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nasa exoplanet archive. they contain (partial) information about extra-solar planets (exoplanets) discovered in last 15-20 years using various methods. the meaning of the various columns present in this dataset are described as follows. pl_name: planet name pl_discmethod:discovery method pl_pnum: number of planets in system pl_orbperl orbital period pl_orbsmax: orbit semi-major axis pl_orbeccen: eccentricity pl_bmassj: planet mass pl_radj: planet radius pl_dens: planet density st_dist: distance st_teff: effective temperature st_mass: stellar mass st_rad: stellar radius last_update: date of last update | 1 |
dataset: the dataset (exoplanets.csv) provided for this homework is taken from the nasa exoplanet archive. they contain (partial) information about extra-solar planets (exoplanets) discovered in last 15-20 years using various methods. the meaning of the various columns present in this dataset are described as follows. pl_name: planet name pl_discmethod:discovery method pl_pnum: number of planets in system pl_orbperl orbital period pl_orbsmax: orbit semi-major axis pl_orbeccen: eccentricity pl_bmassj: planet mass pl_radj: planet radius pl_dens: planet density st_dist: distance st_teff: effective temperature st_mass: stellar mass st_rad: stellar radius last_update: date of last update | 1 |
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--------------------------------------------------------------------------- typeerror traceback (most recent call last) cell in[3], line 27 25 for i in list_of_dists: 26 dist = getattr(stats, i) # statistics parameter for each distribution type ---> 27 param = dist.fit(par) 28 a = stats.kstest(par, i, args=param) # ks test 29 results.append((i, a[0], a[1])) file ~\anaconda3\lib\site-packages\scipy\stats\_distn_infrastructure.py:2613, in rv_continuous.fit(self, data, *args, **kwds) 2610 if not censored: 2611 # note: `ravel()` is called for backwards compatibility. 2612 data = np.asarray(data).ravel() -> 2613 if not np.isfinite(data).all(): 2614 raise valueerror("the data contains non-finite values.") 2616 start = [none]*2 typeerror: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' | 1 |
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金融投资工具概述及投资入门 | 1 |
# capabilities analysis # a2: the program first identifies the distribution of the parameters by using the kolmogorov-smirnov test for goodness-of-fit. import numpy as np import pandas as pd from scipy import stats import warnings warnings.filterwarnings("ignore") # importing data from excel data = pd.read_excel("d:\asq\exels\project 5 data.xlsx") # selecting quality parameters sample_data = data.iloc[0:30] quality_parameter = sample_data.columns # list of distribution types to test list_of_dists = ['alpha', 'logistic', 'dweibull', 'expon', 'exponnorm', 'weibull_min', 'weibull_max', 'norm', 'johnsonsb', 'johnsonsu', 'genextrem', 'lognorm', 'pearson3'] # looping through each quality parameter for j in np.arange(1, 16, 1): par = sample_data.iloc[:, j] results = [] # testing each distribution type for i in list_of_dists: dist = getattr(stats, i) # statistics parameter for each distribution type param = dist.fit(par) a = stats.kstest(par, i, args=param) # ks test results.append((i, a[0], a[1])) results.sort(key=lambda x: float(x[2]), reverse=true) y = pd.dataframe(results, columns=["distribution type", "ks-test", "p-value"]) y = round(y, 3) # displaying the results for each quality parameter print(j, quality_parameter[j], y) nor = y[y['distribution type'] == 'norm'] # if normal distribution if nor.iloc[0, 2] > 0.05: z = (nor.iloc[0, 0], nor.iloc[0, 2]) print('normal distribution with p-value', z) # if non-normal distribution elif y.iloc[0, 2] > 0.05: z = (y.iloc[0, 0], y.iloc[0, 2]) print('non-normal distribution -', z) # if binomial method is to be used else: print('binomial method') | 1 |
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# capabilities analysis import numpy as np from scipy.stats import norm, binom import pandas as pd data=pd.read_exel("d:\asq\exels\project 5 data.xlsx") display(data) | 1 |
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as plt # data m_grams = [50, 100, 150, 200, 250, 300] y_cm = [0.47, 0.91, 1.35, 1.80, 2.32, 2.76] # plot plt.plot(m_grams, y_cm, marker='o') # labeling the plot plt.title('plot of m against y') plt.xlabel('m (grams)') plt.ylabel('y (cm)') plt.grid(true) # show plot plt.show() | 1 |
