diff --git a/.github/workflows/continuous-integration.yaml b/.github/workflows/continuous-integration.yaml
index 1639000..4424731 100644
--- a/.github/workflows/continuous-integration.yaml
+++ b/.github/workflows/continuous-integration.yaml
@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9']
+        python-version: ['3.11']
 
     steps:
     - uses: conda-incubator/setup-miniconda@v2
diff --git a/augur/_version.py b/augur/_version.py
index d3ec452..493f741 100644
--- a/augur/_version.py
+++ b/augur/_version.py
@@ -1 +1 @@
-__version__ = "0.2.0"
+__version__ = "0.3.0"
diff --git a/augur/analyze.py b/augur/analyze.py
index 1d228db..ba3e0aa 100644
--- a/augur/analyze.py
+++ b/augur/analyze.py
@@ -41,6 +41,7 @@ def __init__(self, config, likelihood=None, tools=None, req_params=None):
         self.lk = likelihood  # Just to save some typing
         self.tools = tools
         self.req_params = req_params
+        self.data_fid = self.lk.get_data_vector()
 
         _config = parse_config(config)  # Load full config
         # Get the fiducial cosmological parameters
@@ -128,7 +129,9 @@ def f(self, x, labels, pars_fid, sys_fid):
                 self.tools.reset()
                 self.lk.reset()
                 cosmo = ccl.Cosmology(**_pars)
-                self.lk.update(ParamsMap(_sys_pars))
+                pmap = ParamsMap(_sys_pars)
+                self.lk.update(pmap)
+                self.tools.update(pmap)
                 self.tools.prepare(cosmo)
                 f_out = self.lk.compute_theory_vector(self.tools)
             elif x.ndim == 2:
@@ -146,8 +149,10 @@ def f(self, x, labels, pars_fid, sys_fid):
                             raise ValueError(f'Parameter name {labels[j]} not recognized')
                     self.tools.reset()
                     self.lk.reset()
-                    self.lk.update(ParamsMap(_sys_pars))
+                    pmap = ParamsMap(_sys_pars)
+                    self.lk.update(pmap)
                     cosmo = ccl.Cosmology(**_pars)
+                    self.tools.update(pmap)
                     self.tools.prepare(cosmo)
                     f_out.append(self.lk.compute_theory_vector(self.tools))
             return np.array(f_out)
@@ -186,7 +191,7 @@ def get_fisher_matrix(self, method='5pt_stencil'):
         else:
             return self.Fij
 
-    def compute_fisher_bias(self):
+    def get_fisher_bias(self):
         # Compute Fisher bias following the generalized Amara formalism
         # More details in Bianca's thesis and the note here:
         # https://github.com/LSSTDESC/augur/blob/note_bianca/note/main.tex
@@ -195,8 +200,14 @@ def compute_fisher_bias(self):
         # They should have the same ells as the original data-vector
         # and the same length
         import os
+
+        if self.derivatives is None:
+            self.get_derivatives()
+
+        if self.Fij is None:
+            self.get_fisher_matrix()
+
         _calculate_biased_cls = True
-        _cls_fid = self.lk.measured_data_vector  # Get the fiducial data vector
 
         # Try to read the biased data vector
         if 'biased_dv' in self.config['fisher_bias']:
@@ -216,7 +227,7 @@ def compute_fisher_bias(self):
                     raise ValueError('The length of the provided Cls should be equal \
                                     to the length of the data-vector')
                 _calculate_biased_cls = False
-                self.biased_cls = biased_cls['dv_sys'] - _cls_fid
+                self.biased_cls = biased_cls['dv_sys'] - self.data_fid
 
         # If there's no biased data vector, calculate it
         if _calculate_biased_cls:
@@ -240,13 +251,8 @@ def compute_fisher_bias(self):
             else:
                 raise ValueError('bias_params is required if no biased_dv file is passed')
 
