+from cp.ex09.patient import Patient
+from cp.ex09.appointment import Appointment
+from cp.ex09.dicts.information import conditions_specialties, procedure_costs
+
+from typing import List
+
+def speciality_requirement(appointment: Appointment) -> str:
+ """Get the specialty requirement based on the patient's condition.
+
+ :param appointment: An instance of the Appointment class.
+ :return: The specialty requirement as a string.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return specialty
+
+def create_list_of_appointments(patient:Patient)->List:
+ """Create a list of appointments based on the patient's condition.
+
+ This function takes a Patient object as input and creates a list of Appointment objects
+ based on the patient's condition. Each appointment is associated with a specific procedure
+ and includes the patient, procedure, date, time, and cost.
+
+ :param patient: A Patient object representing the patient's information.
+ :return: A list of Appointment objects representing the created appointments.
+ """
+ five_random_dates = ["2023-06-01 10:00","2023-07-01 14:00","2023-07-21 12:30","2023-07-23 08:20","2023-07-30 16:00"]
+ # TODO: 4 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return list_of_appointments
+
+def compute_total_medical_bills(appointments: List) -> float:
+ """Compute the total medical bills for a list of appointments.
+
+ :param appointments: A list of Appointment instances.
+ :return: The total medical bills as a float.
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return total_costs
+
+if __name__ == "__main__":
+ patient = Patient("John Doe", "Migraine", 38)
+ list_of_appointments = create_list_of_appointments(patient)
+
+ # Ex. 9.4:
+ speciality = speciality_requirement(appointment=list_of_appointments[0])
+ print(speciality)
+
+ # Ex. 9.5:
+ total_costs = compute_total_medical_bills(appointments=list_of_appointments)
+ for appointment in list_of_appointments:
+ print(appointment)
+ print(f"${total_costs}")
+
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/dicts/information.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/dicts/information.py
new file mode 100644
index 0000000000000000000000000000000000000000..9367a119cf14459d00f38dea349a0993e41bc77c
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/dicts/information.py
@@ -0,0 +1,126 @@
+conditions_specialties = {
+ "Acne": "Dermatology",
+ "Eczema": "Dermatology",
+ "Psoriasis": "Dermatology",
+ "Melanoma": "Dermatology",
+ "Rosacea": "Dermatology",
+ "Fracture": "Orthopedics",
+ "Sprain": "Orthopedics",
+ "Osteoarthritis": "Orthopedics",
+ "ACL Tear": "Orthopedics",
+ "Carpal Tunnel Syndrome": "Orthopedics",
+ "Migraine": "Neurology",
+ "Epilepsy": "Neurology",
+ "Multiple Sclerosis": "Neurology",
+ "Parkinson's Disease": "Neurology",
+ "Stroke": "Neurology"
+}
+
+
+procedure_costs = {
+ "Acne": {
+ "Topical Medications": 50.0,
+ "Chemical Peels": 200.0,
+ "Microdermabrasion": 150.0,
+ "Laser Therapy": 300.0,
+ "Extraction of Comedones": 100
+ },
+ "Eczema": {
+ "Topical Corticosteroids": 80.0,
+ "Moisturizers": 30.0,
+ "Antihistamines": 50.0,
+ "Wet Wrap Therapy": 150.0,
+ "Phototherapy": 200
+ },
+ "Psoriasis": {
+ "Topical Medications": 70.0,
+ "Systemic Medications": 300.0,
+ "Biologic Therapies": 500.0,
+ "Phototherapy": 200.0,
+ "Retinoids": 150
+ },
+ "Melanoma": {
+ "Skin Biopsy": 200.0,
+ "Sentinel Lymph Node Biopsy": 400.0,
+ "Wide Local Excision": 500.0,
+ "Mohs Surgery": 600.0,
+ "Imaging Tests": 300
+ },
+ "Rosacea": {
+ "Topical Medications": 80.0,
+ "Oral Antibiotics": 100.0,
+ "Laser Therapy": 300.0,
+ "Photodynamic Therapy": 400.0,
+ "Microdermabrasion": 150
+ },
+ "Fracture": {
+ "X-ray": 150.0,
+ "CT Scan": 300.0,
+ "Closed Reduction": 500.0,
+ "Casting or Splinting": 200.0,
+ "Surgery": 1000
+ },
+ "Sprain": {
+ "Physical Examination": 100.0,
+ "X-ray": 150.0,
+ "MRI": 500.0,
+ "Splinting or Bracing": 200.0,
+ "Physical Therapy": 250
+ },
+ "Osteoarthritis": {
+ "Physical Examination": 100.0,
+ "X-ray": 150.0,
+ "Joint Aspiration": 300.0,
+ "Cortisone Injection": 200.0,
+ "Physical Therapy": 250
+ },
+ "ACL Tear": {
+ "Physical Examination": 100.0,
+ "MRI": 500.0,
+ "Arthroscopy": 1000.0,
+ "ACL Reconstruction": 1500.0,
+ "Physical Therapy": 250
+ },
+ "Carpal Tunnel Syndrome": {
+ "Physical Examination": 100.0,
+ "Electromyography (EMG)": 300.0,
+ "Nerve Conduction Studies": 200.0,
+ "Carpal Tunnel Release Surgery": 1000.0,
+ "Splinting": 150
+ },
+ "Migraine": {
+ "MRI Brain": 500.0,
+ "Electroencephalogram (EEG)": 300.0,
+ "Neurological Examination": 200.0,
+ "Blood Tests": 150.0,
+ "Visual Field Test": 100
+ },
+ "Epilepsy": {
+ "Electroencephalogram (EEG)": 300.0,
+ "MRI Brain": 500.0,
+ "Neurological Examination": 200.0,
+ "Blood Tests": 150.0,
+ "Video EEG Monitoring": 400
+ },
+ "Multiple Sclerosis": {
+ "MRI Brain": 500.0,
+ "Neurological Examination": 200.0,
+ "Lumbar Puncture": 350.0,
+ "Evoked Potential Tests": 250.0,
+ "Blood Tests": 150
+ },
+ "Parkinson's Disease": {
+ "Neurological Examination": 200.0,
+ "MRI Brain": 500.0,
+ "DaTscan": 800.0,
+ "Blood Tests": 150.0,
+ "PET Scan": 600
+ },
+ "Stroke": {
+ "Neurological Examination": 200.0,
+ "MRI Brain": 500.0,
+ "CT Scan": 300.0,
+ "Carotid Ultrasound": 250.0,
+ "Blood Tests": 150.0
+ }
+}
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/employee.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/employee.py
new file mode 100644
index 0000000000000000000000000000000000000000..90897383ecb5d9070a4d700b1becd30a7e4e1b37
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/employee.py
@@ -0,0 +1,49 @@
+"""Exercise 9.0.3: A python class example."""
+
+class Employee:
+ """Employee class."""
+
+ def __init__(self, name: str, age: int, experience: int, department: str, salary: float) -> None:
+ """Initialize an Employee object.
+
+ :param name: the name of the employee
+ :param age: the age of the employee
+ :param experience: the experience (in years) of the employee
+ :param department: the department in which the employee works
+ :param salary: the salary of the employee
+ """
+ self.name = name
+ self.age = age
+ self.experience = experience
+ self.department = department
+ self.salary = salary
+
+ def introduce_yourself(self) -> None:
+ """Print a self-introduction message with the employee's information."""
+ intro_msg = f"My name is {self.name}. I am {self.age} years old. I work in {self.department}, have {self.experience} year(s) of experience, and my salary is {self.salary}."
+ print(intro_msg)
+
+ def give_raise(self, sal_raise: float) -> None:
+ """Give a raise to the employee.
+
+ :param sal_raise: the amount of raise to be given
+ """
+ self.salary = self.salary + sal_raise
+
+
+if __name__ == "__main__":
+
+ employee_1 = Employee("John Doe",30,4,"Sales",30000)
+ employee_2 = Employee("Jane Smith",28,3,"Marketing",45000)
+ employee_3 = Employee("David Johnson",35,10,"Human Resources",60000)
+
+ print("Accessing internal information:")
+ print(employee_1.age)
+ print(employee_2.name)
+ print(employee_3.experience)
+
+ print("Class object before raise:",employee_1.__dict__)
+ print(employee_1.salary)
+ employee_1.give_raise(2000)
+ print("Class object after raise:",employee_1.__dict__)
+ print(employee_1.salary)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/patient.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/patient.py
new file mode 100644
index 0000000000000000000000000000000000000000..afaa50e8c9118baca3c98b317214a25958d4b78a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/patient.py
@@ -0,0 +1,26 @@
+"""Exercise 9.1 + 9.2: Patient class."""
+
+
+class Patient:
+ """Patient class."""
+
+ def __init__(self, name: str, condition: str, age: int):
+ """Initialize a Patient object.
+
+ :param name: the name of the patient
+ :param condition: the condition of the patient
+ :param age: the age of the patient
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+
+if __name__ == "__main__":
+ # Ex. 9.1:
+ patient = Patient("John Doe", "Migraine", 38)
+ # Ex. 9.2 (uncomment line below to check if it prints "Patient: John Doe, Age: 38, Condition: Migraine"):
+ print(patient)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/rectangle.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/rectangle.py
new file mode 100644
index 0000000000000000000000000000000000000000..6406253b4934d2c480119a8008723f2b7fd5e03e
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/rectangle.py
@@ -0,0 +1,76 @@
+"""The Rectangle-exercises 9.1-9.2."""
+import matplotlib.pyplot as plt
+
+class Point:
+ """A class that represents a Point. See Section 15.1."""
+
+class Rectangle:
+ """A class that represents a Rectangle. See Section 15.3."""
+
+def area(rectangle : Rectangle) -> float: #! Compute the area here. Try to use the debugger if you are stuck.
+ """Compute the area of a Rectangle-object.
+
+ :param rectangle: A rectangle-object. Recall it has properties such as ``rectangle.width`` and ``rectangle.height``.
+ :return: The area of the rectangle.
+ """
+ a = rectangle.width * rectangle.height
+ return a
+
+def make_a_rectangle(x, y, width, height):
+ """Create and return a new Rectangle-object. Look at Section 15.3 to see what fields it must possess.
+
+ :param x: The x-position of the lower-left corner.
+ :param y: The y-position of the lower-left corner.
+ :param width: The width of the rectangle.
+ :param height: The height of the rectangle.
+ :return: A new Rectangle object.
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Create and return a Rectangle object here.")
+ return rect
+
+def split_rectangle(rectangle : Rectangle, horizontally : bool) -> list:
+ """Split the rectangle object into two. Either horizontally or vertically.
+
+ For instance, if ``horizontally=True`` this function should return a list such as
+ ``[left_rectangle, right_rectangle]``.
+
+ :param rectangle: A rectangle-object.
+ :param horizontally: If ``True`` the Rectangle is split horizontally otherwise vertically.
+ :return: A list with two Rectangle-objects.
+ """
+ # TODO: 5 lines missing.
+ raise NotImplementedError("Implement function body")
+ return split
+
+def plot_rectangle(rectangle: Rectangle):
+ """Plot the rectangle using matplotlib.
+
+ :param rectangle: The Rectangle to plot.
+ """
+ # TODO: 5 lines missing.
+ raise NotImplementedError("Implement function body")
+
+def rectangle_inception(rectangle, n):
+ r"""Recursively generate a list of n+1 rectangles by applying split_rectangle n times.
+
+ Note that the list should contain n+1 non-overlapping rectangles with the same total area as the original rectangle.
+ The function should begin with a horizontal split.
+
+ :param rectangle: The initial rectangle to split.
+ :param n: How many recursive splits to apply
+ :return: A list of n+1 Rectangle-instances of the form ``[left, right_top, ...]``
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Implement function body")
+ return rectangles
+
+
+if __name__ == "__main__":
+ rectangles = rectangle_inception(make_a_rectangle(0, 0, 10, 10), 10)
+ for r in rectangles:
+ plot_rectangle(r)
+ # Optionally: Let's save the rectangle for later:
+ from cp import savepdf
+ savepdf("rectangles.pdf") # Save your pretty rectangles for later.
+ plt.show()
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/sinus.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/sinus.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a825efa183e43e85491efb0e5b3ef2441cb9616
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/sinus.py
@@ -0,0 +1,86 @@
+"""Project work for week 9, classes I."""
+import math
+
+class Sin:
+ """A class that represents the sinus-function, i.e. sin(x) where x can be any expression."""
+
+
+class Cos:
+ """A class that represents the cosine-function, i.e. cos(x) where x can be any expression."""
+
+
+class Variable:
+ """A class that represents a (named) variable such as ``x``, ``y``, ``x2``, etc."""
+
+
+def make_symbol(symbol : str) -> Variable:
+ """Create a new Variable object with the given name.
+
+ If ``v = make_symbol(x)`` then ``v.symbol`` should be the string ``"x"``.
+
+ :param symbol: The symbol this variable represents, e.g. ``"x"``.
+ :return: A new variable object.
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Implement function body")
+ return v
+
+def sine(arg : object) -> Sin:
+ """Create a new Sin object. The object will represent sin(arg).
+
+ In other words, if we let ``s = sine(arg), then s.arg should be equal to ``arg``. In our case,
+ ``arg`` can be either a ``Variable``, or an object such as ``Sin``.
+
+ :param arg: The argument to sin.
+ :return: An object representing ``sin(arg)``.
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Implement function body")
+ return s
+
+def cosine(arg : object) -> Cos:
+ """Create a new Cos object. The object will represent cos(arg).
+
+ In other words, if we let ``s = cosine(arg), then s.arg should be equal to ``arg``. In our case,
+ ``arg`` can be either a ``Variable``, or an object such as ``Cos``.
+
+ :param arg: The argument to cos.
+ :return: An object representing ``cos(arg)``.
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Implement function body")
+ return s
+
+def format_expression(expression : object) -> str:
+ """Format the given expression and return it as a string.
+
+ The expression is an object either of class Variable, Sin or Cos. The function should format it as a ``str``.
+
+ :param expression: An object to format.
+ :return: A string representation such as ``"cos(x)"`` or ``"sin(cos(y))"``.
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+if __name__ == "__main__":
+ exp = sine(cosine(sine(sine(make_symbol('x')))))
+ print( format_expression(exp) )
+ # print(format_expression(exp), "evaluated in x=0 is", evaluate(exp, 0) )
+ pass
+
+ from sympy import sin, cos, symbols, evaluate, Symbol
+ evaluate_expression = sin.evalf # Get the evaluation-function. Ignore the right-hand side for now.
+ print_expression = sin.__str__ # Get the formatting-function. Ignore the right-hand side for now.
+
+ x = symbols('x') # Define a symbol
+ y = sin(cos(x))
+ print_expression(y)
+ evaluate_expression(y, subs={'x': 0})
+
+ y = sin(x)
+ isinstance(y, sin) # It is an instance of the sin-class
+ from sympy import Symbol
+ y.args
+ isinstance(y.args[0], Symbol)
+ y.args[0] == x
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/vector.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/vector.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4638a008026ce146510af666c1794775b8dde81
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/vector.py
@@ -0,0 +1,223 @@
+"""This file contains all the exercises relating to the Minecraft-example 9.3-9.8."""
+import matplotlib.pyplot as plt
+
+class Vector:
+ """A class that represents a Vector, defined by the endpoint :math:`(x,y)`."""
+
+
+def make_vector(x : float, y : float) -> Vector:
+ """Create a new Vector object with end-points :math:`(x,y)`.
+
+ .. runblock:: pycon
+
+ >>> from cp.ex09.vector import make_vector
+ >>> v = make_vector(2,3)
+ >>> v.x
+ >>> v.y
+
+ :param x: The :math:`x`-position of the vector.
+ :param y: The :math:`y`-position of the vector.
+ :return: A Vector-object with the given end-point.
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return v
+
+def print_vector(v: Vector):
+ """Print a vector object with coordinates (x,y) to the console.
+
+ The output format for a vector with coordinates :math:`x, y` should be simply ``(x, y)`` (on a single line)
+
+ :param v: A vector object instance.
+ """
+ print(f"{(v.x, v.y)}")
+
+def dot(v1 : Vector, v2 : Vector) -> float:
+ r"""Compute the dot-product of ``v1`` and ``v2``, i.e. :math:`\mathbf{v}_1 \cdot \mathbf{v}_2`.
+
+ :param v1: The first vector,
+ :param v2: The second vector,
+ :return: The dot product of ``v1`` and ``v2`` (as a ``float``)
+ """
+ return v1.x * v2.x + v1.y * v2.y
+
+def scale(s : float, v : Vector) -> Vector:
+ r"""Scale the vector :math:`\mathbf{v}` by a factor of :math:`s`.
+
+ :param s: A scalar number
+ :param v: A vector
+ :return: The vector :math:`s \mathbf{v}`.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return w
+
+def add(v1 : Vector, v2 : Vector) -> Vector:
+ r"""Add the two vectors ``v1`` and ``v2`` and return the sum as a ``Vector`` object.
+
+ :param v1: The first vector,
+ :param v2: The second vector.
+ :return: Their sum :math:`\mathbf{v}_1+\mathbf{v}_2`
+ """
+ return make_vector(v1.x + v2.x, v1.y+v2.y)
+
+def sub(v1 : Vector, v2 : Vector) -> Vector:
+ r"""Subtract the two vectors ``v1`` and ``v2`` and return the difference as a ``Vector`` object.
+
+ :param v1: The first vector,
+ :param v2: The second vector.
+ :return: Their difference :math:`\mathbf{v}_1-\mathbf{v}_2`
+ """
+ diff = add(v1, scale(-1, v2))
+ return diff
+
+
+def hat(v):
+ r"""Given a ``Vector`` v1, this function returns a new vector which is orthogonal to v1 ('Tværvektoren' in Danish).
+
+ :param v: A vector :math:`\mathbf{v}`.
+ :return: A ``Vector`` that is orthogonal to :math:`\mathbf{v}`, i.e. :math:`\hat{\mathbf{v}}`.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return v_hat
+
+class LineSegment:
+ """A class that represents a line segment."""
+
+def make_line_segment(p : Vector, q : Vector) -> LineSegment:
+ r"""Construct a ``LineSegment`` connecting the two vectors ``p`` and ``q``.
+
+ Note you have some freedom in terms of how you wish to store ``p`` and ``q`` in the ``LineSegment`` object;
+ i.e. you can either store ``p`` and ``q`` directly similar to the ``Vector``, or perhaps do something slightly
+ smarter.
+
+ :param p: The vector the line segments begins in
+ :param q: The line segment the vector ends in
+ :return: A LineSegment class representing the LineSegment.
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return l
+
+
+def point_on_line(l : LineSegment, t : float) -> Vector:
+ r"""Interpolate between the end-points of the LineSegment.
+
+ Given a line segment, the function will interpolate between the end-points using :math:`t \in [0,1]`
+ and return a Vector. This can be used to compute any point on the LineSegment and :math:`t=0,1` will
+ correspond to the end-points. The order is not important for the following problems.
+
+ :param l: A line defined by the two end-points vectors
+ :param t: Weighting of the two end-point in the inerpolation, :math:`t \in [0,1]`.
+ :return: A ``Vector`` corresponding to :math:`\mathbf{p} + (\mathbf{q}-\mathbf{p})t`.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return s
+
+def plot_vector(v: Vector):
+ """Plot a Vector using matplotlib.
+
+ :param v: Vector to plot.
+ """
+ plt.plot(v.x, v.y, 'ro')
+
+def plot_line(l : LineSegment):
+ r"""Plot a LineSegment using matplotlib.
+
+ :param l: The line segment to plot
+ """
+ p = point_on_line(l, 0)
+ q = point_on_line(l, 1)
+ plt.plot([p.x, q.x], [p.y, q.y], 'k-')
+
+
+class SquareWithPosition:
+ """Corresponds to a square located in space. I.e., each corner has an :math:`(x,y)` coordinate."""
+
+def make_square_with_position(x : float,y : float, width : float) -> SquareWithPosition:
+ r"""Create a ``SquareWithPosition`` instance with lower-left corner at :math:`(x,y)` and the given ``width``.
+
+ Note: It is up to you how you want to store the information in the SquareWithLocation-class. You can for instance
+ choose to store the four corners as ``Vector``-objects, the four sides as ``LineSegment``-objects.
+
+ :param x: :math:`x`-position of the lower-left corner
+ :param y: :math:`y`-position of the lower-left corner
+ :param width: The side-length of the rectangle
+ :return: A ``SquareWithLocation`` object.
+ """
+ # TODO: 5 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return s
+
+
+def intersect(l1: LineSegment, l2: LineSegment) -> Vector:
+ """Get the intersection between two line segments assuming they intersects.
+
+ :param l1: First line
+ :param l2: Second line
+ :return: A ``Vector`` representing the point of intersection.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return p
+
+def do_they_intersect(l1 : LineSegment, l2 : LineSegment) -> bool:
+ """Determine if two line segments intersects.
+
+ :param l1: First line segment
+ :param l2: Second line segment
+ :return: ``True`` if the two line segments intersects, otherwise ``False``.
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return is_intersecting
+
+
+def square_to_lines(sq : SquareWithPosition) -> list:
+ """Return a list of ``LineSegment``-objects corresponding to the four sides.
+
+ :param sq: The square
+ :return: A list of four sides, ``[l1, l2, l3, l4]``
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return lines
+
+def plot_square(sq: SquareWithPosition):
+ """Plot the ``SquareWithLocation`` instance.
+
+ :param sq: The square to plot
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def get_intersections(sq : SquareWithPosition, l : LineSegment) -> list:
+ """Return a list of ``Vector``-objects corresponding to all intersections between the square and line segment.
+
+ :param sq: A square
+ :param l: A line segment
+ :return: A list of 0, 1, or 2 ``Vector``-objects corresponding to the intersections.
+ """
+ # TODO: 1 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return intersections
+
+def plot_intersections(sq : SquareWithPosition, l : LineSegment):
+ """Plot all intersections between the square and line.
+
+ :param sq: A square
+ :param l: A line segment
+ """
+ # TODO: 2 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+if __name__ == '__main__':
+ square = make_square_with_position(1,1,2)
+ line = make_line_segment(make_vector(-1, -1), make_vector(2, 4) )
+ vec = make_line_segment(make_vector(1, 1), make_vector(3, 1) )
+ plot_square(square)
+ plot_line(line)
+ plot_intersections(square, line)
+ plt.show()
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex09/warmup.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/warmup.py
new file mode 100644
index 0000000000000000000000000000000000000000..fad2d07c6c8ad116f869cafda28d23836ba11986
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex09/warmup.py
@@ -0,0 +1,57 @@
+"""Exercise 9.0: Warmup."""
+
+def create_employee_dict(name: str, age: int, experience: int, department: str, salary: float) -> dict:
+ """Create a dictionary representing an employee.
+
+ :param name: the name of the employee
+ :param age: the age of the employee
+ :param experience: the experience (in years) of the employee
+ :param department: the department in which the employee works
+ :param salary: the salary of the employee
+ :return: a dictionary containing the employee information
+ """
+ # TODO: 5 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return employee_dict
+
+
+def give_raise(employee_dict: dict, sal_raise: float) -> dict:
+ """Give a raise to an employee.
+
+ :param employee_dict: the dictionary representing the employee
+ :param sal_raise: the amount of raise to be given
+ :return: the updated employee dictionary with the new salary
+ """
+ # TODO: 3 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return employee_dict
+
+
+
+if __name__ == "__main__":
+ # Hard-coding everything:
+ employee_1_name = "John Doe"
+ employee_1_age = 30
+ employee_1_experience = 4
+ employee_1_department = "Sales"
+
+ employee_2_name = "Jane Smith"
+ employee_2_age = 28
+ employee_2_experience = 3
+ employee_2_department = "Marketing"
+
+ employee_3_name = "David Johnson"
+ employee_3_age = 35
+ employee_3_experience = 10
+ employee_3_department = "Human Resources"
+
+ ## if we want to add a new variable, we need to define a new line of code for each employer:
+ employee_1_salary = 30000
+ employee_2_salary = 45000
+ employee_3_salary = 60000
+
+ # Using dictionaries:
+ employee_1_dict = create_employee_dict(employee_1_name,employee_1_age,employee_1_experience,employee_1_department,employee_1_salary)
+ print("Dictionary before raise:",employee_1_dict)
+ employee_1_dict = give_raise(employee_1_dict,2000)
+ print("Dictionary after raise:",employee_1_dict)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex10/doctor.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/doctor.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1a05e1fe7d595ca4b0434bc13c739763d421494
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/doctor.py
@@ -0,0 +1,99 @@
+"""Exercise 9.x: Doctor class."""
+
+
+from cp.ex09.patient import Patient
+from cp.ex09.appointment import Appointment
+from cp.ex09.dicts.information import procedure_costs,conditions_specialties
+
+from datetime import datetime,timedelta
+from typing import List
+
+
+class Doctor:
+ """Doctor class."""
+
+ # TODO: 134 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+def book_patients_to_doctors(list_of_patients: List, list_of_doctors: List) -> List:
+ """Assign patients to available doctors based on specialty.
+
+ This method iterates over the list of patients and doctors, and assigns each patient to the first available doctor
+ with a matching specialty. The patient's appointments are added to the doctor's schedule.
+
+ :param list_of_patients: A list of Patient objects representing the patients to be assigned.
+ :param list_of_doctors: A list of Doctor objects representing the available doctors.
+ :return: A list of Doctor objects with updated patient assignments.
+ """
+ # TODO: 9 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+if __name__ == "__main__":
+
+ patient1 = Patient("Sophia Hernandez", "Stroke", 67)
+ patient2 = Patient("Michael Johnson", "Psoriasis", 45)
+
+ # Ex. 10.1
+ doctor = Doctor("Dr. Who", "Neurology")
+
+ # Ex. 10.2
+ check = doctor.check_specialty_fit(patient1)
+ print("This should be True:",check)
+ check = doctor.check_specialty_fit(patient2)
+ print("This should be False:",check)
+
+ # Ex. 10.3
+ appointment = Appointment(patient1,"MRI","2023-06-01 10:00", 500)
+ msg = doctor.add_appointment(appointment) # should be fine
+ print(msg)
+ msg = doctor.add_appointment(appointment) # doctor should be busy
+ print(msg)
+ appointment = Appointment(patient2, "Laser Therapy", "2023-06-02 10:00", 300)
+ msg = doctor.add_appointment(appointment) # doctor does not have the right specialty
+ print(msg)
+
+ # Ex. 10.4
+ doctor.add_all_patient_appointments(patient1)
+
+ # Ex. 10.5
+ doctor.display_schedule()
+
+ # Ex. 10.6
+ appointment_to_be_cancelled = doctor.list_of_appointments[3]
+ print("CANCEL THE FOLLOWING:",appointment_to_be_cancelled)
+ doctor.cancel_appointment(appointment_to_be_cancelled)
+ doctor.display_schedule()
+
+ # Ex. 10.7
+ list_of_patients = [
+ Patient("Alice Smith", "Eczema", 35),
+ Patient("Sophia Hernandez", "Stroke", 67),
+ Patient("Michael Johnson", "Psoriasis", 45),
+ Patient("Robert Davis", "Rosacea", 30),
+ Patient("Jennifer Wilson", "Fracture", 42),
+ Patient("David Taylor", "Sprain", 38),
+ Patient("Jessica Miller", "Osteoarthritis", 50),
+ Patient("Christopher Anderson", "ACL Tear", 29),
+ Patient("Sarah Thomas", "Carpal Tunnel Syndrome", 41),
+ Patient("Emily Brown", "Melanoma", 55),
+ Patient("Matthew Martinez", "Migraine", 32),
+ Patient("John Doe", "Acne", 25),
+ Patient("Laura Jackson", "Epilepsy", 60),
+ Patient("Daniel Clark", "Multiple Sclerosis", 47),
+ Patient("Olivia Garcia", "Parkinson's Disease", 58),
+ Patient("William Young", "Acne", 33),
+ Patient("Ava Lewis", "Eczema", 27),
+ Patient("James Lee", "Psoriasis", 39),
+ Patient("Emma Hall", "Melanoma", 43),
+ Patient("Benjamin Adams", "Rosacea", 31)
+ ]
+ list_of_doctors = [Doctor("Dr. Who", "Neurology"),Doctor("Dr. Jones", "Dermatology"),Doctor("Dr. Strange", "Orthopedics")]
+ list_of_doctors = book_patients_to_doctors(list_of_patients,list_of_doctors)
+
+ for doctor in list_of_doctors:
+ doctor.display_schedule()
+
+ # Ex. 10.8
+ for doctor in list_of_doctors:
+ salary = doctor.compute_salary()
+ print(f"{doctor.name} earns ${salary}.")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex10/hospital.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/hospital.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a1853d2ce3bf16527abf6d2ebaa9f3908b14655
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/hospital.py
@@ -0,0 +1,15 @@
+"""Exercise 10.1: A hospital with doctors and patients."""
+
+illnesses = {'hanging toenail': ('feets', 200),
+ 'stepped on lego': ('feets', 100),
+ 'headache': ('head', 100),
+ 'toothache': ('head', 200),
+ }
+
+# TODO: 97 lines missing.
+raise NotImplementedError("Insert your solution and remove this error.")
+if __name__ == "__main__":
+ p = Person("Patricia Smith", 24, 'f') # use the __init__-method to construct the person.
+ print(p) # Call the str-method
+
+ pass
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex10/inherit.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/inherit.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd2043f8fc7ac43d0b74e341bc375fbe3817f597
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/inherit.py
@@ -0,0 +1,58 @@
+"""A small example of how to use the super()-keyword."""
+class Pet:
+ """A basic Pet-class."""
+
+ def __init__(self, name, age):
+ """Create a new Pet object.
+
+ :param name: The name of the Pet
+ :param age: The age of the Pet.
+ """
+ self.name = name
+ self.age = age
+
+ def __str__(self):
+ """
+ Automatically called by ``print(..)``.
+
+ :return: A string representation such as ``"Fido (3 years old)"``.
+ """
+ return f"{self.name} ({self.age} years old)"
+
+class SimpleCat(Pet):
+ """A simple Cat-class. This example contains duplicated code."""
+
+ def __init__(self, name, age, favorite_food):
+ """Construct a new Cat-object. Asides the name and age, it also has a favorite food.
+
+ :param name: The name of the cat, for instance ``'Mr. Whiskers'``
+ :param age: The age, for instance 3.
+ :param favorite_food: The favorite food, for instance ``"fish"``.
+ """
+ self.name = name
+ self.age = age
+ self.favorite_food = favorite_food
+
+class ImprovedCat(Pet):
+ """A more advanced Cat-class. This example uses super() to avoid duplicate code."""
+
+ def __init__(self, name, age, favorite_food):
+ """Construct a new Cat-object. The example function similar to the one before.
+
+ :param name: The name of the cat, for instance ``'Mr. Whiskers'``
+ :param age: The age, for instance 3.
+ :param favorite_food: The favorite food, for instance ``"fish"``.
+ """
+ super().__init__(name, age) # Call the Pet.__init__()-method.
+ self.favorite_food = favorite_food # We stll need to store this one.
+
+
+if __name__ == "__main__":
+ a = SimpleCat("Mr. Whiskers", 3, "fish")
+ print(a)
+ print(a.favorite_food)
+ # Now let's make an improved cat.