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import matplotlib.pyplot as plt import numpy as np # sample data for goals scored by football players goals = np.random.randint(0, 15, 200) # generating 200 random goals scored between 0 and 15 # creating the histogram plt.hist(goals, bins=15, color='skyblue', edgecolor='black') # adding labels and title plt.xlabel('goals scored') plt.ylabel('frequency') plt.title('distribution of goals scored by football players') # displaying the histogram plt.grid(true) plt.show() | 1 |
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https://ispace.uic.edu.cn/pluginfile.php/166476/mod_assign/introattachment/0/linearregression-part1.ipynb?forcedownload=1 | 1 |
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import tensorflow as tf # load your keras model model = tf.keras.models.load_model('path_to_your_model.h5') # save the model in tensorflow savedmodel format model.save('path_to_saved_model_directory') | 1 |
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import matplotlib.pyplot as plt # datos intervalo_clase = ['0-1', '2-3', '4-5', '6-7', '8-9', '10-11', '12-13'] frecuencia_acumulada = [3, 11, 18, 21, 26, 28, 30] porcentaje_acumulado = [10, 36, 59, 69, 85, 91, 97] # crear el gráfico plt.figure(figsize=(10, 6)) # graficar la frecuencia acumulada plt.plot(intervalo_clase, frecuencia_acumulada, marker='o', color='b', label='frecuencia acumulada') # graficar el porcentaje acumulado plt.plot(intervalo_clase, porcentaje_acumulado, marker='o', color='r', label='porcentaje acumulado') # etiquetas y título plt.xlabel('intervalo de clase') plt.ylabel('frecuencia / porcentaje acumulado') plt.title('frecuencia y porcentaje acumulado de accidentes de tránsito') # mostrar la leyenda plt.legend() # mostrar el gráfico plt.grid(true) plt.show() | 1 |
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import matplotlib.pyplot as plt # given data substrate_concentration = [0.10, 0.20, 0.40, 0.80, 1.60, 2.40] reaction_rate = [0.06, 0.09, 0.14, 0.19, 0.25, 0.29] # plotting the data plt.figure(figsize=(8, 6)) plt.scatter(substrate_concentration, reaction_rate, color='blue', label='data points') plt.plot(substrate_concentration, reaction_rate, color='blue', linestyle='--', alpha=0.5) # adding labels and title plt.xlabel('substrate concentration ([s])') plt.ylabel('reaction rate (v)') plt.title('michaelis-menten plot') plt.legend() plt.grid(true) # display the plot plt.show() | 1 |
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to prepare the histogram for the two groups and calculate the mean and standard deviation, we can use python programming language and its data analysis libraries such as numpy and matplotlib. here's the python code to accomplish this: ```python import numpy as np import matplotlib.pyplot as plt # group a data group_a = [82, 76, 89, 84, 69, 77, 84, 95, 89, 84] # group b data group_b = [77, 88, 70, 85, 90, 83, 83, 76, 94, 83] # calculate mean and standard deviation for group a mean_a = np.mean(group_a) std_a = np.std(group_a) # calculate mean and standard deviation for group b mean_b = np.mean(group_b) std_b = np.std(group_b) # plotting the histograms plt.figure(figsize=(10, 6)) # histogram for group a plt.hist(group_a, bins=10, alpha=0.5, label='group a') plt.axvline(mean_a, color='r', linestyle='dashed', linewidth=1, label='mean a') plt.xlabel('scores') plt.ylabel('frequency') plt.title('histogram - group a') plt.legend() plt.show() # histogram for group b plt.figure(figsize=(10, 6)) plt.hist(group_b, bins=10, alpha=0.5, label='group b') plt.axvline(mean_b, color='r', linestyle='dashed', linewidth=1, label='mean b') plt.xlabel('scores') plt.ylabel('frequency') plt.title('histogram - group b') plt.legend() plt.show() # print mean and standard deviation for both groups print('group a - mean:', mean_a) print('group a - standard deviation:', std_a) print('group b - mean:', mean_b) print('group b - standard deviation:', std_b) ``` the code above will generate two histograms, one for group a and one for group b, along with vertical dashed lines indicating the mean score for each group. it will also print the mean and standard deviation for both groups. you can run this code in a python environment, such as jupyter notebook or google colab, to visualize the histograms and obtain the mean and standard deviation values for each group. | 1 |
