python_version stringclasses 3
values | library stringclasses 26
values | version stringlengths 1 6 | problem stringlengths 34 1.02k | starting_code stringlengths 23 1.55k | example_id stringlengths 1 3 | test stringlengths 66 5.96k | solution stringlengths 7 9.39k | type_of_change stringclasses 21
values | name_of_class_or_func stringlengths 0 63 | additional_dependencies stringclasses 31
values | docs listlengths 1 3 | functional unknown | webdev unknown | solution_api_call bool 1
class | api_calls listlengths 0 47 | release_date stringdate 2014-08-01 00:00:00 2024-01-01 00:00:00 | extra_dependencies stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3.10 | django | 4.0.0 |
Complete the function save_existing. Do not run the app in your code. | from django.conf import settings
from django.forms.models import BaseModelFormSet
from django.forms.renderers import get_default_renderer
from django.forms import Form
settings.configure()
def save_existing(formset: BaseModelFormSet, form : Form, obj:str) -> None:
| 101 |
class DummyForm:
def save(self, commit=True):
return 'dummy_instance_value_result'
class MyFormSet(BaseModelFormSet):
def __init__(self, *args, **kwargs):
self.renderer = get_default_renderer()
super().__init__(*args, **kwargs)
fs5 = MyFormSet(queryset=[])
result = save_existing(forms... | return formset.save_existing(form=form,instance=obj)
| argument or attribute change | BaseModelFormSet.save_existing | [
"https://docs.djangoproject.com/en/5.1/topics/forms/",
"https://docs.djangoproject.com/en/5.2/topics/settings/",
"https://docs.djangoproject.com/en/5.2/releases/4.0/"
] | 1 | 1 | true | [
"formset.save_existing"
] | 2021-12 | null | |
3.10 | django | 5.0.0 |
Complete the function save_existing. Do not run the app in your code. | from django.conf import settings
from django.forms.models import BaseModelFormSet
from django.forms.renderers import get_default_renderer
from django.forms import Form
settings.configure()
def save_existing(formset: BaseModelFormSet, form : Form, instance:str) -> None:
| 102 |
class DummyForm:
def save(self, commit=True):
return 'dummy_instance_value_result'
class MyFormSet(BaseModelFormSet):
def __init__(self, *args, **kwargs):
self.renderer = get_default_renderer()
super().__init__(*args, **kwargs)
fs5 = MyFormSet(queryset=[])
result = save_existing(formse... | return formset.save_existing(form=form,obj=instance)
| argument or attribute change | Template.render | [
"https://docs.djangoproject.com/en/5.1/topics/forms/",
"https://docs.djangoproject.com/en/5.2/topics/settings/",
"https://docs.djangoproject.com/en/5.2/releases/5.0/"
] | 1 | 1 | true | [
"formset.save_existing"
] | 2023-12 | null | |
3.10 | django | 5.0.0 |
Complete the get_template_string function so that the template_string variable form the html string provided in comments, making the best use of the templates for form field rendering. Do not run the app in your code.
| import django
from django.conf import settings
from django import forms
from django.template import Template, Context
settings.configure(
TEMPLATES=[
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
},
],
)
django.setup()
def render_output(template_string... | 103 |
template_string= get_template_string()
rendered_output = render_output(template_string)
def normalize_html(html):
# Remove all whitespace and standardize quotation marks to single quotes
normalized = ''.join(html.split())
return normalized
template_string_django_4 = '''
<form>
<div>
{{ form.name.lab... | return '''
<form>
<div>
{{ form.name.as_field_group }}
</div>
</form>
''' | other library or new feature | Template.render | [
"https://docs.djangoproject.com/en/5.0/ref/templates/api/"
] | 1 | 1 | true | [] | 2023-12 | null | |
3.10 | django | 4.0.0 |
Complete the get_template_string function so that the template_string variable form the html string provided in comments, making the best use of the templates for form field rendering. Do not run the app in your code.
| import django
from django.conf import settings
from django import forms
from django.template import Template, Context
settings.configure(
TEMPLATES=[
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
},
],
)
django.setup()
def render_output(template_string... | 104 |
template_string = get_template_string()
rendered_output = render_output(template_string)
def normalize_html(html):
# Remove all whitespace and standardize quotation marks to single quotes
normalized = ''.join(html.split())
return normalized
template_string_django_4 = '''
<form>
<div>
{{ form.name.l... | return '''
<form>
<div>
{{ form.name.label_tag }}
{% if form.name.help_text %}
<div class="helptext" id="{{ form.name.auto_id }}_helptext">
{{ form.name.help_text|safe }}
</div>
{% endif %}
{{ form.name.errors }}
{{ form.name }}
</div>
</form>
''' | other library or new feature | BoundField.as_field_group() | [
"https://docs.djangoproject.com/en/4.0/ref/templates/api/"
] | 1 | 1 | true | [] | 2021-12 | null | |
3.10 | django | 4.0.0 |
Create Square model with a side field and an area field that is calculated as the square of the side. Do not run the app in your code.
| import django
from django.conf import settings
from django.db import models, connection
from django.db.models import F
settings.configure(
DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},
)
django.setup()
def display_side_and_area(square):
return square.side, square.area
... | 105 |
with connection.schema_editor() as schema_editor:
schema_editor.create_model(Square)
square = create_square(side=5)
correct_result = (5, 25)
assert display_side_and_area(square) == correct_result
| models.BigIntegerField(editable=False)
def save(self, *args, **kwargs):
# Compute the area before saving.
self.area = self.side * self.side
super().save(*args, **kwargs)
| other library or new feature | BoundField.models.GeneratedField() | [
"https://docs.djangoproject.com/en/4.0/ref/models/fields/"
] | 0 | 1 | true | [
"models.BigIntegerField",
"super",
"save"
] | 2021-12 | null | |
3.10 | django | 5.0.0 |
Create Square model with a side field and an area field that is calculated as the square of the side. Do not run the app in your code.
| import django
from django.conf import settings
from django.db import models, connection
from django.db.models import F
settings.configure(
DATABASES={'default': {'ENGINE': 'django.db.backends.sqlite3', 'NAME': ':memory:'}},
)
django.setup()
def display_side_and_area(square):
return square.side, square.area
... | 106 |
with connection.schema_editor() as schema_editor:
schema_editor.create_model(Square)
square = create_square(side=5)
correct_result = (5, 25)
assert display_side_and_area(square) == correct_result
| models.GeneratedField(
expression=F('side') * F('side'),
output_field=models.BigIntegerField(),
db_persist=True,
)
| other library or new feature | BoundField.models.GeneratedField() | [
"https://docs.djangoproject.com/en/5.0/ref/models/fields/"
] | 0 | 1 | true | [
"models.BigIntegerField",
"django.db.models.F",
"models.GeneratedField"
] | 2023-12 | null | |
3.10 | django | 5.0.0 |
Create a model based on the given color choices. Do not run the app in your code. | import django
from django.conf import settings
from django.db import models
settings.configure()
django.setup()
color = models.TextChoices('Color', 'RED GREEN BLUE')
class MyModel(models.Model):
class Meta:
app_label = 'myapp'
color = models.CharField(max_length=5, | 107 |
field_choices = list(MyModel._meta.get_field('color').choices)
expected_choices = [('RED', 'Red'), ('GREEN', 'Green'), ('BLUE', 'Blue')]
assert field_choices == expected_choices
| choices=color)
| argument or attribute change | Field.choices | [
"https://docs.djangoproject.com/en/5.0/ref/models/fields/"
] | 0 | 1 | true | [] | 2023-12 | null | |
3.10 | django | 4.0.0 |
Create a model based on the given color choices. Do not run the app in your code. | import django
from django.conf import settings
from django.db import models
settings.configure()
django.setup()
color = models.TextChoices('Color', 'RED GREEN BLUE')
class MyModel(models.Model):
class Meta:
app_label = 'myapp'
color = models.CharField(max_length=5, | 108 |
field_choices = MyModel._meta.get_field('color').choices
expected_choices = [('RED', 'Red'), ('GREEN', 'Green'), ('BLUE', 'Blue')]
assert field_choices == expected_choices
| choices=color.choices)
| argument or attribute change | Field.choices | [
"https://docs.djangoproject.com/en/4.0/ref/models/fields/"
] | 0 | 1 | true | [] | 2021-12 | null | |
3.10 | scipy | 1.7.3 | complete the function that computes the weigthed Minkowski distance between two 1-D arrays, u and v, based on a 1D weigth vector, w. | from scipy.spatial import distance
import numpy as np
def compute_wminkowski(u:np.ndarray, v:np.ndarray, p:int, w:np.ndarray)->np.ndarray: | 109 |
u = np.asarray([11,12,13,14,15])
v = np.asarray([1,2,3,4,5])
w = np.asarray([0.1,0.3,0.15,0.25,0.2])
output = compute_wminkowski(u,v,p=3,w=w)
assertion_value = np.allclose(output, 3.8029524607613916)
assert assertion_value |
return distance.wminkowski(u,v,p=p,w=w) | name change | distance.wminkowski | [
"https://docs.scipy.org/doc/scipy-1.7.1/reference/reference/spatial.distance.html"
] | 1 | 0 | true | [
"scipy.spatial.distance.wminkowski"
] | 2021-11 | null | |
3.10 | scipy | 1.9.2 | complete the function that computes the weigthed Minkowski distance between two 1-D arrays, u and v, based on a 1D weigth vector, w. | from scipy.spatial import distance
import numpy as np
def compute_wminkowski(u:np.ndarray, v:np.ndarray, p:int, w:np.ndarray)->np.ndarray: | 110 |
u = np.asarray([11,12,13,14,15])
v = np.asarray([1,2,3,4,5])
w = np.asarray([0.1,0.3,0.15,0.25,0.2])
output = compute_wminkowski(u,v,p=3,w=w)
assertion_value = np.allclose(output, 9.999999999999998)
assert assertion_value |
return distance.minkowski(u,v,p=p,w=w) | name change | distance.minkowski | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/spatial.distance.html"
] | 1 | 0 | true | [
"scipy.spatial.distance.minkowski"
] | 2022-10 | null | |
3.10 | scipy | 1.8.1 |
Complete the function that computes the matrix exponential of batched matrices, non specified parameters should use the default value | from scipy import linalg
import numpy as np
def compute_matrix_exponential(A: np.ndarray) -> np.ndarray: | 111 |
A = np.array([[[0.25264461, 0.67582554, 0.90718149, 0.65460219],
[0.58271792, 0.4600052 , 0.22265374, 0.98210688],
[0.92575218, 0.66167048, 0.81779481, 0.15405207],
[0.00820708, 0.7702345 , 0.4285001 , 0.87567275]],
[[0.48362533, 0.10258182, 0.58965127, 0.89320413],
[0.11275151, ... |
return np.stack([linalg.expm(A[i]) for i in range(A.shape[0])],axis=0)
| argument or attribute change | linalg.expm | [
"https://docs.scipy.org/doc/scipy-1.8.1/reference/generated/scipy.linalg.expm.html"
] | 1 | 0 | true | [
"range",
"numpy.stack",
"scipy.linalg.expm"