+
+ b = SimpleCat("Mr. Super-duper whiskers", 4, "whatever you are eating")
+ print(b)
+ print(b.favorite_food)
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex10/pit/chief_physician.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/pit/chief_physician.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex10/symbolic.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/symbolic.py
new file mode 100644
index 0000000000000000000000000000000000000000..dde37fa80f0044229abfa26cc68131bd94ebc5b7
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex10/symbolic.py
@@ -0,0 +1,82 @@
+"""Project work for week 10: A do-it-yourself sympy."""
+import math
+
+
+class Expression:
+ """A mathematical expression. Can represent a constant `1`, a symbol such as `x`, or a function such as `sin(x)`."""
+
+ def evaluate(self, substitutions : dict) -> float:
+ """Evaluate the expression and return a floating-point number.
+
+ The input argument is a dictionary which is used to evaluate all symbols. E.g. if ``substitutions = {'x': 3}``,
+ this means all instances of the symbolic expression ``x`` will be replaced by ``3``.
+
+ :param substitutions: A dictionary of substitutions. The keys are strings, and the values are numbers.
+ :return: A floating-point number this expression has been evaluated to.
+ """
+ return 0
+
+ def differentiate(self, variable : str) -> 'Expression':
+ r"""Differentiate the expression with respect to the variable, specified as e.g. ``"x"``.
+
+ Note that if this expression is a function of other variables, we must use the chain-rule to recursively
+ differentiate the other expressions. e.g. :math:`\sin(f(x))' = \cos(f(x))f'(x)`.
+
+ :param variable: The variable we differentiate with respect to (example: ``"x"``)
+ :return: A class that inherits from ``Expression`` which corresponds to the derivative with respect to the given variable.
+ """
+ return Expression()
+
+ def __str__(self) -> str:
+ """Provide a string representation of the expression. The function is called automatically by ``print``.
+
+ :return: A string representation of this expression.
+ """
+ return "Remember to implement the __str__()-function."
+
+# TODO: 246 lines missing.
+
+if __name__ == "__main__":
+ # construct some symbolic expressions
+ c1 = Constant(2)
+ c2 = Constant(3)
+ v = Variable('x')
+
+ # construct expression (2 * x + 3)
+ expr1 = Addition(Multiplication(c1, v), c2)
+
+ print(expr1)
+ print(expr1.differentiate("x"))
+
+ # let's evaluate this expression for x = 4
+ print(expr1.evaluate( {'x': 4} )) # prints 11
+
+ # let's differentiate this expression with respect to x
+ # it should be (2 * 1 + 0) = 2
+ diff_expr1 = expr1.differentiate('x')
+
+ # evaluate the derivative at x = 4
+ print(diff_expr1.evaluate( {'x': 2} )) # prints 2
+
+
+ # construct another expression (x ^ 3)
+ expr2 = Sin(v) # Constant(3))
+
+ # let's evaluate this expression for x = 2
+ print(expr2.evaluate( {'x': 2} )) # prints 8
+ print(expr2)
+ # let's differentiate this expression with respect to x
+ # it should be 3 * x ^ 2 which equals 3 * 2 ^ 2 = 12 at x = 2
+ diff_expr2 = expr2.differentiate('x')
+ print(diff_expr2)
+
+ print( Sin(Cos(Cos(v))).differentiate('y') )
+
+
+ # evaluate the derivative at x = 2
+ print(diff_expr2.evaluate({'x': 2})) # prints 12
+
+
+# Doctor
+# Person
+# Patient
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex11/BMI_analysis.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex11/BMI_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f5f937412fba45abda7c5f10a431d85c7d6f291
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex11/BMI_analysis.py
@@ -0,0 +1,35 @@
+"""Exercise 11.9: BMI analysis."""
+
+import numpy as np
+
+BMI_data = np.array([[1, 1.65, 63.4],
+ [2, 1.70, 87.1],
+ [3, 1.82, 93.7],
+ [4, 1.62, 105],
+ [5, 1.97, 61.3],
+ [6, 1.62, 78],
+ [7, 1.73, 99.6],
+ [8, 1.77, 81],
+ [9, 1.77, 110.7],
+ [10, 1.60, 55.2]]).astype(float)
+
+def calc_bmi(BMI_data:any) -> any:
+ """Calculate the BMI.
+
+ :param BMI_data: matrix with IDs, heights and weights.
+ :return: a vector with calculated BMI (array).
+ """
+ # TODO: 19 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+import matplotlib.pyplot as plt
+
+def visualize_bmi_distribution(new_BMI_data: np.array) -> None:
+ """Create a sorted histogram to visualize the BMI distribution.
+
+ :param new_BMI_data: BMI matrix with calculated BMI and BMI Classification columns.
+ """
+ # TODO: 11 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ # Display the plot
+ plt.show()
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex11/Stable_measurements.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex11/Stable_measurements.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9abdb6f9df4f7f346c940b6a4689c1c3c7d435
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex11/Stable_measurements.py
@@ -0,0 +1,13 @@
+"""Exercise 11.6: Stable Measurements."""
+
+import numpy as np
+def stable_measurements(m: np.array) -> np.array:
+ """Calculate a vector with stable measurements.
+
+ :param m: Original measurements, a vector containing three or more numbers (array).
+ :return: a vector with stable measurements (array).
+ """
+ # TODO: 8 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return S
+
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/ex11/dot_product.py b/src/unitgrade_private/pipelines/tmp/students/cp/ex11/dot_product.py
new file mode 100644
index 0000000000000000000000000000000000000000..458c6db1a2fe6ad68cb75a5af9b6a1dd072f6db7
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/ex11/dot_product.py
@@ -0,0 +1,26 @@
+"""Exercise 11.5: Dot product."""
+
+import numpy as np
+
+def dot_product_a(vec1:list, vec2:list) -> int:
+ """Calculate the dot product of the two vectors based on basic arithmic operations.
+
+ :param vec1: first vector (list).
+ :param vec2: second vector (list).
+ :return: the dotproduct between vec1 and vec2 (int).
+ :raises ValueError: If the two lists does not have the same length.
+ """
+ # TODO: 9 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+
+def dot_product_b(arr1, arr2) -> int:
+ """Calculate the dot product of the two vectors using NumPy.
+
+ :param arr1: first vector (array).
+ :param arr2: second vector (array).
+ :return: the dotproduct between vec1 and vec2 (int).
+ :raises ValueError: If ..
+ """
+ # TODO: 6 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/exam/exam2023spring/readme.md b/src/unitgrade_private/pipelines/tmp/students/cp/exam/exam2023spring/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..c041c5216f5a12754b844c5d316e1d0e835ec09a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/exam/exam2023spring/readme.md
@@ -0,0 +1,2 @@
+This directory is purposefully left empty. During the exam, you will be given a `.zip` file with the content of this directory.
+Replace this directory with the corresponding directory from the `.zip` file to begin working on the exam.
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project0/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project0/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c084ec8e15c131521fae104627661ef70e980b0
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project0/__init__.py
@@ -0,0 +1 @@
+"""Dummy (test) project to test your installation."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf541767912b2c6540dc7fabec481781c6ebbf73
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_grade.py
@@ -0,0 +1,4 @@
+# cp/project0/project0_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c98a94947ffc822a9deb4a3b5e7c1d7d33415f4
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_tests.py
@@ -0,0 +1,37 @@
+from unitgrade import Report, UTestCase
+import cp
+import io
+import unittest
+from cp.ex00 import say_hello
+from unittest.mock import patch
+import importlib
+
+
+class HelloWorld(UTestCase):
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_say_hello(self, mock_stdout):
+ lib = importlib.reload(say_hello)
+ lines = mock_stdout.getvalue().strip().splitlines()
+
+ self.assertEqual(len(lines), 2, msg=f"Wrong number of lines. Your program should print(..) out two lines, you got: {lines}, {say_hello.__file__}")
+
+ print("Testing the first line")
+ self.assertEqual(lines[0], "Hello", msg="You must have somehow changed the first print(...) statement in the program. Did you delete it?") # Rem
+
+ print("Testing the second line")
+ self.assertEqual(lines[1], "World", msg="Your second print statement did not produce the correct output.") # Remember the output must be capitalized.
+
+
+class Project0(Report):
+ title = "Project for week 00 (Try to complete this before the course starts)"
+ remote_url = "https://cp.pages.compute.dtu.dk/02002public/_static/evaluation/"
+
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (HelloWorld, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project0())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..77019ad21f0beafca6a1cfd4d673af65357243a9
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project0/project0_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project0/project0_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project0/unitgrade_data/HelloWorld.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project0/unitgrade_data/HelloWorld.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project0/unitgrade_data/main_config_project0_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project0/unitgrade_data/main_config_project0_tests.artifacts.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project1/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c0d4547b194a0d770d4a822ffd446456f0bc8ee
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project1/__init__.py
@@ -0,0 +1 @@
+"""Lindenmayer system project."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/lindenmayer.py b/src/unitgrade_private/pipelines/tmp/students/cp/project1/lindenmayer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b390f0c9ffc5f36793a1a60332ec31250420591
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project1/lindenmayer.py
@@ -0,0 +1,130 @@
+"""The Lindenmayer project code."""
+import numpy as np
+import matplotlib
+# matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+plt.switch_backend('agg')
+
+koch = 'koch'
+sierpinski = 'sierpinski'
+
+def LindIter(system=koch, N=1):
+ """
+ Compute :math:`N` updates of the given Lindenmayer system and returns the output as a ``str``.
+
+ .. runblock:: pycon
+
+ >>> from cp.project1.lindenmayer import LindIter
+ >>> for n in range(3):
+ ... print(f'{LindIter("koch", n)=}')
+
+ :param system: A string containing the name of the Lindenmayer system currently under scrutiny. The input can take the values ``"koch"`` or ``"sierpinski"``.
+ :param N: The number of iterations that should be calculated.
+ :return: A string containing the output after :math:`N` iterations of the chosen Lindenmayer system
+ """
+ # TODO: 13 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return s
+
+
+def turtle_graph(s):
+ r"""Translate a list of symbols into a list which is twice as long of alternating length/angle specifications.
+
+ The list of symbols are specified as a ``str``.
+
+ .. runblock:: pycon
+
+ >>> from cp.project1.lindenmayer import turtle_graph, LindIter
+ >>> s = LindIter('koch', 1)
+ >>> print(s)
+ >>> turtle_graph(s)
+
+ :param s: A string of symbols representing the state of the system after the Lindemayer iteration.
+ :return: A row vector containing the turtle graphics commands consisting of alternating length and angle specifications :math:`\begin{bmatrix} l_1 & \phi_1 & l_2 & \phi_2 & \cdots \end{bmatrix}`
+ """
+ # TODO: 18 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ return commands
+
+
+def turtle_plot(cmds : list):
+ r"""Turn the input vector of turtle plot commands into a graphical visualisation.
+
+ The function accepts a sequence of input commands generated from :func:`~cp.project1.lindenmayer.turtle_graph`
+ These are turned into a graphical representation by specifying the coordinates of the corners :math:`\vec x = (x, y)`
+ of the straight line segments with a plot command. To find these coordinates, your
+ function should follow the input vector, starting at the origin of the planar coordinate
+ system and adding a new pair of coordinates after every line segment. The line
+ segment has the length :math:`l_i`, and must be drawn at an angle :math:`\phi_i` with respect to the
+ previous line (as specified in the input vector of :func:`~cp.project1.lindenmayer.turtle_graph` ). The initial point
+ is :math:`\vec x_0 = (0, 0)^\top` and the initial direction is along a unit vector :math:`\vec d_0 = (0, 0)^\top`.
+
+ Here, a positive angle corresponds to turning left, and a negative one to turning right. The
+ computation of the vector pointing to the point :math:`\vec x_{i+1}` by using the previous point :math:`\vec x_i`
+ and previous drawing direction :math:`\vec d_i` with
+
+ .. math::
+
+ \vec d_i & = \begin{bmatrix} \cos(\phi_i+1) & − \sin(\phi_i+1) \\
+ \sin(\phi_i+1) & \cos(\phi_i+1) \end{bmatrix} \cdot \vec d_i \\
+ \vec x_{i+1} & = \vec x_i + l_{i+1} \cdot \vec d_{i+1}
+
+ When implemented, you should be able to reproduce the plots given in the problem text as:
+
+
+ .. plot::
+ :include-source: True
+
+ from cp.project1.lindenmayer import turtle_graph, LindIter, turtle_plot
+ s = LindIter(system="koch", N=8)
+ c = turtle_graph(s)
+ f = turtle_plot(c)
+
+ .. note::
+
+ Unlike this example, your plots should include a suitable title, descriptive axis labels, and a data legend where appropriate.
+ You are allowed to present the plots in separate figure windows or as sub-plots
+ in a single figure window.
+
+ :param cmds: List of turtle plot commands of the form :math:`\begin{bmatrix} l_1 & \phi_1 & l_2 & \phi_2 & \cdots \end{bmatrix}`
+ :return: A figure, created using e.g. ``f = plt.figure()``, containing your plot.
+ """
+ # TODO: 14 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+ # Return the figure handle here. I.e. set
+ # f = figure()
+ # ... your plot code here ...
+ # return f
+ f = plt.gcf()
+ return f
+
+def turtle_interactive():
+ """Interactive turtle-drawing program.
+
+ Allows the user to interact with the Lindenmayer system and gives the user the following three options when called:
+
+ #. Choose the type of Lindenmayer system and the number of iterations.
+ #. Generate plots.
+ #. Quit.
+
+ The user must be allowed to perform any reasonable number of Lindenmayer iterations
+ for the chosen Lindenmayer system and subsequent graphical presentation of the
+ obtained strings. The details of how the main script is designed and implemented is
+ for you to determine. It is a requirement that the program is interactive, and you
+ should strive to make it user friendly by providing sensible dialogue options. Consider
+ what you would expect if you were to use such a script, and what you think would
+ be fun. Play around and make a cool script.
+ """
+ # TODO: 7 lines missing.
+ raise NotImplementedError("Insert your solution and remove this error.")
+
+if __name__ == "__main__":
+ s = LindIter(system=sierpinski, N=8)
+ print(s)
+ c = turtle_graph(s)
+ print(c)
+ f = turtle_plot(c)
+ plt.title("Behold the amazing Lindenmayer system")
+ plt.show()
+ turtle_interactive()
+ plt.close()
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe52f1946fe756b7faf39970da2fefa3124300a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_grade.py
@@ -0,0 +1,4 @@
+# cp/project1/project1_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4c7813c3019bf55accb5656027750dc2155088d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_tests.py
@@ -0,0 +1,95 @@
+import matplotlib.pyplot as plt
+from unittest import mock
+from unitgrade import UTestCase, Report, cache, Capturing
+import numpy as np
+from cp.project1.lindenmayer import LindIter, turtle_plot, turtle_graph, koch, sierpinski, turtle_interactive
+from cp.project1 import lindenmayer
+
+
+class Question1_Iter(UTestCase):
+ def test_linditer_koch_N1(self):
+ self.assertEqualC(LindIter(koch, N=1))
+
+ def test_lindite_koch_N4(self):
+ self.assertEqualC(LindIter(koch, N=4))
+
+ def test_lindite_sierpinski_N1(self):
+ self.assertEqualC(LindIter(sierpinski, N=1))
+
+ def test_lindite_sierpinski_N4(self):
+ self.assertEqualC(LindIter(sierpinski, N=4))
+
+
+class Question2_TurtleFunction(UTestCase):
+ @cache
+ def get_lind(self, system, N):
+ return LindIter(system, N)
+
+ def test_turtle_koch_N1(self):
+ self.assertL2(np.asarray(turtle_graph(self.get_lind(koch, N=1))), tol=1e-8)
+
+ def test_turtle_koch_N4(self):
+ self.assertL2(np.asarray(turtle_graph(self.get_lind(koch, N=4))), tol=1e-8)
+
+ def test_turtle_sierpinski_N1(self):
+ self.assertL2(np.asarray(turtle_graph(self.get_lind(sierpinski, N=1))), tol=1e-8)
+
+ def test_turtle_sierpinski_N4(self):
+ self.assertL2(np.asarray(turtle_graph(self.get_lind(sierpinski, N=1))), tol=1e-8)
+
+
+class Question3_TurtleGraph(UTestCase):
+ @cache
+ def get_cmds(self, system, N):
+ return turtle_graph(LindIter(system, N))
+
+ def test_koch_plot(self):
+ turtle_plot(self.get_cmds(koch, N=2))
+ f = plt.gcf()
+ data = f.axes[0].lines[0].get_xydata()
+ plt.close()
+ self.assertL2(data, tol=1e-8)
+
+ def test_sierpinski_plot(self):
+ turtle_plot(self.get_cmds(sierpinski, N=2))
+ f = plt.gcf()
+ data = f.axes[0].lines[0].get_xydata()
+ plt.close()
+ self.assertL2(data, tol=1e-8)
+
+
+class Question4_InteractiveTurtle(UTestCase):
+ @mock.patch('cp.project1.lindenmayer.input', create=True)
+ def test_dictCreateSimple(self, mocked_input):
+ mocked_input.side_effect = ['1', '2', '3']
+ with Capturing() as cc:
+ turtle_interactive()
+ self.assertEqualC(cc[-1])
+
+# This is an example of how the documentation to a function could potentially be checked.
+# class Question5_CheckDocstringPep257(UTestCase):
+# def test_turtle_plot_docstring(self):
+# self.assertEqualC(LindIter(koch, N=1))
+# from pydocstyle.checker import check
+# o = check([lindenmayer.__file__])
+# errs = [k for k in o]
+# x = [e for e in errs if e.definition.kind == 'function' and e.definition.name == turtle_plot.__name__]
+# for err in x:
+# print("> Docstring problem", err)
+# self.assertEqual(len(x), 0)
+
+class Project1(Report):
+ title = "02631: Exam project 2D, the Lindenmayer system"
+ import cp
+ pack_imports = [cp]
+ questions = [
+ (Question1_Iter, 10),
+ (Question2_TurtleFunction, 10),
+ (Question3_TurtleGraph, 10),
+ (Question4_InteractiveTurtle, 10),
+ # (Question5_CheckDocstringPep257, 10)
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project1())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b95c5b24e07ead0a001072a29362f2b4e9a1aaf
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project1/project1_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project1/project1_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question1_Iter.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question1_Iter.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question2_TurtleFunction.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question2_TurtleFunction.pkl
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index 0000000000000000000000000000000000000000..b49932da3ab32d25d4e55cd1d8b12d3463c68432
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question3_TurtleGraph.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question3_TurtleGraph.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question4_InteractiveTurtle.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/Question4_InteractiveTurtle.pkl
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index 0000000000000000000000000000000000000000..88e81389ae1b408f0667508ffcd2e658059aa643
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/main_config_project1_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project1/unitgrade_data/main_config_project1_tests.artifacts.pkl
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index 0000000000000000000000000000000000000000..a9448dfe37d561a5449055ae9f181452788f0ef4
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..890dc18eaf0fafeab3829fd71c6e5c41882a86cc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project2/__init__.py
@@ -0,0 +1 @@
+"""Dummy (test) project from 02465."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca72f7e78e6d34be09dd4cdb70a68c824357ae28
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_grade.py
@@ -0,0 +1,4 @@
+# cp/project2/project2_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d1c625b8afa943a8e5550898892942486c9e571
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_tests.py
@@ -0,0 +1,146 @@
+import string
+from unitgrade import hide
+from cp.ex04.hangman import hangman
+from cp import minput
+from unittest.mock import patch
+import io
+import unittest
+from unitgrade import UTestCase, Report
+from cp.ex04.hangman import is_word_guessed
+from cp.ex04.hangman import get_guessed_word
+from cp.ex04.hangman import get_available_letters
+
+class HangmanIsGuessed(UTestCase):
+ def test_is_word_guessed(self):
+
+ self.assertTrue(is_word_guessed("dog", "tdohg"))
+ self.assertTrue(is_word_guessed("dog", "tdohg"))
+ self.assertFalse(is_word_guessed("dog", ""))
+ self.assertFalse(is_word_guessed("species", "sdcbwegk"))
+ self.assertTrue(is_word_guessed("species", "qseicps"))
+
+
+class HangmanGuessedWord(UTestCase):
+ def test_get_guessed_word(self):
+
+ self.assertEqual(get_guessed_word('cow', 'kcw'), 'c_ w')
+ self.assertEqual(get_guessed_word('apple', ''), '_ _ _ _ _ ')
+ self.assertEqual(get_guessed_word('tasks', 'ws'), '_ _ s_ s')
+
+
+
+class HangmanAvailableLetters(UTestCase):
+ def test_get_available_letters(self):
+ self.assertEqual(len(get_available_letters('')), 26)
+ self.assertEqual(set(get_available_letters('bcdew')), set(string.ascii_lowercase).difference('bcdew'))
+
+
+
+
+
+class Hangman(UTestCase):
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_hangman_startup(self, mock_stdout):
+ try:
+ with unittest.mock.patch('builtins.input', minput([None])):
+ hangman("cow", guesses=4)
+ except GeneratorExit as e:
+ pass
+ out = mock_stdout.getvalue()
+ lines = out.splitlines()
+ self.assertEqual(len(lines), 4, msg='You must print 4 lines')
+ self.assertEqual(lines[0], 'Welcome to hangman! To save Bob from his legal troubles, guess a 3 letter word.', msg='First printed line is wrong')
+ self.assertEqual(lines[1], '----', msg='Second printed line is wrong')
+ self.assertEqual(lines[2], 'You have 4 guesses left.', msg='Third printed line is wrong')
+ self.assertTrue("." in lines[3] and ":" in lines[3], msg="Your fourth line must have both a colon and a period")
+
+ fp = lines[3].split(".")[0].split(":")[1].strip()
+ self.assertEqual(len(fp), 26, msg="The alphabet has 26 letters.")
+ self.assertEqual(set(fp), set(string.ascii_lowercase), msg="You failed to print the alphabet")
+
+
+ def chk_alphabet(self, line, missing):
+ self.assertTrue("." in line and ":" in line, msg="Your alphabet printout must have both a colon and a period")
+ fp = line.split(".")[0].split(":")[1].strip()
+ ab = set( [c for c in string.ascii_lowercase if c not in missing])
+ self.assertEqual(len(fp), len(ab), msg="The alphabet printout has to few characters")
+ self.assertEqual(set(fp), ab, msg="You failed to print the alphabet")
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_hangman_correct(self, mock_stdout):
+ try:
+ with unittest.mock.patch('builtins.input', minput(['w', None])):
+ hangman("cow", guesses=4)
+ except GeneratorExit as e:
+ pass
+ out = mock_stdout.getvalue()
+ lines = out.splitlines()
+ self.assertEqual(len(lines), 8, msg='You must print 8 lines')
+ self.assertEqual(lines[-4], 'Good guess: _ _ w', msg='Format guessed word correctly')
+ self.assertEqual(lines[-3], '----')
+ self.assertEqual(lines[-2], 'You have 3 guesses left.', msg='Third printed line is wrong')
+ self.chk_alphabet(lines[-1], missing='w')
+
+
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_hangman_false(self, mock_stdout):
+ try:
+ with unittest.mock.patch('builtins.input', minput(['q', None])):
+ hangman("doggy", guesses=4)
+ except GeneratorExit as e:
+ pass
+ out = mock_stdout.getvalue()
+ lines = out.splitlines()
+ self.assertEqual(len(lines), 8, msg='You must print 8 lines')
+ self.assertEqual(lines[-4], 'Oh no: _ _ _ _ _ ', msg='Format guessed word correctly')
+ self.assertEqual(lines[-3], '----')
+ self.assertEqual(lines[-2], 'You have 3 guesses left.', msg='Third printed line is wrong')
+ self.chk_alphabet(lines[-1], missing='q')
+
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_hangman_win(self, mock_stdout):
+ try:
+ with unittest.mock.patch('builtins.input', minput(['q', 'd', 'g', 'o', 'y', None])):
+ hangman("doggy", guesses=8)
+ except GeneratorExit as e:
+ pass
+ out = mock_stdout.getvalue()
+ lines = out.splitlines()
+ self.assertEqual(len(lines), 22, msg='You must print 22 lines')
+ self.assertTrue(lines[-1], 'Your score is 20')
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_hangman_loose(self, mock_stdout):
+ try:
+ with unittest.mock.patch('builtins.input', minput(['%'])):
+ hangman("cat", guesses=5)
+ except GeneratorExit as e:
+ pass
+ out = mock_stdout.getvalue()
+ lines = out.splitlines()
+ self.assertEqual(len(lines), 5, msg='You must print 5 lines')
+ self.assertTrue(lines[-1], 'Game over :-(. Your score is 0 points.')
+
+
+
+
+
+
+
+class Project2(Report):
+ title = "Project 2"
+ abbreviate_questions = True
+ questions = [(HangmanIsGuessed, 10),
+ (HangmanGuessedWord, 10),
+ (HangmanAvailableLetters, 10),
+ (Hangman, 30),
+ ]
+ import cp
+ pack_imports = [cp]
+
+
+if __name__ == "__main__":
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project2())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0591af1a8bcb817b52ae4fb128124c130d1eab0
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project2/project2_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project2/project2_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/Hangman.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/Hangman.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..4ea64ff17e61657c7ff82546691b2e059cdef2c2
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanAvailableLetters.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanAvailableLetters.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5fafd68c525834c6bea466b962806913bd611879
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanAvailableLetters.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanGuessedWord.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanGuessedWord.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..a8b2d1a4c414be21be977b69c75ded53d6e38298
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanIsGuessed.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanIsGuessed.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..1d6859db537538955cf78254370ab43d5bfca233
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/HangmanIsGuessed.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/main_config_project2_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project2/unitgrade_data/main_config_project2_tests.artifacts.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..ef83c16c22171bce19dae23ec464e68a039ca4fc
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project3/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..890dc18eaf0fafeab3829fd71c6e5c41882a86cc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project3/__init__.py
@@ -0,0 +1 @@
+"""Dummy (test) project from 02465."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..6763bff5e086d7ccced5b9e7b73b3134967292dc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_grade.py
@@ -0,0 +1,4 @@
+# cp/project3/project3_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e80048e75cd64e3eec142f00064f0f7c79a605
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_tests.py
@@ -0,0 +1,23 @@
+from unitgrade import UTestCase, Report
+from cp.ex00.fruit_homework import add
+
+class AdditionQuestion(UTestCase):
+ """ Problem 1: Adding two numbers """
+ def test_add(self):
+ """Adding two numbers together"""
+ self.assertEqual(add(2, 3), 5) # Test the add-function.
+ self.assertEqual(add(2, -917), -915) # Test the add-function.