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| year 1 | ------------------------------------|--------| assets | | current assets | €300,000| cash and equivalents | €50,000 | inventory | €100,000| other current assets | €150,000| fixed assets | €300,000| property, plant, and equipment | €300,000| liabilities | | current liabilities | €200,000| accounts payable | €50,000 | short-term loans | €150,000| long-term liabilities | €250,000| long-term loans | €250,000| equity | €150,000| partner 1 capital contribution | €50,000 | partner 2 capital contribution | €50,000 | partner 3 capital contribution | €50,000 | | 1 |
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debugger warning: it seems that frozen modules are being used, which may 0.00s - make the debugger miss breakpoints. please pass -xfrozen_modules=off 0.01s - to python to disable frozen modules. 0.00s - note: debugging will proceed. set pydevd_disable_file_validation=1 to disable this validation. | 1 |
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current directory | 2 |
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inputhistoryscope | 2 |
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r language | 2 |
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progress bar | 2 |
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@jupyter | 2 |
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xarray | 2 |
code mirror | 2 |
command cell | 2 |
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url_ https://colab.research.google.com/drive/1psnykx7jwimin-pfyvx4uiw_l5k4bdb8 আমাদের উদাহরণ সংগ্রহের গ্যালারীটি | 1 |
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pep 668 | 1 |
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static_path | 1 |
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caesar latex template | 1 |
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randomforestclassifier | 1 |
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path file | 1 |
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cell.editor | 1 |
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ai/ml algorithms | 1 |
algorithm label | 1 |
key mapping | 1 |
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line numbering | 1 |
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# exports the default jupyterlab workspace $ jupyter lab workspaces export {"data": {}, "metadata": {"id": "/lab"}} $ $ # exports the workspaces named `foo` $ jupyter lab workspaces export foo {"data": {}, "metadata": {"id": "/lab/workspaces/foo"}} $ $ # exports the workspace named `foo` into a file called `file_name.json` $ jupyter lab workspaces export foo > file_name.json $ $ # imports the workspace file `file_name.json`. $ jupyter lab workspaces import file_name.json saved workspace: |
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branches: [ "develop" ] https://github.com/arxiv/arxiv-search/compare/0.5.6...develop#diff-d7b3e1cc0b909c52439e5d8d3f602f70644d0f6b62a54825968d819f7a5bee89/ht207594 | 1 |
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import matplotlib.pyplot as plt # data years = [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021] rates = [0.27, 0.27, 0.28, 0.3, 0.3, 0.28, 0.3, 0.3, 0.32, 0.34, 0.34, 0.32, 0.3, 0.28, 0.31, 0.33, 0.31, 0.33, 0.32, 0.31, 0.38, 0.36] # scatterplot plt.figure(figsize=(10, 6)) plt.scatter(years, rates, color='blue') plt.title('entrepreneurship rates in the u.s. (2000-2021)') plt.xlabel('year') plt.ylabel('entrepreneurship rate per 100,000') plt.grid(true) plt.show() | 1 |
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import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # datos dias = np.array([i for i in range(1, 38)]) # días del 1 al 37 crecimiento = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0.7, 1.2, 2, 3, 3.5, 4.2, 4.7, 5.0, 5.2 | 1 |
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# copiar y pegar en jupyter lab de python %matplotlib inline import numpy as np import matplotlib.pyplot as plt from scipy.special import erfc from ipywidgets import interact, floatslider # parámetros t_c=926 # temperatura en °c d0=2*10**(-5) # constante c en fe fcc en m2/s q=142000 # energia activación c en fe fcc en j/mol d = d0*np.exp(-q/(8.314*(t_c+273))) # coeficiente de difusión c0 = 0.2 # concentración inicial cs = 1.3 # concentración en la superficie # crear una matriz de distancias x = 0.001*np.linspace(0, 2, 1000) # función para calcular y graficar la concentración para un tiempo dado def plot_concentration(t=0.01): c = c0 + (cs - c0) * erfc(x / (2 * np.sqrt(d * t))) plt.plot(x*1000, c) plt.ylim(0, 1.5) plt.xlabel("distancia (mm)", fontname="verdana", size=12) plt.ylabel("% carbono", fontname="verdana", size=12) plt.show() # crear un slider para el tiempo y usarlo para interactuar con la función de graficación interact(plot_concentration, t=floatslider(min=0.01, max=50000.0, step=500, value=0.01)); | 1 |
