Binned quantities

.gitignore

@@ -170,3 +170,4 @@ notebooks/demo_check_footprint.ipynb
 notebooks/demo_calibrate_minimal_cat.ipynb
 notebooks/leakage_minimal.ipynb
 notebooks/demo_add_bands.ipynb
+notebooks/demo_add_bands_to_empty.ipynb

notebooks/leakage_minimal.py

@@ -1,5 +1,5 @@
 # %%
-# Plot binned quantites, see leakage_minima.py
+# Compute binned quantities, see plot_binned_quantities.py
 
 # %%                                                                             
 from IPython import get_ipython                                                  
@@ -13,6 +13,7 @@
     ipython.run_line_magic("load_ext", "log_cell_time")
 
 # %%
+import os
 import numpy as np
 import pandas as pd
 
@@ -28,7 +29,7 @@
 from sp_validation import plots as sp_plots
 
 # %%
-os.getcwd()
+print("pwd:", os.getcwd())
 
 # %%
 # Initialize calibration class instance
@@ -135,28 +136,14 @@
 
 df = calibration.build_df(cat_gal)
 
-# %
-
-num_bins_x = 12
-num_bins_y = 12
+# %%
+num_bins_x = 15
+num_bins_y = 15
 
-quantities, bin_edges = calibration.get_quantities_binned(cat_gal, num_bins_x, num_bins_y)
+s
+quantities, bin_edges = calibration.get_quantities_binned(cat_gal, num_bins_x, num_bins_y, which=which)
 
 # Save binned matrices to files
 for key in quantities:
     io.write_binned_quantity(quantities[key], key, bin_edges)
-
 # %%
-vmin = {"diag": -0.2, "offdiag": -0.2}
-vmax =  {"diag": 1.2, "offdiag": 0.2}
-
-sp_plots.plot_binned(quantities, "response", bin_edges["snr"], bin_edges["size_ratio"], "R", vmin=vmin, vmax=vmax, xlabel="SNR", ylabel=r"$r / r_{\rm psf}$")
-
-# %%
-sp_plots.plot_binned(quantities, "number", bin_edges["snr"], bin_edges["size_ratio"], "R", vmin=1, vmax=np.nanmax(quantities["number"]), xlabel="SNR", ylabel=r"$r / r_{\rm psf}$")
-
-# %%
-vmin = {"diag": -0.2, "offdiag": -0.2}
-vmax = {"diag": 0.2, "offdiag": 0.2}
-
-sp_plots.plot_binned(quantities, "leakage", bin_edges["snr"], bin_edges["size_ratio"], r"\alpha", vmin=vmin, vmax=vmax, xlabel="SNR", ylabel=r"$r / r_{\rm psf}$")

notebooks/plot_binned_quantities.py

@@ -1,5 +1,5 @@
 # %%
-# Plot binned quantites, which are the outputs of leakage_minima.py
+# Plot binned quantities, which are the outputs of leakage_minimal.py
 
 # %%                                                                             
 from IPython import get_ipython                                                  
@@ -11,7 +11,7 @@
     ipython.run_line_magic("load_ext", "autoreload")                             
     ipython.run_line_magic("autoreload", "2")   
 
-# %%                          
+# %%                       
 import matplotlib.pyplot as plt                                                  
 import numpy as np  
 import numpy as np
@@ -28,19 +28,21 @@
 from sp_validation import io           
 from sp_validation import plots          
 
-
-# %%
-# enable inline plotting for interactive sessions                                
-# (must be done *after* importing package that sets agg backend)                 
-if ipython is not None:                                                          
-    ipython.run_line_magic("matplotlib", "inline")    
+
+## %%
+## enable inline plotting for interactive sessions                                
+## (must be done *after* importing package that sets agg backend)                 
+#if ipython is not None:         
+#    print("matplotlib inline")                                                 
+#    ipython.run_line_magic("matplotlib", "inline")    
 
