From aaca811817ecf356879a99197a5f8c0e90e9d328 Mon Sep 17 00:00:00 2001
From: compta Note - This notebook is meant as a draft for a page in the Mars
Mission Minder 2D app addressing the topic of campaigns. Note - Below code contains code for generating the pictures below and
+creates more updated plots. Question - “What is the difference between samples from the two
campaigns Delta Front and Crater Floor?” Every sample from the Perseverance Rover is assigned a campaign. For
@@ -2311,29 +2313,30 @@ DAR F24 How do Campaigns Compare?
DAR Project Name: Mars
Ashton Compton
-2024-12-10
+2024-12-15
2024-12-10
plain. Next the rover traveled to a Delta Fan structure and began
climbing it. Samples taken at the beginning of this journey belong to
Delta Front.
Title: Map of Samples Annotating the Two Campaigns
+Title: Total Mineral Occurrence with Samples grouped by Campaign
Description: Map showing the first 16 Perserverance Samples with the -Crater Floor and Delta Front regions annotated.
-Title: Counting Mineral Occurrence across Samples separated by -Campaign
-Description: Sherloc was grouped by campaign -and the total value of each feature across all samples was summed up and -displayed in the above plot. If a feature had a value of zero for every -sample, it was not included in the plot.
-Title: Pixl Feature Distribution by Campaign
-Description: Box plots showing the -distribution of each feature in Pixl, seperated by Campaign. Note: Sort -low to high
-Title: Libs clustering with Pixl data seperated by campaign on -Ternairy Diagram
-Description: Libs data was clustered and ternairy -plotted along with Pixl data separated by campaign. The Pixl data points -are labelled by name as well. Note-Use the common clusters and use the -non-aggregate plot
+Description: From the SHERLOC dataset, samples were grouped by +campaign. Then the total count of each mineral occurrence was graphed +using the grouped data.
+Title: Sample Compound Distributions, Samples grouped by Campaign
+Description: From PIXL dataset, samples were +grouped by campaign. Then the distribution of each compound measured in +PIXL was graphed using the grouped data.
+Title: Sample Compound Distributions, Samples grouped by Campaign
+Description: LIBS points within 7 meters of a sample were selected +and then grouped by their campaign. The distributions of each compound +measured in LIBS were plotted using the filtered and grouped data.
+Title: Cation Compositions of LIBS and PIXL points
+Description: Cation compositions of each point in +LIBS and each sample in PIXL were calculated, then plotted with LIBS +points grouped by clusters and PIXL samples grouped by campaign.
Conclusions:
In Sherloc, certain minerals are abundant in both campaigns, especially Crater Floor. -Carbonate is common in both campaigns -Organic @@ -2351,6 +2354,8 @@
The pixl graph reveals some big differences between Crater Floor and Delta Front. Namely in Al2O3, CaO, Cr2O3, MgO, P2O5, SO3, & SiO2
+Ternary graph captures trend from Igneous to Sedimentary rock +following decrease in Silicon + Aluminum
CODE: Load libaries Set up dataframes/matrices
Prepare data
#Load in data
@@ -2397,6 +2402,8 @@ 2024-12-10
# # Make the matrix of just mineral percentage measurements
# pixl.matrix <- pixl.df[,2:14]
+#Do Libs
+libs.df <- readRDS("~/DAR-Mars-F24/StudentData/v1_libs_to_sample.Rds")
