Merge pull request #74 from e-koch/master

Updates to some examples scripts; Change numpy version for Travis

.travis.yml

@@ -19,13 +19,13 @@ install:
     - source activate test
 
     # Now install dependencies
-    - conda install --yes numpy
+    - conda install --yes numpy==1.9.0
     - conda install --yes scipy
     - conda install --yes matplotlib
     - conda install --yes pandas
     - conda install --yes statsmodels
     - conda install --yes scikit-learn
-    - conda install --yes astropy==0.4rc1
+    - conda install --yes astropy
 
     # Use pip to install development versions
     - pip install git+https://github.com/dendrograms/astrodendro.git

Examples/analysis_pipeline.py

@@ -1,6 +1,7 @@
 
 import numpy as np
 import turbustat.analysis as ta
+from turbustat.statistics import statistics_list
 import os
 import subprocess
 import sys
@@ -106,20 +107,21 @@
 print "Making distance plots."
 
 ta.comparison_plot(path, comparisons=good_comparison,
-                   out_path=path+"Distance Plots/")
+                   out_path=path+"Distance Plots/",
+                   design_matrix=design_matrix)
 
 # Run the R-script to fit the data to the model
 
 # Must have 0_0 and 2_2 comparisons to run
 
-if not "0_0" in good_comparison and not "2_2" in comparison_plot:
+if "0_0" not in good_comparison and "2_2" not in good_comparison:
     raise StandardError("Model fitting requires 0_0 and 2_2 to be available.")
 
 os.chdir(path)
 
 print "Fitting model of given design."
 
-subprocess.call(['Rscript', path+"FactorialAnalysis.R"])
+subprocess.call(['Rscript', "FactorialAnalysis.R"])
 
 # This should create two output tables of the whole dataset.
 
@@ -128,23 +130,37 @@
 print "Running metric validation."
 
 subprocess.call(['Rscript',
-                 "/Users/eric/Dropbox/code_development/TurbuStat/Examples/noise_validation.r",
+                 os.path.join(turbustat_path, "Examples/noise_validation.r"),
                  path, "10000"])
 
 subprocess.call(['Rscript',
-                 "/Users/eric/Dropbox/code_development/TurbuStat/Examples/signal_validation.r",
+                 os.path.join(turbustat_path, "Examples/signal_validation.r"),
                  path, "10000"])
 
 # Finally, create the model plots
 
 print "Creating model plots."
 
-execfile(turbustat_path+"Examples/effect_plots.py")
+execfile(os.path.join(turbustat_path, "Examples/effect_plots.py"))
 
-effect_plots(path+"DataforFits.csv", path+"ResultsFactorial.csv", save=True,
-             out_path=path+'Model Plots/')
+# Remove PDF_AD from the list
 
-map_all_results(path+"ResultsFactorial.csv", save=True, normed=True,
-                out_path=path+'Model Plots/')
+statistics_list.remove("PDF_AD")
+
+effect_plots("DataforFits.csv", "ResultsFactorial.csv", save=True,
+             out_path='Model Plots/')
+
+# Only show results of the good statistics
+good_stats = ["Cramer", "DeltaVariance", "Dendrogram_Hist",
+              "Dendrogram_Num", "PCA", "PDF_Hellinger", "SCF", "VCA", "VCS",
+              "VCS_Density", "VCS_Velocity", "Skewness", "Kurtosis"]
+
+# THE ASPECT RATIO IS FUNKY
+# NEED TO ADJUST BY HAND
+# Use: p.ion(), and set save_name=None to alter by-hand
+
+map_all_results("ResultsFactorial.csv", normed=False, max_order=2,
+                save_name="map_all_results.pdf",
+                out_path='Model Plots/', statistics=good_stats)
 
 print "Finished!"

Examples/effect_plots.py

@@ -253,18 +253,18 @@ def map_all_results(effects_file, min_zscore=2.0, save_name=None,
                          10,
                          2)
 
-    p.figure(figsize=(16, 7))
     p.imshow(values, vmin=0, vmax=10, cmap=milagro,
              interpolation="nearest")
     p.xticks(np.arange(len(model_effects)), model_effects, rotation=90,
-             fontsize=18)
-    p.yticks(np.arange(len(statistics)), stat_labels, fontsize=18)
+             fontsize=24)
+    p.yticks(np.arange(len(statistics)), stat_labels, fontsize=24)
     cbar = p.colorbar(fraction=0.05, shrink=0.9)
-    cbar.ax.set_ylabel(r'$t$-value', size=18)
-    cbar.ax.tick_params(labelsize=18)
+    cbar.ax.set_ylabel(r'$t$-value', size=24)
+    cbar.ax.tick_params(labelsize=24)
     # Avoid white lines in the pdf rendering
     cbar.solids.set_edgecolor("face")
 
+    p.axes().set_aspect('auto')
     p.tight_layout()
 
     # Save if save_name has been given

Examples/make_distance_plots.py

@@ -46,9 +46,10 @@
     obs_to_fid_path, comparisons=obs_to_fid_comparisons,
     out_path=os.path.join(obs_to_fid_path, "Distance Plots"),
     num_fids=5, design_matrix=design_matrix, obs_to_fid=True, legend=False,
+    obs_legend=True,
     statistics=["Cramer", "DeltaVariance", "Dendrogram_Hist",
-                "Dendrogram_Num", "PCA", "PDF_Hellinger", "SCF", "VCA", "VCS",
-                "VCS_Density", "VCS_Velocity"])
+                "Dendrogram_Num", "PCA", "SCF", "VCA", "VCS",
+                "VCS_Density", "VCS_Velocity", "Skewness", "Kurtosis"])
 
