mac_os = "darwin"
os_name = str(platform.system()).lower()
if os_name == windows_os:
clointfusion_directory = r"C:\Users\{}\ClointFusion".format(
str(os.getlogin()))
elif os_name == linux_os:
clointfusion_directory = r"/home/{}/ClointFusion".format(str(
os.getlogin()))
elif os_name == mac_os:
clointfusion_directory = r"/Users/{}/ClointFusion".format(
str(os.getlogin()))
cf_icon_cdt_file_path = os.path.join(clointfusion_directory, "Logo_Icons",
"Cloint-ICON-CDT.ico")
pi.install_traceback(hide_locals=True, relevant_only=True, enable_prompt=True)
pretty.install()
toaster = ToastNotifier()
local_msg = ""
local_url = ""
local_date = datetime.datetime.now().strftime("%d/%m/%Y")
server_date = ""
def act_on_click():
global local_msg
if "new version" in str(local_msg).lower():\
os.system('cf')
"""
This tutorial shows how to use pyinspect
to produce pretty and informative traceback stacks
"""
# import pyinspect and install the traceback handler
import pyinspect
import numpy as np
pyinspect.install_traceback() # use hide_locals=True to hide locals panels
# make some buggy code
a = np.ones(5)
b = "ignore this" # a local variable not being used
c = np.zeros(4) # ooops, wrong size
a + c # this will give an error
"""
Note: in the traceback a,b will be highlighted because they are in
the line causing the error. 'b' will be shown as well though.
To only show the variables in the error line
pass relevant_only=True to `install_traceback`
To only show relevant variables, pass 'only_relevant=True' to
`pyinspect.install_traceback`.
"""
import numpy as np from pathlib import Path from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from rich.progress import track import pandas as pd from myterial import cyan, teal, indigo, orange, salmon, grey_light, grey, grey_dark, blue_grey_darker from myterial import brown_dark, grey, blue_grey_dark, grey_darker, salmon_darker, orange_darker, blue_grey from scipy.stats import zscore from collections import namedtuple from pyrnn.analysis.dimensionality import get_n_components_with_pca from scipy.stats import ttest_ind, linregress from statsmodels.stats.multitest import multipletests from pyinspect import install_traceback install_traceback() from fcutils.plotting.utils import calc_nrows_ncols, clean_axes, save_figure from fcutils.maths.utils import rolling_mean, derivative from fcutils.plotting.plot_distributions import plot_kde from vgatPAG.database.db_tables import ManualBehaviourTags, Roi, Sessions from Analysis import get_session_data, get_session_tags, get_tags_sequences, speed_color, shelt_dist_color # %% pre_pos_s = 1.5 pre_pos = int(pre_pos_s * 30) n_s_pre, n_s_post = 5, 2 n_frames_pre = n_s_pre * 30
import os
from pathlib import Path
import sys
import brainrender.default_variables
from brainrender.Utils.data_io import save_yaml, load_yaml
from brainrender.Utils.ruler import ruler
import warnings
import pyinspect
pyinspect.install_traceback()
__all__ = [
"DEFAULT_ATLAS",
"BACKGROUND_COLOR",
"DEFAULT_HDF_KEY",
"DEFAULT_NEURITE_RADIUS",
"DEFAULT_STRUCTURE_ALPHA",
"DEFAULT_STRUCTURE_COLOR",
"DISPLAY_INSET",
"DISPLAY_ROOT",
"HDF_SUFFIXES",
"INJECTION_DEFAULT_COLOR",
"INJECTION_VOLUME_SIZE",
"NEURON_ALPHA",
"NEURON_RESOLUTION",
"ROOT_ALPHA",
"ROOT_COLOR",
"SHADER_STYLE",
"SHOW_AXES",
"SOMA_RADIUS",
from PIL import Image
from pathlib import Path
import click
import pyinspect as pi
import os
pi.install_traceback()
@click.command()
@click.argument("fpath")
def get(fpath):
print(f"Extracting gif last frame: {fpath}")
path = Path(fpath)
# get last image
im = Image.open(fpath)
im.seek(im.n_frames - 1)
# save
fld, name = path.parent, path.name
newname = name.replace(".gif", ".png")
im.save(str(fld / newname), format="png")
print(f"Saving image at : {str(fld / newname)}")
if "intro" not in fpath:
os.remove(fpath)
def test_traceback_args():
pi.install_traceback(hide_locals=True)
raise_exception()
pi.install_traceback(hide_locals=False)
raise_exception()
pi.install_traceback(all_locals=True)
raise_exception()
pi.install_traceback(all_locals=False)
raise_exception()
pi.install_traceback(relevant_only=True)
raise_exception()
pi.install_traceback(relevant_only=False)
raise_exception()
pi.install_traceback(keep_frames=0)
raise_exception()
pi.install_traceback(keep_frames=-1)
raise_exception()
pi.install_traceback(keep_frames=5)
raise_exception()
def test_install():
pi.install_traceback()
raise_exception()
raise_exception_with_objs()
import os
from loguru import logger
import sys
from pathlib import Path
from rich.logging import RichHandler
from brainrender import settings
try:
from pyinspect import install_traceback
install_traceback(hide_locals=not settings.DEBUG)
except ImportError:
pass # fails in notebooks
from brainrender.scene import Scene
import brainrender.actors
from brainrender.video import VideoMaker, Animation
from brainrender.atlas import Atlas
base_dir = Path(os.path.join(os.path.expanduser("~"), ".brainrender"))
base_dir.mkdir(exist_ok=True)
__version__ = "2.0.4.7"
# set logger level
def set_logging(level="INFO", path=None):
"""
Sets loguru to save all logs to a file i
brainrender's base directory and to print
import sys
sys.path.append("./")
from pyinspect import install_traceback, search
install_traceback(keep_frames=0, hide_locals=True)
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from datetime import datetime
import matplotlib.pyplot as plt
from loguru import logger
from pathlib import Path
from rich.progress import track
from fcutils.file_io.utils import check_file_exists, check_file_exists, check_create_folder
from fcutils.file_io.io import load_csv_file, save_json
from fcutils.maths.utils import rolling_mean
from analysis.utils.analysis_utils import parse_folder_files
from analysis.utils.utils import calibrate_sensors_data, correct_paw_used, compute_center_of_gravity, get_onset_offset
from analysis import paths
# ---------------------------------------------------------------------------- #
# SETUP #
# ---------------------------------------------------------------------------- #
DEBUG = False # set as true to have extra plots to check everything's OK
# --------------------------------- Variables -------------------------------- #
CONDITIONS = ('WT', ) # keep only data from these conditions
import datajoint as dj
import numpy as np
from pathlib import Path
from rich.prompt import IntPrompt
from scipy import signal
from scipy.stats import zscore
from rich.progress import track
import pandas as pd
import matplotlib.pyplot as plt
from vgatPAG.database.dj_config import start_connection, dbname, manual_insert_skip_duplicate
from pyinspect import install_traceback
install_traceback(keep_frames=2, relevant_only=True)
from fcutils.file_io.io import open_hdf
from fcutils.maths.utils import derivative, rolling_mean
from fcutils.video.utils import get_cap_from_file, get_video_params
from fcutils.plotting.utils import save_figure
from behaviour.tracking.tracking import prepare_tracking_data
schema = start_connection()
fld = Path(
'D:\\Dropbox (UCL)\\Project_vgatPAG\\analysis\\doric\\VGAT_summary\\temp_tagged-mp4'
)
@schema
class Mouse(dj.Manual):
definition = """
mouse: varchar(64)
"""