+
+
+class Project3(Report):
+ title = "Project 3"
+ abbreviate_questions = True
+ questions = [(AdditionQuestion, 10),
+ ]
+ import cp
+ pack_imports = [cp]
+
+
+if __name__ == "__main__":
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project3())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..2634bcdab97912644d02cf5a855e43d0be11834d
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project3/project3_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project3/project3_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('QlpoOTFBWSZTWTWxcRQAqkN/gH/2xFZ7/////+///v////5gs/xzS9z5OeqCgALqqtqnrRQAAoF2AaA9DQRAAUB6e3cC6ts+1t7Hzsr2vhiVCAqo2UDR0AAH1zsLTnNltDQ+DR3MAAAAHYwAAAA2fAAAAAAAADh2AMbAWGAAAAADZYUBYwAAAAAAAAAOwAAAB0AAAAFuAAAAAHDsADGwFhYAA+gAA3GABGwPe+UNzO63t1Hx276yu+0110BcbNBdssSF5tctrYzS2C8jY3WU6NWlaPrejp0KV726ctV1hAD1wOe53HuejrwcEqvlSLHu503eKXW9DSnE1hshNPulg5leduzUDzFt09vlz1bN664fJWT1pvdazk8mG3u0edu83od6dIpO2anoMmWPbRXIZ7OhyiSe3Lr59w77O77Gu925dZ1paHlE3eLbD4vXT1xfd1nN2sq95u09dVXm+8PRTLJ3e9wvtgQ7NUlT7DyddtTob2M7eOw94SmiAgCAmgTQmmTQARM0aSbJT9U/RTTaepMj9U9PUajRmieUwanhApUkmminiDSeoGmTamA0AIDAEwgyZME0xNNNNBKYkIIImk09JpMaaCU9kMKnk1B6IZpB6jTRoNA9TRo/VNDIEnqlJIqemKMjT1GjQGmjQAAAAAAAAAAABEkQgAJpoAg0BDQJowIIZT1PI1G00majJiaAeoBJqIghNA0TUwDVPSYTTJppMp+qeKM1GnqbSDJso0AABob5tV+9fa9tb7i+6ZgyatkiWsdtVaXMSNAquqrufS6ea68nW33zbVqvpTbWKtVdtvoEYEkfK//z+SDtoFba/D/tIUj+lDqv9iv/zz/yNv48mx+H7n/OKxiYurPwXXXOr0tk+f/EMCtdkMSLzKiZISEJItJ4W/84wzn/gpC5uOs7VziYY0UnhFIyiR1FiPyRq2rzK7q9c72OO9YkFNfq/27GOud5QuW0Jbov0zV8+DotRWjK0to+iLVMgTk7E5nXPtB/keBKcfo/x0yBdHP/yN+koiI4x/9p1qqmesvDITAq+/abQQEHeREBUe1QU+7yqii0aNJY22NiqxWvxrXZFlKh+st5ra1Xn+TXWtoDBIEBSBIZyyJyNC2Qg1y8q3Xg16UzHHw7MaOSu087Ox4wPSnVJ8lCBUoFXS0HzFeVPIGDRfmtdppDUFqRTJpA1Hndtj4b/T6u/7b4qYRxwD+zQS0W5/z/6xtUqmwcs3JQmhDtNAiGHU1Er1ae7yk7r0yIg2LDj0zlK5y7UtLJVViOqg2UhpDGurAcdJabblPaWpl1dj8wQzt6SiEDJa1Rfk+icUD5x8r7WlTC1ol7URCRatAiU/JzcViERUkenLMXxNvIP6TQpVIVGbvMdv2rngwt4Z+usf+uX/f90u3Pt9EuIik5bf9pzyxmHZtlRtP3xokf4zYJfw2j/2KZdrow55ZyhMeCPin7vr+HpHz/5O3oIyGYv4rwNMIPpr5QfdCdkI4ob7L7zwn8b/P10xpkzSEYrEkDtYQ6Pt+aDKcq2j/PGIEz+eCbTC6+sSqzh9tIMkj06CEUbEJBj8PcJpR9N2yetvj7TlgwSqlVROM9vzqvosVvJ7dZ6ZMf23Byo7pQKTxhMkKqnuPP6+fGOxhud0mj7/X6ek/5bjtNrPRvR5/NDPNn8GBMvZ9ld/Z8KkX/yw9vHWce0eY59czIkTPpRh1HmtnZ4Uo+jukr4Riu7+WB1ybHjEVFzUbUm8tbSNkclRiuzhxTBe/siirwuZNSSsVKOVthVp5aFN5ncI/No/0mJELp9FbnlXnjtsU35D/Plwgnq5OUcNn//ehFtZ3mpBwEunTKu2TxtaU6+MuWc6Us9smpgPjz1lyrKTZ5c1FeJTbTflauhsQvPlNtcXagi6JWjh3PBpTrTGnCZhbf5ZOieXqwuWoqeqqi3CdCFaJxK3SCa4iDyJcuRtWiZzB09O10U46c51J22glbYxqTL7Y92LTKsPhAQxpd0YYwRqjphKFgkYPleIshbPFS9CUpFVlL9DwToz99WryecPkt+j6Sa6UJ3eqpGCS6ewwpZNV855IMVRo8XgsmN1mmUvaLCXMF7mj5T6X+BL+j5B23K90fNzJAiKxvHHEpAUF+YVmNDkIaIGTQJroNJl1VeFVUm+qDWlBZnmhhJt+Ojbf/Y3bAWq3nJ2zwQXI+zXX27UDNqQikJvuV1M9s4BCS7Go4Diba0FDUpKiUpMIpF0OE46VKrBpjEjyxjHibBladHOjYlFm1kIIQpRszCNXfBpMnAeu7GxERFFBNmgV0+6jm9CuGmu/erorjC7Lu0bVTxjbgaEr1JafUa6QJrdR39e5gwwD9CH6L8y/HpggfFJgGRYFIdyN0w33fdlvs6EcyhzQxw6aQ+i8WHEMY6aQ1Uwaps2sdbZYZjYqiNgXuCZ9S8rfwKcn1a2xpRzJSCcXsXoxSGrzm4qjtLtpgagYaogxa9tjBCHRkZCmYydSzmY1p9s4uraCfF3Rxg0C7jm1gg++s4PcS5WJGt/vmSETF/kDj7SMsUgmzj6tsTEpqa3xcIRNwf/M6PBU5iYOJCjIh9xYN+Rqxo9eyncYKwXI3D03n03voMcTtwyWrZTGWuG1Vgn0oXc403xwSfIVskZd8pSfjhf1TpO9V0354Z3wXFx4qAl6c4ieMQFh8FuMI5yq03HPpQUgeYjErUw4bvnVzAdZ4BYxMTgTxlrYlr+fCR8cN9M6UwWBqJrQmMRyF3bWTd2s96FYffA0ynTBMnLxKlatIMtM2e8imEQtMZZzfAkox6HqqMxCVD8Wxg2Dh562zx5WM15lmZnv+9qWxCRLTCITVOic5d04s2YYHZV3nNUKudsikzOb/xWB8tPswriSH7m4kEpc7k47do1lbXAOFA15cNYzEmlE8BHMx9rliGl2zjqfzyHKy6SjmtKbvALHpxrI5cOGpYnjZ8JygplP5P/rSG/JeZl/vx1aefbkKwI27/BnWbmUcGZsC7wcTiTVOzR+nX+SghX+A7ew4fnHgSMTAbgQZDayXYIM6sSIMEdUeonewaI9lp+7ndixiWo/aesdxg4m9h3F40I4WvwK5xXNokL7U0TyMcWdoPB3YTNkYDBlE9ilSlzDXXT8ulGty4RuYGIuNWMCgTpo5UrMiCkqj6MJqToO9iw5QDaszxJNMSJPifAhspyIcTWc51YPL1z4y9e1NLtN6v9Gp2OZwtBDScyZa9nqdTl0igKdFa5gbKrTKRPoa793CD9nE1LlmSwoOP7ChBnqPqOwpVnFi/SemY6EhYcpRhrejTZskPaSaoLvLa9uzbG01hzMzDKvfGhUxlSvDMuZG2ZqjPJJACEhBompsrF6EmRS9h8ugXQYSq8+vqtBTtz5WvjQgORwv758DfydLhLniWDF1gy6H1SY0Rwfn48Oo7JCMZKH94u/Z/fjVZpusMw9WF3iDjhjdMWC5k45DHs0rWdWrFwkiz8pTNV8IxXW78aZ5kjdfTE0HbS13qKb0lot5EmMDmPxMPSDutocfVsZ9ms4ucZfokbUKvYp+Os5lem+PpIRsbzXGZ+lsih+Xbhobjm4ai95qTztw4yNTBrpltBlx4/rLvfCbSkGI9bMpTdcoKdpy0tXK5FYBeTQ02xCZMhTTRBfCtyTmVWdjzsHE0zzyrgpSlEpTJQe+k8S9vwBFZuCflCiAqRGcGaVtVz+uSSZ9Rzr1Xa2yzhwgLtzW14VLlNArHhZg6BSe5mZsnhoaabV+CivGtC041iUGEGDDqzU81ZBjnSl5TLXoNbAtNWXsRU6ey9cBnO7BWh3QP8fmtdO75N8aYiteLpvemHUT/nR0SZr1pKrX81Z96MzmPkbl6my5kM/CyeOOG21YmcPmy2LbP9rywtxfaPAyNW2FI0fz0x2tOVdQ033L2aP01QixJphVGppN44nbIzKvZVlvc12p3yMM/j25eOiRO99gc0kdDiRJu9kzZxoWdvq8HGKWX3lX/oyfTGwW4kdeojCjUrIiLvNTbvrP82xa1O+hV1nx4O4WzyauBczqrdp1Bm7ZvDEui0eJTKjk5n5F5M5MkobM18+cYF5YvODAxCFFXhbKEKNTIK7VO8s51TcV6lZEXoxhyNRb15M0l1nvryz2831joZGpY115as0pzPN/Lps9960snLlJE4CU7Dk5k3duFZnFYVZYPgRXKGl5ZxTEgATUJHkGZ9ni7Y9cPgfHZBR9Tn3dDvh4QLwH4ISYg4VUkOkKUMmBEDStHCWbXI7E5TbKdTpiYYQUKhcN75CYMLWK4GBJT5SgzVxRxtpamYiQQ9MS1DHDnQnWZOM0GwR2I/krJpVixkXICU0y4uMkfX9XQUJJnNyPU3peZURJDu7JlBy99W3uOuPDjAVoE+Vecp0xjVe8JoafjAYBIKdTUfz5PVm5PgbDtnxYfBpyMJTLhogvGxwMiwQhVwPmvbJsVBArdZYmpOtdGPtMizZVrKFdpjs2xxvQdrFqm2kqPa8mrJ3iqXfViXVaPRnFU0uAZTZXvIMZ82M4OdyBjxUJmupAoXKRqpVyTSwyeUMTKHekmadTKY+W1C+I2L4+80waJZDytNevv3PtQSMh2W205486M4lRwdP3ZcNVpkLBcvE5G1gSdkci7J5amIRxT4yateHM4GpfdzZsCrSqpcB1Mm+Rndt4NtCumdS+PhBtxzCwYa4FdZEmDij6zP5jlbcxwsYDbmUknE6cIaOooVHiRpWrr6sXUYLydcKvAQWqR0L2LLFaN79jIKfJIksrLHgGxWUSLirKnd27JViqx4Imp2786TVTz2npWyhJ4/Dma8NdIa5BvTyaR5aPpqGC7LARgKSBVHKbWVHKzfuLWlwvzTS+4tc1vwc7jV6HhepbN6Q495nGZRGjuxgnY4XBzE8L7SnIRwrbS7c5nDC/j2SyGttgYkjnrlmTnXjvnvC2zMUCLzeunEsw4pMgZ3Gx4nKsDuqRPYm/wk+T06wEs9+mpMl0cdd6aqDAgQiZz6TyLIqmq8hXJOpxoJzhEXWYWELDK58HKZPyirEZB9/u7Bwo4d3AMGw8FgYlriMiXGOqxTSrg2Tyt0x5TXSVMzBy97tQietLUlcfIF7JBWMMmpak7ODpwlBMvWZcwxaZWbhfbQz6VRS9NLaT6UdrFwpCaV8h4nOkk4lrEHCWzUKVkJkGiarUCbTZcOkwgoaEUlKN4tUkhiAzbvkWki9UZNO1gq2BoynLGfCINqp5ct7lm3wetpkaWiJvjjVQEpTCdfKhNNWRmxiWNuOwfs8SRDor4IsG9cjSDxOLh6kbBii21k0YiMV6TtZw9q4yjnq98c2jLFlzKaHHdrxr8SnODOeNccpeaa7TJubFTgpT4o3wyuXK0zZNNlzEVOMHGJXsyrV8iVzlpKWO98Fd2KJ4TZkpntH71fR2B2e6VrExu4+iZj3PVBid8Qgvdq0/VG67jLXBtG3duXtfnd2Fn5w53d5x4X688UY+rCS1M3NTpKVWvPXXv4RhgqKik2jvvyrH+LtrSWsukkeE8RRyN+TGF41boc6l83oHhK2WXm0HAVbZGxeRnTodSwUEICAqzmbiyr0FDYMPgV0k9xsPRieB0FZ/+uR8ZtTbigc+d/yzlMe1Wvr/U5nb2V5+fpXjz5RFE37D4n0bDgO2IQQoTeYI/FfUwm+qg4hNQuw1j73O7IR766Z23JNhI5I7zGaJGV+3+cejSqNHxLwHZk0UQUwUor8rWV6qcMiqIySmUyFHpqnQFFUjUYRFKqvrGd60MqZ09d/Z9L+c8nhZ/wLEHfR9J70cix1kP42wPiVdZp5GG5+YfC1M/+D1ni3588fOX000Wdd95TeRwRx8uUZiPPRyWD5XVYHxn/TywmT4/Z8mEYWwy44WXU24ZyrjlUuXMikzfLFWtWyrv8a750vpXHTTKeHLB4lSuCQIWaid4LfSfYCHPcm+WY+Sb0Q4cgwMQNJnjHrFr3Dt3P9nSc329CMbZaBtyKEHb9s/bwv/R4ePuuafLw0zfnvnCha/X14xhSndn5102XHbmVcCbvrnF8TwEsmkMPU5H6OUhVQwghIsFQb8XITHhf5EmkhSRHZIXZQWNIFpERTeygtDIxtlCbqq/zf6HVEv/IQHNGi6Bt1BKCqUVLOH19lhafKynsTozxTxcIa5rtT4Yt8VK9TrK347dduC+l8TBWXb9+p9Ujw4mvu101TO6O4UCPDwZFSIyCUBiY/XEnQhBasjEngoCxMNGABoQRG0cSWJ/Dtgp/Sh+CKS7umQuwdfhDFEptXKYX+qoYXJm4PM0kmqKr/ruqsukQfQkgEm6Y9UQINr79nFY8qEpP/cxgjWw8CbasNVZePcZAx+5eeh7ECwFgqohiBo/9FTOqSWCUUiilLKKeGjnCmP9Wc6ZmVXRoVOG161RP4JiaJRGmlNEnrDpgiYIP3K6pC98D5hn5zw/W4ljApwj9xTS/d5fH5tf70PGaz3Ew+L33wAcoBWTrsk1GBKv9PD71szITlsP48fQcDp9v4d1RFVVFRJ2oQ34b7pTx00VE7xlDFgsVVWSZ1qAaoa65tC+zubsMCh8yotBX0src+UK5vHd1yd25O7MriW3Cr4XjeKZWuZNc/+7qEkqhIL2Ps4JEwNnR0JSo4DBdkQ/q/wwyYkmGUhnsoDIMDDAUgipFARgGBebURxHDNOwaMJS7VJnTcuQy0zrxUohhP+Lx9S64yxfhrm6UuHh/BiXRw0N4NquqalYopCmUjCyq2PG8ERkWJvGcfCjTXvHCl45/QgXh/MxSBFFXwflX5kahkeOHJ0mVFxjUMiecx4TNYEzFp+zLpa8p/eRu0MBB0iWaqmvr/Pf4q6SS+2PmyTIlv771eP1BQXOC49l6kfZ1k1D+G2B+OGsUwkazHEbzYJiCiBkyDD8x4oq4jHhX/Yr3KXl8hEjMRlWpgWHq06O9M6FEJCo8ZkrYzwwmcnvNpd9gRM5ylTJAInLL9xMCRN2uidIIIG/p8Y2LPN1ZzMva3VhkXoOjJ5BkQzeYwHEbdXbT2JW2dx529R8ZmFm+Kg0bG4cngyF6qDgI2lCdUe1V03zMCSbotNFejVbBYjuDNRiHzU1DX/a0pmjpFJ09ZT54oX6PQr9FJUzwgUcnETcKscDfzNjCuGrXMyRYn3CMwyZC9/B2XMsTs4G2wWTSBiKhyLAmY/iyEsTBngSjSgwc1RF4knehJ3LMeLeRKwkVEaneW4NjjZ3ayana4Wqv1KRFvlcxB5nC9iGLZV/J07/JjAjomueeSWWmcTJm5m6wV1s+PLqs92Z22zWfR6lXxN0qzeNsDezstg/owz3cCZiLPnc8PkOPHbg1V66LBgGTjJOujOOxB7GcLVjDxbJTEesmOwvG7X6UmSHTXe59I5JkaPep7Pb/6Wkb5110m1KfxiP4pyzhe1YJck+iOlHJ73hfdeCF3r3Z/GDevsy9syW/qf1ylrJLSSXa8KSckOQUeynMUx/9i9Vrzv8X8Xwtzl1mOmF/oKVSD6NH6Lvy9UTfOLeJnSUvwaJc5W8ql1Xzywh5mV0mkmOpN+aoXijhlSKTifg5+CbuRzh6rTv36GmfDq8OkWQ6YKI/ARotXXDiHslgT9i9F0ynkP5eX2dzDktWXb8PG/JsynNEPLMkXKd0tFtLA6Mv1pxeIuoi10w+ozZTKNkGoysWuc+3GLd1ivoV6zhJlu4Ptl1z22yzywLrY84NwzalCSgX9Hk179wDKVjphk/J6J4LBmmL/FKiUO7PkLHLyl3dZ+mD2Y6Cbmc+0N25uQIgu9U0Tdi+na5t0sWTOzqQWEe3f6dWfkT+F+Xi9+kmORnMfMFvOVP4PizkvtOl2pp8dvpuxdTDbVbYvvh4yI3RZwsDlEh/aFASRGkssxd7WelVbRZBsk6DpX4J/R2n6kLtpfs3YsZROKm6RVPx/5DVj9bdbNyX5avlmVwxzBBBq4qvlLNIV/9Pyd9Z415y3XDL5fDWbonPR3vN/8IyO1qLxkOli9qySVesZcdLS5d0dL4s37vxbjo3VT9WhAow7pluGyP30WghD91ua46V8e+vBUKJT+qWEpqbvo7YezOfW9Ko62ctyxj6sHaaPTHLvkdah6l9OsZEkhUe30a405Ul0TrjKG+Tn6ZEs1bDz+7IuSnS31P9UXwznbfThWSVtq9fKUTfjTCfl6PKb71fSHu+r6UN4fatVBxxodylPNeKOHyV3lbxvr659xTlQxULYphDkqqK3iH622n1vOWP0bXphfi9t59tq/VKpnJ2Bxa/R3xM984rN4U4rA2UuvKR7VYRVc6qruiBP87nLb1wnx3yZyi/POPNu+0iQsi5fQRzeJWuEu6tF67MTtInE5vHqeEQd6gut39kFDvVJ4eUqKJx7VwyUtnr4yyk1ECivW2PjUngiqUQ7pMkEU8PCCh9fZunHqUx0aSZhyOJ+JIG/N9lanthw7hHCsODFHMk5+zEAOtJwGCwNhILEQO/y1pLj6ViaXAkOFOku7f7sWC7DdJCP7cgMUSE6GMOTA4HdvE5UQ1LfPbhR5UeuUeeL1FSg5AnCtZw+NCSSY+GHNrLATbyw8o78pdFkqL78NclqOa/9XJS3tucerfMiyfljUDJQ9Ne3husMJ8Vhf64adHm2SZYx7ERPjUCUuNSnYnxCVM/kyCXAlqT62r9F7X2pGjZC66lDS9uqlaD68cCVc5RDLEhmsKk6fL8eBwptbuh0mWSdOtkQYvHnViqkHeStC8COMeXApFAcdjc6+fLIojTV4Ez2PHfOhjd9V1tB3y+MXypnlnYp9KVjrZObkabbWjjNuQO5iXfOo7GsypoEtdIeakRBxLBjSmdH56aSJeFw0brphWjfGKsMsJlzrxaROe27lTGH0wcntBBMlEObtBLZCrapBJsNICSZMxKg7ZFMIV3Eb9rQZP4+VTKg+q6b8elUGpRsSWsyXnjSH+fhYuOudDyahjcvVx9czKUzDDl6+9V4T77XMts+79J4oK64ezg8ssiGbyRkZ8InlODjz4ZrwA8bVvfEihTUkUdq0Kw+BwgIQQO+Xo/K7Bwzq0+eZAR387XJifwKtJunZHnwxmYIujhdwxQ0Ik3jXa5OLU57+VGyOHDlI6+4D3MgZIP5OaHsuFGoKIDX3Mn23Tli+eDJB0k6cEz/fpPpyeqg4juP1y+Y7GIEu60WCyOkDZmPrL2OVgxEPP3dj2NdDqz4VxDUd3yWMYkLPd74+YvX04ya2ezFgbi7E4i/JvdYM5IZU/XCat/G68K38KI2ZS11xeE+jMpwcKrUmMMrXLr56V2+BRtPB+lhhdxqoB6KEJOjD38T7Drn2lGDR8G+f5hId2PsX2BqdsdA4CN17BkzHJDSGUgartzNUWnOHI5J6CT9Xjj8zz8J848XvK0r0vPzVJRhIqnsJ7SK50JzpTDC0rrOpYxTUqrVpR8fXK+SpgsWnn7HJYD45evr9+dONejnd+BlWK/S1BRVWQo5HJHvJ9XKjOFbPryoR/X5qqsFizFu9bJkw693JxaDBlSmkaSLX47bRqGKUcWSZNaoEtApzkzcJkS6rJsyhUxoFzKTSqADKaJMS0sRuUWOHByMhC24cz0BEot5Rc6nUmC/6GgDvDNfE1RQhEeVytRjbVQXxTXexOYXoXgMS1v9vX5NSuAwyb/WCBtGG6NqOeWVTMK0GQXkNAxAhkGovJkl1Ltc+5Ei9ce94CnsoPBiJ6XAbG5+feYiRHy9glZHUOvEtoddiw9UOCXDUuR+zueuAYhCBD01la72Om2bgbIXd2Yu0++nXsZMSpn202G8Z4xdZY8KhUDIeo7AqcA1mdqUEUPjwOAe7TQjONc21DFhgJsC0MaJAhSOm2huwgqhJj4SqzPcxoC5x0FGiS8CoVO1xaE/uOTjbklB4OWxDClw1kmi1HN2Yz0iaS91tvjPBUZMgTJgWAU+9zz7h8hCLP4dYuiUOertlHJZoXOzMw1ZUG+kMMfbe5msjyq+eJcvLPJAkIVSkHTsO1TuYrDIoO3bVgHLqZWYol6iZ4+HfE6MJ79CUk0sQqjmOTpymmgIg4E5jNFdhsTp2akGYfUanNvMYkcjxzCdz7RjEPI8dwFAOD7tTAoFyQcoGkD4Z08GwKHya/YGOBYMJHYMWJB+Iesx5hk34N0X/ydZfo99D/UvT81P6kopKUKcYIIjZSZfmwnZBZMvqdnD3fbo+f47Vhh5sBezPhiBRCTVFo1FRoxUWS2jRqotFijbBTNo21M21GqjVjGjRag0QW2TVGKf1dq5qkxUai0m1JV+w1dKSa01RRRtSW1iQWRQiyGQ00rjwrsvXPHOxDCE/naEtNzFdlZe07a8arpq5jGNvTcjJtFoSAxYxWNYsFYqopNWNXuauWjHJKjogVFhFwVpxwO7BOjjDi64J+mZ9ZQGH5DqqY/pTJA0eN/V1NT1dT3D24fs+S6j52jlSnGUVPpel7e6XoWtThLGh9zjx+zad1E0sqRD4NvHc3eheSHn2DplJFtNtEEmWIJFMYoxLlyQuu4FlCATMohEmxMpmZCaSSIQ0sIkJpmxkhoUaNsKTBEmtvs+4RWUNEYSTGkG2gaEZiZRlKMSWMSKYW0yZIElhljEyQ0spNJkiTGw0aTSZSEM1Y1pohQYSKUr3NxTGXp29ruCSDGWA20wZpilDLElikg0kxExk866kPO4J46KIGiEya0SBFAmSBlGKLNa9u7FEyMKsolmlm+t7lJ7dcYSNDAxlMKTCBmmSSmSUki2iIMaSb7fuRizQGSSLKSIgwSSJsyiNACZGiUWtCLEwGGGImCAZSkZIkwMDRCXk3MyAYZkyyIFKMFIzJAzCQYSRkBNe500TEJSS5fOXiTproRgDGoyGTKTJAoxhSw0QZSCZGBkkxTNMW0hRMmMSRkUQpJYhrRAixVXSMNPRRPP18poY1RGJyx4PjVdfl4RlFIZdDwLEMjMQ6QyaGYM8ejXO4PQx/76+YtvPxj7ZlcZxB9X7FUIu56lHpJyH9DO98YVQpT5fPjIOQNKHaDw+b7Tzr0vPo5QUhW2gv6vlgxcHMiJghVkUyLaTCIjJSZkoUykUMzJmSrJYhGWW2SQn0HYkpBGZkwkwbFGCghEUwkUYQwMwKRLSZZL7ZuQYpZCZiZSxSZDIoUzGJKYDJikkyDSYJMBMYiJlCLEJKRL5l9DryiwSk01oIhLIaJCUwy2lEwZDSSEa/Q65EVJGSGMFsaRsCTSa0II1phGGmYphaQs1ISYFGJikURMYpEowhRGJITNiVZqyIlEyWTIaTMSgUKvouiJBiPplyQIpaV5OIpZE2UyDIQSilBRfK3dREUgPZcoD4O1yZLEWCXNcMmRkpGTIWNLMpQCwoRJIqMSiZkbaEjbSmSSQyRpCRZAFlGwoBkUpYRbSMyySQIjJEkxBJJSkEylITSvV1zIyGgW0KBDLaTEhN7duuc3ddMYxgoio2snMONYhgRYiPr7fk7Bgp2uGvd6/Z7RPwb2tlYIQ3d29DtGtCzbszAaHoo5u8OLVfYHut8ujoSz8e2XaHrJ51E+jWnWnGdbzdqXnO+cECK3RMOg9WWlRoLdlwe8E8NAu2KHI+HgRdDlmcRuDK4VtkbboUxDRVRitCq7q3zoZjcQWljIa8PDhaHvBQHtzJyO9NHUuWV3h4Kxb8PAqRjVZ7aNZnAigygsmvBdWDFDxDZPh4KBmHj7tyiFYFLKQELpjUx0zhV7qG3UL2p2LtC8Zwlk96MFZMjNM90tuvFWY808KoXdPuy6suGW7mGxT2dVJzsNA5StvblwbnDG7F2Nx6kGLEzDliqy8u6t4rq66CTK3b2FGhgs0cQ5fvOU42N1GqfwU9Uqxy9BsdajQ2C1cJ2tNj7Om64RGzmb2Ya5ZqN922qd9Qx5g67zHBQqqvzzHzHVNIlaKGXpwjeVHeI1wO+JurHEboqhb661vJYIvnOvcXsaB4bw7RtMbhdX4eEpR09wZd0MQ1MQIkIHzG1e7p9hGSqKO6cya5YTy5detVQfXKqbWXXHZuhyqHyzjn18r++rw8LybML+TeOtp4qYKevNs8tSfTmcPC5cqxQ03HlWJhRy0QYbnHO7dBx7jNdikWrNm8JQvBwJbu3J2+HgvHZdZhG9ZpZtdt9nh4Zz5VK7hQhWBTLHQrNyd3h4ZowbCKVVDtzMzMDM2tccxI7l3QB3cFh3uG6DenZmlTMyeHheA74eBwLe6CtFVB2jpd5WXuHMWduE3DrcMGGCXRybVNVIN0rB3DfZmWPjtCq+01DXFKZlWuwpeHgogpYRFbtV6/hddRN+tPPXtNceuAD2sOTuClraERrhZlz212x6bxDTUKBDyEPqFxVZ1se8H1intXoeODANdB02ud4RZI4i5p2xWnfRUCsjd0pYoY+rslJ2Ce8PCvDwtqtazgbeJB4J1oyW7HYq7lJyq8dDjeTudZUWuq3xczFTdrg7i7GQU6vMT4E17tXMVBZBmrBgu9wdTt9tGwcrJVNuhEOuEVtbeCsyzx7OGhS9Wi4L8Qd8PC/QKVrw5cvqJTD5wjArxtZVrWHQYow54eHGy1bNodnZAFBREEp9D51C8Og2JZPrwVxYh8PCVZ8YRQ1V3MpVsF1tLb2VopBSEG43jYQp9TCxVLhF/TvViBpwGbiCcDlD6p9NDnrCmhsfsr8UrNaRs+p3EBUoNkYvtOYPszRT4VsCK3uObzybG48HIG1w11cgO1LdVeMZqs507w8J6hiywhRHWDLt1V5M8PB8KRJYMSlMjY+GCKBCnVCDcmJY23tV44wfTltrd8PBXvWr5JSaM16/xPiK0cHx++vnZnVWpGvk72hGq1crMzCfupbuvdHW+Y5PzrN8PBgzWonly6DoaxaPcJWsFzUVQzslp2Loq8zmGjeIJoNib4kULqSm2RVi867zh3V3RZ0JxzvdKzlydJZKIKDwzTs3gVwXBZw3WN7A4jNqibfE6Ng6oIsy6j7PDwTGB+94MJOko5ccusuwS1WsW900HbOC3d1uHxFZurBasIddunV925VCLpnQYOFesk1cFM6HdaOPcqyX4eFsXMueHg/XcQtUQnbNA0bM0TrQzjqrjced4DwD8SAQfAhDDMiJk0KhGGCkKI0TWkSFI0iaSMyZZNaaQ0pUIaT57XSkxkpEU0sTMZkIiIkUREQQIGtcNjbcu+FGhhNB+PwNBPqFUdyhpx+HhWZjdGQShG4wR+CLns3w8EMrKCFWN0W2cdQO9FEDILzNR0rFCtIQ2/e82HW15GyPbiAIIfj7VWSnFduswLTpLVbEIafhA4tS8xYQVZKKcqmasZvh4LbwgWqvMo6NyixWmCCeoTPWImA3Qk97KKh12xceiLMYZugh5Ra6ZukphFgFoKB2G7UQszysHaFIzq19vVXKV2KSzkDzi6Gbu7Xh4Xs8RlGrkdHFBorxqsrdrIDbS1XrF7k1a/gO8oUJZCUE0IpkEkUTBDawxpJgjIkYQFEohpIgCBRnGioqosFwbZOGzw04Vm1OBtCG1FAxng2LBpMJjoiGGk79RZyCk1KxJgNW7wFMgh5Xs12diYtRySeJHvvnoQPvughF8QuGdf2VYHeHhQtXnrvw0ggkEnxCkXnCCZ4v1O4Mv3kR5jAaygj1b1aqiErt5HERoQKaxb+OR4H7c1KwdUJ5CmMsgi5ptihPjj5VNojFu7Vm6g0E+Jsh1GY0n73hkRq3l1p9JlZooggEg6ZtooNdSttHJcIWbNKiIsWDFRBCZLBGTNCQRJoMIoiZAqNkiZNaRGEZoyaAMDMwokmRIyXv9vZ3q9SSbNNPAk8vTiMrPPJdZtZ30139grIbPyz76pueHgdY0RMV0Rd5prBA+7pqxYfLw8NGSQjgiPeBmnt7SDpXfUzVDZnHbsiOTtCEr45hB7C3yOWMOTLls4K5QXe1Yt85QWiX6ZNcsiSuGQafDwrRhG+s6PeDueHht3KsLcFQNL2VVoGLKsheord2WOmauxxBYWQyy82wqFG7hSvqwYqBw4j0s8bs5kXCsquWB4Oxc9fh4XWj3gd9KWxAk3VhywpfELzdZe5fsoMu6Fi7CFY5CDsjyHw8OpxYKGWcUS41g0jH4eCizG6zlQ49Nojd0QEX06uv1J/BV152bQRQZoieOJKvgbMzMs2ciqHR9wQ6XhGiJobWCsyZBleHg/LrwWL1PTWNxgab4ujDIu3IcYlm8o0EMXWNm5kNN9InBbV6g77KjFruK3DXVeaMuqkoN5vUw7WdtjcCF4hpdXSHVhoj3hVjaKxBiSQoYLlYNoYzA7SDO0WXQ5xyx2vcNBWMOtQUadVrFqiSfJJM0CcCCsabgQukHDrqA9U7Uugwiw8vc7NvAqvtutpwW7mWQmyO9czAxm0sTuwJnMUdau6fd4eGXC6mivQc5o7w8N2yDWqLR7wYl5d0B1dN0XlDAhpG2+9mr1EuVOEKwZiqvDwzK6dFiFx5s26pZz0hS6nPDdVa5VE5Xh4UUJuSkDruhjFHlwrkcPoBXh4dWC087bnP2eIYCoZUFWVy6nDZD1qADy7aBTm5VxFCquxYdFBQ7fYxN1iqe6h1aLW0e3n1UIps1xcYX4eFWn4eDQciWOVEZRjoYCijIb8PBNEQg9vXBavkqG1YrXvcNvJT413wzq5pm8yXVfGYHB9gQp2QQt+vbeC8o4yCfi4t+NZW9r6OsHAYa8PBS/i/qVeN758XNkYWyMQUX5a92qDl4xNFIlkaLJDN9YrGel7Tu8sZDhyjXKtTl6hL5Pz8PAmHHt5dR2iVzDq/XnaPeGEIiGJ+VszN4RsMLJoIwHFJXCMmTW7HGLpx3TbBmjCEJvHs17m6sXrjTfazRcFKojFViIoqKicGraJImIZmMSWiZCwuut0ljLFGDNCw0CiJERiJEUGIooLsYNpvgvU1fK+7ZlJZ7qbnK5Alr+jXaLG3jx/Kny99lra0tBfZ4eGMmj9vd9ecvTgl99vOiDxs2DnBnTcla9yliDRrTYgMqgQSD7xIBRHHJ6bNxVgyt9vYSrz1i+pcRkN+ViKi0u5d1UurprTWjMIVpmxWKPiCQQQrWWrBHeHgs4yU7o5Deu+UqtEFewWM3YwEaFh2hmU7qxIlpJC7DBDmknBiFbaK3vKjYBDu4btWK6PxAJBFWGNuyRdAmLbFjht1ZsYyCCKrJQ+SJ8SQfEfLEL7L9QtE31fPKjX20q376hg19vqgwlL6Zclxv6fTheFko+HgpL0c8C3bnQ9ZzOY7cFKq7uhi45cyViVWtrdF76la6071Wmd98urUciKsYxhwKCmKJEiMEmImIiZlETYBFMxIlhMUYmlMQiMvOuYpje+bXNr4MPC33DewbvM3WvqF1KnMjcenS6WO29yo9ElWYp8c3q2XmESnpF2S7ZCPiSCSAC5zhKpsbrGx5hVPx97wOqiASSfOolNqNJxx42Vx293Q63btBk+PjSXsx7zD5LBezRPdsu8oVD0oQnZb02be74eFb9yGjNXE6O5YD9lXSNZeCtOjLqi9kqUCQCQYQREDt73Lt3jXh4GgGIwgQSZ7u/AoVOB8n4D2/KhtVte+gEdB+HgTQsGhQ2s+niclM8sHI5d1VIog1RdImC+ore0WMTQdAqR6OtiXqq7zw8OY21ezdzcgby85LFVMXeLs7vDw/E3Mo/Sgp3IjH89JDsyhheYciP15tJzteHEtosDKwQEVY94FAhTw8OFhi72BIHElYyc7rTdnhma/Y4urIPeGwMMHdZoVVAtJ6DZFU8ScQuDfDw4G9gVCubGDg3t0Ky+v181V7MWqhTs5e5tCLzD4FzpdX1YyiEIahox6enVNEOKpuXQNoZUGjkN2XvdhG5eD3hrYQ3pUyJEOnRj7phth8JQ7JZXC5GKG1OPKho5YWqo6Onvcu5G+CqXtUfVpygczIjxojVubfJjtlXVZdmYcTxZvN3mjeJF3ce1lgzKf3we7U+6dyw+trdF2ry6vKR28mZtUYJ6hik+NbmnA9zu8e46WDzzk3XVgxqz1FOamg8BLg4Vj0g7daYLBvXV9W7fBp9xsi6Gvw8ElPcFlabozcyzm1tIEMbid50b43iXGwpWkXpdcrd6O1iqsjRZD68oIZ32Vuq84EIfELDKXjThUgW0I5syvoogvkTVXQc+JelWMyZUNXUO3lSjZw2XN3R9IsxYbGCV6r6QVQmWRDUvrVJUqFRPvrCiFiwl8snh4dTLp8DPheSbwnSLVlm77B3eV+HhdOz4eEgiUldTgjN82vDwRNCjVMiUUuvoKHbV1QxccFoULrw8KMsatpeHh3W0KyhBEPOxYOiVYQoKdcUdt2yXtPobvLN8x8HYDm/ZCatKuPk8IqwfjKioX9VvhtjeSyYOft4DBgq54eF6yNSZyqpZVdt8ZyZFctEOMJ4s0JUfDwL8QhUqzdqu54ailbuHnRxPcw3eA+Y94cLwLM6GWK8onusGxV+THF+uDM5DmJ1RVOyunq3QpyL6jlusnY67ZMwOzfdndsrC8GiBmtWzHjrJdxTgunGjadDtq0MGQTc22cvg9GClVvc2riqByoRu62O7RnckpFHi0lUKC1TMbvw8M8PCqiQeBS35mL1ildZdCxzHXo1Gul9DSsVYrfDwzc3SJsVinEe5+astbjLjKF45Sy0GyC/QuUI2q2tD7cB0cz1zDT6LISDKPsEjwadPHsKG6r4Ks2pB7w6d3WGa8fU6eXt1rG7dJ6JBL6BdfKsukGw7hh05Siuk3t+u7CoZu34huiUNqLgnWdZwPoK59DK22TSFKX7YPeClo3mClQupGauwRbsxEOkjSC2OZfYJqmInVVPBTo1erA1Cevc3M1IlXRUFDUXS3lMGA3N3Zs09QWghB7ayijZFEawnZcjvqiSqiqFZqFEWRcCD3FKzq04O5sdQJIJIJBCXAzOsMCJxdN3K6Zt2NGingvUu9u1KsOwvPeqkqnWTildl9yYu8R4IYu09gnla3kRnIkZK0ZXh4bCwk0VY52L7LqGMaHYBRDtYhBytjRYas2L8PBVFj6lDpa0MoB2QqDgkFO6EtC4XZ8nzwU5D1DRdq7sVM0OnhPlryq3XTaC02Mqz2d1XJO0dVjCi13U1Eklc9djHKb9e5hLkEp92OIZ+cLHPU0Ejh1vvKaDBvBwuxUhhBmSTKRMZKIZBJJm1mJCQmQI0BDSBRmU1pYiQMkkjWhZfNbhGlNMprTAqyUSKSWQwMaKIGMlIMGRMikWRGYyOdUTIxiZbTAFmiKsW0VILKkxiTSoZDKSIk21y7IEjIjDJImUkTCZlNCkZRGBihCxQEEu9/W8yKVYC1kMY2KASmpGSJRQgpRN83WnOcJmkbCTVkSCkpIoGCI2JbRGWQjQkKUyImTQhEUWBCS9295eeKhEabDJGMjRRLBhIxDUDEsGzClFG1ikIhkwsY0EJEyGRJIZJsmSIUkmDJQUsYoQMJIyUYkpoNoIFCyaYCMNGJMxFMlBjPXdkZtYyzUii89deYsgkyFGjTYoDMzKA0IMsgPbzereI130168rzQQHEyqiKgqY6WX0K1s8DPfR4F9+eDtMYxCaNhJCYqIUpIiFGjMmAjNtETZL4+4zSjLaMBJhp3V1DEQKFBLWYkl8d1zG2hSIBhJJCNrNtKYQkDazNLMo0MgGDFKCmNMQZCZkxAybaWaCKIoIkmwRvj7dpNJi8bmRJmCIa0GwhMCUY20skxASaZSLaTSZRgKTUZEzd11JEpmNm+NwwsRUktY1BiNSSwjKJM0pjMIApojNkM87dCBmZGtDKsTKZKTKBixmkGUxoWsyILEJikYyQNKSjLKskxJvbuJmlevi7wjTAUpKMJSgyQKMxRYjJECKL2bjWiasTJCmRFMTTIkBoQjEJGJNNImiTYymJhjSUGhEmNEGVKRhhVmLu7IhIMBlNhFkW0pomECrKRBMiYjGSGDBESTL3uZSYjAjzuUIQ0AEJCZLGFhrzb5nbpXy1veg3QGUFFiirFA6ujnnwMb1pCEPR1n25Slc67nI1yxUEzKTDESRrZO8p5lJ2U7ioPCJae3AVr8Sx2rUkFd1sGG8ui5BS283dLUmHy25KbUg6tCkO9fSitcykOs8EUlKms3TViRCrpsHtWqeaj9OuwLh50L/RJu19Vax0FL5jF9XVrGk7V3xFr1t9xCBYtbGlWdGn6zTQXW4qCCardsPKzPQHWNwqYCLIwc21ptrcNwQHRqjNHZqrrzXu+HhpzBYtNqTQckybOHI2h0UGc+thzsfXytAyXPryfESvtIg5tXKXxHzF2zR7w8CgbFhoIVrCt3ARgsYYRWXZEDqOUVv0xwaHPdopAhi1tVUJ3VTUvpPuVmqDzPvmtu36a1TgYQUow0iVLCh3MwqZl3mnFurzsMjaGVQOfKWKOUICgDtDBWdoSrfQHsOYfWr2r2vHeFbXDulIyUNVyM61EiGeq4qiA1gr2GhviEf2PpU++P3wXb8+IvVyVvCLciyDFZVXcXWitmAsN5e4eE3ux5Q5NZuXvDN7vEGxViPa1dx4V11kCrNHh4LUhVU/2iEBsn6a2/rMzL4s+v8ffge8PvV9PruvOCvO9sK8iFG68D2YR7wlb7OwV1Bw02cNiA2s3JNSpoUcvdwha5LNVW0CaAUS9Vkbm/mbP2WNW/RZ8s+s7ezVrqdw0DSZR2CBZqp0hpNu064hodbiDyhAZ2A8cVL6+33bvU3pwdRy78Ri+HWdo9PFYnDvLKssVdrgcByG76zLD3hGo3vMRjns2kG647VLMorfDwOPL6UExAMw14Q0Y5xRQ7qIINDbGIjcBy8FBbWZDooUQcbeNe94RqxUnM5XntmxjKdbR2CyZgZwaKlIddnZkK7rNJwYUCmu1vTxreHJ6wxZvSbLVUXtgRhU0ltzsu90iG8JQx0eB29ajWDiKxZrlSB3iEUOuHTvW7gju+NvMvtSqqd+vht4dqDAnqre7n0QvJplruCoPe7htF94eGCUtbo0l3VeTQ36QtWTW02aaeVn5AHRny0kdlr5KhCru4MjYjX22y9rQ4HqejTPzVwIIJJPgQSTJfFOIZmNJDJizJEkBEaQEgwQFEiW0pMWIiRNoJCSZgZLIxQISomQqigoiiqwYOug663is1VVkztpnDE/L6/wWohM7RXLbuOMUqG0CtuZXU8d4YjQMF4RYvgbe3i9fDt0RbeCCwqq90Ig4D4ugdd8ykHLwdaRvRCG6R9AQQSfC4mKuZnXV77RNWFib0EIiGNEEgkEeoPLqieC0Re3LFwIkH3sKSuINnR2USP3R+8fd8s2p8PmIOG0LNUiHtqXQFYQfnIe0X2Ct20H+MWWkTathdA0GCQQSCSQfvl6mpUQ+WZgZwY3u0w4bZyVZBAIJbvnBWnw8LtWayxp6Zaoj3hSYWLa5212JXSqrnVboHtELFl3bau4S4JZ6csFxi79KFI72+zOwkcKT3bHWQaZtxljMnZxZ1DNmOLUOaujnj4+JBJBB9tMQiIM1pBUkmMYCIxEiRKRAmJIiM1ppCgDMVNIjBiRFiKorGKK5a3rJi71dNdK0Fc9WIMXoK9j7tg3SOjxHE5e75dO25WU1Mb4N5fVgvSsJdy8q1os+IIBJIBBEw3leCdcYBN0JzxRmhsQbrD8SAfE+Gl3l2LvGK65K9nDJVeRBPiASCDzbEHVlC2rGlTGJKp9KBG8NsbSSdWGkzPVSjqUDLGXEufrqHRur3tI96x7CAfcfLTJHmdBPUeasjcYto4TVgg+BBYZrrtpob66WubrG1QhoIsWSCxjCCgTQMlNMzGiZNhRYmUpIfVt1WJGSYJBQSGFMooiDJMkAoWSY0wxPle7vgu7ekSEYiKhOVVBV8TiMTS65cq2eWa0zToYzYvaeL1lPPq38subEF7raoGlO4ct0kiuDpTuZ4vSMW568ujfo2suyNH5U3nwg474eB9J4eEJMGu7odN2qG2vzZ+NGe5rcDNTX98KGGCrSzhZeUKYyGmSbillHnmR6LDqVbL1YKu2KrDYPbVoEWOXh4YEX1WyCsdPLMc8REMO3Ajd6HaVlzxGYrUH1iEfZ9vNB2idznGhuhuysuEZ88NdZzRtLKeYEJOXPdAV05tMYuhOYG3OlVcpiYnNOxDh19wtk7Q2IHY3oI7c2W5kHGhwxIhsbLgdLZjqIYgTLRdbu1o2gzfLbWDNe68zVArs30pdY2wibujMT5QcaSEN8DMpjtGDTnEWLzjfXz5AUYjRpaEGnQ3OujC1jtkfOfDcVkZ4rxE+wffWtjCst3vZti9u2vFyzg5DQUajF7+eV66zer71k9X1q5glTsG3nHc4u8krOY3jSMR1ttaE9V19ROOeTJxwcx8vM51oj3yKTbOSpLvY9m1nruj7WHZS3w8LRVurxyjvHw8E4EYzqLk2y8FXMuY8HvBG5W96ewqqOW3uVe2eP0w/A+Hh1c9jFBla8d1eGvSkL+kuvicevXgrVndct0kquacGja2ja4LO2q6TJRbeymTCZliWNSykLt0NgWMGx2LOnKZiu4HqIvw8JhGS4KDFW3MfAqIWQcRDZHXWZHmrePgtYrYJRpTTf5i5uz0+ypQkFkoE19JSpC0fpAhY04sB+w5BsCujqt4LLRuZjZyMHy5PTsBCl7gaBGW1yYxZKrMaNvNyq1aLwNtoHpp0Xe6CVrF+HhDV1S6CXo/RJ1E3HwoELz0czUJAwP3Y78lSFFsZgQPh4EWRVxkyzYv4YETRJWpE9N4HF1lAjKC8ctNqzKpkZpQU3CrNS6CuDcio3z8qFkLuW07gm5UV67vzwZmJblykarK8PDa3xZFiMJQ6DBVUGbOCY6ENWjVNBDtnVliu1IXjmiC7Cq3FyQQYpMXjdq9cnPtb5Zm6RTXagpAskIJIdS6bBiWEMjSjJTGkMgll8PfD5bpEZBCZNMmkhkJQmKI0vPj+K74+NINBCZHx9f4v2qtktblNg3jpXYbvmqqySOaI7svMoE11MKr881P1N3hDzdsMI3TdqyY9jx6UdJDjPA+JB8SSQa1BMJJdTGrLywpeTonAQQD4x1Mu1mioBqsZRd1V+HhCSR8kCWPqw18Qq4WEDzvw8KWyQmJCwS/gSRe5ntOdKY94bTbWSqUS0Kk77W7z0q9KcXL3HxIJ59XUSNCxkzX4MR9MQriwcHPFBN1n5BeJBCQR7fhPR6wj9YvelCt8PCkfYPeGXfy8PCo0+JqZxw4O6xT72bdQ2xbI7bUZWIQ5No+67rkmMzTAs9sVrBKPUEDARLIV4g20y7Cy3e8L6kseAk8eFxDnY8Uk0ZNKKUyVKSUYEkCRMhhKMKswjXy+7CSGZEtpMhNKQXi1JBUWDE4VJCG3HXTTi8M8bxrjFiMiZ/FEZn10rkxaqNShd1auk4siFnLgdI3l6gXaaqT1X45Mo7tgJ08L7n7rvge2wLZs4hALQRRiY9714WM9MDl5xEmHGwyGnLFLLutrzgrNgGuC8Mo1GamCwohb8PAoYNYmq34eBGLThuABaodrfZQwPM9HbJFAuwV7w9he6LEGNZufp/nDsVwtkXs9q2mfzMnky50cLvMdMFnQITSDEaZ12WOLswboN8aHhJTqD0dRc44Fb0e9icdWo247NBi5zXlruGExmhAQ2EEVqIjugLvgiBmgpoijJui1DLJ0lkTKCAxVVGT0uLnw32rGsZjumI3vLZ3DQyqCq6kQXZBNkQ3uBoNDlgFX/bA6uvTrPvzpzFEkASPTeTXqQHoiIeiKArUABxOFKAVFFdsUSoAkCCLbdtzRITDRliTIevFzTSTlEZFBZhy8NHAOJzxBBDdAAA6IYGbXRtjuk8Sc6LPi/Dw4Pd3kF2V5cVQvm36q/VkEB/4v0ugytJCvFBHrjphUqsUbcAq8atBflDUpHGiyMSsIHx+dXtBm73K9VlnytjnrCXkyzgomiA7YCYwFqIKyLCAOkHjqsH1egN/759ckPmK/Tb7ei/mv9ls8P+34V92eizdJ6X6p9s4M/34V0+RF8ltF5cr8r4lFckPyohI9BxfqhjT+o/t+f4Hq/GG8+Zp+eWeO0YtrtDH6hQIrMuHgyzDYkPxFX4pZ+Af1Bt+gryQGSj2m3K74Bu/qrGAdkG8HXEMZsxaCvCk7Yd2Wbuv1bFG9GxdIe1CGG0FVEV9vUl8TBd0WnQtOqPWiji5PCOJ7rCJEzu8Rqkg3xGPSWpT0h60zQpRu5dEx0yZ0jCOdOLPMoo/0JzPCYY8bh1S8jEMoGIaTy4hGauuFeOuSeVIsqtR+C+KCUx3oJ1Z/4Q6U8dOP0TxRzwrf5ZxKX4SrWUZil0feQ6natfepflU4cL0ynys+k5xLBBKHU4rJsyZ1PP+tH1f3A47vZHSpirtwc+A/7v6oSBkq/nlrv0usc2Ntw/Uuu1enq9da16q9Xaxa1G1c2r0itcqK0aeOqottGuaK8avFRat4ogWN3TWAskthFgHvN+IfH5VMDOvOnltpVobFzCTdYsARhhi0hDUM6bHlb2SVsGy0tlQ/fvZ4lFdvX0Odx9VxY7Y2j5UTyyeV+6bSZe0eDKDpT11AXA4v2z+oLzBl6Fr3RjNTxBGjCRweuPexq9ZhbVqRXK8e7P7q51y5xXQRXPOs6gqxOuYT/eam+li3TI0PPu9nGybMaqxUgg332nvOJerXOV7L0xbIJXhBgikzfuuTVnUs/67GbVMKlp8wtdzc9kylj0faTbAl1VHTRye53Up5h0m2BktR32uRkS81oPMMZNsi8YqY7SRHjHH0DyAORRwf1xkwfwMjGIlF/BinVv1fCaSOhoYz1d31W899NvL8X6hrX6ujTxM+w3s9qGZ/vE+X/wfH9X0/W/3v/r+k+3s1BouiByATnQsw1f5s1meKFBCqZs54vg0Ioo/X/CkMT8j9hiGaYZ4YVQdZoo7BWDO6RfAJJDTlYe6sQvqNDYhiESV9f+A9+Q3eqPQ55wsOUPP6vyD13VUgqr9OnbIbUeoGdIUFEs/A8OB6KKO4+c+942D0STo/lru+WZzh3abLpPzbuAGfQQYQPQrL6AqsQAJCf9Fn24DI6qqGj4nynUWSiisx/wrAOt3mHtDVuyPTtdh/EL9rypkEx0e3yCGhGfXMj2pBAeZTf0BIb1M17TQ0JD0FdBC3lWLYqHUrluJRs6fe/Eu//4HtwyNJ+/Scg1lLLpnJOsK8j0PuS2LAyUJUJIaVKMxRulj2LlDq8lOoMEXySSEQhBhEICBo66TrPLowNmGtT83b1vp60tRR4Xx1mZ/mmnqeYWF4BcqE/kz1SRN9zOUd1ujrJjR2dkfLoON8McxzCnax2Q/3zqvOCpb1bSu9hohgWYlj1evzh2LTt6A3hvNL4xPhHrCQIag62qSHH22IsySQvYoD36auNm0j7aalT2lTWbaZ8l9/62/zNxKKamgXXkb2xHBDB3T7Q1CGkYmO1nL03LBELooY/mSQoYXM+68CiYPoV6jBgVT6wr1BcYAMgV7qPhuULkTk3VLJJJI2xCtyBu9Jcuuyiu0JTFdZ8Ra1/OQxQ/TmUNrt2/sHZxdzIhfuo29lEN7ZgcDbxzD5Z/iyCJk4JfG9JTf9KZUhIb9A2E2dHwtY4r/4XLiDyh6RCx07wZJ0ETmflC641n+8Y8dkVWet6tGtsCZUih+6UYSLftOrc2aKUT3Sf+88PesKd8fw8vdnXx/87YL+CSTuO8w4KyMhBC/3YYXlHnSCZoO4t6xqSUx8B3l1lEdfw8pi3p3nKHPzYtM/lIdv68QOyY7/35dxx93q3gljkiXFn8MQzM3En9Gd/6nDHmU+HDXemj9l8YDXbg6h24FhzgScs/lwZg/aySDYTA4r3tDGCB3obSysTJ8thi4x/s/sLLuzr/MqMgZ2TpsZxsR+rz+gMiIMFVAmH8gdkHm3i4/N/DkaopT2Uou5QvBOWUO6hMYJnWZY/HnCl/uZk1MGvx92DMTLnT5bMGEVylCzkqSaJAtl86lCCgZS+yuTsbe6y9XL2v48p0P2orKgLCUZ/NFKWnKi4yek3HhyjOqG3jYtSfeHG8njOseRc9rhe6ymi19U7ZsfjlPreElR1RlDw/HOYvnWu7vptZiydnmzpJhrOUuNamc64yBslbDFJKMaY9/RcNtnlcRrbIfnskMdq3lern1I+pXTY5J0hd76qyknQuT5QEnbO9WlIhde7+69q3rLKHSFsqzMFUo001YeEW2rA5ekNEZjDpKaN9OZENC8l8Uc2nD1N0fVcmqi3cMt19LeBNX5ur+Fvr+OcbbYszhBFExvoLMeTe4jl6ng52RZt1WuqwEXHVzddM9NOxrKQuBEhFpFonEqKkhRsSR5xkEzt7LO/enREhBhactF50zFrWZKUsme6pN6XVId84KKqFQVfkw/S3+Pz0/HJbSi0wUzX/qRPxFn623Eb/vOVfZ4FZUVqKavqJpVaNT87RsL8iiD36n7suwWB7xrXjtFb56b3aX6fpesmIJKG8NvhMU/w3nWkUJZuO2juhK95WdVyxsrDro7184MZSRHrnosuXtdemfdd5v4IErwY0jh/OpXsbQlSOWZuntEonrujLpk4uX2ffU0xXXsVhkeDRE+Vpd2Gl1BRuuq6CbM2bRRt8Lo3aOlXjJDC+9lM+FPZLMh9CnR7t49jxGW/P0O1PVOEWuSGcWkltPCnyqyKd98kaTIpKCW8NlCeWTuRDwRBH1VQo82sVN3jikSM9XjcODJhFFkTX708OJXfYV6vkpUhMTlrHU+jAneedbKJauZ0zpySy6VJane73y6lLxYyhCpKomhLUbspH01xbRHZKmeuiWgqcuu+S4rw2+PXw6tAzdctihWOZKnWUUydNPEeXV9IKLBRGtTd4UKKUCKmk0+QrPYe28Gpz4GD3IXRiKTfOk9WlN2deazzphT5acF8K/rW5JxHrwjB5zPtWKaSOSZxYPaBH9z7LDa+UqydtUd+kvCQUTSyduT9Pl1mV0fhttjKrPZrOLqxJK/mfNsSNsx8WWRGD8tna6LvKPF3zxTt88nNEwZ4vknrjlOh6u5z4kLPAomuj+PKXv1+bQeuuHwifNwtz9cWWL6Tt0m6bRa02e6SvNDtiGh14IGqSO7Z4PfyM4qs9fOAcsVMMh8Uhx99cPFPC751ouc4x3ubzfxro1/Ln3KRKvyRZYLnDzXvtE6TllE+nC9FWTA7LD4fDTy5dlOtnK7v6UraxXFrzaR1mW6Luhjx+SUeuHsqng9MZRP0y/jpd5bFDXfcwClCKHLaXWT+dTjPE1xxxpcnMHaSiB2cTnCL+yfA2q1IcL8nSbkv4/jVskSMo6EHv6HPev+Er5O1u/wj/dhyxlW/h3du1uHbtYlynjEIfjFH9Us1h3vsbhugh3+2pZ7Kas+6vmwslZfL41+I6FEwMiYmWeyZmC3n7/8vosl4I4wNcZJB/1n63SfjPzr+vO85iwyeLBdMdrCSSBojPnr3Srcfnvks+188tBgC00SpGAhJcaDW61ijLYGUPP64QOpAY9adyJVF/rEoGAyAWxGSl1fqduJORD7DzEiGAqQNpoPKsAxMJBP3t+4LDDe7ACo3El++7TQLXDEUskxSAYMXEpxtaJhZKWossFLQfS5CXMximLj6zQHUKHCopnzpIpITWOcPYH847dSZuZiOnsMcU6zCRkgSQ1aqGogZB+UD2XUmEUDmWd2gakPxEQ4B347g34CvTxFKWgd2xgl5BR7gvFlI2P4z7ea2KWuQ1J2sxDdHBSqsolTOaREZ+GzsFPz45BGax+p36KYcRjga46+mJcDUUE7tNHI8Uh2gPVoubQduBIaWFNMsbx6ByXsMQTcQgOwknUHXOflqLqFVFEY6HLxm8SAtgqUFB+fQLToNFDsC2bVJISHhAtjiOspsT3XE6dZlZSoSIU6XQlFl3xQuq2HpwPGBB5B6FQuFalWOyfIGoAhkEMIbAaDrNZ0BgORFHmG8gHqIAWEM6xPkEgaBzOl16OsL2tiMtyCzuGPZs5Buc+cb8NCGh23ccWQrX2GriO6pUGj/EoaPPm88z9slYRwc+mjJktYvZiW3rlumdgZIm4KRlkKD0BR5MTPAkTpxTmRHl7ZLxqpo7XCo1TTksYMVgo0ZjAURPMD1ganl8gZALgbBDUGkbUYcPA79TiimZSv8QcKBMRwqkiQVgmh10AHtQuY72X9BR6xDVRRk/Jj1MR0Ww+eY9telDDrOHVRwUeMkHuOBBHUdMOa7OMEfI2xWTJkB42D0yAHcc6TBD9aIwgsCCMIsACgd8B9w0YhBIpCApRmPV/U+tvF2QGjYB9IHvuhL+r1/dc+ntPnOyUFBX8ljVjcJQLFBRRiyRBQFJHs9e557zyVEgPZJANjZJEUjGkkoERPv7oZnyHEQr6TYGJJem49lgNXKlTNd1xld/DkMzvIqiAxRUWD+8YTa/fSUCVo1RIIarqhaIcbppEP0GxzY5FSTESLPqNJo+L9zcIe2U6JhAflJQIRk4VYEYsyCi9qvSmQUYfGcPVJxaI5sqUnlfn6uwtiRKjiwsVkdhVprISENaVC7ZbljuOtOtICtxvxFGninF8xGlI9KYgGxn5CFyWq5GjYqjJmVrddXV2r35I7drYkCIQIx8jAlnRZcEdeKAfeQIRk6Yp6YD2P8NPISZwhoHAoKYUO7ooZz3nPWg3FCenh5DN8sNADjDngPkCDiJfk9Ca1DsXapeAu0h7APE2vmkSjSAXhpKDcbLn1kIENxxDif0yQCAeMawQxjJIxJxIYiEnfv+4o6ww61hg9AbhT+0IA7xiEQkQawfpiaU3FSkqMI8IDkmoHPiikOB7NjtDoVJLBAKiQuJomJoXBV+iz25iO4Iwe0WzaqlSgiMKSA4gPY/fCgW2LtO4QjBJCMEJABkEhBAN/Sv1Vy7w5ktir+SMLD4fozwmKGgOc4RsQkQPQGywbgqKFRTfRc85i04SmMMIKy7EuxB7GgPefB/SnyTCRYlUEQaqBA2DA8mwausIoyFGgzQMxWGcYuB2mFbS9YmbByMAOsfNCQFSQdf8IpTmDjvyaW+OOK4DNpi9GUZk5Mk7i5IFbQNCGR4qsA9Y5wc3T/P2FESvyBc69nUaSBKgF9jtD5Ai7RD5Ij6rSDKTgiWLPVq9HnJOw8a9ZwpNXT8mP6ApgB+wVq2IrZeBFqBEgoEDJmKUUeuZ90v+Xvzj+/9X5/UQ/rwY0blYHbpDSeOfHdL9FphkGwbHKSdMxj+4vVHJrxFh80JY8h7mMuNShBoKJP7LCtXl6vx2ZiiHkBWQfZPdChQ/KkzQAwJcEi6iHfv/6TpwB4+3WHzDghxUeNEqCqQ5l3LIpGAbQ+jqxifQTcy5j4CGBkHzeVfOF18B1qJ5j2HWZQJpQnDYDUorFeY0Kvbv7jcRry5kxZnRdTQz9WK+qGEKxy241nD9JA3f4o7vX7iQsKrtMfhDYTTQbSw07/uZ3jT2n627CoH/2pSLdKSliMAVM1AtCWwB9cgNAiAzfgocizEZgMXLCaoUKQ0pGKR74czTZ2ZVUXNBI9Wv4fOv2oGf6rq1NfwQDeHvDpLeTD0RJFvUSglo1VLYilDStBZyyXuQHHBMSEhIA32JnKoHR7OJ4NmBrNA8ZpaamAdBdVQoD1WQsnozOGWvW9IbrllzSEkjJDVSFMSERgQAbCONB/GGbbxU/ug2HV8NvoZw3GNFhE32Mu4ao5p6SzYCQYPywoj3/JQWmznAGXPeFznr+SwMbZN6jeQu0i0F5NW0GwGh8KxEgYUFGa1BA7X5nAZyLjg/kMgSiA4j75YhGLHAm/rq5EuYqhzqCWA3h/5C4eVz/T5Kziw2EpDWFVRR8mik1akAqnzhditfT52iigPTYqm5MVTJQpXW3ba+dbqrzW8+gFqueQWssVchPWcYaSjdbGu5yz3DzxKjqrv5ozYlZgIL6TVdc1dYkjsgCiqXCW0CBVSpppkeUXhFgq1ZdWmOs4gWiZdDbBIq0R0YUBoYqLgypbJE6BEhWFrBTBVYqxH7NNXV0QKctNNZjAEtORiRbf5hZDCp0rBsExoRblrOKatvVqLLYWOIu7Zg3lO6f8zVTiDQ7LEu3VMIbGMb5MkymAEmEqQy7Dv/rMlgj9gUw/cn6P+R/Uhmbcq5yb0QaKgc7K5r8CrFSQh2YUmrwmFhBQZNLptCRa1YAgQsGcTIM0EwSKUlwobNDoPkSrr4aX2ddj0R8t1sc0fHFzESzBJKrxDfEqJH9RHf7LPiwPFX+dZSGFhYuwQhudAQjyjArnzCsAebhEZd0UMG4h4VdwEhSYBiMKQsGWA0SMCkhbZJLBCiUUabiD+poYw+VxM7UZO8Anh9rraJ23MKnnrvCeXjzBMTzncZ3bk7rSqeNw+yHx5FPdqzR1FBuLGAHlsO4i1BIYh3iuDyDfR2nZ2H+wudD2NcOdf1zKWmsSB9hHRPMMHbQd3STdrzMXHCU0wOeIfeGJzOa6DhXWUBdyA+v0lGMT4iAd01BJ1iNq8I0ul0Kd+IBqPtCBh+5/fxNCau7pscyFw8AqiQCPXC5GQ2sAo672M5HxFUj5iZhwozHaJ0qUPaWywWyiipRRO/Lo+ssLE/uCeOAZykM90ioXQuWTQF9BNBkYpy1+vZ3nd1zF5n6OMOfyrWyWSmfdrgxlUGRXJOBJGrKWJX75SMiCYDBzOU3NHJ8hectUm1rVaOk51iZNKRFsrSIjjZ0RWbOJbMGI6U9zfjO2Fqk9E6TmqrWRfWSHafKQMCJwbV06DGIwa5+Xbbk4VvRkbG8/rdPeniuUcLfcdco3VJ545GTlMkZCDCb2VJPkEE2wm1bBCGrNSSSTSVEwqOIVruf2oPz0B78HHwrPTKCmwTFwSfOVnz2LOKhqmpAoq+exepOxd8iQVRAkgwdxPZytWwLxUyzpeuJcspmyZ504kDGLX24S2NEhXIrdVcdplc6bI0va8mMA0xaUhFTGGYM3xO2NCzmG9jycosjWcnWkSyVpl8MJ4jZ2DmVuqo8UVqjjljOK3u6uFyto0dNTveEFhEbRRozrqZxjN2u5jUsRcW6yMYJBmi70aYtIdDp65yFivbDPHXe9YLsEB0o1W/kTt6tLEUdZbKtKbAbb3Lz1trD2AfEULFr++Vy2ThRUtnioHhEYC+vOarNqgYhiwqIKxgSQGH5lIL8fRCaxsQopoSsMHCyhfuIr3j9PL1jmGLtHAhAMFMYIFS9aWQXmkDMmAyRd7MLXXnW185IZeEqBcqyr5wRH1NnroM/tT8tzqKztQ6MMQhD9B95MAq+HS/Yc9N9s77Xl0MapkbVNmFhRKpdi3MKLLXS9XtOevXFrfDmTr0mdLPR5rbhXgaNDeymDSCw7ro61vU2Jwn7JGmiByHz8CCCvzoLoa+n6U+QCsDUssIMKi2EdIXhRWVXAVLYBgvwv7BGiAdNk2UgV8WzCG6YdsWSGBUIWW8b6cEn/CLvSP2ZZnv6Zso2PX1NE+GLWkVsHI3TGGLOSBCihKlkPKRGsG22NItFhijUBDUZo4mJgNUFMbAKFhMIEZBlOaZMBaCZjMqEGlguHNDfnTN3jDmAxTCHRzg0e3jXgh2QptG2hlCd01xjn12GR7AcIDffnoyiYKSTBOxAiij70Qeq1si8Xm/KzZTSrKS3Y+RmER29jmHoVa2fqIc9d02+mYRgidQsTnWjBFBNHCNaYFWuOmq1VjIvFKs3exmOGAcAveYOIzRAutPojmF3W/N40KtRCyPCEeytdUjvrO0HtgIueXhcJeD40kvDVrnbDYzOFy5NsPlc5xCGFy4fieZfKgqa0WbfY/uzz/WQWRIk+/tQcS9ZEYgUwYCQESIiwyFhDtmqUlBnG4GKaoMGJRschwF1ODQEGAkJADcFBHeQM45kyDOqaDMC3BwG4Zi6pQXGwFgwCrBSHYwuaihxJuIhUmwJuAYKDfiHbLDOEkPTdKRtGLAHGAGjRh/GbW4mDhAo1C4DkftCOB8CBzQ+yl6DS6xTIOIp/hENFokZCEERQYmpYaf6CxCgiIxITmWBy4B1pUabRusVbqGSz5ibR5yZayT3p41Ntqk1DWqHdBDRDSzF1ERGa2b5HFMyyftja2VgaGeYhzLNTolKdQZU4mct19ZRG5tO0rhoBzIGxMpuoJ+TdhZHBHkasAB3Ia6M45nOOkz3JFNbvKQxDfRUcxBPhf9NgHnhrhqVAW/wM2Z17zT3GsYifvCDnQmQn+6IyI/KaBdIH0fTzM8fGOrpmojGAVIwBw7Q3vcXxS7gnVJBCQdpjimdorKQCyt+DKkvDPbXGFA2NFMKBEIIwKEKkIUIVBWukCZz3n3YgmUJ+PUn4pUrlRmDHMPX/V6QWanYoeZYZjo5fiSgo5XreVJQYL9tEDJOmCycAYU4FNtjzBs+SshbCVBK3BwCzo6mISGGf5o88pM4YJEQ7CKKyCBRm3IDgOQ+oMQMiJl0k8NR4erDnj6T4iHsdR+Y6uPw1Wkpo0mvU4Il6KsVUzkPYEUGcBo+y4iHThG2kcc5rfywUGjW9QODQSiGNoY0865ItKXQdDkIIMgq6ABzTsTS0BBHNkVHAQmKJdgaBo/dkbMi46SK6Z000+BTUNrUTfYowaIMUvBqrk9P304k9dwaoBSotRWNLVBDpOviUMmaUJqA/yIgmxRODODYLwrf09eMvy6bn1ddXnJtti0PKhmrL3zYZEaJvdn6QL/IWGRcbarbjjFPVV7aWVTpHBEGokb5ddFa3MRkw/lZxvW8+GbCNnuFbmh1RoyVmis3KTTWz1madODKxlJJgxHC2WTKarnPbjjNwZ659ICDGbBmShIG6QygKREWAEMhiNWU+WJ0xoiFbQsFhS0ESkCLFZIgQSJTEMYnf66wImwATNrZZCwxKkrEPORRkRBIIqCEDPCfuE7xEcfH+GAxBLqhBARiQRDpGlmo0A4QC7iIShnO+0kLPcMuBjNnfGyH9AixAEIsOsC8pHyo/f7pg+kUDcihwipGYpgH1FgPhtOcPwIFEOuPL06bOYOUMAf7I8ULvIh8XsozPsDYEo2mxPjD9v4YDK5D6ziun5TDVGQ6iQJFIbExipnGmFFDUK7kH0gBFwLJSHerp/zKEZFH5jz9UeeVvV7j4wPb1dVdcPdF1CJjkNMKKV0u0mHcdd3xeeeUdpXKzrWYu5OwtrCEwhBUYIlNDCBSiE5V11dsZd2ujcu2tLMkJY3d3O3V/g8u411MpE15d1vGsYWlRhR78+Rm7W4IrPzN3/GXX5qaqd4dpoasa9FFK4xUoKkmuwhhFXoiipaKhCILpwMVsChwrxlWPwD7z9v836Kl6IuSh+GRpbUauRZ4z+eHI6BSoAw9T9tMYvokQ0pXMd6eQ21q5wfGBCSMAgOMDrA+oNBX29lvm7PRrqeHnb3htAaiGBAz0g/H5vnLKkCfr5s4EMb6+877WYXLyrHkvDwJQieJdjEVyKrJYQFQg0g+GZgvWEoohkRIjGcGjYxLmElCCJGanPrwaE6HcXv/WNRirEVkSLgdyaBWDjXdihB2gq6Kso5PJ0xJP991837cE7xNJ4Z/r+h5RlG1v59q0ulpCIaGl2mKowyFDbszsn7use6TBogmJbbEMx6+2MbfxfTBpRvAznGsEeB84426aS0b/Sg5c10nObpAhTJvVva1w5gX5lF2fJkHWSfi+s9J6j3b18kheivdKhaqQNxBaCxQFMkloQojEBiiok7RIfMIamssXoC6DKCBqpUBidUgDAbGFdkYyWUNDVBwx69lmPfJA5xwRxCCRiiwovOgKIeNv7c/O5het5iHrQ0bAsvOJgGaJBYVXdViJbQUiSpQw7romEnr4amprA4Od3aa1NhOAuQ8xljAdFtdgSns8dbgDfgK+7pVvtFTohXc2gwGQZIJLt7QokJ2GTG51oIJCLAiIJipIc2zy3Zz6XLR2buT5oixxgKllMgUlUFDBklAkivYC1SAQemabpKneNS4yRCqnLqmxMw5JoEDBwQo2aLqgaZRFCM8mUb/mf3UlAqMn8SQtck3JZOJBAVrezbkFrs5IqdGaiTbRrldLZNubpjqK2+dTbbsRA0aPNpH+7BJAhEPWXNiGoTEZnT4k2UEXaRz2jFDC0hCmiqiRdol4htbYYnuPEL3Azc4dMDQl8FLHd3TwsGSTh2eHrqXeNvdu/m93v8t9Tr4skSJSNmZImRkRkY338yqtIyBM43uQhgA/jQFjMKWUIJZckisiEIngcTWZjIsXoPZzRrcs9/+H0Sj+aj0FkSjcUPgJAR7Qs/Z7fg6JUbBaV7Uf6IVsUkBYb/fWYg0eHWRfvAtfNxEfwXNAiRArwOnq6OvE9uTheQAqFRB2mLoIIPqx0ODCCpy4Jg+84Bv+CvXh7Hg8vVB8fuG0aqyOOJxYS+8shFVjVMefnbjVhCSRJWfv5hnoHW5UhJNX7H4RryC/kZg9WmMU14IgIktXsbJQSN3ThBJpcUX+wzFgbqKaGKOhoSaaEikmLLeFvGi8MIDJJWN9N5hWErExxLpiTuFTvHuVPMwN5xA+gbB1e0wwH4L2oaX130BmNYnyhsIMLD7HQCf1iBKYEPNAsQkAyJxBADd5lc8/BfymcM2ESRJppXl7DuDhiCJ+qSeX3ZKAy9h+Q+eGJJ+cyClHu99GRzkrqqruxmR1aUToYxg26aplql80qFsduILVRh6LWC9Vkr725qylFxT5iuEBJBCRD1xUJCQJBX6YL3pC8PI+RtESRDMUGWmGiFQIkD6/w+gfM8w8y+IgfJNwhsaY+li6G4zConHOtpS7JhzMGbAYsmGSAKkCcQ+wzw2r4vzdj8ndRUQuvMbHhmyNHjofMTGC8vqYvzfv4+kNnexNU6Zf1oxxknOVpNmeS2SXLOGk1qqSIWGSZEkLR8jAWSQwpc2PHlxF5H+KccEIvMInplQbR1IQMmH4w1aiDAiQLFKi7WNQFJHOVS1VCNVR85VsG8Y/tKoS8YaEqkvceZBaJIO98FMHoZM9bWVLEJGPbctiDugJKWGiRMZAFbGpCjaUtqamkUrShazXyfFfhEBP0p/+IND1vhYcxbsSGCAckQ+M0kApF7AQ1qhxgRSMFhIsAISI0y0NlLMmyJjMr2752+7293zw61e8hCUsMyHAcD/bbrEi+z0B6WSBRF5+ok68QmhuJEQR7RyGCgrUlgiJ0TBjXIXkQQoSGgPfAKhmoGyT+nUe2FIeZ6j3evA5D+aVVEmnWI8wR+4iHtg8L7eD0cEwTaKJ0AeO085s0OkF6YaQsZqCku3eJgaMcmCYLzPmV6CLuOAfn+/rs52SY/E+aywnWq6DGk7wo6duWvcXSYFyzYiEgmY1KMmM3RqSlLtkLkT4YUW+QUQA39+DrkiJnCgMwloihiXPxm1IUHlrKiEkjGBPaU021Abg2z/nMMKY1c2h+I73WI0ibGObhDSbQD/azEoYkS335cT10pZHuhUKEq4/Rt2vWBmR1wMsGqsR6aROR2w2ZqxCKQuJaXEsQykLRCveUS1n01MJTQ1CUV0NUyA6b1s+EnivfUMaIGVMbLaD6krBQrmiVXhTyL0Uk8FcWRgPNcOclCLzu7fjKqQFSHszm7K5EMsQKFIbpYttdhzmq6j972jdH6twNIT1lHmgghOv3bc4e8iEmpDP2M5Tw/v+ggIgIMYxFVYJEIKECKc+Zj/L77/J4fkx/1gd+OF7u91gbCzwKFLhsLHZinKMgYwO4ulx6Oct4WmIXvc67VdMiNUNCrKopaUfJmTQqYMyoRSVrUoNKWvoKKSZUKKxdQIoKajJR3nmvD1cnOSJbpWLhdlindRc43u7gWee/eUt27XtIpdDFyKlCNA1S1JVJSJW2ulujSMC0iOgAyFEiKIIsRhNJIZCzy/rLECG42GbNRnry9W7FMnuiv4TvitodgRzkYHBJugMIBsCLUNa8R1GAycDAsqAvE+QvAjwaMdGK6RN520SHj8pXxFwvQRKQn4eGIYOtkTPyXKTKhzrsI8oee9O0E4fU/Pm3Y09L4XlkRtF8lOM3pT1ZYRQO8b0mCi8RA0GEsjJxNer9PAtnJo6oCBkCgI5kgk0zmj1vwgJFmo3oePeUDpmmRL5JxD6azHQHnXNwmBTrToQToKA3fq4fY8+QvKcNzAlklLMUQFnKnpiFDjPIghASCjBAgBz1wBwgjTALGjKyrZErIRpAMVdq2s78859RsKvCtUO0vRchTC7WJdpuHdMZIW1dsDe+zWUmvtyK3ca4eXBDO1fICqeTav4s0wnBuZ2Cx0qpIoWBl7CDxXfbJpOOMbInBRGUed8rkjKeuKwcP2kFmbXGsFJtFZMYiBp/pEgU/BeX60qQHCF3D5EtWN2wFBnCn163WF7wTGh9HvOBY6zyp+agmBO2HlO4SCMPD0hjrEF60KRVLu7k10u5RDNpLpRTraKrm1GyZto5O5tztLVhlwps2O4jbv0JoEKGBUOTIEAil2yQo0Q3ZTQaOh7navDoMe8vGSq9J6iOsPRlBrXYo6VJxejvtmZeJnDjxEzgO8bq5sSgZDSaywXl2R7jrLPGHfVHWYm2JjfnzeNkfBsI8ovUbyA8MgjWCNLQKftiqtJhfEjZFspyLtoZSt5t1+mGv1TjHhSUZWq0+3Dck9DlU7BCw2quDJ0E2QwkWTrlQ7cxpeJtXj/eMwADRy0REBy2pnJmKzckkJS88bc86lqgwQXxGmbMmhkMXlQzJMWUTAOJM2mAzmvRHAaUGjC3KfQlmXWaTOAx/OIdKVkpYbqyz7JVTDGwQlo4V2WUQNblk1zmTGc3FkwZmSYEwCbiXJowZi7CgooggXQUqiqMIk0u8YYkmm1TFIMVkmTEOBXTRZgTBGMoyWBcxCIlYotajL7UaIO26Q0DG3RSUt0FDhK9gyqczIjSYXQJAxUFjALYKwGEpKEEu8IXhclUKXWCBBCGkgfVljdrk714hd29azPCDsXRGtOxGFXPVDaLV0gGYZTQQzohEhmMVIRTqgOK+JzYNBxN0+BVAs9SYNiJwiDw4zEnnkJJNkMMIqnJCFKikgYQMoglr0KJauJWb0mBkkgN0HcVorIFhdkqoCKhQOhgd4SlSBsrdLNM8ji8/TJXrT3nA1SMZFgoIlmZ0ElDCnYopP2amuDOkCujAiqMV9WCidCcrbUdymBbyqb/VGTDVDqEKs9KBdU7S8vfIGihhlMDRKU4FQhVuLhC4kMMblxuxSEKjG6NDC5QQSwygobwsGKDZLhBLJgUhTRaURaGFo0yrIwoosWio2NNIBgxkAMSkEhal1g0K0CmtuByIONxiENEkc6kBS0RCSRVTOhmoEhC+Pm4VPooyHxwsEZiqmsOcAkIwz/VtCv9SK72iIm6Auq+1Qw3Fk39e3uwP8yEe9d+okVZNTBjACBWo5cQpPZXt7/GxY2J7EsviF0KCAHyDBGBAWI+TTPYh0zOmksx+QyFaqy4CaIttI+dNVaBU6djQ71Os6uYdwc6KOdywdJSVIlQJECrZFZF6TCiUCURBpP44qFkCyTNzmpVtmppqVaKsmoqTVFrukt1ep1LKUl1rhooapKobMcxe7YpMr1YiSMqhqMgxiAgiRkM1OzteMX0VU6DqBnssDom0hyeBPw3Cw+BpT+Q6Yh8yT3smfdVpVVslfzVIIxErejGmL1/YOxcNhQQywMJDSJo+Ulj2MKr4Wy3kOAkVobolkSmNVk2CB2bEQf0ECLEZ9A062Gk6U1tU3Tlb0zxJ137Ci/iblh9eL+hxcTfSWWHDtnyIKxDnCZhc6rXRMAPqecSQAImR8OyJ2SlCEZIyMYSqRoIRYGCmilQ5GdDJEeRpU580NOdUfzfxQD3fjLu7MRgkIqpuUSIqIjEGJCcj+UMHaYh36HtiGZ3NYLnRbHBvYLhRlIMQ+XPgWAD7YKHmH1SxXxtt1hdPfSm67eLburtZXK65Ba8RWt8ivVJNavSjQ8yhuDAxCKOQgf2AGLlQkZWTSoWiC2qhLoaGz8sMkQ4YaobyCURC6bS7807eS9A5GIbDsLGwW8gp3bFzkQqJdwwa7EC4FwvpsUUnOigswcZ/MwS8hiwqkJhgKIhi6CIyMEC2SqqYQrCgK5oAoR4IFCh4UGZibQgMNDNjYe2AYP9iCChlND4CAFgIuak9ihgGCYhBoJgaUyiSISIp0KJ1EBR2EBQKAgCF+uLDY+XrokKfS/j6+2Xs19Hlu3LdBPG26eMUcA5L1GmARgERD8unpgBx5xAfGEIA1BD+2KloJwuDArtTj8SEHMORMApKGEBqpIVsKxPyP8iDGJR16CQO6Li9naEjAgOijMi+17hVzg+hNeo7j+j4WCRUwijGHqPqL/ocWi0wj+WVCkAGwoZCvE8/jYFJ57lU/p6wDp8F86C5PoYKQK9B1nYUEx+SmXqQKlIwVCzagp36SfGY1JGYhsX93T8xkZ+BCyvVAF/SzSbW5RL0otGjR2IZOuqQ0TgRCWbhg4QFIcANmRhgsK3OEnB3m6N5JQahKNSOpAuV3sDn2kA7E3EeoIFAQQ+lK3G2sPdl8CQCMIG87EIh8RkWIJpSeeHp/AK+W4ecN5xrJ9vErzVG8+XzPUM709FRKxhhtoSBQwkvDbJjQ8rLZ4HC66YuzmlBwgPN8iQ75D0kAjzdwp+sUoudoJXZETjDMjIZ6TBLR3jimjHbD5vu3WecMIlj+DRvh7TpwtCPPUGwa8jm3TQtUO2sJtCkGQvy0V4aaaoacjmOwkGAZDy6whgPNCdcN9T/Ci6B37yy6NBVDWLRqUrTZaLUpto2jCrVIMVLM2zaNEkEYL8+TPUPfundZ7jvw3Xen7LLLpGnMT9Xc9HVQpRJCFEK4k3uJDpKOCQ2baC5ngT4XMxMt+/F+w/YfZnTTQUX+ZrWqYXp/7vCotBIkEkgrqU9CSOETYbZNrfGgKBFB+00QDSHEcQHyBHbjizy8cUBBK/b9zGT+WTGXssoaSa2w7RgXNc6hjTIR9Kr1UxhVUBFhDYRYrQjX+Z28ePH0iSwFgDpUzlKwIUwWBCCURLCNzMGeESFOTXxMqXApW6LpWDCZilhAogkuGHWMO+XiDgHyd+HgGCVMPUP7aPjb3nXMkEjBZFH60+eeVGHglPk65Rss/aFFTTM8iQYZP6hTFgWe0ijzmpkt1yaJiPkZLK0eaIuU7p2D+lgQkZzX2FBcctu+ym6S2uxISJpIzduLVpPOWu5yr2GQdKtg6Tu7UkG/fD6bpsbYq/OsJd81MFMyMpTBQim6T+kjiF1RFgwSI4MUGS85xICkEiVHe11qW13WNdKd2NQaJ4piTR2IUDx5jga9sCy6VUx2SqouMtFNNCVaIBJiA4MmTgw4hGembw61CcF3nRWJZmos4l1lFZoQUMRlFDGSoDFGyhbDJjCrijZCwwVUyCZgWcFOw3bN8EgzKYkHCq4MTFsIGgQQw0DQlmLQfcEE/GRIR9Qv3kAXAAO7PZHPyf3ycn5kT42Y1Yw1bkEIDAJsdBnbX+7IGIAagiJ4tCgTYqlFE+Icern7A5oZjByEM4LnCoUIELiQIU1KbEBT6y6u01hiGDqNiZhgxkEnRHKZ6ztiO4IaKdKA2YlyqGFH6SjwKPhdAYSRCwwPpOx4gGDy9rz67m0iHDvkA3G4djdk87i7GWQkYFgmxsmPMXEDj9EOwkfMieezYPTTQ+JT2567jSlHuyKQg9VUkzwNhiHqHXviZAsz7fjcKDoiQrqFQQCqjhjyiJW7EIFGOmJiLYPswcYNCLROoNwoPj/FWi2X5yYlO72Dx+Bxb2qsSCwYV8Oj8TgmHmzpf2IG2DpBwi6SpUvgO81y98X7IU+aGy3ernIqEbWHVIyQIQ5VWnezZUWgYmgO3i6+TGxLbb/Qy7VMQhDEwabf1qnF/Woo02mk6poyIccjr1Kr+Av8h0OQ5eOHAD1BDmeJ5BQeYVT2hC8wowH8VTSyHsutJZSN1UYrBGEqISgYBiIi3KEeSAe8V/cwnefnyA0CaIsNB8UQgwWQsRYHdupXAGJdqCiSIz0iNbAg/OZ3RAzRWoRiIR+MV/Qw2vTi65O7qLWhx0B71FPt2+QcjafK5+HQCP0QTNq+S9Xmr1RU1GjbSVtzzJkNet3i5fI9JVyd54bUbiytAokELgwVoQdWAIYKXY3gNQabYrc2rqbuu27UqslY0y5bup2jWxttojFXW3Zu3TXaaldUyurS19e1a6pLU0QgFnsDtWAERJAZBDnpNnsafhEd8YkW0fMUFrUwfQUUaNxH022bC9UUSg587tFDAysSRLw4Py58feVfz6S9h8M0cDJ9p8V9cs+r9Lr9CWzaGJt9eWRCNTMZvzvzjbc2PjvPuM/XDolmDiLuF4LIJCP2eQwzsxtXkZzkScCSM7TqTn0TtOrKjklzO+egmuzSP7GoVDB8p05UZnyHatxxvY73cmzM9L/iRIYLVYIrkRWjqXUL9Ad5lBzkahnJsHURlGqu27qmgMcRuzZ1cKOGZiElqDxKKGyZKOFdVFGmiCtSAmCLptUw6b4Zu1EBkmksLFCVOjFeoa/y5i08zxmVWqwzqje61DCrHN4Mlsxev2h8Y3W7SWnmSGXJXXiHekT0nTVjRpa8KSGlNNqmjReqg9MFmvrXgZYI8EZVV0Gaqs2Xlbi3szpvMw3eRSoX+Mkrzvrm6XmQvuBoZKNt2qOqj5rvUQGL9TToZKxq1dZisXjGMH37n67YpIfCwBlA0jz+fn9udQH7eY6wj5+eyh311jEdaN+sAfTgMH6mgups2khYhus84tayOtO6K+QR9/Q/VWTS5dcVUo1lDvEg3IYBoTPn2mq7CEMg1MLKAaniFgzGIG5NgXBVFSqJ7vfMlmjgGCBGftTZNciXKbiTSQGyFXiY60ptQd35jCta7R7j2YONCw1FKwtWe/5cZ3weQkcJgji0YlG8/qWazeMmxlLOWGJTGyvWbmxrNd1lLlirUUrW86UaOjFFmxjSz0UbaYs6ta4VYTHfWcC9XvXa0YNXnOJjCSOr4htGYNEW1TC9+BjwiIWEgjLuAjXn3H4j6GE14hmN7BgGi59hLikDxWtW+yrd9zHumHr/ZzMg1G/J3k759UfkgGkEhpwKHMbOBFdOooaurWwJhGW20MeFUJQhO0/x1YypQ6POjxddvcukkKUqPLu1F7u7RiN4AoRlgZwUQlUSwmcAUxzJkCloGhoMx6mbCBIvDdRZtQdT3ZnXrKotQ5+88ipkyFbiIQh3VCiM/NdC333dsp3Hl27u1n3nrpPb1us9tiJcimW9Lt3bFd5dcjmuy56uqurxPXnSu0XTiWBjSEJJ3AtW5B02PC90uZzeVYkjDko+jDZVmIZmFHEQuwIswusYktljLfbDEzjFlNFSqcrQYBmEnsvAsSSNuQHX0HyxNeNjqHdvlAN/B4xhBA3VYVFWUaLVAkMG36/rfKR+KHszWllZjgFlhPe+D5CpvU1rEcjFyLIMiwsER9cEJ3Kvo5Zpizz9eS3QpKFYqxzWmCFaZ/g2ph0NdMmj26iI8DvIdEJGHAqMxkibgtDaVGMCBYshyBbRGhxpDvNr90Db+Pvddjgep53yAa9WZvNA81FHXlUn7g2MG2c1uGdqRTsgWOn00dRxrGmjCFI+NXUCPFlYxZgffIGMIMrSNHVHxf0TVmDE2ZAm0UkYpJPfCnnlea+1l5vNdZLpdOa5taVspUqihITooKkicxQpbIWpClrpktGi0OkM0HNgVJdVI5RvS4UzQTCFCwY488Xl0cSS3ncu6u1l5OXjW8QFMJV2NWEAplokMxqJEmgzrKlMJszqwXOcaEWkrlUfKpzTCOQPPX3mx6au1s2bBZXNExh4G2bFb0gZopxJJIXNQpkUStCoW5KIxJQFOyI6BgpkJL0VCFEIaT2bg2BFMQxcfq1iKXsKBXMArR/D8pX1AJvqPpVk37DL2ZujEKQtRxhiHzx+Fgzt7yNF1tZWlyBMDEhwDWHNEIkC9Q9VB7o8h3CJYdXjjMc7EBPZaBbEdtZQsEA1JylEUPH42JGBYMv5U1PUjuJcLyl1KKbJ1ZLsLYFRJ08sVtz8+EpmzUeZKPOrzm6Nxzoe2jaLC3yXHHsnv7TKjLJVhn4KoYWU7ym+USYkmGctBxV60KK033Kx0qMEkm6qL2vo3EMjM2zBmQvlC5+OvZbQRPpYFA/phGaNjIdXcDFoMYgu0ZsdGMbSsIxtn7oHIWazRk1nJfzYYEuhnPAMxnB4XRcGgyqrtwuclrq+OdtjWKNUzeazjA+l9NBqtVTGN1jrtrnAtAxFZXAqOog52HxpiBFb4pBiUNhzDG9kmqpYyy+KOXVgxBCZqOyZvFlbxhumN7ucs2OkEeIYC1Q6T5MvCCwyq4ww3LE0sO43EFsZawsGysxhfdtqDUbxjPWaKtAIVVVBkVA1QbqkIqmOtxIe3U11grg8pwtGhGVGBRRmQsNsUS9KJJlljFEmyiNEC1TRnLAxRbQxNC3QGmoaxUuGDFKqNkQVVssKjBjG96v04PK8zYRNrnN4uhTJJVQJqLW/yiu2899AwhAoCMX426HrGyejxeF74L3vYFY/X4ZYFD7kKH2nIPHuc7mDw2iOj7YrlzG+YsiHRSZtaZjE6IWoGRggMZojVDGGxg0oJjBKLCsoKMQmEjKKC/AMYEZOUoyWNIGTkXA4KQBtQipUEm8DWlC43Uq8OlDVRXe9uYJx3uY34j1jbWJpjz05oA1dCFVG1ELmAUGAjcLNQhFYtsAs0RKQNLls0HEvEtYGNIJ386R9xFHMCDIhMCFlNRKIpZCV4rz64wYhcEDXC8MWBMMhPGIsGbl9sYgkwHLGNDCotKMLNJahysGAwYOBccEsUXHUEDv7/ca63iYOZ4CJv4G7N2+bVYwKrGFyGxjgS8zl6IqcOSiRCxvAMBD9jspz7qC8hAWRVyHOWLDmMDHpxME3J0u8G7nMAUIQBbGGqqEK8SpaXQ3RZejhAwIhhlCowbJdk8rtXXbXY0bDped4ioXCBSF4lVVMIq3SxSNFhstiwcQ0ONWTxnPY32JThWqKRVosuFiYlGEYJvVYxVWBVHMyTRCcECYYe4PQaTLPzzEt0aEKNbC8oUHWjwKhvzOaedMc6nLWHBMHESfOKPDU+nhRwFWAdEQQYiIUEWij4PhmpjJVyqGIX5VQhgAcRPJWWleko7YJBI8wMzh3UYQ376IYyKsmDhJDnu4mBvEdIwX6YKFClRBCid0WkMwgpjYjalLCZj9lFxthyLoh78rqDdRNmAayNeHPhdwDChdO0JA0HO+Ny4dyqcLddMdmXQmmWMbhuPN3BkgZBhkGvdGiBCAnZnKIqXajQHsMd/BtPPaIFJkOw5ndcgprJGtSwpLAaRptu6gpUApgwpijLcNDZKAMGlhQhRUwFpZujQSXI3ppA7TWa9PScv0cO+GMhi8bEkoyYuihRv9j3pkcNOlMXSy4E5hOsEHcop1BAOF6m09YmBmDFIHTDXEA6ZITiQZ6FVVfxdCg6xRzJr8JalOlFT+RMOr9hy0syPA8alVUDN0qirDgQooSJxIWA1iFJ71oKPYdBXaGbTpvOda2SXloY+w1qOcIJZB00WG0O3IzwzdQeAmdQ8cFm4gjIoICRSDJPmPvOGe9SMMOWIBzIim0SIDiAYpzOCZw+P8/Du89HZaJQykZVpxwKX7j3eZcxkEyUeNXjEKFYbBVMoFUHZlYrS5YYQWYS8yrLgXAIqExiKLugRdYTMK2RSOqPJdNvVmzAew0vZcIkg0C0yUTYJSAe4Ni6wOkMVb6ZuEbCCWihIGQEQodoHHYljVlNkWoFHLqJCEMaMe6rksePBQ7U+XyBKDehzosvUicjnHMTuiuaGG4IA5rL4PNdJe2uqusjRiTMk1XtlzG3pd3czfBZb1/1vJLUZLI39GYiHMg7WNghgDhkBl7R+PEDYAaxQyJzDEzIHZEWBJB+YgrCDuAP25uJIe0UDBDhAZD/aZnCqEVCIjRRUWpKoSFUO+k1sAiDiWU8U95A9VWK/F8TpUfKnQkV+PL+j3QgfYzAibCSbHcf9ffwgyIgW46z14kYv8kEhdnBAyM3fSB5AGTYfIJGFjMc4KzqoVaxLplXmYBEuCTBViwPRDEiHh06cb5FcxqeFhVFlpHSZ0EfzwmuwbAdhMeysiRhHsNB3jiRIbdldEDYEFw4LSdoVte/654SvHcgGEIgO7t3db+3tujUUZLaxZa9uBgtcGgKCKkRLMtIM82t1ctn8xBCEzKFyff2WTu9HO0xa7jnR9U+gMB9gv3BFJHE7DwTPpzrwkhIwY/BNA76qqqvVCiFqpoSRI0FA0k56i4AY+Efv0FFoaJwYZea4XCfIpVRZzg/lfssMQs1U5c7uHO4FGBw2Ngplq5PA0sBcTMkwJW/AHCtqOfrJMYschpMhsIhQ6zH6AYB2z1ORkHiGRgAcwj7DCEIURGkoVxhciBZnBz0hwn6/Cvs8/hMbwnZ/sz2yyyteOU2C9SUSLyHYIbQqBernWiZ7TBUl3GMh/SJi6Bgh7/ruavuPA9wcjx9PtC6kZ7FHDtExA86GKwI7Zm2GxV0225q3S+W1MSyKhIDaKet76oIJkWPC4zK0S9Jl56GNfm+TcMWI3NZfnNzfWNP8Yz6xuqyj6hb8Q0jb8o60cVgNKcvc+sXdE7OtxnJV3T2W4nhFYffhiVEENAkYCOloEOhUmyOzLN7/Fel6allMwkU5pWDMY5ChGjXMxMKBpdo2DQkXgQPRldE8z+KYqJv1Ime+IZ5pQzFxhAV0mY4acyiXD8p1IZyK7TZ6fZR4dp8VnwhJ0jqLlp40sIf94/C1JlGeR+MLFz6q+smMTNFwnsDQBQhe54kOj1ntiVD+/eVaPlKn6Tv8mmZ1L2Xt9P+Ra7OH+8sXJ+4uZsZOGg1Wf+9b/j7n6P/z/8XckU4UJA1sXEUA')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project3/unitgrade_data/AdditionQuestion.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project3/unitgrade_data/AdditionQuestion.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0153af1eeb4d068181d3c7dfa6768d5fe5978c61
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/cp/project3/unitgrade_data/AdditionQuestion.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project3/unitgrade_data/main_config_project3_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project3/unitgrade_data/main_config_project3_tests.artifacts.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..bb81778951ad471957df16283272c3bc1694dad7
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/cp/project3/unitgrade_data/main_config_project3_tests.artifacts.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project4/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project4/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..890dc18eaf0fafeab3829fd71c6e5c41882a86cc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project4/__init__.py
@@ -0,0 +1 @@
+"""Dummy (test) project from 02465."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcd9621448c93ccdcca18ffd9889d65b98b66ecc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_grade.py
@@ -0,0 +1,4 @@
+# cp/project4/project4_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..c90af650fd463770258c8bc5826c9d845d81c55f
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_tests.py
@@ -0,0 +1,23 @@
+from unitgrade import UTestCase, Report
+from cp.ex00.fruit_homework import add
+
+class AdditionQuestion(UTestCase):
+ """ Problem 1: Adding two numbers """
+ def test_add(self):
+ """Adding two numbers together"""
+ self.assertEqual(add(2, 3), 5) # Test the add-function.
