import pandas as pd
import numpy as np
from umap import UMAP
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.cluster import AgglomerativeClustering
from sklearn.neighbors import kneighbors_graph
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
mypath = '*insert file path/waveforms.csv'
data = pd.read_csv(mypath, index_col = 'uid')
print(f'{data.shape[0]} unique experiment identifiers (uid), recorded with a sampling frequency (KHz) of {((data.shape[1]-1)/5)}')
data.organoid.value_counts()
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