diff --git a/_delphi_utils_python/.pylintrc b/_delphi_utils_python/.pylintrc
index f30837c7e..ad0180ed7 100644
--- a/_delphi_utils_python/.pylintrc
+++ b/_delphi_utils_python/.pylintrc
@@ -12,9 +12,9 @@ disable=logging-format-interpolation,
 [BASIC]
 
 # Allow arbitrarily short-named variables.
-variable-rgx=[a-z_][a-z0-9_]*
-argument-rgx=[a-z_][a-z0-9_]*
-attr-rgx=[a-z_][a-z0-9_]*
+variable-rgx=([a-z_][a-z0-9_]*|[a-zA-Z])
+argument-rgx=([a-z_][a-z0-9_]*|[a-zA-Z])
+attr-rgx=([a-z_][a-z0-9_]*|[a-zA-Z])
 
 [DESIGN]
 
diff --git a/_delphi_utils_python/delphi_utils/__init__.py b/_delphi_utils_python/delphi_utils/__init__.py
index 314cb8296..b5c06daa0 100644
--- a/_delphi_utils_python/delphi_utils/__init__.py
+++ b/_delphi_utils_python/delphi_utils/__init__.py
@@ -13,5 +13,6 @@
 from .smooth import Smoother
 from .signal import add_prefix
 from .nancodes import Nans
+from .weekday import Weekday
 
 __version__ = "0.2.1"
diff --git a/doctor_visits/delphi_doctor_visits/weekday.py b/_delphi_utils_python/delphi_utils/weekday.py
similarity index 65%
rename from doctor_visits/delphi_doctor_visits/weekday.py
rename to _delphi_utils_python/delphi_utils/weekday.py
index 529454f80..ba5d75815 100644
--- a/doctor_visits/delphi_doctor_visits/weekday.py
+++ b/_delphi_utils_python/delphi_utils/weekday.py
@@ -3,23 +3,48 @@
 
 Created: 2020-05-06
 """
-
-
-
-# third party
 import cvxpy as cp
-from cvxpy.error import SolverError
 import numpy as np
-
-# first party
-from .config import Config
+from cvxpy.error import SolverError
 
 
 class Weekday:
     """Class to handle weekday effects."""
 
     @staticmethod
-    def get_params(data, logger):
+    def get_params(data, denominator_col, numerator_cols, date_col, scales, logger):
+        r"""Fit weekday correction for each col in numerator_cols.
+
+        Return a matrix of parameters: the entire vector of betas, for each time
+        series column in the data.
+        """
+        tmp = data.reset_index()
+        denoms = tmp.groupby(date_col).sum()[denominator_col]
+        nums = tmp.groupby(date_col).sum()[numerator_cols]
+
+        # Construct design matrix to have weekday indicator columns and then day
+        # indicators.
+        X = np.zeros((nums.shape[0], 6 + nums.shape[0]))
+        not_sunday = np.where(nums.index.dayofweek != 6)[0]
+        X[not_sunday, np.array(nums.index.dayofweek)[not_sunday]] = 1
+        X[np.where(nums.index.dayofweek == 6)[0], :6] = -1
+        X[:, 6:] = np.eye(X.shape[0])
+
+        npnums, npdenoms = np.array(nums), np.array(denoms)
+        params = np.zeros((nums.shape[1], X.shape[1]))
+
+        # Loop over the available numerator columns and smooth each separately.
+        for i in range(nums.shape[1]):
+            result = Weekday._fit(X, scales, npnums[:, i], npdenoms)
+            if result is None:
+                logger.error("Unable to calculate weekday correction")
+            else:
+                params[i,:] = result
+
+        return params
+
+    @staticmethod
+    def _fit(X, scales, npnums, npdenoms):
         r"""Correct a signal estimated as numerator/denominator for weekday effects.
 
         The ordinary estimate would be numerator_t/denominator_t for each time point
@@ -53,57 +78,31 @@ def get_params(data, logger):
 