+ self.assertEqual(add(2, -917), -915) # Test the add-function.
+
+
+class Project4(Report):
+ title = "Project 4"
+ abbreviate_questions = True
+ questions = [(AdditionQuestion, 10),
+ ]
+ import cp
+ pack_imports = [cp]
+
+
+if __name__ == "__main__":
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project4())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbcfdc61b30b37b0101c0bd36de9158ade788690
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project4/project4_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project4/project4_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project4/unitgrade_data/AdditionQuestion.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project4/unitgrade_data/AdditionQuestion.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..de23642f0b21fccef672e08653fd392d359dda44
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/cp/project4/unitgrade_data/AdditionQuestion.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project4/unitgrade_data/main_config_project4_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project4/unitgrade_data/main_config_project4_tests.artifacts.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..146b68beeffd52cfcf2582857463140cb3e06955
Binary files /dev/null and b/src/unitgrade_private/pipelines/tmp/students/cp/project4/unitgrade_data/main_config_project4_tests.artifacts.pkl differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..890dc18eaf0fafeab3829fd71c6e5c41882a86cc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project5/__init__.py
@@ -0,0 +1 @@
+"""Dummy (test) project from 02465."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..73fc42613e86297da4073653e62ed1c64583602a
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_grade.py
@@ -0,0 +1,4 @@
+# cp/project5/project5_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b9bcabbe4d555dbc28f5ca1febe83d8b5b2981
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_tests.py
@@ -0,0 +1,202 @@
+from unitgrade import UTestCase, Report
+import unittest
+import math
+
+class TestSineCosineWeek9(UTestCase):
+ def test_make_symbol(self):
+ from cp.ex09.sinus import make_symbol, Variable
+ v = make_symbol('x')
+ self.assertIsInstance(v, Variable)
+
+ def test_sine(self):
+ from cp.ex09.sinus import make_symbol, sine, Sin
+ v = make_symbol('x')
+ s = sine(v)
+ self.assertIsInstance(s, Sin)
+ self.assertEqual(s.arg, v)
+
+ def test_cosine(self):
+ from cp.ex09.sinus import make_symbol, cosine, Cos
+ v = make_symbol('x')
+ c = cosine(v)
+ self.assertIsInstance(c, Cos)
+ self.assertEqual(c.arg, v)
+
+ def test_format_expression(self):
+ from cp.ex09.sinus import make_symbol, sine, cosine, format_expression
+ v = make_symbol('x')
+ s = sine(v)
+ c = cosine(v)
+ self.assertEqual(format_expression(v), 'x')
+ self.assertEqual(format_expression(s), 'sin(x)')
+ self.assertEqual(format_expression(c), 'cos(x)')
+
+ # Test nested expressions
+ s2 = sine(c)
+ c2 = cosine(s)
+ self.assertEqual(format_expression(s2), 'sin(cos(x))')
+ self.assertEqual(format_expression(c2), 'cos(sin(x))')
+
+
+class TestConstant(UTestCase):
+
+ def test_inheritance(self):
+ from cp.ex10.symbolic import Constant, Expression
+ c = Constant(7)
+ self.assertIsInstance(c, Expression)
+
+ def test_print(self):
+ from cp.ex10.symbolic import Constant
+ self.assertEqual(str(Constant(7)), "7")
+ self.assertEqual(str(Constant(0)), "0")
+ self.assertEqual(str(Constant(3.14)), "3.14")
+
+ def test_negative_print(self):
+ from cp.ex10.symbolic import Constant
+ self.assertEqual(str(Constant(-101)), "(-101)")
+
+
+ def test_evaluate(self):
+ from cp.ex10.symbolic import Constant
+ c = Constant(7)
+ self.assertEqual(c.evaluate({}), 7)
+ self.assertEqual(c.evaluate({"z": -10}), 7)
+
+ def test_derivative(self):
+ from cp.ex10.symbolic import Constant
+ c = Constant(7)
+ d = c.differentiate("x")
+ self.assertIsInstance(d, Constant)
+ self.assertEqual(d.evaluate({}), 0)
+
+class TestVariable(UTestCase):
+ def test_inheritance(self):
+ from cp.ex10.symbolic import Variable, Expression
+ self.assertIsInstance(Variable("x"), Expression)
+
+ def test_print(self):
+ from cp.ex10.symbolic import Variable
+ self.assertEqual(str(Variable("x")), "x")
+
+ def test_evaluate(self):
+ from cp.ex10.symbolic import Variable
+ c = Variable("x")
+ self.assertEqual(c.evaluate({"x": 7}), 7)
+
+ def test_derivative(self):
+ from cp.ex10.symbolic import Variable, Constant
+ c = Variable("x")
+ d = c.differentiate("x")
+ self.assertIsInstance(d, Constant)
+ self.assertEqual(str(d), "1")
+
+class TestAddition(UTestCase):
+ def test_inheritance(self):
+ from cp.ex10.symbolic import Addition, Constant, BinaryOperator
+ self.assertIsInstance(Addition(Constant(1), Constant(2)), BinaryOperator)
+
+ def test_print(self):
+ from cp.ex10.symbolic import Addition, Variable
+ self.assertEqual(str(Addition(Variable("x"), Variable("y"))), "(x + y)")
+
+ def test_evaluate(self):
+ from cp.ex10.symbolic import Addition, Constant, Variable
+ c = Addition(Variable("x"), Constant(3))
+ self.assertEqual(c.evaluate({"x": 7}), 10)
+
+ def test_derivative(self):
+ from cp.ex10.symbolic import Addition, Constant, Variable
+ c = Addition(Variable("x"), Constant(3))
+ d = c.differentiate("x")
+ self.assertEqual(str(d), "(1 + 0)")
+ self.assertEqual(d.evaluate({}), 1)
+
+ def test_derivative_nested(self):
+ from cp.ex10.symbolic import Addition, Constant, Variable
+ c = Addition(Variable("x"), Addition(Constant(3), Variable("x")))
+ d = c.differentiate("x")
+ self.assertEqual(str(d), "(1 + (0 + 1))")
+
+
+class TestMultiplication(UTestCase):
+ def test_print(self):
+ from cp.ex10.symbolic import Multiplication, Constant, Variable
+ self.assertEqual(str(Multiplication(Variable("x"), Constant(3))), "x * 3")
+
+ def test_evaluate(self):
+ from cp.ex10.symbolic import Multiplication, Constant, Variable
+ c = Multiplication(Variable("x"), Constant(3))
+ self.assertEqual(c.evaluate({"x": 2}), 6)
+
+ def test_derivative(self):
+ from cp.ex10.symbolic import Multiplication, Constant, Variable
+ c = Multiplication(Variable("x"), Constant(3))
+ d = c.differentiate("x")
+ self.assertEqual(str(d), "(1 * 3 + x * 0)")
+ self.assertEqual(d.evaluate({"x": 10}), 3)
+
+ def test_derivative_nested(self):
+ from cp.ex10.symbolic import Multiplication, Constant, Variable, Addition
+ c = Multiplication(Variable("x"), Addition(Constant(3), Variable("x")))
+ d = c.differentiate("x")
+ self.assertEqual(str(d), "(1 * (3 + x) + x * (0 + 1))")
+
+
+class TestTrigonometricBasic(UTestCase):
+ def test_sin_print(self):
+ from cp.ex10.symbolic import Variable, Sin
+ sin = Sin(Variable("x"))
+ self.assertEqual(str(sin), "sin(x)")
+
+ def test_cos_print(self):
+ from cp.ex10.symbolic import Constant, Cos
+ cos = Cos(Constant(3))
+ self.assertEqual(str(cos), "cos(3)")
+
+ def test_sin_evaluate(self):
+ from cp.ex10.symbolic import Variable, Sin
+ sin = Sin(Variable("x"))
+ self.assertAlmostEqual(sin.evaluate({"x": 2}), math.sin(2))
+
+ def test_cos_evaluate(self):
+ from cp.ex10.symbolic import Variable, Cos
+ self.assertAlmostEqual(Cos(Variable("x")).evaluate({"x": 2}), math.cos(2))
+
+class TestTrigonometricDerivative(UTestCase):
+ def test_sin(self):
+ from cp.ex10.symbolic import Variable, Sin
+ x = Variable("x")
+ dx = Sin(x).differentiate("x")
+ self.assertEqual(str(dx), "cos(x) * 1")
+
+ def test_cos(self):
+ from cp.ex10.symbolic import Variable, Cos
+ x = Variable("x")
+ dx = Cos(x).differentiate("x")
+ self.assertEqual(str(dx), "(-1) * sin(x) * 1")
+
+ def test_sin_advanced(self):
+ from cp.ex10.symbolic import Variable, Cos, Constant, Multiplication, Addition
+ x = Variable("x")
+ dx = Multiplication(x, Cos(Addition(x, Constant(3)))).differentiate("x")
+ self.assertEqual(str(dx), "(1 * cos((x + 3)) + x * (-1) * sin((x + 3)) * (1 + 0))")
+
+
+class Project5(Report):
+ title = "Project 5"
+ abbreviate_questions = True
+ questions = [(TestSineCosineWeek9, 20),
+ (TestConstant, 10),
+ (TestVariable, 10),
+ (TestAddition, 10),
+ (TestMultiplication, 10),
+ (TestTrigonometricBasic, 10),
+ (TestTrigonometricDerivative, 10),
+ ]
+ import cp
+ pack_imports = [cp]
+
+
+if __name__ == "__main__":
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project5())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1c0617cefc6eea6ecee7944655b52d584e69076
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project5/project5_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project5/project5_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestAddition.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestAddition.pkl
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index 0000000000000000000000000000000000000000..e9c1193de152b507f2c44750b33b1f516807996e
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestConstant.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestConstant.pkl
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index 0000000000000000000000000000000000000000..3f10faacf36f365b02de68f1cc30b3dd38e15823
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestMultiplication.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestMultiplication.pkl
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index 0000000000000000000000000000000000000000..d80a4889da6681b67a990653dee90c0764f4c0ba
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestSineCosineWeek9.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestSineCosineWeek9.pkl
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index 0000000000000000000000000000000000000000..c08f58b08559522222b33094c6647cfdf0e45b48
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestTrigonometricBasic.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestTrigonometricBasic.pkl
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index 0000000000000000000000000000000000000000..4f324f0d4c8bc26bf89fda1b67f06f0f9445b81a
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestTrigonometricDerivative.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestTrigonometricDerivative.pkl
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index 0000000000000000000000000000000000000000..0d4eabade5cfcab995169b51720cdf8a04242f3c
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestVariable.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/TestVariable.pkl
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index 0000000000000000000000000000000000000000..70cf92140ecbd26b9b459ad248876a154b5217b6
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/main_config_project5_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project5/unitgrade_data/main_config_project5_tests.artifacts.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..54f7febe65d5eb1f1c694fa0353b7506d2cb8fc7
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project6/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/project6/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..890dc18eaf0fafeab3829fd71c6e5c41882a86cc
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project6/__init__.py
@@ -0,0 +1 @@
+"""Dummy (test) project from 02465."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5164cfb06239ffb9e952db230c3019f9821cb29
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_grade.py
@@ -0,0 +1,4 @@
+# cp/project6/project6_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_tests.py b/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..5668185578a591e4b7a82ea558ca429e4bc0b5f0
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_tests.py
@@ -0,0 +1,23 @@
+from unitgrade import UTestCase, Report
+from cp.ex00.fruit_homework import add
+
+class AdditionQuestion(UTestCase):
+ """ Problem 1: Adding two numbers """
+ def test_add(self):
+ """Adding two numbers together"""
+ self.assertEqual(add(2, 3), 5) # Test the add-function.
+ self.assertEqual(add(2, -917), -915) # Test the add-function.
+
+
+class Project6(Report):
+ title = "Project 6"
+ abbreviate_questions = True
+ questions = [(AdditionQuestion, 10),
+ ]
+ import cp
+ pack_imports = [cp]
+
+
+if __name__ == "__main__":
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Project6())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_tests_complete_grade.py b/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_tests_complete_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b288ec4a08e16a7954e4a69f2f30898e8a9f98
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/project6/project6_tests_complete_grade.py
@@ -0,0 +1,4 @@
+# cp/project6/project6_tests_complete.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project6/unitgrade_data/AdditionQuestion.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project6/unitgrade_data/AdditionQuestion.pkl
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index 0000000000000000000000000000000000000000..cdb1198b439b5decdcf9f56ffb1dbedc8407e85c
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/project6/unitgrade_data/main_config_project6_tests.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cp/project6/unitgrade_data/main_config_project6_tests.artifacts.pkl
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index 0000000000000000000000000000000000000000..a16a8501224492deef0d5ae18a9a13fa8d0936ee
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week00.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week00.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bc2eeb9b2e122fdaf8e5fd6f7fa6055e82305a3
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week00.py
@@ -0,0 +1,36 @@
+from unitgrade import Report, UTestCase
+import cp
+import io
+import unittest
+from unittest.mock import patch
+import importlib
+from cp.ex00 import say_hello
+
+
+class HelloWorld2(UTestCase):
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_say_hello2(self, mock_stdout):
+ # from cp.ex00 import say_hello
+
+ lib = importlib.reload(say_hello)
+ lines = mock_stdout.getvalue().strip().splitlines()
+
+ self.assertEqual(len(lines), 2, msg=f"Wrong number of lines. Your program should print(..) out two lines, you got: {lines}, {say_hello.__file__}")
+ # print("Testing the first line")
+ # self.assertEqual(lines[0], "Hello", msg="You must have somehow changed the first print(...) statement in the program. Did you delete it?") # Rem
+ # print("Testing the second line")
+ # self.assertEqual(lines[1], "World", msg="Your second print statement did not produce the correct output.") # Remember the output must be capitalized.
+
+
+class Week00Tests(Report):
+ title = "Tests for week 00 (Try to complete these before the course start)"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (HelloWorld2, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week00Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week01.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week01.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd7223101a1fe28bb7c5c5d34df7faa114e7b86b
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week01.py
@@ -0,0 +1,64 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+
+class Taylor(UTestCase):
+ def test_taylor_x1(self):
+ self.assertL2(evaluate_taylor(1))
+
+ def test_taylor_x3(self):
+ self.assertL2(evaluate_taylor(3))
+
+class TaylorVariants(UTestCase):
+
+ def test_taylor_variant_1(self):
+ from inspect import getmembers, isfunction
+ from cp.ex02 import taylor_variant1
+ mm = getmembers(taylor_variant1, isfunction)
+ if len(mm) == 0:
+ raise Exception("You did not define a function in your code")
+
+ name, taylor = mm[0]
+
+ print("Running:")
+ print(f"{name}(x)")
+ self.assertL2(taylor(1.4))
+
+ def test_taylor_variant_2(self):
+ import inspect
+ from cp.ex02 import taylor_variant2
+
+ if hasattr(taylor_variant2, 'y'):
+ self.assertL2(taylor_variant2.y, 0.41666, tol=1e-4)
+
+ source = inspect.getsource(taylor_variant2)
+ lines = source.splitlines()
+ for k, l in enumerate(lines):
+ if l.strip().replace(" ", "").startswith("x="):
+ lines[k] = "x = 9"
+ # y = None
+ s = "\n".join(lines)
+ # global y
+ # locals_ = locals()
+ ldict = {}
+ exec(s, globals(), ldict)
+ # print(y)
+ if 'y' not in ldict:
+ raise Exception("You failed to define y in your test")
+ else:
+ y = ldict['y']
+ self.assertL2(y)
+
+class Week01Tests(Report): #240 total.
+ title = "Tests for week 01"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Taylor, 10),
+ (TaylorVariants, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week01Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week02.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week02.py
new file mode 100644
index 0000000000000000000000000000000000000000..4891fe8a40714fb43b45af19b96283104296c353
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week02.py
@@ -0,0 +1,43 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+from cp.ex02.fibonacci import fibonacci_number
+
+
+class Fibonacci(UTestCase):
+ def test_fibonacci_x0(self):
+ self.assertEqual(fibonacci_number(0), 0, msg="The start of the sequence is wrong -- remember that x_0 = 0!")
+
+ def test_fibonacci_x1(self):
+ self.assertEqual(fibonacci_number(0), 0, msg="The start of the sequence is wrong -- remember that x_1 = 1!")
+
+ def test_fibonacci_x2(self):
+ self.assertEqual(fibonacci_number(6), 8, msg="The first recursive step is not quite right -- remember that x_2 = x_1 + x_0")
+
+ def test_fibonacci_x6(self):
+ self.assertEqual(fibonacci_number(6), 8, msg="There is a problem in your recursion. You did not compute x_6 correctly.")
+
+ def test_fibonacci_x23(self):
+ # Test a large value. I cache the result so I don't have to enter it manually.
+ self.assertEqualC(fibonacci_number(23), 28657)
+
+ def test_fibonacci_x_many(self):
+ # Caching works for arbitrary calls. This test all the values up to 20.
+ for k in range(20):
+ self.assertEqualC(fibonacci_number(k))
+
+
+
+
+class Week02Tests(Report): #240 total.
+ title = "Tests for week 02"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Fibonacci, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week02Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week03.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week03.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ea186ba8d07267dbce78b16eb072f1757462678
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week03.py
@@ -0,0 +1,67 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+
+
+class Week03CompareNumbers(UTestCase):
+ def test_compare_numbers(self):
+ with self.capture() as c:
+ from cp.ex03.compare_numbers import compare_numbers
+ result = compare_numbers(5.,3.)
+ self.assertEqual(result, 'The first number is greater')
+ result = compare_numbers(2.,7.)
+ self.assertEqual(result, 'The second number is greater')
+ result = compare_numbers(4.,4.)
+ self.assertEqual(result, 'Both numbers are equal')
+
+class Week03BodyTemperature(UTestCase):
+ def test_body_Temperature(self):
+ with self.capture() as c:
+ from cp.ex03.body_temperature import body_temperature
+ result = body_temperature(34.5)
+ self.assertEqual(result, 'Hypothermia')
+ result = body_temperature(36.9)
+ self.assertEqual(result, 'Normal')
+ result = body_temperature(37.2)
+ self.assertEqual(result, 'Slight fever')
+ result = body_temperature(38.5)
+ self.assertEqual(result, 'Fever')
+ result = body_temperature(40.1)
+ self.assertEqual(result, 'Hyperthermia')
+
+class Week03BACCalculator(UTestCase):
+ def test_BAC_calculator(self):
+ with self.capture() as c:
+ from cp.ex03.BAC_calculator import BAC_calculator
+ result = BAC_calculator(0.028,80.,0.,2.)
+ self.assertEqual(result,0.02147058823529411)
+ result = BAC_calculator(0.021,70.,1.,2.)
+ self.assertEqual(result,0.020545454545454547)
+
+class Week03DanceClass(UTestCase):
+ def test_Dance_Class(self):
+ with self.capture() as c:
+ from cp.ex03.dance_class import danceClass
+ #result = danceClass([1,1,1,0,0,1,0,1,1,1,0])
+ #self.assertEqual(result,'valid')
+ result = danceClass([0, 0, 0, 1, 1, 1])
+ self.assertEqual(result,'invalid')
+ result = danceClass([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
+ self.assertEqual(result,'invalid')
+ result = danceClass([1,1,1,1,1,0,0,0,0,0])
+ self.assertEqual(result,'valid')
+
+class Week03Tests(Report): #40 total.
+ title = "Tests for week 03"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Week03CompareNumbers, 10),
+ (Week03BodyTemperature, 10),
+ (Week03BACCalculator, 10),
+ (Week03DanceClass, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week03Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week04.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week04.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad8d0acaec36bd2e14404986901c34aec85dfcb0
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week04.py
@@ -0,0 +1,169 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+from cp.ex04.palindrome import is_palindrome
+import io
+import unittest
+from unittest import TestCase
+
+from unittest.mock import patch, call
+from unittest import mock
+class Palindrome(UTestCase):
+ def test_is_palindrome(self):
+ self.assertTrue(is_palindrome('madam'))
+ self.assertTrue(is_palindrome('kk'))
+ self.assertTrue(is_palindrome('m'))
+ self.assertFalse(is_palindrome('gentleman'))
+ self.assertFalse(is_palindrome('ma'))
+
+import unittest
+# import builtin
+
+from cp.ex04 import bug
+# class MyTestCase(TestCase):
+# @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+# def test_print_hello_world_prints_correctly_decorator(self, mock_stdout):
+# # print_hello_world()
+# bug.nice_sign("asdfaf")
+# v = mock_stdout.getvalue()
+# v.splitlines()
+# self.assertEqual(
+# ,
+# 'Hello World!\n' # It's important to remember about '\n'
+# )
+
+# @patch('cp.ex04.bug.print', autospec=True)
+# def test_something(self, print_):
+# with patch('sys.stdout', new = StringIO()) as fake_out:
+# mymodule.urlprint(protocol, host, domain)
+# self.assertEqual(fake_out.getvalue(), expected_url)
+#
+# bug.nice_sign("asdfaf")
+#
+# print_.call_args
+#
+# print_.assert_called_with("print something")
+
+
+
+class Bug(UTestCase):
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_nice_sign(self, mock_stdout):
+ from cp.ex04.bug import nice_sign
+ nice_sign('hello')
+ out = mock_stdout.getvalue()
+
+ self.assertEqual(len(out.splitlines()), 3, msg="You did not print out 3 seperate lines")
+ l1 = out.splitlines()[0]
+ l2 = out.splitlines()[1]
+ l3 = out.splitlines()[2]
+
+ self.assertEqual(l1, "---------")
+ self.assertEqual(l2, "| hello |")
+ self.assertEqual(l3, "---------")
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_last_bug(self,mock_stdout):
+ from cp.ex04.bug import last_bug
+ last_bug()
+ out = mock_stdout.getvalue()
+
+ self.assertEqual(len( out.splitlines()), 3, msg="You did not print out 3 seperate lines")
+ l1 = out.splitlines()[0]
+ l2 = out.splitlines()[1]
+ l3 = out.splitlines()[2]
+
+ self.assertEqual(l1, "------------------------------")
+ self.assertEqual(l2, "| I have written my last bug |")
+ self.assertEqual(l3, "------------------------------")
+
+class Math(UTestCase):
+
+ def test_square_root(self):
+ from cp.ex04.math import square_root
+ self.assertAlmostEqual(square_root(9), 3, places=3)
+ self.assertAlmostEqual(square_root(25), 5, places=3)
+
+ def test_pi(self):
+ from cp.ex04.math import ramanujan
+ self.assertAlmostEqual(ramanujan(), 3.1416, places=3)
+
+
+class Parenthesis(UTestCase):
+
+ def test_matching(self):
+ from cp.ex04.parenthesis import matching
+ self.assertTrue(matching('3x(y+x)'))
+ self.assertTrue(matching('3x'))
+ self.assertTrue(matching('3x(y+(x-1))'))
+ self.assertTrue(matching('3(x-8)^2(y+(x-1))'))
+ self.assertFalse(matching('3x(y+x))'))
+ self.assertFalse(matching('(3x(y+x)'))
+
+ def test_innermost(self):
+ from cp.ex04.parenthesis import find_innermost_part
+ self.assertEqual(find_innermost_part('(3+x)'), '3+x')
+ self.assertEqual(find_innermost_part('3+x'), '3+x')
+ self.assertEqual(find_innermost_part('3x((y+2)y+x)'), 'y+2')
+ self.assertEqual(find_innermost_part('3x((y+(1 - q^2)y+x)'), '1 - q^2')
+
+
+ def test_find_index_of_equality(self):
+ from cp.ex04.parenthesis import find_index_of_equality
+ self.assertEqual(find_index_of_equality("()"), 1)
+ self.assertEqual(find_index_of_equality("(()())"), 3)
+ self.assertEqual(find_index_of_equality("())"), 2)
+ self.assertEqual(find_index_of_equality("())((((("), 2)
+ # self.assertEqual(find_index_of_equality(""), 0)
+ self.assertEqual(find_index_of_equality(")(()())("), 4)
+
+
+class Dialogue(UTestCase):
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_print_the_dialogue(self, mock_stdout):
+ from cp.ex04.parenthesis import print_the_dialogue
+ print_the_dialogue("He said: ''How are you doing?''")
+ out = mock_stdout.getvalue()
+ self.assertEqual(out.strip(), "How are you doing?")
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_print_the_dialogue_twolines(self, mock_stdout):
+ from cp.ex04.parenthesis import print_the_dialogue
+ print_the_dialogue("He: ''How are you doing?'' She: ''Still stuck on my 02002 programming problems''.")
+ out = mock_stdout.getvalue()
+ self.assertEqual(out.strip(), "How are you doing?\nStill stuck on my 02002 programming problems")
+
+class Prefix(UTestCase):
+ def test_common_prefix(self):
+ from cp.ex04.prefix import common_prefix
+
+ self.assertEqual( common_prefix("cat", "cawabunga"), "ca")
+ self.assertEqual(common_prefix("caterpillar", "catapult"), "cat")
+ self.assertEqual(common_prefix("cat", "dog"), "")
+
+ def test_common_prefix3(self):
+ from cp.ex04.prefix import common_prefix3
+ self.assertEqual(common_prefix3("cat", "cawabunga", "catapult"), "ca")
+ self.assertEqual(common_prefix3("cawabunga", "cow", "catapult"), "c")
+ self.assertEqual(common_prefix3("selenium", "sealant", "sensei"), "se")
+ self.assertEqual(common_prefix3("selenium", "apple", "sensei"), "")
+
+
+class Week04Tests(Report):
+ title = "Tests for week 04"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Math, 10),
+ (Palindrome, 10),
+ (Bug, 10),
+ (Parenthesis, 10),
+ (Dialogue, 10),
+ (Prefix, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week04Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week05.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week05.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0f96027d10dd3e4218f84b967e1851fc627f953
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week05.py
@@ -0,0 +1,132 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+
+class Week05Primes(UTestCase):
+ def test_prime(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+ import numpy as np
+
+ with self.capture() as c:
+ from cp.ex05 import primes
+
+ prime_list = np.array([257,181,2503,4871,43,353,2753,3229,2621,3137,3121,877,293,47,997,3023,7,127,13,463,719,4273,307,3617,1597,2377,1433,3061,991,2437,4813,1609,2339,3343,3001,853,4493,1223])
+ non_prime_list = prime_list*2
+
+ check = np.all([primes.is_prime(prime) for prime in prime_list])
+ self.assertTrue(check)
+ check = np.all([not primes.is_prime(non_prime) for non_prime in non_prime_list])
+ self.assertTrue(check)
+
+ prime_list = primes.create_prime_list(2,101)
+ self.assertTrue(isinstance(prime_list,list))
+ self.assertListEqual(prime_list,[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101])
+
+
+
+class Week05Indexing(UTestCase):
+ def __init__(self, *args, skip_remote_check=False, **kwargs):
+ super().__init__(*args, skip_remote_check=skip_remote_check, **kwargs)
+ self.lst_1 = [88, 26, 71, 56, 34, 93, 4, 53, 87, 97, 23, 18, 54, 48, 78, 40, 70, 89, 4, 59, 29, 82, 72, 64, 49, 33, 30, 50, 50, 50, 21]
+ self.lst_2 = [27, 87, 55, 25, 53, 90, 80, 68, 72, 79, 41, 72, 37, 91, 95, 48, 57, 78, 16, 80, 1, 96, 40, 80, 98, 73, 2, 45, 28, 75, 76]
+
+ self.nested_lst_1 = [[88], [26], [71, 56, 34], [93, 4, 53, 87, 97], [23], [18, 54], [48, 78, 40, 70], [89, 4, 59, 29, 82, 72, 64, 49, 33, 30, 50, 50], [50], [21]]
+ self.nested_lst_2 = [[27, 87], [55, 25, 53, 90, 80, 68, 72, 79], [41], [72, 37, 91, 95, 48, 57, 78, 16, 80, 1, 96], [40, 80, 98], [73, 2], [45, 28, 75, 76]]
+
+ def test_add_first_and_last(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+
+ with self.capture() as c:
+ from cp.ex05 import indexing
+
+
+ answer = indexing.add_first_and_last(self.lst_1)
+ self.assertEqual(answer,88+21)
+
+ answer = indexing.add_first_and_last(self.lst_2)
+ self.assertEqual(answer,27+76)
+
+ def test_middle_element(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+ import numpy as np
+
+ with self.capture() as c:
+ from cp.ex05 import indexing
+
+ mid_el = indexing.middle_element(self.lst_1)
+ self.assertEqual(mid_el,40)
+ mid_el = indexing.middle_element(self.lst_2)
+ self.assertEqual(mid_el,48)
+
+
+ def test_slice_list_in_percent(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+ import numpy as np
+
+ with self.capture() as c:
+ from cp.ex05 import indexing
+
+ sub_list = indexing.slice_list_in_percent(self.lst_1,0.1,0.9)
+ self.assertListEqual(sub_list,[56, 34, 93, 4, 53, 87, 97, 23, 18, 54, 48, 78, 40, 70, 89, 4, 59, 29, 82, 72, 64, 49, 33, 30])
+ sub_list = indexing.slice_list_in_percent(self.lst_2,0.45,0.55)
+ self.assertListEqual(sub_list,[91, 95, 48, 57])
+
+
+ def test_get_longest_sublist(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+ import numpy as np
+
+ with self.capture() as c:
+ from cp.ex05 import indexing
+
+ sub_list = indexing.get_longest_sublist(self.nested_lst_1)
+ self.assertListEqual(sub_list,[89, 4, 59, 29, 82, 72, 64, 49, 33, 30, 50, 50])
+ sub_list = indexing.get_longest_sublist(self.nested_lst_2)
+ self.assertListEqual(sub_list,[72, 37, 91, 95, 48, 57, 78, 16, 80, 1, 96])
+
+
+class Week05UpdatingLists(UTestCase):
+ def test_remove_at(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+
+ with self.capture() as c:
+ from cp.ex05 import updating_lists
+
+ in_list = ["one","two","three","four","five","six","seven","eight"]
+ out_list = updating_lists.remove_at(in_list,5)
+ self.assertListEqual(out_list,["one","two","three","four","five","seven","eight"])
+ out_list = updating_lists.remove_at(out_list,0)
+ self.assertListEqual(out_list,["two","three","four","five","seven","eight"])
+
+ def remove_value(self):
+ # Run and capture output
+ # from cp.unitgrade import say_hello
+
+ with self.capture() as c:
+ from cp.ex05 import updating_lists
+
+ in_list = ["one","two","three","four","five","six","seven","eight"]
+ out_list = updating_lists.remove_value(in_list,5)
+ self.assertListEqual(out_list,in_list)
+ out_list = updating_lists.remove_value(in_list,"three")
+ self.assertListEqual(out_list,["one","two","four","five","six","seven","eight"])
+
+class Week05Tests(Report):
+ title = "Tests for week 05"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Week05Primes, 10),
+ (Week05Indexing, 10),
+ (Week05UpdatingLists, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week05Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week06.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week06.py
new file mode 100644
index 0000000000000000000000000000000000000000..64fd532a40b0f22f5b829876870999b33f26fb23
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week06.py
@@ -0,0 +1,25 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+
+
+class HelloWorld2(UTestCase):
+ def test_say_hello(self):
+ from cp.ex00 import say_hello
+ evaluate_taylor(1)
+ self.assertEqual(0, 0)
+
+
+
+class Week06Tests(Report): #240 total.
