-
+
@@ -7703,18 +7703,18 @@
+
1.2 Fields
The first 5 harmonic coefficients of the masked map are:
-[-0.01108506+0.j 0.00632131+0.j 0.0126594 +0.j -0.01295433+0.j
- -0.01352294+0.j]
+[-0.10252715+0.j -0.00173879+0.j 0.1072153 +0.j 0.00386785+0.j
+ -0.09147785+0.j]
The first 5 harmonic coefficients of the mask are:
-[ 8.27262120e-02+0.j -6.89504011e-20+0.j -8.60449099e-02+0.j
- 1.49830650e-19+0.j 7.26256197e-02+0.j]
+[ 8.27262120e-02+0.j -1.48460427e-19+0.j -8.60449099e-02+0.j
+ 3.17723110e-19+0.j 7.26256197e-02+0.j]
-
+
@@ -7866,13 +7866,13 @@ 1.4 Pseudo-$C_\ell$s
Out[5]:
-<matplotlib.legend.Legend at 0x70a588478d40>
+<matplotlib.legend.Legend at 0x790fe4199df0>
-
+
@@ -8020,7 +8020,7 @@ 1.5 Mode-coupling matrices
-In [7]:
+In [8]:
print("Sum of MCM over ell': %.5lf" % (np.dot(mcm, np.ones(len(mcm)))[0]))
@@ -8060,10 +8060,13 @@ 1.6 Mode-decoupled power spectra
-In [8]:
+In [9]:
-# First, estimated decoupled bandpowers. The explicit way of doing this would be to compute
+# As before, we start by computing the mode-coupling coefficients
+w = nmt.NmtWorkspace.from_fields(f, f, b)
+
+# First, estimated decoupled bandpowers. The explicit way of doing this would be to compute
# the binned PCL and the binned MCM. Then, invert the binned MCM, and multiply it by the PCL.
# All this can be done with a simple NaMaster call:
cl_dec = w.decouple_cell(pcl)
@@ -8115,15 +8118,15 @@ 1.6 Mode-decoupled power spectra
-Out[8]:
+Out[9]:
-<matplotlib.legend.Legend at 0x71e72ed7c860>
+<matplotlib.legend.Legend at 0x790fe043b200>
-
+
@@ -8144,7 +8147,7 @@ 1.7 Unbiased estimator
-In [9]:
+In [10]:
nsims = 100
@@ -8191,7 +8194,7 @@ 1.7 Unbiased estimator
-
+
@@ -8211,6 +8214,20 @@ 1.7 Unbiased estimator
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
The first 5 harmonic coefficients of the masked map are: -[-0.01108506+0.j 0.00632131+0.j 0.0126594 +0.j -0.01295433+0.j - -0.01352294+0.j] +[-0.10252715+0.j -0.00173879+0.j 0.1072153 +0.j 0.00386785+0.j + -0.09147785+0.j] The first 5 harmonic coefficients of the mask are: -[ 8.27262120e-02+0.j -6.89504011e-20+0.j -8.60449099e-02+0.j - 1.49830650e-19+0.j 7.26256197e-02+0.j] +[ 8.27262120e-02+0.j -1.48460427e-19+0.j -8.60449099e-02+0.j + 3.17723110e-19+0.j 7.26256197e-02+0.j]
-
+
+
Out[5]:
-
<matplotlib.legend.Legend at 0x70a588478d40>+
<matplotlib.legend.Legend at 0x790fe4199df0>
-
+
+
1.5 Mode-coupling matrices
-In [7]:
+In [8]:
print("Sum of MCM over ell': %.5lf" % (np.dot(mcm, np.ones(len(mcm)))[0]))
@@ -8060,10 +8060,13 @@ 1.6 Mode-decoupled power spectra
-In [8]:
+In [9]:
-# First, estimated decoupled bandpowers. The explicit way of doing this would be to compute
+# As before, we start by computing the mode-coupling coefficients
+w = nmt.NmtWorkspace.from_fields(f, f, b)
+
+# First, estimated decoupled bandpowers. The explicit way of doing this would be to compute
# the binned PCL and the binned MCM. Then, invert the binned MCM, and multiply it by the PCL.
# All this can be done with a simple NaMaster call:
cl_dec = w.decouple_cell(pcl)
@@ -8115,15 +8118,15 @@ 1.6 Mode-decoupled power spectra
-Out[8]:
+Out[9]:
-<matplotlib.legend.Legend at 0x71e72ed7c860>
+<matplotlib.legend.Legend at 0x790fe043b200>
-
+
@@ -8144,7 +8147,7 @@ 1.7 Unbiased estimator
-In [9]:
+In [10]:
nsims = 100
@@ -8191,7 +8194,7 @@ 1.7 Unbiased estimator
-
+
@@ -8211,6 +8214,20 @@ 1.7 Unbiased estimator
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
In [7]:
+In [8]:
print("Sum of MCM over ell': %.5lf" % (np.dot(mcm, np.ones(len(mcm)))[0]))
@@ -8060,10 +8060,13 @@ 1.6 Mode-decoupled power spectra
-
In [8]:
+In [9]:
-
# First, estimated decoupled bandpowers. The explicit way of doing this would be to compute
+# As before, we start by computing the mode-coupling coefficients
+w = nmt.NmtWorkspace.from_fields(f, f, b)
+
+# First, estimated decoupled bandpowers. The explicit way of doing this would be to compute
# the binned PCL and the binned MCM. Then, invert the binned MCM, and multiply it by the PCL.
# All this can be done with a simple NaMaster call:
cl_dec = w.decouple_cell(pcl)
@@ -8115,15 +8118,15 @@ 1.6 Mode-decoupled power spectra
-Out[8]:
+Out[9]:
-<matplotlib.legend.Legend at 0x71e72ed7c860>
+<matplotlib.legend.Legend at 0x790fe043b200>
-
+
@@ -8144,7 +8147,7 @@ 1.7 Unbiased estimator
-In [9]:
+In [10]:
nsims = 100
@@ -8191,7 +8194,7 @@ 1.7 Unbiased estimator
-
+
@@ -8211,6 +8214,20 @@ 1.7 Unbiased estimator
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+