Merge pull request #18 from calico/issue-17

Issue 17 - plot fix to include time zero fit when showing v_inter and v_final

R/impulse_plotting.R

@@ -8,10 +8,12 @@
 #' @return a ggplot
 #'
 #' @export
-kinetics_plotting <- function(augmented_timecourses,
-                              saturation = 0.9,
-                              max_time,
-                              fit_timepoints = 100) {
+kinetics_plotting <- function(
+  augmented_timecourses,
+  saturation = 0.9,
+  max_time,
+  fit_timepoints = 100
+) {
 
   stopifnot("tbl_df" %in% class(augmented_timecourses))
   stopifnot("tc_id" %in% colnames(augmented_timecourses))
@@ -20,8 +22,7 @@ kinetics_plotting <- function(augmented_timecourses,
   present_variables <- intersect(reserved_variables,
                                  colnames(augmented_timecourses))
   if (length(present_variables) == 0) {
-    stop ("no aesthetic variables present; aesthetic variables are: ",
-          paste(reserved_variables, collapse = ", "))
+    cli::cli_abort("no aesthetic variables present; aesthetic variables are: {.field {reserved_variables}}")
   }
 
   checkmate::assertNumber(saturation, lower = 0.5, upper = 1)
@@ -51,6 +52,19 @@ kinetics_plotting <- function(augmented_timecourses,
       tidyr::unnest_legacy(fitted_kinetics)
 
     possible_nest_vars <- c("rate", "t_rise", "t_fall", "v_inter", "v_final", "tzero_offset")
+    missing_nest_vars <- setdiff(possible_nest_vars, colnames(fitted_kinetics))
+
+    if (length(missing_nest_vars) > 0) {
+      for (x in missing_nest_vars) {
+        if (x == "tzero_offset") {
+          fitted_kinetics <- fitted_kinetics %>% dplyr::mutate(tzero_offset = 0)
+        } else {
+          fitted_kinetics <- fitted_kinetics %>%
+            dplyr::mutate(!!rlang::sym(x) := NA_real_)
+        }
+      }
+    }
+
     timepoints <- seq(from = 0, max_time, length.out = fit_timepoints)
 
     fitted_values <- fitted_kinetics %>%
@@ -68,7 +82,7 @@ kinetics_plotting <- function(augmented_timecourses,
         dplyr::filter(model == best_model),
       fitted_values,
       saturation = saturation
-      )
+    )
 
     kinetics_plot <- kinetics_plot +
       geom_rect(data = kinetic_intervals$asympote_aesthetics_df,
@@ -110,12 +124,14 @@ kinetics_plotting <- function(augmented_timecourses,
 
   }
 
-  kinetics_plot
+  return(kinetics_plot)
 }
 
-kinetic_aesthetics <- function(fitted_kinetics,
-                               fitted_values,
-                               saturation = 0.9) {
+kinetic_aesthetics <- function(
+    fitted_kinetics,
+    fitted_values,
+    saturation = 0.9
+    ) {
 
   # find fitted kinetic value at timing coefficient
   time_aesthetics_df <- fitted_kinetics %>%
@@ -132,22 +148,27 @@ kinetic_aesthetics <- function(fitted_kinetics,
 
   # find satuation time for assymptote
   asympote_aesthetics_df <- fitted_kinetics %>%
-    dplyr::select(tc_id, model, rate, t_rise, t_fall, v_inter, v_final) %>%
-    tidyr::gather(par, value, -tc_id, -model, -rate, -v_inter, -v_final) %>%
+    dplyr::select(tc_id, model, rate, t_rise, t_fall, v_inter, v_final, tzero_offset) %>%
+    tidyr::gather(par, value, -tc_id, -model, -rate, -v_inter, -v_final, -tzero_offset) %>%
     dplyr::filter(!is.na(value)) %>%
     # match t_rise to v_inter and t_fall to v_final
-    dplyr::mutate(assymp = dplyr::case_when(par == "t_rise" ~ v_inter,
-                                            par == "t_fall" ~ v_final)) %>%
+    dplyr::mutate(assymp = dplyr::case_when(par == "t_rise" ~ v_inter + tzero_offset ,
+                                            par == "t_fall" ~ v_final + tzero_offset)) %>%
     dplyr::mutate(assymp_type = dplyr::case_when(
       par == "t_rise" ~ "v_inter",
       par == "t_fall" ~ "v_final")) %>%
     # derive saturation time for x-axis
-    dplyr::mutate(t_saturation_start = saturation_time(1 - saturation,
-                                                       value,
-                                                       rate),
-                  t_saturation_end = saturation_time(saturation,
-                                                     value,
-                                                     rate)) %>%
+    dplyr::mutate(
+      t_saturation_start = saturation_time(
+        1 - saturation,
+        value,
+        rate
+      ),
+      t_saturation_end = saturation_time(
+        saturation,
+        value,
+        rate
+      )) %>%
     dplyr::select(tc_id, model, assymp_type, assymp,
                   t_saturation_start, t_saturation_end)
 
