diff --git a/src/scripts/evaluate.py b/src/scripts/evaluate.py
index da3ad8a..6f51ab7 100644
--- a/src/scripts/evaluate.py
+++ b/src/scripts/evaluate.py
@@ -428,7 +428,7 @@ def run_all_sbc(
         samples_per_inference=1_000,
         plot=True,
         save=False,
-        path="../plots/",
+        path="plots/",
     ):
         """
         Runs and displays mackelab's SBC (simulation-based calibration)
diff --git a/test/test_evaluate.py b/test/test_evaluate.py
deleted file mode 100644
index 47e3fe8..0000000
--- a/test/test_evaluate.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import sys
-import pytest
-import torch
-import numpy as np
-import sbi
-import os
-
-# flake8: noqa
-sys.path.append("..")
-from src.scripts.evaluate import Diagnose, InferenceModel
-
-
-"""
-"""
-
-
-"""
-Test the evaluate module
-"""
-
-
-@pytest.fixture
-def diagnose_instance():
-    return Diagnose()
-
-
-@pytest.fixture
-def inference_instance():
-    inference_model = InferenceModel()
-    path = "../savedmodels/sbi/"
-    model_name = "sbi_linear"
-    posterior = inference_model.load_model_pkl(path, model_name)
-    return posterior
-
-
-def simulator(thetas):  # , percent_errors):
-    # convert to numpy array (if tensor):
-    thetas = np.atleast_2d(thetas)
-    # Check if the input has the correct shape
-    if thetas.shape[1] != 2:
-        raise ValueError(
-            "Input tensor must have shape (n, 2) \
-            where n is the number of parameter sets."
-        )
-
-    # Unpack the parameters
-    if thetas.shape[0] == 1:
-        # If there's only one set of parameters, extract them directly
-        m, b = thetas[0, 0], thetas[0, 1]
-    else:
-        # If there are multiple sets of parameters, extract them for each row
-        m, b = thetas[:, 0], thetas[:, 1]
-    x = np.linspace(0, 100, 101)
-    rs = np.random.RandomState()  # 2147483648)#
-    # I'm thinking sigma could actually be a function of x
-    # if we want to get fancy down the road
-    # Generate random noise (epsilon) based
-    # on a normal distribution with mean 0 and standard deviation sigma
-    sigma = 5
-    ε = rs.normal(loc=0, scale=sigma, size=(len(x), thetas.shape[0]))
-
-    # Initialize an empty array to store the results for each set of parameters
-    y = np.zeros((len(x), thetas.shape[0]))
-    for i in range(thetas.shape[0]):
-        m, b = thetas[i, 0], thetas[i, 1]
-        y[:, i] = m * x + b + ε[:, i]
-    return torch.Tensor(y.T)
-
-
-def test_generate_sbc_samples(diagnose_instance, inference_instance):
-    # Mock data
-    low_bounds = torch.tensor([0, -10])
-    high_bounds = torch.tensor([10, 10])
-
-    prior = sbi.utils.BoxUniform(low=low_bounds, high=high_bounds)
-    posterior = inference_instance  # provide a mock posterior object
-    simulator_test = simulator  # provide a mock simulator function
-    num_sbc_runs = 1000
-    num_posterior_samples = 1000
-
-    # Generate SBC samples
-    thetas, ys, ranks, dap_samples = diagnose_instance.generate_sbc_samples(
-        prior, posterior, simulator_test, num_sbc_runs, num_posterior_samples
-    )
-
-    # Add assertions based on the expected behavior of the method
-
-
-def test_run_all_sbc(diagnose_instance, inference_instance):
-    labels_list = ["$m$", "$b$"]
-    colorlist = ["#9C92A3", "#0F5257"]
-    low_bounds = torch.tensor([0, -10])
-    high_bounds = torch.tensor([10, 10])
-
-    prior = sbi.utils.BoxUniform(low=low_bounds, high=high_bounds)
-    posterior = inference_instance  # provide a mock posterior object
-    simulator_test = simulator  # provide a mock simulator function
-
-    save_path = "plots/"
-
-    diagnose_instance.run_all_sbc(
-        prior,
-        posterior,
-        simulator_test,
-        labels_list,
-        colorlist,
-        num_sbc_runs=1_000,
-        num_posterior_samples=1_000,
-        samples_per_inference=1_000,
-        plot=False,
-        save=True,
-        path=save_path,
-    )
-    # Check if PDF files were saved
-    assert os.path.exists(save_path), f"No 'plots' folder found at {save_path}"
-
-    # List all files in the directory
-    files_in_directory = os.listdir(save_path)
-
-    # Check if at least one PDF file is present
-    pdf_files = [file for file in files_in_directory if file.endswith(".pdf")]
-    assert pdf_files, "No PDF files found in the 'plots' folder"
-
-    # We expect the pdfs to exist in the directory
-    expected_pdf_files = ["sbc_ranks.pdf", "sbc_ranks_cdf.pdf", "coverage.pdf"]
-    for expected_file in expected_pdf_files:
-        assert (
-            expected_file in pdf_files
-        ), f"Expected PDF file '{expected_file}' not found"
-
-
-"""
-def test_sbc_statistics(diagnose_instance):
-    # Mock data
-    ranks =  # provide mock ranks
-    thetas =  # provide mock thetas
-    dap_samples =  # provide mock dap_samples
-    num_posterior_samples = 1000
-
-    # Calculate SBC statistics
-    check_stats = diagnose_instance.sbc_statistics(
-        ranks, thetas, dap_samples, num_posterior_samples
-    )
-
-    # Add assertions based on the expected behavior of the method
-
-def test_plot_1d_ranks(diagnose_instance):
-    # Mock data
-    ranks =  # provide mock ranks
-    num_posterior_samples = 1000
-    labels_list =  # provide mock labels_list
-    colorlist =  # provide mock colorlist
-
-    # Plot 1D ranks
-    diagnose_instance.plot_1d_ranks(
-        ranks, num_posterior_samples, labels_list,
-        colorlist, plot=False, save=False
-    )
-"""