] | 2022-05 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that compute the matrix exponential of batched matrices, non specified parameters should use the default value | from scipy import linalg
import numpy as np
def compute_matrix_exponential(A: np.ndarray) -> np.ndarray: | 112 |
A = np.array([[[0.25264461, 0.67582554, 0.90718149, 0.65460219],
[0.58271792, 0.4600052 , 0.22265374, 0.98210688],
[0.92575218, 0.66167048, 0.81779481, 0.15405207],
[0.00820708, 0.7702345 , 0.4285001 , 0.87567275]],
[[0.48362533, 0.10258182, 0.58965127, 0.89320413],
[0.11275151, ... |
return linalg.expm(A) | argument or attribute change | linalg.expm | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.linalg.expm.html#scipy.linalg.expm"
] | 1 | 0 | true | [
"scipy.linalg.expm"
] | 2022-10 | null | |
3.10 | scipy | 1.8.1 |
Complete the function that combines the p values from various independent tests stored in a 1D array
using pearson method, make sure that higher values of the statistic now correspond to lower
p-values, non specified parameters should use the default value | from scipy import stats
import numpy as np
def combine_pvalues(A: np.ndarray) -> tuple[float, float]: | 113 |
A = np.array([0.01995382, 0.1906752 , 0.71157923, 0.44477942, 0.4535412 ,
0.67556953, 0.11174941, 0.85494112, 0.33214635, 0.19103228])
output = combine_pvalues(A)
assertion_value = np.allclose(np.asarray(output),np.asarray([-stats.combine_pvalues(1-A,'fisher')[0],(1-stats.combine_pvalues(1-A,'fisher')[1])]))
... |
output = stats.combine_pvalues(A,'pearson')
return (-output[0], 1-output[1]) | output change | stats.combine_pvalues | [
"https://docs.scipy.org/doc/scipy-1.8.1/reference/generated/scipy.stats.combine_pvalues.html"
] | 1 | 0 | true | [
"return",
"scipy.stats.combine_pvalues"
] | 2022-05 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that combines the p values from various independent tests stored in a 1D array
using pearson method, make sure that higher values of the statistic now correspond to lower
p-values, non specified parameters should use the default value | from scipy import stats
import numpy as np
def combine_pvalues(A: np.ndarray) -> tuple[float, float]: | 114 |
A = np.array([0.01995382, 0.1906752 , 0.71157923, 0.44477942, 0.4535412 ,
0.67556953, 0.11174941, 0.85494112, 0.33214635, 0.19103228])
output = combine_pvalues(A)
expect = np.array([-12.91643003, 0.11905922])
assertion_value = np.allclose(np.asarray(output),expect)
assert assertion_value |
return stats.combine_pvalues(A,'pearson') | output change | stats.combine_pvalues | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.stats.combine_pvalues.html"
] | 1 | 0 | true | [
"scipy.stats.combine_pvalues"
] | 2022-10 | null | |
3.10 | scipy | 1.8.1 |
Write a function that computes the matrix exponential of a sparse array A, non specified parameters should use the default value | from scipy import sparse,linalg
import numpy as np
def compute_matrix_exponential(A:sparse.lil_matrix)->sparse.lil_matrix: | 115 |
A = sparse.lil_matrix((3, 3))
A[0, 0] = 4
A[1, 1] = 5
A[1, 2] = 6
output = compute_matrix_exponential(A)
expect = np.array([
[54.59815003, 0., 0. ],
[ 0., 148.4131591, 176.89579092],
[ 0., 0., 1. ]
])
assertion_value = np.allclose(output.todense(), expect... |
return linalg.expm(A) | name change | linalg | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.sparse.linalg.expm.html"
] | 1 | 0 | true | [
"linalg.expm"
] | 2022-05 | null | |
3.10 | scipy | 1.9.2 |
Write a function that computes the matrix exponential of a sparse array A, non specified parameters should use the default value | from scipy import sparse,linalg
import numpy as np
def compute_matrix_exponential(A: sparse.lil_matrix)->sparse.lil_matrix: | 116 |
A = sparse.lil_matrix((3, 3))
A[0, 0] = 4
A[1, 1] = 5
A[1, 2] = 6
output = compute_matrix_exponential(A)
expect = np.array([
[54.59815003, 0., 0. ],
[ 0., 148.4131591, 176.89579092],
[ 0., 0., 1. ]
])
assertion_value = np.allclose(output.todense(), expect... |
return sparse.linalg.expm(A) | name change | linalg | [
"https://docs.scipy.org/doc/scipy-1.8.1/reference/generated/scipy.sparse.linalg.expm.html"
] | 1 | 0 | true | [
"sparse.linalg.expm"
] | 2022-10 | null | |
3.10 | scipy | 1.8.1 |
Write a function that computes the circular variance: 1-R, where R is the mean resultant vector. | from scipy import stats
import numpy as np
def compute_circular_variance(a: np.ndarray)-> float: | 117 |
a = np.array([0, 2*np.pi/3, 5*np.pi/3])
output = compute_circular_variance(a)
expect = 0.6666666666666665
assertion_value = np.allclose(output,expect)
assert assertion_value |
return 1-np.abs(np.mean(np.exp(1j*a))) | output change | stats.circvar | [
"https://docs.scipy.org/doc/scipy-1.8.1/reference/generated/scipy.stats.circvar.html"
] | 1 | 0 | true | [
"numpy.mean",
"numpy.exp",
"numpy.abs"
] | 2022-05 | null | |
3.10 | scipy | 1.9.2 |
Write a function that computes the circular variance: 1-R, where R is the mean resultant vector. | from scipy import stats
import numpy as np
def compute_circular_variance(a: np.ndarray)-> float: | 118 |
a = np.array([0, 2*np.pi/3, 5*np.pi/3])
output = compute_circular_variance(a)
expect = 0.6666666666666665
assertion_value = np.allclose(output,expect)
assert assertion_value |
return stats.circvar(a) | output change | stats.circvar | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.stats.circvar.html"
] | 1 | 0 | true | [
"scipy.stats.circvar"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the compute_moment function that computes the n-th moment of a distribution dist. |
from scipy.stats import rv_continuous
def compute_moment(dist : rv_continuous, n: int) -> float: | 119 |
from scipy.stats import norm
import numpy as np
dist = norm(15, 10)
n=5
output = compute_moment(dist, n=n)
expect = 6384375.000000001
assertion_value = np.allclose(output,expect)
assert assertion_value |
return dist.moment(order=n) | argument or attribute change | rv_continuous.momentr | [
"https://docs.scipy.org/doc/scipy-1.11.0/reference/generated/scipy.stats.rv_continuous.moment.html"
] | 1 | 0 | true | [
"dist.moment"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the compute_moment function that computes the n-th moment of a distribution dist. |
from scipy.stats import rv_continuous
def compute_moment(dist : rv_continuous, n: int) -> float: | 120 |
from scipy.stats import norm
import numpy as np
dist = norm(15, 10)
n=5
output = compute_moment(dist, n=n)
expect = 6384375.000000001
assertion_value = np.allclose(output,expect)
assert assertion_value |
return dist.moment(n=n) | argument or attribute change | rv_continuous.moment | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.stats.rv_continuous.moment.html"
] | 1 | 0 | true | [
"dist.moment"
] | 2022-10 | null | |
3.10 | scipy | 1.9.2 |
Write a function that computes the determinant of batched matrices (batched in the first dimention), non specified parameters should use the default value | from scipy.linalg import det
import numpy as np
def compute_determinant(A: np.ndarray) -> np.ndarray:
| 121 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape[0])
for i in range(A.shape[0]):
output[i] = det(A[i])
return output | argument or attribute change | scipy.linalg.det | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.linalg.det.html"
] | 1 | 0 | true | [
"range",
"numpy.zeros",
"scipy.linalg.det"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Write a function that computes the determinant of batched matrices (batched in the first dimention), non specified parameters should use the default value | from scipy.linalg import det
import numpy as np
def compute_determinant(A: np.ndarray) -> np.ndarray:
| 122 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return det(A) | argument or attribute change | scipy.linalg.det | [
"https://docs.scipy.org/doc/scipy-1.11.0/reference/generated/scipy.linalg.det.html"
] | 1 | 0 | true | [
"scipy.linalg.det"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the compute_lu_decomposition function that computes the lu decomposition of batched square matrices (batched in the first dimention), non specified parameters should use the default value | from scipy.linalg import lu
import numpy as np
def compute_lu_decomposition(A: np.ndarray) -> tuple[np.ndarray,np.ndarray,np.ndarray]:
# return p, l, u | 123 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
p,l,u = [np.zeros(A.shape) for i in range(3)]
for i in range(A.shape[0]):
p[i],l[i],u[i] = lu(A[i])
return p, l, u | argument or attribute change | scipy.linalg.lu | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/generated/scipy.linalg.lu.html"
] | 1 | 0 | true | [
"range",
"scipy.linalg.lu",
"numpy.zeros"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the compute_lu_decomposition function that computes the lu decomposition of batched square matrices (batched in the first dimention), non specified parameters should use the default value | from scipy.linalg import lu
import numpy as np
def compute_lu_decomposition(A: np.ndarray) -> tuple[np.ndarray,np.ndarray,np.ndarray]:
# return p, l, u | 124 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return lu(A) | argument or attribute change | scipy.linalg.lu | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/generated/scipy.linalg.lu.html"
] | 1 | 0 | true | [
"scipy.linalg.lu"
] | 2023-08 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that returns a lanczos windows, non specified parameters should use the default value | import scipy.signal.windows as windows
import numpy as np
def compute_lanczos_window(window_size:int)->np.ndarray: | 125 |
window_size=31
window = compute_lanczos_window(window_size)
expect = np.array([
3.89817183e-17, 7.09075143e-02, 1.49386494e-01, 2.33872321e-01,
3.22568652e-01, 4.13496672e-01, 5.04551152e-01, 5.93561534e-01,
6.78356039e-01, 7.56826729e-01, 8.26993343e-01, 8.87063793e-01,
9.35489284e-01, 9.71012209e-01,... |
return windows.lanczos(window_size) | other library or new feature | signal.windows.lanczos | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/generated/scipy.signal.windows.lanczos.html"
] | 1 | 0 | true | [
"scipy.signal.windows.lanczos"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that returns a lanczos windows, non specified parameters should use the default value | import scipy.signal.windows as windows
import numpy as np
def compute_lanczos_window(window_size:int)->np.ndarray: | 126 |
window_size=31
window = compute_lanczos_window(window_size)
expect = np.array([
3.89817183e-17, 7.09075143e-02, 1.49386494e-01, 2.33872321e-01,
3.22568652e-01, 4.13496672e-01, 5.04551152e-01, 5.93561534e-01,
6.78356039e-01, 7.56826729e-01, 8.26993343e-01, 8.87063793e-01,
9.35489284e-01, 9.71012209e-01,... |
window = 2*np.arange(window_size)/(window_size-1) - 1
window = np.sinc(window)
window = window / np.max(window)
return window | other library or new feature | signal.windows.lanczos | [
"https://numpy.org/doc/1.20/reference/generated/numpy.sinc.html"
] | 1 | 0 | true | [
"numpy.sinc",
"numpy.max",
"numpy.arange"