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pre_spawn_start | 6 |
ssl | 6 |
pamauthenticator | 6 |
stop | 6 |
uninstall | 6 |
metrics | 6 |
port | 6 |
scopes | 6 |
authentication | 6 |
css | 6 |
github | 6 |
jupyterhub_config.py | 5 |
sudospawner | 5 |
traefik | 5 |
tls | 5 |
extensions | 5 |
changelog | 5 |
configurablehttpproxy | 5 |
spawner_class | 5 |
localauthenticator | 5 |
azure ad | 5 |
lab | 5 |
bind_url | 5 |
notebook | 5 |
username_key | 5 |
kubespawner | 5 |
tornado_settings | 5 |
configurable-http-proxy | 5 |
scope | 5 |
node | 5 |
rbac | 5 |
allowed_users | 5 |
websocket | 5 |
kernels | 5 |
add user | 5 |
virtual environment | 4 |
variables | 4 |
environment | 4 |
root | 4 |
adduser | 4 |
audit | 4 |
spawn | 4 |
c.jupyterhub.authenticator_class | 4 |
403 | 4 |
csrf | 4 |
upload | 4 |
spark | 4 |
image | 4 |
sql | 4 |
profiles | 4 |
network | 4 |
templates | 4 |
create_system_users | 4 |
user_options | 4 |
logout | 4 |
notebookapp | 4 |
jupyterhub-singleuser | 4 |
debug | 4 |
log level | 4 |
nullauthenticator | 4 |
spawners | 4 |
spawn failed | 4 |
jupyterhub_service_url | 4 |
idle | 4 |
auth_state | 4 |
keycloak | 4 |
sqlite | 4 |
roles | 4 |
storage | 4 |
change password | 4 |
mysql | 4 |
culler | 4 |
get_auth_state | 4 |
contentsmanager | 4 |
serverapp | 4 |
r | 4 |
python | 4 |
notebook_dir | 3 |
jupyterhub_config | 3 |
admin user | 3 |
home | 3 |
sharing | 3 |
postgresql | 3 |
nbgrader | 3 |
nvidia | 3 |
subdomain | 3 |
postgres | 3 |
systemctl | 3 |
service | 3 |
profilelist | 3 |
session | 3 |
jovyan | 3 |
jupyterhub_service_prefix | 3 |
jupyterhub_crypt_key | 3 |
volume | 3 |
share | 3 |
admin_users | 3 |
config | 3 |
startup | 3 |
permission | 3 |
logo | 3 |
named | 3 |
template | 3 |
path | 3 |
gateway | 3 |
venv | 3 |
launcher | 3 |
ldapauthenticator | 3 |
packages | 3 |
cross origin | 3 |
auth | 3 |
env | 3 |
named servers | 3 |
compose | 3 |
add_user | 3 |
hub_connect_url | 3 |
tree | 3 |
simplelocalprocessspawner | 3 |
give services access to admin user | 3 |
vscode | 3 |
default_url | 3 |
version | 3 |
refresh_user | 3 |
rstudio | 3 |
tljh | 3 |
lifecycle | 3 |
internal_ssl | 3 |
efs folder access issue | 3 |
windows | 3 |
dummyauthenticator | 3 |
memory | 3 |
localprocessspawner | 3 |
3 | |
redirects | 3 |
users | 3 |
allow_named_servers | 3 |
prepuller | 3 |
global password | 3 |
api/config/tree | 3 |
504 save failed | 3 |
api_tokens | 3 |
dummy | 3 |
resource limit | 2 |
alembic | 2 |
istio | 2 |
profile | 2 |
cpu | 2 |
package | 2 |
nfs | 2 |
alert | 2 |
username | 2 |
authenticators | 2 |
high availability | 2 |
oauth confirmation | 2 |
volumes | 2 |
c.spawner.cmd | 2 |
c.jupyterhub.spawner_class | 2 |
podman | 2 |
passwordsecret | 2 |
export | 2 |
single | 2 |
apply jupyterhub_config changes | 2 |
metric | 2 |
install | 2 |
administrator | 2 |
admin:servers | 2 |
api_token | 2 |
channels | 2 |
voilà | 2 |
file size | 2 |
basehandler | 2 |
license | 2 |
background | 2 |
jupyterhub.service | 2 |
system | 2 |
daemon | 2 |
openshift | 2 |
notification | 2 |
idle culler | 2 |
c.spawner.notebook_dir | 2 |
shutdown_no_activity_timeout | 2 |
jupyterhub.sqlite | 2 |
default url | 2 |
grant shared access | 2 |
azure | 2 |
azuread | 2 |
multi-user | 2 |
kernelspecmanager | 2 |
delete | 2 |
named server | 2 |
dockerfile | 2 |
python version | 2 |
octave | 2 |
schedule | 2 |
null authenticator | 2 |
clusterrole | 2 |
%store | 2 |
store | 2 |
virtual | 2 |
claim_groups_key | 2 |
logging | 2 |
role | 2 |
logger | 2 |
jupyterhub_user | 2 |
authorization | 2 |
base_url | 2 |
permissions | 2 |
umask | 2 |
chown | 2 |
update | 2 |
service account | 2 |
cookie_options | 2 |
anaconda | 2 |
extrafiles | 2 |
c | 2 |
global | 2 |
start | 2 |
cell | 2 |
environment variable | 2 |
add users | 2 |
limit | 2 |
workspace | 2 |
namespace | 2 |
pvc | 2 |
load roles | 2 |
jupyter_notebook_config.py | 2 |
server | 2 |
shared server | 2 |
load_groups | 2 |
callback | 2 |
development | 2 |
shared folder | 2 |
shared | 2 |
docker spawner | 2 |
cryptkeeper | 2 |
hub_bind_url | 2 |
theme | 2 |
aws | 2 |
lambda | 2 |
dns | 2 |
configproxy | 2 |
{username} | 2 |
cookie_max_age_days | 2 |
shell | 2 |
pip | 2 |
source | 2 |
health | 2 |
spawner user config | 2 |
admin_access | 2 |
auth_state_hook | 2 |
python version changing | 2 |
username_claim | 2 |
react | 2 |
cookies | 2 |
extraconfig | 2 |
profile_list | 2 |
activity | 2 |