+ title = "Tests for week 06"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (HelloWorld2, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week06Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week07.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week07.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e64755b15f05352d3aa0f388d06729badf2c30f
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week07.py
@@ -0,0 +1,24 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+
+class HelloWorld2(UTestCase):
+ def test_say_hello(self):
+ from cp.ex00 import say_hello
+ evaluate_taylor(1)
+ self.assertEqual(0,0)
+
+
+
+class Week07Tests(Report): #240 total.
+ title = "Tests for week 07"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (HelloWorld2, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week07Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week08.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week08.py
new file mode 100644
index 0000000000000000000000000000000000000000..f44c95a1d6fc3f8ca40a79273adc3642b9a77e22
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week08.py
@@ -0,0 +1,23 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+from cp.ex02.fibonacci import fibonacci_number
+
+class HelloWorld2(UTestCase):
+ def test_say_hello(self):
+ from cp.ex00 import say_hello
+ evaluate_taylor(1)
+ self.assertEqual(0,0)
+
+
+class Week08Tests(Report):
+ title = "Tests for week 08"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [(HelloWorld2, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week08Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week09.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week09.py
new file mode 100644
index 0000000000000000000000000000000000000000..088d8aedd19bff520a6d2fb34655d56411ddd338
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week09.py
@@ -0,0 +1,344 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+import unittest
+from cp.ex09.vector import Vector, make_vector, print_vector, dot, scale, add, sub, hat, make_line_segment, point_on_line, LineSegment
+from cp.ex09.vector import make_vector, make_square_with_position, square_to_lines, intersect, do_they_intersect, get_intersections
+from cp.ex09.vector import make_vector, make_square_with_position, square_to_lines, intersect, do_they_intersect, get_intersections
+from cp.ex09.vector import make_line_segment
+import io
+from unittest.mock import patch, call
+
+class TestMakeVector(UTestCase):
+
+ def test_make_vector(self):
+ v1 = make_vector(2,3)
+ self.assertIsInstance(v1, Vector, msg="Must return a Vector instance.")
+ self.assertEqual(v1.x, 2)
+ self.assertEqual(v1.y, 3)
+
+ @unittest.mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_print_vector(self, mock_stdout):
+ v1 = make_vector(2, 3)
+ print_vector(v1)
+ out = mock_stdout.getvalue().strip()
+ self.assertEqual(out, "(2, 3)")
+
+class TestVectorOperations(UTestCase):
+ def test_dot_product(self):
+ v1 = make_vector(2,3)
+ v2 = make_vector(4,5)
+ self.assertEqual(dot(v1, v2), 23)
+
+ def test_scale(self):
+ v = make_vector(2,3)
+ s = 2
+ v_scaled = scale(s, v)
+ self.assertEqual(v_scaled.x, 4)
+ self.assertEqual(v_scaled.y, 6)
+
+ def test_add(self):
+ v1 = make_vector(2,3)
+ v2 = make_vector(4,5)
+ v_sum = add(v1, v2)
+ self.assertEqual(v_sum.x, 6)
+ self.assertEqual(v_sum.y, 8)
+
+ def test_subtract(self):
+ v1 = make_vector(2,3)
+ v2 = make_vector(4,5)
+ v_sub = sub(v1, v2)
+ self.assertEqual(v_sub.x, -2)
+ self.assertEqual(v_sub.y, -2)
+
+ def test_hat(self):
+ v = make_vector(2,3)
+ v_hat = hat(v)
+ self.assertEqual(v_hat.x, -3)
+ self.assertEqual(v_hat.y, 2)
+
+
+class TestLineSegmentMethods(UTestCase):
+ def test_make_line_segment(self):
+ p = make_vector(1, 2)
+ q = make_vector(3, 4)
+ l = make_line_segment(p, q)
+ self.assertIsInstance(l, LineSegment)
+
+
+ def test_point_on_line(self):
+ p = make_vector(1, 2)
+ q = make_vector(3, 4)
+ l = make_line_segment(p, q)
+ self.assertIsInstance(point_on_line(l, 0), Vector)
+ self.assertEqual(point_on_line(l, 0).x, p.x)
+ self.assertEqual(point_on_line(l, 0).y, p.y)
+
+ self.assertEqual(point_on_line(l, 1).x, q.x)
+ self.assertEqual(point_on_line(l, 1).y, q.y)
+
+ def test_point_on_line_midpoint(self):
+ p = make_vector(1, 2)
+ q = make_vector(3, 4)
+ l = make_line_segment(p, q)
+
+ self.assertEqual(point_on_line(l, .5).x, (p.x+q.x)/2)
+ self.assertEqual(point_on_line(l, .5).y, (p.y+q.y)/2)
+
+from cp.ex09.vector import SquareWithPosition
+
+class SquareWithLocationTests(UTestCase):
+ def test_make_square(self):
+ sq = make_square_with_position(1, 2, 3)
+ self.assertIsInstance(sq, SquareWithPosition)
+
+ def test_square_to_lines(self):
+ sq = make_square_with_position(1, 2, 3)
+ lines = square_to_lines(sq)
+ self.assertIsInstance(lines, list, msg="Must return a list")
+ self.assertEqual(len(lines), 4, msg="Must return 4 lines")
+ for l in lines:
+ self.assertIsInstance(l, LineSegment, msg="Must return 4 line segments")
+ # get all end-points:
+
+ tpl = lambda v: (v.x, v.y)
+ lmd = lambda l: tuple( set( (tpl(point_on_line(l, 0) ), tpl(point_on_line(l, 1) )) ) )
+ l2 = list( set( [lmd(l) for l in lines] ) )
+
+ self.assertEqual(l2[0], ((4,5), (1, 5)), msg="Problem with first line. It should connect points (4,5) -- (1, 5)")
+ self.assertEqual(l2[1], ((1, 2), (4, 2)))
+ self.assertEqual(l2[2], ((1, 2), (1, 5)))
+ self.assertEqual(l2[3], ((4, 5), (4, 2)))
+
+class TestIntersection(UTestCase):
+ def test_do_they_intersect(self):
+ from cp.ex09.vector import make_vector, make_line_segment, do_they_intersect, intersect, print_vector
+ l1 = make_line_segment(make_vector(1, 1), make_vector(4, 1))
+ l2 = make_line_segment(make_vector(2, -2), make_vector(2, 3))
+
+ self.assertTrue( do_they_intersect(l1, l2), msg="These lines should intersect")
+ self.assertTrue( do_they_intersect(l2, l1), msg="Reverse order; lines should still intersect")
+
+ l3 = make_line_segment(make_vector(2, -2), make_vector(-1, 3))
+
+ self.assertFalse(do_they_intersect(l1, l3), msg="These lines should not intersect")
+ self.assertFalse(do_they_intersect(l3, l1), msg="Reverse order; lines should still not intersect")
+
+ def test_intersect(self):
+ from cp.ex09.vector import make_vector, make_line_segment, do_they_intersect, intersect, print_vector
+ l1 = make_line_segment(make_vector(1, 1), make_vector(4, 1))
+ l2 = make_line_segment(make_vector(2, -2), make_vector(2, 3))
+ isect = intersect(l1, l2)
+ self.assertAlmostEqual(isect.x,2)
+ self.assertAlmostEqual(isect.y, 1)
+
+class GetAllIntersections(UTestCase):
+ def test_get_intersections_none(self):
+ sq = make_square_with_position(1, 1, 6)
+ l = make_line_segment(make_vector(-1, 2), make_vector(0, 0.5))
+ intersections = get_intersections(sq, l)
+ self.assertIsInstance(intersections, list)
+ self.assertEqual(len(intersections), 0)
+
+ def test_get_intersections_one(self):
+ # from cp.ex09.vector import make_vector, make_line_segment, make_square_with_position, get_intersections
+ # from cp.ex09.vector import plot_point, plot_line, plot_square
+ sq = make_square_with_position(1, 1, 6)
+ l = make_line_segment(make_vector(-1, 2), make_vector(4, 3))
+ intersections = get_intersections(sq, l)
+ self.assertEqual(len(intersections), 1)
+ self.assertAlmostEqual(intersections[0].x, 1)
+ self.assertAlmostEqual(intersections[0].y, 2.4)
+
+ def test_get_intersections_two(self):
+ # from cp.ex09.vector import make_vector, make_line_segment, make_square_with_position, get_intersections
+ # from cp.ex09.vector import plot_point, plot_line, plot_square
+ sq = make_square_with_position(1, 1, 6)
+ l = make_line_segment(make_vector(-1, 2), make_vector(9, 4))
+ ls = get_intersections(sq, l)
+ self.assertEqual(len(ls), 2)
+
+ ls = list(set([(ls[0].x, ls[0].y), (ls[1].x, ls[1].y)]))
+
+ self.assertAlmostEqual(ls[0][0], 1, msg="testing x-coordinate of first point")
+ self.assertAlmostEqual(ls[0][1], 2.4, msg="testing y-coordinate of first point")
+
+ self.assertAlmostEqual(ls[1][0], 7, msg="testing x-coordinate of second point")
+ self.assertAlmostEqual(ls[1][1], 3.6, msg="testing y-coordinate of second point")
+
+
+class Week09Warmup(UTestCase):
+ def test_create_employee_dict(self):
+
+ with self.capture() as c:
+ from cp.ex09.warmup import create_employee_dict
+
+ name = "John Doe"
+ age = 30
+ experience = 5
+ department = "IT"
+ salary = 5000.0
+
+ expected_dict = {
+ "name": "John Doe",
+ "age": 30,
+ "experience": 5,
+ "department": "IT",
+ "salary": 5000.0
+ }
+
+ self.assertEqual(create_employee_dict(name, age, experience, department, salary), expected_dict)
+
+ def test_give_raise(self):
+ with self.capture() as c:
+ from cp.ex09.warmup import give_raise
+
+ employee_dict = {
+ "name": "John Doe",
+ "age": 30,
+ "experience": 5,
+ "department": "IT",
+ "salary": 5000.0
+ }
+
+ sal_raise = 1000.0
+
+ expected_dict = {
+ "name": "John Doe",
+ "age": 30,
+ "experience": 5,
+ "department": "IT",
+ "salary": 6000.0
+ }
+
+ self.assertEqual(give_raise(employee_dict, sal_raise), expected_dict)
+
+
+class Week09PatientClass(UTestCase):
+ def test_patient_init(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+
+ name = "John Doe"
+ condition = "Acne"
+ age = 25
+
+ patient = Patient(name, condition, age)
+
+ self.assertEqual(patient.name, name)
+ self.assertEqual(patient.condition, condition)
+ self.assertEqual(patient.age, age)
+
+ def test_patient_str(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ name = "John Doe"
+ condition = "Acne"
+ age = 25
+
+ patient = Patient(name, condition, age)
+
+ expected_str = "Patient: John Doe, Age: 25, Condition: Acne"
+
+ self.assertEqual(str(patient), expected_str)
+
+
+class Week09AppointmentClass(UTestCase):
+ def test_appointment_init(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+
+ patient = Patient("John Doe", "Acne", 25)
+ procedure = "Dermatology Consultation"
+ scheduled_datetime = "2023-06-01 09:00"
+ cost = 150.0
+
+ appointment = Appointment(patient, procedure, scheduled_datetime, cost)
+
+ self.assertEqual(appointment.patient, patient)
+ self.assertEqual(appointment.procedure, procedure)
+ self.assertEqual(appointment.scheduled_datetime, scheduled_datetime)
+ self.assertEqual(appointment.cost, cost)
+
+ def test_appointment_str(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ patient = Patient("John Doe", "Acne", 25)
+ procedure = "Dermatology Consultation"
+ scheduled_datetime = "2023-06-01 09:00"
+ cost = 150.0
+
+ appointment = Appointment(patient, procedure, scheduled_datetime, cost)
+
+ expected_str = "Patient: John Doe, Condition: Acne, Procedure: Dermatology Consultation, Scheduled Time: 2023-06-01 09:00, Cost: $150.00"
+
+ self.assertEqual(str(appointment), expected_str)
+
+class Week09TestFunctions(UTestCase):
+ def test_speciality_requirement(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex09.costs_specialty import speciality_requirement
+
+ patient = Patient("John Doe", "Acne", 25)
+ appointment = Appointment(patient, "Dermatology Consultation", "2023-06-01 09:00", 150.0)
+
+ specialty = speciality_requirement(appointment)
+
+ self.assertEqual(specialty, "Dermatology")
+
+ def test_create_list_of_appointments(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex09.costs_specialty import create_list_of_appointments,procedure_costs
+
+ patient = Patient("John Doe", "Acne", 25)
+
+ appointments = create_list_of_appointments(patient)
+ self.assertEqual(len(appointments), 5)
+ self.assertIsInstance(appointments[0], Appointment)
+ self.assertEqual(appointments[0].patient, patient)
+ self.assertIn(appointments[0].procedure, procedure_costs[patient.condition])
+ self.assertIsInstance(appointments[0].scheduled_datetime, str)
+ self.assertIsInstance(appointments[0].cost, float)
+
+ def test_compute_total_medical_bills(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex09.costs_specialty import compute_total_medical_bills
+ appointments = [
+ Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-01 09:00", 150.0),
+ Appointment(Patient("Jane Smith", "Headache", 30), "Neurology Consultation", "2023-06-02 14:00", 200.0),
+ Appointment(Patient("Michael Johnson", "Fracture", 40), "Orthopedic Consultation", "2023-06-03 10:30", 300.0)
+ ]
+
+ total_bills = compute_total_medical_bills(appointments)
+
+ self.assertEqual(total_bills, 650.0)
+
+
+class Week09Tests(Report):
+ title = "Tests for week 09"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (TestMakeVector, 10),
+ (TestVectorOperations, 10),
+ (TestLineSegmentMethods, 10),
+ (TestIntersection, 10),
+ (GetAllIntersections, 10),
+ (Week09Warmup, 10),
+ (Week09PatientClass, 10),
+ (Week09AppointmentClass, 10),
+ (Week09TestFunctions, 10),
+ ]
+
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week09Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week10.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week10.py
new file mode 100644
index 0000000000000000000000000000000000000000..8030011db910318e836fba6cf9bc8248be998a53
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week10.py
@@ -0,0 +1,261 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+
+
+class TestPerson(UTestCase):
+ # Test Person class
+ def test_init(self):
+ from cp.ex10.hospital import Person
+ # person = Person("John Doe", 35, "m")
+ # self.assertEqual(person.name, "John Doe")
+ # self.assertEqual(person.age, 35)
+ # self.assertEqual(person.gender, "m")
+ self.assertRaises(ValueError, Person, "John Doe", 35, "z")
+
+ def test_str(self):
+ from cp.ex10.hospital import Person
+ person = Person("John Doe", 35, "m")
+ self.assertEqual(str(person), "John Doe, 35m")
+
+class TestPatient(UTestCase):
+ # Test Patient class
+ def test_init(self):
+ from cp.ex10.hospital import Person, Patient
+ patient = Patient("John Doe", 35, "m", "headache")
+ self.assertIsInstance(patient, Person, msg="Patient should inherit from Person")
+ self.assertRaises(ValueError, Patient, "John Doe", 35, "headache", "male")
+
+
+ def test_can_be_treated(self):
+ from cp.ex10.hospital import Patient
+ patient = Patient("John Doe", 35, "m", "headache")
+ self.assertTrue(patient.can_be_treated())
+ patient = Patient("John Doe", 35, "m", "unknown illness")
+ self.assertFalse(patient.can_be_treated())
+
+ def test_str(self):
+ from cp.ex10.hospital import Patient
+ patient = Patient("John Doe", 35, "m", "headache")
+ self.assertEqual(str(patient), "John Doe, 35m: headache")
+
+class TestDoctor(UTestCase):
+ # Test Doctor class
+ def test_init(self):
+ from cp.ex10.hospital import Person, Doctor, Patient
+ doctor = Doctor("Dr. Smith", 45, "f", "head")
+ self.assertIsInstance(doctor, Person, msg="Doctor should inherit from Person")
+ self.assertNotIsInstance(doctor, Patient, msg="Doctor should not inherit from Patient")
+ self.assertRaises(ValueError, Doctor, "John Doe", 35, "feet", "female")
+
+ def test_str(self):
+ from cp.ex10.hospital import Doctor
+ doctor = Doctor("Dr. Smith", 45, "f", "head")
+ self.assertEqual(str(doctor), "Dr. Smith, 45f. Specialization: head")
+
+ def test_can_doctor_treat(self):
+ from cp.ex10.hospital import Doctor, Patient
+ doctor = Doctor("Dr. Smith", 45, "f", "head")
+ patient = Patient("John Doe", 35, "m", "headache")
+ self.assertTrue(doctor.can_doctor_treat(patient))
+ patient = Patient("John Doe", 35, "m", "stepped on lego")
+ self.assertFalse(doctor.can_doctor_treat(patient))
+
+ def test_treatment_cost(self):
+ from cp.ex10.hospital import Doctor, Patient
+ doctor = Doctor("Dr. Smith", 45, "f", "head")
+ patient = Patient("John Doe", 35, "m", "headache")
+ self.assertEqual(doctor.treatment_cost(patient), 100)
+ patient = Patient("John Doe", 35, "m", "toothache")
+ self.assertEqual(doctor.treatment_cost(patient), 200)
+
+
+class Week10TestDoctor(UTestCase):
+ def test_check_specialty_fit(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ patient = Patient("John Doe", "Acne", 25)
+ result = doctor.check_specialty_fit(patient)
+ self.assertTrue(result)
+
+ def test_appointment_datetimes(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment1 = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ appointment2 = Appointment(Patient("Jane Smith", "Headache", 30), "Neurology Consultation", "2023-06-01 09:00", 200.0)
+ doctor.list_of_appointments = [appointment1, appointment2]
+ result = doctor.appointment_datetimes()
+ self.assertEqual(result, ["2023-06-02 14:00", "2023-06-01 09:00"])
+
+ def test_add_appointment_success(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ patient = Patient("John Doe", "Acne", 25)
+ appointment = Appointment(patient, "Dermatology Consultation", "2023-06-01 09:00", 150.0)
+ doctor.list_of_appointments = [Appointment(Patient("Jane Smith", "Headache", 30), "Neurology Consultation", "2023-06-02 14:00", 200.0)]
+ result = doctor.add_appointment(appointment)
+ self.assertEqual(result, "Appointment added for John Doe with Dr. Smith.")
+ self.assertIn(appointment, doctor.list_of_appointments)
+
+ def test_add_appointment_not_available(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ patient = Patient("John Doe", "Acne", 25)
+ appointment = Appointment(patient, "Dermatology Consultation", "2023-06-01 09:00", 150.0)
+ doctor.list_of_appointments = [appointment]
+ result = doctor.add_appointment(appointment)
+ self.assertEqual(result, "Dr. Smith is not available at 2023-06-01 09:00.")
+
+ def test_add_appointment_not_specialty_fit(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ patient = Patient("Jane Smith", "Migraine", 30)
+ appointment = Appointment(patient, "Neurology Consultation", "2023-06-03 10:30", 300.0)
+ result = doctor.add_appointment(appointment)
+ self.assertEqual(result, "Dr. Smith does not have the required specialty for Jane Smith.")
+ self.assertNotIn(appointment, doctor.list_of_appointments)
+
+ def test_display_schedule_empty(self):
+ with self.capture() as c:
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ result = doctor.display_schedule()
+ self.assertEqual(result, "Schedule for Dr. Smith:\nNo appointments.")
+
+ def test_display_schedule(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment1 = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ appointment2 = Appointment(Patient("Jane Smith", "Headache", 30), "Neurology Consultation", "2023-06-01 09:00", 200.0)
+ doctor.list_of_appointments = [appointment1, appointment2]
+ result = doctor.display_schedule()
+ expected_result = "Schedule for Dr. Smith:\n" + str(appointment1) + "\n" + str(appointment2) + "\n"
+ self.assertEqual(result, expected_result)
+
+ def test_cancel_appointment_existing(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ doctor.list_of_appointments = [appointment]
+ result = doctor.cancel_appointment(appointment)
+ self.assertEqual(result, "Appointment canceled for John Doe with Dr. Smith.")
+ self.assertNotIn(appointment, doctor.list_of_appointments)
+
+ def test_cancel_appointment_non_existing(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ result = doctor.cancel_appointment(appointment)
+ self.assertEqual(result, "John Doe does not have an appointment with Dr. Smith at that time.")
+
+ def test_add_all_patient_appointments(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ patient = Patient("John Doe", "Acne", 25)
+ doctor.add_all_patient_appointments(patient)
+ self.assertEqual(len(doctor.list_of_appointments), 5)
+
+ def test_compute_salary_specific_month(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment1 = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ appointment2 = Appointment(Patient("Jane Smith", "Headache", 30), "Neurology Consultation", "2023-06-01 09:00", 200.0)
+ doctor.list_of_appointments = [appointment1, appointment2]
+ result = doctor.compute_salary("06")
+ self.assertEqual(result, 350.0)
+
+ def test_compute_salary_all(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment1 = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ appointment2 = Appointment(Patient("Jane Smith", "Headache", 30), "Neurology Consultation", "2023-06-01 09:00", 200.0)
+ doctor.list_of_appointments = [appointment1, appointment2]
+ result = doctor.compute_salary()
+ self.assertEqual(result, 350.0)
+
+ def test_get_next_available_time_no_appointments(self):
+ with self.capture() as c:
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ result = doctor.get_next_available_time()
+ self.assertRegex(result, r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}")
+
+ def test_get_next_available_time(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex09.appointment import Appointment
+ from cp.ex10.doctor import Doctor
+ doctor = Doctor("Dr. Smith", "Dermatology")
+ appointment = Appointment(Patient("John Doe", "Acne", 25), "Dermatology Consultation", "2023-06-02 14:00", 150.0)
+ doctor.list_of_appointments = [appointment]
+ result = doctor.get_next_available_time()
+ self.assertEqual(result, "2023-06-02 14:30")
+
+
+
+class Week10TestBookPatientsToDoctors(UTestCase):
+
+ def test_book_patients_to_doctors(self):
+ with self.capture() as c:
+ from cp.ex09.patient import Patient
+ from cp.ex10.doctor import Doctor,book_patients_to_doctors
+
+ patients = [
+ Patient("John Doe", "Acne", 25),
+ Patient("Jane Smith", "Multiple Sclerosis", 30),
+ Patient("Mary Johnson", "Rosacea", 35),
+ Patient("David Brown", "Migraine", 40)
+ ]
+ doctors = [
+ Doctor("Dr. Smith", "Dermatology"),
+ Doctor("Dr. Johnson", "Neurology"),
+ Doctor("Dr. Brown", "Orthopedics")
+ ]
+ result = book_patients_to_doctors(patients, doctors)
+ self.assertEqual(len(result[0].list_of_appointments), 10)
+ self.assertEqual(len(result[1].list_of_appointments), 10)
+ self.assertEqual(len(result[2].list_of_appointments), 0)
+
+class Week10Tests(Report):
+ title = "Tests for week 10"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Week10TestDoctor, 10),
+ (Week10TestBookPatientsToDoctors, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week10Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week11.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week11.py
new file mode 100644
index 0000000000000000000000000000000000000000..af3e92137890a19eedaaf9620d8a31be8c24e8a3
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week11.py
@@ -0,0 +1,82 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+
+class Week11DotProducta(UTestCase):
+ def test_dot_product_a(self):
+ with self.capture() as c:
+ from cp.ex11.dot_product import dot_product_a
+ result = dot_product_a([1,2,3,4,5],[3,4,5,6,7])
+ self.assertEqual(result, 85.)
+ result = dot_product_a([2,3,7,8,1,12,5,2,9],[8,4,3,4,5,6,7,17,2])
+ self.assertEqual(result, 245.)
+
+import numpy as np
+class Week11DotProductb(UTestCase):
+ def test_dot_product_b(self):
+ with self.capture() as c:
+ from cp.ex11.dot_product import dot_product_b
+ result = dot_product_b(np.array([1,2,3,4,5]),np.array([3,4,5,6,7]))
+ self.assertEqual(result, 85.)
+ result = dot_product_b(np.array([2,3,7,8,1,12,5,2,9]),np.array([8,4,3,4,5,6,7, 17,2]))
+ self.assertEqual(result, 245.)
+
+import numpy as np
+class Week11BMICalc(UTestCase):
+ def test_calc_bmi(self):
+ with self.capture() as c:
+ from cp.ex11.BMI_analysis import calc_bmi
+ data = np.array([[1, 1.65, 63.4],
+ [2, 1.70, 87.1],
+ [3, 1.82, 93.7],
+ [4, 1.62, 105],
+ [5, 1.97, 61.3],
+ [6, 1.62, 78],
+ [7, 1.73, 99.6],
+ [8, 1.77, 81],
+ [9, 1.77, 110.7],
+ [10, 1.60, 55.2]])
+
+ result = calc_bmi(data)
+
+ expected_result = np.array([[1.0, 1.65, 63.4, 23.29, 'Normal weight'],
+ [2.0, 1.70, 87.1, 30.14, 'Obese'],
+ [3.0, 1.82, 93.7, 28.29, 'Overweight'],
+ [4.0, 1.62, 105.0, 40.01, 'Obese'],
+ [5.0, 1.97, 61.3, 15.8, 'Underweight'],
+ [6.0, 1.62, 78.0, 29.72, 'Overweight'],
+ [7.0, 1.73, 99.6, 33.28, 'Obese'],
+ [8.0, 1.77, 81.0, 25.85, 'Overweight'],
+ [9.0, 1.77, 110.7, 35.33, 'Obese'],
+ [10.0, 1.60, 55.2, 21.56, 'Normal weight']])
+
+ # Check each element of the arrays individually
+ for i in range(len(result)):
+ for j in range(len(result[i])):
+ self.assertEqual(result[i][j], expected_result[i][j])
+
+class Week11StableMeasurements(UTestCase):
+ def test_stable_measurements(self):
+ with self.capture() as c:
+ from cp.ex11.Stable_measurements import stable_measurements
+ result = stable_measurements(np.array([4.3, 5.7, 5.1, 6.4, 7.9, 12.8]))
+ self.assertEqual(result.tolist(), [6.4, 7.9, 12.8])
+ result = stable_measurements(np.array([4.3, 5.7, 5.1, 8, 7.9, 12.8]))
+ self.assertEqual(result.tolist(),[])
+ result = stable_measurements(np.array([8, 7.9, 12.8, 14, 17.5, 18.1]))
+ self.assertEqual(result.tolist(),[12.8, 14, 17.5, 18.1])
+
+class Week11Tests(Report): #30 total.
+ title = "Tests for week 11"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (Week11DotProducta, 10),
+ (Week11DotProductb, 10),
+ (Week11BMICalc, 10),
+ (Week11StableMeasurements, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week11Tests())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week12.py b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week12.py
new file mode 100644
index 0000000000000000000000000000000000000000..9df425b1d8fc459f74f01dab4e8bcdc36176cd67
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/tests/tests_week12.py
@@ -0,0 +1,22 @@
+from unitgrade import Report
+import cp
+from unitgrade import UTestCase
+from cp.ex02.taylor import evaluate_taylor
+
+class HelloWorld(UTestCase):
+ def test_say_hello(self):
+ from cp.ex00 import say_hello
+ evaluate_taylor(2)
+ self.assertEqual(0,0)
+
+class Week12Tests(Report): #240 total.
+ title = "Tests for week 12"
+ pack_imports = [cp]
+ individual_imports = []
+ questions = [
+ (HelloWorld, 10),
+ ]
+
+if __name__ == '__main__':
+ from unitgrade import evaluate_report_student
+ evaluate_report_student(Week12Tests())
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cp/utils/__init__.py b/src/unitgrade_private/pipelines/tmp/students/cp/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7de222064c9f80c98edd660d314614a5a86828ab
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/cp/utils/__init__.py
@@ -0,0 +1 @@
+"""This subpackage is empty. We can use it for general utility files at a later point (or remove it)."""
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/fractions.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/fractions.py
deleted file mode 100644
index a02d4fcca456701a4626bb560d6426da7f8e8465..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_course/fractions.py
+++ /dev/null
@@ -1,53 +0,0 @@
-class Fraction: #!s
- def __init__(self, n, m):
- self.n = n
- self.m = m
-
- def __add__(self, other): #!f f should be the result of adding fraction 'other' to this fraction.
- # Computes f = f1 + f2 (where f1 and f2 are both Fraction-objects and f1=self, f2=other) #!s
- assert(False)
- f = Fraction(other.m * self.n + other.n * self.m, self.n * self.m)
- return f #!s
-
- def __mul__(self, other): #!f f should be the result of multiplying fraction 'other' to this fraction.