] | 2022-10 | null | |
3.10 | scipy | 1.10.1 |
Complete the function that apply a 1D gaussian filter, non specified parameters should use the default value | from scipy.ndimage import gaussian_filter1d
import numpy as np
def apply_gaussian_filter1d(x:np.ndarray, radius:int, sigma:float)->np.ndarray: | 127 |
np.random.seed(42)
x = np.random.rand(100)
radius = 10
sigma= np.pi
output = apply_gaussian_filter1d(x, radius=radius, sigma=sigma)
expect = np.array([
0.56815745, 0.55427942, 0.53042419, 0.50274436, 0.47939431, 0.46608408,
0.46527533, 0.4744513, 0.48749056, 0.49688273, 0.4972386, 0.48550617,
0.46339406,... |
return gaussian_filter1d(x, radius=radius, sigma=sigma) | argument or attribute change | ndimage.gaussian_filter1d | [
"https://docs.scipy.org/doc/scipy-1.10.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.gaussian_filter1d"
] | 2023-02 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that apply a 1D gaussian filter, non specified parameters should use the default value | from scipy.ndimage import gaussian_filter1d
import numpy as np
def apply_gaussian_filter1d(x:np.ndarray, radius:int, sigma:float)->np.ndarray: | 128 |
np.random.seed(42)
x = np.random.rand(100)
radius = 10
sigma= np.pi
output = apply_gaussian_filter1d(x, radius=radius, sigma=sigma)
expect = np.array([
0.56815745, 0.55427942, 0.53042419, 0.50274436, 0.47939431, 0.46608408,
0.46527533, 0.4744513, 0.48749056, 0.49688273, 0.4972386, 0.48550617,
0.46339406,... |
return gaussian_filter1d(x, truncate = radius/sigma,sigma=sigma) | argument or attribute change | ndimage.gaussian_filter1d | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.gaussian_filter1d"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a rank filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import rank_filter
import numpy as np
def apply_rank_filter(A: np.ndarray,rank: int,size:int)->np.ndarray: | 129 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return rank_filter(A,rank,size=size,axes=[1,2]) | argument or attribute change | scipy.ndimage.rank_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.rank_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a rank filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import rank_filter
import numpy as np
def apply_rank_filter(A: np.ndarray,rank: int,size:int)->np.ndarray: | 130 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = rank_filter(A[i],rank,size=size)
return output | argument or attribute change | scipy.ndimage.rank_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"numpy.zeros",
"scipy.ndimage.rank_filter"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a percentile filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import percentile_filter
import numpy as np
def apply_percentile_filter(A: np.ndarray, percentile: int | float,size:int)->np.ndarray: | 131 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return percentile_filter(A,percentile=percentile,size=size,axes=[1,2]) | argument or attribute change | scipy.ndimage.percentile_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.percentile_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a percentile filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import percentile_filter
import numpy as np
def apply_percentile_filter(A: np.ndarray, percentile: int | float,size:int)->np.ndarray: | 132 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = percentile_filter(A[i],percentile=percentile,size=size)
return output | argument or attribute change | scipy.ndimage.percentile_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"numpy.zeros",
"scipy.ndimage.percentile_filter"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a median filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import median_filter
import numpy as np
def apply_median_filter(A: np.ndarray,size:int) -> np.ndarray: | 133 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return median_filter(A,size=size,axes=[1,2]) | argument or attribute change | scipy.ndimage.median_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.median_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a median filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import median_filter
import numpy as np
def apply_median_filter(A: np.ndarray, size:int) -> np.ndarray: | 134 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = median_filter(A[i], size=size)
return output | argument or attribute change | scipy.ndimage.median_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"scipy.ndimage.median_filter",
"numpy.zeros"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a uniform filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import uniform_filter
import numpy as np
def apply_uniform_filter(A: np.ndarray, size: int) -> np.ndarray: | 135 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return uniform_filter(A,size=size,axes=[1,2]) | argument or attribute change | scipy.ndimage.uniform_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.uniform_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a uniform filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import uniform_filter
import numpy as np
def apply_uniform_filter(A: np.ndarray, size: int) -> np.ndarray:
| 136 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = uniform_filter(A[i], size=size)
return output | argument or attribute change | scipy.ndimage.uniform_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"numpy.zeros",
"scipy.ndimage.uniform_filter"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a minimum filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import minimum_filter
import numpy as np
def apply_minimum_filter(A: np.ndarray, size: int) -> np.ndarray:
| 137 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return minimum_filter(A,size=size,axes=[1,2]) | argument or attribute change | scipy.ndimage.minimum_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.minimum_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a minimum filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import minimum_filter
import numpy as np
def apply_minimum_filter(A: np.ndarray, size: int) -> np.ndarray:
| 138 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = minimum_filter(A[i], size=size)
return output | argument or attribute change | scipy.ndimage.minimum_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"scipy.ndimage.minimum_filter",
"numpy.zeros"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a maximum filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import maximum_filter
import numpy as np
def apply_maximum_filter(A: np.ndarray, size: int) -> np.ndarray:
| 139 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return maximum_filter(A,size=size,axes=[1,2]) | argument or attribute change | scipy.ndimage.maximum_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.maximum_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a maximum filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import maximum_filter
import numpy as np
def apply_maximum_filter(A: np.ndarray, size: int) -> np.ndarray:
| 140 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = maximum_filter(A[i], size=size)
return output | argument or attribute change | scipy.ndimage.maximum_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"numpy.zeros",
"scipy.ndimage.maximum_filter"
] | 2022-10 | null | |
3.10 | scipy | 1.11.2 |
Complete the function that applies a gaussian filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import gaussian_filter
import numpy as np
def apply_gaussian_filter(A: np.ndarray, sigma: float) -> np.ndarray:
| 141 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
return gaussian_filter(A,sigma=sigma,axes=[1,2]) | argument or attribute change | scipy.ndimage.gaussian_filter | [
"https://docs.scipy.org/doc/scipy-1.11.1/reference/ndimage.html"
] | 1 | 0 | true | [
"scipy.ndimage.gaussian_filter"
] | 2023-08 | null | |
3.10 | scipy | 1.9.2 |
Complete the function that applies a gaussian filter on batched images (batched in the first dimention), non specified parameters should use the default value | from scipy.ndimage import gaussian_filter
import numpy as np
def apply_gaussian_filter(A: np.ndarray, sigma: float) -> np.ndarray:
| 142 |
A = np.array([[[7.81411439e-01, 1.12331105e-02, 9.39679607e-01, 6.42515536e-01,
6.95548884e-01],
[4.58001321e-01, 7.90049557e-01, 7.41113873e-01, 9.85894466e-03,
4.53976568e-01],
[2.24634401e-01, 4.26973301e-01, 8.91014236e-01, 8.23895514e-01,
9.31046059e-01],
[4.4585... |
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = gaussian_filter(A[i],sigma=sigma)
return output | argument or attribute change | scipy.ndimage.gaussian_filter | [
"https://docs.scipy.org/doc/scipy-1.9.2/reference/ndimage.html"
] | 1 | 0 | true | [
"range",
"numpy.zeros",
"scipy.ndimage.gaussian_filter"
] | 2022-10 | null | |
3.10 | flask | 2.0.0 |
Complete the app_set_up function that set-up the app in such manner that the json encoding returns a sorted list
when called with the eval function provided with the variable
num_set being a set of numbers, use other libraries if needed. Do not run the app in your code. | import flask
app = flask.Flask('test')
@app.route('/data')
def data(num_set):
return flask.jsonify({'numbers': num_set})
def eval(app, data_fn, num_set):
with app.test_request_context():
response = data_fn(num_set)
return response.get_data(as_text=False)
def app_set_up(app: flask.Flask) -> No... | 143 |
import json
app_set_up(app)
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_set):
return flask.jsonify({'numbers': num_set})
class MyCustomJSONHandler2(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, set):
return sorted(list(obj))
return super().default... |
import json
class MyCustomJSONHandler(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, set):
return sorted(list(obj))
return super().default(obj)
app.json_encoder = MyCustomJSONHandler
| argument or attribute change | app.json_encoder | werkzeug==2.0.0 | [
"https://tedboy.github.io/flask/interface_api.json_support.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"default",
"isinstance",
"sorted",
"super",
"list",
"MyCustomJSONHandler"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the app_set_up function that set-up the app in such manner that the json encoding returns a sorted list
when called with the eval function provided with the variable
num_set being a set of numbers, use other libraries if needed. Do not run the app in your code. | import flask
app = flask.Flask('test')
@app.route('/data')
def data(num_set):
return flask.jsonify({'numbers':num_set})
def eval(app, data_fn, num_set):
with app.test_request_context():
response = data_fn(num_set)
return response.get_data(as_text=True)
def app_set_up(app: flask.Flask) -> None... | 144 |
import json
app_set_up(app)
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_set):
return flask.jsonify({'numbers': num_set})
class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