-            self.biased_cls = self.f(_x_here, _labels_here, _pars_here, _sys_here) - _cls_fid
+            self.biased_cls = self.f(_x_here, _labels_here, _pars_here, _sys_here) - self.data_fid
 
-        if self.derivatives is None:
-            self.get_derivatives()
         Bj = np.einsum('l, lm, jm', self.biased_cls, self.lk.inv_cov, self.derivatives)
-
-        if self.Fij is None:
-            self.get_fisher_matrix()
         bi = np.einsum('ij, j', np.linalg.inv(self.Fij), Bj)
         self.bi = bi
diff --git a/augur/generate.py b/augur/generate.py
index 4c8bc8b..cd95c4c 100644
--- a/augur/generate.py
+++ b/augur/generate.py
@@ -231,8 +231,16 @@ def generate_sacc_and_stats(config):
                 ells_here = ells[ells < ell_max]
             else:
                 ells_here = ells
-            for ell in ells_here:
-                S.add_data_point(key, (tr1, tr2), 0.0, ell=ell, error=1e30)
+            # Trying to add bandpower windows
+            ells_aux = np.arange(0, np.max(ells_here)+1)
+            wgt = np.zeros((len(ells_aux), len(ells_here)))
+            for i in range(len(ells_here)):
+                in_win = (ells_aux > ell_edges[i]) & (ells_aux < ell_edges[i+1])
+                wgt[in_win, i] = 1.0
+            win = sacc.BandpowerWindow(ells_aux, wgt)
+            S.add_ell_cl(key, tr1, tr2,
+                         ells_here, np.zeros(len(ells_here)), window=win)
+
             # Now create TwoPoint objects for firecrown
             _aux_stat = TwoPoint(source0=sources[tr1], source1=sources[tr2],
                                  sacc_data_type=key)
@@ -242,7 +250,7 @@ def generate_sacc_and_stats(config):
     return S, cosmo, stats, sys_params
 
 
-def generate(config, return_all_outputs=False, write_sacc=True, force_read=True):
+def generate(config, return_all_outputs=False, write_sacc=True):
     """
     Generate likelihood object and sacc file with fiducial cosmology
 
@@ -257,26 +265,26 @@ def generate(config, return_all_outputs=False, write_sacc=True, force_read=True)
         likelihood object.
     write_sacc : bool
         If `True` it writes a sacc file with fiducial data vector.
-    force_read : bool
-        If `True` it repopulates the likelihood data vector with the contents of the Sacc file
-        generated here. Note: For high-condition covariances where the Cholesky decomposition
-        fails, setting force_read to `True` may result in a `LinAlgError`.
 
     Returns:
     -------
-    noise : float
-       Noise power for Cls for that particular tracer. That is 1/nbar for
-       number counts and e**2/nbar for weak lensing tracer.
-
-    Note:
-    -----
-    The input number_densities are #/arcmin.
-    The output units are in steradian.
+    lk : firecrown.likelihood.ConstGaussian
+        Likelihood object, only returned if `return_all_outputs` is True.
+    S : sacc.Sacc
+        Sacc object with fake data vector and covariance. It is only returned if
+        `return_all_outputs` is True.
+    tools : firecrown.modeling.ModelingTools
+        Modeling tools, only returned if `return_all_outputs` is True.
+    sys_params : dict
+        Dictionary containing the modeling systematic parameters. It is only returned if
+        `return_all_outputs` is True.
+
     """
 