 # %%
 bin_edges = {}
 quantities = {}
 
 # %%
-keys = ["number", "response", "leakage"]
+keys = ["number", "response", "leakage", "w_iv", "mag", "NGMIX_Tpsf_NOSHEAR", "N_EPOCH", "e1_PSF", "e2_PSF", "fwhm_PSF"]
+
 
 for key in keys:
     fname = f"{key}_binned.npz"
@@ -49,21 +51,68 @@
         if xy  != "quantity":
             bin_edges[xy] = result[xy]
     quantities[key] = result["quantity"]
+
+xlabel = "SNR"
+ylabel = r"$r / r_{\rm psf}$"
 
-# %%
-vmin = {"diag": -0.2, "offdiag": -0.03}
-vmax =  {"diag": 1.2, "offdiag": 0.03}
+lines = {
+    "x": [10, 500],
+    "y": [0.5, 3],
+}
 
-plots.plot_binned(quantities, "response", bin_edges["snr"], bin_edges["size_ratio"], "R", vmin=vmin, vmax=vmax, xlabel="SNR", ylabel=r"$r / r_{\rm psf}$")
 
-# %%
-plots.plot_binned(quantities, "number", bin_edges["snr"], bin_edges["size_ratio"], "R", vmin=1, vmax=np.nanmax(quantities["number"]), xlabel="SNR", ylabel=r"$r / r_{\rm psf}$")
 
 # %%
-vmin = {"diag": -0.2, "offdiag": -0.2}
-vmax = {"diag": 0.1, "offdiag": 0.1}
+vmin = {"diag": -0.2, "offdiag": -0.1}
+vmax =  {"diag": 1.2, "offdiag": 0.1}
+
+plots.plot_binned(
+    quantities,
+    "response",
+    bin_edges["snr"],
+    bin_edges["size_ratio"],
+    title="R",
+    vmin=vmin,
+    vmax=vmax,
+    xlabel=xlabel,
+    ylabel=ylabel,
+    lines=lines,
+    close_fig=False,
+)
 
-plots.plot_binned(quantities, "leakage", bin_edges["snr"], bin_edges["size_ratio"], r"\alpha", vmin=vmin, vmax=vmax, xlabel="SNR", ylabel=r"$r / r_{\rm psf}$")
+# %%
+plots.plot_binned(
+    quantities,
+    "number",
+    bin_edges["snr"],
+    bin_edges["size_ratio"],
+    title="n",
+    vmin=1,
+    vmax=np.nanmax(quantities["number"]),
+    xlabel=xlabel,
+    ylabel=ylabel,
+    lines=lines,
+    close_fig=False,
+)
 
+# %%
+vmin = {"diag": -0.2, "offdiag": -0.2}
+vmax = {"diag": 0.2, "offdiag": 0.2}
+
+plots.plot_binned(
+    quantities,
+    "leakage",
+    bin_edges["snr"],
+    bin_edges["size_ratio"],
+    title=r"\alpha",
+    vmin=vmin,
+    vmax=vmax,
+    xlabel=xlabel,
+    ylabel=ylabel,
+    lines=lines,
+    close_fig=False,
+)
 
 # %%
+bin_edges
+# %%

src/sp_validation/calibration.py

@@ -496,8 +496,8 @@ def get_quantities_binned(cat_gal, num_bins_x, num_bins_y=None, which=["response
 
     # Create logarithmic bins in size and SNR
     bin_edges = {}
-    bin_edges["snr"] = cut_to_bins(df_gal, "snr", num_bins_x, type="log", x_min=3, x_max=300)
-    bin_edges["size_ratio"] = cut_to_bins(df_gal, "size_ratio", num_bins_y, type="log", x_min=0.3, x_max=20)
+    bin_edges["snr"] = cut_to_bins(df_gal, "snr", num_bins_x, type="log", x_min=3, x_max=700)
+    bin_edges["size_ratio"] = cut_to_bins(df_gal, "size_ratio", num_bins_y, type="log", x_min=0.3, x_max=10)
 
     # Initialize output dict
     quantities = {}