         ll = (numerator * (X*b + log(denominator)) - sum(exp(X*b) + log(denominator)))
                 / num_days
-
-        Return a matrix of parameters: the entire vector of betas, for each time
-        series column in the data.
         """
-        denoms = data.groupby(Config.DATE_COL).sum()["Denominator"]
-        nums = data.groupby(Config.DATE_COL).sum()[Config.CLI_COLS + Config.FLU1_COL]
-
-        # Construct design matrix to have weekday indicator columns and then day
-        # indicators.
-        X = np.zeros((nums.shape[0], 6 + nums.shape[0]))
-        not_sunday = np.where(nums.index.dayofweek != 6)[0]
-        X[not_sunday, np.array(nums.index.dayofweek)[not_sunday]] = 1
-        X[np.where(nums.index.dayofweek == 6)[0], :6] = -1
-        X[:, 6:] = np.eye(X.shape[0])
-
-        npnums, npdenoms = np.array(nums), np.array(denoms)
-        params = np.zeros((nums.shape[1], X.shape[1]))
-
-        # Loop over the available numerator columns and smooth each separately.
-        for i in range(nums.shape[1]):
-            b = cp.Variable((X.shape[1]))
-
-            lmbda = cp.Parameter(nonneg=True)
-            lmbda.value = 10  # Hard-coded for now, seems robust to changes
-
-            ll = (
-                cp.matmul(npnums[:, i], cp.matmul(X, b) + np.log(npdenoms))
-                - cp.sum(cp.exp(cp.matmul(X, b) + np.log(npdenoms)))
-            ) / X.shape[0]
-            penalty = (
-                lmbda * cp.norm(cp.diff(b[6:], 3), 1) / (X.shape[0] - 2)
-            )  # L-1 Norm of third differences, rewards smoothness
-            scales = [1, 1e5, 1e10, 1e15]
-            for scale in scales:
-                try:
-                    prob = cp.Problem(cp.Minimize((-ll + lmbda * penalty) / scale))
-                    _ = prob.solve()
-                    params[i,:] = b.value
-                    break
-                except SolverError:
-                    # If the magnitude of the objective function is too large, an error is
-                    # thrown; Rescale the objective function by going through loop
-                    pass
-            else:
-                # Leaving params[i,:] = 0 is equivalent to not performing weekday correction
-                logger.error("Unable to calculate weekday correction")
-
-        return params
+        b = cp.Variable((X.shape[1]))
+
+        lmbda = cp.Parameter(nonneg=True)
+        lmbda.value = 10  # Hard-coded for now, seems robust to changes
+
+        ll = (
+            cp.matmul(npnums, cp.matmul(X, b) + np.log(npdenoms)) -
+            cp.sum(cp.exp(cp.matmul(X, b) + np.log(npdenoms)))
+        ) / X.shape[0]
+        # L-1 Norm of third differences, rewards smoothness
+        penalty = lmbda * cp.norm(cp.diff(b[6:], 3), 1) / (X.shape[0] - 2)
+        for scale in scales:
+            try:
+                prob = cp.Problem(cp.Minimize((-ll + lmbda * penalty) / scale))
+                _ = prob.solve()
+                return b.value
+            except SolverError:
+                # If the magnitude of the objective function is too large, an error is
+                # thrown; Rescale the objective function by going through loop
+                continue
+        return None
 
     @staticmethod
-    def calc_adjustment(params, sub_data):
+    def calc_adjustment(params, sub_data, cols, date_col):
         """Apply the weekday adjustment to a specific time series.
 
         Extracts the weekday fixed effects from the parameters and uses these to
@@ -122,14 +121,15 @@ def calc_adjustment(params, sub_data):
         -- this has the same effect.
 
         """
-        for i, c in enumerate(Config.CLI_COLS + Config.FLU1_COL):
-            wd_correction = np.zeros((len(sub_data[c])))
+        tmp = sub_data.copy()
+
+        for i, c in enumerate(cols):
+            wd_correction = np.zeros((len(tmp[c])))
 