jupyterhub.template_paths | 2 |
localp | 2 |
convert | 2 |
manage_groups | 2 |
littlest | 2 |
zero | 2 |
environment variables | 2 |
put | 2 |
put request | 2 |
snapshot | 2 |
sagemath | 2 |
docker compose | 2 |
oauthenticator | 2 |
voila | 2 |
active_server_limit | 2 |
stable | 2 |
genericoauthenticator | 2 |
api token | 2 |
virtualenv | 2 |
jupyter_server_config | 2 |
single user | 2 |
custom functions | 2 |
session-id | 2 |
oauth2 | 2 |
set group access | 2 |
authenticator.allowed_users | 2 |
dependencies | 2 |
anonymous | 2 |
prefix | 2 |
/hub/spawn-pending | 2 |
test | 2 |
load_roles | 2 |
authenticator_class | 2 |
taint | 2 |
label | 2 |
reverse proxy | 2 |
access | 2 |
poststart | 2 |
release | 2 |
minian | 2 |
blocking cross origin websocket attempt. origin | 2 |
dashboard | 2 |
scheduling | 2 |
jupyterhub_singleuser_app | 2 |
graph | 2 |
"/srv/jupyterhub" | 2 |
srv | 2 |
unicode | 2 |
remote | 2 |
terminado_settings | 2 |
mappingkernelmanager | 2 |
_xsrf | 2 |
failed to connect to my hub | 2 |
ubuntu | 2 |
base url | 2 |
rest api | 2 |
creating user | 2 |
upgrade-db | 2 |
dummy authenticator | 2 |
s3 | 2 |
post_stop_hook | 2 |
manage group | 2 |
shared drive | 2 |
trouble | 2 |
allow origin | 2 |
contents_manager_class | 2 |
delete docker | 2 |
tornado | 2 |
python3.7 | 2 |
labextension | 1 |
runtime directory | 1 |
serving notebooks from local directory | 1 |
show directory | 1 |
concurrent_spawn_limit | 1 |
concurrent | 1 |
invalid_client | 1 |
form option | 1 |
dynamic | 1 |
role based | 1 |
virtual service | 1 |
auto_login | 1 |
automatic | 1 |
logrotate | 1 |
file browser | 1 |
options_form | 1 |
error | 1 |
jhub-fluxogram | 1 |
prompt | 1 |
automate | 1 |
cron | 1 |
ad user login | 1 |
proxy_api_ip | 1 |
"system service" | 1 |
system service | 1 |
daemonsets.apps | 1 |
hook-image-puller | 1 |
var | 1 |
download package | 1 |
api/execute | 1 |
storage class | 1 |
start_timeout | 1 |
c.spawner.start | 1 |
pop | 1 |
alert rules | 1 |
metrics_target | 1 |
configurable http proxy | 1 |
log_level | 1 |
guarantee | 1 |
continuous-image | 1 |
api only | 1 |
fallback | 1 |
userdata_method | 1 |
ha | 1 |
high availablity | 1 |
high avalablity | 1 |
puller | 1 |
hist | 1 |
allow_all | 1 |
extra_host | 1 |
c.jupyterhub.extra_log_file | 1 |
extra_start_kwargs | 1 |
dockerspawner.volumes | 1 |
init_s3_hook | 1 |
checkpoint_dir | 1 |
code completion | 1 |
networking | 1 |
check hub version | 1 |
c.spawner.spawn_cmd | 1 |
rstudio proxy configuratin | 1 |
hard way | 1 |
kuber | 1 |
partition | 1 |
jupyter_token | 1 |
postgres_host | 1 |
after_pod_created_hook | 1 |
backup | 1 |
disable pip install | 1 |
generate token | 1 |
named servers are not enabled. | 1 |
server_name named servers are not enabled. | 1 |
server_name | 1 |
analytics | 1 |
ga | 1 |
change | 1 |
singularity | 1 |
you do not have permission to access | 1 |
unix socket | 1 |
hava permission | 1 |
forbidden | 1 |
content manager | 1 |
delete invalid users | 1 |
min seon | 1 |
knu | 1 |
ancestors | 1 |
artifact | 1 |
web security | 1 |
websecurity | 1 |
tls_verify | 1 |
jupyterhub_cookie_host_prefix_enabled | 1 |
max_upload_size_mb | 1 |
uid | 1 |
drop bar | 1 |
choese | 1 |
compress | 1 |
zip | 1 |
xcrf | 1 |
how to download jupyter hub | 1 |
profilees | 1 |
mount | 1 |
credential | 1 |
notifications | 1 |
c.jupyterserverproxy.servers | 1 |
self hosted | 1 |
redirect | 1 |
c.spawner.gpu_options | 1 |
login templates | 1 |
xxx | 1 |
frame-ancestors | 1 |
miniconda | 1 |
pot | 1 |
auth.dummyauthenticator.password | 1 |
config dir | 1 |
open_sessions | 1 |
open_sessios | 1 |
remote access | 1 |
manager | 1 |
kubenetes | 1 |
notebookapp.shutdown_no_activity_timeout | 1 |
spawner.user.get_auth_state() | 1 |
gitlab_user_login | 1 |
copydirectory | 1 |
jupyterhub_crypt_key generate | 1 |
crypt key | 1 |
c.dockerspawner.extra_host_config | 1 |
hub/api/users | 1 |
log out | 1 |
secrettoken | 1 |
% | 1 |
the % commands | 1 |
authorizations | 1 |
bind_port | 1 |
--hub-api-url | 1 |
c.jupyterhub | 1 |
hadas volkov | 1 |
complabngs | 1 |
andre | 1 |
hub-db-dir | 1 |
ecdsa | 1 |
interactiveshellapp | 1 |
interactiveshellapp.exec_lines | 1 |
spawn options | 1 |
pid | 1 |
tensorboard | 1 |
role_mapping | 1 |
xcube | 1 |
darts | 1 |
gsuit | 1 |
c.authenticator. | 1 |
pandas | 1 |
google autheticator | 1 |
lti | 1 |
disabled-password | 1 |
single users | 1 |
data_files_path | 1 |
static | 1 |
content-security-policy | 1 |
frame | 1 |
rame | 1 |
frame ancestors | 1 |
no_confirm | 1 |
attempt to write a readonly database | 1 |