 # Des to Obs
 
@@ -61,5 +62,5 @@
     num_fids=3, design_matrix=design_matrix,
     legend_labels=["Oph A", "IC 348", "NGC 1333"],
     statistics=["Cramer", "DeltaVariance", "Dendrogram_Hist",
-                "Dendrogram_Num", "PCA", "PDF_Hellinger", "SCF", "VCA", "VCS",
-                "VCS_Density", "VCS_Velocity"])
+                "Dendrogram_Num", "PCA", "SCF", "VCA", "VCS",
+                "VCS_Density", "VCS_Velocity", "Skewness", "Kurtosis"])

Examples/make_obs_tables.py

@@ -0,0 +1,38 @@
+
+# Create data tables of the observational results
+# Run from Dropbox/AstroStatistics/Full Factorial/Observational Results/
+
+import os
+import shutil
+
+from turbustat.analysis.convert_results import concat_convert_HDF5
+from turbustat.analysis import convert_format
+
+
+# Obs to Fids
+
+path = "Obs_to_Fid/"
+hdf5_path = "Obs_to_Fid/HDF5/"
+
+convert_format(hdf5_path, 0)
+shutil.move(hdf5_path+"complete_distances_face_0.csv", path)
+
+convert_format(hdf5_path, 1)
+shutil.move(hdf5_path+"complete_distances_face_1.csv", path)
+
+convert_format(hdf5_path, 2)
+shutil.move(hdf5_path+"complete_distances_face_2.csv", path)
+
+# Des to Obs
+
+hdf5_path = "Des_to_Obs/HDF5/"
+
+concat_convert_HDF5(hdf5_path, face=0, interweave=True, average_axis=0)
+shutil.move(os.path.join(hdf5_path, "des_0.csv"), "Des_to_Obs")
+shutil.move("Des_to_Obs/des_0.csv", "Des_to_Obs/des_0_obs.csv")
+
+concat_convert_HDF5(hdf5_path, face=2, interweave=True, average_axis=0)
+shutil.move(os.path.join(hdf5_path, "des_2.csv"), "Des_to_Obs")
+shutil.move("Des_to_Obs/des_2.csv", "Des_to_Obs/des_2_obs.csv")
+
+# Make plots using make_distance_plots.py

Examples/noise_validation.r

@@ -4,7 +4,7 @@
 args = commandArgs(TRUE)
 
 startTime = Sys.time()
-setwd(args[1])
+# setwd(args[1])
 
 FidDes00 = read.csv('distances_0_0.csv', header = T)
 FidFid00 = read.csv('fiducials_0_0.csv', header = T)

Examples/signal_validation.r

@@ -3,7 +3,7 @@
 args = commandArgs(TRUE)
 
 startTime = Sys.time()
-setwd(args[1])
+# setwd(args[1])
 
 FidDes00 = read.csv('distances_0_0.csv', header = T)
 FidDes22 = read.csv('distances_2_2.csv', header = T)

turbustat/analysis/comparison_plot.py

@@ -271,9 +271,10 @@ def _plotter(ax, data, fid_data, num_fids, title, stat, bottom, left,
             ax.plot(x_vals, y_vals, "-o", label="Fiducial " + str(i),
                     alpha=0.6)
     # Set title in upper left hand corner
-    ax.annotate(title, xy=(0, 1), xytext=(12, -6), va='top',
-                xycoords='axes fraction', textcoords='offset points',
-                fontsize=12, alpha=0.75)
+    ax.set_title(title, fontsize=12)
+    # ax.annotate(title, xy=(1, 0), xytext=(0.9, 0.05), va='top',
+    #             xycoords='axes fraction', textcoords='axes fraction',
+    #             fontsize=12, alpha=0.75)
     if left:
         # Set the ylabel using the stat name. Replace underscores
         ax.set_ylabel(stat.replace("_", " ")+"\nDistance", fontsize=10,
@@ -313,11 +314,11 @@ def _plotter(ax, data, fid_data, num_fids, title, stat, bottom, left,
     if legend:
         ax.legend(loc="upper right", prop={'size': 10})
     if labels is None:
-        ax.set_xlim([-1, num_design + num_fids + 5])
+        ax.set_xlim([-1, num_design + num_fids + 8])
         ax.set_xticks(np.append(x_vals, x_fid_vals))
         ax.set_xticklabels(xtick_labels+fid_labels, rotation=90, size=12)
     else:
-        ax.set_xlim([-2, num_design + num_fids + 5])
+        ax.set_xlim([-2, num_design + num_fids + 8])
         xticks = np.append([-1], np.append(x_vals, x_fid_vals))
         ax.set_xticks(xticks)
         ax.set_xticklabels(labels+fid_labels, rotation=90, size=12)
@@ -338,7 +339,7 @@ def _horiz_obs_plot(ax, data, num_fids, shading=False, legend=False):
                    "ic348.13co.fits": "IC 348"}
 
     # Also needs to be generalized
-    colors = ["r", "g", "b"]
+    colors = ["g", "r", "b"]
 
     x_vals = ax.axis()[:2]