             for wd in range(7):
-                mask = sub_data[Config.DATE_COL].dt.dayofweek == wd
-                wd_correction[mask] = sub_data.loc[mask, c] / (
+                mask = tmp[date_col].dt.dayofweek == wd
+                wd_correction[mask] = tmp.loc[mask, c] / (
                     np.exp(params[i, wd]) if wd < 6 else np.exp(-np.sum(params[i, :6]))
                 )
-            sub_data.loc[:, c] = wd_correction
-
-        return sub_data
+            tmp.loc[:, c] = wd_correction
+        return tmp
diff --git a/_delphi_utils_python/setup.py b/_delphi_utils_python/setup.py
index 8d361c0de..1ef144cb4 100644
--- a/_delphi_utils_python/setup.py
+++ b/_delphi_utils_python/setup.py
@@ -7,6 +7,7 @@
 required = [
     "boto3",
     "covidcast",
+    "cvxpy",
     "epiweeks",
     "freezegun",
     "gitpython",
diff --git a/_delphi_utils_python/tests/test_weekday.py b/_delphi_utils_python/tests/test_weekday.py
new file mode 100644
index 000000000..52e6f4f7e
--- /dev/null
+++ b/_delphi_utils_python/tests/test_weekday.py
@@ -0,0 +1,74 @@
+import logging
+
+import numpy as np
+import pandas as pd
+from delphi_utils.weekday import Weekday
+
+
+class TestWeekday:
+
+    TEST_DATA = pd.DataFrame({
+        "num": np.arange(1, 11, 1),
+        "den": np.arange(11, 21, 1),
+        "date": pd.date_range("2020-01-01", "2020-01-10")
+    })
+
+    def test_get_params(self):
+        TEST_LOGGER = logging.getLogger()
+
+        result = Weekday.get_params(self.TEST_DATA, "den", ["num"], "date", [1], TEST_LOGGER)
+        print(result)
+        expected_result = [
+            -0.05993665,
+            -0.0727396,
+            -0.05618517,
+            0.0343405,
+            0.12534997,
+            0.04561813,
+            -2.27669028,
+            -1.89564374,
+            -1.5695407,
+            -1.29838116,
+            -1.08216513,
+            -0.92089259,
+            -0.81456355,
+            -0.76317802,
+            -0.76673598,
+            -0.82523745,
+        ]
+        assert np.allclose(result, expected_result)
+
+    def test_calc_adjustment_with_zero_parameters(self):
+        params = np.array([[0, 0, 0, 0, 0, 0, 0]])
+
+        result = Weekday.calc_adjustment(params, self.TEST_DATA, ["num"], "date")
+
+        # Data should be unchanged when params are 0's
+        assert np.allclose(result["num"].values, self.TEST_DATA["num"].values)
+        assert np.allclose(result["den"].values, self.TEST_DATA["den"].values)
+        assert np.array_equal(result["date"].values, self.TEST_DATA["date"].values)
+
+    def test_calc_adjustment(self):
+        params = np.array([[1, -1, 1, -1, 1, -1, 1]])
+
+        result = Weekday.calc_adjustment(params, self.TEST_DATA, ["num"], "date")
+
+        print(result["num"].values)
+        print(result["den"].values)
+        expected_nums = [
+            0.36787944,
+            5.43656366,
+            1.10363832,
+            10.87312731,
+            5,
+            2.20727665,
+            19.0279728,
+            2.94303553,
+            24.46453646,
+            3.67879441,
+        ]
+
+        # The date and "den" column are unchanged by this function
+        assert np.allclose(result["num"].values, expected_nums)
+        assert np.allclose(result["den"].values, self.TEST_DATA["den"].values)
+        assert np.array_equal(result["date"].values, self.TEST_DATA["date"].values)
\ No newline at end of file
diff --git a/changehc/delphi_changehc/__init__.py b/changehc/delphi_changehc/__init__.py
index a8df7b6ce..2846a8de5 100644
--- a/changehc/delphi_changehc/__init__.py
+++ b/changehc/delphi_changehc/__init__.py
@@ -13,6 +13,5 @@
 from . import run
 from . import sensor
 from . import update_sensor
-from . import weekday
 
 __version__ = "0.0.0"
diff --git a/changehc/delphi_changehc/run.py b/changehc/delphi_changehc/run.py
index c640a00f3..73670798d 100644
--- a/changehc/delphi_changehc/run.py
+++ b/changehc/delphi_changehc/run.py
@@ -184,7 +184,7 @@ def run_module(params: Dict[str, Dict[str, Any]]):
                              file_dict["flu_like"],file_dict["covid_like"],dropdate_dt,"fips")
                 more_stats = su_inst.update_sensor(
                     data,
-                    params["common"]["export_dir"]
+                    params["common"]["export_dir"],
                 )
                 stats.extend(more_stats)
 
diff --git a/changehc/delphi_changehc/update_sensor.py b/changehc/delphi_changehc/update_sensor.py
index 5d9bd00bb..eb68691c9 100644
--- a/changehc/delphi_changehc/update_sensor.py
+++ b/changehc/delphi_changehc/update_sensor.py
@@ -11,13 +11,12 @@
 # third party
 import numpy as np
 import pandas as pd
-from delphi_utils import GeoMapper, add_prefix, create_export_csv
+from delphi_utils import GeoMapper, add_prefix, create_export_csv, Weekday
 
 # first party
 from .config import Config
 from .constants import SMOOTHED, SMOOTHED_ADJ, SMOOTHED_CLI, SMOOTHED_ADJ_CLI, NA
 from .sensor import CHCSensor
-from .weekday import Weekday
 
 
 def write_to_csv(df, geo_level, write_se, day_shift, out_name, logger, output_path=".", start_date=None, end_date=None):
@@ -176,8 +175,8 @@ def geo_reindex(self, data):
 
 
     def update_sensor(self,
-            data,
-            output_path):
+        data,
+        output_path):
         """Generate sensor values, and write to csv format.
 