###
# Read in data as provided.
sherloc_abrasion_raw <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/abrasions_sherloc_samples.Rds")
@@ -2492,14 +2499,8 @@ 2024-12-10
## }
## .Internal(set.seed(seed, i.knd, normal.kind, sample.kind))
## }
-## <bytecode: 0x558e691d4380>
+## <bytecode: 0x559738316f08>
## <environment: namespace:base>
-Used as base for map picture
-#Produce sample map with campaign differences
-# pixl_pos.df$Campaign <- meta.df$Campaign
-# ggplot(pixl_pos.df, aes(x= Lat, y= Lon, color=Campaign, size=1)) +
-# geom_point() +
-# theme_classic()
Make interactive plotly plot for Lithology
# Include all data processing code (if necessary), clearly commented
#Start with lithology
@@ -2521,51 +2522,48 @@ 2024-12-10
geom_col(position=position_dodge(preserve="total"), width=0.6) +
theme(panel.grid.major.x=element_blank(), axis.text.x = element_text(angle = 60, vjust = 1.0, hjust=1, size = 12)) +
labs(x="", y="Count") +
- ggtitle("Sherloc Dataset, Total Mineral Occurance across Samples") +
+ ggtitle("SHERLOC Dataset, Total Mineral Occurances, Samples grouped by Campaign") +
scale_fill_manual(values=c('#d6001c','#54585a'))
ggplotly(p, tooltip = c("campaign",'x', "n"))
-
-
+
+
#Commented out to knit to pdf, picture at top of report
-Create similar plot for Sherloc
-# #Repeat for sherloc
-# #Group by campaign & remove metadata
-# sherloc.df.sorted <- sherloc.df %>% group_by(Campaign) %>% select(-c(Sample))
-#
-# #Turn into long form and only keep positive cases
-# sherloc.df.sorted <- sherloc.df.sorted %>% pivot_longer(2:ncol(sherloc.df.sorted),names_to = "Feature", values_to="Factor") %>% filter(Factor == 1)
-#
-# #Count # of identical cases
-# sherloc.df.sorted <- sherloc.df.sorted %>% count(Feature)
-#
-# #Sort, Crater Floor is High to low & Delta Front is added back in low to high
-# sherloc.df.sorted <- sherloc.df.sorted %>% filter(campaign == "Crater Floor") %>% arrange(desc(n)) %>% ungroup() %>% add_row(sherloc.df.sorted %>% filter(campaign == "Delta Front") %>% arrange(n))
-#
-# p <- ggplot(sherloc.df.sorted, aes(x=factor(Feature, levels = (Feature %>% unique())), y = n, fill = campaign)) +
-# geom_col(position=position_dodge(preserve="total"), width=0.6) +
-# theme(panel.grid.major.x=element_blank(), axis.text.x = element_text(angle = 60, vjust = 1.0, hjust=1, size = 12)) +
-# labs(x="", y="Count") +
-# ggtitle("Sherloc Features Count by Campaign") +
-# scale_fill_paletteer_d(palette = "fishualize::Cephalopholis_argus")
-#
-# ggplotly(p, tooltip = c("campaign",'x', "n"))
-# #Commented out to knit to pdf, picture at top of report
-Make box plots for pixl and sherloc
-#Make box plots
+Make box plots for pixl
+#Make box plot
pixl.lf <- pixl.df %>% select(-c(Sample)) %>% pivot_longer(1:13)
colnames(pixl.lf)<- c("campaign", "feature", "value")
ggplot(data = pixl.lf, aes(x=factor(feature, levels = (feature %>% unique())), y=value, color = campaign)) +
geom_boxplot() +
scale_y_log10() +
- ggtitle("pixl, Compound Distribution by Campaign") +
+ ggtitle("PIXL, Compound Distribution by Campaign") +
labs(x="", y="log10 scale from percent composition") +
scale_colour_manual(values=c('#d6001c','#54585a'))
## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
-
-Code for ternairy plot
+
+#Make box plot
+libs.lf <- libs.df %>% filter(Distance <= 7) %>% select(8:18) %>% select(-c("Lat.pixl","Lon.pixl"))
+
+colnames(libs.lf)[2:9] <- colnames(pixl.df)[2:9]
+colnames(libs.lf)[1] <- "campaign"
+
+libs.lf <- libs.lf %>% pivot_longer(2:9)
+colnames(libs.lf)<- c("campaign", "feature", "value")
+ggplot(data = libs.lf, aes(x=factor(feature, levels = (feature %>% unique())), y=value, color = campaign)) +
+ geom_boxplot() +
+ scale_y_log10() +
+ ggtitle("LIBS, Compound Distribution by Campaign") +
+ labs(x="", y="log10 scale from percent composition") +
+ scale_colour_manual(values=c('#d6001c','#54585a'))
+## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
+## Warning: Removed 118 rows containing non-finite outside the scale range
+## (`stat_boxplot()`).
+
+Description: Libs points under 7 meters from a sample. Distribution of
+compounds with points separated by campaign.
+Code for ternairy plot
#take the sums of the specific elements
pixl_ternary <- pixl.df %>%
mutate(x=(SiO2+Al2O3)/100,y=(FeOT+MgO)/100,z=(CaO+Na2O+K2O)/100) %>%
diff --git a/AppRelated/CampaignQuestionFolder/CampaignQuestionNotebookAshton.pdf b/AppRelated/CampaignQuestionFolder/CampaignQuestionNotebookAshton.pdf
index 985e9655eadb80ca2f430b4abac43547a13e4548..147e3ce0465e8e41d38c09d477ad14ac32343a00 100644
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