diff --git a/docs/changelog.rst b/docs/changelog.rst
index 56ab18d..6374987 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -5,6 +5,11 @@ Change Log
 ==========
 
 
+Version 0.5.1
+-------------
+- Removed working test file
+
+
 Version 0.5.0
 -------------
 - Added new `predict`, `predict_interval`, and `predict_log_odds` methods to GPS tool
diff --git a/docs/conf.py b/docs/conf.py
index 63d5088..67166b3 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -22,7 +22,7 @@
 author = 'Roni Kobrosly'
 
 # The full version, including alpha/beta/rc tags
-release = '0.5.0'
+release = '0.5.1'
 
 # -- General configuration ---------------------------------------------------
 
diff --git a/setup.py b/setup.py
index 695f5e6..9dc9837 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="causal-curve",
-    version="0.5.0",
+    version="0.5.1",
     author="Roni Kobrosly",
     author_email="roni.kobrosly@gmail.com",
     description="A python library with tools to perform causal inference using \
diff --git a/working_test.py b/working_test.py
deleted file mode 100644
index c57e0dd..0000000
--- a/working_test.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from causal_curve.gps import GPS
-import numpy as np
-import pandas as pd
-from scipy.stats import expon
-from scipy.special import logit
-
-
-np.random.seed(333)
-n = 5000
-x_1 = expon.rvs(size=n, scale = 1)
-x_2 = expon.rvs(size=n, scale = 1)
-treatment = expon.rvs(size=n, scale = (1/(x_1 + x_2)))
-
-gps = ((x_1 + x_2) * np.exp(-(x_1 + x_2) * treatment))
-outcome = treatment + gps + np.random.normal(size = n, scale = 1)
-
-truth_func = lambda treatment: (treatment + (2/(1 + treatment)**3))
-vfunc = np.vectorize(truth_func)
-true_outcome = vfunc(treatment)
-
-df = pd.DataFrame(
-    {
-        'X_1': x_1,
-        'X_2': x_2,
-        'Treatment': treatment,
-        'GPS': gps,
-        'Outcome': outcome,
-        'True_outcome': true_outcome
-    }
-).sort_values('Treatment', ascending = True)
-
-
-gps = GPS()
-gps.fit(T = df['Treatment'], X = df[['X_1', 'X_2']], y = df['Outcome'])
-gps_results = gps.calculate_CDRC(0.95)
-
-
-
-gps.predict(np.array([4.1, 4.3, 4.7]))
-gps.predict_interval(np.array([4.1, 4.3, 4.7]))
-
-gps.predict(np.array([5]))
-gps.predict_interval(np.array([5]))
-
-
-
-
-
-
-
-
-####### EXPERIMENTING WITH PREDICTING GIVEN NEW COVARIATE AND TREATMENT DATA
-
-### Using the `predict` method will work like this
-temp_treatment_value = 4
-temp_gps_value = gps.gps_function(temp_treatment_value).mean()
-gps.gam_results.predict(np.array([temp_treatment_value, temp_gps_value]).reshape(1,-1))
-
-
-
-
-
-# Using the `predict_interval` method will work like this
-
-gps.gam_results.prediction_intervals(np.array([temp_treatment_value, temp_gps_value]).reshape(1,-1), width = 0.95)
-
-
-
-
-
-##########################################################
-##########################################################
-
-
-from causal_curve.gps import GPS
-import numpy as np
-import pandas as pd
-from scipy.stats import expon
-
-
-df = pd.read_csv("~/Desktop/advertising.csv")
-
-
-gps = GPS()
-gps.fit(T = df['treatment'], X = df[['age', 'income']], y = df['outcome'])
-gps_results = gps.calculate_CDRC(0.95)
-
-
-
-gps.predict_log_odds(np.array([65]))
-
-
-
-# Using the `predict_log_odds` method
-
-temp_treatment_value = 65
-temp_gps_value = gps.gps_function(temp_treatment_value).mean()
-
-logit(gps.gam_results.predict_proba(np.array([temp_treatment_value, temp_gps_value]).reshape(1,-1)))