         Args:
@@ -192,14 +191,22 @@ def update_sensor(self,
         data.reset_index(inplace=True)
         data_frame = self.geo_reindex(data)
         # handle if we need to adjust by weekday
-        wd_params = Weekday.get_params(data_frame) if self.weekday else None
+        wd_params = Weekday.get_params(
+            data_frame,
+            "den",
+            ["num"],
+            Config.DATE_COL,
+            [1, 1e5],
+            self.logger,
+        ) if self.weekday else None
         # run sensor fitting code (maybe in parallel)
         if not self.parallel:
             dfs = []
             for geo_id, sub_data in data_frame.groupby(level=0):
-                sub_data.reset_index(level=0,inplace=True)
+                sub_data.reset_index(inplace=True)
                 if self.weekday:
-                    sub_data = Weekday.calc_adjustment(wd_params, sub_data)
+                    sub_data = Weekday.calc_adjustment(wd_params, sub_data, ["num"], Config.DATE_COL)
+                sub_data.set_index(Config.DATE_COL, inplace=True)
                 res = CHCSensor.fit(sub_data, self.burnindate, geo_id, self.logger)
                 res = pd.DataFrame(res).loc[final_sensor_idxs]
                 dfs.append(res)
@@ -209,9 +216,10 @@ def update_sensor(self,
             with Pool(n_cpu) as pool:
                 pool_results = []
                 for geo_id, sub_data in data_frame.groupby(level=0,as_index=False):
-                    sub_data.reset_index(level=0, inplace=True)
+                    sub_data.reset_index(inplace=True)
                     if self.weekday:
-                        sub_data = Weekday.calc_adjustment(wd_params, sub_data)
+                        sub_data = Weekday.calc_adjustment(wd_params, sub_data, ["num"], Config.DATE_COL)
+                    sub_data.set_index(Config.DATE_COL, inplace=True)
                     pool_results.append(
                         pool.apply_async(
                             CHCSensor.fit, args=(sub_data, self.burnindate, geo_id, self.logger),
diff --git a/changehc/delphi_changehc/weekday.py b/changehc/delphi_changehc/weekday.py
deleted file mode 100644
index e02997482..000000000
--- a/changehc/delphi_changehc/weekday.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""
-Weekday effects (code from Aaron Rumack).
-
-Created: 2020-05-06
-"""
-
-# third party
-import cvxpy as cp
-import numpy as np
-from cvxpy.error import SolverError
-
-# first party
-from .config import Config
-
-
-class Weekday:
-    """Class to handle weekday effects."""
-
-    @staticmethod
-    def get_params(data):
-        r"""Correct a signal estimated as numerator/denominator for weekday effects.
-
-        The ordinary estimate would be numerator_t/denominator_t for each time point
-        t. Instead, model
-
-        log(numerator_t/denominator_t) = alpha_{wd(t)} + phi_t
-
-        where alpha is a vector of fixed effects for each weekday. For
-        identifiability, we constrain \sum alpha_j = 0, and to enforce this we set
-        Sunday's fixed effect to be the negative sum of the other weekdays.
-
-        We estimate this as a penalized Poisson GLM problem with log link. We
-        rewrite the problem as
-
-        log(numerator_t) = alpha_{wd(t)} + phi_t + log(denominator_t)
-
-        and set a design matrix X with one row per time point. The first six columns
-        of X are weekday indicators; the remaining columns are the identity matrix,
-        so that each time point gets a unique phi. Using this X, we write
-
-        log(numerator_t) = X beta + log(denominator_t)
-
-        Hence the first six entries of beta correspond to alpha, and the remaining
-        entries to phi.
-
-        The penalty is on the L1 norm of third differences of phi (so the third