notebooknotary | 1 |
presentations | 1 |
tab completion | 1 |
jupyterhub_host | 1 |
"config " | 1 |
"%config " | 1 |
"%config ipcompleter" | 1 |
"%config ipcompleter.use_jedi" | 1 |
%config | 1 |
cookiesecret | 1 |
parameterize kernel | 1 |
single user server | 1 |
apply jupyterhub_confic | 1 |
self.user.name | 1 |
null authentication | 1 |
spawn server user token or admin token | 1 |
spawner.cmd | 1 |
url_concat | 1 |
installing python packages | 1 |
vev | 1 |
ownerreferences | 1 |
jupyter notebook | 1 |
jupyterlabs docker | 1 |
jupyterlabs docker install | 1 |
sizing | 1 |
stress | 1 |
load | 1 |
24 hours | 1 |
notebook s3 | 1 |
notebook storage | 1 |
entra | 1 |
notebookdir | 1 |
entrypointtype | 1 |
proxy_class | 1 |
configuration | 1 |
hub | 1 |
generic-oauth | 1 |
cookie secret | 1 |
promtheus | 1 |
openssl | 1 |
external services | 1 |
hardware requirements | 1 |
rest | 1 |
_config.yaml | 1 |
get hub user | 1 |
user manager | 1 |
usermanager | 1 |
scaling | 1 |
api roles | 1 |
admin users | 1 |
swarmspawner | 1 |
spawn admin | 1 |
codecellconfig | 1 |
c.authenticator.enable_auth_state | 1 |
encapsulation | 1 |
auto encapsulation | 1 |
auto encapsulation" | 1 |
auto close | 1 |
spawner default url | 1 |
user audit | 1 |
userdata_params | 1 |
token_params | 1 |
extra_params | 1 |
claims | 1 |
jupyter core | 1 |
scheduler | 1 |
user-scheduler | 1 |
working directory | 1 |
pokemon | 1 |
hpc | 1 |
runtime | 1 |
nb_umask | 1 |
project | 1 |
500 : internal server error | 1 |
future | 1 |
"service account" | 1 |
compute | 1 |
systemdspawner | 1 |
cookie_option | 1 |
cookie_setting | 1 |
wiki | 1 |
samesite | 1 |
multi | 1 |
profile_dir | 1 |
docker image | 1 |
authenticator class | 1 |
cull_internal | 1 |
mappingkernelmanager.cull_internal | 1 |
manage python package install | 1 |
start server | 1 |
serviceaccount | 1 |
traffiek | 1 |
spawner_single | 1 |
jupyterhub_server_name | 1 |
cryptography package | 1 |
upyterhub pages:584] no template for 503 | 1 |
extension-manager | 1 |
websphere | 1 |
deploy ml models in flask | 1 |
npm | 1 |
jupyterhub --config | 1 |
reload | 1 |
realtime | 1 |
syslog | 1 |
requi | 1 |
requirement | 1 |
wrapspawner | 1 |
c.jupyterhub.proxy.pid_file | 1 |
c.spawner.fd | 1 |
c.spawner.environment | 1 |
create cell | 1 |
google cloud | 1 |
allowed_idps | 1 |
idp | 1 |
extra port | 1 |
extraport | 1 |
extraports | 1 |
"www-data" | 1 |
www-data | 1 |
/srv | 1 |
healthcheck | 1 |
username escape | 1 |
admin_group | 1 |
onelogin | 1 |
cookie_secret | 1 |
upgrading | 1 |
start jupyterhub in background | 1 |
files | 1 |
singleuser image | 1 |
bin/bash | 1 |
batchspawner | 1 |
zero-to-kubernate | 1 |
without root | 1 |
jovyan password | 1 |
how to add a column to existing data frame | 1 |
cloak | 1 |
extensioins | 1 |
author | 1 |
repeated api_request | 1 |
jpy_cookie_name | 1 |
c.jupyterhub.db_url | 1 |
--spawner.notebook_dir= |
1 |
folder | 1 |
didnt respond in 30 seconds | 1 |
didnt respond | 1 |
agreement | 1 |
aggreement | 1 |
user aggreement | 1 |
http_timeout | 1 |
jaydebe | 1 |
connection | 1 |
impala | 1 |
arm | 1 |
add user server api | 1 |
container-based | 1 |
monitoring | 1 |
--version | 1 |
webdav | 1 |
space device | 1 |
slurmspawn | 1 |
get_config() | 1 |
smtp | 1 |
startup_poll_interval | 1 |
externally managed proxy | 1 |
oauthenticator.auth0 | 1 |
rtc | 1 |
dockerswarm | 1 |
runtimeerror: proxy failed to start with exit code 1 | 1 |
nodeaffinity | 1 |
port forwarding | 1 |
imagepullsecret | 1 |
image_pull_secrets | 1 |
allow_privilege_escalation | 1 |
common_labels | 1 |
c.configurablehttpproxy.api_url | 1 |
hub_container_port | 1 |
getting token | 1 |
configurable http | 1 |
dark | 1 |
darkmode | 1 |
dark mode | 1 |
hubauth | 1 |
table | 1 |
cert | 1 |
kin | 1 |
kinter | 1 |
t | 1 |
tkinter | 1 |
slurm | 1 |
api_page_default_limit | 1 |
interactiveshell | 1 |
configurable-http | 1 |
custom_html | 1 |
27 | 1 |
1.27 | 1 |
handlers | 1 |
add_handlers | 1 |
handler | 1 |
ram | 1 |
ressource | 1 |
ressources | 1 |
minimal | 1 |
check the single-user server's logs for hints at what needs fixing. | 1 |
single-user server's logs | 1 |
hub_prefix | 1 |
load balancer | 1 |
git clone | 1 |
vpn | 1 |
install python modules | 1 |
chp | 1 |
hub environment | 1 |
proxies | 1 |
htpasswd | 1 |
allowed_organizations | 1 |
home_path_template | 1 |
/websockets | 1 |
/terminals | 1 |
execute | 1 |
dual active | 1 |
hot | 1 |
hotspare | 1 |
hot spare | 1 |
admin password | 1 |
run notebook api | 1 |
add prefix /terminals/websocket/ | 1 |