@@ -156,7 +177,5 @@ kinetic_aesthetics <- function(fitted_kinetics,
 }
 
 saturation_time <- function(saturation, c_time, rate) {
-
   c_time + log(saturation / (1 - saturation)) / rate
-
 }

README.Rmd

@@ -52,10 +52,9 @@ sigmoid_impulse_plot <- function(timecourse_parameters) {
     scale_x_continuous("Time") +
     theme_bw() +
     theme(text = element_text(size = 15), legend.position = "top")
-   
 }
 
-sigmoid_impulse_plot(data_frame(t_rise = 25, rate = 0.25, v_inter = 3, v_final = -3, t_fall = 45))
+sigmoid_impulse_plot(tibble(t_rise = 25, rate = 0.25, v_inter = 3, v_final = -3, t_fall = 45))
 ```
 
 ### sigmoid
@@ -100,3 +99,20 @@ Once conda and impulse are installed, A conda environment configured with Tensor
 ```{r, eval=FALSE}
 auto_config_tf()
 ```
+
+Alternatively, create a minimal environment with:
+
+```{bash venv_setup, eval = FALSE}
+<<PATH TO PYTHON BINARY>> -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip wheel setuptools ipykernel
+pip install -r requirements.txt
+```
+
+Where, requirements.txt is just:
+
+```{bash requirements, eval = FALSE}
+tensorflow==2.9.*
+numpy==1.23.*
+tensorflow-probability==0.17.*
+```

README.md

@@ -18,11 +18,11 @@ value(s) and asymptote(s).
 
 The core functionality of **impulse** is:
 
--   Simulate timecourse parameters and resulting timecourses
--   Fit sigmoid and impulse models to timecourses with or without priors
-    on kinetic parameters
--   Compare sigmoid and impulse models
--   Visualize measurements and parametric fits
+- Simulate timecourse parameters and resulting timecourses
+- Fit sigmoid and impulse models to timecourses with or without priors
+  on kinetic parameters
+- Compare sigmoid and impulse models
+- Visualize measurements and parametric fits
 
 ## The models
 
@@ -33,21 +33,31 @@ between timecourses and kinetic paramters, but the kinetic parameters
 are generally more meaningful since they indicate the timing and
 magnitdue of responses.
 
-A sigmoid with parameters {t\_rise = 25, v\_inter = 3, rate = 0.25} and
-an impulse with two additional parameters {t\_fall = 45, v\_final = -3}
-are shown. The t\_rise of 25 indicates a half-max time of 25 and
-v\_inter of 3 indicates saturation at 3. In the impulse model there is a
-second response with a half-max time of 45 and final asymptote at -3.
+A sigmoid with parameters {t_rise = 25, v_inter = 3, rate = 0.25} and an
+impulse with two additional parameters {t_fall = 45, v_final = -3} are
+shown. The t_rise of 25 indicates a half-max time of 25 and v_inter of 3
+indicates saturation at 3. In the impulse model there is a second
+response with a half-max time of 45 and final asymptote at -3.
 
 ![](man/figures/README-sigmoid_impulse_compare-1.png)<!-- -->
 
 ### sigmoid
 
-![Sigmoid](https://github.com/calico/impulse/blob/master/man/figures/sigmoid.png)
+<figure>
+<img
+src="https://github.com/calico/impulse/blob/master/man/figures/sigmoid.png"
+alt="Sigmoid" />
+<figcaption aria-hidden="true">Sigmoid</figcaption>
+</figure>
 
 ### implulse
 
-![Impulse](https://github.com/calico/impulse/blob/master/man/figures/impulse.png)
+<figure>
+<img
+src="https://github.com/calico/impulse/blob/master/man/figures/impulse.png"
+alt="Impulse" />
+<figcaption aria-hidden="true">Impulse</figcaption>
+</figure>
 
 ## *Impulse* functionality
 
@@ -65,7 +75,7 @@ impulse models since there are natural constraints on parameter values
 which should hold (non-negative rates, non-negative times, rise before
 fall). When these constraints are violated, a good fit may occur, but
 interpretability of timing and effect sizes will be lost. The vignette
-*setting\_priors* describes how to formulate the priors and can be used
+*setting_priors* describes how to formulate the priors and can be used
 to guide the tuning of parameters for other application.
 
 ## Installation
@@ -98,3 +108,20 @@ with TensorFlow can be created using:
 ``` r
 auto_config_tf()
 ```
+
+Alternatively, create a minimal environment with:
+
+``` bash
+<<PATH TO PYTHON BINARY>> -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip wheel setuptools ipykernel
+pip install -r requirements.txt
+```
+
+Where, requirements.txt is just:
+
+``` bash
+tensorflow==2.9.*
+numpy==1.23.*
+tensorflow-probability==0.17.*
+```