-        differences of the corresponding columns of beta), to enforce smoothness.
-        Third differences ensure smoothness without removing peaks or valleys.
-
-        Objective function is negative mean Poisson log likelihood plus penalty:
-
-        ll = (numerator * (X*b + log(denominator)) - sum(exp(X*b) + log(denominator)))
-                / num_days
-
-        Return a matrix of parameters: the entire vector of betas, for each time
-        series column in the data.
-        """
-        tmp = data.reset_index()
-        denoms = tmp.groupby(Config.DATE_COL).sum()["den"]
-        nums = tmp.groupby(Config.DATE_COL).sum()["num"]
-        n_nums = 1  # only one numerator column
-
-        # Construct design matrix to have weekday indicator columns and then day
-        # indicators.
-        X = np.zeros((nums.shape[0], 6 + nums.shape[0]))  # pylint: disable=invalid-name
-        not_sunday = np.where(nums.index.dayofweek != 6)[0]
-        X[not_sunday, np.array(nums.index.dayofweek)[not_sunday]] = 1
-        X[np.where(nums.index.dayofweek == 6)[0], :6] = -1
-        X[:, 6:] = np.eye(X.shape[0])
-
-        npnums, npdenoms = np.array(nums), np.array(denoms)
-        params = np.zeros((n_nums, X.shape[1]))
-
-        # fit model
-        b = cp.Variable((X.shape[1]))
-        lmbda = cp.Parameter(nonneg=True)
-        lmbda.value = 10  # Hard-coded for now, seems robust to changes
-        ll = (cp.matmul(npnums, cp.matmul(X, b) + np.log(npdenoms))
-              - cp.sum(cp.exp(cp.matmul(X, b) + np.log(npdenoms)))
-              ) / X.shape[0]
-        penalty = (lmbda * cp.norm(cp.diff(b[6:], 3), 1) / (X.shape[0] - 2)
-                   )  # L-1 Norm of third differences, rewards smoothness
-        try:
-            prob = cp.Problem(cp.Minimize(-ll + lmbda * penalty))
-            _ = prob.solve()
-        except SolverError:
-            # If the magnitude of the objective function is too large, an error is
-            # thrown; Rescale the objective function
-            prob = cp.Problem(cp.Minimize((-ll + lmbda * penalty) / 1e5))
-            _ = prob.solve()
-        params = b.value
-
-        return params
-
-    @staticmethod
-    def calc_adjustment(params, sub_data):
-        """Apply the weekday adjustment to a specific time series.
-
-        Extracts the weekday fixed effects from the parameters and uses these to
-        adjust the time series.
-
-        Since
-
-        log(numerator_t / denominator_t) = alpha_{wd(t)} + phi_t,
-
-        we have that
-
-        numerator_t / denominator_t = exp(alpha_{wd(t)}) exp(phi_t)
-
-        and can divide by exp(alpha_{wd(t)}) to get a weekday-corrected ratio. In
-        this case, we only divide the numerator, leaving the denominator unchanged
-        -- this has the same effect.
-
-        """
-        tmp = sub_data.reset_index()
-
-        wd_correction = np.zeros((len(tmp["num"])))
-        for wd in range(7):
-            mask = tmp[Config.DATE_COL].dt.dayofweek == wd
-            wd_correction[mask] = tmp.loc[mask, "num"] / (
-                np.exp(params[wd]) if wd < 6 else np.exp(-np.sum(params[:6]))
-            )
-        tmp.loc[:, "num"] = wd_correction
-
-        return tmp.set_index(Config.DATE_COL)
diff --git a/changehc/setup.py b/changehc/setup.py
index d702874b3..e6f42e11e 100644
--- a/changehc/setup.py
+++ b/changehc/setup.py
@@ -4,7 +4,6 @@
 required = [
     "numpy",
     "pandas",
-    "cvxpy",
     "pydocstyle",
     "pytest",
     "pytest-cov",
diff --git a/claims_hosp/delphi_claims_hosp/__init__.py b/claims_hosp/delphi_claims_hosp/__init__.py
index 8a1e78f6c..194c98445 100644
--- a/claims_hosp/delphi_claims_hosp/__init__.py
+++ b/claims_hosp/delphi_claims_hosp/__init__.py
@@ -14,6 +14,5 @@
 from . import run
 from . import smooth
 from . import update_indicator
-from . import weekday
 