     config = parse_config(config)
     # Generate placeholders
     S, cosmo, stats, sys_params = generate_sacc_and_stats(config)
+
     # Generate likelihood object
     lk = ConstGaussian(statistics=stats)
     # Pass the correct binning/tracers
@@ -293,6 +301,7 @@ def generate(config, return_all_outputs=False, write_sacc=True, force_read=True)
     cosmo.compute_nonlin_power()
     tools = ModelingTools(pt_calculator=pt_calculator)
     lk.update(sys_params)
+    tools.update(sys_params)
     tools.prepare(cosmo)
     # Run the likelihood (to get the theory)
     lk.compute_loglike(tools)
@@ -306,7 +315,8 @@ def generate(config, return_all_outputs=False, write_sacc=True, force_read=True)
         st = st.statistic
         st.ready = False
         S.add_ell_cl(st.sacc_data_type, st.sacc_tracers[0], st.sacc_tracers[1],
-                     st.ell_or_theta_, st.predicted_statistic_)
+                     st.ell_or_theta_, st.get_theory_vector(),
+                     window=st.theory_window_function)
     if config['cov_options']['cov_type'] == 'gaus_internal':
         fsky = config['cov_options']['fsky']
         cov = get_gaus_cov(S, lk, cosmo, fsky, config)
@@ -368,9 +378,7 @@ def generate(config, return_all_outputs=False, write_sacc=True, force_read=True)
         print(config['fiducial_sacc_path'])
         S.save_fits(config['fiducial_sacc_path'], overwrite=True)
     # Update covariance and inverse -- TODO need to update cholesky!!
-    if force_read:
-        # Hacky way to overwrite...
-        lk.read(S)
-        lk.measured_data_vector = lk.get_data_vector()
+    lk = ConstGaussian(statistics=stats)
+    lk.read(S)
     if return_all_outputs:
         return lk, S, tools, sys_params
diff --git a/augur/tests/test_analyze.py b/augur/tests/test_analyze.py
index c6d1080..5badba1 100644
--- a/augur/tests/test_analyze.py
+++ b/augur/tests/test_analyze.py
@@ -5,4 +5,4 @@ def test_analyze():
     fish = Analyze('./examples/config_test.yml')
     fish.get_derivatives()
     fish.get_fisher_matrix()
-    fish.compute_fisher_bias()
+    fish.get_fisher_bias()
diff --git a/environment.yml b/environment.yml
index a3bafba..f284945 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,12 +2,11 @@ name: forecasting
 channels:
   - conda-forge
 dependencies:
-  - firecrown
+  - firecrown>=1.7.1
+  - flake8
   - healpy
   - jinja2
+  - numdifftools
   - numpydoc
-  - pip
-  - flake8
-  - pip:
-    - tjpcov
-  - qp
+  - qp-prob
+  - tjpcov
diff --git a/pylintrc b/pylintrc
new file mode 100644
index 0000000..0e8627d
--- /dev/null
+++ b/pylintrc
@@ -0,0 +1,71 @@
+[MAIN]
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use, and will cap the count on Windows to
+# avoid hangs.
+jobs=0
+
+# Minimum Python version to use for version dependent checks. Will default to
+# the version used to run pylint.
+py-version=3.9
+
+# Discover python modules and packages in the file system subtree.
+recursive=yes
+
+# Add custom pylint plugins
+# load-plugins=pylint_plugins.duplicate_code
+
+[MESSAGES CONTROL]
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=c-extension-no-member
+disable=too-few-public-methods
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=any
+
+# Naming style matching correct attribute names.
+attr-naming-style=any
+
+# Naming style matching correct variable names.
+variable-naming-style=any
+
+# Naming style matching correct module level constants names.
+const-naming-style=any
+
+# Naming style matching correct method names.
+method-naming-style=any
+
+# Naming style matching correct function names.
+function-naming-style=any
+
+[STRING]
+
+# This flag controls whether inconsistent-quotes generates a warning when the
+# character used as a quote delimiter is used inconsistently within a module.
+check-quote-consistency=yes
+
+[TYPECHECK]
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=false
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=LF
+
+[DESIGN]
+max-args=20
+max-attributes=20
diff --git a/setup.cfg b/setup.cfg
index fbcdfb7..891b9ec 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -17,7 +17,7 @@ install_requires =
     jinja2
     healpy
     numpydoc
-    qp
+    qp-prob
 
 [options.packages.find]
 exclude =