def default(self, obj):
if isinstance(obj, set):
return sorted(list(obj))
... |
class MyCustomJSONHandler(flask.json.provider.DefaultJSONProvider):
def default(self, obj):
if isinstance(obj, set):
return sorted(list(obj))
return super().default(obj)
app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app)
| argument or attribute change | app.json_encoder | [
"https://tedboy.github.io/flask/interface_api.json_support.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"default",
"isinstance",
"sorted",
"super",
"app.json_provider_class",
"list",
"MyCustomJSONHandler"
] | 2023-09 | null | |
3.10 | flask | 2.0.0 |
Complete the download definition to download the data in the variable attachment_filename. Do not run the app in your code. | from flask import Flask, send_file
from io import BytesIO
app1 = Flask(__name__)
def get_content_disp(app, download_fn):
with app.test_request_context():
response = download_fn()
content_disp = response.headers.get('Content-Disposition')
return content_disp
@app1.route('/download')
def download()... | 145 |
content_disp = get_content_disp(app1, download)
assertion_result = 'filename=hello.txt' in content_disp
assert assertion_result
| attachment_filename=attachment_filename)
| argument or attribute change | flask.send_file | werkzeug==2.0.0 | [
"https://tedboy.github.io/flask/generated/flask.send_file.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 0 | 1 | true | [] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the download definition to download the data in the variable attachment_filename. Do not run the app in your code. | from flask import Flask, send_file
from io import BytesIO
app1 = Flask(__name__)
def get_content_disp(app, download_fn):
with app.test_request_context():
response = download_fn()
content_disp = response.headers.get('Content-Disposition')
return content_disp
@app1.route('/download')
def download()... | 146 |
content_disp = get_content_disp(app1, download)
assertion_result = 'filename=hello.txt' in content_disp
assert assertion_result
| download_name=attachment_filename)
| argument or attribute change | flask.send_file | [
"https://tedboy.github.io/flask/generated/flask.send_file.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 0 | 1 | true | [] | 2023-09 | null | |
3.10 | flask | 2.0.1 |
Implement the function to load the JSON file to config the Flask app. Do not run the app in your code. | import json
import tempfile
from flask import Flask
config_data = {'DEBUG': True, 'SECRET_KEY': 'secret'}
with tempfile.NamedTemporaryFile(mode='w+', delete=False, suffix='.json') as tmp:
json.dump(config_data, tmp)
tmp.flush()
config_file = tmp.name
app = Flask(__name__)
def load_config(config_file: str... | 147 | load_config(config_file)
assertion_result= app.config['DEBUG'] is True and app.config['SECRET_KEY'] == 'secret'
assert assertion_result | app.config.from_json(config_file)
| name change | Flask.config.from_json | werkzeug==2.0.0 | [
"https://flask.palletsprojects.com/en/stable/config/",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"app.config.from_json"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Load the json file to config the Flask app. Do not run the app in your code. | import json
import tempfile
from flask import Flask
config_data = {'DEBUG': True, 'SECRET_KEY': 'secret'}
with tempfile.NamedTemporaryFile(mode='w+', delete=False, suffix='.json') as tmp:
json.dump(config_data, tmp)
tmp.flush()
config_file = tmp.name
app = Flask(__name__)
def load_config(config_file: str... | 148 | load_config(config_file)
assertion_result= app.config['DEBUG'] is True and app.config['SECRET_KEY'] == 'secret'
assert assertion_result
| app.config.from_file(config_file, load=json.load)
| name change | Flask.config.from_file | [
"https://flask.palletsprojects.com/en/stable/config/",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"app.config.from_file"
] | 2023-09 | null | |
3.10 | flask | 2.0.1 |
Complete the safe_join_fail_404 to safely join the path and the subpath, use other libraries if needed. Do not run the app in your code. | import flask
import werkzeug
error404 = werkzeug.exceptions.NotFound
def safe_join_fail_404(base_path: str, sub_path: str) -> str:
# Attempt to join the base path and sub path.
# If the joined path is outside the base path, raise a 404 error.
| 149 |
base_path = '/var/www/myapp'
sub_path = '../secret.txt'
try :
joined = safe_join_fail_404(base_path, sub_path)
except werkzeug.exceptions.NotFound as e:
assertion_result = True
else:
assertion_result = False
assert assertion_result
base_path = '/var/www/myapp'
sub_path = 'secret.txt'
joined = safe_join_... |
joined = flask.safe_join(base_path, sub_path)
return joined
| name change | flask.safe_join | werkzeug==2.0.0 | [
"https://tedboy.github.io/flask/generated/flask.safe_join.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"flask.safe_join"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the safe_join_fail_404 to safely join the path and the subpath, use other libraries if needed. Do not run the app in your code. | import flask
import werkzeug
error404 = werkzeug.exceptions.NotFound
def safe_join_fail_404(base_path: str, sub_path: str) -> str:
# Attempt to join the base path and sub path.
# If the joined path is outside the base path, raise a 404 error.
| 150 |
base_path = '/var/www/myapp'
sub_path = '../secret.txt'
try :
joined = safe_join_fail_404(base_path, sub_path)
except werkzeug.exceptions.NotFound as e:
assertion_result = True
else:
assertion_result = False
assert assertion_result
base_path = '/var/www/myapp'
sub_path = 'secret.txt'
joined = safe_join_... |
joined = werkzeug.utils.safe_join(base_path, sub_path)
if joined is None:
raise error404
return joined
| name change | flask.safe_join | [
"https://werkzeug.palletsprojects.com/en/stable/utils/",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"werkzeug.utils.safe_join"
] | 2023-09 | null | |
3.10 | flask | 2.0.1 |
Complete the function convert_timedelta_to_seconds, use other libraries if needed. Do not run the app in your code. | import flask
import datetime
def convert_timedelta_to_seconds(td: datetime.timedelta) -> int:
| 151 |
import datetime
td = datetime.timedelta(hours=2, minutes=30,microseconds=1)
assertion_results = convert_timedelta_to_seconds(td)==9000
assert assertion_results |
return flask.helpers.total_seconds(td)
| name change | helpers.total_seconds | werkzeug==2.0.0 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"flask.helpers.total_seconds"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the function convert_timedelta_to_seconds, use other libraries if needed. Do not run the app in your code. | import flask
import datetime
def convert_timedelta_to_seconds(td: datetime.timedelta):
| 152 |
import datetime
td = datetime.timedelta(hours=2, minutes=30,microseconds=1)
assertion_results = convert_timedelta_to_seconds(td)==9000.000001
assert assertion_results |
return td.total_seconds()
| name change | helpers.total_seconds | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"td.total_seconds"
] | 2023-09 | null | |
3.10 | jinja2 | 2.11 |
Implement the solution function to return a custom filter. This filter should:
accept the context and a parameter name,
retrieve a variable prefix from the context (defaulting to 'Hello' if not provided),
return a greeting message combining the prefix and the name. Do not run the app in your code. | import jinja2
from jinja2.runtime import Context
from typing import Callable
def setup_environment(filtername: str, filter: Callable[[Context, str], str]) -> jinja2.Environment:
env = jinja2.Environment()
env.filters[filtername] = filter
return env
def solution() -> Callable[[Context, str], str]:
| 153 | greet = solution()
env = setup_environment('greet',greet)
template = env.from_string('''
{{ 'World'| greet }}''')
assertion_results = 'Hi, World!' in template.render(prefix='Hi')
assert assertion_results
assertion_results = 'Hello, World!' in template.render()
assert assertion_results
| @jinja2.contextfilter
def greet(ctx, name):
prefix = ctx.get('prefix', 'Hello')
return f'{prefix}, {name}!'
return greet | name change | jinja2.contextfilter | markupsafe==2.0.1 | [
"https://jinja.palletsprojects.com/en/stable/api/",
"https://jinja.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"greet",
"ctx.get"
] | 2020-01 | null |
3.10 | jinja2 | 3.1 |
Implement the solution function to return a custom filter. This filter should:
accept the context and a parameter name,
retrieve a variable prefix from the context (defaulting to 'Hello' if not provided),
return a greeting message combining the prefix and the name.. Do not run the app in your code. | import jinja2
from jinja2.runtime import Context
from typing import Callable
def setup_environment(filtername: str,filter) -> jinja2.Environment:
env = jinja2.Environment()
env.filters[filtername] = filter
return env
def solution() -> Callable[[Context, str], str]:
| 154 | greet = solution()
env = setup_environment('greet',greet)
template = env.from_string('''
{{ 'World'| greet }}''')
assertion_results = 'Hi, World!' in template.render(prefix='Hi')
assert assertion_results
assertion_results = 'Hello, World!' in template.render()
assert assertion_results
| @jinja2.pass_context
def greet(ctx, name):
prefix = ctx.get('prefix', 'Hello')
return f'{prefix}, {name}!'
return greet | name change | jinja2.pass_context | [
"https://jinja.palletsprojects.com/en/stable/api/",
"https://jinja.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"greet",
"ctx.get"
] | 2022-03 | null | |
3.10 | jinja2 | 2.11 |
Implement the nl2br function which is a custom filter that takes the evaluation context
and a text value, then replaces every occurrence of the substring 'Hello'
with the HTML string '<br>Hello</br>', while respecting the autoescape setting.
You can use the nl2br_core function. Do not run the app in your code. | import re
from jinja2 import Environment, evalcontextfilter
from markupsafe import Markup, escape
from jinja2.runtime import Context
from typing import Callable
def get_output(env, filter_fn):
env.filters['nl2br'] = filter_fn
template = env.from_string('{{ text | nl2br }}')
output = template.render(text='H... | 155 | nl2br = solution()
env = Environment(autoescape=True)
output = get_output(env,nl2br)
expected = '<br>Hello</br> World'
assert output == expected | @evalcontextfilter
def nl2br(eval_ctx, value):
return nl2br_core(eval_ctx, value)
return nl2br
| name change | jinja2.evalcontextfilter | markupsafe==2.0.1 | [
"https://jinja.palletsprojects.com/en/stable/api/",
"https://jinja.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"nl2br",
"nl2br_core"
] | 2020-01 | null |
3.10 | jinja2 | 3.1 |
Write the nl2br function which is a custom filter that takes the evaluation context
and a text value, then replaces every occurrence of the substring 'Hello'
with the HTML string '<br>Hello</br>', while respecting the autoescape setting.