 __version__ = "0.1.0"
diff --git a/claims_hosp/delphi_claims_hosp/run.py b/claims_hosp/delphi_claims_hosp/run.py
index 5072bdcb8..58cba1c56 100644
--- a/claims_hosp/delphi_claims_hosp/run.py
+++ b/claims_hosp/delphi_claims_hosp/run.py
@@ -113,8 +113,11 @@ def run_module(params):
                 params["indicator"]["write_se"],
                 signal_name
             )
-            updater.update_indicator(params["indicator"]["input_file"],
-                                     params["common"]["export_dir"])
+            updater.update_indicator(
+                params["indicator"]["input_file"],
+                params["common"]["export_dir"],
+                logger,
+            )
         logger.info("finished updating", geo = geo)
 
     elapsed_time_in_seconds = round(time.time() - start_time, 2)
diff --git a/claims_hosp/delphi_claims_hosp/update_indicator.py b/claims_hosp/delphi_claims_hosp/update_indicator.py
index 61c4e318e..b4169370d 100644
--- a/claims_hosp/delphi_claims_hosp/update_indicator.py
+++ b/claims_hosp/delphi_claims_hosp/update_indicator.py
@@ -16,10 +16,10 @@
 from delphi_utils import GeoMapper
 
 # first party
+from delphi_utils import Weekday
 from .config import Config, GeoConstants
 from .load_data import load_data
 from .indicator import ClaimsHospIndicator
-from .weekday import Weekday
 
 
 class ClaimsHospIndicatorUpdater:
@@ -133,7 +133,7 @@ def geo_reindex(self, data):
         data_frame.fillna(0, inplace=True)
         return data_frame
 
-    def update_indicator(self, input_filepath, outpath):
+    def update_indicator(self, input_filepath, outpath, logger):
         """
         Generate and output indicator values.
 
@@ -152,7 +152,14 @@ def update_indicator(self, input_filepath, outpath):
         data_frame = self.geo_reindex(data)
 
         # handle if we need to adjust by weekday
-        wd_params = Weekday.get_params(data_frame) if self.weekday else None
+        wd_params = Weekday.get_params(
+            data_frame,
+            "den",
+            ["num"],
+            Config.DATE_COL,
+            [1, 1e5],
+            logger,
+        ) if self.weekday else None
 
