Updating tutorial, updating examples

docs/tutorial.rst

@@ -25,8 +25,13 @@ make it easier to debug.
     (venv)$ pip install thermostat
 
     # if using conda (see note below - conda is distributed with Anaconda)
-    $ conda create --yes --name thermostat pandas
-    $ source activate thermostat
+    # This method uses conda for the virtual environment rather than as a package manager
+    # Note: Using this method will cause problems if you install other packages
+    # with conda. Please use pip instead of conda to install packagesinside of
+    # this virtual environment.
+    $ conda create --yes --name thermostat pip
+    $ conda activate thermostat
+    # We use pip instead of Conda to install the packages.
     (thermostat)$ pip install thermostat
 
 If you already have an environment, use the following:
@@ -38,11 +43,11 @@ If you already have an environment, use the following:
     (thermostat)$
 
     # if using conda
-    $ source activate thermostat
+    $ conda activate thermostat
     (thermostat)$
 
     # if using venv, or virtualenv directly
-    $ source path/to/venv/bin/activate
+    $ source /path/to/venv/bin/activate
 
 To deactivate the environment when you've finished, use the following:
 
@@ -53,7 +58,7 @@ To deactivate the environment when you've finished, use the following:
     $
 
     # if using conda
-    (thermostat)$ source deactivate
+    (thermostat)$ conda deactivate
     $
 
 Check to make sure you are on the most recent version of the package.

scripts/multi_thermostat_tutorial.py

@@ -37,6 +37,9 @@ def main():
 
     # This section finds the metadata files and data files for the thermostats.
     # These point to examples of the various styles of files 
+    # In most cases you will combine Single Stage, Two Stage, and Two Stage ERT
+    # data in the same file.
+
     # Single Stage
     data_dir = os.path.join("..", "tests", "data", "single_stage")
     metadata_filename = os.path.join(data_dir, "metadata.csv")
@@ -57,7 +60,8 @@ def main():
     # "False" to use the logging level instead
     thermostats = from_csv(metadata_filename, verbose=True)
 
-    output_dir = "."
+    # output_dir = "."
+    output_dir = data_dir
     metrics = multiple_thermostat_calculate_epa_field_savings_metrics(thermostats)
 
     output_filename = os.path.join(

tests/data/single_stage/thermostat_example_certification.csv

@@ -1,6 +1,6 @@
 product_id,sw_version,metric,filter,region,statistic,season,value
-test_product,2.0.0a2,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,heating,20.17
-test_product,2.0.0a2,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,cooling,44.36
-test_product,2.0.0a2,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,heating,19.83
-test_product,2.0.0a2,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,cooling,38.44
-test_product,2.0.0a2,rhu_30F_to_45F,tau_cvrmse_savings_p01,all,upper_bound_95,heating,0.09
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,heating,20.17
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,cooling,44.36
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,heating,19.83
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,cooling,38.44
+test_product,2.0.0a3,rhu_30F_to_45F,tau_cvrmse_savings_p01,all,upper_bound_95,heating,0.09

tests/data/two_stage/thermostat_example_certification.csv

@@ -1,6 +1,6 @@
 product_id,sw_version,metric,filter,region,statistic,season,value
-test_product,2.0.0,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,heating,6.96
-test_product,2.0.0,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,cooling,14.08
-test_product,2.0.0,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,heating,7.43
-test_product,2.0.0,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,cooling,14.52
-test_product,2.0.0,rhu_30F_to_45F,tau_cvrmse_savings_p01,all,upper_bound_95,heating,0.03
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,heating,6.96
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,lower_bound_95,cooling,16.33
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,heating,7.43
+test_product,2.0.0a3,percent_savings_baseline_percentile,tau_cvrmse_savings_p01,national_weighted_mean,q20,cooling,17.04
+test_product,2.0.0a3,rhu_30F_to_45F,tau_cvrmse_savings_p01,all,upper_bound_95,heating,0.03