add prefix in /terminals/websocket/ | 1 |
add prefix in jupyterhub /terminals/websocket/ | 1 |
form | 1 |
hub_routespec | 1 |
/progress | 1 |
custom_pages | 1 |
c.jupyterhub.custom_pages | 1 |
jupyterhublab | 1 |
client_id | 1 |
sys.path.append | 1 |
signup | 1 |
openems | 1 |
freeipa | 1 |
time sharing | 1 |
timesharing | 1 |
branding | 1 |
jupyterhub_cluster_base_url | 1 |
caddy | 1 |
repository | 1 |
normalize_username | 1 |
/hub/health | 1 |
8000 | 1 |
disable auth | 1 |
aries monthly | 1 |
verify | 1 |
search within notebook | 1 |
c.kubespawner.http_timeout | 1 |
refresh_pre_spawn | 1 |
version list | 1 |
contents | 1 |
jupyter_notebook_config | 1 |
allowed_users. | 1 |
admin token for all hub apis | 1 |
fix token for all hub apis | 1 |
default token for hub apis | 1 |
default token for server access | 1 |
token_timeout | 1 |
1 | |
array | 1 |
redis | 1 |
hub.domain.tld:443 | 1 |
defaulturl | 1 |
prespawnhook | 1 |
downgrade | 1 |
spawner kubernetes | 1 |
userinfo | 1 |
allow-root | 1 |
db_url | 1 |
cookie timeout | 1 |
user profiles | 1 |
baremetal | 1 |
file list directory | 1 |
api mode | 1 |
admin usuer | 1 |
configuration file | 1 |
upgrade | 1 |
log handlers | 1 |
https://jupyter.org/try | 1 |
rest-api | 1 |
access read:users | 1 |
preexec | 1 |
style | 1 |
oauth_token_expires_in | 1 |
jupyter hub oauth_token_expires_in documentation | 1 |
progress | 1 |
run jupiter notebook | 1 |
jupiter notebook | 1 |
access-control-allow | 1 |
enterprise gateway | 1 |
upgrade jupyter notebook | 1 |
update jupyter notebook | 1 |
drop | 1 |
air drop | 1 |
cookie_max_age | 1 |
clear_login_cookie | 1 |
logout_on_all_devices | 1 |
architecture | 1 |
c.jupyterhub.logging_config | 1 |
kubernete | 1 |
extra | 1 |
extrafile | 1 |
c. | 1 |
serverapp.shutdown_no_activity_timeout | 1 |
secret | 1 |
plugin | 1 |
"there seems to be a problem with the chat backend, please look at the jupyterlab server logs or contact your administrator to correct this problem." | 1 |
there seems to be a problem with the chat backend, please look at the jupyterlab server logs or contact your administrator to correct this problem. | 1 |
cull_idle_servers | 1 |
customization | 1 |
token expire | 1 |
spawner.mounts | 1 |
hooks | 1 |
user-redirect | 1 |
db_uri | 1 |
download using command line | 1 |
download using command | 1 |
download useing command | 1 |
command | 1 |
managed_groups | 1 |
checkpoint | 1 |
branch strategy | 1 |
id | 1 |
pricing | 1 |
jupyterhub_oauth_scopes | 1 |
disk | 1 |
efs | 1 |
env var | 1 |
secrets | 1 |
wrap | 1 |
cli | 1 |
@ | 1 |
anontation | 1 |
anontaion | 1 |
matlab | 1 |
homepath | 1 |
bloque ip | 1 |
jupyterhub_client_id | 1 |
jupyterhub user | 1 |
generic | 1 |
ystore.tempfileystore | 1 |
heatlthchecks | 1 |
ydocextension.ystore_class | 1 |
--ydocextension.ystore_class= | 1 |
reclaim | 1 |
openid | 1 |
enable_signup | 1 |
username_map | 1 |
deploying | 1 |
culling polling period | 1 |
conda env | 1 |
disable download | 1 |
profile_form_template | 1 |
spawner profile_form_template | 1 |
spawner.profile_form_template | 1 |
access permissions | 1 |
templating | 1 |
"jupyterhub-singleuser" | 1 |
groupd | 1 |
authenticator_class: generic-oauth | 1 |
subdomain_host | 1 |
testing/jupyter_configuration.py | 1 |
saml | 1 |
hub_activity_interval | 1 |
delete_user | 1 |
sage | 1 |
bootstrap.py | 1 |
joyvan | 1 |
sample | 1 |
psycopg2 | 1 |
self signed | 1 |
spawnner | 1 |
new kernel | 1 |
fastapi | 1 |
auth-state | 1 |
c.spawner.default_url | 1 |
config examples | 1 |
welcome | 1 |
7b: revisiting the respect study | 1 |
api-only | 1 |
get file | 1 |
api and breaking changes | 1 |
save_auth_state | 1 |
secret storage | 1 |
forward | 1 |
sing | 1 |
icecube | 1 |
provider | 1 |
ldap authenticator | 1 |
install plugin | 1 |
configure-http-proxy | 1 |
configure_http_proxy | 1 |
http_proxy | 1 |
10 min | 1 |
subdomains | 1 |
snowflake | 1 |
latest version | 1 |
tmp | 1 |
configuration reference | 1 |
hostname | 1 |
loadbalancer | 1 |
max_buffer_size | 1 |
notebook max_buffer_size | 1 |
notebook memory | 1 |
"jupyterjub.service" | 1 |
install packages | 1 |
headers | 1 |
emal | 1 |
bin | 1 |
trash | 1 |
before notebook | 1 |
terminal source .bashrc | 1 |
terminal source file | 1 |
source profile | 1 |
installation | 1 |
port 8000 | 1 |
tenant | 1 |
upgrade_db | 1 |
jupyter-singleuser | 1 |
control access with groups | 1 |
group access | 1 |
workload | 1 |
workload identity | 1 |
extra config | 1 |
source kernel | 1 |
how to change login page | 1 |
requirements.txt | 1 |
auth_refresh_age | 1 |