         # run fitting code (maybe in parallel)
         rates = {}
@@ -160,9 +167,11 @@ def update_indicator(self, input_filepath, outpath):
         valid_inds = {}
         if not self.parallel:
             for geo_id, sub_data in data_frame.groupby(level=0):
-                sub_data.reset_index(level=0, inplace=True)
+                sub_data.reset_index(inplace=True)
                 if self.weekday:
-                    sub_data = Weekday.calc_adjustment(wd_params, sub_data)
+                    sub_data = Weekday.calc_adjustment(
+                        wd_params, sub_data, ["num"], Config.DATE_COL)
+                sub_data.set_index(Config.DATE_COL, inplace=True)
                 res = ClaimsHospIndicator.fit(sub_data, self.burnindate, geo_id)
                 res = pd.DataFrame(res)
                 rates[geo_id] = np.array(res.loc[final_output_inds, "rate"])
@@ -174,9 +183,11 @@ def update_indicator(self, input_filepath, outpath):
             with Pool(n_cpu) as pool:
                 pool_results = []
                 for geo_id, sub_data in data_frame.groupby(level=0, as_index=False):
-                    sub_data.reset_index(level=0, inplace=True)
+                    sub_data.reset_index(inplace=True)
                     if self.weekday:
-                        sub_data = Weekday.calc_adjustment(wd_params, sub_data)
+                        sub_data = Weekday.calc_adjustment(
+                            wd_params, sub_data, ["num"], Config.DATE_COL)
+                    sub_data.set_index(Config.DATE_COL, inplace=True)
                     pool_results.append(
                         pool.apply_async(
                             ClaimsHospIndicator.fit,
diff --git a/claims_hosp/delphi_claims_hosp/weekday.py b/claims_hosp/delphi_claims_hosp/weekday.py
deleted file mode 100644
index e02997482..000000000
--- a/claims_hosp/delphi_claims_hosp/weekday.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""
-Weekday effects (code from Aaron Rumack).
-
-Created: 2020-05-06
-"""
-
-# third party
-import cvxpy as cp
-import numpy as np
-from cvxpy.error import SolverError
-
-# first party
-from .config import Config
-
-
-class Weekday:
-    """Class to handle weekday effects."""
-
-    @staticmethod
-    def get_params(data):
-        r"""Correct a signal estimated as numerator/denominator for weekday effects.
-
-        The ordinary estimate would be numerator_t/denominator_t for each time point
-        t. Instead, model
-
-        log(numerator_t/denominator_t) = alpha_{wd(t)} + phi_t
-
-        where alpha is a vector of fixed effects for each weekday. For
-        identifiability, we constrain \sum alpha_j = 0, and to enforce this we set
-        Sunday's fixed effect to be the negative sum of the other weekdays.
-
-        We estimate this as a penalized Poisson GLM problem with log link. We
-        rewrite the problem as
-
-        log(numerator_t) = alpha_{wd(t)} + phi_t + log(denominator_t)
-
-        and set a design matrix X with one row per time point. The first six columns
-        of X are weekday indicators; the remaining columns are the identity matrix,
-        so that each time point gets a unique phi. Using this X, we write
-
-        log(numerator_t) = X beta + log(denominator_t)
-
-        Hence the first six entries of beta correspond to alpha, and the remaining
-        entries to phi.
-
-        The penalty is on the L1 norm of third differences of phi (so the third
-        differences of the corresponding columns of beta), to enforce smoothness.
-        Third differences ensure smoothness without removing peaks or valleys.
-
-        Objective function is negative mean Poisson log likelihood plus penalty:
-
-        ll = (numerator * (X*b + log(denominator)) - sum(exp(X*b) + log(denominator)))
-                / num_days
-
-        Return a matrix of parameters: the entire vector of betas, for each time
-        series column in the data.
-        """
-        tmp = data.reset_index()
-        denoms = tmp.groupby(Config.DATE_COL).sum()["den"]
-        nums = tmp.groupby(Config.DATE_COL).sum()["num"]
-        n_nums = 1  # only one numerator column
-
-        # Construct design matrix to have weekday indicator columns and then day
-        # indicators.
-        X = np.zeros((nums.shape[0], 6 + nums.shape[0]))  # pylint: disable=invalid-name
-        not_sunday = np.where(nums.index.dayofweek != 6)[0]
-        X[not_sunday, np.array(nums.index.dayofweek)[not_sunday]] = 1
-        X[np.where(nums.index.dayofweek == 6)[0], :6] = -1
-        X[:, 6:] = np.eye(X.shape[0])
-
-        npnums, npdenoms = np.array(nums), np.array(denoms)
-        params = np.zeros((n_nums, X.shape[1]))
-
-        # fit model
-        b = cp.Variable((X.shape[1]))
-        lmbda = cp.Parameter(nonneg=True)
-        lmbda.value = 10  # Hard-coded for now, seems robust to changes
-        ll = (cp.matmul(npnums, cp.matmul(X, b) + np.log(npdenoms))
-              - cp.sum(cp.exp(cp.matmul(X, b) + np.log(npdenoms)))
-              ) / X.shape[0]
-        penalty = (lmbda * cp.norm(cp.diff(b[6:], 3), 1) / (X.shape[0] - 2)
-                   )  # L-1 Norm of third differences, rewards smoothness
-        try:
-            prob = cp.Problem(cp.Minimize(-ll + lmbda * penalty))
-            _ = prob.solve()
-        except SolverError:
-            # If the magnitude of the objective function is too large, an error is
-            # thrown; Rescale the objective function
-            prob = cp.Problem(cp.Minimize((-ll + lmbda * penalty) / 1e5))
-            _ = prob.solve()
-        params = b.value
-
-        return params
-
-    @staticmethod
-    def calc_adjustment(params, sub_data):
-        """Apply the weekday adjustment to a specific time series.
-
-        Extracts the weekday fixed effects from the parameters and uses these to
-        adjust the time series.
-
-        Since
-
-        log(numerator_t / denominator_t) = alpha_{wd(t)} + phi_t,
-
-        we have that
-
-        numerator_t / denominator_t = exp(alpha_{wd(t)}) exp(phi_t)
-
-        and can divide by exp(alpha_{wd(t)}) to get a weekday-corrected ratio. In
-        this case, we only divide the numerator, leaving the denominator unchanged
-        -- this has the same effect.
-
-        """
-        tmp = sub_data.reset_index()
-
-        wd_correction = np.zeros((len(tmp["num"])))
-        for wd in range(7):
-            mask = tmp[Config.DATE_COL].dt.dayofweek == wd
-            wd_correction[mask] = tmp.loc[mask, "num"] / (
-                np.exp(params[wd]) if wd < 6 else np.exp(-np.sum(params[:6]))
-            )
-        tmp.loc[:, "num"] = wd_correction
-
-        return tmp.set_index(Config.DATE_COL)
diff --git a/claims_hosp/setup.py b/claims_hosp/setup.py
index 6c88e4383..940e1d165 100644
--- a/claims_hosp/setup.py
+++ b/claims_hosp/setup.py
@@ -4,7 +4,6 @@
 required = [
     "numpy",
     "pandas",
-    "cvxpy",
     "pydocstyle",
     "pytest",
     "pytest-cov",
diff --git a/claims_hosp/tests/test_update_indicator.py b/claims_hosp/tests/test_update_indicator.py
index 27bf1f263..23c901a49 100644
--- a/claims_hosp/tests/test_update_indicator.py
+++ b/claims_hosp/tests/test_update_indicator.py
@@ -7,6 +7,7 @@
 # third party
 import numpy as np
 import pandas as pd
+import logging
 import pytest
 