- # Overwrite to implement f = f1 * f2 #!s
- f = Fraction(other.n * self.n, other.m*self.m)
- assert(False)
- return f #!s
-
- def __truediv__(self, other): #!f f should be the result of dividing this fraction with 'other'
- # Overwrite to implement f = f1/f2, or more specifically self/other. #!s
- f = self.__mul__(Fraction(other.m, other.n))
- return f #!s
-
- def __str__(self):
- """ Creates a string representation. You can use it as `print(str(Fraction(1,2))) to output 1/2"""
- return f"{self.n} / {self.m}" #!s
-
-
-def from_string(s):
- """ Convert the string s to a Fraction(a, b) object. """
- if '+' in s: #!b
- l = [from_string(ss.strip()) for ss in s.split("+")]
- return l[0] + l[1]
- if 'div' in s:
- l = [from_string(ss.strip()) for ss in s.split("div")]
- return l[0] / l[1]
- if '*' in s:
- l = [from_string(ss.strip()) for ss in s.split("*")]
- return l[0] * l[1]
- if '/' in s:
- return Fraction(*[int(ss.strip()) for ss in s.split("/")]) #!b Compute and return a Fraction(a,b) object corresponding to s.
-
-
-if __name__ == "__main__": #!o=a
- f1 = Fraction(1, 2) # Represents 1/2 #!s=a
- f2 = Fraction(3, 5) # Represents 3/5
- print(f"Result of {f1} + {f2} is", f1+f2) #!s=a
- #!o=a
- # Now do some compound tests:
- s = " 1 / 4 * 1 / 2" #!s=b #!o=b
- print("Result of", s, "is", from_string(s))
- assert(False)
- s = "5 / 2 div 10 / 3"
- print("Result of", s, "is", from_string(s)) #!s=b #!o=b
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/fragment.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/fragment.py
deleted file mode 100644
index 42495b68fc3da0bfd035a9640688db3e4a43e692..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_course/fragment.py
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
-from cpp_course.fractions import Fraction, from_string
-
-f1 = Fraction(1, 2) # Represents 1/2 #!i
-f2 = Fraction(3, 5) # Represents 3/5
-print(f"Result of {f1} + {f2} is", f1 + f2)
-
-# Now do some compound tests:
-s = " 1 / 4 * 1 / 2"
-print("Result of", s, "is", from_string(s))
-s = "5 / 2 div 10 / 3"
-print("Result of", s, "is", from_string(s))
-pass
-#!i
-
-
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6.py
deleted file mode 100644
index 8cc6174659c097f70516cbf045da3ef8eb51ad8c..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from unitgrade import UTestCase, Report, cache
-from cpp_course.fractions import from_string, Fraction
-
-class Fractions_from_string(UTestCase):
- def test_from_string_manual(self):
- self.assertEqual(str(from_string("2 / 3 + 4 / 5")), "22/6")
-
- def test_from_string_smarter(self):
- self.assertEqualC(str(from_string("2 / 3 + 4 / 5")))
-
- @cache
- def output(self, problem):
- return from_string(problem)
-
- def test_from_string_smartest(self):
- problems = """
- 2 / 3 + 4 / 5
- 1 / 2 * 3 / 4
- 1 / 2 div 1 / 2
- 1 / 4 + 1 / 2
- 1 / 2 * 2 / 4
- 1 / 2 div 4 / 2"""
- print("\nTesting a bunch of problems...")
- for l in problems.strip().splitlines():
- l = l.strip() # Remove trailing spaces
- print(f"Testing from_string({l}), output ought to be: {self.output(l)}")
- self.assertEqualC(str(from_string(l))) # Actually perform the test.
-
-class Fractions_Basics(UTestCase):
- def test_addition(self):
- f1 = Fraction(1, 2) # Represents 1/2
- f2 = Fraction(3, 5) # Represents 3/5
- self.assertEqualC(str(f1 + f2))
-
- def test_multiplication(self):
- f1 = Fraction(1, 2) # Represents 1/2
- f2 = Fraction(3, 5) # Represents 3/5
- self.assertEqualC(str(f1 * f2))
-
- def test_division(self):
- f1 = Fraction(1, 2) # Represents 1/2
- f2 = Fraction(3, 5) # Represents 3/5
- self.assertEqualC(str(f1 / f2))
-
-
-import cpp_course
-class Week6(Report):
- title = "02393 Programming in C++: Problem set 6"
- pack_imports = [cpp_course]
- questions = [(Fractions_from_string, 10), (Fractions_Basics, 10)]
-
-if __name__ == "__main__":
- from unitgrade import evaluate_report_student
- evaluate_report_student(Week6())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6_grade.py b/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6_grade.py
deleted file mode 100644
index fd11dbb77c1ef8459f783a76addc835596e4ecd1..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_course/tests_ex6_grade.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# cpp_course/tests_ex6.py
-''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
-import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/unitgrade_data/FractionsFromString.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_course/unitgrade_data/FractionsFromString.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_course/unitgrade_data/main_config_tests_ex6.artifacts.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem1_vectors.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem1_vectors.py
deleted file mode 100644
index e986ff9bd9bb473175daa418ddc928015373e181..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem1_vectors.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class Vector:
- def __init__(self, x, y):
- self.x, self.y = x, y
-
-def createField(m, n, v): #!f Create and return a field of the form [ [v11, v12,...], [v21, v22,...],...] here
- return [[Vector(v.x, v.y) for j in range(n)] for i in range(m)]
-
-def displayField(A): #!f Compute and return a string representation of A (see problem description)
- return "\n".join([' '.join([f"({v.x},{v.y})" for v in c]) for c in A])
-
-def addFields(A, B): #!f Create and return a new field corresponding to A+B (element-wise)
- return [ [Vector(v1.x + v2.x, v1.y+v2.y) for v1, v2 in zip(c1,c2) ] for c1,c2 in zip(A,B)]
-
-def scaleField(A, s): #!f Create and return a new field corresponding to A*s (element-wise)
- return [[Vector(v1.x * s, v1.y*s) for v1 in c1] for c1 in A]
-
-if __name__ == "__main__":
- A = createField(3, 3, Vector(1,2)) #!s #!o
- assert(False)
- A[1][1] = Vector(2,2)
- print(displayField(A))
- assert(False)
- print( )
- B = createField(3, 3, Vector(2,-2))
- B[0][0] = B[2][2] = Vector(9, 8)
- print(displayField(B))
- print( )
- assert(False)
- print(displayField(addFields(A, B)))
- assert(False)
- print("Result of scaling A by 2")
- print(displayField(scaleField(A, 2))) #!s #!o
-
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem2_rle.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem2_rle.py
deleted file mode 100644
index 16129eadcc900777d1cde83665212ac610b684e4..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem2_rle.py
+++ /dev/null
@@ -1,41 +0,0 @@
-class Elem:
- def __init__(self, value, times, next):
- self.value = value
- self.times = times
- self.next = next
-
-def displayRLEList(l):
- if l is None: return ""
- return f" {l.value} (x{l.times})" + displayRLEList(l.next)
-
-def reverse(l): #!f
- prev = None
- current = l
- while current is not None:
- n = current.next
- prev = Elem(current.value, current.times, prev)
- assert(False)
- current = n
- return prev
-
-def concatenate(l1, l2): #!f
- hd = l1
- assert(False)
- while l1.next is not None:
- l1 = l1.next
- l1.next = l2
- return hd
-
-def rle_sum(l): #!f
- return 0 if l is None else (l.value * l.times + rle_sum(l.next))
-
-if __name__ == "__main__":
- l1 = Elem(4, 10, Elem(12, 6, Elem(10, 5, None)))
- l2 = Elem(1, 1, Elem(2, 2, Elem(3, 3, None)))
-
- print("The RLE list is: ", displayRLEList(l1))
- print("Other is: ", displayRLEList(l2))
-
- print("The reversed list is: ", displayRLEList(reverse(l1)))
- print("After concatenation, the list is: ", displayRLEList(concatenate(l1, l2)))
- print("The sum of its elements is: ", rle_sum(l1))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem3_groceries.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem3_groceries.py
deleted file mode 100644
index 97401dc33fdbc39b1d2a284810f3579e4deb4bba..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem3_groceries.py
+++ /dev/null
@@ -1,33 +0,0 @@
-class GroceryList:
- def __init__(self):
- # Populate the items:
- self.items = {'Lasagne': (1, "With eggs if available"), "Salmon": (500, "Smoked if available"),
- "Spinach": (300, "Fresh"), "Dessert": (8, "Maybe lagkage?"), }
-
- def add(self, name, quantity=1, notes=""): #!f
- if name in self.items:
- item = self.items[name]
- self.items[name] = (item[0] + quantity, item[1] + ";" + notes)
- else:
- self.items[name] = (quantity, notes)
-
- def remove(self, name, quantity=1): #!f
- if name not in self.items:
- return False
- else:
- self.items[name] = (self.items[name][0]-quantity, self.items[name][1])
- if self.items[name][0] <= 0:
- del self.items[name]
- return True
-
- def copyEntry(self, name, new_name): #!f
- if name not in self.items or new_name in self.items:
- return False
- else:
- self.items[new_name] = self.items[name]
- return True
-
- def display(self): # Don't edit this function; it is used for the test.
- s = [f"> {name=};{quantity=};{notes=}" for name, (quantity, notes) in self.items.items()]
- print("\n".join(s))
- return s
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem4_filter.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem4_filter.py
deleted file mode 100644
index f66aaf6fff30680d14e186d84e1a4473988686ed..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/problem4_filter.py
+++ /dev/null
@@ -1,39 +0,0 @@
-class Buffer:
- def __init__(self, default=-999): #!f
- self.buffer = []
- self.known = set()
- self.default = default
-
- def write(self, n): #!f
- if n not in self.known:
- self.buffer.insert(0, n)
- self.known.add(n)
-
- def reset(self): #!f
- self.buffer.clear()
- self.known.clear()
-
- def occupancy(self): #!f
- return len(set(self.buffer))
-
- def read(self): #!f
- return self.buffer.pop() if len(self.buffer) > 0 else self.default
-
-if __name__ == "__main__":
- b = Buffer()
- print("Current buffer occupancy: ", b.occupancy())
- print("Reading from the buffer returns: ", b.read())
-
- for i in range(10):
- b.write(i*10)
-
- print("Current buffer occupancy: ", b.occupancy())
- for i in range(10):
- b.write(20)
- print("Current buffer occupancy: ", b.occupancy())
- for _ in range(3):
- print("Reading from the buffer returns: ", b.read())
- print("Current buffer occupancy: ", b.occupancy())
- b.reset()
- print("Current buffer occupancy: ", b.occupancy())
- print("Reading from the buffer returns: ", b.read())
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam.py
deleted file mode 100644
index b68bfbe3c62c770d66c894a06112d2d30f9c4b80..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam.py
+++ /dev/null
@@ -1,263 +0,0 @@
-from unitgrade import UTestCase, Report
-import cpp_exam
-from cpp_exam.problem1_vectors import Vector, createField, displayField, scaleField, addFields
-from cpp_exam.problem2_rle import Elem, rle_sum, reverse, concatenate, displayRLEList
-from cpp_exam.problem3_groceries import GroceryList
-from cpp_exam.problem4_filter import Buffer
-
-# class Q1Vectors_Examples(UTestCase):
-# def test_correct_format(self):
-# """ Test your field is in the right format.
-# Hints:
-# * If this test fails, all subsequent tests will fail. Make sure it works!
-# """
-# A = createField(3, 3, Vector(1, 2))
-# self.assertEqual(len(A), 3) # Check there are 3 rows in A
-# self.assertEqual(len(A[0]), 3) # Check there are 3 columns in A
-# v = A[1][1] # Should be a Vector(1, 2) element.
-# self.assertEqual(v.x, 1) # Check that first coordinate is 1
-# self.assertEqual(v.y, 2) # Check that first coordinate is 1
-#
-# def test_displayField(self):
-# A = createField(3, 3, Vector(1, 2))
-# A[1][1] = Vector(2, 2)
-# self.assertEqualC(displayField(A).strip())
-#
-# def test_addFields(self):
-# A = createField(3, 3, Vector(1, 2))
-# A[1][1] = Vector(2, 2)
-# B = createField(3, 3, Vector(2, -2))
-# B[0][0] = B[2][2] = Vector(9, 8)
-# self.assertEqualC(displayField(addFields(A, B)).strip())
-#
-# def test_scaleFields(self):
-# A = createField(3, 3, Vector(1, 2))
-# A[1][1] = Vector(2, 2)
-# self.assertEqualC(displayField(scaleField(A, 2)).strip())
-
-def vector2string(v):
- return f"({v.x},{v.y})"
-
-def check_field(self, A):
- print("\nChecking size and type of field...")
- self.assertIsInstance(A, list)
- self.assertIsInstance(A[0], list)
- self.assertIsInstance(A[0][0], Vector)
- print("Checking all elements of field...")
- for i, r in enumerate(A):
- for j, a in enumerate(r):
- # if i > 3 or j > 3: continue
- print(f"Checking that A[{i}][{j}] = {self.get_expected_test_value()};", "your value was", vector2string(A[i][j]))
- self.assertEqualC(vector2string(A[i][j]))
-
-class Q1Vectors(UTestCase):
- def test_correct_format(self):
- """ Test your field is in the right format.
- Hints:
- * If this test fails, all subsequent tests will fail. Make sure it works!
- """
- A = createField(3, 3, Vector(1, 2))
- self.assertEqual(len(A), 3) # Check there are 3 rows in A
- self.assertEqual(len(A[0]), 3) # Check there are 3 columns in A
- v = A[1][1] # Should be a Vector(1, 2) element.
- self.assertEqual(v.x, 1) # Check that first coordinate is 1
- self.assertEqual(v.y, 2) # Check that first coordinate is 1
-
- def test_createField_small(self):
- # Test a 4x4 field
- check_field(self, createField(4, 4, Vector(1,2)))
- # Test an 8x8 field
- check_field(self, createField(8, 8, Vector(3,8)))
-
- def test_display_field(self):
- A = createField(4, 3, Vector(1,2))
- A[0][0] = A[1][1] = A[2][2] = Vector(0,0)
- self.assertEqualC(displayField(A))
-
- B = createField(6,8, Vector(3, -9))
- B[0][1] = B[2][1] = B[3][2] = B[4][5] = B[5][6] = Vector(0,0)
- self.assertEqualC(displayField(B))
-
- def test_add_fields(self):
- A = createField(4, 3, Vector(1,2))
- B = createField(4, 3, Vector(3, -9))
- check_field(self, addFields(A, B))
-
- def test_scale_fields(self):
- A = createField(2, 4, Vector(1,2))
- check_field(self, scaleField(A, 2))
- A = createField(6, 6, Vector(3, 1))
- check_field(self, scaleField(A, 3))
-
-def make_rle1():
- return Elem(4, 10, Elem(12, 6, Elem(10, 5, None)))
-
-def make_rle2():
- return Elem(4, 2, Elem(5, 3, Elem(3, 5, None)))
-
-def make_rle3():
- return Elem(6, 3, Elem(7, 5, Elem(8, 5, None)))
-
-class Q2RLE(UTestCase):
- def test_reverse_empty(self):
- self.assertEqual(reverse(None), None)
-
- def test_reverse(self):
- self.assertEqualC(displayRLEList(reverse(make_rle1())))
- l = make_rle1() # Test reversal of the tail
- self.assertEqualC(displayRLEList(reverse(l.next)))
-
- def test_concatenate_with_empty(self):
- l = make_rle1()
- print(f"Concatenating {displayRLEList(l)} with empty list")
- self.assertEqualC(displayRLEList(concatenate(l, None)))
-
- l = make_rle2()
- print(f"Concatenating {displayRLEList(l)} with empty list")
- self.assertEqualC(displayRLEList(concatenate(l, None)))
-
- def test_concatenate_with_another(self):
- l1, l2 = make_rle1(), make_rle2()
- print(f"Concatenating {displayRLEList(l1)} with {displayRLEList(l2)}")
- self.assertEqualC(displayRLEList(concatenate(l1, l2)))
-
- def test_concatenate_three_lists(self):
- l1, l2, l3 = make_rle1(), make_rle2(), make_rle3()
- print(f"Concatenating {displayRLEList(l1)} with {displayRLEList(l2)} and {displayRLEList(l3)}")
- self.assertEqualC(displayRLEList(concatenate(l1, concatenate(l2, l3))))
-
- def test_sum_empty(self):
- print(f"Computing sum of empty list")
- self.assertEqual(rle_sum(None), 0)
-
- def test_sum_nonempty(self):
- for l in [ make_rle1(), make_rle2(), make_rle3()]:
- print(f"Computing sum of {displayRLEList(l)}")
- self.assertEqualC(rle_sum(l), 0)
-
-class Q3Groceries(UTestCase):
- def test_add(self):
- gl = GroceryList()
- print("Initial list")
- gl.display()
- print("After adding cheddar:")
- gl.add("Cheddar", 500, "Not too mature")
- gl.display()
- print("After adding more spinach:")
- gl.add("Spinach", 200, "Baby spinach if available")
- gl.display()
- print("After adding even more spinach:")
- gl.add("Spinach", 200, "Frozen is OK")
- gl.display()
-
- def test_remove1(self):
- gl = GroceryList()
- gl.display()
- print("After removing spinach")
- self.assertTrue(gl.remove("Spinach", 200))
- self.assertEqualC(gl.display())
-
- print("After removing more spinach")
- self.assertTrue(gl.remove("Spinach", 100))
- self.assertEqualC(gl.display())
-
- print("Trying to remove even more spinach:")
- self.assertFalse(gl.remove("Spinach", 100))
-
- print("Trying to remove cheddar:")
- self.assertFalse(gl.remove("Cheddar", 100))
-
- def test_copy(self):
- gl = GroceryList()
- print("Initial grocery list:")
- gl.display()
- print("After copying spinach into baby spinach:")
- self.assertTrue(gl.copyEntry("Spinach", "Baby spinach"))
- self.assertEqualC(gl.display())
- print("After copying baby spinach into dessert:")
- self.assertFalse(gl.copyEntry("Baby spinach", "Dessert"))
- print("After copying Cheddar into Spinach")
- self.assertFalse( gl.copyEntry("Cheddar", "Spinach"))
-
-class Q4FilterBuffer(UTestCase):
- def test_if_compiles(self):
- b = Buffer()
- b.write(42)
- b.write(42)
- b.occupancy()
- b.read()
- print("None of the functions crashed! Congrats!")
-
- def test_occupancy(self):
- b = Buffer()
- print("Current occupancy", b.occupancy())
- self.assertEqual(b.occupancy(), 0)
- for i in range(7):
- b.write(i*10)
- print("Current buffer occupancy: ", b.occupancy())
- self.assertEqual(b.occupancy(), 7)
- for i in range(5, 10):
- b.write(i*10)
- print("Current buffer occupancy: ", b.occupancy())
- self.assertEqual(b.occupancy(), 10)
-
- def test_read(self):
- b = Buffer()
- print("Current buffer occupancy: ", b.occupancy())
- print(f"Reading from empty buffer should return default value of {b.default}")
- self.assertEqual(b.read(), -999)
-
- for i in range(5):
- b.write(i*10)
-
- print("Current buffer occupancy: ", b.occupancy() )
- self.assertEqual(b.occupancy(), 5)
- for i in range(3):
- r = b.read()
- print("Reading from the buffer returns: ", r)
- self.assertEqual(r, i*10)
- print("Current buffer occupancy: ", b.occupancy() )
- self.assertEqualC(b.occupancy())
-
- for i in range(8):
- b.write(i * 10)
- print("Current buffer occupancy: ", b.occupancy())
- self.assertEqualC(b.occupancy())
-
- for i in range(6):
- r = b.read()
- print("Reading from the buffer returns: ", r)
- self.assertEqualC(r)
-
- def test_reset(self):
- b = Buffer()
-
- for i in range(5):
- b.write(i*10)
- print("Current buffer occupancy: ", b.occupancy() )
- self.assertEqual(b.occupancy(), 5)
- for i in range(3):
- r = b.read()
- print("Reading from the buffer returns: ", r)
- self.assertEqual(r, i*10)
- print("Current buffer occupancy: ", b.occupancy() )
- self.assertEqualC(b.occupancy())
- b.reset()
- for i in range(8):
- b.write(i * 10)
- print("Current buffer occupancy: ", b.occupancy())
- self.assertEqualC(b.occupancy())
-
- for i in range(3):
- r = b.read()
- print("Reading from the buffer returns: ", r)
- self.assertEqualC(r)
-
-class ExamMay2021(Report):
- title = "Programming in C++: Exam May 2021"
- pack_imports = [cpp_exam]
- questions = [(Q1Vectors, 25), (Q2RLE, 25), (Q3Groceries, 25), (Q4FilterBuffer, 25)]
-
-if __name__ == "__main__":
- from unitgrade import evaluate_report_student
- evaluate_report_student(ExamMay2021())
diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam_grade.py b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam_grade.py
deleted file mode 100644
index d09bb1329d42fc29cd42b16524e54449d5669ae7..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/tests_exam_grade.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# cpp_exam/tests_exam.py
-''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
-import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_Examples.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_Examples.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_addFields.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_addFields.pkl
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diff --git a/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_createField.pkl b/src/unitgrade_private/pipelines/tmp/students/cpp_exam/unitgrade_data/Q1Vectors_createField.pkl
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/exam.py b/src/unitgrade_private/pipelines/tmp/students/intro_python/exam.py
deleted file mode 100644
index d8cf1b269e2829ae11a1282b4fca1be18ad49acb..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/intro_python/exam.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import numpy as np
-from unitgrade import UTestCase, Report, hide
-import intro_python
-from intro_python.problems import water_height, tictactoe, time_angle, astronomical_season, standardize_address
-
-class Q1_WaterHeight(UTestCase):
- def test1(self):
- h0 = 5
- r = np.array([4.5, 0, 1.5, 0, 0, 0.5, 1, 2, 5])
- h = water_height(h0, r)
- print("Water height computed to be", h, "should be", self.get_expected_test_value())
- self.assertEqual(h, 3.0) # Check the height is 3.0
-
-
-class Q2_AstronomicalSeason(UTestCase):
- def test_seasons(self):
- season = astronomical_season('09/12-2020')
- print("Season was computed to be", season, "it was supposed to be", self.get_expected_test_value())
- self.assertEqualC(season)
-
-
-
-class Q3_TimeAngle(UTestCase):
- def test_angle(self):
- a = time_angle(hour=8, minute=20)
- print("Angle was", a, "it was supposed to be", self.get_expected_test_value())
- self.assertEqualC(a)
-
-
-class Q4_TicTacToe(UTestCase):
- def test_tic_tac(self):
- board = np.array([[2, 1, 1],
- [1, 1, 2],
- [2, 0, 0]])
- score = tictactoe(board)
- print("Score for board was", score, "it is supposed to be", self.get_expected_test_value())
- self.assertEqualC(score)
-
-
-
-class Q5_StandardizeAddress(UTestCase):
- def test_standardize_address(self):
- s = standardize_address('New York 10001')
- print("Address computed to be", s, "was supposed to be", self.get_expected_test_value())
- assert(False)
- self.assertEqualC(s)
-
-
-
-
-class Exam2021(Report):
- title = "Introduction to Python: Exam spring 2021"
- pack_imports = [intro_python]
- questions = [(Q1_WaterHeight, 20),
- (Q2_AstronomicalSeason, 20),
- (Q3_TimeAngle, 20),
- (Q4_TicTacToe, 20),
- (Q5_StandardizeAddress, 20)]
-
-if __name__ == "__main__":
- from unitgrade import evaluate_report_student
- evaluate_report_student(Exam2021())
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/exam_grade.py b/src/unitgrade_private/pipelines/tmp/students/intro_python/exam_grade.py
deleted file mode 100644
index 67b272c3fa4478273b3f9ea2a69d03e824a5ae17..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/intro_python/exam_grade.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# intro_python/exam.py
-''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
-import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/problems.py b/src/unitgrade_private/pipelines/tmp/students/intro_python/problems.py
deleted file mode 100644
index d6f738567b918f4abedbb39d563de5b306008c7d..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/students/intro_python/problems.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import numpy as np
-
-def astronomical_season(date):
- """ Problem 1. Given a date (as a string) return the season (as a string)
- Hints:
- * The date is a string in the format <dd> <mm>, for instance '18 04' is the 18th of March.
- * The season must be a string which can be either 'winter', 'spring', 'summer' or 'autumn'.
- """
- dd = int(date[0:2]) #!b
- mm = int(date[3:5])
- if mm<3 or (mm==3 and dd<20):
- season = 'winter'
- elif mm<6 or (mm==6 and dd<21):
- season = 'spring'
- elif mm<9 or (mm==9 and dd<23):
- season = 'summer'
- elif mm<12 or (mm==12 and dd<21):
- season = 'autumn'
- else:
- season = 'winter' #!b Compute the season here.
- return season
-
-
-def standardize_address(address):
- """ Given an address, return a standardized version (see question text)
-
- Hints:
- * The function s.isnumeric() may be of help to check if a string s is a number.
- """
- s = address.replace('_', ' ') #!b
- if not s[0].isnumeric():
- i = s[-1::-1].find(' ')
- i = len(s) - 1 - i
- s = s[i+1:] + s[i] + s[:i] #!b Compute the address here
- return s
-
-def tictactoe(board):
- """ Given a tic-tac-toc board configuration, return whether it is valid or not.
-
- Hints:
- * Remember that 'board' is a numpy array. Check what the function np.all(A, axis) does.
- * You can 'flatten' an array using A.ravel().
- """
- def wins(b): #!b
- return np.all(b, axis=0).sum() + np.all(b, axis=1).sum() + np.all(b.ravel()[[0, 4, 8]]) + np.all(b.ravel()[[2, 4, 6]])
-
- ones_wins = wins(board == 1)
- twos_wins = wins(board == 2)
- d = np.sum(board == 1) - np.sum(board == 2) # 0 or 1 for a valid board
- assert(False)
- if d < 0 or d > 1 or (ones_wins + twos_wins) > 1:
- score = -1
- elif ones_wins:
- score = 1
- elif twos_wins:
- score = 2
- else:
- score = 0 #!b compute the board score (an integer, either 0, 1 or 2)
- return score
-
-def time_angle(hour, minute):
- """ Given hours and minutes (integers), return the angle of the small hand.
-
- Hints:
- * The angle is between 0 and 360.
- * Noon and midnight, 12:00 and 24:00, is both 0
- * Use % for remainder. For instance 8 % 5 is 2 and 24 % 10 = 4 (see lecture notes)
- """
- hour = hour % 12 #!b
- hour_hand = 360 * (hour + minute / 60) / 12
- minute_hand = 360 * minute / 60
- a = hour_hand - minute_hand
- assert(False)
- if a < 0:
- a = a + 360 #!b Compute the angle of the small hand here
- return a
-
-def water_height(h0, r):
- """ Problem 1: Compute the water height.
-
- Hints:
- * Remember the height must be a non-negative number.
- """
- h = h0 #!b
- for ri in r:
- h = max(h + ri - 2, 0) #!b Compute the water height h here.
- return h
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Problem1.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Problem1.pkl
deleted file mode 100644
index 15baef336eff2b1034fdb3e6def2a77b7b19b5ac..0000000000000000000000000000000000000000
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q1_WaterHeight.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q1_WaterHeight.pkl
deleted file mode 100644
index 834d08b7bbdcf8ebf2e06cfe57953e687871396a..0000000000000000000000000000000000000000
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diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl
deleted file mode 100644
index 2d677c3561d435b085828c376c30118f918b7c5d..0000000000000000000000000000000000000000
Binary files a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q2_AstronomicalSeason.pkl and /dev/null differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl
deleted file mode 100644
index 8528aeef3c8c029a9a8af2b1bc198d19a0463555..0000000000000000000000000000000000000000
Binary files a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q3_TimeAngle.pkl and /dev/null differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl
deleted file mode 100644
index 163e3b6bbf380c2ec67ca64c32d89bee735a97be..0000000000000000000000000000000000000000
Binary files a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q4_TicTacToe.pkl and /dev/null differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl
deleted file mode 100644
index a55324933d96a979dce39176c27427e4ef8b5ea2..0000000000000000000000000000000000000000
Binary files a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/Q5_StandardizeAddress.pkl and /dev/null differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl b/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl
deleted file mode 100644
index d323d4cf41dd5b63d5f45f495924e2f57c13bc78..0000000000000000000000000000000000000000
Binary files a/src/unitgrade_private/pipelines/tmp/students/intro_python/unitgrade_data/main_config_exam.artifacts.pkl and /dev/null differ
diff --git a/src/unitgrade_private/pipelines/tmp/students/requirements.txt b/src/unitgrade_private/pipelines/tmp/students/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc21402b08fd2dd2b8c053a75b0700b81b4ab0cd
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/requirements.txt
@@ -0,0 +1,8 @@
+dtumathtools
+# sympy
+unitgrade
+# pydocstyle # For what?
+# numpy<=1.23
+# matplotlib<=3.6
+# PyQt5<=5.13.2 # 5.15.8 has a problem with matplotlib. Trying this version.
+-e .
\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/students/setup.py b/src/unitgrade_private/pipelines/tmp/students/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..c14dc562ccfcce2fc456735c95f7f261e4e01e47
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/students/setup.py
@@ -0,0 +1,10 @@
+from setuptools import find_packages, setup
+
+setup(
+ name="cp",
+ packages=find_packages(),
+ version="2023.0",
+ description="Toolbox for Introduction to Python Programming",
+ author="Tue Herlau",
+ license="See licensing file",
+)
diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token
deleted file mode 100644
index 1a0ef426f0359ec6fd12754b46385cba59ec162f..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Exam2021_handin_40_of_100.token
+++ /dev/null
@@ -1,207 +0,0 @@
-# This file contains your results. Do not edit its content. Simply upload it as it is.
----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Project0_handin_0_of_10.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Project0_handin_0_of_10.token
new file mode 100644
index 0000000000000000000000000000000000000000..1958e293adc0913a9f9dd9e2162405181e95b8e3
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221000 - Alice - 1 March, 2022 518 PM/Project0_handin_0_of_10.token
@@ -0,0 +1,3376 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token
deleted file mode 100644
index 68797b0b56e3e0418752dee0d9f778ccfac9ae26..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Exam2021_handin_80_of_100.token
+++ /dev/null
@@ -1,207 +0,0 @@
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Project0_handin_0_of_10.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Project0_handin_0_of_10.token
new file mode 100644
index 0000000000000000000000000000000000000000..1958e293adc0913a9f9dd9e2162405181e95b8e3
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221001 - Bob - 1 March, 2022 518 PM/Project0_handin_0_of_10.token
@@ -0,0 +1,3376 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token
deleted file mode 100644
index 74ab5a3d17172fbf232bac79b3b1e04cd6252cc7..0000000000000000000000000000000000000000
--- a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Exam2021_handin_60_of_100.token
+++ /dev/null
@@ -1,207 +0,0 @@
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\ No newline at end of file
diff --git a/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Project0_handin_0_of_10.token b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Project0_handin_0_of_10.token
new file mode 100644
index 0000000000000000000000000000000000000000..1958e293adc0913a9f9dd9e2162405181e95b8e3
--- /dev/null
+++ b/src/unitgrade_private/pipelines/tmp/zip/116607-35260 - s221002 - Charlie - 1 March, 2022 518 PM/Project0_handin_0_of_10.token
@@ -0,0 +1,3376 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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