add user api | 1 |
comorbidipy | 1 |
missing credentials | 1 |
dtabase | 1 |
enterprise-gateway | 1 |
spawner config | 1 |
spawning | 1 |
jupyterhub.auth.dummyauthenticator | 1 |
net_admin | 1 |
security_context | 1 |
thousand | 1 |
shared volume | 1 |
culling | 1 |
nodegroupsize | 1 |
affiliation | 1 |
get 404 | 1 |
wide | 1 |
width | 1 |
sudospawn | 1 |
169.254.169.254 | 1 |
iptables | 1 |
trusted | 1 |
c.spawner.args | 1 |
user_cmd | 1 |
users_cmd | 1 |
user path | 1 |
system_users | 1 |
system_user | 1 |
redi | 1 |
redicts | 1 |
重定向 | 1 |
jovy | 1 |
pagination | 1 |
pycharm | 1 |
iframe | 1 |
refused to frame 'http://localhost:8230/' because an ancestor violates the following content security policy directive: "frame-ancestors 'self'". | 1 |
theme configuration | 1 |
migrate | 1 |
scp | 1 |
kernal | 1 |
adding ikernal | 1 |
panel | 1 |
jupyterhub.custom_html | 1 |
installation pack | 1 |
custom css | 1 |
azureadoauthenticator | 1 |
ecs | 1 |
elastic | 1 |
jpy_api_token | 1 |
jupyter notebook localhost change | 1 |
remove users | 1 |
remove | 1 |
jupyterhub-core | 1 |
untrusted | 1 |
hekm | 1 |
heml | 1 |
probe | 1 |
hub_connect_ip | 1 |
allowed_user | 1 |
%env | 1 |
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from pyecharts.charts import pie from pyecharts import options as opts # 准备数据 data_pair = [("类别1", 40), ("类别2", 21), ("类别3", 17), ("类别4", 13), ("类别5", 9), ("其他", 5)] # 创建一个pie对象 pie = pie() # 添加数据,设置半径和中心位置 pie.add("", data_pair, radius=["40%", "75%"], center=["50%", "50%"]) # 设置全局配置项 pie.set_global_opts( title_opts=opts.titleopts(title="扇形图示例"), legend_opts=opts.legendopts(orient="vertical", pos_top="15%", pos_left="2%") ) # 在jupyter notebook中直接渲染图表 pie.render_notebook() | 1 |
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#6 syawal 1444h fisika fmipa unej import numpy as np import coolingroutine as cr import matplotlib.pyplot as plt #%matplotlib inline times = np.linspace(0, 20, 201) #kasus ke 1 temps_beforecooling = cr.cube_before_cooling(times, **cr.cooling_parameters) plt.plot(times, temps_beforecooling) plt.xlabel('time (min)') plt.ylabel('coffee temperature (degrees celcius)') plt.show() #kasus ke 2 temps_aftercooling = cr.cube_after_cooling(times, **cr.cooling_parameters) plt.plot(times, temps_aftercooling) plt.xlabel('time (min)') plt.ylabel('coffee temperature (degrees celcius)') plt.show() #kasus ke 3 temps_cont = cr.continuous_sugar_cooling(times, **cr.cooling_parameters) plt.plot(times, temps_cont) plt.xlabel('time (min)') plt.ylabel('coffee temperature (degrees celcius)') plt.show() #kasus ke 4 temps_n = cr.sugar_specifiedtime_cooling(times, [50, 100, 150], **cr.cooling_parameters) plt.plot(times, temps_n) plt.xlabel('time (min)') plt.ylabel('coffee temperature (degrees celcius)') plt.show() #kasus ke 5 plt.plot(times, temps_beforecooling, label='cube before cooling') plt.plot(times, temps_aftercooling, label='cube after cooling') plt.plot(times, temps_cont, label='continuous') plt.plot(times, temps_n, label='3 times of dropping cube') plt.xlabel('time (min)') plt.ylabel('coffee temperature (degrees celcius)') plt.legend() plt.show() | 1 |
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import matplotlib.pyplot as plt # definir los datos hitos_completados = 70 entregables_defectuosos = 15 # etiquetas para las barras categorias = ['progreso del proyecto', 'calidad de los entregables'] # alturas de las barras alturas = [hitos_completados, entregables_defectuosos] # colores para las barras colores = ['blue', 'orange'] # crear el gráfico de barras plt.figure(figsize=(8, 6)) plt.bar(categorias, alturas, color=colores) # agregar etiquetas y título plt.xlabel('indicadores') plt.ylabel('porcentaje') plt.title('progreso del proyecto vs. calidad de los entregables') # mostrar el gráfico plt.ylim(0, 100) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.show() | 1 |
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# import کتابخانه مورد نیاز import matplotlib.pyplot as plt # تعریف مختصات ستاره x = [1, 2, 4, 1, 3] y = [4, 3, 2, 1, 1] # ترسیم ستاره plt.figure() plt.plot(x, y) plt.show() | 1 |
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skipped non-installed server(s): bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyright, python-language-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server 0.00s - debugger warning: it seems that frozen modules are being used, which may 0.00s - make the debugger miss breakpoints. please pass -xfrozen_modules=off | 1 |
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