You can use the nl2br_core function. Do not run the app in your code. | import re
from jinja2 import Environment, pass_eval_context
from markupsafe import Markup, escape
from typing import Callable, Union
from jinja2.runtime import EvalContext
def get_output(env, filter_fn):
env.filters['nl2br'] = filter_fn
template = env.from_string('{{ text | nl2br }}')
output = template.ren... | 156 |
nl2br_filter = solution()
env = Environment(autoescape=True)
output = get_output(env,nl2br_filter)
expected = '<br>Hello</br> World'
assert output == expected
|
@pass_eval_context
def nl2br(eval_ctx, value):
return nl2br_core(eval_ctx, value)
return nl2br | name change | jinja2.pass_eval_context | [
"https://jinja.palletsprojects.com/en/stable/api/",
"https://jinja.palletsprojects.com/en/stable/changes/"
] | 1 | 1 | true | [
"nl2br",
"nl2br_core"
] | 2022-03 | null | |
3.10 | scipy | 1.11.1 |
complete the following function that check if all the batch of matrices are invertible, using numpy 1.25.1. | import warnings
from scipy.linalg import det
import numpy as np
warnings.filterwarnings('error')
def check_invertibility(matrices: np.ndarray) -> np.bool_: | 157 |
matrices = np.array([
[[1, 2],
[3, 4]],
[[0, 1],
[1, 0]],
[[2, 0],
[0, 2]]
])
assertion_value = check_invertibility(matrices)
assert assertion_value
matrices = np.array([
[[1, 2],
[3, 4]],
[[0, 1],
[1, 0]],
[[2, 0],
[0, 2]],
[[0, 0],
[... |
return np.all(det(matrices))
| argument or attribute change | linalg.det | numpy==1.25.1 | [
"https://docs.scipy.org/doc/scipy/release/1.11.1-notes.html"
] | 1 | 0 | true | [
"numpy.all",
"scipy.linalg.det"
] | 2023-06 | null |
3.10 | scipy | 1.9.1 |
complete the following function that check if all the batch of matrices are invertible, using numpy 1.21.6 | import warnings
from scipy.linalg import det
import numpy as np
warnings.filterwarnings('error')
def check_invertibility(matrices : np.ndarray) -> np.bool_ : | 158 |
matrices = np.array([
[[1, 2],
[3, 4]],
[[0, 1],
[1, 0]],
[[2, 0],
[0, 2]]
])
assertion_value = check_invertibility(matrices)
assert assertion_value
matrices = np.array([
[[1, 2],
[3, 4]],
[[0, 1],
[1, 0]],
[[2, 0],
[0, 2]],
[[0, 0],
[... |
return np.alltrue([det(A) for A in matrices])
| argument or attribute change | linalg.det | numpy==1.21.6 | [
"https://docs.scipy.org/doc/scipy/release/1.9.1-notes.html"
] | 1 | 0 | true | [
"numpy.alltrue",
"scipy.linalg.det"
] | 2022-10 | null |
3.10 | scipy | 1.11.1 |
Complete the function count_unique_hmean that takes a 2D numpy array as
input and returns the number of unique harmonic mean values across
the rows of the array, counting each nan value as unique. We are using numpy 1.25.1 | import numpy as np
from scipy.stats import hmean
def count_unique_hmean(data: np.ndarray) -> int:
# data shape: (n, m)
# n: number of arrays
# m: number of elements in each array | 159 |
data = np.array([
[1, 2, 3],
[2, 2, 2],
[1, np.nan, 3],
[4, 5, 6],
[np.nan, 1, np.nan],
[1, 2, 3]
])
assertion_value = count_unique_hmean(data) == 5
assert assertion_value
|
hmean_values = hmean(np.asarray(data), axis=1)
unique_vals = np.unique(hmean_values, equal_nan=False).shape[0]
return unique_vals
| output change | stats.hmean | numpy==1.25.1 | [
"https://docs.scipy.org/doc/scipy/release/1.11.1-notes.html"
] | 1 | 0 | true | [
"scipy.stats.hmean",
"numpy.unique",
"numpy.asarray"
] | 2023-06 | null |
3.10 | scipy | 1.8.1 |
Complete the function count_unique_hmean that takes a 2D numpy array as
input and returns the number of unique harmonic mean values across
the rows of the array, counting each nan value as unique. We are using numpy 1.21.6 | import numpy as np
from scipy.stats import hmean
def count_unique_hmean(data: np.ndarray) -> int:
# data shape: (n, m)
# n: number of arrays
# m: number of elements in each array | 160 |
data = np.array([
[1, 2, 3],
[2, 2, 2],
[1, np.nan, 3],
[4, 5, 6],
[np.nan, 1, np.nan],
[1, 2, 3]
])
assertion_value = count_unique_hmean(data) == 5
assert assertion_value
|
hmean_values = []
for arr in data:
if np.isnan(arr).any():
hm = np.nan
else:
hm = hmean(arr)
hmean_values.append(hm)
hmean_values = np.asarray(hmean_values)
non_nan_vals = hmean_values[~np.isnan(hmean_values)]
counts_non_nan = np.unique(non_nan_v... | output change | stats.hmean | numpy==1.21.6 | [
"https://docs.scipy.org/doc/scipy/release/1.8.1-notes.html"
] | 1 | 0 | true | [
"scipy.stats.hmean",
"hmean_values.append",
"numpy.isnan",
"numpy.unique",
"numpy.sum",
"numpy.asarray",
"any"
] | 2022-05 | null |
3.10 | scipy | 1.11.1 |
Complete the function compute_hilbert_transform. We are using numpy 1.25.1 | import numpy as np
from scipy.signal import hilbert
def compute_hilbert_transform(a, b, dtype=np.float64):
# compute_hilbert_transform should return the Hilbert transform of the
# a and b arrays stacked vertically, with safe casting and the specified
# dtype.
# raise TypeError if needed
| 161 |
a = np.array([1.0, 2.0, 3.0], dtype=np.float32)
b = np.array([4.0, 5.0, 6.0], dtype=np.float64)
assertion_value = False
try :
compute_hilbert_transform(a, b, dtype=np.float32)
except TypeError:
assertion_value = True
assert assertion_value
b=b.astype(np.float32)
computed = compute_hilbert_transform(a, b, dtyp... |
stacked = np.vstack((a, b), dtype=dtype,
casting='safe')
return hilbert(stacked)
| argument or attribute change | signal.hilbert | numpy==1.25.1 | [
"https://docs.scipy.org/doc/scipy/release/1.11.1-notes.html"
] | 1 | 0 | true | [
"scipy.signal.hilbert",
"numpy.vstack"
] | 2023-06 | null |
3.10 | scipy | 1.8.1 |
Complete the function compute_hilbert_transform. We are using numpy 1.25.1 | import numpy as np
from scipy.signal import hilbert
def compute_hilbert_transform(a: np.ndarray, b: np.ndarray, dtype=np.float64) -> np.ndarray:
# compute_hilbert_transform should return the Hilbert transform of the
# a and b arrays stacked vertically, with safe casting and the specified
# dtype.
# rai... | 162 |
a = np.array([1.0, 2.0, 3.0], dtype=np.float32)
b = np.array([4.0, 5.0, 6.0], dtype=np.float64)
assertion_value = False
try :
compute_hilbert_transform(a, b, dtype=np.float32)
except TypeError:
assertion_value = True
assert assertion_value
b=b.astype(np.float32)
computed = compute_hilbert_transform(a, b, dtype... |
if not (np.can_cast(a.dtype, dtype, casting='safe') and np.can_cast(b.dtype, dtype, casting='safe')):
raise TypeError('Unsafe casting from input dtype to specified dtype')
a_cast = a.astype(dtype, copy=False)
b_cast = b.astype(dtype, copy=False)
stacked = np.vstack((a_cast, b_cast))
... | argument or attribute change | signal.hilbert | numpy==1.21.6 | [
"https://docs.scipy.org/doc/scipy/release/1.8.1-notes.html"
] | 1 | 0 | true | [
"b.astype",
"TypeError",
"scipy.signal.hilbert",
"numpy.vstack",
"a.astype",
"not",
"numpy.can_cast",
"result.astype"
] | 2022-05 | null |
3.10 | flask | 2.0.0 |
Complete the app set-up for the json encoding to return only the unique values (each NaN being a different value) contained
in the numpy array when called, we are using numpy 1.21.6. Do not run the app in your code. | import flask
import json
import numpy as np
app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
return flask.jsonify({'numbers': num_arr})
def eval(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return response.get_data(as_text=False)
class M... | 163 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
n_nan = np.sum(np.isnan(obj))
unique_vals = obj[~np.isnan(obj)]
... |
n_nan = np.sum(np.isnan(obj))
unique_vals = obj[~np.isnan(obj)]
unique_vals = np.append(np.unique(unique_vals), [np.nan]*n_nan).tolist()
return unique_vals
return super().default(obj)
app.json_encoder = MyCustomJSONHandler
| argument or attribute change | app.json_encoder | numpy==1.21.6 werkzeug==2.0.0 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://numpy.org/devdocs/release/1.21.0-notes.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 0 | 1 | true | [
"numpy.append",
"default",
"numpy.unique",
"numpy.isnan",
"numpy.sum",
"tolist",
"super"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the app set-up for the json encoding to return only the unique values (each NaN being a different value) contained
in the numpy array when called, we are using numpy 1.25.1. Do not run the app in your code. | import flask
import numpy as np
app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
return flask.jsonify({'numbers': num_arr})
def eval_app(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return response.get_data(as_text=True)
class MyCustom... | 164 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
def default(self, obj):
if isinstance(obj, np.ndarray):
n_nan = np.sum(np.isnan(obj))
unique_va... |
if isinstance(obj, np.ndarray):
unique_vals = np.unique(obj, equal_nan=False)
return unique_vals.tolist()
return super().default(obj)
app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app)
| argument or attribute change | app.json_encoder | numpy==1.25.1 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://numpy.org/doc/2.2/release/1.25.0-notes.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 0 | 1 | true | [
"default",
"isinstance",
"numpy.unique",
"unique_vals.tolist",
"super",
"app.json_provider_class"
] | 2023-09 | null |
3.10 | flask | 2.0.0 |
Complete the app set-up for the json encoding to perform a fast copy and
transpose when given a numpy array before flattening and converting
the result to a list,we are using numpy 1.21.6. Do not run the app in your code. | import flask
import json
import numpy as np
from numpy import fastCopyAndTranspose
app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
return flask.jsonify({'numbers': num_arr})
def eval(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return r... | 165 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
res = obj.T.copy().flatten().tolist()
return res
return ... |
res = fastCopyAndTranspose(obj).flatten().tolist()
return res
return super().default(obj)
app.json_encoder = MyCustomJSONHandler
| argument or attribute change | app.json_encoder | numpy==1.21.6 werkzeug==2.0.0 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://numpy.org/devdocs/release/1.24.0-notes.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 0 | 1 | true | [
"default",
"numpy.fastCopyAndTranspose",
"flatten",
"tolist",
"super"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the app set-up for the json encoding to perform a fast copy and
transpose when given a numpy array before flattening and converting
the result to a list, we are using numpy 1.25.1. Do not run the app in your code. | import flask
import numpy as np
import warnings
from numpy import fastCopyAndTranspose
warnings.filterwarnings('error')
app = flask.Flask('test1')
@app.route('/data')
def data(num_list):
return flask.jsonify({'numbers': num_list})
def eval_app(app, data_fn, num_arr):
with app.test_request_context():
... | 166 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
def default(self, obj):
if isinstance(obj, np.ndarray):
res = obj.T.copy().flatten().tolist()
r... |
res = obj.T.copy().flatten().tolist()
return res
return super().default(obj)
app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app)
| argument or attribute change | app.json_encoder | numpy==1.25.1 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://numpy.org/doc/2.2/release/1.25.0-notes.html",
"https://flask.palletsprojects.com/en/stable/changes/"
] | 0 | 1 | true | [
"default",
"obj.T.copy",
"flatten",
"tolist",
"super",
"app.json_provider_class"
] | 2023-09 | null |
3.10 | flask | 2.0.0 |
Complete the stack_and_save function, we are using numpy 1.21.6. Do not run the app in your code. | import flask
import werkzeug
import numpy as np
error404 = werkzeug.exceptions.NotFound
def stack_and_save(arr_list: list[np.ndarray],base_path : str,sub_path : str, casting_policy: str, out_dtype: type) -> tuple[str, np.ndarray]:
# Attempt to join the base path and sub path.
# If the joined path is outside t... | 167 |
base_path = '/var/www/myapp'
sub_path = '../secret.txt'
a = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
b = np.array([[7, 8, 9], [10, 11, 12]]).astype(np.float64)
arr_list = [a, b]
casting_policy = 'safe'
out_dtype=np.float64
stacked_correct = np.vstack(arr_list).astype(np.float64)
try :
joined, stacked ... |
joined = flask.safe_join(base_path, sub_path)
casted_list = []
for arr in arr_list:
if not np.can_cast(arr.dtype, out_dtype, casting=casting_policy):
raise TypeError('Cannot cast array')
casted_list.append(arr.astype(out_dtype, copy=False))
stacked = np.vstack(casted_l... | name change | flask.safe_join | numpy==1.21.6 werkzeug==2.0.0 | [
"https://tedboy.github.io/flask/werk_doc.tutorial.html",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://numpy.org/devdocs/release/1.21.0-notes.html"
] | 1 | 1 | true | [
"flask.safe_join",
"TypeError",
"arr.astype",
"numpy.vstack",
"casted_list.append",
"numpy.can_cast"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the stack_and_save function, we are using numpy 1.25.1. Do not run the app in your code. | import flask
import werkzeug
import numpy as np
error404 = werkzeug.exceptions.NotFound
def stack_and_save(arr_list: list[np.ndarray],base_path : str,sub_path : str, casting_policy: str, out_dtype: type) -> tuple[str, np.ndarray]:
# Attempt to join the base path and sub path.
# If the joined path is outside t... | 168 |
base_path = '/var/www/myapp'
sub_path = '../secret.txt'
a = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
b = np.array([[7, 8, 9], [10, 11, 12]]).astype(np.float64)
arr_list = [a, b]
casting_policy = 'safe'
out_dtype=np.float64
stacked_correct = np.vstack(arr_list).astype(np.float64)
try :
joined, stacked... |
joined = werkzeug.utils.safe_join(base_path, sub_path)
if joined is None:
raise error404
stacked = np.vstack(arr_list,casting=casting_policy,dtype=out_dtype)
return joined, stacked
| name change | flask.safe_join | numpy==1.25.1 | [
"https://tedboy.github.io/flask/werk_doc.tutorial.html",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://numpy.org/doc/2.2/release/1.25.0-notes.html"
] | 1 | 1 | true | [
"numpy.vstack",
"werkzeug.utils.safe_join"
] | 2023-09 | null |
3.10 | flask | 3.0.0 |
Complete the app set-up so that, when given a batch of matrix,
the json encoding compute the determinants of each matrix,
before flattening and converting the result to a list, we are using scipy 1.11.1. Do not run the app in your code. | import flask
import numpy as np
from scipy import linalg
app = flask.Flask('test1')
@app.route('/data')
def data(num_list):
return flask.jsonify({'numbers': num_list})
def eval_app(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return response.get_data(as_t... | 169 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
def default(self, obj):
if isinstance(obj, np.ndarray) and len(obj.shape)==3 and obj.shape[-1]==obj.shape[-2] :
... |
res = linalg.det(obj)
return res.tolist()
return super().default(obj)
app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app) | argument or attribute change | app.json_encoder | scipy==1.11.1 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://docs.scipy.org/doc/scipy/release/1.11.1-notes.html"
] | 0 | 1 | true | [
"default",
"scipy.linalg.det",
"res.tolist",
"super",
"app.json_provider_class"
] | 2023-09 | null |
3.10 | flask | 2.0.0 |
Complete the app set-up so that, when given a batch of matrix,
the json encoding compute the determinants of each matrix,
before flattening and converting the result to a list, we are using scipy 1.8.1. Do not run the app in your code. | import flask
import json
import numpy as np
from scipy import linalg
app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
return flask.jsonify({'numbers': num_arr})
def eval(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return response.get_da... | 170 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray) and len(obj.shape)==3 and obj.shape[-1]==obj.shape[-2] :
res = np.zeros(obj.s... |
res = np.zeros(obj.shape[0])
for i in range(obj.shape[0]):
res[i] = linalg.det(obj[i])
return res.tolist()
return super().default(obj)
app.json_encoder = MyCustomJSONHandler
| argument or attribute change | app.json_encoder | scipy==1.8.1 Werkzeug==2.0.0 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://docs.scipy.org/doc/scipy/release/1.8.1-notes.html"
] | 0 | 1 | true | [
"default",
"scipy.linalg.det",
"numpy.zeros",
"range",
"res.tolist",
"super"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the app set-up so that, when given a batch (first dim) of values,
compute the harmonic mean along the second dimension (It should handle nan values),
before flattening and converting the result to a list, we are using scipy 1.11.1. Do not run the app in your code. | import flask
import numpy as np
from scipy.stats import hmean
app = flask.Flask('test1')
@app.route('/data')
def data(num_list):
return flask.jsonify({'numbers': num_list})
def eval_app(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return response.get_da... | 171 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(flask.json.provider.DefaultJSONProvider):
def default(self, obj):
if isinstance(obj, np.ndarray):
res = hmean(obj,axis=1).tolist()
return... |
res = hmean(obj,axis=1).tolist()
return res
return super().default(obj)
app.json_provider_class = MyCustomJSONHandler
app.json = app.json_provider_class(app)
| argument or attribute change | app.json_encoder | scipy==1.11.1 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://docs.scipy.org/doc/scipy/release/1.11.1-notes.html"
] | 0 | 1 | true | [
"default",
"scipy.stats.hmean",
"tolist",
"super",
"app.json_provider_class"
] | 2023-09 | null |
3.10 | flask | 2.0.0 |
Complete the app set-up so that, when given a batch (first dim) of values,
compute the harmonic mean along the second dimension (It should handle nan values),
before flattening and converting the result to a list, we are using scipy 1.8.1. Do not run the app in your code. | import flask
import json
import numpy as np
from scipy.stats import hmean
app = flask.Flask('test1')
@app.route('/data')
def data(num_arr):
return flask.jsonify({'numbers': num_arr})
def eval(app, data_fn, num_arr):
with app.test_request_context():
response = data_fn(num_arr)
return response.g... | 172 |
app2 = flask.Flask('test2')
@app2.route('/data2')
def data2(num_arr):
return flask.jsonify({'numbers': num_arr})
class MyCustomJSONHandler2(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
res = np.zeros((obj.shape[0],1))
for i_arr in range(obj.shape[0]... |
res = np.zeros((obj.shape[0],1))
for i_arr in range(obj.shape[0]):
if np.isnan(obj[i_arr]).any():
res[i_arr] = np.nan
else:
res[i_arr] = hmean(obj[i_arr])
res = res.flatten().tolist()
return res
... | argument or attribute change | app.json_encoder | scipy==1.8.1 Werkzeug==2.0.0 | [
"https://flask.palletsprojects.com/en/stable/api/",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://docs.scipy.org/doc/scipy/release/1.8.1-notes.html"
] | 0 | 1 | true | [
"res.flatten",
"tolist",
"default",
"scipy.stats.hmean",
"numpy.zeros",
"numpy.isnan",
"range",
"any",
"super"
] | 2021-05 | null |
3.10 | flask | 3.0.0 |
Complete the save_exponential function, we are using scipy 1.11.1. Do not run the app in your code. | import flask
import werkzeug
from scipy import linalg
import numpy as np
error404 = werkzeug.exceptions.NotFound
def save_exponential(A: np.ndarray, base_path: str, sub_path: str) -> tuple[str, np.ndarray]:
# Attempt to join the base path and sub path.
# If the joined path is outside the base path, raise a 40... | 173 |
base_path = '/var/www/myapp'
sub_path = '../secret.txt'
import numpy as np
a = np.random.random((4,3,3))
expected = np.zeros(a.shape)
for i in range(expected.shape[0]):
expected[i] = linalg.expm(a[i])
try :
joined, results = save_exponential(a,base_path, sub_path)
except werkzeug.exceptions.NotFound as e:
... |
joined = werkzeug.utils.safe_join(base_path, sub_path)
if joined is None:
raise error404
output = linalg.expm(A)
return joined, output
| name change | flask.safe_join | scipy==1.11.1 | [
"https://tedboy.github.io/flask/werk_doc.tutorial.html",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://docs.scipy.org/doc/scipy/release/1.11.1-notes.html"
] | 1 | 1 | true | [
"scipy.linalg.expm",
"werkzeug.utils.safe_join"
] | 2023-09 | null |
3.10 | flask | 2.0.0 |
Complete the save_exponential function, we are using scipy 1.8.1. Do not run the app in your code. | import flask
import werkzeug
from scipy import linalg
import numpy as np
error404 = werkzeug.exceptions.NotFound
def save_exponential(A: np.ndarray, base_path: str, sub_path: str) -> tuple[str, np.ndarray]:
# Attempt to join the base path and sub path.
# If the joined path is outside the base path, raise a 40... | 174 |
base_path = '/var/www/myapp'
sub_path = '../secret.txt'
import numpy as np
a = np.random.random((4,3,3))
expected = np.zeros(a.shape)
for i in range(expected.shape[0]):
expected[i] = linalg.expm(a[i])
try :
joined, results = save_exponential(a,base_path, sub_path)
except werkzeug.exceptions.NotFound as e:
... |
joined = flask.safe_join(base_path, sub_path)
output = np.zeros(A.shape)
for i in range(A.shape[0]):
output[i] = linalg.expm(A[i])
return joined, output
| name change | flask.safe_join | scipy==1.8.1 Werkzeug==2.0.0 | [
"https://tedboy.github.io/flask/werk_doc.tutorial.html",
"https://flask.palletsprojects.com/en/stable/changes/",
"https://docs.scipy.org/doc/scipy/release/1.8.1-notes.html"
] | 1 | 1 | true | [
"range",
"numpy.zeros",
"scipy.linalg.expm",
"flask.safe_join"
] | 2021-05 | null |
3.9 | sympy | 1.9 | Create a function named custom_generateRandomSampleDice that accepts two parameters: a die object and an integer X. The function should generate and return a list containing X random outcomes sampled from the given die | from typing import List
from sympy.stats import Die, sample
import sympy.stats.rv
def custom_generateRandomSampleDice(dice: sympy.stats.rv.RandomSymbol, X: int) -> List[int]:
return | 175 |
dice = Die('X', 6)
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
def test_custom_generateRandomSampleDice():
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning) # Capture all warnings
output = custom_generateRan... | [sample(dice) for i in range(X)] | new func/method/class | stats.sample | scipy==1.8.0 | [
"https://docs.sympy.org/latest/modules/stats.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"range",
"sample"
] | 2021-06 | null |
3.9 | sympy | 1.9 | Create a custom_computeDFT function that accepts an integer parameter n. This function should compute the Discrete Fourier Transform (DFT) matrix of size n times n and show it explicitly. | import sympy
from sympy.matrices.expressions.fourier import DFT
def custom_computeDFT(n: int) -> sympy.ImmutableDenseMatrix:
return | 176 |
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
from sympy import Matrix, I, Rational
def test_custom_computeDFT():
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning) # Capture all warnings
output = custom_comput... | DFT(n).as_explicit() | new func/method/class | sympy.physics.matrices.mdft | [
"https://docs.sympy.org/latest/modules/physics/matrices.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.matrices.expressions.fourier.DFT",
"as_explicit"
] | 2021-06 | null | |
3.9 | sympy | 1.9 | Create a function named custom_laplace_transform that takes two parameters, t and z. This function should compute the Laplace transform of the 2×2 identity matrix (eye(2)) with respect to the variable t and the transform variable z using the laplace_transform function from Sympy. The function should return a single tup... | from typing import Tuple
from sympy import laplace_transform, symbols, eye
import sympy
def custom_laplace_transform(t: sympy.Symbol, z: sympy.Symbol) -> Tuple[sympy.Matrix, sympy.Expr, bool]:
return | 177 | t, z = symbols('t z')
from sympy import Matrix
output = custom_laplace_transform(t,z)
expected = (Matrix([
[1/z, 0],
[ 0, 1/z]
]), 0, True)
assert output == expected
| laplace_transform(eye(2), t, z, legacy_matrix=False) | new func/method/class | laplace_transform | [
"https://docs.sympy.org/latest/modules/integrals/integrals.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"eye",
"laplace_transform"
] | 2021-06 | null | |
3.9 | sympy | 1.11 | Create a function named custom_trace that takes a single parameter n, which represents a numerical input. The function should instantiate a trace object using the provided number and return the resulting trace object. | import sympy.physics.quantum
import sympy
def custom_trace(n: int) -> sympy.physics.quantum.trace.Tr:
return | 178 | from sympy.physics.quantum.trace import Tr
expect = 2
assert custom_trace(2) == expect | sympy.physics.quantum.trace.Tr(n) | breaking change | trace | [
"https://docs.sympy.org/latest/modules/physics/quantum/state.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.physics.quantum.trace.Tr"
] | 2022-03 | null | |
3.9 | sympy | 1.11 | Create a custom_preorder_traversal function that instantiate and return the preorder_traversal object with existing expression. | import sympy
def custom_preorder_traversal(expr: sympy.Expr) -> sympy.core.basic.preorder_traversal:
return | 179 | expr = sympy.Add(1, sympy.Mul(2, 3))
expect = [7]
assert list(custom_preorder_traversal(expr)) == expect | sympy.preorder_traversal(expr) | breaking change | preorder_traversal | [
"https://docs.sympy.org/latest/tutorials/intro-tutorial/manipulation.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.preorder_traversal"
] | 2022-03 | null | |
3.9 | sympy | 1.11 | Create a function called custom_parse_mathematica that takes a Mathematica expression (as a string) and converts it into a Sympy expression using the parse_mathematica function. In addition, modify the resulting expression so that every occurrence of the function symbol F is replaced by a new function that, when evalua... | from sympy.parsing.mathematica import parse_mathematica
from sympy import Function, Max, Min
import sympy
def custom_parse_mathematica(expr : str) -> int:
return | 180 | expr = "F[6,4,4]"
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = 24
assert custom_parse_mathematica(expr) == expect
assert not any(isinstance(warn.message, S... | parse_mathematica(expr).replace(Function("F"), lambda *x: Max(*x)*Min(*x)) | new func/method/class | parse_mathematica | [
"https://docs.sympy.org/latest/modules/parsing.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"Min",
"Max",
"sympy.parsing.mathematica.parse_mathematica",
"Function",
"replace"
] | 2022-08 | null | |
3.9 | sympy | 1.12 | Create a function called custom_pinJoint that creates a pin joint connecting two bodies—a parent and a child. The joint should be defined such that: a PinJoint in the parent to be positioned at parent.frame.x with respect to the mass center, and in the child at -child.frame.x. Return a PinJoint object that properly co... | from sympy.physics.mechanics import Body, PinJoint
import sympy.physics.mechanics
def custom_pinJoint(parent: sympy.physics.mechanics.Body, child: sympy.physics.mechanics.Body) -> sympy.physics.mechanics.PinJoint:
return | 181 | parent, child = Body('parent'), Body('child')
pin = custom_pinJoint(parent, child)
expect1 = parent.frame.x
expect2 = -child.frame.x
assert pin.parent_point.pos_from(parent.masscenter) == expect1
assert pin.child_point.pos_from(child.masscenter) == expect2 | PinJoint('pin', parent, child, parent_point=parent.frame.x,child_point=-child.frame.x) | argument change | sympy.physics.mechanics.PinJoint | [
"https://docs.sympy.org/latest/modules/physics/mechanics/masses.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"PinJoint"
] | 2023-05 | null | |
3.9 | sympy | 1.12 | Create a function called custom_pinJoint that creates a pin joint connecting two bodies—a parent and a child. Configure the joint such that the connection point on the parent body is at parent.frame.x (relative to its mass center), and the connection point on the child body is at -child.frame.x. The function should ret... | from sympy.physics.mechanics import Body, PinJoint
import sympy.physics.mechanics
import sympy as sp
def custom_pinJoint_connect(parent: sympy.physics.mechanics.Body, child: sympy.physics.mechanics.Body) -> sympy.physics.mechanics.PinJoint:
return | 182 |
parent, child = Body('parent'), Body('child')
pin = custom_pinJoint_connect(parent, child)
assertion_value = isinstance(pin.coordinates, sp.Matrix)
assert assertion_value
assertion_value = isinstance(pin.speeds, sp.Matrix)
assert assertion_value | PinJoint('pin', parent, child, parent_point=parent.frame.x,child_point=-child.frame.x) | output behaviour | sympy.physics.mechanics | [
"https://docs.sympy.org/latest/modules/physics/mechanics/masses.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"PinJoint"
] | 2023-05 | null | |
3.9 | sympy | 1.13 | Create a function named custom_check_carmichael that takes an integer parameter n as input. The function should determine whether n is a Carmichael number. It should return the result, which is a Boolean value indicating if n is a Carmichael number. | from sympy import *
def custom_check_carmichael(n: int) -> bool:
return | 183 |
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
n = 561
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
from sympy import is_carmichael
expect = True
output = custom_check_carmichael(n)
assert output == expect
... | is_carmichael(n) | breaking change | sympy.functions.combinatorial.numbers.carmichael.is_carmichael
| [
"https://docs.sympy.org/latest/modules/core.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"is_carmichael"
] | 2023-07 | null | |
3.9 | sympy | 1.13 | Create a function named custom_function that takes two parameters: an integer n and an integer k. The function should compute the divisor sigma function. The divisor sigma function returns the sum of the k powers of the divisors of n. | from sympy import *
def custom_function(n: int, k : int) -> int:
return | 184 | n = 6
k = 1
output = custom_function(n, k)
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = 12
assert output == expect
assert not any(isinstance(warn.message, ... | divisor_sigma(n, k) | breaking change | sympy.ntheory.factor_.divisor_sigma | [
"https://docs.sympy.org/latest/modules/core.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"divisor_sigma"
] | 2023-07 | null | |
3.9 | sympy | 1.13 | Create a function named custom_function that takes two parameters: K and a. Here, K represents a finite field created using the GF class from Sympy, and a is an element of that finite field. The function should convert the field element a into its corresponding integer representation and then return this integer. | from sympy import GF
from sympy.polys.domains.finitefield import FiniteField
def custom_function(K: FiniteField, a: FiniteField) -> int:
return | 185 | K = GF(6)
a = K(8)
output = custom_function(K, a)
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = 2
assert output == expect
assert not any(isinstance(warn.me... | K.to_int(a) | new func/method/class | ModularInteger.to_int() | [
"https://docs.sympy.org/latest/modules/polys/domainsref.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"K.to_int"
] | 2023-07 | null | |
3.9 | sympy | 1.13 | Create a custom_generateInertia function that accepts an input and returns a symbolic representation of a rigid body’s inertia tensor relative to a specified reference frame, constructed using the symbolic variables for its principal moments of inertia. Based on the provided code, give the complete function and import ... | from sympy import symbols