 # first party
@@ -25,6 +26,7 @@
 DATA_FILEPATH = PARAMS["indicator"]["input_file"]
 DROP_DATE = pd.to_datetime(PARAMS["indicator"]["drop_date"])
 OUTPATH = "test_data/"
+TEST_LOGGER = logging.getLogger()
 
 
 class TestClaimsHospIndicatorUpdater:
@@ -93,7 +95,8 @@ def test_update_indicator(self):
 
             updater.update_indicator(
                 DATA_FILEPATH,
-                td.name
+                td.name,
+                TEST_LOGGER
             )
 
             assert len(os.listdir(td.name)) == len(
diff --git a/doctor_visits/delphi_doctor_visits/__init__.py b/doctor_visits/delphi_doctor_visits/__init__.py
index 0aceb317a..bbb519448 100644
--- a/doctor_visits/delphi_doctor_visits/__init__.py
+++ b/doctor_visits/delphi_doctor_visits/__init__.py
@@ -15,6 +15,5 @@
 from . import sensor
 from . import smooth
 from . import update_sensor
-from . import weekday
 
 __version__ = "0.1.0"
diff --git a/doctor_visits/delphi_doctor_visits/update_sensor.py b/doctor_visits/delphi_doctor_visits/update_sensor.py
index 068d2a058..1bb068059 100644
--- a/doctor_visits/delphi_doctor_visits/update_sensor.py
+++ b/doctor_visits/delphi_doctor_visits/update_sensor.py
@@ -17,10 +17,10 @@
 import pandas as pd
 
 # first party
+from delphi_utils import Weekday
 from .config import Config
 from .geo_maps import GeoMaps
 from .sensor import DoctorVisitsSensor
-from .weekday import Weekday
 
 
 def write_to_csv(output_df: pd.DataFrame, geo_level, se, out_name, logger, output_path="."):
@@ -125,7 +125,14 @@ def update_sensor(
         (burn_in_dates >= startdate) & (burn_in_dates <= enddate))[0][:len(sensor_dates)]
 
     # handle if we need to adjust by weekday
-    params = Weekday.get_params(data, logger) if weekday else None
+    params = Weekday.get_params(
+        data,
+        "Denominator",
+        Config.CLI_COLS + Config.FLU1_COL,
+        Config.DATE_COL,
+        [1, 1e5, 1e10, 1e15],
+        logger,
+    ) if weekday else None
     if weekday and np.any(np.all(params == 0,axis=1)):
         # Weekday correction failed for at least one count type
         return None
@@ -145,7 +152,10 @@ def update_sensor(
         for geo_id in unique_geo_ids:
             sub_data = data_groups.get_group(geo_id).copy()
             if weekday:
-                sub_data = Weekday.calc_adjustment(params, sub_data)
+                sub_data = Weekday.calc_adjustment(params,
+                                                   sub_data,
+                                                   Config.CLI_COLS + Config.FLU1_COL,
+                                                   Config.DATE_COL)
 
             res = DoctorVisitsSensor.fit(
                 sub_data,
@@ -169,7 +179,10 @@ def update_sensor(
             for geo_id in unique_geo_ids:
                 sub_data = data_groups.get_group(geo_id).copy()
                 if weekday:
-                    sub_data = Weekday.calc_adjustment(params, sub_data)
+                    sub_data = Weekday.calc_adjustment(params,
+                                                       sub_data,
+                                                       Config.CLI_COLS + Config.FLU1_COL,
+                                                       Config.DATE_COL)
 
                 pool_results.append(
                     pool.apply_async(
diff --git a/doctor_visits/setup.py b/doctor_visits/setup.py
index d7c0fe0a9..4a4b62bb6 100644
--- a/doctor_visits/setup.py
+++ b/doctor_visits/setup.py
@@ -5,7 +5,6 @@
     "numpy",
     "pandas",
     "sklearn",
-    "cvxpy",
     "pytest",
     "pytest-cov",
     "pylint==2.8.3",