from sympy.physics.mechanics import ReferenceFrame
import sympy.physics.vector
def custom_generateInertia(N: sympy.physics.vector.frame.ReferenceFrame, Ixx: sympy.Symbol, Iyy: sympy.Symbol, Izz: sympy.Symbol) -> sympy.physics.vector.dyadic.Dyadic:
from sympy. | 186 |
N = ReferenceFrame('N')
Ixx, Iyy, Izz = symbols('Ixx Iyy Izz')
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
from sympy.physics.mechanics import inertia
expect = Ixx * ... | physics.mechanics import inertia
return inertia(N, Ixx, Iyy, Izz) | new func/method/class | sympy.physics.mechanics | [
"https://docs.sympy.org/latest/modules/physics/mechanics/masses.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"inertia"
] | 2023-07 | null | |
3.9 | sympy | 1.13 | Implement a function named custom_function that accepts a single parameter eq, which is expected to be a symbolic equation (an instance of Sympy's Eq class). The function should compute the difference and return the expression. | from sympy import *
import sympy
def custom_function(eq: sympy.Equality) -> sympy.Expr:
return | 187 | x, y = symbols('x y')
eq = Eq(x, y)
output = custom_function(eq)
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = x - y
assert output == expect
assert not any... | eq.lhs - eq.rhs | new func/method/class | Eq.rewrite(Add) | [
"https://docs.sympy.org/latest/modules/core.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [] | 2023-07 | null | |
3.9 | sympy | 1.13 | Create a custom_generatePolyList function that accepts a polynomial object and returns a list of its coefficients based on its internal representation. | from sympy import symbols, Poly
import sympy
def custom_generatePolyList(poly: sympy.Poly) -> list[int]:
return | 188 |
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
x = symbols('x')
p = Poly(x**2 + 2*x + 3)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = [1,2,3]
assert custom_generatePolyList(p) == expect
assert not any... | poly.rep.to_list() | new func/method/class | DMP.rep attribute | [
"https://docs.sympy.org/latest/modules/core.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"poly.rep.to_list"
] | 2023-07 | null | |
3.9 | sympy | 1.13 | Using Sympy’s mechanics module, create a mechanical system involving three rigid bodies—a wall, a cart, and a pendulum. The wall serves as the inertial (fixed) reference, while the cart is connected to the wall by a sliding joint along the wall’s x-axis. The pendulum is attached to the cart via a rotational joint about... | from sympy import symbols
from sympy.physics.mechanics import (
Particle, PinJoint, PrismaticJoint, RigidBody)
import sympy
import sympy.physics.mechanics
def custom_motion(wall: sympy.physics.mechanics.RigidBody, slider: sympy.physics.mechanics.PrismaticJoint, pin: sympy.physics.mechanics.PinJoint) -> sympy.Matrix:
... | 189 | l = symbols("l")
wall = RigidBody("wall")
cart = RigidBody("cart")
pendulum = RigidBody("Pendulum")
slider = PrismaticJoint("s", wall, cart, joint_axis=wall.x)
pin = PinJoint("j", cart, pendulum, joint_axis=cart.z,
child_point=l * pendulum.y)
from sympy import symbols, Function, Derivative, Matrix, sin,... | System
system = System.from_newtonian(wall)
system.add_joints(slider, pin)
return system.form_eoms()
| new func/method/class | sympy.physics.mechanics.JointsMethod | [
"https://docs.sympy.org/latest/modules/physics/mechanics/masses.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.physics.mechanics.System.from_newtonian",
"system.add_joints",
"system.form_eoms"
] | 2023-07 | null | |
3.9 | sympy | 1.13 | Using the SymPy's mechanics module, define a function named custom_body that takes two strings as inputs—one representing the name of a rigid body and the other representing the name of a particle—and returns a tuple containing a rigid body and a particle created with those names.
| from sympy.physics.mechanics import *
import sympy.physics.mechanics
def custom_body(rigid_body_text: str, particle_text: str) -> tuple[sympy.physics.mechanics.RigidBody, sympy.physics.mechanics.Particle]:
return | 190 | rigid_body_text = "rigid_body"
particle_text = "particle"
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
exp1, exp2 = custom_body(rigid_body_text, particle_text)
assert e... | RigidBody(rigid_body_text), Particle(particle_text) | new func/method/class | sympy.physics.mechanics.Body | [
"https://docs.sympy.org/latest/modules/physics/mechanics/masses.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"RigidBody",
"Particle"
] | 2023-07 | null | |
3.9 | sympy | 1.9 | Implement a function named custom_symbol that takes an input parameter index, which represents an indexed expression or symbol. The function should extract all the free symbols present in the given indexed object and return it. | from sympy import Indexed, Symbol
import sympy
from typing import Set
def custom_symbol(index: Indexed) -> set[Symbol]:
return | 191 |
a = Indexed("A", 0)
output = custom_symbol(a)
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
A = Symbol('A')
A0 = Indexed('A', 0)
expect = {A, A0}
assert o... | index.free_symbols | new func/method/class | expr_free_symbols | [
"https://docs.sympy.org/latest/modules/tensor/indexed.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [] | 2021-06 | null | |
3.9 | sympy | 1.9 | Write a custom_create_matrix function that takes two lists (first and second) and returns a matrix composed of these two lists as rows. | from sympy import Matrix
import sympy
def custom_create_matrix(first: sympy.Matrix, second: sympy.Matrix) -> list[int]:
return | 192 |
first = [1,2]
second =[3,4]
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expected_shape = (2, 2)
expected_content: list[list[int]] = [[1, 2], [3, 4]]
output =... | Matrix([first, second]) | new func/method/class | sympy.polys.solvers.RawMatrix | [
"https://docs.sympy.org/latest/tutorials/intro-tutorial/matrices.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.Matrix"
] | 2021-06 | null | |
3.9 | sympy | 1.9 | Write a custom_function that accepts a Sympy Matrix and returns a flat, read-only copy of its data | from sympy import Matrix
import sympy
def custom_function(matrix: sympy.Matrix) -> list[int]:
return | 193 | m = Matrix([[1, 2], [3, 4]])
output = custom_function(m)
output[0] = 100
assertion_value = m[0, 0] == 1
assert assertion_value
assertion_value = output[0] == 100
assert assertion_value
| matrix.flat() | new func/method/class | DenseMatrix._flat | [
"https://docs.sympy.org/latest/tutorials/intro-tutorial/matrices.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"matrix.flat"
] | 2021-06 | null | |
3.9 | sympy | 1.9 | Write a custom_function that accepts a Sympy SparseMatrix and returns its dictionary-of-keys representation. | from sympy import Matrix
import sympy
def custom_function(matrix: sympy.Matrix) -> list[int]:
return | 194 | m = Matrix([[1, 2], [3, 4]])
output = custom_function(m)
output[(0, 0)] = 100
assertion_value = m[0, 0] == 1
assert assertion_value
assertion_value = output[(0, 0)] == 100
assert assertion_value
| matrix.todok() | new func/method/class | SparseMatrix._todok | [
"https://docs.sympy.org/latest/modules/matrices/sparse.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"matrix.todok"
] | 2021-06 | null | |
3.9 | sympy | 1.10 | Write a custom_bottom_up function that applies a bottom-up traversal to expr with a lambda function on each node. | import sympy
def custom_bottom_up(expr: sympy.Expr) -> int:
return | 195 | expr = sympy.Add(1, sympy.Mul(2, 3))
expect = 7
assert custom_bottom_up(expr) == expect | sympy.bottom_up(expr, lambda x: x.doit()) | breaking change | sympy.bottom_up | [
"https://docs.sympy.org/latest/modules/core.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.bottom_up",
"x.doit"
] | 2022-03 | null | |
3.9 | sympy | 1.10 | Write a custom_use function that traverses the expression so that every subexpression is evaluated, and returns final evaluated result. | import sympy
def custom_use(expr: sympy.Expr) -> int:
return | 196 | expr = sympy.Add(1, sympy.Mul(2, 3))
expect = 7
assert custom_use(expr) == expect | sympy.use(expr, lambda x: x.doit()) | breaking change | sympy.use | [
"https://docs.sympy.org/latest/tutorials/intro-tutorial/manipulation.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.use",
"x.doit"
] | 2022-03 | null | |
3.9 | sympy | 1.11 | Write a custom_is_perfect_square function that check if the input is a perfect square. | import sympy
def custom_is_perfect_square(n: int) -> bool:
return | 197 | import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = True
output = custom_is_perfect_square(4)
assert output == expect
assert not any(isinstance(warn.me... | sympy.ntheory.primetest.is_square(n) | new func/method/class | carmichael.is_perfect_square | [
"https://docs.sympy.org/latest/modules/ntheory.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.ntheory.primetest.is_square"
] | 2023-07 | null | |
3.9 | sympy | 1.11 | Write a custom_is_prime function that check if the input is prime | import sympy
def custom_is_prime(n: int) -> bool:
return | 198 | import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = True
output = custom_is_prime(13)
assert output == expect
assert not any(isinstance(warn.message, S... | sympy.isprime(n) | new func/method/class | carmichael.is_prime | [
"https://docs.sympy.org/latest/modules/ntheory.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"sympy.isprime"
] | 2023-07 | null | |
3.9 | sympy | 1.11 | Write a custom_divides function that checks whether the integer p divides the integer n evenly (i.e., with no remainder). | import sympy
def custom_divides(n: int, p: int) -> bool:
return | 199 | import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
expect = True
output = custom_divides(10,2)
assert output == expect
assert not any(isinstance(warn.message, ... | n % p == 0 | new func/method/class | carmichael.divides | [
"https://docs.sympy.org/latest/modules/physics/quantum/operator.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [] | 2023-07 | null | |
3.9 | sympy | 1.12 | Write a custom_array_to_matrix function that convert input array into matrix by import correct sympy modules. | from sympy import Matrix, symbols, Array
import sympy
def custom_array_to_matrix(array: sympy.Array) -> sympy.Matrix:
from sympy.tensor.array.expressions. | 200 |
a1, a2, a3, a4 = symbols('a1 a2 a3 a4')
array_expr = Array([[a1, a2], [a3, a4]])
import warnings
from sympy.utilities.exceptions import SymPyDeprecationWarning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", SymPyDeprecationWarning)
from sympy.tensor.array.expressions.from_arr... | from_array_to_matrix import convert_array_to_matrix
return convert_array_to_matrix(array)
| breaking change | sympy.tensor.array.expressions.conv_* | [
"https://docs.sympy.org/latest/modules/matrices/matrices.html",
"https://docs.sympy.org/latest/explanation/active-deprecations.html"
] | 1 | 0 | true | [
"convert_array_to_matrix"
] | 2023-05 | null |
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